rural industrial entrepreneurship
TRANSCRIPT
Rural Industrial Entrepreneurship
The Case of Bardhaman District in West Bengal
Subrata Dutta
Promotoren : Professor Dr. Henk Folmer Hoogleraar in de Algemene Economie Wageningen Universiteit, Nederland Professor Dr. Wim J.M. Heijman Hoogleraar in de Regionale Economie Wageningen Universiteit, Nederland Co-promotor : Professor Dr. Amita Majumder Economic Research Unit Indian Statistical Institute, Kolkata, India Promotiecommissie : Professor Dr. Arie Kuyvenhoven, Wageningen Universiteit Professor Dr. Gerrit Antonides, Wageningen Universiteit Dr. Han H.L. Oud, Universiteit van Nijmegen
Dr. Sudha Loman, Wageningen Universiteit Dit onderzoek is uitgevoerd binnen de 'Mansholt Graduate School of Social Sciences'
Rural Industrial Entrepreneurship
The Case of Bardhaman District in West Bengal
Subrata Dutta
Proefschrift
ter verkrijging van de graad van doctor
op gezag van de rector magnificus
van Wageningen Universiteit
prof. dr. ir. L. Speelman
in het openbaar te verdedigen
op woensdag 9 juni 2004
des namiddags te 16.00 uur in de Aula
Subrata Dutta
Rural Industrial Entrepreneurship: The Case of Bardhaman District in West Bengal Ph.D. Thesis, Wageningen University, Wageningen, The Netherlands, 2004 ISBN 90-8504-046-9 Key words: Rural Development / Rural Industry / Entrepreneurship / Non-farm Sector / Non-agricultural Sector / Culture / LISREL Model / Simultaneous Equations System / Bardhaman (Burdwan) District / West Bengal / India All rights reserved
To
My mother, my wife (Sumita) and my son (Sagnik)
vii
Acknowledgements
It was Professor Asim Chaudhuri of Rabindra Bharati University, Kolkata, who brought
me to academia. So, at the very beginning, I pay my gratitude to him. I remember, as a
teacher in Bachelors and Masters, he drew our attention by his fascinating lectures on
classical political economy. I had a hidden passion for studying lives but found no sincere
indulgence. Four years after I completed my Masters in economics, coincidentally one
day I saw Professor Chaudhuri at the library of the United States Information Services at
Kolkata and he encouraged me to switch over from non-academia to the area of research.
I entered. But the new world was not that interesting to me simply because I lacked
something. What was that something? – Something like a clear vision that helps one
pierce the depth of ideas first, then bridge the ideas with aims, and finally reach the aims.
So, it’s a journey. And making the journey worth is an art, and, of course, science too.
That is what I could first perceive when I came in touch with Professor Henk Folmer at
Wageningen University in the Netherlands. He is the main supervisor of my study. I owe
my gratitude to him since he is the person from whom I learned how a complete research
work should be designed and carried out. His repeated critical notes on my drafts
stimulated me to improve the thesis a lot. In my eyes, he is a dedicated scientist. Not only
as a supervisor, but also as a human being he is a very nice and fantastic person indeed.
I am indebted to my second supervisor Professor Wim Heijman of Wageningen
University because he introduced me with Professor Folmer in 2000. I remember that
first I started my Ph.D. study at Maastricht School of Management (MSM) in 1999. But I
had to look for a supervisor outside of MSM because there was no faculty available in the
area of my interest, i.e. development economics. So, I met Professor Heijman while he
was visiting MSM as a guest faculty and that was the beginning of our journey. During
the period of study, Professor Heijman controlled many aspects of organisation, which
have been helpful to carry out the work smoothly. My co-supervisor in India, Professor
Amita Majumder of the Economic Research Unit, Indian Statistical Institute, Kolkata,
helped and encouraged me a lot during the study. Her valuable guidance right from
viii
preparing the questionnaires to completing the thesis considerably enriched the whole
work. Without her continuous support, especially during the estimation of the LISREL
model, I could not have finished this study. I express my sincere gratitude to Professor
Majumder. I am sincerely grateful to Dr. Han Oud of Nijmegen University, the
Netherlands, for his valuable suggestions during estimation of the LISREL model.
Without his help too, it would not have been possible for me to finish the study. I have
learnt a lot from him since he is one of the few experts in LISREL.
I have been introduced with Professor Gerrit Antonides at our department in Wageningen
University just two weeks back and I am really thankful to him for his constructive
remarks on my thesis. My special thanks go to Dr. Rein Haagsma, Dr. Sudha Loman, Dr.
Pierre van Mouche, Dr. Johan van Ophem, and Dr. Jan Rouwendal of our department in
Wageningen University for offering me all kinds of study support I requested for and a
very nice academic atmosphere at Wageningen. I also convey my thanks to Professor
Asok Maiti and Dr. Subhendu Chakrabarti of the Indian Statistical Institute for their
moral support. I am also thankful to Dr. Wilbert Houweling of Wageningen University
for his several helps.
I am grateful to the Maastricht School of Management (MSM) for their all-out support
during this study. Especially, the comments of Professor M. van Beugen and Dr. L.
Alcorta on my proposal are gratefully acknowledged. I thank Mr. Jos Linssen and Mr.
Ron Soeren for they have taken care of several official aspects of my study. I
acknowledge the support of Ashish Kumar (India), S.P. Premaratne (Sri Lanka), Osama
Isdudi (Palestine) and Md. Mewan (Sudan) – my Ph.D. batch mates at MSM.
The support I have received from the two great ladies, i.e. two secretaries of our
department at Wageningen University – Ms Annelies Coppelmans and Ms Margaret van
Wissen, is really unforgettable. It is difficult for me to express my feeling of gratitude
towards them in words. Many many thanks to them for what they have done for me. I
thank my Ph.D. batch mates at Wageningen—Judith Cornelisse (the Netherlands),
Muyeye Chambwera (Zimbabwe), Pius Odunga (Kenya), Hoang Nguyen-Huy (Vietnam),
ix
Morteza Chalak (Iran)—for their friendship and support. Especially I want to mention the
name of Judith for she has provided me with enormous support. Anybody will be proud
of having a friend like Judith. I thank the fieldworkers—Dilip Rana, Pranab Datta, Tirtha
Dey, Satrajit Goswami and Shirsendu Gupta—for their participation in data collection in
Bardhaman district.
I am deeply shocked that my father – who passed away last December—could not see
this book. This dissatisfaction of mine will never be covered up. But the good news is
that I have become a father in November last year. My son, Sagnik, has brought new
lights to our family. My mother is the source of all my inspiration. My respects go to her
for all her love and support. My wife never seriously complained for not providing her
with even a little company; rather she provided me with direct helps (along with indirect
support) during my study in several ways—sometimes in composing texts in word file, or
in inserting data in excel file. I am really thankful to her for her all kinds of support.
Special gratitude goes to my three aunts (my mother’s sisters) who have always loved me
and taken care of me. I also thank all my other friends and well-wishers in India as well
as in the Netherlands.
Subrata Dutta
Wageningen, 9 May 2004
x
xi
CONTENTS
Acknowledgements vii
Contents xi
Abstract xvii
Map of Bardhaman district xix
1 INTRODUCTION 1
1.1 Introduction 1
1.2 Poverty: The problem 2
1.3 Poverty in India (with special reference to rural poverty) 5
1.4 Rural poverty in West Bengal 8
1.5 Poverty alleviation 11
1.6 Definition of small-scale industries in India 12
1.7 What is misleading? 15
1.8 Definition of rural industry 16
1.9 Definition used in the present study 19
1.10 The objective of the study and the research question 20
1.11 Significance of the study 21
1.12 Organisation of the study 26
2 THE STUDY AREA 29
2.1 Introduction 29
2.2 Murshidabad: the starting place of British regime 30
2.3 Bengal in retrospect 31
2.4 A brief history of Bengali entrepreneurship 33
2.5 Agriculture in West Bengal 37
2.5.1 The growth debate 37
2.6 Industry in Wets Bengal 44
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2.7 Urbanisation and infrastructure in West Bengal 48
2.8 Urban centres and small-scale industries in West Bengal 51
2.9 Rural industrialisation and the role of infrastructure in rural towns 55
2.10 Bardhaman’s economy in retrospect: a glimpse 59
2.11 Recent Bardhaman: a glimpse 61
APPENDIX 2.1: Overall description of Bardhaman district 66
3 ISSUES RELATING TO RURAL INDUSTRIES: A REVIEW OF
LITERATURE 71
3.1 Introduction 71
3.2 Agriculture-Industry linkage: The classical political economy framework 73
3.2.1 The Physiocrats and Adam Smith 73
3.2.2 Karl Marx 76
3.3 Categorisation of non-farm sector 79
3.4 Kuznets’s growth theory 82
3.5 Relationship between agriculture and industrial growth 86
3.6 Linkages: theories and debate 90
3.7 Japanese experience in brief 102
3.7.1 Agriculture/non-agriculture linkage 102
3.7.2 Transformation from traditional industries to modern industries 104
3.8 Reasons for household participation in rural non-farm activities 105
3.9 Secondary and seasonal employment in non-farm activities 106
3.10 Sectoral composition of rural non-farm employment 107
3.11 Hymer-Resnick model vs. Ranis-Stewart models 113
3.11.1 Hymer-Resnick model 113
3.11.2 The Ranis-Stewart models: colonials and post-colonials 114
3.11.3 Colonial archetypes 119
3.11.4 Post-colonial archetypes 120
3.12 Urbanisation and role of rural infrastructure in rural industrialisation 127
3.13 Conclusions 133
xiii
APPENDIX 3.1: Distribution of rural workers across non-farm sectors in
selected countries, male and female 135
APPENDIX 3.2: Women’s share in total employment by sector in selected
countries, rural and urban 136
APPENDIX 3.3: Percentage of non-agricultural workers in rural workforce
in India, 1961-1983 137
4 ENTREPRENEURSHIP AND CULTURE 139
4.1 Introduction 139
4.2 Entrepreneurship 143
4.3 Theories of entrepreneurship: Schumpeter and his previous and later 143
4.4 Achievement motive in economic growth: psychological view 153
4.5 Family environment and entrepreneurship 159
4.6 Culture and development 164
4.7 Darwinian principles of evolutionary change and technical culture 174
4.8 Some culturally sensitive models 178
4.8.1 Entrepreneurship among the Amish 179
4.8.2 Entrepreneurship in the indigenous people in Alaska 180
4.8.3 Entrepreneurship in the Canadian Sub-Arctic 180
4.8.4 Entrepreneurship in Laos 180
4.8.5 Entrepreneurship in the kingdom of Lesotho 181
4.9 Conclusions 182
APPENDIX 4.1: Plato, Aristotle and McClelland 184
APPENDIX 4.2: Characteristics of Stationary Societies 187
5 DATA COLLECTION 191
5.1 Introduction 191
5.2 Secondary data 193
5.3 Primary data: sampling procedure 193
5.4 The structure of the questionnaires 195
5.5 Selection and training of field workers 198
xiv
5.6 The pilot survey 198
5.7 The survey 199
5.8 Conclusions 201
APPENDIX 5.1: Questionnaire for farmers who are engaged in non-farm
manufacturing activities 202
APPENDIX 5.2: Questionnaire for farmers who are NOT engaged in
non-farm manufacturing activities 218
6 DETERMINANTS OF NON-FARM MANUFACTURING ENTRE-
PRENEURSHIP OF FARMERS: THEORETICAL CONSIDERA-
TIONS AND EMPIRICAL FINDINGS 225
6.1 Introduction 225
6.2 Basic concepts and assumptions of theoretical considerations 226
6.2.1 Rationality and decision making 226
6.2.2 The rational entrepreneur 228
6.3 Non-farm entrepreneurship 231
6.4 Explanatory variables 231
6.4.1 Sex 231
6.4.2 Age and age squared 235
6.4.3 Marital status 239
6.4.4 Children 241
6.4.5 Education 243
6.4.6 Political position of the farmer 245
6.4.7 Financial family support 249
6.4.8 Work-effort or fate 251
6.4.9 Marriage relationship 253
6.4.10 Involvement in agriculture 255
6.4.11 Types of crops 258
6.4.12 Wealth 259
6.4.13 Innovation 261
6.4.14 Risk 264
xv
6.4.15 Summary of the explanatory variables and descriptive statistics 266
6.4.16 Simultaneity bias 266
6.5 The LISREL model 269
6.5.1 The measurement model 270
6.5.2 The structural model 270
6.5.3 Submodels 272
6.5.4 The theoretical and the sample covariance matrices 274
6.5.5 Identification 277
6.5.6 Estimation of the model 278
6.5.7 Model judgment and model identification 280
6.6 The results 282
6.6.1 The measurement model 282
6.6.2 The structural model 283
6.7 Conclusions 289
APPENDIX 6.1: The Full Results 294
7 CONCLUSIONS 333
7.1 Summary of the study 333
7.2 Policy recommendations 341
7.3 Further research recommendations 343
REFERENCES 345
SAMENVATTING EN CONCLUSIES (SUMMARY AND
CONCLUSIONS IN DUTCH) 365
ABOUT THE AUTHOR 371
xvi
xvii
Abstract
For a living, most of the rural people in developing countries are primarily dependent on
agriculture. If the farmers, who have investible surplus generated from agriculture, are
interested in non-farm entrepreneurship then rural economy can find an industrial route of
development. With this consideration, the study has posed the research question as to
what determines non-farm entrepreneurship among farmers and thus attempted to identify
the factors that may influence farmer’s non-farm entrepreneurship.
The theoretical part constituted a set of 13 hypotheses which in turn led to formulation of
two questionnaires in order to collect data—one questionnaire was for interviewing the
farmers who were engaged in non-farm manufacturing activities and the other
questionnaire was for interviewing the farmers who were engaged in farming only. So far
as the investigation part of the study is concerned Bardhaman district of the state of West
Bengal in India was selected because during 1980s and 1990s the state has experienced
high agricultural growth compared to the previous decades, which implies that farmers
might have been able to gather surplus generated from agricultural development and
therefore it was considered interesting to study non-farm entrepreneurship of farmers of
West Bengal. Five administrative blocks were randomly selected from the eastern part
(agricultural part) of Bardhaman district, and then six panchayats have been randomly
selected from each block, and finally 10 farm households were randomly selected from
each panchayat, i.e. totally 300 samples were randomly selected for interviews.
The LISREL (LInear Structural RELations) approach was applied to estimate the model
which was constituted in the form of simultaneous equations system that included a set of
10 equations (indicating interdependencies between the endogenous and explanatory
variables) with a consideration of the hypotheses of the theoretical model; and we applied
the LISREL approach, by using its maximum likelihood estimator, since this approach
can control for simultaneity bias in the model, and simultaneously deal with latent
xviii
variables and the observable variable or, as we may say, can simultaneously estimate the
measurement model and the structural model.
The farmers who are married, engaged in producing three crops year, and risk takers have
been found to have a relatively high probability to become non-farm entrepreneurs. The
farmers who are relatively wealthy and have high levels of education have been found to
be less likely in becoming non-farm entrepreneurs whereas age of farmer has indirect
positive impact on non-farm entrepreneurship via marriage and indirect negative impact
on non-farm entrepreneurship via risk attitude and wealth. The number of children of a
farmer has been found to have an insignificant effect on non-farm entrepreneurship, but
interestingly non-farm entrepreneurship has been found to have a positive impact on the
number of children. Three exogenous variables—viz. age squared, farmer’s primary
involvement in agriculture either as a landowner or as a sharecropper, and farmer’s faith
in work-effort or fate – have been found to be highly insignificant and therefore have
been removed from the structural model. Three explanatory variables – viz. political
affiliation of farmer, financial family support, marriage relation, and innovativeness –
have also been found to have insignificant impacts on non-farm entrepreneurship.
xix
Map of Bardhaman District (without scale)
Chapter 1
Introduction
1.1 Introduction
The rural economy of the so-called developing countries is currently the subject of much
discussion. What is the typical condition of the rural poor in most of the developing
countries? In an informal fashion of writing, Schumacher (1973) described that the work
opportunities in villages of the developing countries are so restricted that the poor people
cannot work their way out of misery. They are under-employed or totally unemployed,
and when they do find occasional work their productivity is exceedingly low. Some of
them have land, but often too little. Many have no land and no prospect of ever getting
any. There is no hope for them in the rural areas and hence they drift into the big cities.
The concentration of non-farm sectors in a few urban areas, and the wage gap between
rural and urban areas result in a huge rural-urban migration and concentration of
unemployed workers in urban areas (Todaro, 1980). Thus rural unemployment becomes
urban unemployment. The growing number of unemployment in urban areas necessitates
finding a way to create jobs outside agriculture and outside cities focusing on a growth
process that would boost the demand for rural non-agricultural activities. Hence creating
demand in rural areas for locally produced non-food goods and services becomes an
important element in the process of economic development (Mellor, 1976; Bell and
Hazell, 1980). Rapid agricultural growth may raise rural income and, consequently, the
landowning class may create demand for non-food items. But the demand side alone has
the limited scope to change the situation of the rural economy until and unless the
increased demand is positively responded by the supply of rural non-farm goods and
services, otherwise the increased demand may find route towards the urban industrial
goods and services. Agricultural land-owning group has vital role to play in rural
industrialisation by investing the surplus, earned from agriculture, to diversified non-farm
Introduction
2
activities. From both scientific and policy point of view this is a subject of rural non-farm
entrepreneurship. Further expansion of the existing low-productivity rural industrial units
as well as low-quality goods will not simply help. The rural land-owning group’s
involvement in non-farm activities may have to incorporate a modern outlook since
combating the modern, large-scale, urban industrial sector requires a steady shift of the
rural land-owning group’s affinity from traditional culture to modern or technical culture.
Thus the modernisation of the traditional sector (often synonymous with agricultural
sector or rural sector) may go hand in hand with rapid and wide rural industrialisation.
Bringing about technological change through innovation in the rural non-agricultural
sector is often held to be responsible for modernising the remote villages. In such a
perspective, the main focus of this study is to ascertain the determinants of rural non-farm
entrepreneurship among the farmers.
From section 1.2 to 1.5 in this chapter, we present the problem statement. From section
1.6 to 1.9 we discuss the definitions of small-scale industry and rural industry. In section
1.10, we set the main objective and the research question for the study. Section 1.11
states the significance of the study and section 1.12 presents the organisation of the whole
study.
1.2 Poverty: The Problem
Poverty being an age old curse in the developing countries is a big challenge for the
twenty-first century. Poverty and its correlates like illiteracy, malnutrition, ill health etc.
offer the people of a poor nation the utter bleakness of the future. The World
Development Report (World Bank, 1990), which represents a landmark study on poverty
in developing countries, experimented with a choice of two poverty lines: $275 and $370
per person per year, expressed in 1985 purchasing power parity (PPP) prices. The range
was chosen to reflect the fact that the poverty lines of some of the poorest nations fall
between these two limits. Table 1.1, as furnished by Ray (1998), puts together poverty
data from two World Development Reports. In 1990, over one billion individuals were
Chapter 1
3
estimated to earn less than $370 per year ($420 per year at 1990 PPP prices). The picture
does not look very hopeful. Only East Asia experienced very high rates of growth and the
absolute number of poor declined in five years. And it is observed that the absolute
number of poor did not increase in East Europe. But the absolute numbers of the poor for
all other regions, as shown in Table 1.1, rose significantly between 1985 and 1990. The
overall percentage of people in poverty (at $370 line) was roughly constant over this
period at 30% of the population of all developing countries. It is also somewhat clear
from the table that why South Asia and Sub-Saharan Africa are to be designated as the
areas where mass poverty in the world is geographically concentrated.
Table 1.1: Poverty in developing regions, 1985 and 1990 (using “universal” poverty lines)
1985 1990
Ultra-poor (Under $275)
Poor (Under $370) Poor Region
HCa (millions)
HCRb (%)
HCa (millions)
HCRb (%)
HCa (millions)
HCRb (%)
Sub-Saharan Africa 120 30 184 48 216 48 East Asia 120 9 182 13 169 11 South Asia 300 29 532 52 562 49 East Europe 3 4 5 7 5 7 Mid. East/N. Africa 40 21 60 31 73 33 L. America/Caribbean 50 12 87 22 108 26 All LDCs 633 18 1051 31 1133 30 Notes for a and b: HC is head count and HCR is head count ratio. A natural measure that
appears to mind is simply to count the number of people below the poverty line. One might be interested in the numbers per se or the relative incidence of the poor. In the latter case, one has to divide the number of poor people by the total population of the country or region under consideration. The first measure is known as the head count (HC) and the latter as the head-count ratio (HCR) which is just head count as a fraction of population.
Source: Ray (1998: 257), Table 8.1; Original source: World Bank (1990).
Introduction
4
Table 1.2: Rural and urban poverty in selected countries as per World Development Report, published in 1990
Region and country
Rural population (% of total population)
Rural poor (% of total
poor)
Infant mortality (per 1000 live
births)
Access to safe water (% of population)
Rural Urban Rural Urban Sub-Saharan Africa
Côte d’Ivoire 57 86 121 70 10 30 Ghana 65 80 87 67 39 93 Kenya 80 96 59 57 21 61 Asia India 77 79 105 57 50 76 Indonesia 73 79 74 57 36 43 Malaysia 62 80 - - 76 96 Philippines 60 67 55 42 54 49 Thailand 70 80 43 28 66 56 Latin America
Guatemala 59 66 85 65 26 89 Mexico 31 37 79 29 51 79 Panama 50 59 28 22 63 100 Peru 44 52 101 54 17 73 Venezuela 15 20 - - 80 80 Source: Ray (1998: 260), Table 8.2; Original source: World Bank (1990).
Rural poverty in particular is very acute in the developing countries. Poverty in rural
areas is significantly higher than that in urban areas. “Even countries with substantial
advances in creating an equitable agriculture display higher rural poverty than their
national averages” (Ray, 1998: 259). Table 1.2 summarizes rural-urban disparities in
poverty, as well as two major indicators of well-being, for selected countries. The other
studies provide us with some more information about rural poverty in Asia, particularly
in some Indian states. In Asia, the proportion of the population living in absolute poverty
in rural areas is 75 % (Mellor, 1995: 9). Agarwal (1986) has noted for all India that the
poor are likely to be found in agricultural labourer households. A study has shown that
the rural poor in India allocate about 80 per cent of their total expenditure on food, but,
Chapter 1
5
yet they do not get enough nutrition in terms of calories and fat (Dev et al, 1994: 245;
Lipton, 1983: 35). Let us now review rural poverty of India in a nutshell.
1.3 Poverty in India (with special reference to rural poverty)
Before independence, the colonial government was primarily concerned with the
maintenance of law and order, defence and tax collection and lacked an explicit
development policy. Public investment decisions were governed more by profitability
considerations than by any concern for long-run growth or equity. The period was
marked by economic stagnation, particularly in the agricultural sector. The growth rate of
agriculture was around 0.3 per cent per annum in the first half of twentieth century.
Aggregate real output increased at a rate of less than two percent per annum during the
period 1900-1950. In per capita terms, it was less than half a per cent. There was some
growth in the large-scale manufacturing sector which was, however, nullified by the
decline of traditional industries. Capital formation was only about six per cent of Net
Domestic Product (Dev et al, 1994).
At the time of independence in August 1947, India was faced with problems of
rehabilitating the economy disturbed by the Second World War and partition of the
country, and of achieving rapid economic growth to emancipate the millions of its
population from poverty, hunger and malnutrition. The economy was predominantly
agrarian, with large inequalities in the distribution of resource endowments among people
and across geographical regions. Unemployment and underemployment was prevalent.
And saving, capital formation, income levels and hence living standards were all very
low (Dev et al, 1994).
Of course, there have been some improvements during last 50 years after independence.
Aggregate economic statistics on the evolution of poverty in India point fairly
unambiguously towards steady, albeit slow, progress in the reduction of poverty
(Jayaraman and Lanjouw, 1999). The poverty headcount (based on various rounds of the
Introduction
6
National Sample Survey Organisation’s household surveys of consumption) declined
from 53% in 1970 to 36% in 1991 (Datt, 1995; Datt and Ravallion, 1996). This decline
was driven principally by the fall in poverty in rural areas (from 55% to 37%), where the
bulk of the population still lives and where the incidents of poverty have always been
highest. The poverty gap in rural areas declined at 1.77% per year between 1951 and
1992 (compared to a fall of 0.82% per year for the rural headcount over this period); and
in rural areas the squared poverty gap declined at 2.55% per annum (Datt, 1995). These
figures suggest that the poorest of the poor, in per capita consumption terms, have seen
the more pronounced improvements.
Other indicators of well-being generally lend support to the notion that living standards
have been rising in India over time. Female literacy rates, for example, rose from below
10% in 1950 to 39% by 1990; male literacy rates expanded from 27% to 64% over the
same period; life expectancy in India in 1950 was only 32.1 years, by 1990 rising to 59.2
years (Drèze and Sen, 1995). In another study, Drèze and Srinivasan (1996) examine
National Sample Survey data for 61 constituent regions (loosely defined in terms of
agroclimatic characteristics) in India in 1972/73 and 1987/88. They find little evidence of
regional “pockets” where the incidence of poverty has risen.
The picture from secondary data, in terms of direction of change, is thus fairly positive.
Much of the impetus behind the decline, albeit slow, of rural poverty over time can be
found in the process of agricultural intensification which has taken place, at a varying
pace, across rural India. What is equally clear, however, is that the absolute levels of
deprivation are still very high. This is true in terms of consumption-based poverty, and
also in terms of social indicators. India in the 1990s has been still far from its goal of
universal primary education, and freedom from hunger and preventable illness
(Jayaraman and Lanjouw, 1999). Even against a background of declining poverty in
general, it is clear that at any given moment there are certain subgroups of the population
who face a high risk of falling into poverty. Instances of drought (or flood) in rural India
are common, and can spell dramatic increases in poverty (Hazell and Ramasamy, 1991).
After all, rapid population growth represents one factor which can result in increased
Chapter 1
7
impoverishment. Village studies have noted this as a force which can offset rising
productivity, and occasionally result in a decline in per capita incomes (see, for details,
Jayaraman and Lanjouw, 1999). The population in the working age-group (15-59 years)
is the fundamental supply-side factor in the labour market (Dev et al, 1994: 264).
Employment for all the people of this age group is a far cry.
Moreover, one should not be very much encouraged by the incidence of reduction in rural
poverty in India for a particular short period of time because rural poverty in India is very
dynamic in nature, which may fall in a span of few years and then again may go up in the
subsequent years. Mellor and Desai (1985) have provided, primarily based on
Ahluwalia’s estimates of the incidence of rural poverty in India for 1956/57 to 1977/78,
an opportunity to understand the dynamic behaviour of the poverty variable. Table 1.3
shows this picture.
Table 1.3: Changes in the incidence of rural poverty in India, 1956/57 to 1977/78
Subperiod Changes in the percentage of rural population in poverty
1956/57 to 1960/61 Decreased from 54% to 39% 1960/61 to 1966/67 Increased from 39% to 57% 1966/67 to 1971/72 Decreased from 57% to 41% 1971/72 to 1974/75 Increased from 41% to 50% 1974/75 to 1977/78 Decreased from 50% to 39%
Source: Mellor and Desai (1985: 195)
It is clear from the above table that the incidence of rural poverty in India is dynamic,
changing substantially even from one year to the next. But, more importantly, it shows
that for a period of almost two decades there was neither a rising nor a declining trend in
the incidence of Indian rural poverty. This depicts that in independent India the incidence
of rural poverty has not consistently experienced a steady decline.
During the period, West Bengal, a state of India and the study area of the present
research, was no exception and could not extend substantial well-being to its rural poor.
In a study on Bengal (both West Bengal and Bangladesh), van Schendel (1991: 238) has
Introduction
8
stated that two-thirds of the rural population of Bengal were utterly poor. Let us now take
a slight insight into rural poverty of West Bengal.
1.4 Rural Poverty in West Bengal
In a review of several studies, Chatterjee (1998) has observed a very high head count
ratio (per cent), i.e. nearly 80 per cent, for 1973-74 in West Bengal.1 This is, according to
him, partly due to the prevalence of drought in that year. The year 1977-78 saw heavy
rainfall and floods in many parts of the state and the head count ratio is found to be 76.85
per cent. In 1983, the rainfall was moderate to low. But, after 1983, West Bengal
experienced consistently good rainfall. For the years 1973-74, 1977-78 and 1983 even the
average standard of living fell below the poverty in rural areas and this again reached the
poverty line level in 1986-87 in rural areas, when the head count ratio fell to 60.50 per
cent. In 1988-89, the head count ratio became 53.10 per cent. For the signs of sharper
decline of rural poverty in the state in the second half of 1980s, the productivity growth
of West Bengal agriculture (discussed in Chapter 2) in that period is often seen to be
responsible by the scholars. But, in a review of different studies, Rogaly et al (1995)
concluded that, due to the effect of agricultural development in West Bengal in 1980s,
while many gained increased employment and higher wages, relative poverty has
increased and the quality of life of the poorest might have decreased. Besides, Beck
(1994), in a case study in the three villages of West Bengal, observes that poverty rose in
those villages during the late 1980s, even though he documents an increase in incomes of
the poorest households. In his observation in those three villages, employment was
available for only half the year at most, so the villagers had to resort to cuts in
consumption and supplement their diet by wild foods. Beck asked people, who were
regularly hungry, some questions about the amount they ate and shortfalls of food. He
came to know from the respondents the followings:
1 For a detailed review of different measurements of poverty for West Bengal, see Chatterjee (1998).
Chapter 1
9
“Food had to be bought every day, as the cash was not available to buy in
stocks at cheaper prices. Little of respondents’ food requirements came
from ration2 shops. Ration rice, which was about 20 paisa3 cheaper than
rice bought locally with cash, was taken by all except three Fonogram4
respondents when it was available, but the ration shop was open only once
a week, the supplies erratic and the quality of the rice very bad” (Beck,
1994: 136).
Beck (1994) has also provided a representative daily shopping bill, from a Bithigram (a
West Bengal village) household with two adults and two children under six. This shows
the amount the household members estimated they would need to suffice for one day, and
fits closely with other estimates, when adjusted for household size. The bill is given in
Table 1.4.
Table 1.4: A representative daily household shopping bill
Item Amount
Price (in rupees)
Rice 2 kg 6.50 Potatoes 1 kg 1.40 Oil 50 gm 1.20 Chilli 25 gm 1.00 Lentils 100 gm 1.00 Spices 1.50 Mustard oil .50 Tea leaves .40 Pana 1.00 Khoinib .20 Salt .20 Vegetables 1.00 Total 15.90 a Betel leaf; b A kind of tobacco which is cheaper; Source: Beck (1994: 137)
2 This is public distribution system in India through which commodities are sold at administered prices (lower than the open market prices). 3 Paisa (or, paise) is the unit of rupee, Indian currency. 4 This is a village in West Bengal.
Introduction
10
If both the partners of a couple worked, the woman earned eight rupees a day and the
man ten rupees. So they would have enough to meet their daily needs. In Beck’s (1994)
observation, employment was available for only half the year at most, so the respondents
had to resort to cuts in consumption and supplement their diet by wild foods. It was not a
surprise to hear that this household, along with fifty-six of the other fifty-nine households
replying, spent more or less all their daily income on food on a regular basis. Only three
households out of sixty said that they were getting an adequate amount of food.
The impoverishment is also seen, if the daily wage for the casual labourer in rural West
Bengal is looked into. The wage for casual labour is uniform within villages, but it varies
across villages considerably (Bardhan and Rudra, 1981, 1986; Rudra, 1982, 1984;
Walker and Ryan, 1990; Dasgupta, 1993). Table 1.5 displays a remarkably wide range of
wages in a cluster of villages in Illambazar in West Bengal. The daily wage per casual
labourer in most of the villages is either Rs. 5.00 or Rs. 3.00 plus 1.5 kg rice. This wage
could hardly suffice the subsistence requirements of a small family on daily basis with
one member in employment. And, also, this kind of casual employment is not available
throughout the year.
Table 1.5: Wage rates in Illambazar cluster, West Bengal Daily wage rates
Number of villages
Cash only Rs. 4.00 4 Rs. 5.00 10 Rs. 6.00 6 Rs. 8.00 1 Cash and kind Rs. 2.00 + 1.5 kg rice 5 Rs. 3.00 + 1.5 kg rice 22 Rs. 3.50 + 1.5 kg rice 1 Rs. 4.00 + 1.5 kg rice 2 Rs. 6.00 + 1 meal 1
Source: Rudra 1984, (Table 2).
Chapter 1
11
1.5 Poverty Alleviation
The poverty alleviation programmes as are undertaken in the state of West Bengal are
basically the same ones that are implemented in all other states across India. Most of the
poverty alleviation schemes are designed centrally and the implementing agencies for
these programmes are the states.
Land in rural areas is an important asset or an important means of production. Everybody
in villages does not have land. Access to assets other than land—redistribution of which
is somewhat difficult—was thought to be a provider of additional income to the poor. A
very wide range of such assets can be listed. They may include livestock, raw material for
arts and crafts for artisans, small implements or even a better means of transport (a
bullock cart or bicycle or rickshaw). The most important programme which is aimed at
providing additional income to the poor by giving them productive assets of the type
listed above is the Integrated Rural Development Programme (IRDP). In a review of
some case studies, Vyas and Bhargava (1995) find that IRDP has been successful in very
special circumstances—where the asset and employment base of the poor is much secure.
IRDP has done well in relatively developed and prosperous areas, but its performance in
the backward and remote areas has been poor.
One of the major programmes for skill development is Training of Rural Youth for Self-
Employment (TRYSEM). The objective of the programme is to provide youth with some
skills so that they can be self-employed. In a review of TRYSEM’s performance in the
nine states in India, no significant success was observed.5 Vyas and Bhargava (1995)
state that TRYSEM is neither linked with industrial policy nor linked with the rural
industrialisation process. Since the needs are not identified after any systematic review of
demand, the training does not always help the poor to improve their levels of living.
5 The nine states include Andhra Pradesh, Bihar, Gujarat, Haryana, Karnataka, Kerala, Maharashtra, Rajasthan, and West Bengal.
Introduction
12
Two employment generating programmes are National Rural Employment Programme
(NREP) and the Rural Landless Employment Generating Programme (RLEGP). These
programmes were modified into a combined programme called Jawahar Rozgar Yojana
(JRY) in 1989-90. In a study on the state of Bihar, JRY was found to be the most visible
rural development programme (see Sharma, 1995). This may be because of the fact, as
Parthasarathy (1995) comments, that the JRY gave greater flexibility to village
panchayats in the choice of projects. Anyway, Parthasarathy finally found JRY to be
inconsistent with the spirit of multi-level planning since it was not adequately related to
agricultural development.
Given such a background, it is more or less clear that different employment generating
programmes in India need to be given greater importance in order to combat poverty
since poverty in the country is still very acute. At the same time, as a poverty alleviation
programme, promotion of small industries in a wider scale should be taken into
consideration.
1.6 Definition of Small-Scale Industry (SSI) in India:
Since the study is related with the rural industrial sector, we need to define which the
rural industries are. Before doing so, first we are to furnish the definitions of the small
scale industrial (SSI) units, which have been formulated by the government of India. In
the next section, we will discuss the definition of rural industries with a review of
existing literature and thereafter we will give our definition of rural industry that has been
followed in the present study.
The definitions of small-scale industrial units and ancillary industrial units have
undergone several changes in the past. The Government, as they claim, had to make these
changes mainly keeping in view the price escalation over the past years. Small-scale
industries have been defined in terms of the upper ceiling of investment in plant and
machinery (original value) alone since 1966. The investment ceiling for plant and
Chapter 1
13
machinery (original value) which was then fixed at Rs. 7.5 lakhs (Rs. 1 lakh = Rs.
100,000) in the case of SSI units and Rs. 10 lakhs for ancillary units has been revised
upwards several times thereafter as detailed below (see Table 1.6):
Table 1.6: Investment Ceiling for Plant & Machinery (Rs. in Lakhs)
Description 1966
1975 1980 1985 1991 1999
Small-scale industries 7.5 10 20 35 60 100** Ancillary industries 10 15 25 45 75 100** Export oriented small-scale industries
- - - - 75 ***
Tiny enterprises - - 2* 2* 5 25 Small-scale service & business (industry related) enterprises
- - - - 5 10
Notes: * Located in rural areas and towns with maximum population of 5 lakhs (i.e. 500,000); ** In 2001, the Government of India has enhanced the investment ceiling from 100 lakhs (i.e. 1 crore) to 500 lakhs (i.e. 5 crores) for those small-scale industries and ancillary industries who are engaged in manufacturing of 41 specified items. *** All small-scale units which export more than 50% of their output are classified as export oriented units.
Source: Report on the Second All-India Census of Small-Scale Industrial Units and the official website of the Ministry of Small-Scale Industries, Government of India.
The most recent definitions of small-scale units formulated by the Government of India
in 1999 need some more clarifications which are stated below.
1.6.1 Small-Scale Industries:
A small-scale industrial unit is an industrial undertaking in which the investment in fixed
assets of plant and machinery,6 whether held on ownership term or on lease or by hire-
purchase, does not exceed Rs. 100 lakhs (i.e. 1 crore).
1.6.2 Ancillary Industrial Units:
Introduction
14
An ancillary industrial unit is an industrial undertaking which is engaged or is proposed
to be engaged in the manufacture or production of parts, components, sub-assemblies,
tooling or intermediates or rendering of services and the undertaking which supplies or
renders or proposes to supply or render not more than 50 per cent of its production or
services, as the case may, to one or more other industrial undertakings and whose
investment in fixed assets in plant and machinery, whether held on ownership terms or on
lease or on hire purchase, does not exceed Rs. 100 lakhs (i.e. 1 crore).
[Please note that, in 2001, the Government of India has again enhanced the investment
ceiling from 100 lakhs (i.e. 1 crore) to 500 lakhs (i.e. 5 crores) for those small-scale
industries and ancillary industries who are engaged in manufacturing of 41 specified
items.]
1.6.3 Export oriented small-scale industries:
All small-scale units which export more than 50 per cent of their output are classified as
export oriented units.
1.6.4 Tiny Enterprises:
The “tiny” concept was introduced in 1977. As a follow up on “Policy Measures for
Promoting and Strengthening Small, Tiny and Village Enterprises” laid in the Parliament
on 6 August, 1991 the limit for tiny enterprises was enhanced from Rs. 2 lakhs to Rs. 5
lakhs, irrespective of the location of the unit. In 1999, the definition has been changed
again. All small-scale units where investment on plant and machinery (excluding land
and building) is upto Rs. 25 lakhs are classified as tiny industries.
6 This does not include the fixed assets of land and building.
Chapter 1
15
1.6.5 Industry Related Small-Scale Service & Business Enterprises (SSSBE):
Industry related service/business enterprises with investment on plant and machinery
(excluding land and building) upto Rs. 10 lakhs are entitled to be registered under
SSSBE.
1.7 What is Misleading?
It should be noted here that the Government of India has clearly indicated that no small-
scale or ancillary industrial undertaking referred to above shall be subsidiary of or owned
or controlled by any other industrial undertakings. But, in a study on India’s small-scale
industries, Taub and Taub (1989: 17) observed that an indeterminate number of
enterprises was included in counting that formally met all the criteria for a small-scale
industry but did not fit the larger conception of small-scale units as independent
entrepreneurship in action. These were the industries established by large-scale
manufacturers as, in effect, wholly-owned subsidiaries of the larger concern.
Manufacturers set these units up because they could then take advantage of the some of
the benefits available to small-scale units, such as more liberal labour laws and tax
advantages. They did not, however, represent a new or additional contribution to the
industrial economy, and in that sense their inclusion in statistics on small-scale sector
growth, in numbers, output, or whatever, is misleading. Therefore, following this
argument, the governmental claim which states that the small-scale sector presently
accounts for 40 per cent of the industrial production of the national economy seems to be
exaggerating and misleading.
In addition, the associations or organisations of these kinds of small-scale industrial units
have very powerful lobbies in the central government. Therefore, they are very strong in
influencing the government to formulate the definitions of small-scale industries, i.e. to
increase the investment ceiling, in their favour so that they can run their de facto large- or
medium-scale units under the purview of small-scale definition in order to exploit all the
Introduction
16
governmental benefits given to small-scale sector. As a result, having been very poorly
organised and due to having very weak influence on the governmental policy making
body, the real small-scale sector or the tiny sector has failed to be the beneficiary of the
small-scale industrial policy.
1.8 Definition of Rural Industry
The term ‘rural industry’ (or ‘rural industrialisation’) is a confusing term to define. There
is no particular definition. It is also difficult to differentiate between rural and non-rural
industries. The term ‘rural industry’ is often considered to be synonymous with cottage
industries (which constitute household based petty production activities) and,
consequently, ‘rural industrialisation’ with the development and promotion of cottage
industries. This view would, however, be too simplistic and narrow (Islam, 1987).
Currently, rural industries do not necessarily mean only cottage industries. Manufacturing
enterprises using modern machines and tools can also be regarded as rural industries. It is
also not necessary that the rural industries must have a direct link with agriculture.
Diversification of the rural economy through the introduction and promotion of small-
scale manufacturing enterprises (but not necessarily cottage-based ones only) should
essentially mean rural industrialisation (Islam, 1987). But the size of the industries is
important which means it should be of small-scale type (we will define this ‘small-scale’
notion of rural industry in our own definition presented in the next section). Saith (1991)
has argued that no unique and universally appropriate definition can be elicited from the
diverse experiences of ‘rural industrialisation’ in the process of economic development.
Often we use the terms like rural non-agricultural activity or rural non-farm activity.
Broadly speaking, non-farm or non-agricultural activities can include all those activities
which are undertaken outside agriculture. In this sense, rural non-agricultural activity is
essentially a residual category (Basant and Kumar, 1989). Unni (1991) defines that non-
agricultural activities include all economic activities other than crop production and allied
agricultural activities such as animal husbandry, plantations, fishing, forestry etc. They
Chapter 1
17
therefore include agricultural processing and trade (conventionally classified as part of
the manufacturing and commerce sectors, respectively), as well as construction, mining,
transport, and financial and personal services (Rosegrant and Hazell, 2000). Saith (1992)
distinguishes between rural industrial sector and rural non-agricultural sector. According
to him, the rural industrial sector constitutes one part of the rural non-agricultural sector.
The latter also includes various services, household based petty production activities and
non-agricultural labour, which in turn includes work on rural public works programmes
and creation of public infrastructure.
In a study on rural industry, UNDP et al (1988) have defined micro-enterprises with 0-4
employees and small enterprises with 5-25 employees, located in the countryside and in
villages and towns as rural industry. The study has included manufacturing (the
transformation of materials into finished or intermediate physical products) and a few
activities, such as metalworking repair shops (which use much the same equipment and
skills as their counterparts in pure manufacturing). Production and repairs are often
carried out by the same firm. It seems that processing of agricultural products like jam-
jelly-pickle making is included into the definition formulated by UNDP et al, but drying
of raw agricultural produce—e.g., of grains and tobacco—has been categorically
excluded from the their study.
The location factor is also important. The enterprises should be based in rural areas on the
one hand, and the process of rural industrialisation has to involve the rural people, either
as labourers or as entrepreneurs, on the other. The United Nations defines the term ‘rural’
to include locations with up to 20,000 population, the practical definition varies from
country to country. In most cases, some urban industries are also considered by the
scholars and policy makers as rural industries. They ignore the fine border between a
rural and an urban area. The argument is: “Where transport infrastructure and the
marketing and trade network are well-developed, or where general urbanisation is very
marked, larger urban areas may be regarded as locations for rural industries, as long as
such areas provide a comparable environment to small towns” (UNDP et al, 1988: 12).
Often newly settled urban areas (shanty towns) retain many of the rural characteristics of
Introduction
18
the migrants from countryside. Small enterprises in such environments should also be
considered as rural industries, irrespective of absolute population figures. Islam (1987: 3)
argues that “if people in villages have access to employment opportunities available in
nearby small rural towns or market centres (or the so-called ‘rural growth centres’, as
they are often termed), such locations should also be covered by the term rural, in the
present context”. According to Rosegrant and Hazell (2000), rural can be defined as any
locality that exists primarily to serve an agricultural hinterland. They argue that, in
contrast, urban economies are driven by manufacturing, government or some other
economic base independent of agriculture. Given this view, rural areas include all the
rural settlements, central market places and towns that are linked together through
economic transactions related to the agricultural economy. In this connection, we can
refer to Saith (1991) who has emphasized more on the linkage approach than the
locational approach. According to him, there could be several types of industries which
though located in the smaller urban centres nevertheless display exceedingly high rural
linkages, either through a high dependence on rural labour and/or rural raw materials
within production processes which are labour and raw-material intensive.
Another concept is ‘village industry’ which is nothing but a quite similar concept to rural
industry. The term has got a distinct identity in India since it is pronounced with
Mahatma Gandhi’s ideology of economic self-dependence. ‘Village industries’ attracted
Mahatma Gandhi’s attention in 1935. At that point of time, there was no specific
definition given to village industries. The industries in rural areas processing local raw
material for local markets with simple techniques and equipment were categorised under
‘village industries’. After independence, the Khadi and Village Industries Commission
(KVIC) was set up by an Act of Parliament in 1956 to foster the development of khadi7
and village industries in rural areas. After that this Act has been amended quite a few
times. Village industry in the amended Act of 1987 has been defined as follows:
7 Khadi is an indigenous textile in India. The discovery of charka (spinning wheel) was EUREKA for Mahatma Gandhi who made it an integral part of the freedom movement and later inspired the khadi movement establishing All India Spinners Association. After independence, the Government of India brought khadi and village industries into the mainstream of planning process integrating them with the overall economic development.
Chapter 1
19
“Village industry shall mean an industry which is located in a place with a
population not exceeding 10,000 (or such other figure as may be
prescribed from time to time) which produces goods and services, with or
without the use of electric power, and in which the fixed capital
investment per head of artisan/worker does not exceed Rs. 15,000 or such
other sum as may be prescribed from time to time.
However, any non-manufacturing facility or unit that may be located in
place with population exceeding 10,000 in connection with the sole
purpose of promoting, maintaining, assisting (including mother units) or
managing a village industry as defined, shall be deemed to be a village
industry” (KVIC Report, 1994: 37).
1.9 The Definition Used in the Present Study
In the present study, only manufacturing enterprises are considered. In the literature
review, presented above regarding the definitional aspect, we have found four criteria
(viz. technology, investment, personnel, and location) that help in defining rural
enterprise. Let us now set our definition of rural enterprise considering these four criteria.
Technology:
By technology, we mean the production technique used in the non-farm enterprise. It is
broadly divided into two categories—one is primitive and the other is modern. Manually
produced goods as well as products produced by hand-made-machines are called to be
the products of primitive technology. Things produced by machine-made-machines are
called to be the products of modern technology.
Investment:
Introduction
20
For investment criterion, we follow the Government of India’s definition for tiny
enterprises, formulated in 1999 (see Table 1.6 and the corresponding description in this
chapter).
Personnel:
This criterion is not strict at all. Any number of employees will be considered.
Location:
As regards location factor, we include village, rural retail market, rural wholesale market,
and small rural town in the panchayat area. We exclude only municipality area of the
district.
1.10 The Objective of the Study and the Research Question
Absence or presence of entrepreneurship is generally caused by economic conditions on
the one hand, and the personal characteristics and family environment as well as the
psycho-socio-cultural realities on the other. These conditions or realities vary from region
to region and from community to community. In the present study, we are concerned with
rural socio-economic conditions of West Bengal. The core purpose of the study is to
examine a micro issue i.e. whether or not an individual farmer opts for non-agricultural
manufacturing entrepreneurship. We now furnish below the specific objectives of this
study:
• We are to examine personal characteristics of the farmer, which influence on his
willingness of non-farm entrepreneurship;
• We are to understand the influence of economic factors (including financial
family support) of the farmer on his willingness to non-farm entrepreneurship;
• We are to examine the influence of the farmer’s agricultural/occupational
background on his willingness to accept diversified, industrial activity as a
profession;
Chapter 1
21
• We are to perceive the influence of political factor on the farmer’s interest
towards non-farm entrepreneurship;
• We are to see the impact of the psycho-socio-cultural factors on the willingness of
the farmer to be a non-farm entrepreneur;
• We are to understand the influence of the factors relating to risk and innovation
on the farmer’s willingness towards non-farm entrepreneurship.
In this perspective, the main research question that this study poses is:
What are the determinants of non-farm manufacturing entrepreneurship of farmers?
1.11 The Need for the Promotion of Rural Industrial Entrepreneurship (or rural
industrialisation): Significance of the Study
Employment generation in rural areas is very important. Government’s popular
programmes, which do not have deep-rooted vision of development, cannot reap fruit of
sustainable growth. Encouraging policies need to be adopted to channel local capital from
agriculture to non-agriculture, given the fact that free labour is ready to be another factor
of production. What is needed is to encourage non-farm entrepreneurship among the rural
land-owning farmers, because this group constitutes the rural capitalists who can afford
to invest into new (diversified) activities and generate employment for the rural poor.
Thus, the issue of rural industrial entrepreneurship for the growth of rural non-farm
activities and for generating employment provisions assumes special significance. Let us
go into more detail.
It is known to us that the most of the developing countries are predominantly agrarian in
nature and at the same time it is also known to us that the scope of labour absorption in
agriculture is limited (Dunham, 1991: 18; J. Harriss, 1991). So far agriculture and allied
activities have been the major sources of employment in the rural areas in India,
providing 77 per cent of the total rural employment. But with the declining capacity of
Introduction
22
agriculture to absorb labour, the problem of unemployment will persist unless non-
agricultural sources of employment are found. Many researchers have noted that rising
agricultural production is no longer followed by increases in the demand for labour (for
example, Vyas, 1985; Lanjouw and Lanjouw, 1995). The creation of non-farm jobs in
rural areas on a large enough scale is possible only by promoting rural industries.
On the other hand, the observable trends over the past few decades in the less developed
countries suggest that the industrial sector has been unable to generate employment
growth at a rate which can make any appreciable impact on the high levels of
unemployment. This is due to the high capital intensity of modern industrial production
techniques. Large enterprises are not able to produce adequate spread effects, either in
terms of the number of people benefiting from them or in terms of the geographical area
covered. Large undertakings are generally concentrated in a few ideal locations and they
lead to highly polarized development. They also tend to appropriate a major share of
developmental facilities, public economic services, fiscal and financial incentives, etc.
An industrialisation strategy guided by the goal of meeting the needs of the rural poor not
only leads to a different composition of products and of techniques, but it also contributes
to stopping the drift to the cities. In this sense, rural industrialisation is also relevant for
the urban centres, because any effort to treat urban poverty (which is also substantial)
without tackling rural poverty is destined to be overwhelmed by increased migration. By
raising the level of living of the poor people in the countryside, it reduces the pressure to
leave the farmsteads and to expand urban expensive services.
With a few notable exceptions, until two decades ago, little attention was paid to rural
industrialisation and rural non-farm activities except in the form of a few passing
references to it in the planning exercises and plan documents of most countries. Of late,
however, rural industrialisation has become a “fast-moving bandwagon” on which most
developing countries think they can hitch a cheap ride to rural development. One of the
factors underlying the bandwagon effect, with development planners now suddenly
regarding the rural non-agricultural sector as a panacea, has been the massive projection
Chapter 1
23
of the successful, even if exceptional, cases: China and the four (industrialising) East
Asian countries. In India, the role of rural industries was underscored by two major
contending development paradigms proposed in the early post-independence period. The
Gandhian ideology of development gave pride of place to villages and village industries.
The ideological and economic essence of this approach was one that was totally rejected
by the mainstream Nehru-Mahalanobis ideology of rapid industrialisation based on an
accelerated expansion of the modern heavy industries sector.8 But the new-found
emphasis has also to do with an increasing awareness of the relative failure of the
previous industrialisation-oriented development strategies to alleviate the problems of
rural poverty and underemployment, as well as an increasing acquiescene to the narrower
political constraints within which future labour-oriented poverty alleviation strategies can
be adopted. The following discussion appears to be of some importance at the present
conjecture.
Observable trends over the past few decades clearly reveal a worsening in the
employment balance in the developing economies. Rates of employment growth—say, at
average levels of labour productivity—have fallen far short of the rates of labour force
growth. In so far as high (and in many poor countries, rising) growth rates of population,
as well as increasing participation rates (especially in countries where the female
participation rate had been low to begin with), are likely to be relatively permanent
features, the prognosis with regard to the balance is also bleak. On the one hand, the rate
of labour transfer into the modern industrial and service sectors has been uniformly low,
and in many countries has been unable to fully absorb the net additions to the urban
workforce arising from natural increase. This is due partly to the low rates of
industrialisation, but is also the result of the high capital intensity of such industrial
8 We can get an idea of Mahalanobis’s (1968) development ideology from one of his articles, where he wrote: “We must be clear….that no radical cure of unemployment and underemployment would be possible without a rapid growth of modern industries. We must produce an increasing quantity of steel every year. We must produce increasing quantities of heavy machinery and electrical equipment. More and more goods would be then produced by increasing utilization of our domestic resources. To do this, we must, of course, steadily and rapidly increase our domestic savings.”
Introduction
24
growth as has taken place. Hence, any notion that industrial expansion will soon siphon
off the rural backlog of unemployment and underemployment seems patently unrealistic.
On the other hand, the labour absorptive capacity of agricultural intensification strategies
has also proved to be quite limited. In the Asian group, with the exception of Thailand,
there are few possibilities of pushing out the cultivation frontier much, and additional
irrigation—necessary for raising the multiple cropping intensity—is becoming
increasingly expensive in terms of resources. Concomitant agrarian differentiation and
structural shifts induced by capital-oriented patterns of technological change have
aggravated the situation by intensifying ongoing process of rural marginalisation and
proletarianisation of resource-poor rural classes and communities. Indeed, if the Green
Revolution were to continue according to current trends, a net loss of employment per
hectare could occur once farm mechanization extends to such crucial agricultural
operations as harvesting on a wider scale. In view of the declining real earnings of
workers, and widening income disparities in town and countryside, the pessimistic
scenario acquires a keen political edge from the point of view of governments. In this
context, the rural non-farm sector expected to provide cheap jobs for the rural poor.
The developmental rationale and potential role of rural industries are much wider.
Though the immediate objective assigned to rural industries is usually rural employment
generation, focusing on the landless poor, rural industries can achieve a good deal more.
A set of six arguments needs to be mentioned (Saith, 1992).
1. By taking industry to the villages, large-scale rural out-migration can be checked. This
reduces the pressure on scarce urban housing, and infrastructural and other services. By
the same token, wage costs are lower in rural areas where the marginal costs of
migration, town housing and transport, and higher living expenses do not have to be
incurred.
2. To the extent that additional employment is created in towns, matching flows of marketed
surplus of food grains have to be ensured (assuming rising total real earnings), whereas
Chapter 1
25
extensive rural industrialisation based on the employment of surplus rural labour
bypasses the need to induce and manage such transfers. Usually, imperfect or inadequate
marketing of food grains leads to increases in food prices which then erode the real
entitlements of other poor classes.
3. Rural industrialisation could stop the skill drain from the countryside if it provided a
sufficiently lucrative alternative for employment as workers or owner-managers. New
rural enterprises could simultaneously generate modern skills in the rural workforce. A
parallel argument could be extended to rural investible surpluses, which could be
absorbed directly in local income generation instead of being siphoned off through the
banking system, lying idle or circulating in mostly non-productive local money-lending
circuits.
4. Rural industries could utilize local “slack” resources which are not capable of being used
in urban, modern industry. The social cost of such raw materials is extremely low, and
the benefits quite high, as in the case of various waste-recycling activities.
5. At a wider level, rural industrialisation can lead an economy to a greater degree of
regional and sectoral equality and decentralisation. This aspect might be quite important
in systems characterised by geo-natural obstacles to economic integration and where the
transport networks are weak or expensive. Here, rural industries could be the agent of
localized growth that is not overly dependent on fragile and unpredictable links to the
(distant) industrial centre.
6. Rural industrialisation could have significant spin-offs for agricultural development as
well. The extension and deepening of the skill profile of the rural workforce is one
extremely important factor. Another is the interdependent relationship that rural
industrialisation has with the creation of new rural infrastructure, which in turn raises
agricultural productivity, e.g. through better roads, canals, storage facilities and so on.
These activities are typically best executed on the basis of local knowledge and felt need.
Again, almost everywhere, a major element of rural industrialisation is a set of activities
Introduction
26
which support agricultural production directly, e.g. the maintenance, repair and
progressive improvement of farming and agricultural processing implements. Local
small-scale fertilizer plants, brick kilns and stone quarries, local irrigation and power
generation schemes also fall into this category, whose benefits deriving are augmented
through the dynamic backward and forward linkages generated by such local intersectoral
relationships.
This comprehensive listing of the potential roles of rural industrialization explains why it
is treated as some sort of panacea in the present debates on strategic reorientations. A
note of caution might therefore be in order. It would be quite unwarranted to assume any
automaticity in the realization of what are at best the potential benefits of rural
industrialisation. As a process, it does not come in the form of a standardized package. A
question central to the later part is the identification of conditions which promote or
induce entrepreneurship in rural industrial sector. More specifically, identification of the
determinants of non-farm entrepreneurship of the land-owning farmers has got major
focus in the present study.
1.12 Organisation of the Study
The organisation of the whole study is as follows. Chapter 2 broadly describes the state of
West Bengal as the study area. In the latter part, the chapter goes down to Bardhaman
district of the state. It gives a brief history of entrepreneurial background of Bengal. It
provides description of the state’s agricultural and industrial background. It emphasizes
the state’s rural industrialisation as well as urbanisation. Chapter 3 of this book
extensively reviews the literature in the field of rural industry. The discussion
considerably covers the issue of linkage between agriculture and industry and presents
the linkage debate. The chapter incorporates categorization of the non-farm sector. It
points out very briefly the Japanese experience of transition. Amongst other issues, the
chapter emphasizes the role of urbanisation and development of rural infrastructure in
promoting rural industrialisation. Chapter 4 deals with extensive literature pertaining to
Chapter 1
27
the theory of entrepreneurship. It looks at entrepreneurship from the cultural point of
view. Chapter 5 presents the story about data collection for this study. Chapter 6 firstly
presents the theoretical considerations of the empirical model and secondly analyses data
through the LISREL model.9 Chapter 7 presents the conclusions, summary and the
recommendations for policy and further research.
9 The full form of LISREL is LInear Structural RELations.
Introduction
28
Chapter 2
The Study Area
2.1 Introduction
Our study area is Bardhaman district of West Bengal state in India. India was and still
remains to be a vast country. A general discussion on India as a whole cannot give a true
picture of the country. In terms of geography, climate, people, culture, and so many other
aspects, its different parts are of so diverse nature that a common overall assessment of a
particular theme of the country may not provide a complete scene of the play that is
happening across the regions or states of India. It may call for a disaggregative level
analysis—at least a statewise analysis. But a statewise full analysis of the nation may
form such a huge volume that it may take a shape of an epic. We restrict our discussions
in the area of the state of West Bengal as well as Bardhaman district. Before doing so, we
present a very brief background of European and Indian entrepreneurship in India before
independence.
The base of the Indian industry was not so strong since the country had been ruled by the
foreign rulers for about two hundred years. “British paramountcy in India in 1900 was
very much in evidence. The unchallenged political supremacy of the British favoured,
among other things, the exercise of economic power by Europeans resident in India who
derived special advantages from linguistic and racial identification with the rulers of
India…. One important industry where government patronage was crucial and where
Indians were rarely to be found was the engineering industry. Large government
contracts for construction and engineering were rarely, if ever, given to Indian firms.
Since engineering firms in a poor economy with little industry had to depend mainly on
contracts placed by public authorities, there were practically no large Indian firms”
(Bagchi, 1970).
The Study Area
30
The European firms did not bring capital from Europe to India. The inflow of foreign
private capital into India was roughly zero. The European firms mostly gathered their
capital from their earnings in trade, industry, banking, and employment in the army or
various government departments in India. Most of the big managing agency houses made
their money in trade or as labour contractors. Before the First World War, many Indians
from the professional classes had entered the field of industry but, according to Bagchi
(1970), industry which was not backed by a large trading and financing organisation was
not destined to succeed. Given such an industrial background during the British rule,
India started its independent journey in 1947. As we have mentioned earlier, we will
restrict our following discussions in West Bengal and then Bardhaman district, not on
whole India.
2.2 Murshidabad: starting place of British regime
Why do we start from Murshidabad? The place of Murshidabad in West Bengal assumes
historical importance. History of British India started from the place of Murshidabad in
Bengal when, through the battle of Plassey in 1757, Robert Clive occupied the place.
Murshidabad is a small village located towards north of Kolkata (formerly known as
Calcutta) and can be reached after a six-hour train ride. The town was formerly the
capital of Bengal. It attained its prosperity by being the centre of silk production as well
as the seat of government administration. Those visiting the town will find a number of
temples near the place where the Marwaris, residents originally of western Rajasthan (a
state of India), used to carry out their commercial activities. These temples were built by
the Marwaris themselves. Robert Clive wrote a very interesting account of the town as it
was after Plassey battle of 1757:
“The town of Murshidabad was as large as London. The number of its
population was large and the inhabitants were rich. Some of them were
wealthier than that of London. If they really wanted to expel the
Chapter 2
31
Europeans living there, they could have done it at any time they wished”
(see Noboru, 1970: 13).
The silks produced in Murshidabad came to the attention of the British East India
Company in 1656, and resulted in its introduction into the European market. Thereafter,
silk factories sprung up around the town of Kashimbazar. The Dutch and French
immigrants also took part in the movement which eventually converted Murshidabad into
a base area for the European traders-turned-powers to carry on their colonial activities.
The town prospered for more than 200 years as one of the few commercial centres in East
India and became the focal point where the Marwari community built their foundations.
This town which used to enjoy an unprecedented prosperity in the past has become a
calm village today. The town itself has gradually collapsed and the industry has declined.
The silk factories which had once produced one of the principal export products have
disappeared from the town. Its place has been succeeded by the handicraft industry of the
village. Moreover, with the loss of its position as the seat of government administration,
the handicraft industry which processed ivory products destined for use in the palace is
no longer in existence today. Indeed, the prosperity and decline of Murshidabad
represents one of the most significant chapters in the history of India. The colonial rule of
the British deprived its chance of growing into a large city. The town which was
comparable to London in the past has turned into a rural village.
2.3 Bengal in retrospect
Boyce (1987: 3) wrote: “Early travellers to Bengal were struck by its great prosperity. Six
centuries ago the Moroccan explorer Ibn Battuta, whose travels took him to Persia,
China, Sumatra, and Timbuktu, reported: ‘This is a country of great extent, and one in
which rice is extremely abundant. Indeed, I have seen no region of the earth where
provisions are so plentiful’ (Yule, 1866: 457).” The French traveller Bernier, who visited
Bengal around 1660, recorded similar impressions:
The Study Area
32
“Egypt has been represented in every age as the finest and most fruitful
country in the world, and even our modern writers deny that there is any
other land so peculiarly favoured by nature; but the knowledge I have
acquired of Bengale, during two visits paid to that kingdom, inclines me to
believe that the pre-eminence ascribed to Egypt is rather due to Bengale.
The latter country produces rice in such abundance that it supplies not
only the neighbouring but remote states…. Bengale abounds likewise in
sugar…. The three or four sorts of vegetables which, together with rice
and butter, form the chief food of the common people, are purchased for
the merest trifle, and for single roupie twenty or more good fowls may be
bought. Geese and ducks are proportionately cheap. There are also goats
and sheep in abundance…. Fish of every species, whether fresh or salt, is
of the same profusion. In a word, Bengale abounds with every necessary
of life” (Bernier, 1914: 437-9).
Bernier (1914: 439) also described Bengal’s handloom textiles, which then ranked among
the world’s greatest industries: “There is in Bengale such a quantity of cotton and silks,
that the kingdom may be called the common storehouse for those two kinds of
merchandise, not of Hindoustan or the Empire of the Great Mogol only, but of all the
neighbouring kingdoms, and even of Europe.” In 1790 Lord Cornwallis, the Governor-
General of Bengal appointed by the British East India Company, also paid tribute to the
land’s fertility: “We have, by a train of the most fortunate events, obtained the dominion
of one of the most fertile countries on the face of the globe, with a population of mild and
industrious inhabitants, perhaps equal to, if not exceeding in number, that of all the other
British possessions put together” (Firminger, 1917: 542).
The British East India Company was attracted to Bengal above all by the prospect of a
monopoly over the lucrative trade in Bengali textiles. With the development of a
mechanised textile industry in Britain, however, the British rulers sought to eliminate
Bengali competition by means of trade restrictions and the imposition of prohibitive
Chapter 2
33
duties. Not only were Bengali textiles shut out of the British market, but even within
India taxes discriminated against local cloth (Lamb, 1955: 468; Sinha, 1970: 11-12, 41-
3). As a result, while industry developed in Britain, it withered in Bengal. The weavers
were thrown back on to the land.
The decline of cotton was followed by the rise of the cultivation of jute, the world’s
second most important natural fibre, which is used to make rope, Hessian, and carpet
backing. In 1947, with the end of British rule in India, the province of Bengal was
partitioned between India and Pakistan. East Bengal went to Pakistan with a new name
East Pakistan1 (which became independent in 1971 as a sovereign country entitled
Bangladesh) and West Bengal fell into the part of India. Spate (1954) has cited West
Bengal’s disproportionate urban concentration and food deficit, voicing the accepted
wisdom that West Bengal’s agriculture was qualitatively as well as quantitatively inferior
to that of East Bengal (now Bangladesh). But, afterwards, according to Boyce (1987),
West Bengal’s agriculture no longer seems markedly inferior to that of its eastern
neighbour, but Spate’s verdict may well be endorsed by future historians. We will come
to this discussion later.
2.4 A Brief History of Bengali Entrepreneurship
Indeed, it was in and around Kolkata that the beginnings of the industrial age in India
were seen. Kolkata has a tradition of establishment of industries older than in Mumbai
(formerly known as Bombay). The jute industry, perhaps the oldest and largest in terms
of generation of employment and export earnings in pre-independence years, was (and
continues to be) located in a large number of small towns situated up to 60 kilometres
north of Kolkata along the river Hooghly. Kolkata and its sister city Howrah were (and
are) home also to a large steel fabricating industry manufacturing bridges, coaches,
wagons and other equipments for the railways, mining equipment and machine tools
besides repair of ships (Nath, 2000). But there is now a sense of dismay at the depths to
1 East Pakistan was unofficially called as East Bengal.
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34
which Kolkata and Eastern India have fallen, compared to the growth and prosperity that
have touched other regions of India since independence in 1947. The economy of Bengal
as well as the rest of India has been in transition since the imposition of British rule over
the subcontinent in the later half of the eighteenth century. This transitional history has
been well-summarised by Gupta (1991). Earlier, the Dutch had come to Pipli in the
province of Orissa in 1627; by 1655, they were trading both at Hooghly and Balasore in
Bengal. The French came to India in 1688, when they founded the trading post of
Chandernagore, Bengal, in pursuit of Colbert’s decision to establish French trading posts
on the mainland of India. French trade with India reached impressive heights with the
appointment of Dupleix as governor of Chandernagore in 1732. Chandernagore, which
did not have a single ship in 1732, is reported to have “possessed fifteen or twenty
vessels in daily use by the Company’s employees when he [Dupleix] left in 1742” (Sinha,
1965: 35-36).
The British, however, outstripped the French and the Dutch. A series of treaties between
the British East India Company and the successive Nawabs of Bengal gradually
transferred political powers and functions into the hands of the Company. From then
onwards European monopoly in industrial activities prevailed in Bengal. It was early in
the twentieth century when a movement started whose motive was to break the system of
foreign monopoly capitalism and to enable the educated Bengalis to break out of the
narrow confines of service and professions into the wider fields of commercial and
industrial enterprise. The year 1905 saw the beginnings of the “swadeshi” movement—
ostensibly a protest against Curzon’s decision to partition Bengal—whose principal
objective was to boycott the use of British goods and develop reliance on indigenous
products. Bengalis wanted to escape the narrow sector (narrow in the sense that it
excluded industrial and business activities) of the economy to which they had been
confined:
“Living on an inelastic income derived partly from landed property and partly
from clerical service in government and mercantile offices, the majority of the
educated Bengalis found their economic horizon extremely limited. They were
Chapter 2
35
eager to enter the world of business and industry, but lack of capital stood in their
way. The big landlords and the successful lawyers and doctors who were in a
position to supply the capital were reluctant to put money into the hands of young
and untrained persons, and even a man with some training found it difficult to
raise sufficient capital, partly because of the poverty of the people and partly
because of the fear of powerful foreign competition. The fear was real and was
bred from experience. Rabindranath’s brother, Jyotirindranath Tagore, started an
Inland River Steam Navigation Service in 1884 to break the exclusive European
monopoly of river navigation. His swadeshi venture, however, collapsed from the
unequal and unfair competition which he faced from the Flotilla Company
managed by the Hoare Miller group. This pathetic yet magnificent gesture by the
grandson of Dwarkanath Tagore taught educated Bengalis the lesson that
swadeshi ventures were extremely likely to end in debt and financial ruin” (Ray,
1984: 150-151).
The Bengali entrepreneurs did not succeed in their efforts, but the year of 1914 was
provided something of a watershed in the realignment of economic power on the Indian
subcontinent. In the business world of Kolkata, one saw a remarkable breakthrough for
Indian capital. In Kolkata, the investments of Indians rose by 1,609.04 per cent between
1914 and 1922. According to Gupta (1991), European businessmen felt intimidated by
the disappearance of the favourable pre-war political climate and by the increasing
transfer of power to Indians through constitutional reform. They quickly abandoned their
long term policies of reinvesting profits earned in India and began to transfer them to
England as quickly as possible.
This was the time when, according to Gupta (1991), Marwaris emerged in Bengal as a
leading business community (see also Lamb, 1955). Gupta stated that Indian
businessmen, especially Marwaris, had amassed enormous profits during the war.
Actually, from the middle of the nineteenth century, migrating Oswal, Agarwal, and
Maheshwari traders from Rajasthan in Western India had begun to settle in Kolkata and
displaced the Bengalis as the principal collaborators of the expanding British companies.
The Study Area
36
These businessmen kept to their traditional trading activities during the war. After the
war, flush with capital, they aggressively began to finance new ventures. Unlike the
swadeshi period of a decade earlier, shares in companies were fully subscribed in a very
short time. Even Bengali businessmen began to make some headway, especially in such
areas as tannery and leather works, chemicals and pharmaceuticals, pottery, cement, and
coal mines.
Nath (2000) describes that the jute and steel fabrication industries as well as the tea
gardens of Darjeeling and Assam were owned before independence by the Scots. Tea is
one of the largest exports of India and Kolkata is the principal packaging and export
centre. After independence, the Scots’ place has been taken not by the Bengalis but
mainly by the Marwaris. The able and affluent Bengali young men were and continue to
be content with becoming executives, lawyers, physicians, scientists and other
professionals. But with the exception of the Indian Iron and Steel Company, established
by Biren Mukherjee, and some companies manufacturing pharmaceuticals and toiletries
no large industrial enterprise was established by a Bengali.
In Bengal, as Kling (1967) points out, the Marwaris and other non-Bengali business
castes have dominated modern manufacturing and trade. As a result of the lack of
economic opportunity in Rajasthan for the Marwaris and the lack of indigenous
entrepreneurship in Bengal, the Marwaris began entering key entrepreneurial positions in
trade and finance in Bengal, especially in Kolkata, in the nineteenth century. Kling
focuses on the antipathy toward business activity by Bengalis. The “Bengali middle
classes were averse to trade and industry and preferred the liberal professions” (Bagchi,
1970: 240). The lack of indigenous entrepreneurship in Bengal stems in part from the
discrimination and duplicity of the British in the nineteenth century and the low esteem
(of Bengalis) placed on business occupations. In the early nineteenth century,
participation of Bengalis in activities competitive with the British, such as international
trade, resulted in the subsequent exclusion of Bengalis from modern business. In a
number of cases in the 1880s, Bengali firms with established reputations were defrauded
by British partners, an action that led many Bengali businessmen to retreat from trade and
Chapter 2
37
commerce. In addition, although Bengalis admired the boldness and daring of the ancient
romantic merchant-prince and the modern industrialist, they placed a low value on the
profession of traditional businessmen such as the petty trader. The Bengali business
classes, having spurned traditional business and being excluded from modern business,
could find no middle ground suitable for their talents. This lack of participation in
traditional business virtually precluded upward mobility to modern business.
2.5 Agriculture in West Bengal
In a book on agricultural growth in West Bengal and Bangladesh, Boyce (1987)
documented the slow growth of agricultural output in West Bengal (and Bangladesh)
between the 1950s and end of 1970s. According to his conclusions, the simple
exponential growth rate of agricultural output in West Bengal over the period 1949-80
was 1.74 per cent, far below the rate of population growth. Using a slightly different
method, he estimated the rates of growth of aggregate agricultural output for the state as
1.42 per cent and 2.25 per cent for the subperiods 1949-64 and 1965-80 respectively. The
scenario started changing from 1980s. Let us now turn into the agricultural growth debate
of West Bengal.
2.5.1 The growth debate
The West Bengal agriculture started experiencing high growth since the early 1980s.
Harriss (1992) observes that a quite dramatic spurt in agricultural production marks the
end of ‘agrarian impasse’ of West Bengal. The reason he has marked to the remarkable
output growth is the development of private shallow tubewell irrigation. According to
him, all this has taken place in the absence of any reform of the agrarian structure. He
asserts that the development in agriculture is an outcome of some growth in suitable
technology and the reverse situation of the previous kind of extremely unfavourable
fertiliser-paddy price ratio. However, Harriss has received counterattack from Sanyal et
The Study Area
38
al (1998) who found difficulty in agreeing with the attempt of playing down the role of
institutional intervention through land reform measures in accelerating agricultural
production in West Bengal in recent times. They held the land reform measures, initiated
by the Left Front government, responsible the agricultural growth. “Agriculture in West
Bengal was, for the first time, poised for a change when the Left front rule was
established in 1977. ‘Operation Barga’ assumed the dimension of a movement in the
countryside within a few months of its launching in October 1978. Quick recordings of
the names of bargadars and granting legal rights to cultivate land was a major incentive
for the marginal and small peasants to raise production. Provisions were also made for
institutional credit and subsidies to the sharecroppers and assignees of the vested land to
remove their dependency on landlords or money lenders. The impact of these provisions
was felt in agricultural investments and output growth in the 1980s” (p. 2979).
Another dimension, which is the most vibrant one, has been added to the debate centring
the ‘high growth’ in agriculture in West Bengal—especially concerning the method of
calculating the growth rate and the reasons behind the occurrence of such growth. In the
following, we are going to try to present the core of the debate.
Using an index number series on aggregate agricultural production, the exponential
growth rate for all West Bengal for the period 1981-82 to 1990-91 was an impressive 6.4
per cent per annum (Saha and Swaminathan, 1994). The growth performance is
remarkable in comparison to the annual growth rate of agricultural output for the period
1965-80 which was estimated by Boyce (1987: 68) and found to be 2.2 per cent. Some of
the notable features of the districtwise pattern of growth in production, as estimated and
described by Saha and Swaminathan (1994), are as follows (see Table 2.1). With the
exception of Darjeeling and Jalpaiguri, the index of aggregate crop production grew by
over 5 per cent annually in all the districts of West Bengal. By any standard, and
particularly that of the performance of preceding years, this performance is exceptional.
Bankura and Birbhum were ranked at the top with growth rates of 9.5 and 9.3 per cent
respectively. Midnapur and Purulia followed with growth rates of 8.5 and 8.4 per cent.
Next in rank were Nadia and Howrah. In these six districts, growth rates were higher than
Chapter 2
39
the state average. In relation to growth in 1965-80, there has been a tremendous increase
in the growth rate of total output in almost all the districts.
In general, as during 1965-80, the high growth districts are those in the Gangetic plains of
West Bengal. The northern sub-Himalayan districts of Jalpaiguri, Darjeeling, Coochbehar
and West Dinajpur continued to lag behind the southern districts. A comparison of the
position of districts in terms of rates of growth of aggregate crop output in the two
periods, 1965-80 and 1981-90, shows a perceptible change in the ranking of districts over
time (see Table 2.2, the ranking in which follows Table 2.1). Many of the districts that
were ranked in the middle for the period 1965-80 such as Birbhum, Bankura and
Midnapur moved to the top of the ranking in the second period. Purulia witnessed a
remarkable transformation from its last position in 1965-80 to fourth position in 1981-90.
Table 2.1: Decomposition of growth rates of aggregate agricultural production in West Bengal by district (1965-80 and 1981-90).
1965-80** 1981-82 to 1990-91 District Production Production Area Productivity
West Bengal 2.74 (0.33) 6.4 (0.9) 1.2 (0.2) 5.2 (0.8) Bardhaman 3.85 (0.49) 6.4 (0.9) 1.2 (0.5) 5.1 (0.6) Birbhum 1.63 (0.54) 9.3 (1.9) 1.3 (0.4) 8.0 (1.6) Bankura 2.55 (0.90) 9.5 (1.8) 2.3 (0.5) 7.2 (1.0) Midnapur 2.68 (0.53) 8.5 (1.6) 1.3 (0.2) 7.1 (1.0) Howrah 4.30 (0.93) 7.6 (1.0) 2.7 (0.7) 4.9 (1.0) Hooghly 5.82 (0.60) 6.0 (0.6) 1.7 (0.6) 4.2 (0.8) 24-Parganas* 2.49 (0.68) 6.1 (1.6) 0.5 (0.5) 5.6 (0.9) Nadia 3.95 (0.65) 7.6 (0.9) 2.0 (0.3) 5.5 (0.8) Murshidabad 3.15 (0.57) 5.7 (1.0) 0.6 (0.2) 5.1 (0.9) West Dinajpur 1.19 (0.44) 5.5 (0.8) 0.8 (0.2) 6.2 (1.0) Malda 3.21 (0.52) 5.7 (0.6) 2.3 (0.4) 3.5 (0.7) Jalpaiguri 1.28 (0.56) 2.1 (0.8) 0.26 (0.8) 1.9 (0.9) Darjeeling 1.28 (0.82) 3.0 (0.8) 2.2 (0.8) 0.8 (1.2) Coochbehar 1.04 (0.39) 5.2 (0.6) 1.0 (0.5) 4.2 (0.3) Purulia 0.25 (1.23) 8.4 (3.0) 3.9 (1.5) 4.4 (3.0)
Notes: Index number for all crops has base 1981-82 = 100; Standard errors are shown in the parentheses. *Only North 24-Parganas has been considered for the 1981-82 to 1990-91 period while in the 1965-80 period both north and south parts have been included.
Source: Saha and Swaminathan (1994); **Boyce (1987: 259)
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Table 2.2: Ranking of districts in descending order by rate of growth of aggregate agricultural production in West Bengal, 1965-80 and 1981-90
Rank* 1965-80
1981-90
1 Hooghly Bankura 2 Howrah Birbhum 3 Nadia Midnapur 4 Bardhaman Purulia 5 Malda Nadia 6 Murshidabad Howrah 7 Midnapur Bardhaman 8 Bankura 24-Parganas 9 24-Parganas Hooghly
10 Birbhum Malda 11 Jalpaiguri Murshidabad 12 Darjeeling West Dinajpur 13 West Dinajpur Coochbehar 14 Coochbehar Darjeeling 15 Purulia Jalpaiguri
Note: *Based on estimates in Table 2.1. Source: Saha and Swaminathan (1994).
To check the question as to whether or not the agricultural growth occurred has been
influenced by exceptionally good rainfall, Saha and Swaminathan (1994) have included
an index of rainfall (defined as actual rainfall as a percentage of normal rainfall) in the
exponential growth equation.2 The growth estimates have shown that weather elasticities
were insignificant in all but four districts. The estimates of growth did not change
substantially when adjustments for weather were made. Therefore, according to the
authors, the growth performance of agriculture in the 1980s cannot be explained in terms
of unusually good weather conditions. However, the growth was traced by the authors as
an effect of two reasons. First, improved farming practices and the use of high-yielding
varieties (HYV) on a larger scale than before (although, at the same time, the authors
found important barriers to HYV adoption) and the second reason which they found the
most important one is implementation of the programme of land reform along with
decentralisation of power, which was executed by the Left Front government after it had
assumed power in 1977. The authors wrote: “The transition in West Bengal’s agricultural
production performance (and its excellent comparative performance among the states of
2 See also Dev (1987).
Chapter 2
41
India) occurred after a landmark programme of land reform and after the establishment of
new, democratic panchayat institutions in the West Bengal countryside. Panchayats are
active in different production-related activities. Panchayats are involved in water
management; rural workers were organised to sink shallow tubewells to build contour
bunds and to do irrigation-related earthwork of different kinds. Panchayats are reported to
be involved in ensuring that cultivators receive electricity for agriculture, and in the
allocation of rural credit. Panchayats are expected to distribute ‘mini-kits’ or input
packages to small cultivators” (p. A-10).
Saha and Swaminathan’s (1994) thesis have faced vehement criticism from
Chattopadhyay and Das (2000) who have argued in favour of including an explanatory
variable called as rainfall in the model but questioned the treatment of this variable as a
proxy for weather and the consideration of total rainfall index as an aggregative measure.
Since volatile fluctuation in the agricultural production data is quite disturbing, these
authors have remarked that induction of an additional variable, e.g. rainfall, to minimize
the effect of volatile fluctuations in the data on the estimated growth rate is justified. In
their own words:
“The inclusion of rainfall as an explanatory variable in the trend equation
certainly increases the accuracy of growth rate estimations, but treating
this variable as a proxy for weather and estimating total rainfall index as
an aggregative measure are likely to introduce measurement error in the
variable. As a result, the coefficient of rainfall estimated by the OLS
method will be both biased and inconsistent. In other words, the estimated
coefficient will not reflect the true effect of rainfall on production. That is
why, it seems, Saha and Swaminathan (1994) failed to find any significant
effect of rainfall upon agricultural production in West Bengal during
1980s even when the rainfall was quite good during the period and was
expected to have positive effect upon production”3 (p. 120).
3 See also Datta Roy (1994).
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The authors have applied Durbin’s ranking method for measuring the impact of rainfall
on agricultural production in order to obtain both unbiased and consistent estimate of the
coefficient of rainfall. The coefficients of rainfall have been found to be statistically
significant and their regression results conform to their expectation that agricultural
production in West Bengal is still dependent on rainfall and fluctuations in rainfall index
significantly positively contribute to fluctuations in agricultural production in the state.
The authors have alerted that before fitting a trend equation and estimating the growth
rates from the trend equation one should carry out necessary adjustments in the data for
satisfying the criteria of randomness, non-autocorrelation, homoscedasticity and
stationarity of time-series data.4 They argue that the use of R2 or Adj R2 as a measure of
goodness-of-fit and, therefore, as a criterion for choosing the best equation is not
sufficient. The authors claim that their estimate is more reliable as it satisfies all the
econometric criteria. In the result of their estimation (for districtwise growth rates, see
Table 2.3), the annual growth rate of agricultural production in West Bengal during 1977-
78 and 1994-95 was found to be 3.65 per cent which is much lower than the growth rate
estimated by Saha and Swaminathan for the period 1981-82 to 1990-91.5 In this context,
Chattopadhyay and Das (2000) have concluded that “the rate of growth of agricultural
production in West Bengal during the Left Front rule has been certainly higher than that
during the pre-Left Front rule, but much below the growth rate as estimated by some of
4 The authors have remarked that while most of the researchers report in their analysis the value of D-W statistic as a proof of presence or absence of autocorrelation, they hardly make necessary correction in the model in case autocorrelation is present. According to them, the “properties of randomness and homoscedasticity are hardly checked perhaps on the assumptions that non-randomness of the disturbance term is not a serious drawback and heteroscedasticity is not a problem of time series data. In the time series data on agricultural production the heteroscedasticity may be very much present even when the observations are aggregative provided that one part of the aggregate accounts for a major share in the total and the variability in that part is a function of some variables that change over time, such as unirrigated marginal land brought under cultivation, rainfall, etc. Various studies on the fluctuations in agricultural production have pointed out that the extents of fluctuations have in many cases been either divergent or convergent implying thereby that the condition of homoscedasticity in the agricultural production data is not satisfied (pp. 119-120)” 5 “Further, the increase in the growth rate during this period over the pre-Left Front period (2.08 per cent) by 1.57 percentage point is inconformity with the growth of agricultural inputs used in the state during this second period” (p. 128). But the growth rate of 6.4 per cent, as estimated by Saha and Swaminathan (1994), in West Bengal agriculture appears to be much higher than what can be explained by the amounts of inputs used during this period (Datta Roy, 1994: 1883-84).
Chapter 2
43
Table 2.3: Growth rates of districtwise agricultural production in West Bengal, 1957-58 to 1976-77 and 1977-78 to 1994-95.
Growth rates@ Kinked trend equation
Name of the district
1957-58 to
1976-77
1977-78 to 1994-95
Trend break$
Adj R2 D-W Runs
Bardhaman 2.786* (.0011)
3.837* (.0013)
1.051* (.0022)
.9912* 1.515k 15+
Birbhum 2.086* (.0020)
2.435* (.0023)
0.349 (.0039)
.9357* 2.258 c 26***
Bankura 1.269* (.0023)
4.807* (.0026)
3.538* (.0044)
.9569* 1.838 c 17+
Midnapur 1.537* (.0043)
5.074* (.0048)
3.537* (.0082)
.8753* 2.025 c 21+
Howrah 4.049* (.0044)
4.220* (.0049)
0.171 (.0084)
.9151* 1.789 c 17+
Hooghly 4.261* (.0025)
4.727* (.0028)
0.466 (.0048)
.9746* 2.010 c 22+
24-Parganas 2.007** (.0027)
2.702** (.0031)
0.695** (.0039)
.8391* 1.785 c 19+
Nadia 3.911* (.0016)
4.461* (.0018)
0.550 (.0081)
.9884* 2.509 k 23+
Murshidabad 3.068* (.0038)
3.583* (.0043)
0.515 (.0072)
.8973* 1.641 c 16+
West Dinajpur 1.948* (.0017)
3.144* (.0013)
1.196* (.0027)
.9754* 1.558 k 17+
Malda 2.742* (.0039)
3.740* (.0025)
0.998* (.0044)
.9392* 1.777 c 20+
Jalpaiguri 2.050* (.0021)
1.484* (.0017)
0.566 (.0034)
.9214* 2.082 c 20+
Darjeeling 2.513* (.0034)
3.325* (00.41)
0.812 (.00670
.8939* 2.031 c 16+
Coochbehar 1.792* (.0033)
2.314* (.0037)
0.522 (.0063)
.8199* 1.851 c 21+
Purulia 0.687 (.0071)
3.325* (.0065)
2.638* (.0112)
.5622* 1.752 c 21+
Notes: ***, ** and * are significant at 10, 5, and 1 per cent level, respectively. c indicates presence of no autocorrelation, k implies inconclusive at 5 per cent level and non-autocorrelated at 1 per cent level, + random disturbance term, figures in parentheses are standard errors. Growth rates are in per cent per annum form. @ estimated from equation: ln yt or yt = a + b1D1t + b2D2t, t = actual time – break point. Here D1 = 1 and D2 = 0 for the period of 1957-58 to 1976-77 and D1 = 0 and D2 = 1 for the period of 1977-78 to 1994-95. Growth rate for the period 1957-58 to 1976-77 = estimated b1*100 or (estimated b1/harmonic mean)*100; and growth rate for the period 1977-78 to 1994-95 = estimated b2*100 or (estimated b2/harmonic mean)*100. $ Estimating equation: ln yt or yt = a + b1t + b2D2t, and trend break calculated from estimated b2.
Source: Chattopadhyay and Das (2000).
The Study Area
44
the early researchers. Further, the conclusion drawn by these researchers that the rainfall
did not have any significant effect on agricultural production and, by implication
therefore, the higher growth rate achieved during the Left Front rule was due to the
government’s land reform policy and ‘panchayati raj’ system, not due to any external
factor like the occurrence of good rainfall in most of the years, is not acceptable because
of erroneous treatment of rainfall as a variable free of measurement errors and application
of inappropriate technique for measurement of the effect of rainfall” (p. 132).
In the above perspective, it seems that the counterarguments presented by Chattopadhyay
and Das (2000) deserve some credits over the arguments presented by Saha and
Swaminathan (1994), although the thesis of the latter scholars has the merit of
introducing the agricultural development of West Bengal through contributing to the
literature. However, the debate may continue with regard to the method of measuring
actual growth rate of agriculture in West Bengal, but there has already been a consensus
among scholars that since the early 1980s West Bengal agriculture has experienced
higher output growth than what it has witnessed in the first three decades of the post-
independence period.
2.6 Industry in West Bengal
In the following, we mainly present a brief chronological history of industrial
performance in West Bengal which was placed at top in British India and then gradually
started lagging behind the other leading states. West Bengal was foremost among the
Indian states at the time of India’s independence, in terms of concentration of industrial
capital in the field of organised manufacturing sector. Her share in all-India stock of
capital in the manufacturing sector was 24.6 per cent in 1950. In 1993-94, the share had
declined to just 8.8 per cent (Banerjee, 1998). The rate of decline, however, was not
uniform. For a comprehensive introductory note on the industrial background of West
Bengal, we can refer to Raychaudhuri and Chatterjee (1998: 3061) who note:
Chapter 2
45
“In a less developed economy, industrialisation is synonymous with
economic development and one way to boost up industrial activity is
public sector investment in capital goods and large-scale manufacturing
sector. The much quoted Mahalanobis strategy had the same message for
India’s industrialisation. West Bengal’s industrial development also
benefited to a large extent from the public sector investment in the decades
of 1950s and early 1960s. Although large-scale public sector investment
gave a fillip to industrial development in West Bengal, the decline of the
same from the mid-1960s led to a big slump and sickness of industries all
around. One problem to another—labour unemployment led to the politics
of gherao, while the latter led to flight of capital to other states.”
Before independence, as Dasgupta (1998) reviewed, Bengal (including Bangladesh)
became the leading industrialised state in the erstwhile British India. In 1921, Bengal
accounted for 35.1 per cent of the total number of industrial workers in India. In 1939
(just prior to the Second World War), Bengal continued to be the major industrialised
state in India accounting for 28.7 per cent of the total number of industrial workers in
British India. In this regard, a comparative picture between Bengal and some other states
is shown in Figure 2.1. In 1946, according to the first census of manufacturing industries
in India (which covered all factories employing 20 or more workers using power) West
Bengal continued to lead other states in terms of its share in total number of industrial
workers in India, though in terms of value added in the manufacturing sector, West
Bengal was second to Bombay (including Gujarat). This is shown in Table 2.4. In 1948,
“West Bengal had a much higher level of employment in the engineering industries than
Bombay. In the case of chemicals and food, drink, [and] tobacco, Bombay had
marginally higher employment than West Bengal; and in chemicals (leaving out dyeing
and bleaching) West Bengal had greater employment. In other industries such as paper
and leather, West Bengal had higher employment than Bombay. It is only in textiles, in
terms of employment, that Bombay had a clear superiority over West Bengal. Moreover,
the industrial structure of West Bengal was more diversified than that of Bombay”
(Dasgupta, 1998: 3049).
The Study Area
46
Source: Dasgupta, 1998
Table 2.4: Registered factories and factory employment in major Indian states, 1946
Number of registered factories
Total number of employees
Value added (Rs. crore)*
West Bengal 1218 509120 57.32 Bombay 959 500267 87.66 Madras 1244 144931 15.25 Uttar Pradesh 559 166763 21.71 Bihar 316 93523 19.66
*Rs. 1 crore = Rs. 100,00,000. Source: Dasgupta (1998)
Planned industrialisation began in India from 1951 with the beginning of the First Five-
Year Plan. During first 15 years, West Bengal prospered well. In the period between
1951 and 1965, the value of industrial output in West Bengal increased by 287 per cent.
Registered factory employment in West Bengal in the same period increased from
6,51,944 to 8,80,270. But West Bengal fell behind Maharashtra in terms of the number of
Figure 2.1: Per cent of industrial workers in some states
of British India, 1921 and 1939
0
5
10
15
20
25
30
35
40
Madras Bombay Bengal UttarPradesh
Punjab
per c
ent
19211939
Chapter 2
47
industrial licences received (Dasgupta, 1998).6 Figure 2.2 illustrates the comparative
positions of some major states. Actually, the declining trend-point of industrial
performance of West Bengal has been indicated from the data of 1946, as shown in the
Table 2.4 which tells us that Bombay (or Maharashtra) had higher value added though
West Bengal had higher employment. Dasgupta has noted that this feature prevailed till
1965. Higher value added with lower employment would indicate that industry is more
efficient as it generates more non-wage value added which implies greater profitability.
Moreover, the Indo-Pak war of 1965 coupled with harvest failures in the country in two
consecutive years, 1965-66 and 1966-67, brought in an industrial recession. “The
recession of the mid-1960s did not affect all industries uniformly. The major industries
that were affected were food processing, textiles and engineering goods industries, in
particular metal products and transport equipment industries. Within the transport
equipment group, the most adversely affected was the railways equipment industry. After
registering an average annual growth rate of 31.8 per cent during 1960-65, the industry
registered negative growth rates during 1967, 1968 and 1969, respectively” (Dasgupta,
1998: 3050). The evidences found from the book of Basu (1991: 103) tell us that, in
1959, the share of income earned from registered industry in West Bengal to that of India
was 22.2 per cent. In 1963, it became 23.1 per cent. According to him, the share of West
Bengal started going down after 1963—initially at slow pace, but afterwards rapidly. In
1986-87, it came down to 7.4 per cent and 6.5 per cent in 1989. Another review made by
Raychaudhuri and Chatterjee (1998) suggests that the share of the value added in the
industrial sector in West Bengal to that of India as a whole is below 10 percent and shows
a consistent decline since 1970s. The trend rate of growth of industrial production as a
whole during 1980-81 to 1995-96 for West Bengal lags far behind the corresponding rate
for India as a whole. Figure 2.3 gives the comparative picture of the index of industrial
production in India and West Bengal.
6 License system has been abolished in 1991.
The Study Area
48
Figure 2.2: Statewise distribution of applications for licence and licences issued, 1956-66
0
500
1000
1500
2000
2500
3000
3500
4000
Andhra
Prad
esh
Mahara
shtra
Madras
Uttar P
rades
h
Wes
t Ben
gal
Bihar
Mysore
No. of applicationsNo. of licences issued
Source: Dasgupta, 1998
2.7 Urbanisation and infrastructure in West Bengal
The city of Kolkata is the product of the colonial trade. The city did not come out through
endogenous development of economic activities. The exogenous factors played crucial
role in its emergence. We come to know from the review made by Giri (1998) that
industrial growth, primarily based on export-oriented jute industry, took place around
Kolkata because of the port facility, internal river transport network based on Ganges and
the railway infrastructure. Another external factor which contributed to urbanisation and
also urban concentration in West Bengal immediately before independence was huge
refugee migration from the eastern part of Bengal (now Bangladesh). The historical
perspective of urbanisation in West Bengal suggests that in the pre-independence era
Chapter 2
49
urbanisation process in West Bengal was determined largely by the exogenous factors
rather than being a part of the endogenous development of the region (Dasgupta, 1995).
“The result was a monocentric urban development concentrated in and around Calcutta
[Kolkata], with some urban development in the mining belt of Asansol” in Bardhaman
district (Giri, 1998: 3033).
Figure 2.3: Index of industrial production in India and West Bengal, 1980-81 to 1995-96 (Base: 1980-81=100)
0
50
100
150
200
250
300
1980
-81
1981
-82
1982
-83
1983
-84
1984
-85
1985
-86
1986
-87
1987
-88
1988
-89
1989
-90
1990
-91
1991
-92
1992
-93
1993
-94
1994
-95
1995
-96
Inde
x IndiaWest Bengal
Source: Raychaudhuri and Chatterjee (1998)
After independence, the level of urbanisation in West Bengal was 23.88 per cent in 1951.
Although the state had a level of urbanisation above the all-India average (17.29 per
cent), it was fourth in rank, preceded by Maharashtra (28.75 per cent), Gujarat (28.23 per
cent) and Tamil Nadu (24.35 per cent). However, Giri (1998) observes that West Bengal
experienced a slower rate of urbanisation compared to all-India average in the post-
independence period. The gap between urbanisation level of West Bengal and all-India
average sharply declined from 6.59 percentage points in 1951 to 1.67 percentage points
The Study Area
50
in 1991 (see Table 2.5). The comparative picture of the growth rates of urbanisation,
calculated from the Table 2.5, between West Bengal and India is shown in the Figure 2.4.
Table 2.5: Urbanisation in West Bengal and India
Urbanisation level West
Bengal All-India
Gap
(1) (2) (1)-(2) 1951 23.88 17.29 6.59 1961 24.45 17.97 6.48 1971 24.75 20.22 4.53 1981 26.47 23.34 3.13 1991 27.39 25.72 1.67
Source: Giri (1998)
In West Bengal, significant diffusion of urban centres has not occurred after
independence. According to Bhattacharya (1998), the structural system of the state—
nourishing the Kolkata-centric urbanised regions—is found to remain mono-nuclear over
time. Strengthening a major primate city like Kolkata and neglecting the rural sector in
terms of building infrastructure does not help the economy grow as a whole. Migration of
workforce addresses the other side of the issue. In Mellor’s (1995) opinion,
unemployment in rural sector creates problem in urban sector. There is a common
argument that urbanisation is accompanied by a shift of employment and other inputs
from the predominantly rural agricultural sector to the predominantly urban industrial and
service sectors. In the third world countries, with high population growth rate and limited
scope for extending agriculture, unemployment is pervasive in the rural areas. The wage
rate that prevails in rural agriculture is not sufficient to generate demand for non-
agricultural commodities. In such a situation, the manufacturing sector plays the role of
the prime mover behind the urbanisation process and migration from rural to urban areas
takes place. Giri (1998) observed that the rate of urbanisation in West Bengal was closely
associated with the change in the proportion of workforce engaged in the manufacturing
sector. “During the 1960s the pace of urbanisation was the lowest and the proportion of
workers engaged in manufacturing declined from 14.60 per cent in 1961 to 13.87 per cent
in 1971. In the 1980s the slowing down of the urbanisation rate was also accompanied by
Chapter 2
51
a decrease in the share of manufacturing. In contrast during the 1970s an increase in the
share of manufacturing in the workforce was accompanied by an increased rate of
urbanisation” (p. 3037).
Figure 2.4: Rate of growth of urbanisation in West Bengal and India, 1961-1991
2.391.23
6.95
3.483.93
12.52
15.43
10.2
0
2
4
6
8
10
12
14
16
18
1961 1971 1981 1991
Rate
of g
row
th
West BengalAll India
Source: Giri (1998)
2.8 Urban Centres and Small-Scale Industries in West Bengal
Of the total number of 45,954 small industrial units in the state of West Bengal, 8,877
units (19.32%) were found to be located in the metropolitan areas, 16,519 units (35.9%)
in the urban areas and remaining 20,558 units (44.74%) in the rural areas. Considering
the whole volume of the rural areas in the state, the percentage share of the small units
located in the rural areas to the total number of small units in the state is not significantly
higher than that located in the urban areas to the total number of small units in the state.
The Study Area
52
This is shown in Table 2.6 which gives us more explicit picture. West Bengal consists of
17 districts. The district of Kolkata, which is the capital of the state, is almost completely
a metropolitan area. 99.78% units of this district were found to be located in the
metropolitan area. Kolkata is surrounded by the three districts viz. Howrah, North 24-
Parganas and South 24-Parganas and one can always expect strong metropolitan
influence on these surrounding districts. In Howrah, 74.32% of the small scale units were
found to be located in urban areas and 25.68% in the rural areas. This urban
concentration of small enterprises is also seen in North and South 24-Parganas. The
percentage share of the number of small units in the urban areas to the total number of
small units in each of these two districts is higher than that of the number small units in
the rural areas to the total number small units.
Small industrialisation is related with the degree of urbanisation. Not so many small
industries have flourished in those districts of West Bengal which are less urbanised. In
other words, there has been no significant rural industrialisation in the rural-led districts
of West Bengal. The evidence on urbanisation indicates that much of the growth in
factory production and employment is geographically concentrated in a small area of the
state. For example, the Kolkata Urban Agglomeration alone accounted for 64.11 per cent
and 63.64 per cent respectively of the urban population in the state in 1971 and 1981
(Government of West Bengal, 1987-88: Statistical Appendix, 3). This means that,
excluding this Kolkata agglomeration centre, the whole state holds only about 35%
urbanisation which indicates a very poor rate to induce industrialisation in the state. The
growth of small enterprises was inhibited by less urbanisation in the region.
Further, as can be seen from Table 2.7, West Bengal has an extremely skewed pattern of
urbanisation when compared with an industrialised state like Tamil Nadu. Though West
Bengal is not too far behind in the average degree of urbanisation, with 26.5 per cent of
the population located in urban areas as compared with 33 per cent in Tamil Nadu, only 7
of its 16 districts have more than a fifth of its population in urban centres as compared
with 12 out of 16 in Tamil Nadu.
Chapter 2
53
Table 2.6: Dispersal of small enterprises in West Bengal Enterprises located in
Rural Urban Metropolitan Sl. No.
Name of the district
Total number of enter-
prises Number Per cent Number Per cent Number Per cent
(1) (2) (3) (4) (5) (6) (7) (8) (9) 1 Bankura 1,690 1,435 84.91 2,55 15.09 - - 2 Birbhum 2,088 1,526 73.08 562 26.92 - - 3 Bardhaman 3,280 1,958 59.7 1,322 40.3 - - 4 Kolkata 8,897 3 0.03 17 0.19 8,877 99.78 5 Coochbehar 593 310 52.28 283 47.72 - - 6 Darjeeling 696 247 35.49 449 64.51 - - 7 West Dinajpur 781 4 17 53.39 364 46.61 - - 8 Hooghly 3,049 1,953 64.05 1,096 35.95 - - 9 Howrah 7,369 1,892 25.68 5,477 74.32 - -
10 Jalpaiguri 1,329 1,103 82.99 226 17.01 - - 11 Malda 1,060 828 78.11 232 21.89 - - 12 Midnapur 4,140 3,369 81.38 771 18.62 - - 13 Murshidabad 1,770 1,237 69.89 533 30.11 - - 14 Nadia 1,981 1,257 63.45 724 36.55 - - 15 South 24-
Parganas 2,455 915 37.27 1,540 62.73 - -
16 Purulia 1,570 1,040 66.24 530 33.76 - - 17 North 24-
Parganas 3,206 1,068 33.31 2,138 66.69 - -
TOTAL 45,954 20,558 44.74 16,519 35.95 8,877 19.32 Note: *Percentages with respect to total number of units in column (3). Source: Report on the Second All-India Census of Small-Scale Industrial Units, Department
of Small-Scale Industries, Ministry of Industry, Government of India.
If we go down to the level of tehsils/blocks, 63 per cent of the tehsils/blocks in West
Bengal have an urbanisation level of less than 10 per cent, while only 28 per cent of the
tehsils/blocks in Tamil Nadu are characterised by less than 10 per cent urbanisation. It
means that not only is a larger proportion of the population resident in rural areas in West
Bengal, but the urban population is squeezed into a few pockets, which are themselves
unevenly distributed across the state. Since the manner in which urban areas are defined
tends to include segments where there has been a significant development of factory
industry, this would mean that even the limited direct and indirect employment effects of
the growth of factory production have not touched much of the state’s population and
regions. Thus rural sources of employment in general and agricultural employment in
particular have to be the means of livelihood for a major part of the region and its
population.
The Study Area
54
Table 2.7: Urbanisation in West Bengal and Tamil Nadu as per 1981 census
District Percentage of urban to total
population
Number of Tehsils/ Blocks
Number of T/B with nil urbanisation
Number of T/B with 0-10% urbanisation
WEST BENGAL 26.5 Coochbehar 6.9 8 2 7 Jalpaiguri 14.1 14 5 8 Darjeeling 27.6 13 7 8 West Dinajpur 11.2 16 9 10 Malda 4.8 10 7 9 Murshidabad 9.4 21 11 12 Nadia 21.6 14 5 8 24-Parganas* 38.9 55 23 26 Kolkata (Metro.) 100.0 Howrah 45.1 12 3 6 Hooghly 29.5 20 9 12 Midnapur 8.5 39 26 31 Bankura 7.6 19 14 16 Purulia 9.0 18 11 13 Bardhaman 29.4 27 10 14 Birbhum 8.3 14 8 9 TAMIL NADU 33.0 Chennai (Metro.) 100.0 Chengalpattu 38.9 12 2 3 North Arcot 23.5 13 1 4 South Arcot 15.7 12 4 6 Dharmapuri 9.4 8 2 5 Salem 28.9 9 1 3 Periyar 22.0 6 0 1 Coimbatore 20.5 6 0 0 Nilgiri 48.9 4 0 0 Madurai 36.2 13 0 3 Tiruchirapalli 26.1 10 0 1 Thanjavur 23.1 16 2 3 Pudukottai 13.3 6 1 4 Ramanathapuram 28.2 18 1 3 Tirunelveli 34.6 14 1 3 Kanniyakumari 17.2 4 1 3 Note: * Here the 1981 Census provides the data for the undivided district of 24-Parganas which
was later divided into two districts, i.e. North 24-Parganas and South 24-Parganas. West Bengal Industrial Development Corporation (WBIDC) data individually shows that the rate of urbanisation in North 24-Parganas is 51.23% whereas the rate in South 24-Parganas is only 13.30%, although both the districts are close to Calcutta. This fact correlates with the growth of small enterprises in the way that the number of small enterprises in South 24-Parganas is less than that in the other surrounding districts of Calcutta.
Source: Chandrasekhar, 1993. Original source: Census of India, 1981, various reports.
Chapter 2
55
2.9 Rural Industrialisation and the Role of Infrastructure in Rural Towns
In the state of West Bengal, underdeveloped areas are mainly called backward areas
which are faced with very less urbanisation. The metropolitan district Kolkata is
considered as non-backward area. So are the parts of three other districts surrounding
Kolkata. Table 2.8 shows the distribution of units in backward and non-backward areas.
It is remarkable here that the non-backward area in the state consisting of only the whole
part of Kolkata district and some parts of other three surrounding districts together holds
42.83% share of the total number of small units in the state, whereas the huge backward
area in the state holds only 57.17% share. This picture shows a clear urban concentration
of small enterprises. The area of Greater Kolkata not only includes Kolkata district and
parts of its three surrounding districts but also a substantial part of Hooghly district,
which enjoys a quick metropolitan link with Kolkata. The locational advantage of
Hooghly, as it is a part of Kolkata agglomeration, gives its small enterprises an urban
environment to grow in number (see Table 2.8). Apart from the large Kolkata
agglomeration, if we look at the other districts individually in the Table 2.8 then
Bardhaman and Midnapur districts naturally draw the attraction for holding the small
enterprises more than 3000 in each. This requires further explanation. After the Kolkata
agglomeration, another important largest urban area in the state is the Asansol-Durgapur
agglomeration. This agglomeration belongs to the district of Bardhaman. The main towns
in this district are Asansol, Bardhaman, Durgapur, Burnpur, Kulti, Raniganj and
Chittaranjan. Due to its dispersed urban nature, Bardhaman district holds a significant
number of small enterprises. In this perspective, only the case of Midnapur district is
exceptionally different. It includes only Midnapur town, Kharagpur town, Kharagpur
railway settlement and a growing town Haldia. The rate of urbanisation in this district is
only 8.5% (see Table 2.7). Yet, according to Table 2.8, the number of small enterprises in
this district is exceptionally very high. Apart from Midnapur district, the rural picture in
West Bengal is miserable. Rural industrialisation in the state has not been promoted.
Infrastructure and services are heavily concentrated in the metropolitan city Kolkata only
and the adjacent districts have gained some peripheral impacts. Sufficient infrastructure
facilities are not dispersed throughout the state.
The Study Area
56
Table 2.8: Distribution of enterprises in backward and non-backward areas
Enterprises located in Backward area Non-backward area
Sl. No.
Name of the district
Total number of enterprises Number Per cent* Number Per
cent* (1) (2) (3) (4) (5) (6) (7)
1 Bankura 1,690 1,690 100 0 02 Birbhum 2,088 2,088 100 0 03 Bardhaman 3,280 3,280 100 0 04 Kolkata 8,897 1 0.01 8,896 99.995 Coochbehar 593 593 100 0 06 Darjeeling 696 696 100 0 07 West Dinajpur 781 781 100 0 08 Hooghly 3,049 3,049 100 0 09 Howrah 7,369 86 1.17 7,283 98.83
10 Jalpaiguri 1,329 1,329 100 0 011 Malda 1,060 1,060 100 0 012 Midnapur 4,140 4,140 100 0 013 Murshidabad 1,770 1,770 100 0 014 Nadia 1,981 1,981 100 0 015 South 24-
Parganas 2,455 181 7.37 2,274 92.63
16 Purulia 1,570 1,570 100 0 017 North 24-
Parganas 3,206 1,979 61.73 1,227 38.27
TOTAL 45,954 26,274 57.17 19,680 42.83 Note: * Percentage with respect to total number of enterprises in column (3). Source: Report on the Second All-India Census of Small-Scale Industrial Units,
Department of Small-Scale Industries, Ministry of Industry, Government of India.
Rural industrialisation requires investment on basic infrastructure throughout the state
either to help grow rural towns in a geographically dispersed way or to provide rural
towns with basic infrastructure facilities. Regardless of agroclimate, the denser the
infrastructure, rural town services and population, the greater the earnings from the rural
non-farm sector (Reardon et al, 1998). In general, the quality and quantity of physical
infrastructure (e.g. roads) and social infrastructure (e.g. schools) tend to be correlated
with population density and the development of rural towns. More developed
infrastructure and denser population means lower transaction costs for market products
(farm or non-farm) and a greater availability of inputs (electricity, water etc.) at a lower
cost. Hence, infrastructure quality and quantity have often been identified as key
determinants of non-farm business investments. Islam (1987) has also argued that both
Chapter 2
57
physical and social infrastructure play an important role in rural industrialisation.
According to him, the contrasting experiences of Taiwan and Korea amply demonstrate
this.
In Taiwan province of China, the shift made by rural households to non-farm sources of
income began in the late 1960s. Structural reforms in the late 1960s stimulated the
spectacular expansion of an outward-oriented export economy. Manufacturing grew by
20 per cent per year, leading the way in the sustained double-digit growth of GNP. The
consequent pace of labour absorption in the industrial sector took the steam out of the
population pressure on the land frontier. The growth of industry is evenly spread across
space—a well-known and much lauded feature of the Taiwanese economy. Urban centres
are themselves geographically dispersed and infrastructure is also well distributed,
making it possible for industrial estates to flourish in the smaller towns (Reardon et al,
1998).
The opposite picture was seen in the republic of Korea throughout its rapid growth period
in the 1960s and 1970s. Why? It was observed that manufacturing activities were
concentrated in just two growth poles: Seoul in the north and Pusan in the south, along
with the adjacent provinces. The population in the other provinces remained dependent
on agricultural occupations. This was because of the fact that infrastructure and services
were heavily concentrated in the urban centres. The option of commuting from the
countryside was constrained by an inadequate rural road network. Instead, there was
considerable migration to the cities (Reardon et al, 1998).
Finally, let us mention the study carried out by Chapman and Wanmali (1981) who have
argued that successful development is correlated with an extensive and general regional
urbanisation. They noted that urbanisation in India is at too low a level to facilitate the
diffusion of a modern commercial sector in traditionally agricultural areas; in particular
there are too few towns of the smaller sizes to allow for the proper integration of the
urban and rural sectors which hinders modernisation of the latter. In West Bengal, as we
have seen that the small scale enterprises are concentrated in Kolkata and its immediate
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satellites, this is a result of strengthening a major primate city which is seen to be the
centre of introverted economic systems. Development policy has to take cognizance of
the fact that it cannot rely upon nor utilize the urban framework, but has to be directed at
the rural level. It calls for a massive scheme of the infrastructure development in rural
Bengal which in turn will facilitate the growth and development of rural non-agricultural
sector. This also, of course, mirrors Rostow’s attitude towards capital accumulation for
the agricultural sector in the early stages of growth. Dasgupta (1993) remarks that it is
hard to imagine that investment projects in rural infrastructure could be anything but
worthwhile. Mody (1981) appreciates the necessity of resource flows into agriculture
during the initial period of development for building rural infrastructure, whether or not
these are reversed subsequently. The success of industrialisation will depend to a large
extent not only on the capability of agriculture to generate surpluses but also on whether
and how these surpluses can be channelled into industrial investment.
2.10 Bardhaman’s economy in retrospect: A glimpse
Bardhaman is a pre-dominantly agricultural district of West Bengal, although some parts
of the district are industrially developed. In the early period of the (British) East India
Company’s rule Raja of Bardhaman was at the top of the landed society in the district and
at the bottom was the cultivator of the soil. In between them there was a group of landed
aristocrats who did neither enjoy the status of the Raja nor lived very ordinarily like the
cultivators. Commercialisation of agriculture was introduced by the British and the
system transformed from subsistence farming to cash crop production—mainly rice. The
rich farmers profited from the new system as the prices of food crops rose. But the
system did not favour the small farmers. “It forced the small peasant to mortgage his land
to the money lenders or rich peasants and ultimately to sell it to the latter. Thus the
jotedar-cum-rural creditors possessed large holdings and they became prosperous at the
cost of primary producers. But their accumulation of land was not propelled by an urge
for the development of large-scale farming on capitalist lines” (A.K. Dutta, 2002: 72).
The large farmers had sufficient fund, but they did not invest it for technological up-
Chapter 2
59
gradation or in innovation in order to raise agricultural productivity. They restored to
sharecropping cultivation because surplus could easily be extracted through
sharecropping which entailed almost no risk on the part of the landlords. Finally, A.K.
Dutta remarked that the district of Bardhaman during the British rule represented an
agrarian system where the prevalence of a backward form of cultivation and production
was found. Thus, from the producer’s side, capitalist farming could not grow in the
district.7 From the demand side, lack of purchasing power of the village people gave no
fillip to the grain production on the one hand and, on the other, “in the absence of rapid
industrialisation, the urban market for agricultural products underwent no expansion”
(A.K. Dutta, 2002: 73). Moreover, due to the limited domestic market the cash crops
could not bring in sustained long-term development in agricultural sector. Although there
was considerable growth of cultivation and exports of cash crops, local farm economy
received marginal benefits from it and consequently capitalist farming did not grow since
the producer himself was not directly involved in exporting his produces. Rice mills
emerged in the district as an agro-based industry, but no other agroindustry developed in
the district so far. Rice milling industry was located in semi-urban areas, primarily in
subdivisional towns and the district headquarter.
The pressure of population was not felt in Bardhaman during the second half of the 19th
century. Population remained almost static till 1921. However, the surplus labour force
was found in 20th century Bardhaman. Moreover, the population of the district increased
after 1921 (see Table 2.9 which is further placed in Figure 2.5 that shows a steady growth
of population after 1921). But the picture of the non-agricultural sector which could have
absorbed the surplus labour force was not impressive at all. The only big industry in
Bardhaman was coalmining. With the progress of coalmining industry, iron and pottery
works emerged in the western part of the district. The iron works started at Barakar in
1875 by Barakar Iron Works Company, Limited, which however collapsed in 1879.
About two years later, the government restarted the works and ran them for about eight
and a half years. It was renamed as the Bengal Iron and Steel Company Ltd.
7 For a detailed review of literature and discussion on the complex agrarian structure in Bardhaman district during the British rule, see the book of A.K. Dutta (2002).
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Source: A.K. Dutta (2002)
Besides coal and iron, Raniganj/Asansol subdivision was famous for pottery works. Clay-
works were manufactured on a large scale in Raniganj and Durgapur. Messers Burn and
Company Limited of Raniganj excelled in using the fire-clay for manufacturing
stoneware pipes, red-colour roofing tiles, bricks, and pottery. The pottery works of this
company seem to have been started in 1860. The other famous companies were the
Raniganj Pipe Works and Bengal Firebrick Syndicate of Kulti. In tiny sector, a special
branch of iron and steel works is cutlery in which blacksmiths manufacture knives,
scissors etc. The most famous and primary centre of cutlery industry in Bardhaman
district was Kanchannagar, a suburb in the south-west of Bardhaman town, five miles
away from Bardhaman railway station. It is often said that the cutlery industry in
Kanchannagar is five hundred years old. But there is no historical documentation about it.
The industry is still traceable at Kanchannagar but it has lost its former glory long ago.
In the past, some other indigenous handicrafts also existed in Bardhaman district. They
include weaving industry, brass and bell-metal works, conch-shell manufactures and so
on. Weaving occupied an important place in the traditional indigenous cottage industry of
the district. Of the three branches of weaving industry—cotton, silk and wool, the first
Table: 2.9: Population in Bardhaman, 1891-1951
Year Population 1891 1391880 1901 1528290 1911 1533874 1921 1434771 1931 1575699 1941 1890732 1951 2191667
Figure 2.5: Population in Bardhaman, 1891-1951
0
500000
1000000
1500000
2000000
2500000
1880 1900 1920 1940 1960
Population
Chapter 2
61
two flourished in the district. From 1880s the weaving industry in Bardhaman as well as
in Bengal faced hard competition from machine-made Manchester cloth and the
indigenous industry started decaying. Nonetheless, 18,117 persons were engaged in
different sections of cotton weaving industry in Bardhaman in 1891. The chief centres of
the weaving industry in the district were Purbasthali, Kalna, and Manteswar in which
about 2000 persons were daily employed in 1908-09. According to 1921 census, there
were 3942 handlooms in Bardhaman district (see A.K. Dutta, 2002: 203).
In this section, we have tried to present a very brief description of Bardhaman’s
economy, mainly in the field of agriculture and industry, in the pre-independence era
based on available evidences. The literature suggests that commercial agriculture had not
led to the technical transformation of agriculture. The large farmers could increase their
landed property, but it did not result in substantial increase in production; rather it had
encouraged cultivation in smaller farms by sharecroppers who continued to retain labour-
intensive techniques in their production units. On the other hand, the industrial scenario
in the district did not show any remarkable picture. Rice milling industry was found to be
the most important food-processing industry of Bardhamn. The rice mills in Bardhaman
were really big but modernisation in the mills of the district notably began in the 1960s
and became widespread in the 1970s. The modernisation took place in the milling plant
only; processing of paddy still followed the traditional method.
2.11 Recent Bardhaman: A glimpse
In the present section, we offer the brief and salient aspects of the economy of
Bardhaman district in recent time. A detailed overall description of the district is
presented in APPENDIX 2.1. To start with, one can always observe that rice is the major
crop of West Bengal. During the 1980s and 1990s, rice production in the state increased
rapidly. So far as rice production is concerned, Bardhaman is traced as an important
district in the state. In Neil Webster’s (1990: 47) version: “It is one of the main rice-
producing districts in the region and was the first district to be chosen for the intensive
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Agricultural Development Programme in the state, in 1962, and thereby became a
forerunner in the green revolution.” Saha and Swaminathan (1994) showed that in 1980-
81 and also in 1990-91 shares of Bardhaman district in rice production were the highest
in the state (see Table 2.10). Again, Saha and Swaminathan considered the period
between 1980-81 and 1990-91and Bardhaman was found to be one of the important rice-
producing districts of the state (see Table 2.11).
Table 2.10: Share of Districts in rice production in West Bengal
Table 2.11: Growth of rice production in West Bengal (by district), 1980-81 to 1990-91
Percentage of total rice production
District Production District
1980-81 1990-91 Midnapur 16.8 14.6 West Bengal 6.19 (4.5)Bardhaman 13.4 13.6 Bardhaman 6.19 (4.7)24-Parganas 12.4 11.1 Birbhum 6.4 (3.4)Bankura 7.9 8.1 Bankura 7.4 (3.5)Birbhum 7.8 7.8 Midnapur 6.4 (3.6)Hooghly 6.9 5.8 Howrah 7.6 (4.9)WestDinajpur 6.6 7.4 Hooghly 4.1 (2.9)Murshidabad 5.7 7.5 24-Parganas 5.9 (3.4)Purulia 4.7 3.6 Nadia 9.9 (8.2)Jalpaiguri 3.9 2.4 Murshidabad 7.6 (3.9)Malda 3.8 4.7 West Dinajpur 5.7 (5.0)Nadia 3.7 6.7 Malda 6.0 (7.3)Coochbehar 3.7 3.8 Jalpaiguri 1.2 (0.8)Howrah 2.0 2.2 Darjeeling 3.0 (1.9)Darjeeling 0.7 0.7 Coochbehar 4.6 (5.3)All districts 100 100 Purulia 5.7 (2.0)
Source: Saha and Swaminathan (1994) Source: Saha and Swaminathan (1994)
More recent data show that yield rates of total cereals (including rice) in Bardhaman
district in 1998-1999 and 1999-2000 were found to be 3056 and 2738 kgs/hectare
respectively. The yield rates of total cereals in Bardhaman district in both the years were
the highest of all districts (Evaluation Wing, Directorate of Agriculture, Government of
West Bengal, 2001). This can be depicted as a justification for why we have selected
Bardhaman district as our study area. Since Bardhaman district is the main rice/cereal
producing district in the state, it may be interesting to examine the farmers’ participation
Chapter 2
63
in non-agricultural, industrial, activities through investment of their surplus generated
from agriculture.
Let us now turn towards the industrial picture of the district after independence.
Durgapur, located in the district of Bardhaman, had been projected in the fifties and
sixties as the second city of West Bengal as far as industries are concerned. During the
period covered by the Second and Third Five Year Plans, a number of public sector
enterprises were set up in Durgapur. The most important among them was the Durgapur
Steel Plant which not only provided employment to thousands of people but also
facilitated the setting up of upstream as well as downstream ancillary industrial units.
There is also the Alloy Steel Plant in Durgapur, which produces a large number of metal
alloys and which also feeds the demand of downstream metal product units. In the
chemical sector, Durgapur Chemicals Ltd. is a large plant located in Durgapur. Keeping
in view the fact that the district of Bardhaman is agriculturally well developed, the
government had set up the Hindustan Fertilizers Ltd. at Durgapur. Another public sector
unit at Durgapur is Durgapur Projects Ltd.
The public sector industries are located outside Durgapur too. At Chittaranjan, there is
the giant Chittaranjan Locomotive Works which is the largest manufacturer and supplier
of railway engines in India. The Indian Iron and Steel Company (IISCO) is situated at
Burnpur. The present financial situation of IISCO is not satisfactory. Hindustan Cables at
Rupnarayanpur is also a large public sector enterprise. The Eastern Coalfields Ltd., the
public sector mining company, is also located in Bardhaman district. However, it is to be
noted here that the large public sector undertakings are all concentrated in the two
western subdivisions of the district, viz. Durgapur and Asansol, while the three eastern
subdivisions, viz. Bardhaman, Kalna, and Katwa, have got very little share of the public
investment in the large-scale industrial sector.
In the private sector too, there are a number of large-scale industrial units in Bardhaman
district. The major large-scale private industrial units of Bardhaman district are listed
below.
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1. J.K. Paper Mill, J.K. Nagar 2. Raniganj Paper Mill, Raniganj 3. Philips Carbon Black Ltd., Durgapur 4. Burn Standard Co., Durgapur 5. Shanky Wheel, Durgapur 6. IFB Agro, Durgapur 7. Dishergarh Power Supply Corporation, Dishergarh 8. Aluminium Factory, Asansol
Apart from the above, thirteen new large industrial projects were implemented in the
district during 1999-2000. The list of the projects implemented along with other details is
furnished below:
Table 2.12: Projects implemented in Bardhaman district during 1999-2000 Sl.
No. Name of the company Product Project cost
(Rs. in crore) 1. Bengal Nestor’s Industries Ltd. Dairy products 3.50 2. Bhaskar Shrachi Alloys Ltd. Casting of iron and steel, and steel ingots 3.00 3. Bhaskar Shrachi Alloys Ltd. Silicon/ferro manganese, ferro silicon 9.90 4. Elegant Commerce Ltd. Heavy rounds 19.60 5. Jai Balaji Sponge (P) Ltd. Sponge iron 3.88 6. Jolla Steel Pvt. Ltd. M.S. Ingot 1.40 7. Khalsa Tubes Pvt. Ltd. Lancing pipes 2.20 8. Maithan Alloys Ltd. Ferro alloys 7.10 9. Sethia Oils Ltd. Refined oil, Vanaspati, fatty acid 7.50
10. Shyam Ferro Alloys Ltd. Ferro alloys plant 11.50 11. Shyama Cast Ltd. Sponge iron 10.00 12. Sova Ispat Alloys (P) Ltd. High carbon ferro manganese alloys 5.37 13. Srinivasa Ferro Alloys Ltd. H.C. ferro manganese and H.C. ferro
alloys 5.33
Source: Economic Review, 2000-2001
Several other large- and medium-scale projects are under construction in the district. The
list is furnished in the following table:
Chapter 2
65
Table 2.13: Projects presently under construction in Bardhaman district Sl. No.
Name of the company Product Project cost (Rs. in crore)
1. Adhunik Steel Ltd. Steel ingots/billets 4.50 2. Bardhaman Iron and Steel Co. Pvt.
Ltd. Mini steel plant 2.00
3. Bengal Recycles and Fabrications Pvt. Ltd.
M.S. ingots 8.72
4. Core Ceramics Burnt clay fly-ash bricks
13.75
5. Corporate Ispat Alloys Ltd. Ferro alloys 7.00 6. D.K. Paul and Co. Pvt. Ltd. Steel ingots 3.16 7. Haldia Steels Ltd. Ferro alloys 5.44 8. Howrah Gases Ltd. Clinker slag, grinding
unit 4.11
9. Howrah Gases Ltd. Sponge iron 2.73 10. Indian Institute of Aviation Ltd. Aviation training centre 8.00 11. Jagadamba Fiscal Services (P) Ltd. Polythene compound of
all types 3.11
12. Jai Balaji Sponje Pvt Ltd. Sponge iron mills, steel strips
35.00
13. Jalan Cold Storage Pvt. Ltd. Cold storage 3.00 14. Jawala Steel Ltd. M.S. ingots 46.84 15. Larsen and Tubro Ltd. Portland cement 150.00 16. Ma Chandi Durga Ispat (P) Ltd. Ferro alloys 14.00 17. Monnet Ferro Alloys Ltd. H.C. ferro chrome, ferro
manganese 8.85
18. Rashmi Ispat (P) Ltd. PP/HDPE woven fabrics 5.03 19. Rescon Chemicals (P) Ltd. Rice-husk ash nodules 3.33 20. S.D. Tools (P) Ltd. Forging steel 2.00 21. Shyama Cast Ltd. M.S. ingots 3.00 22. Stalberg Gmbh Flux for steel making 27.00 23. Super Semelter Ltd. - 24.04 24. Vinayak Steels (P) Ltd. Sponge iron steel
casting 80.40
Source: Economic Review, 2000-2001
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APPENDIX 2.1: Overall Description of Bardhaman District
Location of Bardhaman District
Bardhaman, sometimes called and spelt as Burdwan (as pronounced by the British), is
located between the latitude 23053’ N and 22056’ N and between the longitude 88025’ and
86048’. The district has a total area of 7024 sq. kms. and is bordered by Birbhum district
in the north, Murshidabad district in the north-east, Nadia in the east, Hooghly and
Bankura in the south and Purulia in the south-east.
Climate and Rainfall
The climate of the district is generally hot and the western part, mainly covered by
Asansol and Durgapur Sub-Divisions, is drier than the eastern part which is very fertile.
The district enjoys ample monsoon showers and is agriculturally very well developed.
Sometimes, it suffers from floods due to the overflowing of Damodar and Ajay rivers
during monsoon.
On an average, Bardhaman district experiences the maximum rainfall from July to
September. The mean annual rainfall of the district is around 1470 mm. The temperature
of the district varies between 50 C and 440 C. April and May are the hottest months and
December and January are the coldest. Table 1 furnished below gives us a picture of
rainfall of Bardhaman district.
Table 1: Rainfall pattern of Bardhaman district for the period 1997-1999 Year
1997 1998 1999
Actual Rainfall (in mm)
1529 1261 1964
Source: District Statistical Handbook 1997-1999
Chapter 2
67
Soil
The soil of the part of Bardhaman district is of rich alluvial variety and is perfectly
suitable for intensive cultivation of paddy, wheat, potatoes and other crops and
vegetables. The soil of the western part of the district is reddish and is not that fertile.
Rivers
The district of Bardhaman is extremely fortunate of being girdled by three major rivers—
the Hooghly on the east, the Ajay on the North and the Damodar on the south.
Apart from these three, there are myriads of minor rivers and streams across the district.
The Hooghly, the Ajay and the Damodar are all perennial rivers.
Forest
The aggregate forest area of the district is 25196 hectares and the forests are mainly
spread over the western part of the district. The forests are mainly covered by Sal and
Kendu trees. The main forest products are timber and fuel-wood.
Distribution of Population
Bardhaman is one of the most populous districts in West Bengal. According to the 1991
Census, it has an aggregate population of 6050605, the population density being 861
persons per sq. km. Raniganj, Jamuria, Niamatpur, Dishergarh, Burnpur, Andal and
Durgapur are densely populated areas while the blocks of Ketugram, Mangalkote, Galsi
II, Khandaghosh, Raina I and II, Bhatar, Ausgram, Jamuria I and II are sparsely
populated regions.
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Administrative Set up
The district comprises five administrative sub-divisions viz Bardhaman Sadar, Durgapur,
Asansol, Kalna and Katwa. The Development Blocks coming under the individual Sub-
Divisions are as follows:
Bardhaman Sadar: Bardhaman I and II, Ausgram I and II, Bhatar, Memari I and II,
Jamalpur, Raina I and II, Khandaghosh, Galsi II.
Asansol: Salanpur, Barabani, Raniganj, Jamuria.
Durgapur: Galsi I, Andal, Durgapur-Faridpur, Kanksa, Pandabeswar.
Katwa: Mangalkote, Ketugram I and II, Katwa I and II.
Kalna: Purbasthali1 and 2, Kalna I and II, Monteswar.
In all, there are 31 administrative blocks throughout the 5 Sub-Divisions. The district has
32 police stations, 31 Panchayat Samitis, and 278 Gram Panchayats.
There are nine municipalities (M) and two municipal corporations (MC) in Bardhaman
District. The names of municipalities, municipal corporations and the sub-divisions to
which they fall under are given in Table 2 below:
Table 2: Distribution of municipal areas in the five sub-divisions Sub-Division
Municipality (M) and municipality corporation (MC)
Bardhaman Bardwan (M), Guskara (M), Memari (M)
Asansol
Kulti (M) , Raniganj (M), Jamuria (M), Asansol (MC)
Kalna Kalna (M)
Katwa Katwa (M), Daihat (M)
Durgapur Durgapur (MC)
Chapter 2
69
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70
Chapter 3
Issues Relating to Rural Industries: A Review of
Literature
3.1 Introduction
This chapter discusses the literatures which have dealt with the various issues related to
rural industries. The role of agriculture in economic development with special reference
to industrial development is one of the major points of discussion that has been
highlighted in the following sections. More explicitly, a substantial portion of the chapter
discusses the interlinkage between agriculture and industry (industry in general and rural
industry in particular). The discussion on such interlinkage has got a long tradition,
starting from the classicists to the present day economists. During last few decades, many
authors have put agricultural development at the central place as an engine of prosperity
while discussing early stages of development of a nation. It is known to all that the
economies of the most of the developing nations are predominantly agrarian in nature.
Agriculture is the main pillar of those economies because most the people of the
developing nations live in villages and earn their living from farming and trades related
to farming. Rural industries in those economies are often seen to have been very much
dependent on agriculture. In the countries like India and Bangladesh, for instance, rice-
milling is an example of rural industrial activities. Before rural electrification, labour-
intensive dhenki method was used to process rice and hence dhenki constituted the single
largest source of non-farm employment for rural women. After electrification, dhenki was
replaced by rice milling. In both the cases (i.e. before electrification and after
electrification), rural non-farm economy has remained heavily dependent on agriculture
as well as agriculture-based off-farm activities except some specialized craftsmanship. A
traditional (agricultural) society may gradually start moving towards modernisation
through diversification of rural economy, in the first place, from agriculture to
agriculture-based industrial activities and thereafter to pure non-agriculture. Therefore,
for modernization, industrialization and prosperity of rural sector, diversification of rural
Issues Relating to Rural Industries: A Review of Literature
72
economic activities is very important—especially in those countries where marginal and
landless peasants constitute a sizeable population. But diversification of rural agrarian
economy, according to some economists, finds viable route of prosperous ladder only
after the economy is self-sufficient in producing agricultural output. There are some other
scholars who reserve their rights to give agriculture all credit for bringing about
successful rural industrialisation. They believe that agricultural growth may be a
necessary condition for the development of rural non-farm sector but not a sufficient
condition. This debate has been vividly described in this chapter amongst other issues. In
this connection, development of village infrastructure is an inevitable issue for a
complete discussion on rural industrialisation which, in turn, relates to the issue of
urbanisation. In this context, we can refer to Deininger and Olinto (2001: 455) who
opine: “There is little doubt that the importance of rural non-farm employment, which in
many countries already constitutes an important sector of the rural economy, will greatly
increase as agriculture becomes more and more integrated into global markets and as the
links between urban and rural areas intensify.”
The organisation of the chapter is as follows. Section 3.2 offers a brief presentation of the
discussion on agriculture-industry linkage in the classical political economy. Section 3.3
gives us the categorization of the rural non-farm sector. In section 3.4, Kuznets’s Growth
Theory is presented. An econometric model on the relationship between agriculture and
industrial growth is presented in section 3.5. Section 3.6 discusses the theories and
theoretical debate regarding the rural growth linkages. The role of agriculture in Japanese
industrialisation is briefly described in Section 3.7. The next section, i.e. section 3.8,
highlights the reasons for household participation in rural non-farm activities. Section 3.9
discusses the non-farm jobs as secondary and seasonal employment. Sectoral
composition of rural non-farm employment is described in section 3.10. Section 3.11
offers the Hymer-Resnick Model and Ranis-Stewart Models which raise the agriculture-
rural industry linkage debate through the theoretical models. We present relevant
discussion on urbanisation in section 3.12. The chapter concludes with the section 3.13.
Chapter 3
73
3.2 Agriculture-Industry Linkage: The Classical Political Economy Framework
3.2.1 The Physiocrats and Adam Smith
The analysis of the linkages between agriculture and the rest of the economy dates back
to Quesnay in 18th century France (Harriss, 1987; Bharadwaj, 1987; Eapen, 1999). Some
members of the Physiocratic School laid great stress upon the agricultural sector which
produced an economic surplus or net product (“produit net”) and so played a very
significant role in economic development (Gide and Rist, 1915).
Quesnay distinguishes three social classes:
I. The first is a productive class consisting entirely of agriculturists—perhaps
also of fishermen and miners.
II. The second is a proprietary class, including not only landed proprietors, but
also any who have the slightest title to sovereignty of any kind in the society.
This class is seen as the bearers of the responsibility of survival of feudalism,
where the two ideas of sovereignty and property are always linked together.
The identity of this second class needs to be explained further. The
classification of basic social groups in the Physiocratic model was made with
primary reference to the relation in which each group stood to the net product.
The main distinction which they emphasized was that between the ‘productive
class’ (i.e. those engaged in agricultural production) and the ‘sterile class’ (i.e.
those engaged in non-agricultural activities). The sterile class has been
defined in the third category (see below). In the no-man’s-land between these
two classes, partaking to some extent of the character of each but belonging
definitely to neither, lay the class of proprietors. This class consisted of the
landowners, the king, and the clergy, who were assumed to receive, in the
form of rent, taxes, and tithes respectively, the value of the net product which
agriculture annually yielded. (Meek, 1962: 20)
Issues Relating to Rural Industries: A Review of Literature
74
III. The third is a sterile class, consisting of merchants and manufacturers (mainly
constituting self-employed artisans and craftsmen),1 together with domestic
servants and members of the liberal professions.
To the Physiocrats, the agriculturists were seen to be the productive class. Hence, the first
class, being the only productive class, must supply all that flow of wealth. But
agricultural products alone do not suffice for the upkeep of Class I. Gide and Rist (1915)
argue that Class I also requires manufactured goods, which it must get from the sterile
class. On the other side, salaries obtained by the sterile class are employed in buying the
necessaries of life and the raw material of industry.
The most strange part of this theory is that the class III includes manufacturers on the one
hand but ignores manufacturers as a productive class on the other (agriculturists were
considered to be the only productive class by Quesnay). What led Quesnay to his
conclusion, which astonished his contemporaries no less than subsequent generations of
economists, was a belief that industry as constituted in France in the seventeenth and
eighteenth centuries could make no kind of net contribution to the nation’s tax revenues.
Its ‘net product’ (produit net), or taxable capacity, or economic surplus, was zero, which
meant that at best, if its support cost the rest of society nothing, it could make no
contribution to the military and welfare needs of the state (Eltis, 1988). In less favourable
circumstances where industry actually needed to be subsidised or protected, such
diversions of real resources would impoverish the primary producing sector which
provided the surpluses on which French governments relied. Criticism came up from the
fact that manufacturing and commercial states like Venice and Holland had accumulated
wealth and power, so how could it possibly be argued that agriculture provided the
ultimate source of all wealth and of all net government revenues? Quesnay insisted that
taxable industrial and commercial profits could arise only where businesses had managed
to achieve elements of monopoly power. Eltis (1988) said that this had arisen in
Quesnay’s Europe in a variety of ways. States frequently granted monopoly privileges to
political supporters, or else they sold future monopoly rights for current cash, or they
allowed corporations with monopoly power to emerge by protecting their own countries’
industries. Such policies were prevalent throughout Europe and they had allowed
1 The artisan (manufacture) sector was believed not to produce any surplus (or net product) and
Chapter 3
75
extremely profitable corporations to emerge. And it was also true that industrial
innovators could sell at monopoly prices, but these would disappear as soon as others
learned to make the same new products. In addition, France’s great jewellers and
furniture makers of the ancien régime had temporary monopoly-rights over their
distinguished products which allowed them to sell at home and overseas at very high
prices which yielded financial surpluses.
Whatever be the argument, in the context of our core discussion the significance of
Quesnay’s theory is that the linkage between class I and class III, i.e. linkage between
agriculture and manufacturing (although sterile) has gained recognition in the
Physiocratic school of thoughts.2
In contrary to Quesnay, Adam Smith stressed upon manufacturing sector by recognising
that industry contributes also to ‘net product.’ While talking about natural progress of
wealth or opulence Adam Smith divided the “course” into three different stages. The
greater part of the capital of every growing society is, first, directed to agriculture,
afterwards to manufactures, and last of all to foreign commerce. Therefore, the great
similarity between Quesnay and Adam Smith is that both of them discussed the role of
agriculture in the process of economic development. Adam Smith believed that
agriculture had the potential to provide a vastly greater economic surplus than industry,
but in his judgement the surplus industry offered was not zero (Eltis, 1988). In addition,
hence called sterile. 2 In this context, we can refer to Meek (1962: 21-22) who has described such linkage concept from the Physiocrats’ point of view. If the net product is increasing from year to year, the level of agricultural output and, therefore, the general level of economic activity will rise. An increasing net product means, in the first place, that the landowners will spend more on agricultural produce; they will also spend more on manufactured goods, and the makers of these goods, whose incomes are thus increased, will in their turn spend more on agricultural produce; thus the aggregate demand for agricultural produce will increase, output will be stimulated, the net product will increase further, and the general level of economic activity will rise progressively from year to year. An increasing net product means, in the second place, that more will be available for investment. The landowners will have more to spare for investment in improving their land; the agricultural entrepreneurs, in so far as they happen at the time to be sharing in the net product, will have more to spare for maintaining and expanding their fixed and working capital; thus aggregate investment in agriculture will increase, and once again output will be stimulated, the net product will increase further, and the general level of economic activity will rise progressively from year to year. The eventual result of this process will be the attainment of maximum level of output consistent with the state of the country’s resources and the existing techniques. If the net product is decreasing from year to year, on the other hand, a cumulative downward trend in the
Issues Relating to Rural Industries: A Review of Literature
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industry could be expected to provide external benefits of great importance to the whole
economy through the productivity advances associated with the division of labour. The
important difference between Smith and Quesnay is that Smith believed industrial profits
could include an element of economic surplus in the sense that industrial capitalists can
save and invest from their profits, with the result that they have the potential to add to the
growth of the economy. Smith’s main objection to Quesnay’s argument is of course its
neglect of the enormous advantages a society can obtain from the division of labour
which can be taken further in industry than in agriculture. The division of labour in
industry leads to more extensive subdivisions of as well as expansion in industrial
employment, and hence to the achievement of higher productivity through the invention
of superior machinery. Smith argues that a country which successfully develops its
industry can attain far more favourable terms of trade between agricultural produce and
manufactures than one that is still without a substantial industrial sector. Although it is
clear that Smith stressed more on industry than agriculture, he also mentioned of the
interlinkage between agriculture and industry. According to him, the great commerce of
every civilised society is carried on between the inhabitants of the town and those of the
country. “The country supplies the town with the means of subsistence and the materials
of manufacture. The town repays this supply by sending back a part of the manufactured
produce to the inhabitants of the country” (Adam Smith, 1776: 479).
3.2.2 Karl Marx
In dealing with the process of capitalist development, Karl Marx believed that the
capitalist relations would ultimately engulf agriculture as well as industry with a
concentration of property in land, the proletarianisation of peasants, large productivity
gains of capitalist agriculture, displacement of working capital and labour previously
engaged in small-holdings and artisan manufactures.
Marx (1954)3 viewed the capitalistic system of production as the destructive force of
rural domestic industry. We quote him from Capital (Vol. 1): “Formerly divided among a
level of economic activity will take place, leading either to complete ruin or to the achievement of a sort of sub-optimum equilibrium at a very low level.
Chapter 3
77
number of small producers, who cultivated it themselves and with their families spun it in
retail fashion, it is now concentrated in the hand of one capitalist, who sets others to spin
and weave it for him” (Marx, 1954: 698). Hence, the process started supplying wage-
labour to industry. He added: “Formerly, the peasant family produced the means of
subsistence and the raw materials, which they themselves, for the most part, consumed.
These raw materials and means of subsistence have now become commodities; the large
farmer sells them, he finds his market in manufacturers. Yarn, linen, coarse woolen
stuffs—things whose raw materials had been within the reach of every peasant family,
had been spun and woven by it for its own use—were now transformed into articles of
manufacture, to which the country districts at once served for markets. The many
scattered customers, whom stray artisans until now had found in the numerous small
producers working on their own account, concentrate themselves now into one great
market provided for by industrial capital. Thus hand in hand with the expropriation of the
self-supporting peasants, with their separation from their means of production, goes the
destruction of rural domestic industry, the process of separation between manufacture
and agriculture. And only the destruction of rural domestic industry can give the internal
market of a country that extension and consistence which the capitalist mode of
production requires” (pp. 699-700). The thinning-out of the independent, self-supporting
peasants brought about an increase in the number of industrial proletariat in the urban
sector. In spite of the smaller number of the cultivators, the soil brought forth as much or
more produce, after as before, because the revolution in the conditions of landed property
was accompanied by improved methods of culture, greater co-operation, concentration of
means of production and because not only were the agricultural wage-labourers put on
the strain more intensely, but the field of production on which they worked for
themselves, became more and more contracted. With the setting free of a part of the
agricultural population, therefore, their former means of nourishment were also set free.
The peasants, expropriated and cast adrift, must buy their value in the form of wages,
from his new master, the industrial capitalist. Thus, formation of glut of agricultural
produce was not possible. “That which holds good of the means of subsistence holds with
the raw materials of industry dependent upon home agriculture” (pp. 697-698).
3 This edition was first published in 1954 by Progress Publishers, Moscow. We have quoted Marx
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78
There are two aspects in Marx’s view described above. Marx believed that agriculture
and rural domestic industry go hand in hand, but capitalistic mode of production draws a
separation between agriculture and domestic industry since in order to enter into the
internal market the capitalistic production requires the destruction of rural domestic
industry. Another aspect is implicitly present in the above text and we can find a
connection of this subtext to the present situation of many developing countries. In the
following, we discuss the second aspect and it may sound relevant in the light of the
present discussion regarding rural industry although it is not related with the interlinkage
between agriculture and industry.
Marx’s core argument, considering the capitalistic system of production as the
destructive force of rural domestic industry, is still very pertinent in the context of the
present situation of the many developing countries, especially when we see that the
multinationals or the big business houses are entering the markets of rural domestic
industry. A glaring example, given by Taori and Singh (1991), is of potato wafers, the
demand for which is estimated to be of the order of Rs 400 crore4 out of which Rs 40
crore worth is supplied by the large-scale sector and the remaining still by small
entrepreneurs, shopkeepers etc. So many rural entrepreneurs, scattered across the
country, are self-employed in the small-scale potato wafer industry. They may lose their
incomes if the big companies enter their markets. However, because of aggressive
marketing, the demand for potato wafers in the large-scale sector is recording a growth of
30 per cent per annum. The days are not far off when television advertisements will have
a tremendous impact on the rural population as well, replacing the local sources of
supply. With the onslaught of the modern expensive advertisements on television which
covers a large share of the population with novel noises of sales campaign and packaging,
the rural industries have a difficult time for surviving.
In the classical political economy framework, the agriculture-industry linkage has been
articulated very lucidly. The expansion of non-agricultural activities has been viewed
primarily in terms of the changing relationship between agriculture and industry.
Agriculture, initially, is the mainspring of all economic activities, including within its
fold very simple and primitive forms of industry viz. processing of raw materials within
here from the 1986 edition of the book.
Chapter 3
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the household or artisan production which may sometimes be a supplement to a peasant
agriculture. Industry at this stage is considered more an appendage of agriculture, in
which situation one can hardly speak of interlinkages. However, with the growing
division of labour and increasing specialisation in the process of capitalist development,
industry separates spatially and organisationally from agriculture and the sectoral
distinction becomes clearer. Over time the interlinkages between agriculture and industry
grow through the progress of technology and diversification of production in terms of
inputs and outputs (Eapen, 1999).
3.3 Categorisation of the Non-farm Sector
The discussion so far points to the fact that the non-farm sector does not consist of a
homogeneous set of activities in terms of income and productivity levels. While
observing the non-homogeneous characteristics, duality in the non-farm sector has been
identified by the two researchers in relatively early studies. Mukhopadhyay and Lim
(1985) suggested that the rural non-farm sector consists of two sub-sectors. Sub-sector 1
are enterprises run on a more or less stable basis with an eye on surplus generation and
growth, using primarily hired labour and a certain degree of technological sophistication.
Sub-sector 2 consists of products and/or activities which are often, though not always,
seasonal, which are run with the help primarily of unpaid family labour, using rather
primitive technology, catering mostly to the local market and responding more to the
supply side of the labour market than to the market demand for output. There are both
labour supply side and product demand side stimuli to the growth of rural non-farm
sector, or ‘push’ and ‘pull’ factors from the point of view of labour absorption. Sub-
sector 2 substantially responds to the supply side of the labour market because of the fact
that this sub-sector is very much labour intensive whereas rural labour force is primarily
dependent on agriculture. Therefore, sub-sector 2 finds an inflow of labour force in the
slack agricultural seasons since agricultural labourers lose their jobs in farming during
slack period.
4 Rs 1 crore = Rs 10000000.
Issues Relating to Rural Industries: A Review of Literature
80
Rural industry has also been categorised, based more on the products, into three types.
First, the production of low quality and cheap variety of goods meeting certain
consumption needs, using locally available raw material and primitive techniques.
Second, agro-processing industries. Third, transitional location of modern industry in
rural areas which leads over time to these areas being absorbed as urban centres
(Chandrasekhar, 1993).
Another interesting categorisation is based on the locational versus the linkages approach
to rural industrialisation (Saith, 1991). The definition of rural industry in the first
approach is primarily based on location in rural areas. It helps as a safety valve to contain
urban congestion. In the second approach rural industry is viewed from the point of
whether it generates sufficient linkages in the rural sector. In Saith’s (1991) argument,
preference is given to the linkage approach, and the sector is understood to include all
economic activities which display sufficiently strong rural linkages, irrespective of
whether they are located in designated rural areas or not. The following four categories
thus emerge:
(a) rural-located, rural-linked;
(b) rural-located, urban-linked;
(c) urban-located, urban-linked;
(d) urban-located, rural-linked.
Only the third category is completely urban. The other three categories, connected either
through location or linkage with the rural population, cover a remarkably wide variety of
activities and enterprises, ranging from household-based cottage and handicrafts
production to relatively medium sized, modern, and complex industrial plants. Which
approach—locational or linkage—is found more meaningful depends upon the objectives
of policy and the concrete circumstances of the economy. Saith (1991) has argued that it
is necessary to emphasize that the generation of rural linkages is not connected with a
deep rural location. Whether a locational criterion is super-imposed on the linkage one
would depend upon the case with which resources, including the rural labour force, could
move from their rural residential locations to urban work-places. The rural industrial
sector should be viewed from the rural end, and the key criterion for defining an
industrial enterprise or other economic activity as ‘rural’ is whether it generates
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81
significant developmental linkages with the rural sector. According to Saith, one simple
index of the intensity of linkage effects could be the percentage of the gross output value
of the enterprise that is accounted for by the rural sector either through receipts for rural
raw materials purchased by the enterprise, or through income flows received in the form
of wages or profits for labour or capital provided by the residents of the rural sector.
Restricting the index to the disposition of the value added by the enterprise would be
inappropriate since it would exclude the linkage through the raw material purchases made
in the rural sector. Not all industries located in designated rural areas would necessarily
show high levels of rural linkage. This might be especially true for cases where modern
medium or large-scale industrial enterprises are being invited through incentives to
relocate their plants in designated rural area. Although these industries are entitled to take
the pressure off the urban centre, they might still operate within the high urban linkage
effects more or less intact. So the policy makers should be aware of this phenomenon of
rural linkage effects.
Now we turn to the three sources of demand, mentioned below, for the products and
services of rural non-farm activities. World Bank (1978) has made the following
categorisation:
1. Non-food consumer as well as durable (e.g. wooden furniture) goods and
services, the demand for which rises as rural incomes increase;
2. Inputs and services to agriculture (including tools and equipment, repair services,
transport, processing, and supporting infrastructure and works), the demand for
which rises with agricultural development;
3. Manufactured goods and handicrafts, the demand for which stems from markets
in other regions or from abroad. This category of products has no direct demand
linkage with the local rural area and is mainly produced for the specific target
buyers of other regions.
Almost the same kind of categorisation has been made by Islam (1987). According to
him, the broad components of the demand for products of rural industries are: (a)
household demand, (b) intermediate demand and (c) export demand. A large part of the
products of rural industries are meant for final consumption by rural households or use as
inputs in the agricultural sector.
Issues Relating to Rural Industries: A Review of Literature
82
3.4 Kuznets’s Growth Theory
What is the position of the rural non-agricultural sector in the growth models? The
models have divided the whole economy into two broad sectors—agriculture and
industry—as it was observed in the classical political economy framework. Promotion of
the rural non-farm economy as an employment-oriented strategy has assumed no
significance in most of the growth theories. Saith (1992) has pointed out that economic
theorising within both the classical and neo-classical traditions, as well as analyses of
structural transformation along the lines of Clark, Kuznets and Chenery, have generally
ignored the existence of the rural non-farm sector. In the broad sweep of the process of
economic transformation of an agrarian economy into a modern industrial one, this
subsector, sandwiched between two major ones, has implicitly been regarded as a
manifestation of incomplete transition to the full-blown industrial economy where the
rural sector sheds all its non-farming functions, which are regrouped within the modern
sector.
Let us take Kuznets’s views as example. Kuznets has talked about the direct linkage
between agriculture and non-agriculture. The important features are:
1. Expansion of the non-agricultural sector is strongly reliant on domestic
agriculture, not only for a sustained increase in the supply of food, but also for
raw materials used in manufacturing products such as textiles.
2. Because of the strong agrarian bias of the economy during the early stages of
economic growth, the agricultural population inevitably forms a substantial
proportion of the home market for the products of domestic industry, including
the market for producer goods as well as consumer goods.
3. Because the relative importance of agriculture in the economy inevitably declines
with economic growth and development, agriculture is seen as a principal source
of capital for investment elsewhere in the economy. Thus the development
process involves the transfer of surplus capital from agriculture to the non-
agricultural sector. Similarly, development also entails the transfer of surplus
labour from agriculture to non-agricultural occupations, especially over the long
term.
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In the above features, Kuznets has made it clear that during the early stages of
development agriculture serves as an engine of non-agricultural growth, but in the long
run as non-agricultural sector grows the relative importance of agriculture declines
caused by resource transfer from agriculture to non-agriculture. This “long run”
relationship between agriculture and non-agriculture has been shown by Kuznets
mathematically. He found an inverse relationship between these two sectors in the long
run.
An expression derived from Kuznets shows how the agricultural sector’s share of GDP
growth is related to the product of agriculture’s initial share of GDP and the relative rates
of growth of agricultural and non-agricultural net products (see, for example, Ghatak and
Ingersent, 1984). Let
aP = agricultural net product
nP = non-agricultural net product
P = total national product
Then:
na PPP += (1)
and
nn
na
a
a PPP
PPP
P∂
+∂
=∂ (2)
Writing ar for aa PP /∂ and nr for nn PP /∂ :
nnaa rPrPP +=∂ (3)
Therefore, nnaa rPPrP −∂= (4)
and PrP
PrP nnaa
∂−=
∂1 (5)
Substituting for P∂ on the right hand side of equation (5) from equation (3):
nnaa
nn
rPrPrP+
−= 1
nnaa
aa
rPrPrP+
=
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84
( ) aannaa rPrPrP /1
+=
aann rPrP /11
+= (6)
Kuznets’s formula expressing an inverse relationship between agriculture’s share of GDP
growth ( )PrP aa ∂/ and the product of the ratio of sectoral shares of GDP ( )an PP / and the
ratio of sectoral growth rates ( )an rr / is given by equation (6). This equation implicates
that in a developing economy where per capita incomes are rising, growth in the
agricultural sector can be expected to lag behind non-agricultural sector. It means that in
a rapidly growing economy non-agriculture starts playing more important role than
agriculture (although Kuznets, as we have seen before the above mentioned mathematical
presentation, has recognised the crucial role of agriculture during the early stages of
economic development. in the mathematical presentation he has talked about an economy
which is not greatly dependent on agriculture, rather where non-agricultural sector has
already gained momentum). This may happen for three reasons. First, the demand for
food and other agricultural products is generally less income-elastic than the demand for
non-agricultural products. Mellor (1976) observed that agricultural labourers in India,
represented mainly by landless workers, spend over three-quarters of increments to
consumption on agricultural commodities and 59 per cent on foodgrains alone. He also
observed that, in successively higher expenditure classes, the proportion of increments to
expenditure spent on foodgrains declines rapidly. Second, due to scientific advances and
associated technological innovations in agriculture farmers become increasingly reliant
on inputs purchased from the non-farm sector of the economy. Third, because the
demand for off-farm marketing services—distribution, storage and processing—is more
elastic than the demand for agricultural products at the farm gate.
In recognising the development trends making for the declining relative importance of
the agricultural sector in the long term, it is necessary to avoid the trap of overlooking the
critical importance of domestic agriculture’s product contribution to the maintenance of
an adequate rate of economic growth in the short term. This is the trap that a number of
developing countries have fallen into in opting for a strategy of rapid industrialisation
without parallel development in agriculture (Ghatak and Ingersent, 1984). This kind of
Chapter 3
85
strategy hampers growth process due to the fact that levels of agricultural productivity
per capita are too low to sustain rapid industrialisation on the basis of a home market. A
direct leap to more advanced modern industry is rendered inefficient on account of a
wide array of absorption problems which raise the incremental capital-output ratio to
extremely high levels. Due to high capital-output ratio large industries provide no scope
for raising huge employment opportunities as well as purchasing power at mass level. On
the one hand, these industries are unable to enter the export market and, on the other
hand, domestic markets are too narrow to sustain such an industrial sector at anywhere
near optimal capacity utilization. In such a situation, agriculture is viewed as a prior
phase necessary for laying the basis for industrial acceleration (Saith, 1992). In India, for
example, the capital-goods-oriented Mahalanobis industrialisation strategy has been
unable to generate employment at an acceptable rate. The debate on whether agricultural
development can lay the foundation stone for industrial acceleration is described in
section 3.6.
In this perspective, Kuznets’s work is significant for two reasons. First, it gives
importance to agricultural growth as an engine of non-agricultural growth. Second, it
proves that with the increase in non-agricultural income over time the agriculture’s share
to GDP goes down. But a tremendous vacuum shows off in his approach when he
remains completely silent about the rural non-agricultural economy as a crucial separate
existence in the analysis of the development process. He discusses the non-agricultural
(industrial) sector of an economy as a whole but does not involve the critical role of rural
non-farm activities during an economy’s transformation from traditional situation to
modernisation. It is interesting that the Lewisian framework (very briefly described
herewith) too, despite focusing essentially on the analytic of the very transition, ignores
the rural non-farm economy, except for an aside which despatches ruined artisanal groups
to the same lifestyle and mobility path as surplus labour in agriculture. Lewis model
viewed the problem of surplus labour absorption as essentially as one of intersectoral
labour transfer through industrialisation which would accelerate capital accumulation and
also raise per capita output in the economy since industry, typically, was more productive
than agriculture. The steady expansion of the capitalist industrial sector would draw upon
the “unlimited supply” of rural labour from a low productivity agricultural sector at a
constant real wage to the point labour was no longer infinitely elastic (Lewis, 1954).
From this “turning point” which would suggest a tight labour market situation, the
Issues Relating to Rural Industries: A Review of Literature
86
benefits of industrial expansion would trickle down to workers and the rural population
through higher wage rates. The process of industrialisation, therefore, was expected to
remove underdevelopment and rural poverty. This whole framework holds no position
for the rural non-farm economy during labour transfer from agriculture to industry.
3.5 The Relationship between Agriculture and Industrial Growth: An Econometric Model
Hwa (1989) has developed a model based on the relationship between agriculture and
industrial growth. According to him, the relationship between agriculture and industry is
one of interdependence and complementarity. For example, agriculture supports
industrialisation by providing a source of labour, capital and raw materials to other
sectors, and by generating demand for industrial products. At the same time, agriculture
receives from industry modern farm inputs, advanced technologies, and consumption
goods to increase its productivity.
For testing the statistical significance of this relationship, Hwa has developed the
following non-linear model of the Chenery-Syrquin (1975) type, which relates the rate of
industrial growth (.Ι ) to per capita income (YN) and the rate of agricultural growth (
.Α ):
.Ι = f [
.Α , lnYN, (lnYN)2] + u, (1)
where u is a randomly distributed error term.
The model is derived in the following way. First, assume that the rates of growth of
industry and agriculture are both non-linear functions of per capita income variables:
.Ι = αI lnYN + βI (lnYN)2 + εI
(2)
and
.Α = αA lnYN + βA (lnYN)2 + εA, (3)
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87
where εI and εA are random errors.
These are simplified reduced form models for the determination of industrial and
agricultural growth. The per capita income variables are used as summary measures of
the stage of economic development, in addition to being measures of final demand.
Alternatively, these regressions can be thought of as establishing ‘norms’ for the rates of
growth of industry and agriculture with reference to the stage of economic development,
as measured by the level of per capita income. It is possible to test the hypothesis of
whether countries with higher industrial growth in relation to ‘normal’ industrial growth
are also those with higher agricultural growth with reference to its norm. This can be
made by regressing the residuals in (2) on the residuals (3):
εI = γεA + u, γ > 0, (4)
where u is a randomly distributed error term.
Substituting (2) and (3) into (4) and rearranging the terms yields:
.Ι = γ
.Α + (αI - γαA) lnYN – (γβA - βI) (lnYN)2 + u. (5)
This equation is the explicit form of (1). It also implies that the disparity between
industrial and agricultural growth, .Ι –
.Α , is a second order non-linear function of per
capita income.
The estimation of (5) is conducted using two cross-country samples: one consists of 63
countries for the decade of the 1960s and the other has 87 countries for the decade of the
1970s. Both samples include developing as well as developed countries. The estimation
results based on the ordinary least square (OLS) method are presented in Table 3.1.
Issues Relating to Rural Industries: A Review of Literature
88
Table 3.1: Estimated regression coefficients for annual industrial growth .Ι
Independent variables
Equation Period lnYN (lnYN)2 .
Α Constant R2
I 1960-70 9.277* (2.7)
- 0658* (2.8)
- - 24.331* (2.1)
0.12
II 1970-79 13.068** (3.4)
- 0.904** (3.4)
- - 40.594** (3.1)
0.13
III 1960-70 6.548 (1.8)
- 0.458 (1.8)
0.491* (2.1)
- 16.730** (2.1)
0.18
IV 1070-79 9.477** (2.6)
-0.649* (2.6)
0.722** (4.1)
- 29.873* (2.4)
0.28
Notes: The numbers under the respective coefficients in parentheses are t-statistics. The coefficients with a significance level above 5% are indicated by * and those above 1% by **. The notations have the following meanings: .Ι = The average annual rate of growth of industry, comprised of mining, manufacturing, construction, and electricity, water, and gas. YN = GNP per capita in 1970 and 1979 US dollars respectively, for the 1960-70 sample and the 1970-79 sample.
.Α = The average annual rate of growth of agriculture.
Source: Hwa (1989)
According to Hwa’s analysis, the regressions with the per capita income variables alone
(equations I and II) depict a parabolic curve, a result that indicates that at a relatively low
level of income, industrial growth will increase as per capita income increases, and that
when per capita income reaches a certain level, the rate of industrial growth will reach a
maximum and then taper off. This outcome, as Hwa states, confirms the hypothesis as
formulated in (2).
Equations III and IV show that the growth rate of agriculture is a statistically significant
variable in explaining industrial growth and that it has raised the R2s significantly for
both samples, especially for 1970-79. Although the statistical significance of the per
capita income variables were reduced, the results unambiguously confirm the hypothesis
that countries with the above-normal performance in industry are also those associated
with the above-the-norm performance in agriculture over the development process that is
manifested through a continuous rise in per capita income.
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89
In the above model, Hwa concludes that agricultural development leads to industrial
growth via an increase in per capita income. This was his hypothesis too. In testing his
hypotheses, he has taken only three explanatory variables as mentioned above. This has
practically made his study too narrow to examine the influence of other factors/variables
on industrial growth as well. Besides, simultaneity bias in the model has not been taken
into consideration. For example, Hwa has not checked whether or not agricultural growth
or per capita income could be influenced by industrial development. In all sense, the
model failed to form a complete structure. Lastly, or should it have been firstly (!), the
model has been termed by Hwa as a non-linear econometric model which is actually
nothing but a linear model.
In the same article, Hwa has developed another model, more explicitly a production
function model following the Cobb-Douglas production function, based on the
relationship between agriculture and overall economic growth.5 Both the models have
5 Hwa has used the following Cobb-Douglas production function following the Balassa (1987) model: Y = CKαLβelog R (1) where Y = gross domestic product; C = a scale parameter; K = capital stock; L = the labour force; and elog R = the rate of technological change over time, which is taken to be synonymous with productivity change. Rewriting the variables in (1) in terms of the rate of change over time yields:
.....RLKY ++= βα (2)
In the literature of production function analysis, the productivity change (.R ) in production
function (2) is frequently treated as a ‘residual’, and the production function is estimated accordingly. The argument presented assumes that the rate of productivity change is positively
influenced by both the rates of agricultural growth (.Α ) and export growth (
.X ) but is negatively
related to the rate of inflation (.P ):
.R = a + γ
.Α + θ
.X + η
.P + ε , γ, θ > 0, η < 0, (3)
where a is a constant term and ε is a residual, assumed to be randomly distributed. Combining (2) and (3) yields:
.
Y = a + γ.Α + α
.K + β
.L + γ
.Α + θ
.X + η
.P + ε (4)
where .
Y = the average annual rate of growth of GDP;
Issues Relating to Rural Industries: A Review of Literature
90
reserved no place for rural non-farm economy or rural industrial sector as a vital player in
the process of development. The problem lies in the fact that rural non-farm sector is not
seen as an independent contributor to economic growth and it is a concept which is not
true.
3.6 Linkages: Theories and Debate
It is recognised by many economists that linkage mechanism plays an important role in
economic activities. Linkages between agriculture and industry have been focal issue in
many academic works. In the following we are going to discuss the views of many
authors regarding linkage mechanism.
Hirschman (1958) has developed the concept of production linkages as inducement
mechanisms for stimulating economic activities through backward and forward linkage
effects. He defines linkage from the standpoint of supply of output to other production
sectors and demand for input from other sectors. The backward linkages relate to the
input provision or derived demand, i.e., every non-primary economic activity will induce
supply of the inputs needed in that activity through domestic production. The forward
linkages relate to the output utilisation, i.e., every activity that does not by its nature cater
exclusively to final demands, will induce attempts to utilize its outputs as inputs in some
new activities. This has been schematically shown in the Figure 3.1. (For a detailed
scheme of linkages between agriculture and small scale industries adapted from Harriss
(1987) based on a study in North Arcot in India, see Figure 3.2).
.
K = the average annual rate of growth of capital, proxied by the average investment rate;
.L = the average annual rate of growth of the labour force;
.X = the average annual rate of growth of exports;
.P = the average annual rate of inflation.
Please note that although equation (4) includes the export and agricultural growth rates as regressors, it is essentially a production function and is not a national income accounting identity.
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Figure 3.1: Forward and backward production linkages
FORWARD PRODUCTION LINKAGE
BACKWARD PRODUCTION LINKAGE
ECONOMIC ACTIVITY WHICH SUPPLIES ITS OUTPUTS AS
INPUTS
ECONOMIC ACTIVITY
WHICH INDUCES DEMAND FOR INPUTS
SOME NEW ACTIVITIES
DOMESTIC PRODUCTION
It is important to note here that in defining backward linkages the supply of inputs
through imports from outside (not always through domestic production) should also be
taken into consideration and this may be termed as leakage of “local” growth linkages.
Nurkse (1961) has advocated the basic principle of “linked progress” in the two broad
sectors, farming and manufacturing. He describes that in a very poor country a given
increase in a manufacturing is likely to require a greater agricultural advance than in one
that is not so poor. Conversely, a given increase in food output is likely to support a
larger increase in manufacturing in an area where income per head is already fairly high
than in one where it is still very low. According to him, the problem of low-income area
is this: there is not a sufficient market for manufactured goods in a country where
peasants, farm labourers and their families, comprising typically two-thirds to four-fifths
of the population, are too poor to buy any factory products, or anything in addition to the
little they already buy. There is a lack of real purchasing power reflecting the low
productivity in agriculture. The other side of the same coin is that the local economy
cannot supply the food needed to sustain the new industrial workers. Therefore, industrial
development for domestic markets requires a complementary advance on the farm front,
TO FROM
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92
a rise in agricultural productivity. The equilibrium relation between the two rates of
advance may vary in the course of time as well as between countries. But farming and
manufacturing, as Nurkse emphasises, must move forward together, though not
necessarily at the same rate.
The effort to specify the linkages, empirically and quantitatively, between these two
sectors is much more recent (Harriss, 1987). Of particular importance was the
expenditure linked multiplier effect of growing agricultural income, viz. consumption
linkages, in generating rural based, small-scale, labour intensive industries, highlighted
by Mellor (1976) in his “new strategy of agriculture-led growth” (see also Johnston and
Kilby, 1975). Mellor’s argument is that increased food production based on cost
decreasing technology can make large net additions to national income. If this income
accrues especially to larger cultivators (say, those in the upper middle deciles), a large
portion of it will be spent on non-agricultural goods and services. But Islam (1986) has
stressed on egalitarian growth in agriculture. He stated, “…if the benefits of growth in
agriculture are not widely distributed, the pattern of consumer demand will not change
[in this manner] and hence this source of incentives for non-farm activities may not
materialize” (pp. 172). This is a hypothesis which is not tested in Islam’s paper, but it
seems to contrast with Mellor’s argument because Mellor finds larger cultivators’
additional income as crucial generator of demand for rural non-farm goods and services
whereas egalitarian growth approach of Islam emphasizes wide distribution of
agricultural income amongst rural people. Saith’s comments are also significant in this
regard. He has described Mellor’s version of “Agriculture First” (AF) strategy as the
most complete and internally consistent one. In this connection, Saith has explained:
“AF, especially when it is not based on a concentration of resources on the large farmer,
is likely to generate diverse forward and backward linkages on the consumption and
production sides. These are likely to induce a local supply response from the rural non-
farm economy, thus creating a virtuous circle of rural development. Such expansion is
also likely to be characterized by much higher labour absorption rates; over time, such a
successful rural process is also bound to widen markets for the mainstream industrial
sector, and thereby lay the basis for a sustainable form of development based on an
expansion of the home market” (1992: 103). In a study on China, Ho (1986) has argued
that the interlinkage between agriculture and rural industries followed the classic pattern
of enlarging the market for the latter’s products and helping the former’s growth through
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a more efficient supply of inputs, including labour. UNDP et al (1988) have stated that
consumer demand caused by growth in agricultural income provides the major impetus
for rural industrialisation.
Figure 3.2: Agricultural linkages of registered small-scale industries (number of units)
FORWARD PRODUCTION LINKS
Agro-based consumer goods
Machinery Primary Agro-processing
Machinery Transport
Machinery Capital goods Inputs
Machinery/Spares Machinery
BACKWARD PRODUCTION LINKS Source: Adapted from Harriss (1987)
In addition to the above, there is much debate on Mellor’s argument. Harriss (1987) has
pointed out that the size of the multiplier effect, which was emphasized by Mellor, not
only depends on mere rise in agricultural income but at the same time also depends on: 1)
whether the growth linkages of agriculture6 are stronger or weaker than those of industry;
2) whether consumption linkages are stronger or weaker than production linkages and 3)
whether local linkages are stronger or weaker than non-local linkages. These points may
be clearer from the conclusions of her work. In a complex setting of North Arcot (a
6 Mellor (1976: 161-162) has defined growth linkages of agriculture in the following way: “…the increased efficiency of technologically advanced farming allows a large net increase in national income, which provides the dynamics of growth led by the agricultural sector.”
AGRICULTURE
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district in the state of Tamil Nadu in India), Harriss (1987) found that the non-farm
economy in that agrarian region was led by more than rice (the main crop of the region)
and more than agriculture. In particular, an examination of employment and incomes in
mercantile, industrial and government activities revealed not only a massive
concentration but also the existence of a sizeable economic sector where income and
demand for income elastic goods are relatively high. Secondly, while agricultural growth
might have stimulated industrial expansion, such expansion was seen in activities which
were production linkages, both backward (inputs) and forward (processing),7 and not in
activities which could be regarded as consumption linkages. And, thirdly, production
activities have been found to have important non-local links. So it was not always local
industry that benefited, and that when the industry was local, it was not necessarily small-
scale, nor indeed labour intensive. The industry that is generated in the growth linkage
process might prove to be overwhelmingly urban.
In a detailed study on Uttar Pradesh in India, Papola (1987) has concluded that rural
industrialisation needs to be viewed not merely as an adjunct to agricultural growth but as
an independent element of a strategy for generating non-agricultural employment and
incomes in rural areas. He suggests that input supplying and servicing activities like
manufacture and repair of agricultural implements are likely to develop in rural areas
with agricultural growth. But, at the same time, he argues that higher incomes generated
by agricultural growth is found to facilitate an improvement in the situation of some rural
industrial activities producing goods of general use and capable of adapting to new
pattern of demand, but the larger volume of agricultural produce available for processing
tends to shift the processing industries to town. Thus, the hypothesis, as formulated by
Papola (1987), that agricultural growth leads to industrialisation in rural areas both in
terms of diversification and improved performance seems only partially validated in the
Indian case. Therefore, he concluded that rural industrialisation “would have to be seen in
a perspective wider than that limits it to the needs of and opportunities provided by
agricultural growth. In that sense, it has to be a part of the policy and strategy of
industrialisation in general, and location and diversification of industries in particular,
and not merely a programme of protection and promotion of village and agricultural
related industries” (p. 106). Harriss (1991: 455) has argued that “agricultural growth may
7 In the study, the author has found that forward production links are more important than
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be a necessary condition for rural diversification…, it is certainly not sufficient.”
Dunham (1991) has also argued that, in a dynamic situation where agricultural
productivity and incomes are increasing, the potential for local growth in the non-farm
sector is likely to depend on a range of circumstances. Important amongst these will be
(1) the nature of the particular product or service (and especially whether or not it is
technically ‘inferior’8 to those imported); (2) the industrial history of the area; (3) the
accessibility or degree of closure of the local economy; and (4) purchasing power and the
size of the local market. Dunham stated that if large farms are numerous and there is a
high inequitable distribution of assets and income, then Mellor’s thesis seems likely to be
justified (because the rich would spend the money for non-food commodities and
services). But the situation is not always as clear-cut as that. It is by no means clear that
large-scale commercial farms will determine consumer demand for non-farm goods.
According to Dunham, the pattern of demand that emerges is likely to be dependent on
agrarian structure consisting of three factors mentioned below:
1) Farm size distribution: The reason is that distribution of money income in the
rural agrarian sector is broadly consistent with that of land;
2) Household size (the number of mouths): The relatively larger household size
would create higher demand for non-farm goods and services provided that the
household belongs to upper or upper-middle deciles;
3) Values that shape the consumption behaviour of rural households within a
particular area: The traditional households who are always hesitant to accept
modernisation may hamper the growth of new industries with innovative product
or service concepts.
Chandrasekhar (1993) has also questioned the idea of combined (agriculture and non-
agriculture) rural dynamism. In a study on West Bengal, he observed that the demand for
manufactures resulting from increases in income above a certain critical minimum is
catered to by the urban-based units. Lastly, despite making a lengthy criticism, Dunham
ultimately admitted that Mellor’s key study is of contemporary relevance for two reasons.
First because expansion of rural non-farm sector, due to its higher employment
backward production links.
Issues Relating to Rural Industries: A Review of Literature
96
generation provision, is a growing policy priority. And second because many aspects of
his macro framework are still very pertinent today. For instance, Mellor advocated a
more open economy, stressing comparative advantage in international trade with the
import of capital-intensive manufactures and the export of labour-intensive manufactures.
He emphasized the need for strong anti-inflationary measures at national level in order to
keep food prices down. To promote small-scale industrial sector, Mellor stressed on
identification and removal of market bottlenecks—particularly with regard to input
supplies and to infrastructure which were observed to be creating uncertainties for local
industrial expansion. He favoured less government control and suggested to switch the
government’s emphasis from regulation to facility support.
One thing has been very clear from different studies that agricultural development may
not be the sufficient condition for rural non-farm growth but for increase in rural non-
farm income agricultural development, of course, is regarded to be the necessary
condition.9 Why agriculture is so important has been described in a recent study by Asian
Development Bank (2000). They have argued that most rural non-farm employment in
8 Urban centres tend to be the locations for manufacturing based on methods of mass production and thus the urban industries have the opportunities to exploit the economies of scale. The conditions for rural industries are different. 9 Based on the particular experience of Latin America, Reardon et al (2001: 397) do not recognize agriculture even as a necessary condition for non-agricultural growth. They say that demand for non-farm goods and services can be driven by “motors” other than the agricultural sector. Demand is driven by any motor that raises local incomes and the pool of investment capital and thus increases in rural non-farm wage and self-employment through production and expenditure linkages. For example, an increase in tourism (service sector) can induce growth in manufactures (e.g., local wine-making) and in agriculture itself. Moreover, the “motor” does not even have to be local, as long as the local economy is “open” in that workers can commute and local farm and non-farm firms can sell to the area where the motor is churning. For example, a mine or a big city in a coastal region could induce non-farm employment growth in the nearby highlands. Behind this tendency in Latin America, Mellor (1989) found an argument. He said that there seems to be a tendency for agriculturally-led growth to be less effective in Latin America than in Asia. The most important reason for that is the tendency for the distribution of land in Latin America to be highly skewed. Since the addition to net national income from technological change in agriculture is distributed largely to landowners, the benefits are skewed to high-income people, with a consequent tendency for the goods comprising the marginal propensities to consume to have a larger import content and a higher capital intensity. The result is smaller domestic employment and income multipliers and greater dependence on foreign markets—which are innately less stable, more risky, and hence have lower net returns—for growth in food output. The consequence that lower efficiency in the conversion of agricultural growth has for overall growth in Latin America is a lower employment multiplier in the non-agricultural sector and hence lower economic returns to investment in agriculture. The solution lies with a broader distribution of land and a greater relative emphasis on technological change for small-holders. But this may be unsatisfactory because of poor land resources and the high costs of distribution to the small-holder part of bimodal agricultural production systems.
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Asia arises in services, trade, and household manufacturing activities. These are
dominated by small, part-time, mostly family businesses that are highly labour-intensive.
These activities depend to a large extent on local and regional demand. But where is
effective demand in poor rural regions? Here is the importance of agricultural growth. It
generates enormous purchasing power among the rural population for non-food consumer
goods and services and therefore supports rapid growth in services and trade in rural
areas, and provides a nascent market for an emerging manufacturing sector. Rapid
agricultural growth supplies basic food, raw materials for agro-industry, and exports and
frees up foreign exchange for the importation of strategic industrial and capital goods. It
releases labour and capital to the non-farm sector. It reduces poverty by increasing labour
productivity and employment in rural areas, by generating more remunerative
opportunities for rural-urban migration, and by lowering food prices for all.
In a study Asian Development Bank (2000) has observed that while an agricultural
revolution was necessary during the early stages of the transformation, not all the
countries that experienced successful agricultural revolutions started to industrialise and
grow rapidly. Several other key factors are also needed to enable countries to
successfully convert agricultural growth into national economic growth. All the important
ones are mentioned here:
• Agricultural growth must be equitable, so that it puts increased purchasing power
into the hands of the rural masses and not just a privileged few. Although the green-
revolution technology was basically scale-neutral, access to rural resources –
particularly land – has been a key determinant of the equitability of agricultural
growth.
• A well-developed infrastructure in rural areas is required to foster the links between
the farm and non-farm sectors. Villages need to be connected to local towns so that
agricultural inputs and outputs can flow freely and so that people can go shopping.
Rural towns also need good infrastructure, especially roads, electricity, schools,
sewers, water, and communications, in order to attract new firms and to grow. In
particular, rural areas must not be allowed to be left behind in the use of modern
communications technology to forge links with larger markets. Since the transaction
costs of poor infrastructure are borne unequally by the poor (because poor
Issues Relating to Rural Industries: A Review of Literature
98
infrastructure increases disparities in rural-urban price relationships), improvements
help level the playing field for the most disadvantaged.
• As agriculture develops and food security diminishes as a major constraint, countries
need to move quickly toward market liberalisation and pro-trade and pro-investment
policies. Protecting domestic industries and overhauling exchange rates penalise
agricultural growth and impede the development of competitive industries that
should be at the forefront of export-led growth. They also shield a country from the
new technologies that are embedded in many imports and that have the potential to
raise economic efficiency and competitiveness significantly.
• As agriculture develops, rural non-farm activities become more dynamic. Strong
rural financial institutions are required to mobilise resources and allocate them
efficiently to promote a wide array of economic activities. Both farm and non-farm
enterprises and households demand a broad range of financial services, including
savings facilities, not just credit.
• Promotion of a legal and regulatory environment (e.g., to secure property rights and
enforce contracts) that will help to promote trade, commerce, and manufacturing is
necessary.
• Investment in human capital and especially in rural education is necessary to ensure
continued productivity increases in agriculture and to allow rural workers to be more
readily absorbed in non-agriculture if necessary. Training should be provided in
relevant technical, entrepreneurial, and management skills.
• Industrialisation policies that foster the development of all kinds of rural non-farm
activities and not just manufacturing are needed to be initiated and implemented.
Despite the relatively small importance of manufacturing in rural employment,
policy makers have been enamoured of this sector; rural industrialisation policies
have showered manufacturing firms with all kinds of preferential tax, subsidy,
licensing, and regulatory benefits, as well as with targeted and subsidised credit and
technical assistance programmes. Moreover, these policies have typically favoured
larger capital-intensive manufacturing firms (the lure of the shiny rice mill or shoe
factory), and neglected small labour-intensive firms and informal household
manufacturing activities. It is necessary to revamp rural industrialisation policies to
(a) be more inclusive, perhaps by redefining them as “rural enterprise” policies; and
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(b) to remove all unnecessary subsidies and protective policies that prevent rural
firms from becoming competitive in the marketplace.
In the first point mentioned above, Asian Development Bank has focused on uneven
distribution of income which is caused by lack of access of rural poor to land resource.
But surprisingly they did not talk about extensive land reform as a measure which is an
effective instrument to cure uneven distribution of the fruit of agricultural growth and,
therefore, to reach equitability. In the third point, Asian Development Bank has argued
that: “As agriculture develops and food security diminishes as a major constraint,
countries need to move quickly toward market liberalisation and pro-trade and pro-
investment policies.” They have argued that any kind of protection impede the
development of competitive industries. But there should be a big question mark about
this argument. The counter argument is as follows. As agriculture develops, purchasing
power of rural people increases. In this situation, if market is open to all, multinationals
and the big business sector will naturally tap this new market. Is it possible for small and
tiny business sector to compete with the multinationals and big business house? Is it not a
competition between two unequal sectors? In this case, authors of Asian Development
Bank have ignored this reality and favoured market liberalisation. Conversely, United
Nations (1990) have recognised the reality and advocated protection for rural industrial
products. They have expressed the following opinion.
Rural industrial products tend to face competition from domestic large-
scale industries as well as imported goods. Therefore a large number of
potential rural industrial products such as pins, plastics, pencils, cups, etc.,
in addition to agriculture supporting manufactured goods, may need
protection. Favourable fiscal measures are required in this regard (United
Nations, 1990: 28).10
10 United Nations (1990: 89) have also opined that, in India, although there is a realisation that some products need to be reserved for rural industries, very few rural industries have been set up producing equipment and inputs for use by the agricultural sector. Excepting the development of some centres for repair and maintenance of implements and engines in rural markets, rural industries based on backward production linkage have not made much progress. The counter argument is found in Mellor (1976: 173) who has argued that the linkages arising from increased foodgrain production cannot have their full stimulative effect on growth unless restraints on expanded production in the domestic consumer goods sector are removed. In this case, according to him, it is institutional deficiencies which are most likely to restrain development. Growth in industrial production may face a constraint from institutional barriers particularly with respect to
Issues Relating to Rural Industries: A Review of Literature
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Why is protection needed? Three aspects are very important in this regard. One is poor
quality of products produced by rural industries. Next is distribution cost. Lastly, it is
publicity cost. So far as quality is concerned, competition between urban (large) and rural
(small) industries does not stand at all. It is said that due to inferior quality demand for
most of the rural industrial products are income inelastic. Delivering this argument, one
should not talk in favour of wiping out small rural enterprises. Actually the rural small-
scale sector has a very important place in a developing economy even if the sector
produces poor quality goods. The reason is that the poor-quality-goods-producing
industries may sometimes play critical role during transition from primitive techniques of
production to modernisation. The second thing is about distribution/transaction cost of a
product. Large firms supply bulk quantity of goods whereas small scale sector has a
limited capacity. Naturally, distribution cost per unit greatly differs between these two
sectors. That means, big houses incur lower distribution/transaction cost than the small
sector does. This has an impact on pricing of the product. Advertisement or publicity is
also an important factor in connection to reaching market. Big houses can afford
publicity in the print and electronic media but the rural small scale sector cannot. On the
other hand, cultural factors are not that much rigid in India to resist big firms to penetrate
a local market as we have seen the reverse in Japan. In the Japanese case, an important
factor which operated in an almost unique fashion was the stability of the cultural tastes
and consumer behaviour of the population in the face of market contacts with the West.
Japan borrowed great deal from its Western competitors, but still managed to retain a
powerful preference for traditional forms of consumption biased in favour of local rural-
linked community industrial products. The significance of this virtually unique feature
becomes evident by comparison with India at a point, say in the mid-nineteenth century,
when the two systems had less economic distance separating them (Saith, 1992). British
rule in India is considered to be responsible for this comparison between these two
nations.
One of the most harmful effects of a foreign rule is the imposition on the
conquered peoples of the ideals of the conquerors; and the newly created
Indian “bourgeoisie” showed itself during the latter half of the last century
capital, input, and output markets. Public policy must diagnose the bottlenecks and make appropriate adjustments.
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extremely ready to accept European standards and to pour scorn over
everything Indian. This was specially so in the case of the arts. To follow
European fashions was considered the hall-mark of enlightenment.
Consequently the products of indigenous industries suffered… It was
perhaps natural for this class to act as it did: it was itself entirely a product
of British rule (Gadgil, 1971: 414).11
The Japanese case provides a sharp contrast. Rosovsky (1961: 53 & 86) argues that:
Japan brought to economic development a built-in resistance to the
corroding influence of the demonstration effect—the deep commitment to
a traditional economy that has continued to be productive to this day. The
modern sector has succeeded because it has climbed on to the shoulders of
the traditional sector…. Two hundred years of isolation solidified a style
of life until it could not be demolished, even by the powerful impact of an
industrial revolution (cited in Broadbridge, 1966: 19 and Saith 1992: 50).
We were discussing about market liberalisation. Of course, it is necessary to move
toward market liberalisation to encourage the development of competitive industries but
the crucial question is: When is it necessary? What is the exact time? Just after
agricultural development any attempt toward market liberalisation will simply inhibit
growth of rural small-scale sector. Rural enterprises should be provided time to exploit
increased local demand which is the result of agricultural growth. In other words,
domestic small scale sector should be given opportunity to grow first. At the same time, it
is also true that in the current era of market liberalisation it is really tough for the
developing countries to protect their markets for their own domestic industries, especially
when they are under heavy pressure from the World Bank and the International Monetary
Fund (IMF) due to the dependency of developing countries on these institutions for loans.
What is the alternative then? We will try to conceptualise an alternative possibility for the
development of rural small-scale sector in Chapter 6 focusing on the issue as to whether
or not rural small enterprises can grow with the involvement of farmers who have
investible surplus generated from agriculture.
11 Cited in Saith (1992: 50).
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3.7 The Japanese Experience in Brief
3.7.1 Agriculture/Non-agriculture Linkage
If a backward Asian country, as Japan was in 1850, is to modernise as rapidly as Japan
did, it may seem necessary that it satisfies some conditions. Is a strong linkage between
agriculture and rural industry one of them? According to Nakamura (1966), one of the
interesting aspects of the growth process involves the transfer of resources, labour and
capital, from the traditional sector of a developing economy to the modern sector. More
explicitly, growth first starts in the traditional sector and then spreads into the industrial
sector. Usually the traditional sector is taken to be synonymous with the agricultural
sector. Almost universal acceptance has been accorded to the general proposition that
Japan’s industrialisation during the Meiji era was heavily dependent on the rapid growth
of agricultural production. Most of the increase in rural industrial activity in Japan—
almost entirely financed and operated by rural landlord-merchants—was induced by the
existence of surplus capital and by the development of national market including the rural
market (since peasants’ real income rose).
Smith (1988) found some evidence of the farm/non-farm linkage during Japanese
industrialisation (1750-1920). He saw mainly labour participation in both farm and non-
farm activities. Farm families did not engage exclusively in farming; nearly all members
worked concurrently at other occupations.
In discussions of Japan’s modern economic growth Meiji resource transfer has
deservedly received much attention. The Meiji transfer, however, was not a new
development. It was a continuation of a trend that began in the Tokugawa period owing
to economic changes of that period. It was indicated that industrial and commercial
activity increased during the Tokugawa period, particularly in the rural areas. According
to Preston (2000), “Tokugawa was an agricultural society. Over 80 per cent of the
population were peasant farmers, scattered across the country in hundreds of small
villages. The staple crop was rice, grown in paddy fields which required collective work.
In the early years of the shogunate the economy was mainly subsistence as a result of the
upheavals of decades of civil wars. However, with stability the economy became
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successful and output rose, artisanal skill based manufacture prospered and
commercialisation slowly spread.” Beginning about 1800, rural industrial and
commercial activity assumed national importance. The significance of the changing
structure of rural production is that a continuous transfer of rural labour and capital from
agricultural production to non-agricultural production must have taken place. The
increase in rural non-agricultural production is also evidence of resource transfer.
Sugihara (1996) stated that the traditional historiography has assumed the availability of
cheap labour, drawn from the countryside, to be a crucial factor for Japan’s
industrialisation. After Meiji Restoration of 1868 a strong central state was created and
landlordism developed, and rural savings were transferred, through the land tax and the
high rent, to industry for investment. In this way, agriculture provided industry with
labour and with capital.12
Almost universal acceptance has been to the general proposition that Japan’s
industrialisation during the Meiji era was heavily dependent on the rapid growth of
agricultural production. The argument is summarised below.
Agricultural production and real income are said to have risen at more than two percent
per year which is somewhat more than twice the 0.9 per cent growth rate of population.
Since the agricultural labour force declined somewhat during this period, it is claimed
that the speed of growth is attributable to agricultural developments which caused labour
productivity to increase at about 2.6 per cent per year. A proposition of major importance
is therefore advanced that this remarkable increase in agricultural labour productivity
released a cheap and “unlimited” supply of labour to other sectors since most of the
population growth was taking place in farm families.
There is an important proposition about Japanese economic development that the speed
of growth of agricultural production was responsible for a large transfer of savings to
12 In an article, Robison (1971) has shown that transfers of capital and labour from a lower productivity sector such as agriculture, to a higher productivity sector such as industry, is by itself a distinctive source of economic growth. An explanation of this view is found in Hwa’s (1989) argument which states that rapid agricultural growth makes the situation feasible to increase per capita domestic consumption, exports of agricultural products, and absorption of the agricultural labour force by the industrial sector. Therefore, it enhances the resource transfer from agriculture to industry.
Issues Relating to Rural Industries: A Review of Literature
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other sectors. Landlords are believed to be responsible for most of these savings, and they
invested a large part of their savings in the non-agricultural sector as entrepreneurs in
their own right.
3.7.2 Transformation from Traditional Industries to Modern Industries
For a low income country, the rural industrial activities hold tremendous potentials. Let
us take the classic example of Japan which is regarded to be a success story in small-
scale industrial sector. The industrialisation in Japan did not take place overnight. It has a
background. The concurrent growth of agriculture, traditional industry and modern
industry was a major characteristic of the Meiji industrialisation. Traditional industry
gave its all support to the economic growth of Japan. Some evidence is found in the study
of Sugihara (1996). He observed that, according to the 1909 Factory Survey report, 72
per cent of factories (defined as a concern employing five or more persons) had no power
supply, and 5 per cent operated on the Japanese-style (small) water mill, while the
factories equipped with steam engines, gas- or petrol-operated engines or motors,
consisted of 23 per cent. Some 58 per cent of factory workers were employed in small
factories (employing between five and 100 persons). In addition, production employing
less than five persons, which was excluded in the Survey, is estimated to have amounted
to 51 per cent of total industrial production.
In the next 50 years, Japan showed tremendous improvements. The main goal of the
Meiji State was to narrow the large technological gap between Japan and the West. But
the more relevant question was how Western technology and organisation could be
absorbed into a non-European economy with a very different commodity and technology
mix and institutional framework. Thus, the Meiji government was much more concerned
with the exploitation of rural human resources and their technical and managerial
knowledge. During the second half of the Meiji period, a large amount of local and
central government expenditure went into the establishment of commercial and technical
schools, commercial museums and industrial experimental and testing stations.
Between the two World Wars, the rural household economy was slowly transformed into
the urban household economy, and traditional small-scale production was slowly
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transformed into modern large-scale production. After the disruption of World War II,
the process of urbanisation accelerated and the rise of big business became apparent in
the 1950s and 1960s. The proportion of city dwellers in the total population raised from
38 per cent in 1950 to 76 per cent in 1975. The Japanese economy shifted its base from
the rural household to the urban household at this point, and a persistent rise in wages
resulted. Smith (1988) has said that much of Japan’s economic growth in the twentieth
century was achieved by the expansion of traditional industries with the aid of relatively
modest technical and organisational modifications.
3.8 Reasons for Household Participation in Rural Non-farm Activities
Decisions made by rural households concerning the form and extent to their involvement
in rural non-farm activities (either starting enterprises or entering the wage labour
market) generally depend on two main factors (Reardon et al, 2000):
The incentives offered, such as the relative profitability and risk of farm and rural non-
agricultural activities;
The household’s capacity (determined by education, income and assets and access to
credit, etc.) to undertake such activities.
When opting for undertaking rural non-agricultural activities, farm households may be
motivated by:
• “pull” factors, such as better returns in the non-farm sector relative to the farm sector;
and
• “push” factors, which include in particular:
♠ an inadequate farm output, resulting either from temporary events (e.g. a drought)
or longer-term problems (e.g. land constraints);
♠ an absence of or incomplete crop insurance and consumption credit markets (to use
as ex-post measures for harvest shortfalls);
Issues Relating to Rural Industries: A Review of Literature
106
♠ the risks of farming, which induce households to manage income and consumption
uncertainties by diversifying and undertaking activities with returns that have a
low or negative correlation with those of farming;
♠ an absence or failure of farm input markets or input credit markets, compelling
households to pay for farm inputs with their own cash resources.
3.9 Secondary and Seasonal Employment in Non-farm Activities
Non-agricultural activities provide considerable amount of part-time employment to rural
population on the one hand. Still much more part-time and periodic non-agricultural work
often remains uncaptured because of its seasonal nature on the other (Basant and Kumar,
1989). Many small and landless farmers undertake non-farm work in the slack seasons.
Construction and irrigation works are common examples, but repair and maintenance
jobs, which can be postponed until the slack season, are important as are a number of
processing, service, and commercial activities that expand after the harvest, while the
products of a number of manufacturing activities can be stocked (World Bank, 1978).
(Non-farm workers, similarly, often undertake farm work as a secondary occupation, of
which crop cutting during the peak season and animal husbandry are examples; what
follows concentrates on secondary non-farm work, however).
There are large swings in farm and non-farm employment over the agricultural cycle.
There is no period when there is no non-farm employment, and non-farm work competes
with farm work even during the peak, when it occupies roughly one-third of the working
hours of the labour force. World Bank (1978) showed that over the whole cycle, non-
farm work accounts for about 40 per cent of working hours; during the slack season, it
accounts for 75 per cent of working hours. Seasonal fluctuations of this kind in farm and
non-farm work are common in all agricultural regions. In some regions, irrigation
obviously acts to increase farm employment relative to non-farm employment in the dry
season. Seasonal fluctuations in non-farm employment remain significant, even in these
regions, however.
Chapter 3
107
Secondary employment in non-farm activities is particularly important for the small and
landless farmers. Some evidence is available from the World Bank (1978) study. A
survey in Pakistan found that 48 per cent of farm families relied on secondary sources of
income from non-farm activities, which contributed 23 per cent to total income. For small
and landless farm families, however, the percentages are much higher; 70 per cent
undertake non-farm work, and the contribution of such work to their incomes is 39 per
cent. For small and landless farmers in the Republic of Korea, the contribution of non-
farm work to income is 42 per cent; in north Thailand, the contribution is 76 per cent
under “less intense” cropping and 42 per cent under intense cropping, illustrating the
point previously made: that even in irrigated regions, secondary employment in non-farm
activities remains important. In Zambia, for farms of around two hectares, about 22 per
cent of total farm income (allowing for subsistence income) is from non-farm work,
while the contribution to cash income is about 30 per cent. Although there are marked
variations among countries in the extent of secondary employment in non-farm work for
farm families, the available information from other countries also confirms that such
employment is particularly extensive and important for small and landless farm
families.13
3.10 Sectoral Composition of Rural Non-farm Employment
Basant and Kumar (1989) comment that rural households in developing countries are
seldom so specialised that the work of all household members throughout the year falls in
13 Some evidence is given by UNDP et al (1988) which are furnished below. The information provided are not clear whether they are regarding secondary or part-time employment, but they seem to be relevant in connection with the present discussion. In Tanzania, four industrial branches—food products, textiles, wood products and metal products—accounted for 88% of the value added in rural small industrial sector and 75% of employment in manufacturing enterprises in 1978. In Senegal, 20% of artisans are in artistic trades (e.g., jewellery and weaving), 50% in utility trades (clothing, metal wood and leather) and remainder in a variety of services. Non-household grain-milling is a recent activity in which rural women are engaged. In Indonesia, according to 1974 data, five branches accounted for 85% of the total value added in Rural industrial sector; food (44%), wood, rattan and bamboo products (21%), structural clay products (9%), textiles and clothing (6%), and metals and machinery (4%). Reardon et al (2001) have shown that—in Chile, Colombia, Costa Rica, Honduras, Mexico, Panama and El Salvador—rural non-farm employment, in both absolute and relative terms, has continued to grow rapidly. The Ecuador study by Elbers and Lanjouw (2001) reports that non-farm activities constituted 20% of rural employment in 1974 versus 36.4% in 1994. For data on share of rural non-agricultural workers in India (statewise), please see APPENDIX 3.3.
Issues Relating to Rural Industries: A Review of Literature
108
a single economic sector. One of the most critical limitations of aggregate data is their
inability to cover (capture) the range of occupations and types of employment status of
individuals, let alone of households as economic units. Moreover, comparisons across
countries are complicated by differences in definitions of rural areas, of the total work
force, and of different non-farm sectors. The general compositional pattern of rural non-
farm employment, according to the observation of World Bank (1978), appears to be
approximately 20 to 30 per cent in manufacturing; 20 to 35 per cent in services, including
government services; 15 to 30 per cent in commerce; 5 to 15 per cent in construction; 5
per cent in transport; and the rest in utilities and other activities. Rosegrant and Hazell
(2000) have given a picture of employment shares for a number of South and Southeast
Asian countries which is shown in Table 3.1.
Because part-time and temporary employment are important in many agricultural and
non-farm activities, the employment data tend to underestimate the importance of some
activities, though the bias is likely to be small when expressed in share rather than in
absolute terms. Non-farm sectors are not always defined in the same way; the biggest
differences tend to arise in the definitions of the “service” and “other” sectors
(Bangladesh departs most from other country definitions).
Despite these differences in definitions, the data in Table 3.1 show a remarkably
consistent story across countries. The non-farm economy accounts for 40 to 60 per cent
of total national employment and the rural non-farm economy accounts for 20 to 50 per
cent of total rural employment. Differences between South and Southeast Asian
economies are also surprisingly small. While rapidly developing (until recently)
economies like Indonesia and Thailand now have very little agricultural employment in
their urban areas—only 9.4 and 1.9 per cent, respectively, of total urban employment—
the non-farm share of rural employment is not much different from those of other
countries. This no doubt reflects the fact that as rural settlements grow and diversify, they
soon become classified as urban rather than rural areas in the census data. Even so, the
share of non-farm employment in total national employment was not much higher in
Indonesia and Thailand in the mid-1990s than in Sri Lanka in 1981 or Pakistan in 1992-
93.
Chapter 3
109
Table 3.1: Employment shares by activity in rural & urban areas, selected countries (percent)
Total
Employment
Economy
Agri. Nonfarm
Manu-
facturing
Transport Trade Services Finance
Construction
Other
Bangladesh
(1991)
Rural 60.1 39.9 6.8 4.0 3.3 50.2
Urban 15.1 84.9 8.1 6.5 3.4 50.1
Total 54.6 45.4 7.3 5.1
…………. 35.4 ……….....
…………. 31.8 ……….....
…………. 33.8 ………..... 3.3 50.4
Sri Lanka
(1981)
Rural 55.7 44.3 19.8 8.3 16.5 25.2 1.5 6.6 22.1
Urban 7.3 92.7 16.0 9.7 23.9 28.7 2.8 3.7 15.0
Total 45.2 54.8 18.5 8.8 19.2 26.5 2.0 5.5 19.5
Pakistan
(1992-93)
Rural 63.8 36.2 19.0 10.4 21.9 26.8 0.7 19.4 1.8
Urban 5.8 94.2 22.6 10.6 28.9 26.0 2.4 7.1 2.4
Total 47.6 52.4 20.8 10.5 25.4 26.4 2.6 13.2 2.1
India
(1993-94)
Rural 76.9 23.1 30.7 6.9 19.4 11.6 4.6
Urban 17.7 82.3 22.2 12.7 25.9 3.1 2.3
Total 61.5 38.5 28.5 8.4 21.1
…….. 26.8 ……..
…….. 38.3 ……..
…….. 29.8 …….. 9.4 2.8
Philippines
(1980)
Rural 74.0 26.0 20.9 11.9 13.2 32.1 3.0 11.5 7.4
Urban 18.3 81.7 19.4 11.3 14.9 35.9 7.1 8.1 3.3
Total 51.4 48.6 19.9 11.5 14.3 34.7 5.8 9.2 4.6
Indonesia
(1995)
Rural 63.1 36.9 23.8 8.2 31.7 24.2 0.5 9.4 2.2
Urban 9.4 90.6 20.0 8.0 30.1 31.1 2.4 6.8 1.6
Total 45.9 54.1 21.8 8.1 30.9 27.9 1.5 8.0 1.8
Thailand
(1996)
Rural 49.9 50.1 30.3 5.1 22.1 21.5 1.3
Urban 1.9 98.1 22.6 7.0 29.9 9.7 2.0
Total 39.7 60.3 27.6 5.8 24.8
…….. 19.7 ……..
…….. 28.8 ……..
…….. 22.8 …….. 17.4 1.6
Source: Rosegrant and Hazell (2000)
Issues Relating to Rural Industries: A Review of Literature
110
The composition of the non-farm economy also shows remarkable similarities across
countries. Service activities dominate the non-farm economy in both rural and urban
areas, followed by manufacturing and trade. Service activities (including much of the
“other” activities in Bangladesh and Sri Lanka) are more dominant in the lower income
South Asian countries, while trade and manufacturing are about equal in importance to
services in the Southeast Asian countries.
Within manufacturing, as observed in the study of World Bank (1978), most rural
employment is accounted for by four broad groups of activities: (a) food processing; (b)
textiles and wearing apparel; (c) wood, including saw-milling, furniture making, and
general carpentry; and (d) metal, including blacksmithing, welding, fabrication, and the
making of tools and equipments. All four categories appear to have retained their
importance no matter what level of development has been reached in a particular country.
Within each category, however, there are considerable differences among countries. A
particularly noticeable feature of manufacturing activities in rural areas (as elsewhere) is
their diversity, both with regard to manufacturing techniques used, and with regard to the
types and quality of the final product. To a large extent, this diversity is linked to the
nature and income level of the markets, which determine both the range and the quality
of products in demand. On the supply side, local income levels (labour costs), availability
of capital, level of infrastructure, and degree of competition from large scale industries
are the most important factors determining production techniques and product types. In
Africa, for example, the rural metal working sector is largely confined to blacksmithing
and welding. In irrigated regions in Pakistan Punjab and the Indian state of Tamil Nadu,
the sector is much more advanced, and includes small scale manufacturing of diesel and
electric tubewell pumpsets, an activity that has quickly become an important source of
rural employment in recent years. In Taiwan, where the rural metalworking sector has
similarly diversified in response to agricultural modernisation, industrial decentralisation,
drawing on cheap labour and good infrastructure, has been an additional factor affecting
the diversity, as well as the level and growth of employment. Handicrafts, textiles, food
and crop processing, carpentry, and wood products similarly differ enormously among
regions, with many old crafts declining in importance and new ones emerging as a result
of the development of the rural economy. Construction, commercial transportation, and
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111
service activities, which together account for as much as two-thirds of rural non-farm
employment, are, by their nature, linked to local markets. As with rural manufacturing,
the techniques used, and the types and quality of products and services offered, differ
considerably among regions. In construction, half of employment is typically in the
construction of dwellings, and farm and other buildings, while the remainder is largely in
roads and civil works. In commerce, retail trade accounts for three-quarters of total
employment, while the other quarter is in trade and financial services. In services, half of
the employment is typically in business, repair, community, personal and various
recreational services; and other half—in regions where there is an active development
programme—is generally in government, with educational and medical services also
being a large employer. (In regions without active development programmes, services
such as health and personal care, education, and the training of apprentices are provided
for by the families and small businesses themselves, and occupy much of the family’s
time.)
Rural towns can be expected to have an employment structure that reflects their
economic links to agriculture, while urban towns typically have a more independent
economic base. On average, according to Hazell and Haggblade (1993), the non-farm
employment share for rural areas increases from 26 per cent to 36 per cent when rural
towns are added to the definition of rural areas. Moreover, the non-farm employment
share increases quickly with size of locality and is 81 per cent even for rural towns. In
India, non-farm employment share also increases sharply with locality size. In rural
towns (defined as having populations between 5,000 and 100,000), 76.4 per cent of the
work force was employed in non-farm activities in 1971 (see Table 3.2). Services and
household manufacturing activities are the most important sources of employment in
rural areas, whereas employment in rural towns is more nearly dominated by trade and
services. In urban towns, trade and services are also important sources of employment,
but manufacturing dominates. Unlike rural areas and towns, manufacturing employment
in urban towns is nearly all in formal non-household activity; household manufacturing
accounts for a mere 3.9 per cent of total employment.
Issues Relating to Rural Industries: A Review of Literature
112
Table 3.2: Employment shares by activity and size of Locality, India (per cent)
Total
Employment
Manufacturing Non-farm
Employment
Economy
Agri Non-
farm
Household Non-
household
Transport Trade
Services
Construction
Other
India
(1971)
Rural 84.9
15.1 21.6 15.7 5.9 15.7 35.3 3.9 2.0
Rural
Towns
23.6 76.4 8.6 19.5 10.5 25.4 25.4 4.5 1.8
Urban
Towns
4.7 95.3 3.9 30.3 12.0 21.5 27.8 3.5 0.7
Source: Hazell and Haggblade (1991: 518)
Considering all the developing countries of the world, examination of the composition of
rural non-farm activities must reveal considerable diversity with respect to sector and
function, types and quality of products, and technology of production. Such diversity
poses special problems for the design of development assistance, particularly as regards
the kinds of intervention required from the public sector. Taken separately, each kind of
rural non-farm activity—there are over 20 broad categories—accounts for only a small
fraction of rural employment; taken together, however, rural non-farm activities become
an important source of employment.
However, Basant and Kumar (1989) make a critical comment that estimates of labour
force by economic activity generally provide a classification of workers according to
their principal sector of employment or occupation. Such estimates are likely to
underestimate the extent of non-agricultural work because it is commonly a secondary
source of income on a part-time or seasonal basis in rural areas. Such underestimation is
likely to be more in regard to the activities of female workers than for those of male
workers (for women’s share in non-farm activities, see APPENDICES 3.1, 3.2 and 3.3).
Chapter 3
113
3. 11 Hymer-Resnick Model Vs. Ranis-Stewart Models
The interlinkages between agriculture and industry have been presented as a theoretical
model by Hymer-Resnick in 1969. The importance of this model lies in the fact that it
incorporates three sectors viz. agricultural sector, urban industrial sector, and rural non-
farm sector. But, generally speaking, rural non-farm sector did not bear any positive
indication in the model and the development process mainly focused on the linkages
between export of agricultural products to the urban sector and import of urban
manufactured goods to the rural sector. Therefore, although rural non-farm economy
finds a place in the Hymer-Resnick model, it is not seriously taken as an important
instrument of playing a vital role in the development of home economy. It is the Ranis-
Stewart (1993) model which countered the Hymer-Resnick considerations and not only
tried to trace the position of the rural non-agricultural economy but also tried to search
for the positive role of this sector by developing a series of models based on four
situations like unfavourable colonial case, favourable colonial case, unfavourable post-
colonial case, and favourable post-colonial case. Let us now turn towards them.
3.11.1 Hymer-Resnick model
In Hymer-Resnick (1969) model, the pre-colonial era was a self-sufficient peasant
economy. To serve their own needs, peasants used only labour to produce two types of
goods—food and some simple non-agricultural produce. The non-agricultural activities—
defined as ‘Z-goods’—consist of the household or village production of handicrafts and
services, including some textiles, garments and food processing for village consumption.
However, as the colonial era increasingly linked the rural economy with the world
economy, two related developments occurred: on the one hand, profitable new
opportunities in the form of minerals and cash crops for world markets opened up,
offering an alternative use of rural labour, i.e., new export activities developed; and on
the other, consumption of imported manufactured goods, produced in the metropolitan
centres, became possible and, at times, was mandated. These imported products (M)
often were of higher quality and fulfilled a wider range of needs than Z-goods.
Consequently, labour was induced (sometimes forced) to move out of the production of
(non-traded) Z-goods and into the production of (non-domestically consumed) cash crops
Issues Relating to Rural Industries: A Review of Literature
114
(AE); the foreign exchange so earned permitted the purchase of imported manufactures
which substituted for Z-goods production and permitted the further expansion of AE
activities. As a consequence, there was a decline of the Z-goods sector and an expansion
of both exports and imports. The production of food for domestic consumption was
assumed to be broadly unaffected by (nor to affect) these developments.
The Hymer-Resnick displacement process is illustrated in Figure 3.3. 0EA ZO in quadrant I
represents the production possibilities curve as the fixed supply of labour (i.e., all rural
labour after the society’s food needs have been met) is allowed to shift between the
production of non-traded Z-goods and the production of non-domestically consumed AE
goods. Cash crop exports AE are exchanged for imports (M), through international
exchange at some terms of trade OPO (quadrant IV). Using a O45 line to project OMO
imports onto the horizontal axis (in quadrant II), ZOMO then represents the consumption
possibilities curve, corresponding to the production possibilities curve 0EA ZO, given the
terms of trade OPO. A price consumption curve (P.C. Curve) can be constructed in
quadrant II representing the locus of all tangencies between the consumer indifference
map (between Z and M goods) and alternative terms of trade. Given terms of trade OPO,
equilibrium at time zero is then established at 1 for consumption and at 1’ for production;
production of AE is then given by Oa* and that of Z-goods by OZ*, representing Z
displacement of ZO Z*, compared with a no-trade position. If the terms of trade improve
to OP’ (in quadrant IV), the consumption possibilities curve shifts out to ZOM’, and the
consumption (production) equilibrium to 22’, showing further Z displacement by M
goods, as the substitution effect overcomes the income effect and shifts consumption
towards M. If Z is assumed to be an inferior good, both the price and income effects are
negative and there is substantial Z displacement in response to further improvements in
the terms of trade.
3.11.2 The Ranis-Stewart Models: Colonial and Post-colonial
The Hymer-Resnick model, as described above, was intended primarily to apply to the
colonial era. But, according to Ranis-Stewart (1993), even in that era the assumptions
made were not universally applicable, either with respect to the likely movement of the
Chapter 3
115
terms of trade or the inferior character of Z-goods. But, especially when extended to the
post-colonial era, there are number of assumptions which clearly do not necessarily
apply. The following are the major departures on which Ranis-Stewart focus:
1. The Z-goods sector in the Hymer-Resnick model is broadly homogeneous, composed
of traditional non-agricultural activities carried out in individual households or at the
village level. However, Ranis-Stewart stressed that rural non-agricultural activities, in
fact, range from household and village production on a very small scale, producing very
simple, low quality products, to small modern factories using mechanical horsepower,
sometimes using imported technology, and producing modern higher quality products.
Ranis-Stewart divide the Z-sector into two groups: ZT, covering traditional household and
village processes and products, and ZM, covering non-traditional or modernising rural
non-agricultural processes and products. The latter are more likely to be located in rural
towns, rather than in households or villages. Some ZM activities existed even in the
colonial era, at least in some countries, but they clearly may become more important in
the post-colonial period. Science and technology inputs, domestic and foreign, contribute
to the enhanced productivity of such ZM activities. An example of ZT would be household
rice milling or handloom weaving, while metalworking or machinery repair shops would
constitute typical ZM activities.
2. A major development of the post-colonial era has been emergence of a modern
industrial sector, located predominantly in urban areas. This sector, initially at least, was
devoted to replacing imports. Ranis-Stewart call this the U-sector. In the presence of the
U-sector, the Hymer-Resnick displacement of Z-goods by imports is no longer a
dominant feature; instead it is the U-sector which now displaces Z-goods, actually or
potentially. Ranis-Stewart call this displacement of Z-goods by U-goods, U-
displacement. The assumption that labour is the only factor of production in non-
agriculture must now be dropped as the U-sector imports capital and other inputs in
exchange for the AE cash crop exports.
3. It should also be recalled that the agricultural sector (A) is really composed of two sub-
sectors: the agricultural cash crop export sector (AE), and a domestically oriented food
producing agricultural sector (AD), which has up to now been behind a curtain. In both
periods, but especially the post colonial era, domestic agriculture can be assumed to have
Issues Relating to Rural Industries: A Review of Literature
116
the potential for dynamic growth as it is subject to productivity-raising technology
change. This feature of AD—not incorporated into the Hymer-Resnick model—has two
relevant consequences. First, as fewer resources may be required to feed the total
population, the sub-sector can, over time, release land and labour for use elsewhere, i.e.,
the production of more AE and Z-goods, permitting an outward shift of the AEZ
production possibility frontier. Second, a dynamic AD can give rise to stronger linkages
between domestic agriculture and rural non-agriculture than export agriculture typically
provides. Such linkages run both from agriculture to rural non-agriculture and from rural
non-agriculture back to agriculture. The former include consumption linkages as well as
forward and backward production linkages.
Here, the linkages from agriculture to non-agriculture and vice-versa are required to be
critically viewed, because such linkages are not that straightforward as it is usually
assumed. To understand this, let us go back to Mellor (1989: 307) again who said that “if
the rate of growth of the demand for labour can be accelerated, demand for food can
grow rapidly, absorbing the increase in supply induced by technological change. If the
food supply increases more rapidly than food demand, real food prices will fall.” That is
exactly what has happened in India after the occurrence of the Green Revolution. “The
first period of the Green Revolution was one of substantial gains in farm profits. But the
rapid gains in production during the late 1970s did not translate into further advances in
income because the prices of agricultural products fell” (Binswanger and Quizon, 1989:
116). Mellor states that some reduction in output prices can be absorbed by producers
without a decline in output or net income since new technology increases factor
productivity. But what does happen when agricultural prices go down beyond Mellor’s
“some reduction?” Simply, the farmers will start losing profit margin and, consequently,
production will be hampered. “During the early Green Revolution period (1965-66), the
real per capita income of the rural population of India rose by about 8 per cent. However,
these gains were rapidly eroded. The sobering point is that in 1980-81 real rural per
capita income appears to have been only about 2 per cent higher than in 1960-61”
(Binswanger and Quizon, 1989: 130). In such a situation, considering open and closed
economy, two remedies can be thought of. One, looking for export markets for
agricultural produces could be a way out. But the problem may persist if the crops
produced in the region are not demanded by the people of the other regions (for example,
Chapter 3
117
other neighbouring regions may have also reached self-sufficiency in agricultural
production). Second, promotion of rural non-agricultural activities may be another
option. Let us explain it further. Rural income rises in the early period of green revolution
and that is the high time for at least partial diversification of rural economic activities
from agriculture to non-agriculture through transfer of resource like capital, given the fact
that surplus agricultural labour force will be absorbed in non-agriculture. If the second
2
1
M
Z
M
O 0
EA
O45
O45 MO
MO
PO
AE
M’
M’ P’
ZO
Z*
Z’ P.C. Curve
II
Consumption Possibilities Curves
I
Production Possibilities Curve
III IV
a*
1’
2’
Figure 3.3: Unfavourable colonial case (Hymer-Resnick)
Source: Ranis and Stewart (1993)
Issues Relating to Rural Industries: A Review of Literature
118
option happens to have been accepted in the rural economy, then the people engaged in
non-agriculture will create further demand for food products and prices of agricultural
goods will be able to retain a certain level for sustaining viable production. In such a
situation, linkages from rural non-agriculture to agriculture may work in a positive
direction. But this is merely a subject of rural entrepreneurship—which involves a crucial
question i.e. whether or not a farmer is willing to invest his surplus capital to non-
agriculture. The present study primarily focuses on this issue and we will discuss this
issue later in other chapters. Let us now confine ourselves to Ranis-Stewart model again.
Ranis and Stewart consider three dimensions of the linkages from rural non-agriculture to
agriculture (which, according to them, have more been neglected). An improvement in
agricultural markets or improved internal terms of trade resulting from additional rural
non-agricultural activity; improved science and technology knowledge, often embodied
in modern inputs; and a change in attitudes among farmers, who, as a result of the
growing availability of consumer goods and additional opportunities for investment in
rural non-agricultural activities, have a greater incentive to raise productivity and
accumulate savings.
4. In the colonial era, the Hymer-Resnick assumption of improving external terms of
trade or improving opportunities for AE may have been broadly correct. But in the post-
colonial era this does not seem to correspond to the facts; most less developed countries’
terms of trade have either remained constant or deteriorated. If deteriorating terms of
trade occur in the Hymer-Resnick model, Z-goods production tends to increase. Ranis-
Stewart assume constant terms of trade in their discussion.
Hymer-Resnick model was based on colonial era, whereas Ranis-Stewart have mainly
focussed on the post colonial era. But Ranis-Stewart argue that in both the colonial and
post-colonial eras, conditions may be relatively favourable or unfavourable for the
achievement of rural balanced growth. So, they found it useful to contrast the extremes of
the unfavourable colonial archetype (the Hymer-Resnick case) with the more favourable
archetype.
Chapter 3
119
3.11.3 Colonial Archetypes
3.11.3.1 The Unfavourable Colonial Archetype: The Hymer-Resnick Case
The distinguishing features of this case are that colonial policies inhibit the development
of domestic industry through a combination of mercantilist restrictions by the mother
country on the pattern of trade and investment, coupled with the relative neglect of food
producing agriculture. The focus of government attention is on the export of minerals and
cash crops and the auxiliary overhead services required to bring them to market; less
attention is paid to agriculture for domestic consumption. AE is often concentrated in
large holdings, yielding an unequal distribution of income and therefore probably
relatively weak linkages with non-agriculture, because, in this case, it is assumed that
linkages can be strong only when rural income distribution is not extremely skewed.
Where the primary export consists largely of minerals the resulting rural income
distribution is even more unequal and the rural linkages even weaker. As a combined
consequence of these features, the Z-goods sector is likely to be dominated by traditional
household goods ZT, with negligible ZM activity. In this context ZT is gradually displaced
by imported consumer goods, while domestic agriculture tends to stagnate relatively.
This is the Hymer-Resnick prototype, discussed herewith and summarised in Figure 3.3.
3.11.3.2 The Favourable Colonial Archetype
In circumstances more favourable for rural development, the colonial government, for its
own reasons, focuses more attention on food producing agriculture (usually because the
export and the domestic food crop are one and the same, e.g. rice) and occasionally even
resorts to land reform. In these circumstances, the agricultural sector tends to be
unimodal, i.e., composed of large numbers of individual small-holders, rather than
bimodal as in the unfavourable archetype (i.e., with plantations hiring landless
agricultural workers); consequently agricultural income is more equally distributed,
yielding stronger linkages with non-agricultural activity. In this favourable case, the
colonial government is less restrictive with respect to local entrepreneurs, permitting
indigenous industrial development to progress naturally as a consequence of the various
linkages.
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120
Under these assumptions, illustrated in Figure 3.4, as agricultural productivity increases,
more labour and land are released permitting an outward shift of the production
possibilities curve from 0EA ZO to 0
EA Z’, thereby also leading to an outward shift in the
consumption possibilities curve from ZOMO to Z’MO, in the second quadrant. Z is now
increasingly made up of ZM which is much more dynamic and innovative in character
than the ZT it replaces. As a consequence of both these quantitative and qualitative shifts,
the displacement of Z by M goods, predicted by Hymer-Resnick, is considerably
weakened and, indeed, may not occur, as modernising rural industries are better able to
compete with imports.
Both the substitution and income effects are now likely to be positive, leading to a new
equilibrium position at 2 in quadrant II. Consequently, Z-goods production increases
relative to the pessimistic Hymer-Resnick case.
3.11.4 Post-colonial Archetypes
3.11.4.1 The Unfavourable Post-colonial Archetype
The major development of the post-colonial era is the emergence of a U-sector, as a
consequence of the well-known import substitution policy syndrome. In the unfavourable
case, urban industry is encouraged, relative to both Z-goods production and domestic
agriculture, by government policies affecting the internal terms of trade, the exchange
rate, the interest rate, and the allocation of infrastructure, credit and foreign exchange.
The U-sector is mainly composed of large-scale domestic enterprises, public and private,
as well as multinational companies. Foreign institutions—both private and public—
typically help finance this urban oriented import substitution process. Within agriculture,
export crops, whose earnings are needed to help fuel the import substitution process, are
usually favoured relative to food crops in terms of research, extension, subsidised inputs
availability, etc. Land and income are likely to remain unequally distributed, especially in
the AE activities, with increases in purchasing power directed largely towards U-goods.
Consequently, rural linkages remain weak, both as a result of the neglect of the food
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121
producing agricultural sector and the U-displacement process which consists in the
displacement of Z (and M) goods by the newly developing urban consumer goods
industries. While some ZT production will continue—partly due to the persistence of
rural poverty and partly the ‘natural’ protection afforded by transport costs (especially in
large countries)—the potential development of the ZM sector is severely affected.
3.11.4.2 The Favourable Post-colonial Archetype
In more favourable post-colonial circumstances, the Z-goods sector represents a dynamic
element in interaction with a growing food producing agricultural sector. This results
from a more favourable environment in a number of dimensions. First, in this case the
government does not discriminate against domestic agriculture relative to traditional
primary export activities. Consequently, productivity expands in the AD sector and
additional resources are released to the rest of the economy. Second, since income
generated within AD is more likely to be equally distributed than within AE, especially
when land-saving or labour-using technologies are adopted, that sector’s expansion
generates stronger potential demand for the output of rural non-agricultural activities.
Third, government policy with respect to the allocation of infrastructure and the macro-
economic environment are generally more evenhanded between rural and urban industry
as well as between agriculture and industry.
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122
Z
M
O 0EA
O45
MO
PO
AE
M’
ZO
Z’
II I
III IV
1
2
Figure 3.4: Favourable colonial case
Source: Ranis and Stewart (1993)
Chapter 3
123
'kM
okM
2
U’ UO'EA 0
EA
Z
U, MC
O
P
AE
MK, MC
ZO
II I
Figure 3.5: Unfavourable post-colonial case
III IV
1
Source: Ranis and Stewart (1993)
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124
As a consequence of the above, the rural industrial sector faces relatively more
favourable conditions, both from the perspective of demand, via linkages with
agriculture, and of supply, via the dynamics of investment and the adoption of new
technology. The resulting modernisation transforms Z from a sector largely composed of
very small, low productivity, household and village enterprises of the ZT type to one
increasingly represented by ZM activities, i.e., composed of small factories using modern
(often imported and then adapted) technologies and producing products of a more
ZT 2
U’ UO'EA 0
EA
Z
U, MC
O
P
AE
MK, MC
ZM III
III IV
1
Figure 3.6: Favourable post-colonial case
Source: Ranis and Stewart (1993)
Chapter 3
125
uniform (and often higher) quality than the traditional ZT goods, selling at favourable
prices relative to the output of the U-sector and imports. The modernised ZM sector may
eventually also export, through subcontracting relations with the U-sector as well as
through direct channel. ZM gradually displaces ZT and the Z-goods sector retains a
substantial, and likely growing, importance, at least until the labour surplus has been
eliminated. U-displacement is now much less in evidence, since the U-sector itself is less
artificially nurtured and is forced to compete and/or induced to establish complementary
relations with the Z-goods sector. At some point the U-sector will begin to supply the ZM
with domestically manufactured capital goods, while the ZM sector supplies the U-sector
with partly processed agricultural goods and/or serves as a subcontractor for the
production of U-goods. Far from exhibiting Hymer-Resnick stagnation, the Z-sector thus
becomes one of the dynamic elements in industrial development, with a high rate of
capital accumulation, technology change, and employment expansion. This, in turn, acts
as a stimulant to agricultural growth, through rural non-agriculture to agriculture
linkages. This pattern of development tends to be associated with a high overall rate of
growth, a more egalitarian income distribution and regional balance, in contrast to the
unfavourable archetype.
Figures 3.5 and 3.6 illustrate these two post-colonial extremes. The horizontal axis to the
right of the origin now indicates a combination of U-goods and Mc, imported consumer
goods, the proportion of imports in the supply of total consumer goods declining as
import substitution proceeds. Earnings from agricultural exports are now spent
increasingly on imports of capital goods (Mk) and to a diminishing extent on imports of
consumer goods (Mc), both shown on the vertical axis below the origin. The consumption
possibilities curve (e.g. ZOUO in Figure 3.5) represents the possible combinations of the
consumption of Z-goods and the consumption of U-goods. As illustrated in the diagram,
once imported consumer goods have been fully replaced by capital goods imports, the
proceeds from agricultural exports are translated, first, into larger amounts of Mk, and
then, through domestic production, into larger volumes of U.
In the unfavourable case, shown in Figure 3.5, while the productivity and output of AE
increase, the productivity of the Z-sector remains unchanged, shifting the production
possibilities curve outward, from ZO 0EA to ZO '
EA . The consumption possibilities curve
Issues Relating to Rural Industries: A Review of Literature
126
therefore shifts correspondingly, from ZOUO to ZOU’. The substitution effect acts to
reduce the consumption of Z-goods and to increase the consumption of U. Since the Z
sector remains overwhelmingly ZT, the income effect also tends to be negative, i.e., ZT is
likely to be an inferior good. So this too acts to reduce Z-consumption. Consequently, the
equilibrium position moves from 1 to 2, with reduced production and consumption of Z-
goods.
Finally, Figure 3.6 represents the favourable post-colonial case. Here productivity
increase in the AD sector releases land and labour for the enhanced production of AE and
Z, and consequently there is an outward shift in the production possibilities curve in
quadrant I. Moreover, ZT is gradually replaced by ZM, with an increase in productivity
enhancing the potential production of Z goods. The outward shift of AE is, however,
relatively smaller than in the unfavourable case, as less attention is paid to that sector and
more to domestic agriculture. In this case, the income as well as the substitution effects
on Z-goods consumption is positive because of the increasingly modern characteristic of
ZM goods, leading to a new equilibrium at 2 in quadrant II of Figure 3.6. Consequently,
both Z-goods production and consumption expand, supported by strong linkages with
domestic agriculture as well as expanding complementary relations with the U-sector
over time.
Definitely the favourable post-colonial model shows a very positive as well optimistic
picture so far as the transformation of ZT to ZM is concerned. This can be achieved
through favourable institutional intervention. Heavy investment on rural infrastructure
(both hard and soft) may facilitate such transformation. According to Ranis-Stewart, the
favourable post-colonial model fits well into Taiwan case. Taiwan was a colony of Japan
from 1895 till the end of the World War II. Japan was interested almost exclusively,
certainly prior to 1930, in Taiwan’s agricultural output, particularly sugar and rice, as a
complement to the Japanese home economy which was beginning to run into food
shortages after the turn of the century. As a consequence, Japanese colonial policy was
heavily rural-oriented, as evidenced by the 1905 land reform. The attention consistently
paid both to physical infrastructure (e.g. irrigation and roads) and organisational
infrastructure (e.g. the substantial agricultural research expenditures).
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3.12 Urbanisation and the Role of Infrastructure in Rural Industrialisation
To some writers, the connection between industry and the city was, perhaps, too obvious
to require further elucidation. Others found a ready explanation at hand. Hoselitz (1951)
has argued that the economic development of underdeveloped countries is contingent
upon the introduction of industry; industrialisation, in turn, is associated with urban
growth. Davis and Golden (1955) have also found no difficulty in maintaining that city
makes its own peculiar contribution to the process of economic development, or in
asserting the existence of a direct relationship between urbanisation and industrialisation
in which the former indicates and stimulates the latter. But, can a city or town grow
without the support of infrastructure? In Lampard’s (1955) view, the great urban
explosion of the second half of the 19th century was seen to stem directly from
improvements in communications which served to concentrate economic opportunities in
locations which offered the greatest cost advantages in the procurement, processing, the
distribution of goods. He has strongly argued that, in a culture of economic competition,
producers always tend to scatter or concentrate according to the principle of minimum
cost. Producers prefer urban sites in order to have an access into better transfer facilities,
broader and more flexible labour markets, numerous auxiliary business services like
banking, insurance, brokerage, utilities, or fire and police protection. Conventional
production theory has led many to suppose that economies of scale and mobility of
factors would eventually bring all activities into great centres, but the persistence of
small-scale plants and widely dispersed towns is not necessarily a token of irrationality
(Lampard, 1955). Oberai (1993) has also argued that public sector investment in
infrastructure development (power generation, water treatment, transportation systems,
etc.) is concentrated in the urban centres in order to exploit economies of scale. Industrial
firms located in cities thus reap substantial cost benefits because of their access both to
infrastructure and to large and diversified markets for labour and other inputs. Anderson
(1971) has argued that whether in ancient, medieval, or modern times, if there is technical
invention that leads to social change, its point of beginning tends most likely to be in
urban centres.
According to Gilbert and Gugler (1982), the world depression of the 1930s and the
effects of the Second World War led to the spontaneous process of industrial expansion
in the larger Third World nations. They have argued that the initial stage of
Issues Relating to Rural Industries: A Review of Literature
128
industrialisation saw the establishment of companies producing directly for the consumer
market. Many of these products were only affordable by the higher income groups in the
society, most of whom lived in the major cities. Consequently, most market oriented
companies tended to concentrate in the largest cities. Even while talking about the rural
industries in China, Gilbert and Gugler (1982) have observed that after 1949 there has
been a programme of rural industrialisation but industry has continued to locate in major
cities or in nearby satellite towns.
So far as rural small enterprises are concerned, UNDP et al (1988) have stated a distinct
opinion. They have argued that small industry is less dependent on the urban locational
amenities which are a veritable life-line for large industry development. According to
them, in the post-World War II period, development strategies were followed in many
developing countries, which tended to concentrate on public investment and ownership to
promote large-scale industrialisation. This was the case in Nehru-Mahalanobis strategy in
India too (Saith, 1992). Agriculture and other rural activities were often neglected or
subordinated to pursuit of modernisation through large-scale industrialisation and
urbanisation (Chakravarty, 1987; UNDP et al, 1988). Large firms’ intention is clear. At
big cities, commercial and financial services are relatively well developed and a
concentration of purchasing power exists. Hence, at early stages of development, most
industries act as if urbanisation economies associated with sheer city size are crucial and
the intermediate city system suffers (Hamer, 1985). Also, Friedmann (1973) has argued
that ‘core-region’ policies have generally stressed large-scale industrial complexes. This
strategy overlooks the long-term consequences of building up an entrepreneurial tradition
that has its roots in the bazaar (rural trade centre) economy. This calls for an alternative
strategy which would aim at deconcentration. A feasible proposition then can be this: the
city may be brought into the countryside, not the whole city, to be sure, for the people
continue to live on their own farms and in the villages, but vital elements of it should be
present in rural areas (Friedmann, 1973). Establishing some vital urban elements to rural
areas may be termed as rural urbanisation and this process can gain momentum through
infrastructure development. In Bunce’s (1982) view, a recurrent theme in the study of
rural settlement problems is stagnation and decline in community infrastructure. Lerner
(1958) has argued that rural energies would have to be released by placing villages in
better communication with the urban centres. Friedmann and Douglass (1976: 372)
suggest that the countryside should be transformed by “introducing and adapting
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elements of urbanism to a specific rural setting. This means: instead of encouraging the
drift of rural people to cities by investing in cities, encouraging them to remain where
they are by investing in rural districts and to transmute existing settlements into a hybrid
form we call agropolis or city in the fields.” An agropolitan district is thus defined by
Friedmann (1988) as containing, at least, one small urban centre and a population of
between 40,000 and 60,000 persons. Agropolitan districts according to this definition are
relatively small and the population within such a district can reach the centre within a few
hours. Ærøe (1992) too is an advocate of this concept. In Friedmann’s approach, the main
purpose of the urban centre is to strengthen its surrounding rural base and to improve the
life of the community. Rural development is on the agenda, and the role of the urban
centre is to support this development through provision of services and social
infrastructure.14 Bunce (1982) also mentions this common principle of concentrating
investment in strategic growth centres. The logic of this is that if growth occurs at a
number of centres, the surrounding rural areas will benefit through a ripple effect. This is
discussed below in more detail.
14 Tangri (1964) says that urbanisation is neither a necessary nor a sufficient condition for economic growth. He argues that “we cannot determine the role of urbanisation without estimating the economic costs or benefits of such urban phenomena as anomie, political and ideological ferment, and transformation of cultural and social values” (Tangri, 1964: 383). So far as the economic cost of urbanisation is concerned, he raises the right question because urbanisation needs a huge investment on infrastructure. There is a category of goods, such as roads, electricity, cables, ports, irrigation canals, and potable water supplies, whose availability is a precondition for the growth of anonymous markets, and for the production and distribution of raw materials and outputs (Rogers and shoemaker, 1971; Brown, L.A. and Lentneck, 1973). They form an economy’s infrastructure. Now, the answer to the question of economic cost of infrastructure is sought into the following discussion. According to Dasgupta (1993), now it is a contingent fact that the production of infrastructure involves large fixed costs relative to the size of the population involved in their use. (Commodities that we have labelled public goods also often satisfy this property). In rural communities of poor countries they are often large relative to average income, which is another way of saying the same thing. If this is the case, then who will invest on infrastructure in rural areas? It can be argued, both analytically and by an appeal to evidence, that the production and use of infrastructure is hampered if decisions are left exclusively to the private sector (Scherer, 1980; Stiglitz and Mathewson, 1986; Tirole, 1988; Panzar, 1989). The reason is that, because of large fixed costs, the average cost of production is less than the marginal cost when the level of output is optimal. This means that setting price equal to marginal cost of production entails losses, something a private producer would wish to avoid. This forms the classical reason for government involvement in the production of infrastructure (Guesnerie, 1975; D. Brown and Heal, 1979, 1983; Beato and Mas-Colell, 1985; Dierker, 1986; and D. Brown, Heller and Starr, 1990). This view was supported by Dasgupta (1993) too. He argues that, for very poor regions, the infrastructure has to be supplied free of charge, the expenditure being financed by general taxation.
Issues Relating to Rural Industries: A Review of Literature
130
Urban population is dependent on the acquisition of an agricultural surplus (Gilbert and
Gugler, 1982). Hence an urban centre in a rural setting plays a positive role in rural
development. Johnson (1970) has noted that there is one central place for every 16
villages in Europe compared with one for every 157 villages in the Middle East. This gap
in the urban size distribution lowers agricultural productivity because there is nowhere to
sell the produce and no opportunities for buying consumer goods which might stimulate
farmers to increase production. Bendavid-Val (1991) has argued that rural towns are
important links in marketing agricultural exports from the area. Their export marketing
function may include commodity processing; grading and packaging; storage, bulking,
and depot activities; wholesale trade; transportation activities; and the maintenance,
repair, and supply services to support these functions. Some argue that agricultural
development itself necessarily requires growth in industry and services. Mellor (1976,
1995: 8) has already emphasised this rural dynamism (through forward and backward
linkage effects) and strongly argued that “agricultural growth also accelerates non-
agricultural growth and therefore will not automatically eliminate the problems of
urbanisation.” Here is the importance of small urban centres in the agricultural districts
and thus the concept of ‘growth centre’ holds a venerable place in the regional
development (Gilbert and Gugler, 1982). A growth centre serves as an agent in an
agropolitan district. The growth centre notion conceives of an urban complex containing
a series of industrial enterprises. The centre provides a focus for the development of the
centre’s hinterland. Industries that consume agricultural products will create a market for
the region’s farmers, as will the growing concentration of urban inhabitants. Mellor
(1995) argues that where non-agricultural activity is fomenting the growth of secondary
urban centres, the better the modern communications the better these firms can compete
and look outward. Liedholm et al (1994) have also argued that rural towns help grow
small enterprises. They serve as centres for marketing farm produces, for obtaining wage
employment, for engaging in non-farm enterprises, and for investing (Bendavid-Val,
1991). Towns, as centres of local trade, are the primary vehicles for multiplying into
more livelihoods for more people in the area. Bendavid-val (1991) has argued that rural
towns, given a welcoming environment, are logical business locations for many types of
entrepreneurs. Considering a link between the large urban centre and the rural areas
through market town, Smith (1975) has developed a market chain shown in Figure 3.7.
Such a chain calls for a strategy to strengthen the link, as shown in the Figure, through
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131
development of public infrastructure. For another schematic diagram developed on the
basis of the overall conceptual discussion presented above, see Figure 3.8.
Figure3.7: Market chain
Urban primary centre Market town Rural wholesale market Rural retail market Source: Smith (1975)15 Courtesy: Royal Anthropological Institute of Great Britain and Ireland, London.
15 Cited in Whyte (1982: 22).
Issues Relating to Rural Industries: A Review of Literature
132
Figure 3.8: Relationship between urbanisation and rural industrialisation
requires facilitates creates We draw an end to this discussion with an example. On the eve of independence, Punjab
(in India) was industrially backward in comparison with the other states of India. But, by
the beginning of the 1960s, Punjab had become known as the land of small-scale
enterprises (Bhalla, 1995). Technological breakthrough in Punjab agriculture was led by
large-scale planned investment in rural infrastructure right from the beginning of
planning in 1951. Urbanisation accelerated during 1961 to 1981, particularly in
agricultural marketing cum trading towns. The plan investment in Punjab gave top
priority to rural infrastructure such as irrigation and power, agriculture, community
development, credit, markets, and research and extension, which together constituted
nearly 70 percent of the total outlay during all the plans. The central government has
made a considerable outlay for direct assistance and for infrastructure. By 1985, nearly
all villages were linked by metalled roads, and the proportion of surfaced roads per 100
square kilometres of area far exceeded that for India as a whole. Since agriculture is still
the largest sector of the Punjab economy, the extent of urbanisation is limited. But the
“notable feature of Punjab’s pattern of development is that many rural areas have
acquired urban functions and amenities owing to their fairly well-developed
infrastructure and the good road connections between most villages, but have retained
their rural characteristics. Urbanisation statistics fail to reflect this important
phenomenon” (Bhalla, 1995: 103). There is no single dominant city in Punjab. There are
URBANISATION
DEVELOPMENT OF RURAL INFRASTRUCTURE
(THROUGH PUBLIC INVESTMENT)
RURAL GROWTH CENTRES/MARKETS
RURAL INDUSTRIALISATION
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133
large numbers of equally important major centres and a host of others running down to
smaller and smaller centres that still are important sources of non-agricultural
employment. The Punjab experience illustrates that rapid agricultural growth coupled
with dispersed infrastructural facilities stimulates growth in other sectors through input,
output and consumption linkages.
3.13 Conclusions
This chapter focuses on various issues relating to rural industries. The subjects include
rural growth linkages, urbanisation, secondary or seasonal employment in rural non-farm
activities, and sectoral composition of rural non-farm employment. Primarily, the chapter
emphasizes the interlinkages between agriculture and industry and tries to look for the
place where the rural non-farm economy does stand in the existing literature and to
ascertain whether the sector is given its due respect for its role in the development of
home economy. In this connection, the present chapter has started the discussion on
interlinkages from the time of the Physiocrats and covered the linkage debate appeared in
literature during the last few decades. While Quesnay was found to have been
concentrated in agriculture for its surplus generating role, Adam Smith was found to be
an advocate of industry as a leading sector (which adds value through division of labour)
in the development of an economy. Marx found capitalistic mode of production as a
destructive force of small farming as well as rural cottage industry. According to him,
small farming groups turn into wage earners under the capitalistic system and,
accordingly, rural home-based industry finds the ruinous path. Thereafter, in this chapter,
we present the Kuznets’s model and Hwa’s model on relationship between agriculture
and industry, where we find agriculture as a significant engine of development but find
no place of rural industry as an important connector of transformation of a traditional
sector towards industrialisation. Although Hymer-Resnick model takes the rural non-
farm economy into consideration, the model ignores/fails to define particular
developmental role of this sector and puts significance to the linkage between rural
agricultural sector and urban industrial sector. It is John W. Mellor who has been very
renowned for theorizing resource transfer from agriculture to rural industry. Although
Mellor gave importance to other factors like rural infrastructure, he recognized
agriculture as a primary engine for the development of the overall rural economy in
Issues Relating to Rural Industries: A Review of Literature
134
general and for the development of the rural industrial sector in particular. He initiated a
debate, but the other scholars have more or less agreed with him with regard to the
dynamic role of agriculture in the process of growth of rural economy. Later Ranis-
Stewart took up the Hymer-Resnick model and showed the shortcomings of the Hymer-
Resnick by developing a series of new models with an objective to pinpoint the position
of rural non-agricultural sector in different situations starting from unfavourable colonial
case to favourable post-colonial case. In such a discussion on rural industry, we take the
issue of urbanisation or rural infrastructure into consideration in a separate section, since
urbanisation facilitates the road to industrialisation.
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135
APPENDIX 3.1: Distribution of rural workers across non-farm sectors in selected countries, male and female (per cent)
Country Total Nonfarm
Manufac- turing
Transport Trade Services Finance Construction Other
Bangladesh (1991)
Male 100 6.5 4.6 …………..... 39.4 ………..........
3.7 45.8
Female 100 8.4 0.4 …………..... 12.2 ………..........
0.8 78.2
Sri Lanka (1981)
Male 100 18.8 9.8 18.1 20.6 1.6 7.7 23.4 Female 100 24.9 1.6 8.9 46.1 1.4 1.2 15.9 India
(1993-94)
Male 100 26.8 8.4 21.1 ………. 27.2 ……….
12.6 3.8
Female 100 48.7 0.6 14.2 ………. 25.9 ……….
7.1 3.2
Philippines (1980)
Male 100 16.5 19.0 9.6 22.9 3.1 18.6 10.4 Female 100 27.9 0.6 19.0 46.8 2.8 0.4 2.5
Indonesia (1995)
Male 100 19.9 12.5 23.7 26.0 0.7 14.5 2.8 Female 100 30.9 0.3 46.2 21.0 0.2 0.3 1.0
Thailand (1996)
Male 100 25.3 8.0 17.8 ………. 17.3 ……….
29.9 1.7
Female 100 37.5 1.0 28.2 ………. 23.1 ……….
9.7 0.5
Source: Rosegrant and Hazell (2000)
Issues Relating to Rural Industries: A Review of Literature
136
APPENDIX 3.2: Women’s share in total employment by sector in selected countries, rural and urban (per cent)
Country Total Nonfarm
Manufac- turing
Transport Trade Services Finance Construction Other
Bangladesh (1991)
Rural 14.7 18.2 1.5 …………..... 5.1 ………..........
3.9 22.8
Urban 12.1 15.9 1.4 …………..... 5.7 ………..........
3.3 15.7
Sri Lanka (1981)
Rural 17.9 22.3 3.4 9.6 32.8 16.1 3.3 12.9 Urban 18.5 24.3 5.5 7.6 33.7 22.9 5.8 11.4 India
(1993-94)
Rural 19.4 30.3 1.8 14.0 ………. 18.6 ……….
11.9 16.9
Urban Philippines
(1980)
Rural 38.6 51.5 2.0 55.4 56.3 36.5 1.2 13.1 Urban 38.2 34.6 5.3 39.1 56.0 38.9 1.9 15.9
Indonesia (1995)
Rural 35.6 46.2 1.1 51.9 30.8 16.9 1.3 15.0 Urban 33.5 35.7 3.0 44.8 37.0 29.0 3.7 11.9
Thailand (1996)
Rural 41.3 51.0 8.1 52.7 ………. 48.5 ……….
18.5 17.7
Urban 45.4 46.1 14.3 48.4 ………. 56.7 ……….
24.5 40.0
Source: Rosegrant and Hazell (2000)
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APPENDIX 3.3: Percentage of non-agricultural workers in rural workforce in India, 1961-1983 States 1961 1971 1972-73 1977-78 1981 1983 Total Share All India 14.3 15.2 16.7 15.8 18.6 Andhra Pradesh 17.8 17.3 17.6 17.0 22.8 Bihar 13.1 10.4 15.3 12.6 16.5 Gujarat 11.8 14.1 13.0 17.4 15.4 Haryana 16.3 21.5 18.3 21.2 22.3 Himachal Pradesh 10.0 17.8 12.2 17.7 12.9 Karnataka 11.7 15.5 15.2 15.4 15.8 Kerala 39.7 38.0 34.6 42.3 36.9 Madhya Pradesh 9.0 9.4 8.6 10.4 10.0 Maharashtra 10.1 12.7 14.2 13.6 14.3 Orissa 12.9 14.9 15.0 14.9 20.9 Punjab 23.9 20.5 18.5 21.1 17.5 Rajasthan 9.0 12.6 11.6 12.9 13.3 Tamil Nadu 17.9 19.1 22.0 18.3 25.4 Uttar Pradesh 12.8 12.3 17.3 12.9 18.0 West Bengal 20.3 18.2 24.4 21.8 26.4 Male All India 16.3 16.3 19.3 19.5 18.3 22.4 Andhra Pradesh 20.0 19.7 21.4 19.7 19.9 25.6 Bihar 14.7 11.3 17.8 16.9 13.6 18.7 Gujarat 15.9 15.8 16.1 15.6 19.4 21.1 Haryana 19.4 21.6 19.9 22.5 24.1 27.8 Himachal Pradesh 15.4 23.4 18.9 22.6 26.6 22.9 Karnataka 13.9 15.9 14.8 16.8 16.4 18.4 Kerala 40.4 38.9 44.3 40.8 42.1 42.2 Madhya Pradesh 11.7 10.8 9.6 10.8 12.8 12.8 Maharashtra 14.8 16.1 17.6 19.6 19.6 20.4 Orissa 12.1 14.1 18.4 15.4 15.9 21.8 Punjab 23.2 20.0 20.6 22.2 21.9 22.5 Rajasthan 11.9 13.5 15.6 17.5 16.4 19.0 Tamil Nadu 20.7 20.9 24.6 26.1 22.4 31.1 Uttar Pradesh 13.9 12.9 18.1 19.8 13.6 21.3 West Bengal 20.0 18.4 22.1 22.3 21.8 26.9 Female All India 10.3 10.6 14.3 11.9 9.7 12.3 Andhra Pradesh 14.8 12.0 13.8 14.6 11.4 18.7 Bihar 9.9 5.7 14.8 11.0 6.9 11.8 Gujarat 4.8 6.4 8.7 5.6 6.8 7.4 Haryana 8.7 21.4 15.6 9.5 7.7 9.7 Himachal Pradesh 3.5 3.7 1.6 1.8 3.5 2.4 Karnataka 7.9 14.2 10.9 12.5 10.9 11.8 Kerala 38.0 35.4 41.4 27.4 38.5 29.1 Madhya Pradesh 5.5 5.5 4.4 5.3 6.5 6.1 Maharashtra 4.4 5.4 6.6 7.8 5.4 7.1 Orissa 14.5 21.1 18.3 14.2 12.2 19.0 Punjab 31.6 65.9 37.0 10.0 14.1 7.4 Rajasthan 4.5 6.4 8.4 4.7 5.2 6.1 Tamil Nadu 13.2 12.9 15.6 16.4 11.1 18.2 Uttar Pradesh 9.3 7.4 15.0 10.9 8.3 10.3 West Bengal 21.8 16.0 43.1 31.3 21.8 24.9 Source: Unni (1991)
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Chapter 4
Entrepreneurship and Culture
4.1 Introduction
The central problem in growth, as some orthodox economists all assume, is capital
formation. Of course, some have stressed ‘technical progress’ or ‘technological
creativity’ in their studies related to developing countries. But the studies which have
given main thrust on savings and capital formation have implicitly (behind the veil)
assumed that sufficient technological creativity to carry forward economic growth is
present in all societies. This assumption may fit well the developed countries but not the
developing countries at all. In development studies literature, sometimes lack of
technological creativity is regarded as an inhibitive factor to growth. In a discussion
pertinent to increase in productivity, Hagen (1962) gave priority to technological
creativity over capital formation. This was apparent to the first great student of economic
growth, Schumpeter, who wrote:
The slow and continuous increase in time of the national supply of
productive means and savings is obviously an important factor in
explaining the course of economic history through the centuries, but is
completely overshadowed by the fact that development consists primarily
in employing existing resources in a different way, in doing new things
with them, irrespective of whether those resources increase or not
(Schumpeter, 1949: 68).
Schumpeter’s statement is true not merely in some vague qualitative way but also in a
definite quantitative sense. Robert Solow (1957) has estimated that of the increase in
output per man-hour in the United States from 1909 to 1949, not more than 13 per cent
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was due to increase in capital. Between 87 and 90 per cent was due to other changes,
which may be lumped under the broad heading of technological progress (though some of
them may not belong there). This statistical estimate implies that if there had been no
increase in the quantity of capital used per worker, and changes in productive equipment
had been made only by replacing equipment as it wore out with new equipment
embodying new ideas, we would have had at least 87 per cent of the increase in output
per man-hour that we actually had; only 13 per cent or less is attributable to increase in
the quantity of physical plant and equipment used per worker.
Although technological creativity has got some attention in economics, all other
entrepreneurial characteristics like achievement motivation, propensity to take risk,
arbitrage, organising capability etc. have either been ignored or got relatively less
importance in mainstream economics. To fill up this vacuum, entrepreneurship has been
considered an interesting subject in the field of psychology since, some may argue,
entrepreneurial motivation is primarily generated from the mind of a person concerned,
which cannot be incorporated into the theoretical models (production or distribution
theory of the firm). Only the Austrian school of economics has been maintaining a series
of serious studies of entrepreneurship within the field of economics. In this regard, we
will present a brief analytical history of the studies of entrepreneurship within the field of
economics in this chapter.
The one side of the story is that entrepreneurship has been neglected in the theories of
economic development. The other side is that, while studying entrepreneurship,
economists generally do not bother about culture. But, on the contrary, the study of
culture tries to hold or make room for all activities of a society in its holistic womb. The
definition of culture, most often quoted from Tylor (1871), is: “Culture is that complex
whole which includes knowledge, belief, art, morals, law, custom, and any other
capabilities and habits acquired by man as a member of society.” It is quite natural that,
as Ogburn (1964) has stated, different students will emphasize different aspects of culture
as most significant, and in the future important new ideas about culture may be
discovered. In the above mentioned definition of culture, Tylor has not reserved any
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distinct place for entrepreneurship or entrepreneurial capabilities, but he has kept
provision to include “any other capabilities” to “that complex whole” of culture for the
future students. Entrepreneurial capability is one of the “any other capabilities” and thus
culture is reflected through entrepreneurial act (like other acts) of a set of population. A
particular culture has been defined by Redfield (see Ogburn 1964: 3) as “an organized
body of conventional understandings, manifest in act and artefact, which, persisting
through tradition, characterises a human group.” Thus, in this broad definition of culture,
entrepreneurial capability can take its place to “manifest in act” and, consequently, to
“characterise a human group.”
Berger (1991) has stated that a more comprehensive approach to entrepreneurship must
be interdisciplinary, comparative, and, above all, must take culture seriously. But there is
limitation to conduct such an interdisciplinary study and this limitation has been cited by
Ogburn (1964: 9-10) who has said that: “There are interrelationships between the parts of
modern society as truly as in the cultures of preliterate peoples. But these
interrelationships tend to be neglected by modern scientists because of their specialisation
in particular fields.” Berger (1991) has roughly distinguished between two camps:
economists on the whole are inclined to see entrepreneurship as a variable dependent
upon economic factors and largely independent of culture, and scholars from other
disciplines tend to see entrepreneurship as a variable deeply embedded in culture.
Economists, taking as a given the basic motivation to maximize one’s gain, postulate that
entrepreneurial activities will emerge more or less spontaneously when economic
conditions are favourable. Hence the members of this camp—the “mainstream”
economists of neoclassical frame—emphasize the pre-eminent importance of the
availability of capital, access to markets, labour supply, raw materials, and technology.
They formulate their analyses in terms of “economic opportunity conditions” and
“economic risk,” and their analytical models use a combination of rather narrowly
defined, functionally interrelated factors in more or less mechanistic ways.1 In contrast,
1 Entrepreneurship has not received much recognition from the mainstream economists. Moreover, economists (except very few) who have emphasised entrepreneurship for economic development did not try to relate entrepreneurship with culture. This trend has been observed from the early period of economic theories. For instance, priority is given, by the Physiocrats, to
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anthropologists, historians, psychologists, and sociologists emphasize in varying and
often contradictory terms the influence of non-economic factors such as social norms and
beliefs, psychological motivations for achievement, the legitimacy of entrepreneurship,
questions of social “marginality,” and the “internal fit” between any and all of these in
the rise of modern entrepreneurship. According to Berger (1991), because mainstream
economists with their clearly defined analytical models currently dominate the discussion
and are influential in shaping present-day economic policies, the analysis of
entrepreneurship in cultural terms has remained elusive. If non-economic factors are
taken into consideration, they are typically those pertaining to the political-economic
system, while wider questions of society and culture remain largely ignored. Berger
(1991) states that few have tried to enrich their studies from a cross-cultural and historical
perspective, and even fewer have attempted to come to grips with the changing nature of
cultures and society. Berger strongly argues that since entrepreneurship is embedded in
culture, such dynamics must be incorporated into our studies of it.
In this context, our discussion is broadly divided into two sections. First, we try to see
how entrepreneurship has been dealt with in the field of economics, and, in the next part,
we discuss the issue of entrepreneurship with an interdisciplinary approach (with special
reference to India as well as West Bengal). Finally, we end up our discussion with some
culturally sensitive models of entrepreneurship and conclusions.
capital. Despite giving priority to capital, they didn’t ignore entrepreneurial capabilities, but cultural factors are totally absent while they talk about entrepreneurial capabilities. They assertively say that the rehabilitation and further development of agriculture was the main precondition of general economic advance. How, then, was this to be achieved? One of the chief hindrances to further development, the Physiocrats recognised, was the prevalence of small-scale, capital starved, subsistence farming. What was required in the countryside above all, they argued, was not men, but wealth, i.e. capital. Simultaneously, Quesnay puts emphasis on entrepreneurial capabilities. He says that the normal income of the agricultural entrepreneur includes a “reward due for the trouble, work, and risks of his enterprise” (see Meek, 1962). But, in this line of discussion, we did not find even a meagre mention of culture in the Physiocratic school of thought.
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4.2 Entrepreneurship
The word ‘entrepreneur’ itself has an interesting history and it appeared first in French
according to Encyclopaedia Britannica, long before there was any general concept of an
entrepreneurial function. By the early 16th century men engaged in leading military
expeditions were referred to as entrepreneurs. From this usage it was easy to move to
applying the word ‘entrepreneur’ to other type of adventures. After 1700, ‘entrepreneur’
was a word which was frequently applied by the French to government road, bridge,
harbour and fortification contractors. The same term was later applied to architects.
Seeing such activities as the entrepreneurial function Bernard F. de. Bolidor, says
Hoselitz, defined it as buying labour and material at uncertain prices and selling the
resultant product at a contracted price (see Gautam, 1979).
Let us supplement it with Hoselitz’s (1951) words. The word ‘entrepreneur comes from
the French word ‘entreprendre’, which means ‘to do something’, and it was originally
used in the Middle Ages in the sense of ‘a person who is active, who gets things done’.
Swedberg (2000) adds that the first economic theory of entrepreneurship is to be found in
a work entitled Essays on the Nature of Commerce in General (circa 1730), written by a
Paris banker of Irish extraction, Richard Cantillon (circa 1680-1734), who had a real flair
for economic analysis.
Let us now discuss the evolution of the concept of entrepreneurship in economics.
4.3 Theories of Entrepreneurship: Schumpeter and his Previous and Later
The crucial role of entrepreneurship in an economic system has, in many cases, been
characterised by private ownership of capital. But the entrepreneur virtually deserves his
own identity, different from capitalist. Blaug (1986: 76) remarks: “a businessman need
not be a ‘capitalist’ or ‘manager’ but he must be a decision-maker, whether he liked it or
not. It is his function and this function alone that deserves the title of ‘entrepreneurship’.”
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The analysis of entrepreneurship should have occupied a central role in the investigations
of economists. But, as Blaug (1986: 76-77) has commented with deep despair, “when we
open any current text book of elementary economics, we discover that entrepreneurship is
hardly mentioned, or mentioned only in passing. Is this some sinister conspiracy of
silence…?” Always this was not the case. The strange disappearance of the entrepreneur
from the centre of the stage of economic debate has a long history which is brilliantly
depicted in Blaug’s work. Barreto (1989) has also done a good deal of job in presenting a
similar history, starting from the period of neoclassical era. Following mainly Blaug’s
work combined with some arguments of Barreto (1989) and Peneder (2001), here is a
summary presented below.
In the book entitled The Wealth of Nations (1776), Adam Smith clearly distinguished the
functions between the capitalist and the manager, and he emphasised that “profits” of the
capitalist exclude the “wages” of management as payment for “the labour of inspection
and direction.” But, Adam Smith did not distinguish between the capitalist as the
provider of the “stock” of the enterprise and the entrepreneur as the ultimate decision
maker. He used the terms “projector” and “undertaker” as the English equivalents of the
French word “entrepreneur” but only as synonymous for the business proprietor. This
failure to separate the entrepreneurial function from that of pure ownership of capital
became almost common practice of all the English classical economists. Thus, the term
“entrepreneur” or any of its English equivalents is totally absent in the writings of David
Ricardo and so is the concept of the businessman as the principal agent of economic
change.
Blaug comments that this explanation of the neglect of entrepreneurship in English
classical political economy appears somewhat unconvincing. Blaug’s argument is as
follows.
It is to be that the concept of entrepreneurship—as containing a function quite distinct
from that of both the capitalist and the manager—had already been formulated by a
renowned French economist of the eighteenth century, Richard Cantillon, who had
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written some twenty years before Adam Smith. Cantillon had pointed out the fact that
discrepancies between demand and supply in a market create opportunities for someone
to buy cheap and to sell dear and this sort of arbitrage performed by some agent brings
competitive markets into equilibrium. He called people who take advantage of these
unrealised profit opportunities “entrepreneurs”, that is, those who are willing to buy at a
certain price and sell at an uncertain price. (Moreover, he noted that action of this kind
need not involve manufacture and need not absorb the personal funds of the entrepreneur,
although it frequently did.) Anyway, the core argument that Cantillon offers is that
entrepreneurship is a matter of foresight and willingness to assume risk, which is not
necessarily connected with the employment of labour in some productive process.
Cantillon, therefore, left no doubt of the difference between the functions of the
entrepreneur and the capitalist.
Adam Smith, as Blaug (1986) stated, read Cantillon but did not take the analysis of
entrepreneurship as a serious concern. Similarly, David Ricardo had read the writings of
Jean Baptiste Say’s, who leaned heavily on Cantillon in distinguishing between the
provision of capital to a business enterprise, on the one hand, and the function of
supervision, direction, control, and judgment, on the other. Nevertheless, there is no
considerable indication of the special role of entrepreneurship in Ricardo. It is evident
that Ricardo and all other leading English classical economists regarded production and
the investment of capital as a more or less automatic process, involving no critical
decision-making and certainly no risky judgment or imagination of any kind. Ricardo
recognised the capitalist’s role to introduce a novel improvement in production. For
example, a new machine is liable to reap extra returns. And this is a fact which Ricardo
knew very well but this did not lead him to single out the capacity to innovate as the
feature which distinguished one capitalist from another.
Blaug states that exactly the same thing is true of Marx. Marx knew well that capitalists
can borrow all their capital from banks. That is why he regarded “interest” on capital as a
deduction from the “profits” of the enterprise. He also knew that the special skills of
managers, including the skills of monitoring and supervising the labour force, can be
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hired from the labour market. But the entrepreneur is missing in his writings. He never
considered whether the residual income left over after paying the interest on borrowed
capital and the wages of management corresponds to any particular economic function,
for example, the function of buying inputs at certain prices and selling the output at
uncertain prices, as a result of which there may be losses rather than profits. He must
have thought that either decision-making under uncertainty, which is what is involved in
operating a business enterprise, entails no risks, or if it does, there is an unlimited supply
of people in a capitalist economy willing to take such risks. Whatever be the case, Marx
simply conflated the functions of the capitalist and the entrepreneur and in that sense
simply continued the task where Adam Smith and David Ricardo left.
For the first entirely adequate argument in favour of the entrepreneurial role, we must
look at the nineteenth-century German economist, Johann von Thünen. Blaug remarks
that his remarkable but hopelessly obscure book, The Isolated State, Volume II
(published in 1850), defines the gains of the entrepreneur as to which is left over from the
gross profits of a business operation after paying (1) the actual or imputed interest on
invested capital, (2) the wages of management, and (3) the insurance premium against the
calculable risk of losses. The rewards of the entrepreneur, von Thünen went on, are
therefore the returns for incurring those risks which no insurance company will cover
because they are unpredictable. Since novel action is precisely the condition under which
it is impossible to predict the probability of gain or loss, the entrepreneur is “inventor and
explorer in his field” (Hébert and Link, 1982: 45-47).2
John Stuart Mill’s Principles of Economics (published in 1848) popularised the term
“entrepreneur” among English economists but failed to break the tradition of the Smith-
Ricardo concept of the entrepreneur as simply a multifaceted capitalist. The general
equilibrium theory of Léon Walras, a central figure in the marginal revolution which
ushered in the era of the neoclassical economics, provides a perfect example of how the
new microeconomics caused the entrepreneur, as it were, to disappear. Walras tells us
that every productive agent in a competitive economy is rewarded according to his
2 Cited in Blaug (1986).
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marginal product, that is, the increment of output which is contributed by the marginal
unit of that agent. Now suppose that some economic agent hires others to produce a
certain product. The hiring agent will be forced by competition to pay all the agents
whom he employs their marginal product. Initially, this may leave him with something
over and above the marginal product of his own services. If so, this merely induces the
hired agents themselves to become the hiring agent because the hired agents get attracted
by the positive residual and consequently enter the market as hiring agents. Thus they
eliminate the positive residual. If, on the other hand, the residual proves to be negative,
the hiring agent ceases to be a residual income recipient and rents the use of his services
to others at the value of its marginal product. In either case, the residual always tends to
become zero. The hiring agent is, of course, the entrepreneur but Walras assumed that
entrepreneurship is not itself a factor of production but rather a function that can be
carried out by any agent, say, the capitalist or the salaried manager. In any case, with a
zero residual income, the total product is, as neoclassical economists liked to pronounce,
exactly “exhausted” when all productive agents are paid their marginal products. When
perfect competition exists in the market and when we reach short-run and long-run
equilibrium, labour receives “wages” in accordance with the marginal product of labour,
capital receives “interest” in accordance with the marginal product of capital goods, but
“profits” are eliminated and the entrepreneur, as Walras said, “neither benefits, nor loses”
(Hébert and Link, 1982: 63-64).3 For an extended historical presentation of the absence
of the entrepreneur in microeconomic theory, covering almost whole neoclassical era and
modern theory of the firm, one may consult chapter 2 and chapter 3 of Barreto (1989).
Now we can raise a fundamental question: why does microeconomic theory neglect
entrepreneurship? So long as economic analysis is preoccupied with the nature of static
equilibrium under conditions of perfect competition, there is simply no room in the
theory either for entrepreneurship or for profit as a reward of risks associated with
uncertainty. The growing popularity of general equilibrium theory sets the seal on the
possibility of theorising entrepreneurship. As a matter of fact, static equilibrium school of
thought increasingly became the mainstream economics. Despite valiant attempt to shift
3 Cited in Blaug (1986).
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microeconomics from static concept to dynamic concept, large parts of modern
economics remain trapped in a static framework. Actually the core of neoclassical
economics lacked any true theory of the competitive process; what it truly possessed is
the theory of the outcome of that process in an equilibrium state. In short, it emphasized
equilibrium at the expense of disequilibrium. By assuming that all economic agents have
free access to all the information they require for taking decisions, decision-making in the
large part of modern economics is trivialised into the mechanical application of
mathematical rules for optimisation. No wonder then that the mainstream economics of
today is rich in the treatment of consumer behaviour, the profit maximising decisions of
business firms (in short-run equilibrium), the theory of wages, the theory of interest, the
theory of international trade, etc., but poor in the analysis of technical change, the growth
of big business, the causes of the wealth and poverty of nations—and the theory of
entrepreneurship (see also Baumol, 1968; Leff, 1979; Kirzner, 1979).
Apart from Blaug’s presentation of arguments, we can refer to Barreto (1989) who
analysed the question as to why the entrepreneur is nonexistent into orthodox
microeconomic theory. Barreto argues that as soon as the entrepreneur takes its place in
the orthodox microeconomic theory, consistency of the theoretical base is violated. This
violation is not tolerable for the sake of the building of the theory. The explanation of
Barreto is as follows.
1. Why can’t the entrepreneur as innovator be incorporated into the modern theory
of the firm? Because the production function exactly describes every possible
input-output relationship; because the logic of rational choice requires that the
ends and means be known and given. That is, in orthodox theory, the searcher
knows what he is looking for, and where to look for it. The introduction of
innovation requires relaxing these core assumptions. But this is impossible. If
relaxed, then the consistent, interlocking nature of the theory would be destroyed.
2. Why can’t the entrepreneur as uncertainty-bearer be incorporated into the
modern theory of the firm? Because perfect information, a basic assumption,
guarantees that every agent’s expectations will be exactly fulfilled. The
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introduction of uncertainty demands relaxing this assumption. But this relaxation
would prevent the application of the logic of rational choice. Although, by such
relaxation, decision-making would have some real meaning, the resulting
destruction of the consistent framework of the theory is not acceptable.
3. Why can’t the entrepreneur as arbitrageur be incorporated into the modern
theory of the firm? Because perfect information prevents the introduction of
ignorance, a requirement for arbitrage. Once again, relaxing this assumption
would definitely cause the immediate breakdown of the model, which is too
costly.
There is other school of thought, who brings entrepreneurship into theory. Blaug links up
the debate. The virtual consensus about the negligence of entrepreneurship has been
seriously questioned at several occasions in the twentieth century. The first occasion
came with the publication of Frank Knight’s Risk, Uncertainty and Profit (published in
1921), an acknowledged classic of modern economics. Knight started with elaboration of
von Thünen’s distinction between “risk” and “uncertainty”. There are many uncertainties
in economic life such as the chances of dying at a certain age. Their objective probability
can be calculated and correspondingly they can be shifted via insurance to the shoulders
of others. Such risks thus become an element in the costs of production, a deduction from
and not a cause of profits or losses. There are other uncertainties, however, which can
never be reduced to objective measurement because they involve unprecedented
situations.
The excellence of Knight’s argument was to show that the presence of true “uncertainty”
about the future may allow entrepreneurs to earn positive profits despite perfect
competition, long-run equilibrium and “product exhaustion”. Production takes place in
anticipation of consumption, and since the demand for factors of production is derived
from the expected demand of consumers for output, the entrepreneur is forced to
speculate on the price of his final product. But it is impossible to determine the price of
the final product without knowing what payments are going to be made to the factors of
production. The entrepreneur resolves this dilemma by guessing the price at which output
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will sell, thereby translating the known marginal physical products to the factors of
production into their anticipated marginal value products. Although the factors are hired
on a contractual basis and therefore must be paid their anticipated marginal value
product, the entrepreneur as a residual income recipient may make a windfall gain if
actual receipts happen to be greater than forecasted receipts.
Ten years before the appearance of Knight’s book, the young Schumpeter had
contributed a wholly different view of the economic problem in The Theory of Economic
Development (published in 1911). In this book, entrepreneurship and its connection with
dynamic uncertainty is placed at the centre of economic inquiry. Schumpeter developed
his argument by constructing a model of an economy in which technical change of any
kind is absent. Such an economy, he contended, would settle down to a repetitive and
perfectly routine economic process in which there is no uncertainty about the future.
Hence, there would be no profits in such an economy. Turning towards counter argument,
Schumpeter (1942) finds that the capitalist system cannot be understood except in terms
of the conditions giving rise to entrepreneurship, because the entrepreneur as innovator is
the source of all dynamic change in an economy. Schumpeter consequently separates
entrepreneurship from other economic functions which may or may not be fulfilled by the
same individual. For example, the capitalists are characterized by the ownership of the
means of production, the management undertake the administration of a running concern,
and the inventor produces ideas, whereas the entrepreneur gets things done (see Peneder,
2001). Distinguishing between “invention” and “innovation”—the discovery of new
technical knowledge and its practical application to industry—and defining “innovations”
broadly as the introduction of new technical methods, new products, new sources of
supply, and new forms of industrial organisation, Schumpeter traced all disrupting
economic change to innovations and identified the innovator with the entrepreneur.
After Schumpeter’s theory had arrived, mainstream economic theory has continued to
neglect Schumpeter’s writings on entrepreneurship as it continues to neglect Knight’s
theory of profits because neither fits in with static equilibrium analysis. The theory of
entrepreneurship has, however, been given a new lease of life by the modern Austrian
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school, descending from Ludwig von Mises and Friedrich von Hayek. Peneder (2001),
amongst others, states that it was Ludwig von Mises who firmly established the idea of
the market as an entrepreneurial process, not as an equilibrium state. The market is such
an entrepreneurial process which is driven by the daring, imaginative, speculative
actions of entrepreneurs who see opportunities for pure profit in the conditions of
disequilibrium. In the two books, Competition and Entrepreneurship (published in 1973)
and Perception, Opportunity and Profit (1979), a student of von Mises, Israel Kirzner,
has sought once again to persuade his fellow economists that the properties of
disequilibrium states deserve as much attention as those of equilibrium states.
Disequilibria are due to intertemporal and interspatial differences in demand and supply
and hence give rise to unrealised profit opportunities. The essence of entrepreneurship,
for Cantillon as much as for Kirzner, consists in the personal alertness to such potential
sources of gain. There is a subtle change of emphasis in Kirzner’s discussion of
entrepreneurship from that of Schumpeter’s: Schumpeter always portrayed the
entrepreneur-innovator as a disequilibrating force disturbing a previous equilibrium,
whereas Kirzner (1973; 1979) depicts him as seizing upon a disequilibrium situation and
working to restore equilibrium. As Demsetz has said (in Blaug, 1986), entrepreneurship
in new Austrian theory is “little more than profit maximisation in a context in which
knowledge is costly and imitation is not instantaneous.” A more promising approach to
the theory of entrepreneurship is offered in a study by Mark Casson who synthesises and
extends previous works by Knight, Schumpeter, Kirzner and many others. Casson (1982:
23) defines an entrepreneur as “someone who specializes in taking judgemental decisions
about the coordination of scarce resources.” An entrepreneur is someone who reaches a
different decision from other people in the face of identical circumstances either because
of access to better information or because of a different interpretation of the same
information. The entrepreneurial function is, in principle, performed in all societies by
individuals whose judgement differs from the norm.
This brief history, presented mainly by Mark Blaug, of the ideas of entrepreneurship
gives us a theoretical discussion on entrepreneurship which has virtually deserved an
important place in economic development through technological change, but has always
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been neglected by mainstream economic studies. Another important thing is that the
scientists who advocated the positive role of entrepreneurs in economic development did
not talk a single word about culture or environment or value in the society. The non-
economic factors are completely ignored whereas so far as entrepreneurship is concerned
it is such an economic factor which somehow maintains a relationship with the non-
economic factors. It is also surprisingly noted that Blaug, in his critical appraisal, himself
has neither made any remark about the cultural factors, nor has he tried to raise the issue
with relevant literatures. In a different study, at least one attempt of theorisation has been
made by Lipset (2000)4 through introduction of “deviants” in anti-entrepreneurial
cultures. The role of the group of deviants in breaking the tradition in a society is of
immense significance. The logic of value analysis implies that the creation or expansion
of roles which are not socially approved in terms of the traditional values should be
introduced by social deviants. Lipset argues, while talking about the values and
entrepreneurship in Latin America, that this hypothesis is basic to much of the literature
dealing with the rise of the businessmen in different traditional societies. Let us go for an
elaboration.
We start with Schumpeter. Although Schumpeter did not talk about culture, a meagre, or
should we say implicit, is observed while he points out that the key aspect of
entrepreneurship, as distinct from being a manager, is the capacity for leadership in
innovation, for breaking through the routine and the tradition (see Schumpeter, 1961: 74-
94). The approach which emphasizes the theory of deviance assumes that those who
introduce change must be deviants, since they reject the traditional elite’s ways of doing
things. As Hoselitz (see in Lipset, 2000: 117) puts it, “a deviant always engages in
behaviour which constitutes in a certain sense a breach of the existing order and is
contrary to, or at least not positively weighted in the hierarchy of existing social values.”
In societies in which the values of the dominant culture are “not supportive of
entrepreneurial activity, someone who is relatively outside of the social system may have
a particular advantage in entering an entrepreneurial activity. The restraints upon
entrepreneurial activity imposed by the network [of social relations] would be less
4 Originally published in 1970.
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effective against such a person. Thus, an immigrant may be outside of many of the
networks of the nation and freer to engage in entrepreneurial activity” (Kriesberg, 1963).5
The creative role of the deviant, or the outsider, has in part been conceptualised by the
term “marginal man,” who for various reasons is partially outside the culture in which he
is living, is less socially integrated in the structures which maintain conformity, and is,
therefore, not so committed to the established values of the larger order. Hence men of
this sort are more likely to be receptive to possibilities for change (see Lipset, 2000).
It is not always the case that innovating entrepreneurs in developing societies must be
recruited disproportionately from the ranks of social “deviants.” Rather, it points with
Weber to the fact that many minority groups have not shown such propensities (see
Lipset, 2000). Clearly the Catholic minorities in England, or other Protestant countries,
were much less likely than the general population to engage in entrepreneurial activity. In
his analysis of the divergent consequences for economic behaviour of Protestantism and
Catholicism, Max Weber pointed to the greater business accomplishments of the
Protestant majority as compared to the Catholic minority in Germany. The key issue, as
Weber has indicated, is the value system of the various groups involved. Latin America
and some other less developed traditional societies are so vulnerable to economic cultural
“deviants” because the predominant values of the host culture are in large measure
antithetical to rational entrepreneurial orientations. Where national values support
economic development, the Weberian emphasis on value would suggest that the
innovating business elite would be drawn not from deviants but rather from the “in-
groups,” from persons with socially privileged backgrounds (see Lipset, 2000).
4.4 The Achievement Motive in Economic Growth: Psychological Views
The question of why some countries develop rapidly in the economic sphere at certain
times and others do not is in itself of great interest. Usually, rapid economic growth has
been explained in terms of “external” factors—favourable opportunities for trade,
5 Cited in Lipset (2000: 117).
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unusual natural resources, or conquests that have opened up new markets or produced
internal political stability. But McClelland (1966), a psychologist, has been interested in
the internal factors—in the values and motives men have that lead them to exploit
opportunities, to take advantage of favourable trade conditions; in short, to shape their
own identity.
So far as human motivation is concerned, the question which comes first is: How can
human motives be identified? The problem of seeking an answer to this question lies in
the fact that what people say about their motives is not a reliable basis for determining
what those motives really are. Freud and the other psychoanalysts convinced us, rightly
or wrongly, in analyzing the psychopathology of everyday life in terms of dreams and
neurotic symptoms.6 McClelland (1966) planned to study human motivation by coding
objectively what people spontaneously thought about in their waking fantasies. His
method was to collect such free fantasy, in the form of brief stories written about
pictures, and to count the frequency with which certain themes appeared. He was able to
demonstrate that the frequency with which certain “inner concerns” appeared in these
fantasies varied systematically as a function of specific experimental conditions by which
he induced motivational states in the subjects. Eventually, he was able to isolate several
of these inner concerns, or motives, which, if present in great frequency in the fantasies
of a particular person, enabled him to know something about how he would behave in
many other areas of life. Chief among these motives was what he termed “the need for
Achievement” (n Achievement)—a desire to do well, not so much for the sake of social
recognition or prestige, but to attain an inner feeling of personal accomplishment.
6 Freud demonstrated repeatedly that the “obvious” motives—the motives that the people themselves thought they had or that a reasonable observer would attribute to them—were not, in fact, the real motives for their often strange behaviour. By the same token, Freud showed the way to a better method of learning what people’s motives were. He analyzed dreams and free associations: in short, fantasy or imaginative behaviour. Stripped of its air of mystery and the occult, psychoanalysis has taught us that one can learn a great deal about people’s motives through observing the things about which they are spontaneously concerned in their dreams and waking fantasies.
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In this regard, it is worth mentioning the name of the German sociologist, Max Weber,
whose perceptive analysis of the connection between Protestantism and the spirit of
capitalism had greatly impressed McClelland. Weber’s argument is as follows. The
distinguishing characteristic of Protestant business entrepreneurs and of workers,
particularly from the pietistic sects, was not that they had invented the institutions of
capitalism or good craftsmanship, but that they went about their jobs with a new
perfectionist spirit. The Calvinistic doctrine of predestination had forced them to
rationalize every aspect of their lives and to strive hard for perfection in the positions in
this world to which they had been assigned by God. According to this description of the
behaviour of these people given by Weber, McClelland concluded that they must
certainly have had a high level of n Achievement. Perhaps the new spirit of capitalism
was none other than a high need for achievement—if so, then n Achievement has been
responsible, in part, for the extraordinary economic development of the West.
McClelland has made a historical study based on ancient Greece. He coded imaginative
literary documents: poetry, drama, funeral orations, letters written by sea captains, epics,
etc. Literary documents written during three different historical periods (dealing with
similar themes) were found. Three different periods are: 900 B.C.-475 B.C. (the period of
economic growth or prosperity); 475 B.C.-362 B.C. (the period of economic climax); and
362 B.C.-100 B.C. (the period of economic decline). Hesiod wrote on farm and estate
management in the early period; Xenophon, in the middle period; and Aristotle, in the
late period. The measure of economic growth was computed from information supplied
by Heichelheim in his Wirtschaftsgeschichte des Altertums. Heichelheim records in detail
the locations throughout Europe where the remains of Greek vases of different centuries
have been found. Of course, these vases were the principal instrument of Greek foreign
trade, since they were containers for olive oil and wine, which were the most important
Greek exports. Getting the information about where the vase fragments have been found,
McClelland was able to compute the trade area of Athenian Greece for different time
periods. His concern was Athenian Greece, because the later period of Hellenistic Greece
represented another civilization. That is why he did not consider the period of Hellenistic
Greece in his study. Now we go to the results of the study.
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When all the documents had been coded, it was found, as predicted by McClelland, that
the level of n Achievement was highest during the period of growth prior to climax of
economic development in Athenian Greece. In other words, the maximum n
Achievement level preceded the maximum economic level by at least a century.
Furthermore, that high level of n Achievement had fallen off by the time of maximum
economic prosperity, thus foreshadowing subsequent economic decline. A critical note,
relating McClelland’s findings to Aristotle and Plato’s philosophy, is presented in
APPENDIX 4.1.
A similar methodology was applied, with the same results, to the economic development
of Spain in the sixteenth century and to two waves of economic development in the
history of England (one in the late sixteenth century and the other at the beginning of the
industrial revolution, around 1800). The n Achievement level in English history (as
determined on the basis of dramas, sea captains’ letters, and street ballads) rose, between
1400 and 1800, twice, a generation or two before waves of accelerated economic growth
(incidentally, at times of Protestant revival).
McClelland has also tested the hypothesis on modern nations. Again there are two
variables. Independent variable is n Achievement manifested in literature and dependent
variable is economic development. For independent variable, what type of literary
document could be used that would be equally representative of the motivational levels of
people in India, Japan, Portugal, Germany, the United States, and Italy? Two cultures
may express their achievement motivation in a different literary form. But, by that time
McClelland had gained experience through the historical studies (stated above) that
certain types of literature usually contain much more achievement imagery than others.
This is not too serious as long as time changes are dealt with. He decided to use
children’s stories, for several reasons. They exist in standard form in every modern
nation. The stories are imaginative. They are not often influenced by temporary political
events, if selected from those used in the earliest grades. He collected children’s readers
for the second, third, and fourth grades from every country where they could be found for
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two time periods, which were roughly centred around 1925 and around 1950. He got
some thirteen hundred stories, which were all translated into English. Code was used on
proper names, so that the scorers would not know the national origins of the stories. The
tales were then mixed together, and coded for n Achievement. McClelland (1961: 74)
described how the scoring of the stories was done. That was done on the basis of desires
for success, anticipations of success and failure, and various types of instrumental ability
(or blocks to achievement). To get a total score for a country, each achievement-related
story was given a score of +2, each further subtype of imagery in such a story a score of
+1 (with the limitation that each subtype can be scored only once per story), each
possibly achievement-related story a score of +1, and each unrelated story a score of 0
(see also McClelland et al, 1953).
The next problem he faced was to find a measure of economic development which is
dependent variable. The problem was to insure comparability. Economists consider
national income figures in the per capita terms to be the best measure available; but they
are difficult to obtain for all countries, and it is hard to translate them into equal
purchasing power. Ultimately, McClelland came to rely chiefly on the measure of
electricity produced: the units of measurement are same all over the world; the figures are
available from the 1920s on; and electricity is the form of energy (regardless of how it is
produced) that is essential to modern economic development. In fact, electricity produced
per capita correlates with estimates of income per capita in the 1950s around .90 anyway.
The result he found is interesting. The correlation between the n Achievement level in the
children’s readers in 1925 and the growth in electrical output between 1925 and 1950, as
compared with expectation, is a quite substantial .53, which is highly statistically
significant. It is especially interesting that n Achievement level in 1950 is not correlated
either with previous economic growth between 1925 and 1950, or with the level of
prosperity in 1950. McClelland argues that this result strongly suggests that n
Achievement is a causative factor—a change in the minds of men which produces
economic growth rather than being produced by it.
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In another psycho-social theory, Hagen (1962) relegates economic variables to a
relatively minor role and has put an emphasis on certain aspects of the personality. He
used the term “innovational personality.” Innovation involves two steps: arriving at a new
mental conception, and converting it into action or into material form. Hagen (1962: 50)
raised a point: “…why is technological creativity taken for granted in the economic
theories? One reason no doubt is ethnocentricity. In Western societies we see
technological creativity at work around us and assume that it is a fact of human nature
rather than a culturally acquired trait. Therefore we assume that if technological progress
is not going forward it must be because there are barriers to the capital formation in
which it will be embodied. But there is another and perhaps more important reason. For
reasons which were valid in the context in which they were first applied, economic
thought since the second quarter of the nineteenth century, in which present-day
economists are steeped, has ignored not only creativity but also the process of
technological change itself.” In explaining the term “innovational personality” Hagen
adds: “When it is stated that innovation requires creativity, the reader should not assume
that the term ‘creativity’ refers to genius. Creativity exists in varying degrees; the man
who conceives of an improvement in a can opener as well as the man who conceives of
the theory of relativity is creative. Technological progress results from the actions of men
characterised by varying degrees of creativity. The discussion of creativity refers,
therefore, not merely to the limiting case of genius but to the quality of creativity in
general, in whatever degree it may be found in a given individual” (p. 88).
According to Hagen, creative individuals are those who primarily respond productively.
The unproductive, uncreative individual who responds with unacceptable fantasies may
shut them out from his conscious mind, but he senses dimly the emotional surges within
him and fears what is going on in his unconscious. Finding impulses in himself, which he
regards as evil or foul or dangerous, he is afraid of letting his unconscious processes
come to the surface for fear that dangerous or evil or vile urges will appear. Hence his
unconscious processes are not only primarily unproductive; even insofar as they are
productive, they are unavailable to him. The results do not appear in his conscious mind.
The creative individual, on the other hand, is not afraid of his unconscious processes, and
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their results appear in his conscious mind. In the technical terms of psychoanalysis, he
can “regress in the service of the ego.”7
This psycho-social theory of Hagen provides an alternative way of thinking to bye pass
the building blocks of economic development or to break the vicious circle of poverty.
But the problem is that the poor people in poor countries have remained poor for
generations. In consequence, a culture of poverty might have developed amongst them.
Hence, it is quite natural that they may suppress their fantasies in their unconscious
minds and may not try to bring them out to their conscious surface as well. This could
happen because of fear. Despite this limitation, Hagen’s approach is interesting for
presenting a unique idea in the field of economic development and social change. We
should not stick to the economic line of thinking; rather we should seek other avenues
with a holistic view to find a path of economic development of the poor nations.
4.5 Family Environment and Entrepreneurship
Is achievement motivation a crucial factor for generating entrepreneurship? Or, is it
family background which plays a greater role in reproducing entrepreneurs? Many argue
in favour of the second. Family environment often really matters and many examples
may support this view. Examples will come later. Let’s first start with a theoretical
model. A mathematical model of entrepreneurship has been developed by Ghosh (1989)
and it is shown that ‘supply of enterprise’ is crucially dependent on environment. Ghosh,
in his article, has not defined what enterprise is. Let us pick up the definition from
Schumpeter (1911) who wrote that the carrying out of new combinations (innovations) is
to be called ‘enterprise’ and the person who carries out such thing is to be called the
‘entrepreneur.’ Although the model has been developed in relation with entrepreneurship
in agriculture-based subsistence economy (not industry-based entrepreneurship), it is
found interesting as well as relevant for the present chapter. Ghosh (1989) has presented
the model by writing
7 Hagen has borrowed these words from Kris (1952).
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0,0);,( >Ω∂∂
>∂∂
Ω=S
ESESS (1)
Equation (1) states that production of surplus S is an increasing function of first,
enterprise E, supplied in the subsistence economy. This surplus can be viewed as various
types of goods are produced. For example, a subsistence economy, which usually
produces foodgrains, may branch out into production of commercial crops or industrial
goods. Secondly, supply of surplus S also depends on endowment Ω . The endowment
variable can be looked at as combinations of available land, capital, technology and other
assets. One can, following the subsistence economy models, assume Ω to be constant.
However, in a model where entrepreneurship is considered, perhaps one should let
endowment (Ω ) vary. This is because, with accumulation, land and technology can be
improved, new capital can be bought, and investment in human capital can be made.
With this in mind, one can make endowment an increasing function of enterprise (E).
This gives us
Ω = Ω (E) dΩ /dE > 0 (2)
It is now asked what determines supply of enterprise. Following the logic of the
subsistence literature one can say that it is the limited needs of the members of the
subsistence sector which restricts the supply of enterprise. Then it can be written as
E = E (N) dE/dN > 0 (3)
Equation (3) states that supply of enterprise (or entrepreneurship) is an increasing
function of needs. The extent of needs in a subsistence economy is limited. Needs, in
these societies, are limited not because the members therein are contended with their
existing circumstances. They are limited because the existing circumstances have forced
them to accept some sort of low level of living from which it is difficult to break out.
This last observation is represented by the following equation
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N = N(V) dN/dV > 0 (4)
This can be stated as needs (N is a function of environment V). The meaning of the term
environment will emerge from the following discussion.
Now there is a system of four equations with four endogenous variables S, Ω , E, and N
and one exogenous variable V. Combining equations (1) and (2) one can write
dEE
SESdS ⎟
⎠⎞
⎜⎝⎛
∂Ω∂
⋅Ω∂∂
+∂∂
= (5)
From (5) one can write
EdEE
ESS
SE
ES
SdS
⎟⎟⎠
⎞⎜⎜⎝
⎛⎟⎠⎞
⎜⎝⎛
Ω⋅
∂Ω∂
⎟⎠⎞
⎜⎝⎛ Ω
⋅Ω∂∂
+⋅∂∂
= (6)
Expression (6) states that the rate of change of surplus (dS/S) is determined by enterprise
elasticity of surplus (∂ S/∂ E . E/S), endowment elasticity of surplus, enterprise elasticity
of endowment, and the rate of change of enterprise (dE/E).
From equations (3) and (4) one can have
VdV
NV
VN
EN
NE
EdE
⎟⎠⎞
⎜⎝⎛ ⋅∂∂
⎟⎠⎞
⎜⎝⎛ ⋅∂∂
= (7)
which says that the rate of change of enterprise is determined by needs elasticity of
enterprise, environment elasticity of needs and rate of change of environment (dV/V).
Looked at from this point of view the importance of environment becomes obvious. If
environment is stagnant, i.e., if dV/V=0, then from (7) dE/E=0 and hence dS/S in (6) is
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also equal to zero. In other words, supply of enterprise which is crucial to accumulation is
itself crucially dependent upon environment.
Let’s now go into cases based in India. For a large proportion of firms in India, the basic
unit of entrepreneurship is the extended family. India’s industrialists are usually members
of old trading families, which frequently exercise control of a number of firms through
the managing agency firm. The successful entrepreneurs and top executives, interviewed
by Gupta (1991), were either born into families with business interests or were drawn
into business either by choice or, as they themselves liked to describe it, by some quirk of
fate. Included in the group interviewed were Marwari businessmen, as well as
entrepreneurs from West Bengal, Uttar Pradesh, Northern India, and Assam.
The Marwaris who were interviewed made no secret of their burning desire, from
childhood, to be in business. It was not simply a desire to participate in business
activities, but to outshine all others. This was radically different from the orientation of
most Bengali entrepreneurs whose driving ambition, as they were growing into manhood,
seemed to be to compete and excel in examinations, to be brilliant if possible in academic
fields. “We Bengalis believe and indoctrinate our children into believing,” said one, “that
business is bad, that the guy running around in a Mercedes-Benz is unquestionably evil.”
Perhaps, here, the meaning of evil is a manifestation of the image of a blackmarketeer.
Gupta also met Bengali businessmen who said, “Business is in my blood,” or words to
that effect. Other saw their fathers in business and admitted that the thought of going to
work for someone else had never occurred to them. This group does not constitute an
innumerable number of Bengali populations, of course. In all societies, there are always
some people who move opposite to the mainstream. Consequently, the above mentioned
observation of Gupta does not show a remarkable diverse nature of Bengali
entrepreneurial culture. Clearly, the family environment proved to be the most critical in
developing entrepreneurial instincts.
Just as Bengal idealised the intellectual life, other parts of India held up other career
ideals. A highly successful entrepreneur from northern India admitted that, within his
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community, it is taken for granted that business is worthwhile and rewarding occupation.
Another respondent of northern India said, “Most of my family members are either in
military or government service. My father discouraged me from government service.
Instead, my aunt, a doctor, persuaded me to join medical college. I left after only ten days
of classes.” Others had dreams to realize. An engineering graduate from prestigious
Indian Institute of Technology said, “I had dreams of a decent life, a car, a house. After a
year working for a major managing industry house in Kolkata, I had a new car, a
bungalow, a club membership, but a boring job.” A trip to Japan was all he needed to
make up his mind about starting his own business. There is an interesting finding of the
study of Gupta (1991: 110) which is as follows:
Among those we interviewed, the general consensus was that, compared with the
rest of the country, there was less and less entrepreneurship as one moved
eastward. “There’s a lot of family education in Marwari and Gujarati families,”
said one. “They train their children for business, instil discipline, whereas
Bengalis and Assamese don’t.”
The respondents, as Gupta wrote, tended to agree that life was easy in Bengal once upon
a time. “Making a living, providing for two meals for one’s family, were comparatively
easier in Bengal than in the rest of the country, especially arid spaces like Rajasthan and
Gujarat,” said one Bengali businessman. And the most successful entrepreneurs in India
are from these two states. During British period the zamindar (landlord) class emerged
which later, by extension, created a bhadralok (so called gentleman) or babu (white-
collar worker) culture in West Bengal and this is hardly compatible with
entrepreneurship. Middleclass Bengali mothers discourage their sons from joining
businesses. It is not uncommon for generations of Bengalis to find in their family circles
lawyers, doctors, and teachers, but no businessmen. Currently, for economic security,
government jobs are the priority. In business, one may gain or lose huge. This unforeseen
future is not welcomed by the Bengali average middleclass families. Families still prefer
to marry their daughters to government servants or to those holding even clerical jobs in
the public sector rather than to businessmen (Gupta, 1991). Therefore, family
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environment plays a crucial role in promoting entrepreneurship in a society. So far as
business profession is concerned, Bengali entrepreneurs generally receive moral support
from their own people only when they turn around as successful entrepreneurs, not before
that. But an entrepreneur may not be successful from the very first day of his career and,
therefore, his initial journey may happen to be very lonely. Appreciation at the initial
stage should be seemed to be more important than that after the turning around towards
success. So, family environment, along with McClelland’s achievement motivation and
Hagen’s personality traits, is said to be another factor that has great influence on
entrepreneurial rise/fall of a society. In this section, we have discussed how family
environment holds responsibility in generating entrepreneurship. In the next section we
will turn to the discussion of cultural root of India in influencing entrepreneurship.
4.6 Culture of Development (in Indian Perspective)
This section of our study provides a debate on whether certain aspects of Hindu culture
and religious values embedded in the social system acted as a major barrier to economic
development. Max Weber (1958) believed that Hinduism adversely affected economic
development. Other-worldly and fatalistic orientation of the Indians had an adverse
influence on economic development. Mishra (1962) indicated that belief in karma and
rebirth retarded economic development of India. Hindu concept of karma implies that a
person’s previous life’s good/bad acts result in the good/bad conditions of the present
life. That means nothing but surrendering to fate. Common belief in fatalism indicates
that every action of a person is regulated by fate. Kapp (1963) has also stressed the
negative role of the doctrine of karma and social institutions (like caste, joint family and
kinship) in economic development. Tilman (1963) pointed out that the caste system stood
in the way of economic development as it restricted social mobility. On the other hand,
there are scientists like Singer (1956),8 Srinivas (1958), Dube (1963; 1976), Rao (1969)
8 Singer (1956) wrote: In my study of the Madras industrialists, as well as from my observation of other Hindus, I found that Hinduism also generates in its believers a “salvation anxiety” about how to escape from the effects of one’s own past actions and the endless cycles of rebirths. The anxiety is not an intolerable one, however, that leads to an overwhelming pessimism and
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and Tinberg (1978) who argued that traditional Indian culture has several elements that
could encourage and stimulate economic growth. Especially, Rao pointed out that the
Veerasaivas of Karnataka were puritanical, propagating that work was heaven, and tried
to stimulate entrepreneurial spirit among its followers. Tinberg pointed out that
Marwaris, in spite of their conservatism, were highly entrepreneurial businessmen.
Gupta (1991) interviewed some entrepreneurs. One from several respondents said:
“Religion has played a great part in my life. Faith in God has given me strength. My
religion has helped me tide over numerous crises.” Another said, “I have a little temple in
my office. I have Lord Krishna in my office, and he has played a big part in my life.” A
third person said, “Religion is a great corrective. I don’t go to temples, but I have a
temple in my house. I spend some time in it every morning. Religion pulls you back
when you’re on the verge of doing something wrong.” Gupta describes that most
entrepreneurs kept religion out of their business on a day-to-day level, but there were
some expectations. In some companies, a priest visits every day to offer prayers and
flowers to Goddess Lakshmi, Lord Ganesh, or Krishna. The practice is much more
widespread around special festivals when new images of the gods are installed in many
organisations. A Marwari entrepreneur remarked, “Religion motivates you to remain
honest. After all, I have to go to God. How do I answer him? A man wouldn’t want to
adulterate if he’s conscious of his religion. I can’t adulterate medicines that might kill a
person. Ultimately, God is there to take the final account.” This is one side of the story.
The other side is as follows as it is seen in Gupta’s study. One of the younger Marwari
respondents said, “Personally, I couldn’t care less. The elders of the family are more
traditional. So we never start anything new, never hire anyone, on a Friday. I see no logic
in it.” Religious belief strongly influences a businessman to stay out of major actions on a
particular day of a week. The detrimental part of this kind of exercise is that religion
often determines what businesses one stays out of. The mighty Birlas chose to stay out of
defeatism or to a despairing burden of sin and guilt. That one becomes good by good deeds and bad by bad deeds is taken as an inexorable law of fate (karma) but not necessarily as a denial of freedom of choice and action in the present or as a reason for not exercising effort, intelligence, foresight, and resourcefulness in taking advantage of opportunities to improve one’s condition in this life and the next.
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the hotel business because of the necessity to consider serving non-vegetarian food. “My
family are all vegetarians,” said one Marwari entrepreneur. “They didn’t want me to
make potato chips with artificial chicken flavour. They felt if the public had the slightest
suspicion that the same oil is used to fry vegetarian and non-vegetarian products (even if
artificially ‘flavoured’), they wouldn’t buy the chips.” Another Marwari said, “My wife
wouldn’t want to earn a single penny from the death of a single bird.”
In Gupta’s study several of the Eastern Indian respondents were categorical in denying
that religion played any part in their business. They admitted, however, that most
successful Indian businessmen are probably quite religious, even if religion had no part in
their business. “He sticks to his religion,” suggested one entrepreneur by way of
explanation, “because he is afraid to upset the mental equanimity, the balance, which he
derived from religion and which was a part of him when [he] became successful. He will
not part with religion, then, for fear it will upset the overall configuration that he knew as
present when he found success.”
The emerging picture is very mixed: for some, religion plays a role, and for some it does
not. In this context of discussion, Alexander and Kumaran (1992) suggested that while
there were elements of traditional values and institutions which could adversely affect the
process of socio-economic development, there were also elements in the culture which
could stimulate and sustain development. In such a context, according to them, the speed
of development is dependent upon the elements, which are embedded into the culture and
society and which are responsible for strengthening and weakening the development
process. But the discussion cannot end up with this simplified conclusion. Let us try to go
into further expansion.
The corrosive impact of colonialism and the negative aspects of traditional cultures of
India have, since Rammohun Roy’s time, been chosen by turn, and sometimes together—
depending upon who the commentator is—to explain the socio-economic backwardness
of the country. We have talked about Max Weber’s view above. India had failed to
develop capitalism and rational attitudes to life generally because, Weber argued, her
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religions were ‘other-worldly’. The cosmos was seen by Indians as being essentially
moral—the rich deserved their richness and the poor their poverty—and, therefore, the
question of changing it did not arise. Human beings were tied by a stern determinism to
reaping the fruits of their actions, one incarnation after another, and the only way out of
human bondage and suffering was renunciation and ultimately release from the never-
ending cycle of birth, death and rebirth. This was then the ideology, the dharma, which
legitimized the caste system. The effects of the notion of ritual purity/impurity as
attached to occupations, and of the caste system generally, or development were judged
by Weber as being ‘negative’—incapable of “giving birth to economic and technical
revolutions from within or even of facilitating the first germination of capitalism”
(Weber, 1958: 112). Moreover, since ideologies are far more difficult to change than
modes of production, Weber could hardly have been optimistic of the chances of
capitalism and industrial development even under colonial rule. But, writing after the
railway system and modern industry had actually arrived in India, Weber perceived
loopholes in the rigid framework of the caste system, because caste system did not allow
free mobility of people from one occupation to another, especially from an odd
profession to a noble one. Perhaps, free occupational mobility was mainly seen in the
urban industrial areas. In fact, Habib (1969) commented that occupational changes had
not been all that unknown in pre-British India as Weber erroneously believed.
Morris’s (1960) studies have shown that not only has caste not proven to be an
impediment to the formation of an industrial working class, but Indian entrepreneurs also
have shown themselves to be ‘aggressive, rational and creative.’ He concludes:
There is no precise definition of a ‘Hindu value system’ that can be identified as a
significant obstacle to economic growth or change. Nor does the value system
working through the caste system exhibit any decisive impact on the process of
change. While jati seems to be the operative unit it behaves historically as
extended kin groups have done elsewhere. For example, the description of
entrepreneurial behaviour in nineteenth- and early twentieth-century India
resemble similar activities in Renaissance Europe and seem to reflect primarily
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the limited scope of economic opportunities rather than any specific form of
social structure (p. 607).
Gunnar Myrdal’s Asian Drama was published in 1968. The whole work is devoted to
showing how material poverty of Asian nations in general and of India in particular is the
result of a deadly combination of, on the one hand a social structure—and its associated
cultural ideology—which is rigid, non-innovative, inequalitarian and indifferent (if not
inimical) to development, and on the other a development strategy which mistakenly puts
its faith almost exclusively in physical investment, ignoring the overall institutional
setting which includes a corrupt and ‘soft’ state (Vol. 1, p. 57ff). Myrdal also discussed
the productivity consequences of poor health and irrelevant education. His conclusion
was that the nations of Asia were caught in a vicious circle of underdevelopment and a
wide range of drastic changes in existing institutional structures, as also in development
strategies, was urgently needed. The narrow notion of standard of living, he argued,
should be replaced by a broad-based concept of levels of living—of social systems
defined not only by output and incomes and conditions of production but also by levels of
living, attitudes to life and work, institutions, and appropriate policies (Vol. 3, p. 1860ff).
Another element is consumerism which should be mentioned in the light of present
discussion, because consumerism comes in the wake of consumption and consumption
leads to production and production leads to income. Consumerism, in our opinion, is a
state of mind which provokes a person to buy things. Madan (1983) has made a point in
this regard. “There are, of course, other elements also which have contributed to the
making of the socio-economic impasse in India, but among these I would like to focus
attention on the phenomenon of runaway consumerism in the midst of widespread
poverty and deep socio-economic inequalities—without meaning to imply that this
explains everything—as a matter of the gravest concern” (p. 35). The inevitability of the
emergence of consumerism in the economies, particularly those characterized by
capitalist enterprise, has been recognized for long. But, in a developing economy, a social
frustration may arise out of consumerism. Madan (1970) helps us to identify this
problem:
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Another major development of the sixties has been what may be called the
legitimization of Consumption as a major value in Indian society. The peasant’s
demand for finer varieties of foodgrain or mill-made cloth; his desire to give his
children school education; the ubiquitous bicycle and transistor; the acceptance of
the refrigerator as a ‘necessity’ of upper middle-class life; the seemingly
insatiable demand for motor cars, air-conditioners and such other luxuries among
richer people—these are a few of the many manifestations of the new attitude to
consumption. Consumerism is coming in the wake of consumption. This, of
course, also gives a keener edge to income disparities and to one’s sense of
disappointment over the inability to buy the many consumers’ goods and services
that have become available. Here then is major source of social frustration (p. i).
This problem of frustration remains at the side of the have-nots. So far as the Indian
‘uprising’ middle-class people are concerned, another kind of mental complex may
remain in their minds. With the increase in their incomes they may be hesitant or resist
themselves to buy more consumer as well as luxury goods keeping the thing in mind that
many around them do not have food even. A social value works behind this motive which
can only be identified as emotional attitude rather than rational attitude. Not the affluent
class, only the uprising middle class bears this suffering because they have come across a
very struggling life for survival. It may happen to be the case that they could be in fears
of unknown evils of consumerism or what may be explained in the way that they are not
psychologically prepared to consume in bulk amount after their long fight against
poverty. Of course, this attitude changes but it changes gradually, not overnight. This
view about Indian middle-class consumers is simply a hypothesis and has never been
tested. Another argument can go like the way that the middle-class people, having been
afraid of unsecured future (since, in India, there is no social security like the developed
countries), may put main thrust on savings, not on consumption.
What lies at the core of the received/borrowed models of development from the West is
the notion of growth as an unlimited process—the more we have, the better off we are.
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But the thinkers at the other pole criticize the view of mindless consumerism and the loss
of autonomy. According to them, consumerism infantalizes people by turning them ‘into
a herd of compulsive consumers’ (Roszak, 1969: 16). Consumerism will ultimately
destroy everything, values as well as resources: for as Gandhi once warned, the earth
produces enough for everybody’s need but not for everybody’s greed (see Madan, 1983:
37).9
Hence the core of the economic development of a nation is not only due to the several
economic factors but also embedded in the culture of the people. Economics may no
longer continue to be separated from culture. Some argue that if a traditional culture
stands in the way of economic development, then modern Western culture should be
introduced for the sake of a speedy course of economic development. Professor D.N.
Majumdar, one of the founding fathers of Indian anthropology, conceived such kind of
concept.10 In a memorial lecture T.N. Madan has explained Majumdar’s view in detail
(see Madan, 1983). Modern Western culture, based on science and technology, was the
model for Majumdar and he advocated interventionist policy, backed by applied social
science, to achieve modernisation, but with due regard for the specific character of
India’s society. Modernisation or social development and economic growth were seen by
Majumdar as generally desirable and interrelated goals—as an unquestioned ‘good’—
with the clear implication that the country needed to develop in its villages and cities, in
its home and offices, i.e. a culture which would initiate, promote and sustain
development. One was not to wait and watch for the ‘unfolding’ of the potentialities of a
culture but to take charge of the process of innovation, not only to speed it up but also to
give it a particular content and direction. Whatever stood in its way was to be eradicated.
Since development had not been generated from within the traditional cultures, it had to
be introduced into them from without (see Madan, 1983).
9 Perhaps this is the reason that most of the western people consider Gandhi a great politician and social reformer but not a good economist. 10 Majumdar was not very happy with the role of economists. Noting the long-lasting interest of economists in rural studies, but castigating them for a preoccupation with the perfection of techniques of quantitative analysis and a neglect of ‘the cultural background’ and ‘the interrelation that exists between different sets of social phenomena,’ he called for a holistic approach to the problems of development (see Madan 1983).
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In contrary to the notion of imposing Western model, there is another notion which is
called endogenous path of development. Endogenous development supports the idea of
self-reliance; it points the way to self-directed development—to original styles of
development—which alone can ensure authenticity of the effort. It underscores respect
for cultural identity and, as a corollary, future options directed towards a wide range of
types of development that are based on the fundamental socio-cultural features and
relations of various societies. At the same time it also grounds itself in the ideal of a
redemptive world community. In short, what the notion of endogenous paths of
development seeks to highlight is that the issue today is not how to alter cultural
traditions and reshape social institutions to bring about one particular kind of
development of all nations; the real issue is how to envisage the goals and strategies of
development so as to bring out the best potential of each culture in the realisation of
universally valued life-styles (Madan, 1983). Even, despite criticizing the negative
aspects of Hindu culture, Max Weber did not ask India to see its future in the image of
the West (see Madan, 1983: 28). The Japanese experience should be instructive in this
regard. One of the makers of the Japanese miracle, the engineer-planner-bureaucrat
Saburo Okita, has written:
….economic growth of a country is essentially a result of economic efforts made
by the people who live there, and nothing else….there is no cure-all medicine for
economic development; each country has its own background history, religion
and tradition. Any economic development scheme cannot be planned without
paying due attention to these factors…. Japanese society of today maintains many
elements that are far from being Western, and still Japan is the only non-Western
country that has, more or less, succeeded in modernizing its economy. Many
traditional elements played a vital role for the success of this process (Okita,
1979: 12f).
According to Madan (1983), the concept of modern culture which seeks to make the
whole world look alike, live alike, think alike, and feel alike is a dull concept.
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Modernization of culture should not mean sameness because sameness is dullness of the
mind and it deadens creative faculties. The peoples of a uniformly modernized world
would have nothing to tell one another: they would become speechless. A crucial point
which Alatas (1977: 28) makes is that “although modern societies share many features in
common, they need not belong to the same type.” The affirmation of the positive value of
cultural pluralism is the cornerstone of the new thinking on alternative visions or models
of development. Such rethinking calls for a high degree of self-awareness on the part of
intellectuals: neither mere imitation nor cultural arrogance will promote this process
(Madan, 1983).
While talking about entrepreneurship and economic development, the cultural issues
should be incorporated into that discussion. Why they should be added into the discussion
has been well-argued by two scholars. Hoselitz (1960: 155) states that industrial
“entrepreneurship can develop only in a society in which cultural norms permit
variability in the choice of paths of life, and in which the relevant processes of
socialization of the individual are not so completely standardized and demanding
conformity to a prescribed pattern that the bases for appropriate personality development
leading to productive orientation are absent.” Leff (1979: 46) has been more assertive in
commenting (based on some scholars’ previous researches) that the socio-cultural
conditions associated with the emergence of entrepreneurship indicates pessimism
concerning the prospects for less-developed countries’ generating sufficient
entrepreneurship to achieve high rates of economic development.
Now, in such a context, the question which inevitably arises is: Does culture change?
Yes, of course, it does. If not, then a traditional society would have remained so for ever.
The world history does not legitimize the notion of unchanging culture. “The period of
the combined Dark and Middle Ages in Europe is notorious for its ignorance of natural
phenomena and for its lack of original thinking in science. The ignorance and the
folkloristic character of the natural history of the period are not to be wondered at, for
Europe did not descend into the Dark Ages….” (Barnett, 1953: 68-69). Culture which is
generally considered to be a stable component of a society has been proved a changing
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process in the study of Lerner and Pevsner (1958). They examined the process of
modernisation in the Middle East and indicated that modernisation was associated with
an increase in literacy, increase in media exposure, increase in per capita income, and
political participation. Literacy and education became the basic components in the
transformation of the mental outlook of the members of a traditional society. Literacy
became the sociological pivot in the activation of psychic mobility and education was
found to the most powerful force shaping people’s modernity. Geographic mobility,
urban contact and participation in mass media also were found to be associated with
individual modernisation.
Regarding change in culture Barnett (1953) states that the more a man knows about a
given set of data, or about diverse sets of data, the more likely he is to develop something
new. Among some people there is a tradition of learning, a stimulation of curiosity, and a
nurturing of the quest for new and diverse knowledge. In a few parts of the world,
libraries, publication centres, schools, and apprentice systems have been developed for
the wide dissemination and perpetuation of ideas. On the other hand, in many societies
the acquisition of knowledge is a privilege of the few who are selected either formally or
indirectly by economic or inheritance preferences. According to Barnett, knowledge is a
prerequisite for innovation and innovation is the basis of cultural change. The ideal of
breadth and depth of knowledge does not mean that innovation is confined to specialists
and encyclopaedists. It means only that added resources multiply the chances that new
thoughts will occur, and that resources may be increased by intensive and extensive
explorations. It means, too, that innovation is impossible for an individual beyond the
limits of his understanding of his experiences. Breadth and depth of ideas increase the
frequency with which anyone is likely to conceive of something new. By the same token
it is an auspicious condition for acceleration in cultural change.
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4.7 Darwinian Principles of Evolutionary Change and Technical Culture
It seems to be interesting to see how changes in economy can be explained by the
Darwinian theory of evolutionary change and how the entrepreneur generated from an
environment of technical culture plays a significant role in evolutionary economics. How
can we use biological phenomena as social phenomena? In reply, Greenfield and Strickon
(1981: 486-487) tell us: “Although we recognize quite clearly that biological and social
phenomena are not the same, we propose to think of the social as if it were like the
biological, so that we may use the model of evolutionary process Darwin gave us as the
metaphor for thinking about the ways in which social phenomena are produced.” So “we
are using the Darwinian imagery metaphorically, not literally” (p. 488). Greenfield and
Strickon explained that among the varied individuals that constitute any breeding
population, Darwin noted, some are better able to utilize the resources of their
environment than others (principle of variation). Those better able to utilize resources
produce more offspring relative to the other members of the population (principle of
cumulation). Over time this differential reproduction results in a shift in the average
characteristics of the population (principle of selection). This is the process of evolution
(see Greenfield and Strickon, 1981).
Let us go into more detail. Evolutionary change refers to a specific kind of dynamic
process which is performed by the simultaneous interplay of the three functional elements
of variation, cumulation and selection. These three elements are found in Darwinian
theory. The phenomenon of the complex interdependencies that work for the emergence
of new species has been termed by Darwin “correlation of growth”: “I mean by this
expression that the whole organisation is so tied together during its growth and
development, that when slight variations in any one part occur, and are accumulated
through natural selection, other parts become modified” (Darwin, 1859: 182).11 Darwin
talked the emergence of new species, whereas, using Darwin’s frame as a metaphor,
Peneder (2001) spoke of the evolution of an economy. According to Peneder, the above
mentioned three functional elements form the building blocks which are essential to any
11 Cited in Peneder (2001).
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evolutionary explanation of economic change, including the dynamics of the market
process. In simple and general terms, Peneder’s (2001) explanation is as follows:
“First of all, in a system lacking any source of variation there can be no change
at all. Secondly, in any system, which does not include the element of
cumulation, single variations lack the force necessary for the generation of
different paths of development. …Finally, without the mechanism of selection,
the system lacks any meaningful direction or guidance of motion; i.e. any
arbitrary historical variations are preserved and might be indefinitely amplified
via cumulative effects” (p. 18).
How do we relate the above mentioned principles to our perception of free market
processes, characterized by perpetual motion, rivalry, and entrepreneurial alertness to
new profit opportunities? Peneder continues:
“We should however attempt a positive formulation, which more directly relates
to our concern for the process of entrepreneurial competition. In this particular
context, the principle of variation can be applied synonymously with the
entrepreneurial function of creative response, i.e. the realisation of new
competitive practices, such as the introduction of new products or processes,
new marketing methods, organisational reform, or refined pricing strategies.
Similarly, the principle of selection is tantamount to competition. It constitutes
the essential negative feedback or control device that keeps the system within its
proper boundaries, as defined by the external constraints of economic scarcity.12
12 About principle of selection, Peneder (2001: 21) has given further explanation: “Selection is the principle source of guidance in the system, imposing a certain direction on its movements towards the alleviation of relative scarcities, which influence the system as a whole.” Regarding the theory of natural selection, there is existence of a catchy but misleading notion of “survival of the fittest” in Darwin’s work. Peneder (2001) informs us that Darwin unwisely adopted it in his later editions, which did not appear in his first edition (and generally considered best) of the ‘Origins.’ It is hard to say any one is fitter or more advanced than another. Huxley (1873: 397), one of Darwin’s most effective defenders, suspected that the “fallacy has arisen out of the unfortunate ambiguity of the phrase ‘survival of the fittest.’ ‘Fittest’ has a connotation of
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Finally, the principle of cumulation allows successful competitive practices to
continue and expand over time. Only the simultaneous interplay of all three
functional elements can establish the principle capacity of a social system to
discover and learn about its opportunities to generate profits by alleviating the
impact of external constraints on the system” (p. 19).
Figure 4.1: Evolutionary economic change
cumulation selection variation Evolutionary change through variation, cumulation and selection
‘best’; and about ‘best’ there hangs a moral flavour. In cosmic nature, however, what is ‘fittest’ depends upon the conditions.”
Education system that pays due respect to technical culture
Economic development through technological change
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Following the Darwinian framework, variation is essential to bring about changes in the
economy. Actually, by variation, the entrepreneurial function of creative response gets
recognition, because the entrepreneur initiates change in the economy through
innovation. But, from the same framework, it is not clear at all as to when/where such
creative entrepreneurial function generates. What is the conducive atmosphere which
helps the entrepreneur to go for innovative action? Sweeney (1987) offers an argument
that there are certain components that are conducive for the creation of an innovation-
friendly environment. They are:
1. the education and training system in a region;
2. the quality and intensiveness of the information flows between firms and
entrepreneurs and their friends;
3. the cycle time between need and delivery of information for decision-making;
4. the technical culture and progressiveness which determine the innovative
potential of a region.
Of the four components, the first one and the last one—which raise the issues like
‘education’, ‘training’ (let us say vocational training or technical training) and ‘technical
culture’—are very crucial for initiating variation in a traditional rural society where static
attitude of people has become inhibitive to industrialisation and modernisation of the
region. It is almost known to all that education and training are key components of social
as well as economic change but very less has been discussed about ‘technical culture and
progressiveness which determine the innovative potential of a region.’ According to
Sweeney (1987), the division between culture and technology are becoming a barrier to
innovation. The author argues that we should not single them out since “[i]ndustrial
action has to reconcile technical, economic and aesthetic needs” (Lang, 1983).13 As a
strong protagonist of technical culture, Sweeney (1987: 31) argues: “The great cathedrals
of Europe, for example, were not the brainchild of an artist without technology or a
technologist without art.” In a disappointed voice, Sweeney states: “Culture came to be
13 Cited in Sweeney (1987).
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associated solely with the humanities, literature, music, art—and pure science. It was and
is snobbish culture. Technology and industry are considered to be somehow at a lower
level, tedious, dirty and certainly not creative activities” (p. 32). “The alienation between
technology and culture has been reinforced by an education system which has organised
itself to associate culture and creativity with the humanities and ‘pure’ sciences.
Technology, the creation and application of ‘know-how’ has been increasingly associated
with all that is anti-social or at best a ‘vulgar’ art” (p. 33). Of course, Sweeney’s
arguments are too strong to gain support. But, on the other hand, we cannot escape from
the argument that Sweeney further extends: “Society, influenced in attitude by the
education system, including its lack of understanding of technology, has in effect
ostracised technology, industry, and industrial culture from its culture” (p. 33). Let us
now try to see the problem from another angle.
Referring to the developing world, except a few pockets where the large industrialists and
the businessmen live, the city and the suburbs are mainly dominated by the middleclass
people who usually were/are very likely to show technical culture a path of departure
from the mainstream culture towards marginal culture. And, consequently, when this
class, due to its majority, represents the policy making body or formulates educational
policy, introduction of technical culture gets support of lower priority. At the same time,
the rural society is so traditional and so heavily dependent on agriculture that technical
culture or industrial culture is not likely to enjoy considerable indulgence over there too;
rather folk culture and folklore are still very popular in remoter villages. In a society of
such typical dual characteristics, people lack technical culture which is the driving force
of generating innovation and progressiveness that further leads to variation in production
system and finally guides or directs the society towards evolutionary economic change.
Therefore, technical culture should be treated as a dynamic element of evolutionary
economics, in absence of which an economy may get stagnant. For a schematic
presentation of the above discussion, see Figure 4.1.
4.8 Some Culturally-Sensitive Models
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Is there really any relationship between entrepreneurship and culture? Dana (1998) states
that the most important environmental variable that explains entrepreneurship
phenomenon is the cultural context. In the following section the role of cultural context in
entrepreneurship is discussed by examining entrepreneurship in different cultural groups.
These models are developed by Dana (1998). The role of these models is important to
signify as to why the study of culture is crucial while undergoing entrepreneurial study of
a particular set of people. Different cultural groups maintain their own cultural values.
One culture may encourage the members of its group to promote entrepreneurship, while
the other culture may not. Entrepreneurship is not just a function of opportunity, but
rather is a function of cultural perceptions of opportunity. This is the hypothesis that
Dana tries to test by the following models.
4.8.1 Entrepreneurship among the Amish
The Amish form a religious group which evolved in Europe, but which spread to
America, with concentrations in Ohio, and in Pennsylvania. The culture of these people
values asceticism, frugality, thrift and work as well as humility. Adults teach their young
that work is pleasurable. In order to maintain their cultural values, the Amish try to avoid
close contact with people who do not hold the same traditions. Furthermore, due to
religious discrimination in the past, the Amish often exhibit a mistrust of outsiders.
Consequently, given the choice, the Amish prefer not to work for companies in
mainstream society. Hence, the Amish prefer to be self-employed or to work amongst
themselves, as it is their belief that a community of believers is the context for life. It is a
different model of Western kind of entrepreneurship. Unlike Western entrepreneurship
which often implies technological innovation, entrepreneurs among the Amish do not
even use electricity.
The primary motive of such self-employment among the Amish is neither profit nor
prestige, but rather the maintenance of cultural values, separately from mainstream
society such as to emphasise humility over pride. Self-employment is perceived as much
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a social activity as an economic activity. Although the outsider world is penetrating
Amish territory, the Amish have managed to hold on to their cultural values. They
continue to exhibit a propensity for entrepreneurial behaviour. Their society is self-
sustaining, with virtually no unemployment, and their membership is growing.
4.8.2 Entrepreneurship among the Indigenous People in Alaska
The results of an exploratory study by Dana in Nome, Alaska reveal that Eskimos in
Nome have a lower tendency to become entrepreneurs than do non-natives. Although the
Eskimos make up the majority of the population, only 21.9 per cent of the entrepreneurs
are Eskimos. Furthermore, only 7.1 per cent of the Eskimo respondents claimed to have
actively sought opportunity for profit. Among those who actively sought profit
opportunities, 94.4 per cent are non-natives. Only 1.6 per cent of the sample consisted of
opportunity-seeking Eskimos, while 98.4 per cent were either non-native or Eskimos who
passively identified an existing opportunity for entrepreneurship. This study suggests that
entrepreneurship should not be viewed as a function of opportunity, but rather as a
function of cultural perceptions of opportunity.
4.8.3 Entrepreneurship in the Canadian Sub-Arctic
A field study using ethnographic methodology compared self-employment among
different ethnic groups in the Canadian north. Significant differences were found between
aboriginals and non-aboriginals. Aboriginals tended to express entrepreneurship in the
form of informal self-employment, reflecting traditional activities, such as hunting and
fishing. In contrast, entrepreneurship among non-aboriginals was found to be the result of
opportunity identification. Again this study supports the hypothesis (of Dana) that
entrepreneurship is not merely a function of opportunity but rather a function of one’s
cultural perception of opportunity.
4.8.4 Entrepreneurship in Laos (Asia)
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Dana gives an account of entrepreneurship in Laos, explaining the impact of religion and
culture on enterprise. The national religion of the Laos Kingdom (dating back to 1353) is
Theravada Buddhism. Associated cultural beliefs include high social status being given to
monks, and entrepreneurship being relegated to outsiders with inferior social standing.
Central to Theravada values is the ultimate goal of extinguishing unsatisfied desires on
the premise that unsatisfied desires are believed to cause suffering and that suffering can
be eliminated if its cause (desire) is eliminated. A respectable man, therefore, should not
work towards the satisfaction of materialistic desires, but should rather strive to eliminate
desire itself. Given that entrepreneurship may be perceived as a means to satisfy desire
(e.g., for power or materialistic goods), entrepreneurial behaviour is shunned. Lao men
are thus excluded from entrepreneurship. Yet, the same Lao folk tales (which reinforce
the belief that a religious man should not be self-employed), encourages women to accept
a heavy burden in exchange for home, protection and security. Even the Lao currency
portrays agricultural work being done exclusively by women.
The economy in Laos is growing. There are several hundreds (licensed) foreign
entrepreneurs in this country. Most of the 2,500 ethnic Chinese in Vientiane are
entrepreneurs. Many Moslems, Australians and Thais own enterprises in Laos. Numerous
Lao women have stands at local markets, but for Lao men, cultural values make
entrepreneurial work taboo.
4.8.5 Entrepreneurship in the Kingdom of Lesotho (Africa)
In general, different cultures have different frames of reference. Culture dictates that
certain property cannot be bought or sold in exchange of cash. In every society there
exists some restriction on economic exchange. Democracies have imposed a cultural ban
on treating people as a commodity. Laws prohibit slavery, baby-selling, trade of human
organs etc. Along the same lines, Basuto (people of Lesotho are known as Basuto) culture
dictates what is morally acceptable in that society, and what may not be treated as a
commodity. For example, a cow is not treated as a commodity by the people of Lesotho.
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For this reason to essential to differentiate between two types of property in Lesotho: (1)
property for personal consumption; and (2) property with social value. This distinction is
necessary because, according to the Basuto culture, there is a lack of convertibility
between these categories of property. However, cultural values have created a one-way
barrier inhibiting a Basuto from giving up property with social value in exchange of cash
or in exchange of property for personal consumption, although the norm dictates to trade
cattle in exchange of a bride. Thus, a Basuto might not have money for food but, yet,
culture prevents him from selling his house, which falls under the category of property
with social value.
In this way, due to the lack of convertibility between types of property, it is possible to be
wealthy in one type of property while poor in the other. Western societies measure degree
of wealth; in Lesotho, in contrast, it is possible to be very poor and wealthy
simultaneously. Therefore, it is quite evident, according to Dana, that Western models are
not necessarily applicable in such a context.
Not in that strict manner, but some kind of social value is attached with some properties
like land, gold etc. in rural India. Properties like land and gold are saleable in India but
still some rural people try to keep them for not losing social prestige. This is the
characteristic of traditional cultures (characteristics of stationary societies and changing
societies which are given in the APPENDIX 4.2 are nicely furnished by Ogburn (1964)).
Often this kind of trait inhibits economic growth. For instance, a rural landlord, despite
incurring continuous losses in his agricultural activities, may not try to gradually
diversify or shift in industrial activities by selling his land properties.
4.9 Conclusions
This chapter has a unique feature in conceiving the idea of seeing entrepreneurship from
a cultural point of view. It first presents a brief analytical history regarding the
disappearance of entrepreneurship from the mainstream economics. It combines the
discussion with arguments offered by the Austrian school of thought in favour of
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entrepreneurship. Entrepreneurship in connection with cultural values has been discussed
with due significance. The main emphasis has been given to examine, with the context of
the existing literature, whether cultural values play any role in influencing
entrepreneurship. It is very difficult to close the chapter with a clear conclusion, because
culture has multifaceted role to play so far as entrepreneurship is concerned. Some
community may find encouragement for entrepreneurship from within its own particular
religious culture, like Hindu culture, while some other community from same religious
culture may not find encouragement. In the empirical chapter, we will further examine
the issues, amongst others, relating to whether or not the cultural factors influence
entrepreneurship in our study area, Bardhaman district of West Bengal state.
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APPENDIX 4.1
Plato, Aristotle and McClelland
One comment should be made in connection with McClelland’s findings. In
McClelland’s study, Aristotle’s time has been marked as the period of economic decline.
But n Achievement should have been at maximum level during Aristotle’s time due to the
strong influence on society of his philosophy of materialism which is supportive to
capitalism. And accordingly, in this connection, his time should have been considered as
the period of prosperity or economic growth and the subsequent period as the climax. We
explain it in more detail.
All through the brilliant work Aristotle comes forth as a fundamental materialist and
teaches that matter is the principle of the living reality of the world existing around us
(Losev and Takho-Godi, 1990a). Earlier, Plato had an idealistic world view; he is
acknowledged to be the founder of European idealism. He was the first to supply an
idealistic basis for the primacy ideas over matter. The idea of an object is something
essentially, vitally and reasonably necessary for us to understand the object, to make
relation with it, to use it. The idea of an object is necessary for us to reconstruct it and
determine its uses. In this sense, every object and in general everything that exists in the
world has its own idea, its own meaning. Without ideas it would be impossible to
distinguish one thing from another, and thus all reality would be transformed into
formless and incomprehensible chaos. The idea of an object indicates the aggregate of an
object’s essential characteristics, its composition and structure, its purpose, and its
meaning in general. We have then an integral unit that contains within it something that is
not contained in its individual components. A table can be of different colours, it can be
big or small, it can be decorated or repaired, it can be broken into pieces, burned and
turned to ash. But can this be done to the idea of the table? Can the idea of the table be
pale or dark, red or brown, heavy or light? Can the idea of a table be smelled or touched?
The table itself can be smelled and touched, but what about the idea? Can the idea of the
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table be broken into pieces and turned to ash? Of course, water can freeze and boil, but
can the idea of water freeze and boil (Losev and Takho-Godi, 1990b: 86-87)?
On the contrary, Aristotle starts the definition of a thing with matter. A thing is, first,
matter, second, form (idea), third, operative cause, and fourth, a certain expediency or
utility. Idea or eidos (as it is pronounced in Greek) does not exist separately, but is always
embodied in matter. Aristotle conceives the idea of the thing to be not separate from the
thing and off somewhere else, but within the thing itself. Aristotle at one point was in full
agreement with Plato, i.e., he (Aristotle) did not himself deny the existence of ideas.
Nevertheless Aristotelianism was a revolution with regard to Platonism which recognised
the existence of a separate, heavenly world of ideas. Aristotle admitted that the idea of
the thing could be anywhere, even outside the thing. However, whatever functions of the
idea of the thing were involved, the most important for Aristotle was precisely the
presence of the idea within the thing itself, the functioning of the idea of the thing within
the thing itself, the complete absence of any gap between the two and of any dualism. For
example, we breathe air, but we don’t breathe the idea of air. If a person were to be put in
an airless place, no idea of air in its pure form would save him from suffocation (Losev
and Takho-Godi, 1990a).
Although there may not be any relationship between Aristotle’s materialism and
McClelland’s n Achievement, the question which inevitably arises here is that if n
Achievement of a section of population can be identified from the contemporary
literatures and be correlated with contemporary economic situation, then how come the
impact of Aristotle’s revolutionary materialistic world view has a positive correlation
with the declining period of the Greek economy? Not only this materialistic view, there
are more in Aristotle in connection with the present discussion.
Aristotle conceptualised Mind as prime mover. The fact is that everything in the cosmos
moves, and every movement depends on another movement. For Plato, the World Soul
governs the cosmos. For Aristotle, it is Mind which moves absolutely everything and
consequently is life as eternal energy, but which is itself immobile, because its mobility
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would require some other cause, and there is nothing above it. If Mind governs the
cosmos then there must be a positive influence of Mind on a civilisation’s entrepreneurial
energy.14 But McClelland’s study gives us a different result. Also, according to Aristotle,
anger is necessary, and no battle can be won without it—unless it fills mind and fires the
soul; it must serve, however, not as a leader, but as the common soldier; and Aristotle
stands forth as the defender of anger, and forbids us to cut it out (Losev and Takho-Godi,
1990a: 174-75). Although anger is not synonymous to dream or fantasy, it could have
been treated as one of the “inner concerns”.
Our main confusion toward McClelland’s consideration is how the time of Aristotle’s
revolutionary materialistic world view can be positively correlated with the declining
economic growth. Otherwise, McClelland deserves overwhelming praise for his
extraordinary imagination and innovative research direction in the area of economic
development in general and entrepreneurial energy in particular. Lipset (2000: 111) said:
“Perhaps the most impressive comparative evidence bearing on the significance of value
orientations for economic development may be found in the work of David McClelland
and his colleagues….”
14 A somewhat supportive view is seen in the works of some Austrian theorists who have brought the discussion of the act of entrepreneurship more into the realm of mental processes. For example, Mises saw entrepreneurship as action, which was by nature always speculative, since the future cannot be known. Entrepreneurs made decisions, which could bring profit or loss—but
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APPENDIX 4.2
Characteristics of Stationary Societies
In stationary societies what is done is good because it has always been done.
Experimentation or new methods would not be looked upon with favour if they should be
presented. The prestige of the past is exceptionally great, and the elders who know it best
are greatly respected. Fate and inevitability are accepted, and since there is little thought
of changing conditions, efforts toward adjustment and modification face inhibitions and
control. Those who succeed have character. Authority of the past and of elders counts.
Law is majestic; rules of moral conduct are set forth in detail and are followed. The
mores are strictly obeyed, and deviation from them not permitted. The people have
excellent manners. There is likely to be good deal of sentiment attached to institutions.
Ritual and ceremonialism are prominent. The conditions are favourable for art, religion,
and for class lines. Especially is a stationary society a well-balanced, harmonious society.
Characteristics of Changing Societies
In a changing society the attitude is one of seeking improvement. There is always a better
way. The new tends to be favoured somewhat. Progress is a feature of the social mind.
Optimism tends to prevail, and the social philosophy may favour pragmatism. The past is
like a dead hand, something to get away from. The position of youth is strong, and young
men often rise to influence. Authority as power yields to reason and evidence, but in
crises dictators arise. There is no great respect for law, and crime is more frequent. Moral
codes are ineffective, and good conduct rests upon intelligence in problem-solving.
Mores are of slight significance. Manners are bad, and the egos of others become very
annoying. Behaviour is more in accordance with biological nature and animal tendencies.
Sentiment about institutions does not flourish while the ceremonial tends to decline.
it was in the mental act of decision making by the entrepreneur that profit originated: “Profit is a product of the mind, of success in anticipating the future state of the market” (Mises, 1951: 21).
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Conditions do not favour rigid barriers between the classes, and the milieu is somewhat
difficult for art. Traditional religion finds a more hostile environment. There is no great
harmony in culture. The times seem out of joint, and there is much maladjustment
between the different parts of culture, due to the lagging of some changes behind others.
The different parts of culture are moving at unequal rates of speed.
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Chapter 5
Data Collection
5.1 Introduction
Jan Tinbergen (1965), the renowned Dutch economist, makes us clearly understand the
need of operational development policy oriented research: “It is the requirement that
research undertaken in direct or indirect support of development policies should be as
‘operational’ as possible. In a general way we may say that there are two types designing
scientific activity. One type is directed at satisfying our curiosity and the other at
reaching certain aims of policy. As a rule scientific activity starts in the first way. When it
is discovered that scientific knowledge can be used to further certain concrete aims part
of the activity takes the second form. Of course there is no fundamental difference
between the two aims; upon closer thought, both in some way are forms of reaching
higher levels of satisfaction. The differences are that the first type of scientific activity
satisfies a few people very intensively and large parts of the community only in a weak
way; while the second type of scientific activity is directed at the satisfaction of more
pressing needs of large sections of society and undertaken in a more systematic way.”
Ours is the second type of research and, of course, is an empirical research. Data
collection through field survey is the most important part of an operational, development-
policy-oriented, empirical work if the study is solely dependent on primary data. Whether
the study requires secondary data or primary data is subject to the objective of the study.
The objective helps setting hypotheses or research questions which are reflected through
the theoretical considerations/model of the study. For testing hypotheses or answering
research questions the researcher may need data. If the data available in government or
non-government offices, which are mostly secondary data, can serve the purpose of the
researcher concerned then primary data may not be needed. But if the secondary data
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does not fulfil the demand of the study then the researcher may have to go to the field for
collecting primary data in search of the information required for the purpose. Especially,
for micro level study the researcher finds almost no option other than going to the field
survey because often secondary data available are found to be of macro nature. Even if
secondary micro data are available, the researcher may not find them sufficient or up-to-
date to perform at least a satisfactory research. Honestly speaking, primary data
collection is such a job which is interesting but, however, often realised as a time-
consuming as well as pain-staking work. So far as data collection in rural areas of
developing country is concerned, it is not an easy task at all. Any way, the job is the part
of the game and the researcher needs to play the game very carefully right from the very
beginning for the sake of yielding interesting findings (or discoveries) concerning the
subject under study.
Keeping the theoretical considerations (or theoretical model) in mind, the researcher is to
formulate the questionnaires which usually require very careful attention because a silly
mistake in this work may lead the whole study towards a wrong direction or may make
the study incomplete. In the present study, as we have said in chapter 1, we are to identify
the determinants of non-farm entrepreneurship among farmers. Data collection has been
based on two types of questionnaires. In the first type of questionnaire, we focused on the
farmers who had been diversifying into non-farm manufacturing sector. In the second
type of questionnaire, we focused on farmers who had been retaining farming only. The
first category includes both the firm- and farm-level data-collection from a sample of
heads of farm households who are also engaged in non-farm manufacturing business.
And the second category includes mainly the farm-level data-collection, including some
non-farm related perceptions, from a sample of heads of farm households who are not
engaged in non-farm manufacturing business.
In this chapter, we present a detailed description of the data collection. Section 5.2
introduces us with the secondary data that were collected. Section 5.3 describes the
sampling procedure for collecting primary data. In section 5.4, we find the structure of
the questionnaires. Section 5.5 tells us the story of the fieldworkers’ selection and
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training. Section 5.6 discusses the pilot survey, whereas the story of organising the main
survey is discussed in section 5.7. Section 5.8 mentions the timing of the survey. Section
5.9 gives us the statistics of number of observation. Section 5.10 describes the problems
occurred during field survey. Checking of the filled-out questionnaires has been
discussed in section 5.11. Section 5.12 tells us how the reliability of the data has been
tried to be maintained. Section 5.13 presents the conclusion of this chapter.
5.2 Secondary data
Although the core of the present work is dependent on primary data, the study, like any
other research work, uses some secondary data for better understanding of the study area.
The secondary information we used include historical background of entrepreneurship in
the study area, agricultural as well as industrial performance of West Bengal and of
Bardhaman district, urbanisation and the condition of rural infrastructure in the study area
etc. Various publications and reports were reviewed.
5.3 Primary data: sampling procedure
We have already given the rationale in chapter 2 as to why we have selected Bardhaman
district as our study area. So, selection of the district involves no sampling; the district is
chosen purposely. Sampling played an important role in next part of the operational
work. Sampling involved three stages:
1. selection of the administrative blocks in Bardhaman district;
2. selection of the panchayats (cluster of some villages) in the blocks;
3. selection of the households.
The important considerations in selecting the administrative blocks were to avoid the
region of the district where the heavy industries are located and to concentrate in the
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region where the farmers are found in number. If we broadly divide the district into two
parts, then we find that the western part (Durgapur-Asansol area) of the district is
dominated by the heavy industry sector, whereas the eastern part can be regarded as the
agricultural area. Consequently, our survey has been based on the eastern part of the
district, i.e. the blocks have been randomly selected from the eastern part of the district.
Table 5.1: Administrative blocks and panchayats that have been randomly selected for fieldwork
Administrative Block
Panchayat
Raina-II Uchalan
Painta-II Gotan Pahalanpur Kaiti Arui
Galsi-II Sati Nandi
Galsi Adrahati Sanko Khano Gohogram
Kalna-II Baidyapur
Pindira Kalyanpur Badla Borodhamas Purba Satgachhia
Mangalkote Kaichor-I
Mangalkote Godista Maghigram Simuliya-I Simuliya-II
Bardhaman-II Barsul-I
Barsul-II Baikunthapur-I Baikunthapur-II Nabasta-I Hatgobindapur
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195
Bardhaman district is divided into 31 administrative blocks. Roughly 20 blocks constitute
the district’s eastern part. Of 20 blocks, five have been randomly selected for this study
(see Table 5.1). Thereafter, from each block, six panchayats have been selected randomly
(see Table 5.1). And, then lastly, 10 farmers have been randomly selected from each
panchayat. For selecting the farm households, we faced trouble in getting the name-list of
the farmers. At last, we found a solution. We met the chief of the pachayats for getting
the name list. From that list, we randomly selected the names of the respondent-farmers.
It is important to note here that we interviewed two kinds of farmer—one, who were
engaged in farming and non-farm manufacturing activities; two, who were engaged in
farming only. So, accordingly, we made two lists of the names in each panchayat and
then we randomly selected the respondents from those lists.
5.4 The structure of the questionnaires
As we said earlier that we have visited two kinds of farmer for interviews—the farmer
engaged in farming and non-farm manufacturing activities and the farmer not engaged in
non-farm manufacturing activity. We constructed two questionnaires for the purpose—
one questionnaire was for the first kind of farmer (questionnaire 1) and the other was for
the second kind of farmer (questionnaire 2). The purposes of both the questionnaires were
same, i.e. identification of the determinants of non-farm entrepreneurship among the
farmers. The two questionnaires differed only regarding one issue: we deleted the
questions relating to non-farm industrial unit from the questionnaire which was meant for
the farmers who were not engaged in non-farm activities. For both the questionnaires (i.e.
questionnaire 1 and questionnaire 2), see APPENDICES 5.1 and 5.2.
At the beginning of the questionnaires, we presented a brief introduction for why this
research is conducted. The panchayat authorities, the educated respondents, and any
government officials might want to know the purpose of this research. For that purpose,
we included such an introduction. For the fieldworker, there are number of instructions in
the questionnaires. They include general instructions and instructions relating to
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particular questions. These instructions were incorporated into the questionnaires in order
to help the fieldworker to conduct interviews smoothly. These instructions included some
definitions too. For example, the differences among the locations like village, rural
growth centre and small town have been described in the questionnaires; the difference
between primitive technology and modern technology has also been described. They have
been very useful tips for the fieldworkers.
In questionnaire 1 (i.e. the questionnaire which was meant for the farmer who was
engaged in non-farm activity), the questions were grouped into fourteen sections dealing
with different types of information. The question sections are as outlined below:1
1. Household composition: family size, sex, age, marital status, education level,
occupation.
2. Economic status of the household: farm size, size of homestead, kind of
irrigation system used, kind of puddle system used, kind of harvesting method
used, number of livestock.
3. Farm related question: primary identification of the farmer (big land owner or
sharecropper?), crops produced in a year.
4. Occupational background in non-farm activities: number of non-farm
enterprises owned, primary motivation for starting non-farm business, year of
establishment, reasons for selecting the product line, type of ownership, type
of business.
5. Locational factors: location of the non-farm enterprise, reasons for selecting
the location, obstacles faced in starting up non-farm unit.
6. Technology/machinery: kind of technology used in the firm, procurement of
machine.
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7. Financial aspects of the non-farm enterprise: capital, sources of capital,
expansion of firm.
8. Employees/workers: number of workers, wage.
9. Raw materials: kind of raw materials used (agricultural or non-agricultural),
procurement of raw material, availability of raw material.
10. Consumption linkages: consumer’s demand for the farmer’s non-farm
products, competition.
11. Forward production linkages: producer’s demand for the farmer’s non-farm
products.
12. Performance data of the non-farm unit: production capacity (per month), total
output (per month), costs, profit, increase/decrease in demand for the
products, future plans, government policies for the small-scale industries.
13. Risk/psychological factor/sociological factor/cultural factor: marriage
relationship with business family, faith in fate or work effort, risk,
innovativeness.
14. Political factor: political affiliation of the farmer.
In questionnaire 2 (i.e. the questionnaire which was meant for the farmer who was not
engaged in non-farm activity), the questions were grouped into six sections. Sections 1, 2,
3, 13 and 14 of questionnaire 1 hold same in the questionnaire 2. In addition,
questionnaire 2 incorporates a section relating to the perception of the farmer about the
non-farm manufacturing businesses.
1 Not all information gathered is used in this study whose main focus is on the determinants of NFE. The information relating to the performance of non-farm entrepreneurship will be used in a follow up study.
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5.5 Selection and training of fieldworkers
In selecting fieldworkers, we took their educational level and experience into
consideration. In such a study, a fieldworker’s role is very important because he/she
determines the success of interviews and the quality of data collected. The questionnaires
were drafted in English and it was the duty of the fieldworkers to translate it into Bengali,
the local language. Literal translation of a question was not always expected to bring out
actual information from the respondent during interview. It was expected that sometimes
fieldworker might be needed to gossip with respondent to get to the actual information.
So, key considerations for recruitment of fieldworkers were their education and
experience. Fortunately, we got five trained and experienced fieldworkers from the
economics department of Bardhaman University (located in the study area). They were
all Masters in economics and were engaged in different projects of economics
department. All of them were from the same district, i.e. our study area. We did not
consider the fieldworkers from districts other than Bardhaman because an overall
familiarisation with the district’s geography and with the district’s transportation system
was important.
Just before fieldwork we sat together with the fieldworkers to convey them a brief story
and the objective of the study, to tell them what we want, to make them familiarize with
each question of the questionnaires, and to train them how to approach to interviews with
villagers.
5.6 The pilot survey
A pilot survey was first organised and conducted in order to test our questionnaires and to
familiarize the fieldworkers with the procedure of interview. In the pilot survey, we faced
little trouble with some questions of the questionnaires. For example, during pilot survey
we observed that the respondent mixed up his cultivable land and homestead when he
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199
was asked question about the size of his landholding. So, after pilot survey, we split the
question into two subparts—one is about the respondent’s cultivable land size and the
other is about his homestead. In total, ten trial interviews were conducted in the pilot
survey. In addition, the pilot survey helped us to determine the time needed for an
interview.
5.7 The survey
Organisation
In a meeting, we divided and distributed the geographical areas to the fieldworkers for
conducting the field survey. We sat with the transportation details and designed how to
reach the destination areas. In remotest villages, fieldwork was expected to be very time-
consuming due to unavailability of transport vehicles. We were well aware of the fact
that we might have to walk kilometre after kilometre due to lack of sufficient intervillage
public or private transport facility. So, we organized our field-visits accordingly.
Timing
The fieldwork was conducted between November 2002 and March 2003. We chose this
time period because this period covers two seasons like winter and spring. In West
Bengal (except the Himalayan area) winter is not that severe; so winter is the right time
for fieldwork. Summer and monsoon are not suitable seasons in West Bengal to carry out
fieldwork. We now go into detail about the collection of data for the present study. Only
the head of the household was interviewed.
Number of observations
In this chapter, the procedure used for data collection is discussed, including the sampling
procedure and household interviews. The plan of the fieldwork involved the interviewing
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of 300 farmers. But finally we could interview 290 farmers. Ten farm households among
our samples remained non-respondents. Of 290 farm households, we interviewed 169
farmers who were found to have been engaged in non-farm manufacturing activities. And
we talked to 121 farmers who were engaged in farming only.
Problems faced during fieldworks
Each of the sample households was visited. Some problems were encountered during the
field implementation of household interviews. As we said earlier, 10 farmers were found
to be non-respondents. They were not found at home and at work as well. Instead, the
female members of these households could not answer the questions. So we had to reject
them.
The availability of the farmer was a real problem. We could not make any appointment
prior to the interviews. So, we directly visited the household on the day of interview. In
some occasions, the household heads were not found at home. In that case, we had to
rush either to the farm or to the crop-field to meet the farmer. We conducted many
interviews on the crop-field. For this reason, in the remotest places time management was
a problem.
Checking of the completed questionnaires
Everyday, at the end of interviews the questionnaires were collected from the
fieldworkers and checked for completeness, mistakes, omissions, and irrelevant
responses. Identified mistakes and problems were discussed with the fieldworkers. As
regards mistakes, arrangements were made to go back to the respondents concerned and
to correct the mistakes on the next day. In a very few cases, respondents had to be
reinterviewed.
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Reliability of the data
Accuracy of collected information is very important aspect of an empirical research. If
the interviewer does not correctly perceive information provided by the respondent then
inaccuracy may exist in data. On the other hand, if the respondent is unable to provide
accurate information then also data may produce errors.
In the fieldwork of our study, some respondents could not correctly answer the questions
relating to financial aspects of their firm like production capacity, fixed capital, working
capital etc. In such cases, we talked to the respondent’s educated elder children or other
relatives around (if found any) and clarify things. Also, we ourselves tried to bring out
the actual information not by asking specific questions, but by holding a relevant chat.
5.8 Conclusions
This chapter intended to offer an overview of the data collection procedure including the
descriptions of the questionnaires and the fieldwork details. Data collection included
gathering secondary and primary data. Secondary data were required for a better
understanding of the study area and other issues, whereas primary data were required for
the empirical model of this study. Primary data collection was divided into parts. The first
part was meant for testing the questionnaire, which is called as pilot survey, and the
second part constitutes the main survey of the study. During the interviews some
problems were encountered. Through innovative means the problems were tackled in
order to reduce data inaccuracy. Information collected from the household interviews was
organised and processed into a form that can be utilized to address the study questions.
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APPENDIX 5.1 QUESTIONNAIRE 1
SURVEY ON RURAL INDUSTRIES IN BARDHAMAN DISTRICT OF WEST BENGAL
(Questionnaire for farmers who are engaged in non-farm manufacturing activities) Location of survey (To be filled out by the interviewer immediately before interview): Block: Panchayat: Village / rural town: Post office: INTRODUCTION: We are carrying out a research project on various aspects of rural industry in Bardhaman district. Our objective is to study the problems and prospects of rural industrialisation in the district. This project will be trying to raise the issues relating to the interest of your village as well as yourself. More specifically, we are studying the problems of investments in non-farm activities and, accordingly, our study is concerned with, first, identification of the obstacles of the rural industrial sector at micro as well as macro level and, second, formulation of eradicative measures of those obstacles. So, we are sure that the findings of this research will be directly or indirectly beneficial for you through some specific policy recommendations for industrial development of your district. This research is being done by a team comprising one researcher and supervisors from Wageningen University, the Netherlands and Indian Statistical Institute, Kolkata, India. We have come here to talk to you about agricultural and industrial development of your village, infrastructural condition in general and some aspects of your own household, your farm, your non-farm activities (if any) in particular. We are not from any government department or tax office, so please don’t worry about the questions. You can trust us and freely exchange your dialogues with us. We promise that we will not use this data for anything except our academic research purpose. We invite your wholehearted cooperation in this work. INSTRUCTIONS FOR THE FIELD INVESTIGATOR: In this questionnaire we have used the terms ‘your household’ and ‘your household member’ frequently. Here household consists of family-head (usually male), his spouse, his sons and daughters including those (children) who are not living in the same house any more and have started families of their own. For the present study this definition of household is formulated in the light of the Indian tradition. For the interview the family-head should usually be our respondent and household would be considered to be the unit of observation. As a field investigator of this project you should be careful about where to use the term ‘you’ and where to use ‘your household member’ (or you can directly use a term like ‘your son / your daughter / your wife’ instead of ‘your household member’ wherever applicable). For example, if the respondent himself is the owner of the business, then you can use the term ‘you’, otherwise ‘your household member’ (to be more precise, use ‘your son/daughter/wife’ as instructed above).
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A. General Background Name of the family head: 1. Respondent’s household composition
Education
Occupation
Secondary
Sl. No.
Household members
(see note below for
abbreviations) (Respondent
first)
Sex (M/F)*
Marital status**
Age
General*** Technical/ vocational
Primary (1) (2)
1. H1
2. H2
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
*M = 1, F = 2; **Married = 1, Never married = 2, Divorced = 3. ***Never went to school = 1, Incomplete primary = 2, Primary = 3, Incomplete secondary = 4, Secondary = 5, Higher Secondary = 6, Bachelors = 7, Masters = 8. Note: family head = H1, head’s spouse = H2, their children = C1, C2, and so on; children of C1 = C11, C12, and so on; children of C2 = C21, C22, and so on. (Hence C11, C12, etc. are grandchildren to H1 and H2). B. Economic Status of the Household
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INSTRUCTION FOR THE FIELD INVESTIGATOR: From this section the properties such as land, farm, livestock, non-farm businesses etc. owned by family-head’s children (who are not living in the same house any more) will be counted together with that of the household directly selected by us. 2. (a) Your household is engaged in: only agriculture [1] both agriculture and non-agriculture [2] (b) How many number of farm units do you (your household) have?:
one [1] more than one (specify) [2]: …………………. (c) Who is the owner of the largest unit?: myself [1] spouse [2] son [3] daughter [4] 3. If your household is engaged in both agriculture and non-agriculture, which one is the main source of income?
agriculture [1] non-agriculture [2]
4. What is the size of your household’s landholding?:
(i) homestead (in katah/bigha2): …………………………….. (ii) cultivable land (in katah/bigha): ………………………… 5. Does your household irrigate the land?: yes [1] no [2] (a) If yes, what kind of irrigation system does your household have?:
manual: from canal (traditional system)[1] tube well [2]
shallow pump or diesel pump [3] other (specify) [4]: ……………………. 6. What kind of puddle system does your household have? only traditional system: bullock drawn plough [1] power tiller [2] tractor [3]
2 Indian measures.
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7. What kind of harvesting method does your household follow? manual [1] mechanical [2] 8. Does your household have livestock? yes [1] no [2] (a) If yes, how many (fill-out the following table)?: Bullocks:
Cows:
He buffaloes:
She buffaloes:
Calves:
Goats:
C. Some Farm Level Data 9. How is your household involved in agriculture?
primarily as a land-owning farmer [1] primarily as a share-cropper [2]
10. Now I am going to ask you some questions about the crops your household produces. Please inform me which crops you produce. Season Plot
no. Name of crop Area (kottah/bigha) Average no. of workers
1
2
3
4
Kharif
5
1
2
3
4
Rabi
5
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1
2
3
4
Summer
5
D. Occupational Background in Non-agricultural Activities INTRODUCTION: In the present section, we will ask you some questions about your household’s non-farm business activities. These questions will be asked about the enterprise established personally by you or any of your household members. 11. (a) If your household owns non-farm enterprise/s, then how many?: one [1] more than one (specify) [2]: …………… (b) Who started the enterprise/s?:
I myself [1] I and other member/s of the household (specify the other member/s by spouse, son, daughter, etc.) [2]: 1) ……………………………………………. 2) ……………………………………………. 3) ……………………………………………. 4) ……………………………………………. other member/s of the household (specify by spouse, son, daughter, etc.) [3]: 1) ……………………………………………. 2) ……………………………………………. 3) ……………………………………………. 4) …………………………………………….
(c) In case your household owns multiple non-farm enterprises then which one is the most important (mention the name of the enterprise)?:
……………………………………………………………………………………. (d) Who started the most important one (specify by spouse, son, daughter, etc.)?:
1) ……………………………………………. 2) ……………………………………………. 3) ……………………………………………. 4) …………………………………………….
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[INSTRUCTION FOR THE FIELD INVESTIGATOR: By enterprise we mean the most important one identified in 11(c)] 12. What was your (your household member’s) “primary” motivation for establishing the enterprise?:
farming is not that much profitable anymore [1] household (member) desired to invest surplus from agricultural activities [2] household (member) did not like agricultural activities[3] other (specify) [4]: ……………………………………..
13. What is the year of establishment?: ……………… 14. What are the reasons for selecting this product line?:
I (household member) wanted to offer a product which is somewhat new in the market [1] the product has a good market demand [2] I (household member) have some skill in this line [3] this product line needs very little skill [4] this product line requires low capital [5] raw material is locally available3 for this product [6] other (specify) [7]: …………………………………… ………………………………………………………….
15. Type of ownership of the non-farm enterprise:
proprietorship [1] partnership [2] ◊ with household member/s [2.1] ◊ other than household member/s [2.2] cooperative [3] other (specify) [4]: …………………………….
16. Type of business:
only manufacturing [1] only assembling [2] only repair [3] both manufacturing and assembling [4] both manufacturing and repair [5] both assembling and repair [6]
3 In this questionnaire, the term ‘local’ indicates the concerned administrative block (where we are going to carry out survey) of the district. ‘Non-local’ would mean the places outside the concerned administrative block.
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manufacturing, assembling, and repair [7] job-work [8] service [9] other (specify) [10]: ………………………………………….
E. Locational Factors INSTRUCTIONS FOR THE FIELD INVESTIGATOR: 1) Village is an area which falls under gram panchayat, but does not look like a bazaar or a trading centre. Village is considered to be a residential place. 2) Rural growth centre falls also under gram panchayat, but typically looks like a bazaar. It is mainly a trading centre of village. 3) Small town is an area which falls under municipality. It is mainly an urban area in a rural setting. 17. (a) What is the location of the non-farm enterprise?
village [1] rural growth centre [2] small town [3]
(b) (i) Is the non-farm enterprise situated at the household’s home land? yes [1] no [2] (ii) If no, how far is it from the home?: …………………………………. 18. What were the reasons for selecting this location?:
space available [1] it is close to home [2] land is relatively cheap [3] labour is cheap [4] its proximity to market [5] its proximity to the source of major raw material [6] it has necessary infrastructure [7] other (specify) [8]: ……………………………………
19. (a) Did you (your household member) experience obstacles in starting up / expanding the non-farm business? yes [1] no [2] (b) If yes, then what were the obstacles you (your household member) experienced?
poor roads [1] power cut [2] power with low voltage [3]
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lack of water supply [4] lack of skilled labour [5] poor consumer (lack of purchasing power) [6] bazaar is situated at a distant location [7] other (specify) [8]: ……………………………………. F. Technology / Machinery INTRODUCTION: Technology here mainly indicates the production techniques used in the non-farm enterprise. It is broadly divided into two categories — one is primitive and the other is modern. (1) Manually produced goods as well as products produced by hand-made-machines are called to be the products of primitive technology. (2) Things produced by machine-made-machines are called to be the products of modern technology. 20. (a) What kind of technology is used in the firm?: primitive modern (b) If primitive technology, then it is: manual [1]
traditional machines and equipments [2] (c) If modern technology, then it is: new machines [1]
second hand [2] (i) If second hand, give the reasons: second hand machines are cheaper [1] new machines are not available [2] other (specify) [3]: …………………….. [INSTRUCTION FOR THE FIELD INVESTIGATOR: If the production technique is manual (where no machines or equipments are used), then go to question 23.] 21. Where did you (your household member) procure your (his/her) machines?
own village [1] within block [2] within district [3] nearest town (specify) [4]: any other town/city (specify) [5]: other parts of the state (specify) [6]: outside state (specify) [7]:
22. How did you (your household member) procure this equipment?
directly from small industrial producer [1] directly from large industrial producer [2] small shop (e.g. retail shop) [3] big shop (e.g. area distributor) [4]
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G. Financial Aspects of the Non-farm Enterprise 23. (a) How much was the total capital employed (in Rupees)?: (b) How much was the fixed capital for (i) Land & building: ………………………
(ii) Machinery and equipments: ………………... (c) How much was the working capital?: ……………………………………………. 24. What was the source/s of capital for initial investment (fill out the following table):
Source Amount Location of source 1. Bequest Rs.
2. Own savings Rs. From
agriculture Past job (specify what/where)
3. Borrowed from relations
Rs. From agriculture
Other occupation (what/where)
4. Banks Rs. Location
5. Government Rs. Specify the scheme
6. Wholesalers Rs. Location
7. Individual money lender
Rs. Location
8. NGOs Rs. Location
9. Other (specify) Rs. Location
25. Have you (your household member) been expanding the non-farm business?
yes [1] no [2]
[INSTRUCTION FOR THE FIELD INVESTIGATOR: If the respondent/entrepreneur has not been expanding the non-farm business, then go to question 27.]
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26. (a) Have you (your household member) experienced problems in financing
expansion?: yes [1] no [2] (b) If yes, then what are the problems you (your household member) have been
facing?: do not have own funds [1]
high cost (interest) of borrowing [2] large security requirement [3] high transaction cost (if any) [4] other (specify) [5]: …………………………………………… …………………………………………………………………
(c) If no, then how were funds generated?
Source
Amount Location of source
1. Bequest
Rs.
2. Own savings Rs. From agriculture
Past job (specify what/where)
3. Borrowed from relations
Rs. From agriculture
Other occupation (what/where)
4. Banks
Rs. Location
5. Government
Rs. Specify scheme
6. Wholesalers
Rs. Location
7. Individual money lender
Rs. Location
8. NGOs
Rs. Location
9. Other (specify):
Rs. Location
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H. Employees / Workers 27. (a) Total number of workers employed (including proprietor/partners) Now: Initially: 28. What is the average wage/day : highest wage/day : lowest wage/day :
local casual wage/day : local agricultural wage/day : I. Raw Materials 29. Now we need some information about raw materials which are used in the firm. Please give us following information.
Do you use: Procured from:
(a) Agricultural raw materials: yes [1] no [2]
Local [1] / outside [2]
(b) Non-agricultural raw materials: yes [1] no [2]
Local [1] / outside [2]
30. Raw material is available: throughout the year [1] only for some months (mention) [2]: …………………. J. Consumption Linkages 31. Demand for your (your household member’s) non-farm goods/services comes from:
rural Bengal [1] urban Bengal [2] both rural and urban Bengal [3] outside West Bengal [4] 32. Locally who are the consumers of your (your household member’s) business?
no local consumers [1] local rich farm households [2]
local poor farm households [3] local rich non-farm households [4]
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local poor non-farm households [5] almost all local consumers [6] 33. (a) Does the product of your (your household member’s) business face competition?
yes [1] no [2]
(b) If yes, then it is from other local small units [1] local medium or large units [2] non-local small units [3] non-local medium or large units [4] (c) What kind of competition is it? it is in terms of price [1] it is in terms of quality [2] it is in terms of both [3] K. Forward Production Linkages (producer goods) 34. (a) Are there producers who use your (your household member’s) business products?
yes [1] no [2] (b) If yes, then who uses the products? agricultural sector [1]
industry [2] construction [3] both agriculture and industry [4] both agriculture and construction [5] both industry and construction [6] agriculture, industry and construction [7] other sectors (specify) [8]: ………………
(i) Within agriculture the users of your (your household member’s) business products
are: local small farmers [1]
local large farmers [2] non-local small farmers [3],
Specify location: ……………………… non-local large farmers [4],
Specify location: ……………………… (ii) Within industry the users of your (your household member’s) business products
are:
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local small units [1] local large units [2] non-local small units [3],
Specify location: ………………………. non-local large units [4],
Specify location: ………………………. (iii) Within construction your (your household member’s) business product is
purchased by local users [1] non-local users [2]
35. Does your (your household member’s) business have sub-contracting arrangements? yes, local medium and/or large units [1]
yes, non-local medium and/or large units [2] no [3] L. Performance Data 36. Firm performance:
1. Production capacity In a month (value in Rs.):
2. Total output (actual) In a month (value in Rs.): Raw materials per month:
Power per month:
Packaging per month:
Other costs (including sales tax)
per month:
Interest per month:
3. Total costs (Rs.)
Capital service cost per month:
No. of hours per day in peak agricultural season
4. Total working hours (including own and hired labour) No. of hours per day in
slack agricultural season
5. Per month how much is your profit after meeting all costs?
37. (a) Do you think demand for the product of your (your household member’s) business has grown in the last year [1] has remained steady [2] has declined [3]
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(b) Give reasons for your answer: (c) In case demand has increased, it originates from:
mainly local rural market [1] mainly non-local rural market [2]
mainly local urban market [3] mainly non-local urban market [4]
all of the above [5] 38. (a) (i) Do you (your household member) intend to shut down the business?:
yes[1] no [2]
(ii) If yes, why?: (iii) If no, what are the future plans?:
to maintain status quo [1] to expand production [2]:
(a) with existing capital and labour[2.1], (b) with more labour [2.2], (c) with more capital [2.3];
to diversify into new products [3] to start another unit [4]
39. (a) In expanding/maintaining the business do you (your household member) face problems other than financial ones? yes [1] no [2] (b) If yes, then what are those problems? uncertainty with raw materials [1]
inadequate rural infrastructure [2] lack of adequate distribution channels [3] not enough training available for
entrepreneurs [4] business information not available [5]
other (specify) [6]: …………………….. …………………………………………... 40. (a) Are you (your household member) aware of government policies for SSI?4
yes [1] no [2]
4 SSI stands for small-scale industries.
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(b) If yes, then (i) mention policies which are favourable to promote and protect your (your household member’s) business: (ii) mention policies which need modifications to promote and protect your (your household member’s) business more effectively:
(iii) mention policies which need to be introduced to promote or protect your (your household member’s) business: M. Risk Factor / Psychological Factor (Achievement Motive) / Sociological Factors / Cultural Factors 41. Will you establish a marriage relationship with a business family? yes [1] no [2] 42. Do you believe that
your fate will determine the future of your life? [1] your efforts will determine the future of your life? [2] 43. What would you do if you were given a large amount of money, say Rs. 50 lakhs?: start a business [1]: ◊ One of the businesses that are currently being run by others[1.1], ◊ An innovative/new business that is not being run by others [1.2],
Specify the kind of new business: ………………………. travel to different places [3]
save in bank and enjoy interest [4] donate [5] implement any kind of fantasy/imagination (specify) [6]: …………….. ……………………………………………………………………………... N. Political Factors 44. (a) Do you (or any of your household members) hold any portfolio in local Panchayat/Municipality? yes [1] no [2] (b) If yes, then specify the person: …………………………………………………. (c) If yes, then specify the position:
chief of the local government body [1]
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deputy chief of the local government body [2] ordinary member of the local government body (belonging to ruling party) [3] ordinary member of the local government body (belonging to opposition party) [4]
INSTRUCTION TO THE FIELD INVESTIGATOR FOR THE FOLLOWING QUESTIONS: The following questions are not to be asked to the respondent, rather to be filled out by the field investigator. 45. (a) Who is the MLA (member of legislative assembly) of this constituency?: …………………………………………………………………………………….. (b) Which party does he/she belong to?
CPI(M)5 (largest member of the ruling coalition) [1] Any of the other members of ruling coalition (specify) [2]: ........
Opposition (specify) [3]: ……………………………….
5 CPI(M) stands for Communist Party of India (Marxist).
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APPENDIX 5.2 QUESTIONNAIRE 2
SURVEY ON RURAL INDUSTRIES IN BARDHAMAN DISTRICT OF WEST BENGAL
(Questionnaire for farmers who are NOT engaged in non-farm manufacturing activities)
Location of survey (To be filled out by the interviewer immediately before interview): Block: Panchayat: Village / rural town: Post office: INTRODUCTION: We are carrying out a research project on various aspects of rural industry in Bardhaman district. Our objective is to study the problems and prospects of rural industrialisation in the district. This project will be trying to raise the issues relating to the interest of your village as well as yourself. More specifically, we are studying the problems of investments in non-farm activities and, accordingly, our study is concerned with, first, identification of the obstacles of the rural industrial sector at micro as well as macro level and, second, formulation of eradicative measures of those obstacles. So, we are sure that the findings of this research will be directly or indirectly beneficial for you through some specific policy recommendations for industrial development of your district. This research is being done by a team comprising one researcher and supervisors from Wageningen University, the Netherlands and Indian Statistical Institute, Kolkata, India. We have come here to talk to you about agricultural and industrial development of your village, infrastructural condition in general and some aspects of your own household, your farm, your non-farm activities (if any) in particular. We are not from any government department or tax office, so please don’t worry about the questions. You can trust us and freely exchange your dialogues with us. We promise that we will not use this data for anything except our academic research purpose. We invite your wholehearted cooperation in this work. INSTRUCTIONS FOR THE FIELD INVESTIGATOR: In this questionnaire we have used the terms ‘your household’ and ‘your household member’ frequently. Here household consists of family-head (usually male), his spouse, his sons and daughters including those (children) who are not living in the same house any more and have started families of their own. For the present study this definition of household is formulated in the light of the Indian tradition. For the interview the family-head should usually be our respondent and household would be considered to be the unit of observation. As a field investigator of this project you should be careful about where to use the term ‘you’ and where to use ‘your household member’ (or you can directly use a term like ‘your son / your daughter / your wife’ instead of ‘your household member’ wherever applicable). For example, if the respondent himself is the owner of the business, then you can use the term ‘you’, otherwise ‘your household member’ (to be more precise, use ‘your son/daughter/wife’ as instructed above).
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A. General Background Name of the family head: 1. Respondent’s household composition
Education
Occupation
Secondary
Sl. No.
Household members
(see note below for
abbreviations) (Respondent
first)
Sex (M/F)*
Marital status**
Age
General*** Technical/ vocational
Primary (1) (2)
1. H1
2. H2
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
*M = 1, F = 2; **Married = 1, Never married = 2, Divorced = 3. ***Never went to school = 1, Incomplete primary = 2, Primary = 3, Incomplete secondary = 4, Secondary = 5, Higher Secondary = 6, Bachelors = 7, Masters = 8. Note: family head = H1, head’s spouse = H2, their children = C1, C2, and so on; children of C1 = C11, C12, and so on; children of C2 = C21, C22, and so on. (Hence C11, C12, etc. are grandchildren to H1 and H2). B. Economic Status of the Household
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INSTRUCTION FOR THE FIELD INVESTIGATOR: From this section the properties such as land, farm, livestock, non-farm businesses etc. owned by family-head’s children (who are not living in the same house any more) will be counted together with that of the household directly selected by us. 2. What is the size of your household’s landholding?:
(i) homestead (in katah/bigha6): …………………………….. (ii) cultivable land (in katah/bigha): ……………………… 3. Does your household irrigate the land?: yes [1] no [2] (a) If yes, what kind of irrigation system does your household have?:
manual: from canal (traditional system)[1] tube well [2]
shallow pump or diesel pump [3] other (specify) [4]: ……………………. 4. What kind of puddle system does your household have? Only traditional system: bullock drawn plough [1] Power tiller [2] Tractor [3] 5. What kind of harvesting method does your household follow? Manual [1] Mechanical [2] 6. Does your household have livestock? yes [1] no [2] (a) If yes, how many (fill-out the following table)?: Bullocks:
Cows:
He buffaloes:
She buffaloes:
Calves:
Goats:
6 Indian measures.
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C. Some Farm Level Data 7. Your household is engaged in: only agriculture [1] both agriculture and non-agriculture [2] 8. How is your household involved in agriculture?
primarily as a land-owning farmer [1] primarily as a share-cropper [2]
9. Now I am going to ask you some questions about the crops your household produces. Please inform me which crops you produce. Season Plot
no. Name of crop Area (kottah/bigha) Average no. of workers
1
2
3
4
Kharif
5
1
2
3
4
Rabi
5
1
2
3
4
Summer
5
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D. Non-farm Business Related Data 10. (a) Had any of your household members started a business which went bankrupt?: yes no (b) If yes, why did it go bankrupt?:
uncertainty with raw materials [1] inadequate rural infrastructure [2]
lack of adequate distribution channel [3] not enough training available for
entrepreneurs [4] business information not available [5]
other (specify) [6]: …………………….. …………………………………………... 11. (a) Has/have any/some of the household members thought of starting a non-farm business? yes no (b) If yes, who (specify them by name/s)?1) …………………………………….. 2) …………………………………….. 3) …………………………………….. 4) …………………………………….. (c) If yes, why didn’t he/she/they start a business?:
uncertainty with raw materials [1] inadequate rural infrastructure [2]
lack of adequate distribution channel [3] not enough training available for
entrepreneurs [4] business information not available [5]
other (specify) [6]: …………………….. …………………………………………... 12. (a) In your opinion, which non-farm businesses of your locality have flourished most in the last two years (mention two)?:(1) ………………………………………………… (2) ………………………………………………… (b) Give reasons for your answer:
For business (1): it supplies a new kind of product/service (innovative product/service)[1] it supplies a better quality product/service [2] other (specify) [3]: ………………………………………………………
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For business (2): it supplies a new kind of product/service (innovative product/service)[1] it supplies a better quality product/service [2] other (specify) [3]: ………………………………………………………
(c) You think that these businesses are going well, but then why didn’t you (or any of your household members) start any of them?:
we have never been in non-farm business [1]; we don’t have the special skill needed [2]; number of competitors is rising rapidly, so demand will fall soon [3]; other (specify) [4]: ……………………………………….....
………………………………………………………………………….
13. (a) Does your household think that government policies for SSI7 are responsible for your household’s apathy in non-farm business?:
yes [1] no [2]
not aware of government policies [3] (b) If yes, then mention policies which are responsible for your household’s apathy in non-farm business: E. Risk Factor / Psychological Factor (Achievement Motive) / Sociological Factors / Cultural Factors 14. Will you establish a marriage relationship with a business family? yes [1] no [2] 15. Do you believe that
your fate will determine the future of your life? [1] your efforts will determine the future of your life? [2] 16. (a) Until now you have been just engaged in farming. But if you are now interested in a non-farm business, will you get any support from your family? yes [1] no [2] (b) If yes, it is only verbal support [1] only financial support [2] only labour support [3] all of the above [4] other (specify) [5]: …………………….. 7 SSI stands for small-scale industries.
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17. What would you do if you were given a large amount of money, say Rs. 50 lakhs?: start a business [1]: ◊ One of the businesses that are currently being run by others[1.1], ◊ An innovative/new business that is not being run by others [1.2],
Specify the kind of new business: …………………………. travel to different places [3]
save in bank and enjoy interest [4] donate [5] implement any kind of fantasy/imagination (specify) [6]: …….. ……………………………………………………………………………... F. Political Factors 18. (a) Do you (or any of your household members) hold any portfolio in local Panchayat/Municipality? yes [1] no [2] 19. If yes, then specify the person: …………………………………………………. 20. If yes, then specify the position: chief of the local government body [1] deputy chief of the local government body [2]
ordinary member of the local government body (belonging to ruling party) [3] ordinary member of the local government body (belonging to opposition party) [4]
INSTRUCTION TO THE INVESTIGATOR FOR THE FOLLOWING QUESTIONS: The following questions are not to be asked to the respondent, rather to be filled out by the field investigator. 21. (a) Who is the MLA (member of legislative assembly) of this constituency? …………………………………………………………………………………….. (b) Which party does he/she belong to?
CPI(M)8 (largest member of the ruling coalition) [1] Any of the other members of ruling coalition (specify) [2]: …. Opposition (specify) [3]: ……………………………………...
8 CPI(M) stands for Communist Party of India (Marxist).
Chapter 6
Determinants of Non-farm Manufacturing Entrepre-
neurship of Farmers: Theoretical Considerations and
Empirical Findings
6.1 Introduction
In this chapter, first we present some theoretical considerations (supported by some
empirical evidences) to account for the differences of involvement of farmers in non-farm
manufacturing activities. The success or failure of rural industrialisation programme in a
predominantly agrarian economy is due to the section of the farmers. From our study, we
exclude the probability of involvement of landless labourers in non-farm manufacturing
entrepreneurial activities in manufacturing sector with an assumption that this section
cannot afford to invest money in a manufacturing business. Even if a few people are
involved in some repair works or so, we exclude them from our study if they do not
possess any cultivable land. Since the large part of the rural capital belongs to the farming
group, diversification of rural economic activities from agriculture to non-agriculture
largely depends on the farmers’ willingness of being non-farm entrepreneurs—i.e.
whether or not an individual farmer opts for non-agricultural manufacturing
entrepreneurship. The distinction of the farmers’ involvement in non-farm
entrepreneurship provides us with an opportunity to determine empirically whether or not
different factors are important. Hence, the second part of this chapter deals with the
empirical analysis of several factors responsible for determining non-agricultural
entrepreneurship of farmers. In other words, in the second part of this chapter, we have
tried to test the hypotheses of the theoretical considerations through the LISREL
estimation results. We have described the LISREL model later in this chapter.
The model integrates the personal characteristics of the farmers (age, education, marital
status and number of children); the factors relating to the farmers’ agricultural
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
226
background (whether the farmer is principally a owner or sharecropper,1 and whether or
not the farmer produces three crops viz. aman rice, boro rice and potato in a year); the
financial factors (wealth of the farmer, and financial support from the farmer’s family);
the psycho-socio-cultural factors (whether the farmer has faith in work-effort or fate, and
whether the farmer is willing to arrange his daughter’s or sister’s marriage with a non-
farm businessman); the factors relating to risk and innovation; and the political factor
(whether the farmer is holding an office in panchayat, the local government).
In chapter 4, we have extensively discussed the different theoretical concepts relating to
entrepreneurship along with an emphasis on psycho-socio-cultural issues which have
assumed much negligence until now in the literature of economics. In the present chapter
we emphasize the determinants of non-farm entrepreneurship of farmers.
In the model, the individuals are assumed to be rational decision makers who relate their
existing resources that they control to the requirements of starting their non-farm
manufacturing business. These resources include land, labour, and financial capital. In
addition to these, the opportunities—that a potential non-farm entrepreneur may have to
find out from his own personal characteristics and his milieu as well as to make use of
them—include the person’s education, political affiliation, psychological state of mind
i.e. achievement motive, and cultural environment.
6.2 Basic concepts and assumptions of the theoretical considerations
6.2.1 Rationality and Decision Making
The concept of rational behaviour is frequently used in economic theory. What is meant
by rational behaviour? Maurice Allais provides us with a clear definition, generally
accepted by economists (see Godelier, 1972: 12):
“We have to have recourse to the definition which seems to emerge from
scientific logic, by which a man is considered rational when:
1 Sharecroppers are not categorized as landless labourers. Usually sharecroppers own some amount of land which is not considerably big.
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227
(a) he pursues ends that are mutually coherent, and
(b) he employs means that are appropriate to the ends pursued.”
Analysis of rational behaviour is thus seen as a theoretical investigation aimed at
discovering the conditions under which it is possible to attain a certain objective, taking
into account a certain set of constraints. J. Bénard put it in the following way (see
footnote in Godelier, 1972: 12). The quest for an economic optimum consists in choosing
the best means of arriving at the ends that are regarded as the best. If we speak of an
optimum, this means, therefore, that we agree that choices are possible, that is, that
several procedures exist for arriving at the same end, and that these can be arranged in
order of preference. In other words, at a single point of time there must be alternatives
that can be substituted one for another, and there must also be criteria of choice.
Determination of the optimum will result from combining these two series of elements.
In a review, Sen (1987) has found two quite distinct motivations in the development of
the concept of rational behaviour.
1. It is interesting to know how one could behave rationally in a given situation;
2. The second concern is the possible use of models of rational behaviour in
explaining and predicting actual behaviour.
The exercise is done in two steps. First, we characterize rational behaviour and second,
following that, we assume that actual behaviour is rational because, in normal case,
through actual behaviour an individual, according to his/her own preference, picks up the
best means among alternatives in order to arrive at the ends that are considered by that
individual as the best. In this way, the characterization of rational behaviour may end up
specifying the predicted actual behaviour as well. In the following discussion, the
primary concern is with the way rational behaviour has been characterized.
Rational behaviour under uncertainty will be taken up here, but before that the more
elementary case when there is no uncertainty has to be dealt with. According to Sen
(1987), although there are many different approaches to rational behaviour under
certainty, it is fair to say that there are two main approaches to this question. The first
emphasizes internal consistency: rationality of behaviour is identified with a requirement
that choices (in making a decision) from different subsets should correspond to each
other in a cogent and systematic way. The condition of internal consistency, which seems
to command most attention in formal economic theory among others, is binaries which
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
228
require that the choices from different subsets can be seen as maximizing solutions from
the respective subsets according to some binary relation R (xRy standing for ‘x being
preferred or indifferent to y’). The second common approach to rational behaviour under
certainty sees human beings as tirelessly fostering their respective self-interests. Many
other motivations are important in human behaviour in general, but it is certainly true that
the assumption of the ‘economic man’ relentlessly pursuing self-interest in a fairly
narrowly defined form has played a major part in the characterization of individual
behaviour in economics for a very long time (Sen, 1987).
So far as the present study is concerned, rational behaviour under uncertainty is to be
regarded as an important phenomenon since an entrepreneur lives in an uncertain world.
Future does not always move along the predicted line, so the proposition of present
investment for future benefit holds considerable amount of uncertainty. The extension of
the modelling of rational behaviour from certainty to uncertainty involves both (1) the
characterization of uncertainty, and (2) taking note of uncertainty in making actual
decisions over alternative courses of actions.
The model that has been most extensively used in the context of uncertainty is that of
‘expected utility.’ This takes the form of weighing the value of each of the outcomes by
the respective probabilities of the different outcomes. The probability-weighted overall
‘expected value,’ thus derived, is then maximized in this approach to rational choice
under uncertainty.
6.2.2 The Rational Entrepreneur
The behaviour of entrepreneurs, that is, the totality and succession of acts of decision-
making and management by which they direct the activity of the enterprises, makes up
the essential aspect of economic practice under a system. And this practice is dominated
by the problem of investment-choice, that is, the problem of measuring the efficiency of
investments. The theory of rational behaviour for entrepreneurs thus undertakes to break
down its elements into the series of strategic acts that are the entrepreneur’s
prerogative—determination of investment possibilities, forecasting of the consequences
bound up with each of these, choice between alternatives, ways of carrying out
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investment—and to determine for each of these the optimum conditions for its
accomplishment. Knowledge of these conditions therefore supplies the norms, principles
or recipes for maximizing profit of enterprise. These norms determine the forms of
behaviour and forms of organisation (institutions, structures) that are best adapted to the
end that is aimed at. The conditions are not merely economic but also psychological,
sociological, legal, etc., and in order to analyze them the help will have to be taken from
psychologists, sociologists, lawyers, and, above all, mathematicians (Godelier, 1972: 31).
Taking these into consideration our study in this chapter includes a few variables which
relate to the psychological and sociological factors, along with the economic ones and
personal characteristics of the entrepreneur.
From a psychological point of view, the difference comes out when Godelier emphasizes
‘appetite for power’ and, also, when our concern centres ‘need for achievement’
introduced by McClelland (discussed in chapter 4). In Godelier’s (1972: 32) version, the
psychologists and sociologists have begun to study the motivations and aptitudes of the
head of an enterprise, and shown that desire for ‘gain’ is not the only motive behind his
actions, this often being combined with appetite for power. In a traditional society where
culture of non-agricultural entrepreneurship is not commonly present, the problem is
more related with initiating ‘need for achievement’ than with emphasizing ‘appetite for
power.’
As we have said earlier, decision making about the firm is prerogative of the
entrepreneur. Determination of investment possibilities is a crucial part of starting an
enterprise. Investment is considered as present sacrifice for future benefit (Hirshleifer,
1987). Individuals, firms, and governments all are regularly in the position of deciding
whether or not to invest, and how to choose among the options available. An individual
might have to decide whether to buy a bond, plant a seed, or undertake a training course;
a firm whether to purchase a machine or construct a building; a government whether or
not to erect a dam. In the theory of intertemporal choice, the object of investment is taken
to be to optimize one’s pattern of consumption over time. According to Hirshleifer
(1987), the elements needed to determine an individual’s investment decisions are: (a) his
endowment, in the form of a given existing income stream over time; (b) his preference
function (i.e. time preferences), which orders in desirability all possible time-patterns of
consumption; and (c) his transformation set, which specifies the possibilities (productive
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
230
opportunities) for transforming the original endowment into other time-combinations of
consumption.
Again, whether or not a person is willing to be an entrepreneur is a question of decision
making. Decision making, particularly with respect to the business world, had
traditionally been considered to be an art, although it has recently been evolved into a
science. Scientific decision making tends to hold the similar meaning of rational decision
making. Any individual who is faced with a choice among possible actions obviously
needs to devise some method of selection in order to reach a decision. The consequences
of possible actions must be evaluated in terms of some criteria of desirability. In the real
world, this problem of decision making among alternatives is complicated by the fact that
an individual is rarely sure that his predictions regarding the future consequences of
various actions are correct (Brownlee and Buttrick 1968).
In any event, the general problem of making decisions may still usefully be broken into
three parts. First, the environment in which one finds himself typically sets limits to the
alternative actions which are available. At one extreme is the man who can choose only
the frying pan or the fire; at the other is the possessor of Aladdin’s lamp, for whom
anything is possible. Second, there is the problem of predicting the consequences of each
set of actions that might be chosen—how much profit will be made if this rather than that
is done, for example. Such predictions are usually subject to error, the size of the error
being determined by unpredictable and, in part, unknown factors in the environment as
well as by the means used to make the predictions—the model, the data employed, and
the person doing the predicting. How to predict can be considered an economic problem
in itself. Third, there is the task of constructing the criteria by which the consequences of
alternative actions may be compared and ranked in order from best to worst (Brownlee
and Buttrick 1968).
The setting in which the decision maker is placed differs from model to model, the
assumptions made about the decision maker’s objectives differ from model to model, but
the models are tied together by a common thread wherein efficient decision making is
sought. Irrespective of his problems or motivations, it has been assumed throughout that
the decision maker will make an effort to utilize the best of his ability and knowledge to
resolve these problems, in light of his recognized alternatives and their perceived
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231
consequences. He will want his decisions to be based on reason, the decision maker is
presumed to want to behave rationally (Horowitz, 1969).
Lastly and cautiously, we all expect other people to have rational motives, though we
know very well that our own acts are half of them due to impulse. Moreover, individuals
differ, and they differ as decision makers. In a world immersed in uncertainty one can be
irrational and survive. But, of course, there is no place for impulse in economics.
Given the above background, we now propose to employ the variables that are likely to
be the systematic factors determining whether or not a farmer will be a non-farm
entrepreneur. The influence of the different variables on the tendency of the farmer’s
non-farm entrepreneurship is presented in a schematic diagram in Figure 6.12 and the
theoretical justification (along with some empirical evidences) in favour of employing the
variables are furnished below. A short description of the variables is given in Table 6.1
(see section 6.4.15). Table 6.2 presents the descriptive statistics of all the variables (see
section 6.4.15). In this chapter, we will present the LISREL model which is actually the
simultaneous equations model and which deals with latent variables. This model will be
estimated in order to control simultaneity bias, i.e. to explain interdependence among
dependent and explanatory variables.
6.3 Non-farm entrepreneurship (NFE)
Non-farm entrepreneurship (NFE) of the farmer is the dependent variable of the model.
It’s a binary variable indicating that the farmer who is a non-farm entrepreneur belongs to
category 1 and the farmer who is not a non-farm entrepreneur belongs to category 0. In
section 6.4, we are going to discuss the explanatory variables in detail.
6.4 Explanatory variables
6.4.1 Sex
2 Reverse dependency, i.e. impacts from non-farm entrepreneurship on explanatory variables is not represented in Figure 6.1.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
232
In the present study, initially sex/gender of the farmer had been considered a variable in
construction of the theoretical as well as empirical model, but, during the present
literature survey it became clear that we might not be able to find women entrepreneurs
engaged in non-farm manufacturing activities in the agricultural regions. The arguments
drawn from existing literature are as follows.
Figure 6.1: The conceptual framework
In a country like India, rural elites (even the rural middle class too) are generally very
orthodox and conservative in nature. In such a society, women find their place at home
and men are responsible for outside work. In urban areas the trend is somewhat different,
of course. But, in general, women—except a very few—are not found to play an
entrepreneurial role in manufacturing business in a developing economy like India. In a
study on large farmers and rural industrialists in central Gujarat, Rutten (1995) found
that, within all the entrepreneurial families, only male members were responsible for the
daily running of the farm or industrial enterprise. That is why he comments that the
sociological study he carried out is not only primarily about entrepreneurs but also
Age Marital status No. of children Education
Farmer’s involvement in agriculture Types of crops produced
Political position of the farmer
Wealth/socioeconomic status Financial family support
Marriage relationship Work-effort or fate?
Innovation Risk
Non-farm entrepreneurship
Pers
onal
cha
ract
eris
tics
Agricultural/occupational factors Financial factor and family support
Political factor Innovation and risk
Psycho-socio-cultural factors
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primarily about men. Participation of women in manufacturing business activities in the
other Asian developing economies is not remarkable as well. However, in some cases,
married women appear to be helping-hand in their husband’s manufacturing business. Let
us give an example here. In a study on the Philippines, Carroll (1965) observed that out
of 92 entrepreneurs (in manufacturing business) 87 were found to be men and only five
women. There is a long tradition of business activity by women in the Philippines, and
belief that they make particularly shrewd and energetic traders. Then, why is the number
of women entrepreneurs (in manufacturing sector) so poor in the Philippines? Actually
Carroll (1965) found plentiful evidence of the important roles played by women in the
establishment of many of the enterprises studied; but they did not appear to have played
the entrepreneurial role. More frequently their role was that of treasurer, corresponding to
their traditional role at home. In this regard, Rutten’s observation based on his study in
central Gujarat in India can be mentioned. Rutten (1995: 65) observed that the women
who were co-owners [in official papers] were never present in the industrial unit. This, he
continued, did not mean that the owners’ spouses and female kinsmen played no role. But
Rutten remained superficial by mentioning only the ‘role’ of women in this regard and, at
the same time, by not mentioning which particular ‘role’ women played. Within the area
of economics, not so many research-works have been conducted in different regions of
the world in order to study women’s (who do not appear to be entrepreneurs, but join
hands of their husbands for performing some duties in business activity) role, if any,
played in family business.
In all societies—either traditional or modern—some social or human values might have
stood on the way of development in general and women’s active participation in different
economic activities in particular. The degree of retaining traditional values or maintaining
traditional human occupations in the static pre-industrialised countries is much higher
than that in the dynamic industrialised countries. Therefore, bringing about a change in
values bears great significance in this regard. It will be clearer if an example is given. Let
us take up the case of the USA. Fry (1993) has observed that dramatic change in values
among women in the USA led them (who were potential entrepreneurs) to dive into the
field of entrepreneurship leaving their traditional occupations. According to Fry, in the
USA the number of women-owned business grew from 2,612,621 in 1982 to 4,114,787 in
1987, a 57.5 per cent increase. The primary reason for growth in the number of women-
owned ventures can be traced ultimately to the 1970s. During this decade, an
unprecedented number of women moved out of the traditional roles of housewife,
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
234
teacher, nurse, and secretary. Some became entrepreneurs immediately after high school
or college. Some went the corporate route and then moved into entrepreneurship. Some
raised families and then started their own ventures. Some took over ventures left by
husband or fathers. Others started their own from scratch. This is the picture of a highly
industrialised society where changes in social values played an important role in
changing women’s attitude towards their occupation. In this context, let us now take a
look at a developing country for a comparison. In a study on women entrepreneurs of
NGOs in India, Handy et al (2002) found that all the women entrepreneurs they studied
used to share a feminist ideology. It is to be noted here that feminism is very much an
urban phenomenon and has not yet diffused in rural traditional society even in the
smallest possible volume. This view gains support from Handy et al (2002) when they
write that their study was conducted in and around the city of Pune in Maharashtra, not in
the rural base. They observe that most of the Indian female NGO runners are the products
of the so called urban society, not the products of traditional rural (agricultural) society.
In such a country, one can hardly expect the existence of women entrepreneurs in non-
farm manufacturing sector in the agriculture-based rural areas. As regards women’s role
in rural agricultural society, Rutten (1995) has observed the changes in Patidar
community in central Gujarat in India and remarked that as a result of their early
economic rise and their aspiration to higher status, the Patidar farmers were among the
first of the present-day entrepreneurial families to remove their women from direct
involvement in field labour. For the wealthier Leva Patidars, this happened as early as the
1930s, while many members of the lower sub-castes of Kanbi and Kadva Patidars did not
make this change until the end of the 1950s. They employed hired labourers instead of
using their female family members. According to Rutten (1995), the overall outcome of
these recent changes in working pattern for the women of the entrepreneurial families is
that housekeeping has become their main daytime activity. The major part of the day’s
work of these women consists of preparing and serving the food to other members of the
household. Lastly, to conclude this section, we informally hypothesize that no female
non-farm manufacturing entrepreneurs will be found in the study area. Based on the field
survey, we will informally observe this variable in the section of data analysis later in this
chapter.
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6.4.2 Age and age squared (AGE)
Hypothesis 1: The combined impact of age and age2 of the farmer on his entry into non-
farm manufacturing entrepreneurship follows an inverted U-curve.
The argument is as follows.
In terms of age, which group of people is most likely to enter the field of non-farm
entrepreneurship? Younger or older? Younger persons do possess a spirit of doing
something in life and have strong urge to get established in the professional field. On the
other hand, people of older ages may possess less ambition and accordingly may not find
stimulus to start a new business. But, at the same time, we have to keep in mind that older
people have greater experiences, which may be regarded as one of the positive factors for
entry into non-farm entrepreneurship. Older people can make use of their experiences
earned from different fields to combat/avoid uncertainties. The combined effect leads to
the hypothesis that the impact of age and age2 of the farmer on his entry into non-farm
manufacturing entrepreneurship follows an inverted U-curve (see Figure 6.2).
Figure 6.2
We now review the literature. Age of entry of an entrepreneur has a pivotal role to play in
the performance of enterprise. Studies of Sharma (1980), Deshpande (1982), and Rao
(1986) have shown that the age at which entrepreneurs enter industry has much to do
with the growth of enterprises. Young persons are generally more energetic, change
prone, progressive, and innovative than the older ones are. In this context, Joseph (2003)
Probability of entry
age of entry
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236
has hypothesized that the entrepreneur-manager’s performance is affected by his age of
entry, because an entrepreneur has to play many roles in a small scale industrial unit. For
example, he is the owner, worker, financier, technical expert, and manager. Over a period
of time an entrepreneur acquires additional skills, technical as well as managerial, which
would help him perform better3.
Bird’s (1989) study shows that there are three age groups that yield large numbers of
entrepreneurs. The first group is made up of those in their early twenties who start a
business soon after graduation from high school or college. A second group consists of
those in their late twenties to early thirties who work for someone else for a while, but
have the intention of starting a venture as soon as they have amassed the necessary
capital and experience. A third group consists of those who had no intention of starting a
non-farm business but who encounter a compelling reason to do so later in life. Bird
observes that a significant subgroup in this latter category is those who start a venture
after retiring. A supporting observation is found in another study too. Singh (1986)
observes that once middle age sets in, there is a tendency not to take any risks and to
postpone the idea of entrepreneurship till after retirement.
According to Ramamurthy and Kumar (1990), the best age for entry into innovative
establishments was observed to be between 20 and 40 years. Young persons have greater
attraction towards entrepreneurial ventures. The same phenomenon was found in Singh’s
(1986) study of entrepreneurs in Mumbai. In his study, the maximum concentration was
between the ages of 20 and 30 years. The average age at which entrepreneurs actually
started business was found to be 28 years. However, in the same study for entrepreneurs
of Tamilnadu it was found that the respondents started the enterprises in their later age.
But, in general, it is observed that younger entrepreneurs tend to take risks whereas the
older ones have less risk-taking capacity.
Entrepreneurship at different ages may be influenced by maturity as well as experience of
an entrepreneur on the one hand and lengthy pay-back period on the other. First we 3 There is also a relationship between age and quality of performance. Mishra (1991), while discussing performance, points out that the successful entrepreneurs were relatively younger in age. Unsuccessful entrepreneurs mostly belonged to the older age group. The mean age of successful entrepreneurs was 41 years whereas unsuccessful entrepreneurs had the mean age of
Chapter 6
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discuss maturity and experience. In normal cases, maturity and experience increase when
age goes up. Fry (1993) observed that almost 65 per cent of entrepreneurs launched
ventures during their twenties or thirties. According to Fry, this time period is referred to
as an entrepreneurial window. He says that prior to this period most entrepreneurs do not
have sufficient experience or capital to launch a successful venture. An entrepreneur, who
starts a business at an early age, gets time to get more matured and earn wider experience
in his profession. This further broadens the scope of his success in the long run. Hence,
after staring a business a young entrepreneur gains a wider range of maturity and
experience through the process of learning by doing and this gain may play a role of
causative factor for his better performance. On the other hand, starting a business at an
older age may face certain disadvantage. As a man ages, he may become more risk averse
and may not want to risk his savings on a venture that might fail. Consequently, the
person may not leave his existing work, even though the work may not be highly
satisfying.
Pay-back period is another important aspect of the decision of starting up a business.
Usually a business, especially a manufacturing business, does not pay the entrepreneur
back a substantial amount of investment immediately after starting a business. So, it is
natural to guess that an older entrepreneur is not likely to bear a lengthy pay-back period,
because he has to get financially established in his profession and prove himself worthy
in his society at least earlier than a relatively younger entrepreneur.
The study by Sekaran (1986) shows that an individual may prioritize different facets of
his life at different times depending on his needs of the moment and the stage he is in.
For instance, most individuals assign top priority to their careers until they acquire a firm
foothold in them. Raising a family will be paramount to individuals in their late twenties
and thirties. Consolidating career gains will attain significance when people reach their
forties and fifties. Later, they may give more of their time to preparing for relaxation, and
they may become more involved in temple, church, community, or other activities. Thus,
entry will vary in the different stages of the life cycle of the people. Depending on how
they prioritize the different aspects of their lives at different stages, individuals will
assign different criteria for success. At the early stages of an adult life, the propensity to
build a good career is higher than that in later stages. In the field of entrepreneurial
48 years. In a study, Joseph (2003) found that 72% of the entrepreneur-managers belonged to the age group of 21-40. Of this more than 50% belonged to the age group of 21-30.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
238
career, it can be assumed that fresh spirit and enthusiasm at young age may lead an
individual to take high risk. As the age increases, risk taking propensity decreases, i.e.
people become more risk averse as they age. At older age, shift of an individual from one
organisation to another, or from one company to another, or even from one nature of job
to another is not that uncommon. But shift from non-entrepreneurial field to
entrepreneurship is unlikely unless the person concerned is forced to shift by the
intervention of circumstances, e.g. loss of job, reduction in salary or wage, etc. Similarly,
in the rural economy of a developing country, older people generally stick to agricultural
profession (since agriculture is their traditional occupation) and are not likely to shift
from farming to non-farm entrepreneurship unless they are forced by circumstances like
fall in production in successive years, tremendous fall in agricultural prices due to rising
production in successive years, etc. We will go to these issues further in the analysis of
our data in the later part of this chapter.
Let us turn to the issue of ‘success of life’ again. Sekaran (1986) tried to look at the
success of life from another angle as well. According to her, for one individual, success
might connote a peaceful, happy home that resonates with joy and laughter. To another
individual, success might mean not only involving himself in career and family activities
but also making a significant contribution to the community’s advancement. In the first
place, for both the cases, success means economic success, ignoring which a person can
make neither a ‘happy home’ nor a ‘significant contribution to the community’s
advancement.’ Whether it is home or community, man’s most coveted goal is the
enhancement of his own personal power and influence (Burgess and Locke, 1960), which
is highly related with economic success achieved through competition and struggle.
Because competition and struggle play such a large part in all life, some scholars (see, for
example, Lehman 1953) have advanced the theory that the desire for power is the most
universal and the most fundamental of all human motives. If we consider these statements
to be true then probability of being entrepreneur among younger age groups is higher,
because people in older age groups are likely to realize that an entrepreneur can only
enhance his personal power and influence after he becomes successful and that success
requires a long walk of hard-work with dedication starting from the entrepreneur’s
younger age. Accordingly, people among older age groups are likely to show
comparatively less interest in entering into the field of non-farm entrepreneurship leaving
the occupations they are engaged in. This view is supported by an empirical finding in the
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work of Joseph (2003). She observed that as the age increases, the percentage of
entrepreneurs entering manufacturing is decreasing. She found that only 28% of the
enterprises were started by entrepreneur-managers who were of 41 years and above.
6.4.3 Marital Status (MARS)
Hypothesis 2: The probability of being non-farm entrepreneurs among the married
farmers is higher than that among the unmarried farmers.
Usually the farm family is an economic partnership with all members having an interest
and a stake in the success of the occupational enterprise. Work brings the farm family
together. Burgess and Locke (1960) studied six communities in the USA viz. Old Amish,
El Cerrito, Irwin, Sublette, Harmony, and Landaff and depicted that in a farm family, the
father, mother, and children work together in making the living, with the father doing the
outside work, the mother taking care of the house, and the children being given and
accepting responsibilities.
This occupational cohesion between two partners of a married couple in a farm family is
quite common in nature almost all over the world. The wife of a farmer not only works at
home but also helps in farming when urgently needed. It is a universal culture of the farm
families. Hence, the picture is same in India. In this sense, a farmer, while entering into
the field of non-farm entrepreneurship, finds an added advantage from his marital status
if he is married, since he has got an opportunity to avail the service of his wife as a free
labour. In this regard, we can cite from Burgess and Locke (1960: 70-71):
“In all six communities the family is a working unit with the members
having specialized responsibilities. The father is responsible for operating
the farm, specifically the field work. The mother manages the house,
generally takes care of the garden and chickens, and in emergencies may
help in the field. Her housework includes cooking, washing, ironing,
mending, and making house dresses, …, and frequently children’s clothing.
She also does a great deal of canning and preserving of foods, and some
baking.”
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
240
In the housework, the contribution of the housewife is enormous. At the same time, it is
not expected that in the traditional societies the wife will go to provide the husband with
some help at their own firm. In the low income group of rural society, husbands may
allow their wives to work outside home for earning money as wages, but farmers of the
middle income group and capitalist group will not be in a position to lose their social
status by allowing their wives to work outside home, even in their own farm (or firm). It
has been discussed earlier with an example of the Patidar community in Gujarat.
According to Ross (1976), in agricultural societies the division of labour between the
sexes is clearly defined, and remains so until industrialisation upsets the pattern. Even in
early stages of industrial development women are not considered to be capable of doing
male-type jobs, particularly at the higher levels of administration (see also Boserup,
1970). Once the process of industrialisation has reached a certain point, the demand for
labour increases; and this directly and indirectly produces factors which encourage
women to move out of the home into the former male domain of work. Some of these
factors are growing economic insecurity of women as industrialisation progresses, the
spread of education that gives women wider visions and skills, and the gradual
disappearance of the original social taboos on women working outside the home (Kara,
1972). From sociological viewpoint, two ideologies are related with the women’s role in
industrialisation. One is the ideal of housewife and the other is the ideal of employed
woman. Both of these ideals were formulated publicly in the nineteenth century. The
ideal of housewife was expressed in the statement: Family is the woman’s natural place.
This was originally the ideal of the middle class, from whom it spread out to the working
class (Jallinoja, 1989). This tended to distinguish between the role of husband and wife
more sharply than earlier. Husbands became the sole breadwinners of the family, while
wives devoted most of their time to childcare and household management (Tilly and
Scott, 1978). The ideal of the employed woman was presented publicly mainly by those
women who struggled for their liberation and independence (Jallinoja, 1989). These
kinds of conflicting views do not exist in traditional societies (mainly in rural areas which
are far from urban influence). In the rural areas of a country like India, only one rule
applies, i.e., housewife mainly contributes to housework. In a pre-industrialised society
this role of women may positively impact on the productivity of their husbands at
farm/firm by their contribution to daily work at home.
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The other thing is that marriage may be viewed as an incentive for the farmer to go for
non-farm entrepreneurship in order to support his wife. How? Wives are not paid for their
works at home. So, the reciprocal part of the story is that, in exchange of the free service
of the wife in the family and occasionally in the work, the husband often gets morally
obliged to provide his wife with financial support. In the Indian society like many other
societies, it is a cultural tradition that husband should be responsible for his wife’s well-
being. It is a Hindu custom that the husband is liable to take a pledge during the marriage
ceremony that he would continue to provide his wife with food and clothing for whole
life. In this regard, we may conclude that the male partner of a married couple in rural
India can find himself in a better social position or upper social status in their own
community, if he can provide his wife (or family) with better financial conditions than
the others do.
There is a counter argument too. The probability of being non-farm entrepreneurs among
the unmarried farmers may be higher than that among the married farmers. In such case,
the above hypothesis will be rejected. The (younger) farmers may not have been
interested to get married before they are well established in their occupation, especially
when they are planning or taking initiatives to start a non-farm manufacturing enterprise
which is not their traditional occupation. A new business generally involves tremendous
risks which in turn demands substantial amount of physical labour and almost 24-hour
mental involvement from the entrepreneur. At this situation, a prospective (younger)
entrepreneur may not like to take extra responsibilities through his marriage (which
involves psychological factor), because uncertainty in business does not leave the
entrepreneur with tension-free mind unless and until he finds his firm feet on the
professional ground (which involves economic factor). Thus such a phenomenon
involves both psychological and economic factors and therefore the likelihood of young
manufacturer’s being unmarried finds justification in psycho-economic analysis.
6.4.4 Children (CHIL)
Hypothesis 3: The probability of being non-farm entrepreneurs among the farmers who
have children is higher than that among the farmers who do not have children.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
242
Since children in farm families help their parents at home as well as at work, they can be
treated as positive factor if their parents tend to perform non-farm (manufacturing)
entrepreneurial venture. We have already discussed the culture of farm family in a
nutshell in the previous section. Here we specifically emphasize the role of children.
Children in farm families are to spend a considerable amount of time either at home (in
case of girls) or at farm (in case of boys). Burgess and Locke (1960) said that the children
assist their parents, the degree of responsibility depending on their age. They attain
economic importance and status at a much earlier age than in cities. For example, the
Amish assign youngsters definite tasks at an early age. The following excerpt from a
personal document illustrates the roles played by the children in the collective occupation
of farming:
On the farm we children had duties to perform. My sister helped mother
prepare meals and do the laundry, cleaning, and other things about the
house. My brothers and I had tasks to do when we were five or six years
old. At first it was carrying in fuel, later caring for stock and working in the
garden, and by the age of ten or twelve, working in the field. We all
worked together to make the farm a go (Burgess and Locke, 1960: 61).
Thus, children’s contribution in farm families bears importance. It seems that a farmer
who is going to diversify his activity by starting-up a non-farm manufacturing enterprise
may also capitalize his children’s contribution in the family and firm. In the traditional
societies, family appears to be an important phenomenon so far as starting-up of a new
business venture is concerned. The reason is as follows. It is known to everybody that in
a dual economy like India where urban large-industrial world has got western corporate
influence, the rural economy still exists in its traditional form. Therefore, in a society
where professional ethics are not yet developed as such, the idea that only family
members are reliable in business bears some truth. Besides, the trust the entrepreneur
feels toward his children enables him to delegate part of his functions to the children
corresponding to their ages. For this reason, it is the family enterprise which appears at
the very beginning of any industrialisation process. Corporations make their appearance
at more advanced levels of development (Derossi, 1971).
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Responsibility of being a father can be considered to be an incentive for a farmer in
becoming a non-farm entrepreneur for earning higher income. A child need to be fed,
clothed, kept in good health, looked after, and schooled (Ray, 1998). All these require a
good income of father, because in most of the societies including India a father is mainly
responsible for providing his children with all the basic needs mentioned above.
Therefore, it is hypothesized that a farmer who has child/children has an incentive to be a
non-farm entrepreneur than a farmer who does not have a child. In the similar vein,
probability of being a non-farm entrepreneur increases with the number of children of the
farmer.
6.4.5 Education (EDU)
Hypothesis 4: The probability of being non-farm entrepreneurs among the farmers
increases with their educational achievements.
Earlier a common stereotyped belief about entrepreneurs was that they were at the low
level of education. To be sure, many of the outstanding entrepreneurs of the last century
have had little formal schooling.
Occasionally, however, some studies come up, which go against the common
assumptions (see, for example, Hornaday and Aboud, 1971; Cooper, 1975; and Mancuso,
1975). In a study on the relation between education and success of entrepreneurs based in
Atlanta metropolitan area in the USA, Douglass (1976) shows that entrepreneurs are
more educated than the general population, although their educational advantage may not
contribute directly to their success in business. As for developing nations, Derossi (1971:
163) confirms that “wherever studies have been carried out in the developing countries,
the entrepreneur emerges as one of the most highly educated members of his
community.” The writer continues: “In developing countries, where illiteracy is still
widespread and where the percentage of highly educated people is extremely low, the
majority of entrepreneurs are the men with higher education.” An example is given based
in Mexico. In the country, 38% of the population is illiterate and less than 1% receives
higher education. In contrast, 68% of the businessmen in the sample had a university
degree. Only three people out of 143 were found to have received primary education.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
244
Education may reduce the informational costs associated with starting a new
manufacturing enterprise. Education may also augment skills in the most profitable
manner, particularly if the technology is complex (Schultz, 1975). Technical
entrepreneurs seem to have at least Bachelors Degree, often in engineering, and
frequently hold Masters Degrees. Therefore, in the high-tech field, the education level is
higher (Douglass, 1976; and Fry, 1993). In a broad sense, entrepreneurship not only
requires the support of physical capital, but also the support of human capital. The second
one can be augmented by investing time and money in education. Objective oriented
proper education can make such a person who is skilled in production, a person who can
operate sophisticated machinery, a person who can create new ideas and new methods in
economic activity (Ray, 1998). And all these are qualities of a person, which resemble
with the qualities of an entrepreneur. Thus education has a positive impact on
entrepreneurship. Now let us turn the discussion with a basic question what education is.
What is education? The question was raised by Schumacher (1973). His own answer is
important here. Education is the transmission of ideas which enable man to choose
between one thing and another. We know how to do many things, but do we know what
to do? Let us make it more specific in the context of our discussion. Education may
influence input choice of a production and thus selection of improved or better inputs
may have a positive impact on output and income. For example, in agriculture, farmers
experience how seed choice affects quality and volume of output. Moreover, farmers,
who have been diversifying from agriculture to non-agricultural manufacturing activities,
or at least thinking of diversification, presently realize the need for improved education.
An example is due to Rutten (1995). Much earlier, almost all the village families in his
study area relied on the availability of primary and secondary education in the village.
But, during his visits in the villages, Rutten observes that there is a tendency among the
families to send their children to primary and secondary schools outside the village. This
is done particularly by those families who have economic interests outside agriculture. As
their situation improved in terms of wealth and mobility as a result of their economic
activities in trade and industry, the families have started to consider sending their children
to be educated at expensive private institutions outside the village. The purpose of this is
to provide their children with not only an educational but also a socio-cultural
background which will help them “set up industrial enterprises in the future” (Rutten,
1995: 265).
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Also, for this variable we may find counter argument, approval of which may reject the
above mentioned hypothesis. The probability of being non-farm entrepreneurs among the
farmers may decrease with their educational achievements. The farmer may be more
interested in getting a government or semi-government job (where the gain is not
conditional upon any risk factor) with the increase in his education, rather than starting a
non-farm business (where the gain is fully conditional upon financial risk). More
explicitly, security in getting monthly salary is assured in government jobs, so educated
farmer may opt for secure earning.
Small non-farm manufacturing venture is not a lucrative or prestigious profession for an
educated member of farm family. The educated member does neither show his interest in
farm activities of his family, nor in starting a non-farm business. All these are last options
whereas the first option is to secure a permanent income. Moreover, social prestige is
augmented if a family member is attached with the public sector, be it a job of clerk or
whatever. Mainly financial security is the root cause of such a tendency of the farmer.
Even the rich farmers are not exceptions. This may result in the consequence where we
may find the farmers who are engaged in non-farm manufacturing activities are relatively
less educated.
6.4.6 Political position of the farmer (POLIT)
Hypothesis 5: The farmer has a higher likelihood to become a non-farm entrepreneur if
he is occupying a position in the local government i.e. panchayat.
It is assumed that a farmer who is occupying a position in local government body may
have a higher chance of availing different financial or allied packages of the government,
which are allocated for individual development of rural population and this may create an
extra opportunity for the farmer to become a non-farm entrepreneur in order to earn a
higher income from outside agriculture. Before we proceed with this discussion, the issue
of political affiliation of the farmer needs to present a background. In the traditional
system of India, the rural elites (usually belonging to the upper castes), because of their
superior socio-economic status, enjoyed dominance in all sectors of activities and had
virtually monopolized the socio-economic institutions in the society. As they enjoyed a
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
246
monopoly of wealth, political power, and education, they have naturally been put in an
advantageous position to avail the inflow of resources and the additional technical
knowledge in the village to further enhance their socio-economic status in the society.
This had, thus, led to the emergence of a group of progressive farmers mainly from the
upper echelons of the society most favourably endowed with material resources and
wealth (Singh, 1985). Saith (1992: 62) has argued that “the local elites have almost
invariably been in a stronger economic position to divert the resource inflows in their
favour and to take better advantage of the new opportunities opened up by the package
intervention.” Chambers (1983: 132) has also argued: “Local elites stand as nets between
the poorer people and outside world, in the sense that they catch and trap resources and
benefits.”
The above mentioned socio-political structure of rural society has taken a different shape
by the intervention of the democratic institution of Panchayati Raj. The impact of the
intervention of the Panchayati Raj institution in the rural society requires an attention
towards the macro observation. Although we are here concerned about micro-study, it
seems to be highly interesting if we start the present discussion from macro point of view
and thereafter narrow it down to the micro focus. Such a presentation may help us
understand the scenario better. The introduction of Panchayati Raj and the subsequent
rural development programmes had the effect of establishing a separate infrastructure for
economic development to initiate a process of transformation of the social and economic
life of villagers (Desai, 1969). The objective was to decentralize and transfer the political
as well as economic power from the upper caste rural elite (mainly large farmers) to the
representative body elected by the local people. It is true that previously the categories of
large farmers and small-scale industrialists were increasingly merging into one class of
rural capitalists. It is this class of rural capitalists which had become socially and
politically the most powerful group in the Indian countryside. They dominated the
various social and political organisations at the local level. In the 1950s and 1960s in
Gujarat, as Rutten (1995) described, being in the position of political dominance,
members of the rural entrepreneurial families and many of the large farming families
were responsible for establishing public facilities. They were actively involved in getting
public funds to connect the locality with electricity, to put in water pipes and to construct
school buildings. In most cases, they themselves were the main beneficiaries: the
connections to the water and electricity mains were confined to the residential areas in the
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centre of the village inhabited by the middle and high castes and the schools were mostly
attended by children of these same communities. Nowadays, this picture has changed in
several parts of the country through the intervention of the democratic institution. The
gradual shift in power in favour of the lower castes (or lower class) affected the balance
of power in the village panchayats. Following the decline in power of the upper caste
landlords and entrepreneurs, most upper caste people “began to distance themselves from
local politics, which had become for them a low status profession, dominated by
members of the lower castes” (or lower economic class elsewhere) (Rutten, 1995: 307).
In a general sociological discussion on Indian villages, caste factor inevitably comes in
talks. But when the discussion is about the West Bengal villages, caste factor loses its
prime importance. Caste in the West Bengal villages has lost its stigma and
discriminatory meaning. In this respect, a revolutionary change has been brought about
(Lieten, 1992). Although this is not true for cent per cent case, caste factor plays much
less role in West Bengal than in other Indian states. That is why, for West Bengal, class
based study finds relevance instead of caste based sociology. It is worth mentioning here
that people of upper castes may also belong to lower economic class. Seen in such
perspective, at the level of elected village bodies in West Bengal, one would expect a
communist party to have a high percentage of agricultural labourers and poor peasants
(Lieten, 1992), i.e. the people of lower economic class (whereas, as we have said earlier,
the local bodies are dominated by the people of lower castes in some other Indian states).4
It is said that the Left Front government has earned huge popularity from the rural people
of lower economic profile due to the massive implementation of land reforms or land-
ceiling act (through the programme of ‘Operation Barga’) by which lands of the landlords
over and above the land ceiling have been redistributed to the marginal peasants and
landless labourers. Rudra (1981), Khasnabis (1981) and others present different view
about the story of land reforms made by the Left Front government. Rudra (1981) argued
that the CPI(M) has been rather slow on land reforms, and has suggested that the process
of land distribution had been going on since 1953. The Left Front government continued
the line of the previous Congress governments to confirm that if a political party aims at
majority support among the agricultural population, it cannot achieve that by not
betraying the most exploited and the most oppressed sections of the rural masses (Rudra,
1981: A-61). The interests of the weaker sections, the argument continues, will
automatically be sacrificed in trying to maintain an alliance with the rich peasants.
4 Since 1977, a coalition, called as the Left Front Government, led by the Communist Party of India (Marxist) (CPIM or CPM), has been ruling the state of West Bengal.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
248
Khasnabis (1981: A44-A45) appreciates the political will of the CPI(M) to implement
reforms, but squarely challenges ‘the shameless compromise’ with the state structure:
“This reduces an erstwhile revolutionary programme to an ordinary reformist one. Thus
[the political will] was conditioned and constrained by the will to serve the institutions of
the class society where they run the government.” This simply depicts that the upper class
has a strong influence on the Left Front government. Lieten (1992) criticizes Rudra,
Khasnabis and others for they have not correctly understood the programmatic
understanding of the agrarian question by the CPI(M) and finally concludes that “the type
of economic restructuring which has taken place has delivered many of the goods” (p.
289). If we believe Lieten and go on further in believing that the poor people are
benefited in the Left Front rule, still it is evident from the study of Harriss (1993) on a
small West Bengal village, called as Jungul in Birbhum district, that there is a positive
relationship between the holding of important political position by a person even if he is
proved to be a erstwhile poor man and the mobilisation of resources in favour of the
person concerned. Here we link up the above written macro discussion with the micro
incident or individual evidence. Harriss (1993: 1243) describes: “Politics are
important….and it is surely significant in the Jungul story that the first elected member of
the panchayat, after the Left Front government came to power, was a CPI(M) supporter
from the low ranking mal community. He was subsequently the panchayat prodhan (chief
of the local government), in a panchayat which has always been dominated by the
CPI(M). It cannot be coincidental that his family has acquired a comparatively large
amount of land, a shallow tube-well and an oil engine, or that he himself should now
have secured a position in government service.
In this perspective, it is hypothesized that the farmers who have direct political affiliation,
especially who are holding offices in panchayats, are powerful people in the rural society
and can benefit by mobilizing different kinds of resources in their favour. Therefore, this
section of the rural population has a higher likelihood to become non-farm entrepreneurs.
There may be some argument in favour of the alternative hypothesis, i.e. the farmer may
have higher likelihood to become a non-farm entrepreneur if he is not occupying a
position in the local government i.e. panchayat. The farmer who is holding office in
panchayat may find no time left for non-farm manufacturing activities. Both his farm
occupation and political occupation together may take all his time and energy. Similarly,
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running a manufacturing enterprise demands substantial amount of time of a day from the
entrepreneur. Consequently, a farmer is forced to select one activity out of the two. Due
to lack of time, the farmer who is occupying a position in panchayat has no scope to
establish or run a non-farm business even if he has the opportunity to channel some
public facilities/resources, distributed and disbursed through government machinery, in
his favour. Therefore, the farmer has a higher likelihood to become a non-farm
entrepreneur if he is not occupying a position in the local government i.e. gram
panchayat.
6.4.7 Financial family support (FSUP)
Hypothesis 6: The farmer has a higher probability of becoming a non-farm entrepreneur
if he enjoys financial support from his family.
In traditional societies, the family is often the only locus where a concentration of capital
can be found. It can, therefore, provide the basic elements which are necessary in setting
up a business: capital and organisation (Derossi, 1971). According to Joseph (2003),
main role of the family in the entrepreneurial activity is the financial support. Kinship
links play an important part at every stage and in every aspect of entrepreneurial activity.
They represent one of the most important sources of financial aid in starting an industry
(see Madan, 1993). In a study, Singh (1986) concludes that the willingness to take risks is
not very high in the low income groups but is fairly high in the middle class which
perhaps is inspired by a desire to get into the higher income groups (the study mainly
included middle and low income groups, not the high income group). For people coming
from lower income group, such movement towards entrepreneurship would mean
relatively higher risk. What is implicit in Singh’s observation is that the low income
group may have suffered from higher risk due to their weak strength/support of family
finance. This is because of the fact that once an individual of low income group loses his
invested money he may be left with bankruptcy. This fear might have led the low income
group to be risk-averse. The important role of financial family support in starting small
manufacturing enterprise has also been observed in the Philippines. Carroll (1965: 158)
found that “a major share of the original capital for the enterprise was provided in most
cases by either the entrepreneur himself or his family.”
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
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Let us discuss the theoretical importance of family support in rural economy. Family acts
as a unit in rural areas of developing countries. Derossi (1971) depicts that it is not only
the natural focus of individual emotion, but it also represents the strongest social unit.
Hence, family as a total unit has the power of going beyond an individual member of the
unit so far as the decision making is concerned. An example can be taken from Rutten
(1995). In rural Gujarat, Rutten finds that the surplus accumulated by the families of large
farmers and by the families of small-scale industrialists is substantial. “Part of it is
reinvested in the farm or factory, resulting in an increase in capital-intensity and in scale
of agricultural or industrial operation. Part of the accumulated surplus is invested outside
the agricultural or industrial sector, resulting in a move towards diversification of
economic activities by these families” (p. 233). It should be noted here that Rutten talks
of families, not of the individuals. Hence, family matters so far as the decision making of
the business is concerned. Moreover, in rural India joint family or extended family still
exists.5 The highly developed sense of jointness and family feeling is very much there in
the rural families. Rutten found, while studying the Patidar community, that agricultural
land, for a long time, has played a great emotional role in tying the family members of
the community together, as a result of which family-centrism has become an important
characteristic of the behaviour and attitude of the members of this community. But, in
such a family structure, who does take the decision of investment? We may assume that a
despotic head of the family takes all decisions and others just obey. The family behaviour
would then be just a reflection of the head’s choice function. Family welfare—in terms of
revealed preference—would then have to be seen as the maximum implicit in the head’s
choice function. But it is difficult to assume that in actual societies family heads do
typically have such complete command over all economic actions of everyone in the
family (Sen, 1983). However, it is a controversial issue and bypassing the controversial
part it can be concluded that investment decision of a non-farm manufacturing business
idea conceived by a member of an agrarian family would require support from the family
since a single member does not have sole control over financial aspects of the whole
family.
5 An extended joint family includes parents, married sons and their wives and children, and often also other relatives along the male line of descent, such as the family of the father’s brother and father’s sister.
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6.4.8 Work-effort or fate? (FATE)
Hypothesis 7: The farmer has a higher likelihood to become a non-farm entrepreneur if
he believes in work-effort, not in fate.
Entrepreneurship demands a number of specific personal qualities. Foremost among these
is an optimistic outlook (Derossi, 1971). The man who does not have an optimistic
attitude cannot become an entrepreneur because an entrepreneur deals with substantial
amount of financial risk. And this risk factor, which is of course calculated risk, has some
relationship with optimism. A pessimist will never take entrepreneurial risk. Hard-work
constructs the road of optimism. It’s not fate which directs the line of the future. In a
traditional society like India, most of the people have faiths on supernatural power and
fate is considered to be something which is linked to the will of god. The traditional
belief is: it’s not human’s work-effort, rather it’s god who makes the future—either
prosperity or destitution—of a human; and, therefore, the future of a human is dependent
on his/her fate, not on his/her work-effort. Moreover, in Hindu ideology, one’s karma
(action) of the previous life has the total impact on his/her karma of the present life.
Whatever karma one is performing now had been programmed in his/her fate before
he/she was born. Thus fate is given by god, and human cannot change it. Hence, believers
of fate are not expected to bring change in their lives either through entrepreneurship or
through other initiatives/activities which require a substantial amount of work-effort.
Even if someone has been on the line of sharp prosperity in life by hard-work and
entrepreneurial zeal, then it may be said that he/she is directed by his/her karma.
According to Derossi (1971: 169), the entrepreneur must believe in the possibility of
change, that the environment can be mastered and that he himself can introduce the
required change. On the contrary, the environment cannot be mastered by human being in
the traditional line of thinking. Of course, there are some successful entrepreneurs who
strongly believe in fate. Naturally, as we have indicated earlier, such kind of people may
argue that their work-efforts have obviously made important contribution in achieving the
goal, but finally their successes have appeared as god’s blessings.
Another quality among many others should belong to a prospective entrepreneur, which,
according to Derossi (1971), is that an entrepreneur should be able to turn an
unfavourable situation to his own advantage. Alternatively, as Derossi (1971: 170)
suggests, “it may be said that the entrepreneur has a Machiavellian approach, believing
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
252
that obstacles can be manipulated and, far from being frustrated by difficulties, finding
them, on the contrary, challenging and stimulating.” Here one thing needs further
explanation. First, for some people, an unfavourable situation may be considered an
inevitable reflection of their karmas, whereas, for some others, an unfavourable situation
may be considered a challenge which can be overcome by hard-work. In business, there
are very few difficulties which hard work, thrift, and common sense cannot overcome.
Naturally, the second category of people, who believe in work-efforts, has the quality of
becoming entrepreneurs. In the present chapter, our discussion is about the determinants
of non-farm entrepreneurship in farmers and in doing so we must not forget that the
farmers who are not non-farm entrepreneurs are of course regarded as farm entrepreneurs
by profession. How did they become entrepreneur (i.e. farm entrepreneur), if we assume
they all are fate-believers? How did they take risk? The answer is simple. Most of the
farmers inherited the agricultural profession from their previous generation. It was not a
new occupational entry for them. For entering into a new field, one’s self-confidence has
a crucial role to play and he has to understand that he is not at the mercy of conditions
and events beyond his control and, also, he has to tend to underestimate obstacles.
In this perspective, we may conclude that the farmer who believes in work-efforts, not in
karma or fate, has higher likelihood of inviting challenging career in life through non-
farm manufacturing business.
The above arguments were presented based on common logic but the reality in India
differs from the logical arguments. A counter argument may support the alternative
hypothesis. That is, the farmer might have a higher likelihood to become a non-farm
entrepreneur if he believes in fate, not in work-effort. Let us first try to understand what
the reality is. Most of the businessmen are religious in this part of the world. In the
reality, one can find that almost all Hindu businessmen in India—starting from the
biggest to the smallest one—has placed the images of the god, Ganesha, and the goddess,
Lakshmi in their factories/shops/offices and often they start daily operation of their
business activities after worshipping those images. This indicates that the industrialists
and businessmen are very much faithful to the gods or goddesses, or, in other words, to
their fates, because they usually worship gods with an expectation to have a good fortune
in their business profession.
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One may argue that fate itself is not a scientific concept, so it cannot play any scientific
role in generating entrepreneurship. This is not true, because fate may be an unscientific
concept but faith-in-fate is a psychological phenomenon which may involve achievement
motivation and determination in a particular activity. There are many people who actually
win a game or cross a hurdle due to their hard-work and merit but still they believe that
fate favours them from behind the screen and helps them achieve the goal. In such cases,
faith of a person in good fate may play positive role, i.e. his belief may provoke him
towards an effort. Therefore, we may hypothesize that the farmer has a higher likelihood
to become a non-farm entrepreneur if he believes in fate.
6.4.9 Marriage relationship (MAREL)
Hypothesis 8: The farmer has a higher likelihood to become a non-farm entrepreneur if
he is willing to arrange his daughter’s or sister’s marriage with a non-farm businessman.
In India, unlike the West, marriage partners of the young boys and girls are selected by
the parents (exceptions are seen in some families). Usually, parents of a girl give
preference to a marriage arrangement with a boy of a well-to-do family. To an individual
(who is family head), the marriage of the daughter/sister is associated with social and
economic status. In this regard, the meaning of social and economic status varies from
region to region or from community to community within the country. For some
community, bridegrooms of high-paid government servants may be preferred for the
prospective brides in order to reach higher status, and guardians/fathers belonging to
some other community may want to make marital relationships for their daughters with
well-established self-employed bridegrooms.
Profession and occupation greatly influence the matrimonial alliances in every country.
Settled occupation and stability in life provide confidence to youthful talents. Commonly,
for all practical purposes a male is supposed to be an earning member whereas a female is
a dependent counterpart. The traditional Hindu society favours this kind of relationship.
In general population, as we have indicated earlier, two categories of people are found in
India. Some people have ideas of constancy and stability of a profession. On the other
hand, there are some people who would like to choose prospective male mates who are
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
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settled in private firms and business. This is a sort of adventure in social living.
Matrimonial alliances based on such links are less secure but more prosperous in nature
(Reddy, 1978). In case of matrimonial alliance in West Bengal, preference is given to the
people who are in stable profession, especially to the government servants. In a study on
two communities, Malmaddi and Haveripeth, in Dharwar city in north Karnataka in
India, Kadetotad (1979) finds that one’s social position is counted on the matrimonial
relationship of his daughter and, therefore, many people like to arrange their daughters’
marriage with the persons who are occupationally in a better position. But the meaning of
“occupationally better position” differs from community to community. For example, the
entrepreneurial community, such as the Marwari community, may prefer self-employed
bridegrooms for their daughters, whereas the Bengali people may prefer bridegrooms
who are in secure jobs, like government jobs. Before we go into more detail about this,
let us present the observations of Reddy (1978): In Indian society, the social status of a
qualified medical doctor or a full-fledged engineer or a technological expert sometimes
excels or supersedes the usual family status and its history. The British imperialism and
its legacy have given the Indians altogether new patterns of social values with respect to
services. For instance, IAS and IPS job holders are placed at the top of the hierarchy of
the social values with respect to marriage alliances in the Indian society.6 Next to these
are class-I gazetted officers, executive heads of the departments, services of educational
status, and last in the scale comes the category of the private sector employees. Private
sector job holders (except for rare cases) enjoy low status in the marriage market mainly
due to its insecure nature. Especially Bengali people love security in occupation. While
searching for daughter’s bridegroom, a father first looks for a boy who is in government
service, corresponding to his own status.
The farmers of West Bengal are not the exceptional ones. Some well-to-do, educated,
elderly farmers themselves have been in the profession of school-teaching (mostly semi-
government jobs) simultaneously with their traditional profession of farming. Also, most
of the young farmers of the present generation, who have finished university courses,
primarily like to be engaged in government sector. On the other hand, the farmer, who is
the father of an adult unmarried daughter, may like to give her daughter to a bridegroom
6 IAS and IPS stand for Indian Administrative Service and Indian Police Service respectively. In the government sector, both these positions enjoy highest facilities as well as respect since these two positions are at the top of the administrative hierarchy.
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who is in secure job. In the context of above discussion, it may be concluded that a
farmer’s desire for making a marital relationship (for his daughter/sister) with a non-farm
business family is positively related with his likelihood of being non-farm entrepreneur.
6.4.10 Involvement in agriculture (AGRI)
Hypothesis 9: The farmer has a higher probability of becoming a non-farm entrepreneur
if he is an independent (wealthier) farmer, not a sharecropper.
Let us first divide the farmers into three categories: a) large landowner-cum-tenant, b)
fixed-rent tenant (medium sized landowner-cum-tenant), and c) sharecropper (only tenant
or little landowner-cum-tenant). Large land-owners are those who either cultivate the
land on their own or lease out the land to the tenant or do both. In some cases, some
amount of ‘reverse tenancy’ may be found in the agrarian sector. This concept suggests
that large farmers lease in land from the small farmers, particularly if the former possess
indivisible irrigation assets which can provide water to the holdings in question. The
fixed-rent tenants are those who pay a fixed sum of money to the landlord in return for
the right to cultivate the land.
Those are characterized as sharecroppers—a kind of tenants—who yield to the landlords
an agreed-upon share of the crop. For instance, fraction of tenanted output under share
tenancy is usually 50% in India.
In the present study, we divide the farmers into two categories. First category includes
both the large landowner-cum-tenant and fixed-rent tenant together, i.e. fixed-rent tenant
merges with the large landowner-cum-tenant. And the second category includes only the
sharecropper. The reasons we have made two divisions are as follows. First, fixed-rent
form is largely a variety of Latin American tenancy (Ray, 1998). Very limited fixed-rent
tenancy cases are present in Asia. Primarily, sharecropping has received considerable
attention in South Asian literature (Boyce, 1987; Ray, 1998). That is why we have
ignored separate entity of this group in our study. Second, richer tenants engage in fixed-
rent tenancy and, that is the reason why we have put them in the group of the large
landowners-cum-tenants, who are also richer. Both these groups are richer than the
sharecropper, who is identified as relatively poor tenant. Why richer tenants go for fixed-
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
256
rent tenancy is described in the following. In the case of fixed-rent tenancy, risk is borne
by the tenant. The landlord is relieved of all risk. The rent is the same whether the crop
does well or not. Thus in this sense, fixed-rent tenancy requires that the tenant be willing
and able to bear the risks of agricultural production. This is generally so if the tenant has
substantial wealth of his own (Ray, 1998). In this perspective, our study broadly
considers two groups of farmers—wealthier farmer and sharecropper.
We have already initiated the discussion that the sharecropper is relatively poor, which
means that the sharecropper has very limited financial capacity that may not support him
to be a non-farm manufacturing entrepreneur, because a non-farm business may require a
lump-sum initial investment. Ray (1998) argues that in the case of sharecropping
arrangement the tenant is small and averse to risk: if a given fraction of output is paid as
rent, then the tenant is, to some extent, insulated against output fluctuations, because he
can share some of these fluctuations with the landlord. The weak economic strength of
the sharecropper suggests that sharecropping probably reflects, on the whole, land-leases
from relatively large landowners to relatively small landowners. Consequently, so far as
farming is concerned, families that own relatively large land may have better access to
working capital than families that sharecrop. This again clearly indicates that the
relatively large landowners are financially capable of becoming non-farm entrepreneur
whereas the sharecroppers do not possess that capacity.
Also, from another point of view, the sharecropper does not qualify for non-farm
entrepreneurship. Let us explain it in brief. The sharecropper has an incentive to
undersupply his effort due to economic reason. If the effort of the sharecropper cannot be
monitored and controlled by the landlord, the tenant has an incentive to undersupply his
effort, because, under the sharecropping contract, part of the output produced by him gets
siphoned off to the landlord. It is true that he may give full effort if he starts a non-farm
manufacturing business where there is no such tenancy agreement, i.e. where he himself
is the sole owner of the business. But, at the same time, one should also consider the
psychological setting of the sharecropper who has usually been undersupplying his effort
(when there has been no monitoring and control from the landowner) with an expectation
that he will never enjoy full amount of output in exchange of his hard effort. This
psychological setting of the sharecropper-farmer may affect his effort negatively in non-
farm business since at the initial stage of a non-farm manufacturing business one should
not expect full return against the effort given. The start-up cost cannot be recovered
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overnight. In the competitive market, a new product may require a time period to
substantially capture the market. And, so, the sharecropper may find an incentive to
undersupply his effort in his non-farm manufacturing business. Given such consideration,
the sharecropper does not have the quality to become an entrepreneur. On the contrary,
the landowner, unlike the sharecropper, has not got any scope to develop such
psychological setting (in himself) which may negatively affect the supply of his effort.
Another important aspect is that the large or medium landowner (or independent farmer)
may make use of a piece of his big landholding for the purpose of establishing his non-
farm manufacturing enterprise, if necessary. The sharecropper mainly earns his living by
sharecropping on his landlord’s land, so he lacks such opportunity. Even if the
sharecropper owns a small amount of land, he is not expected to start a non-farm unit on
his small land by stopping farming substantially, because such action may be recognised
as a big gamble for him. To be more specific, in such a case, the sharecropper is not
expected to have been a prospective hopper from farming to non-farm activities, i.e. from
at least little certainty to complete uncertainty, at the cost of the gamble with his own
land.
For this variable too, we may find some counter argument which may support the
alternative hypothesis. The farmer may have a greater probability of becoming a non-
farm entrepreneur if he is a sharecropper, not an independent (wealthier) farmer.
The sharecropper has the higher likelihood to become a non-farm manufacturing
entrepreneur. The rationale for this statement implicitly lies in the fact of incorporating
more family labour by the sharecropper in the cultivation process. Let us go into more
detail. The large owner uses hired labour whereas the sharecropper uses family labour as
far as possible. In a study on Bangladesh, Boyce (1987) observes that family size is larger
for those households that lease in. Boyce argues: “For both landowner and tenant,
sharecropping may well represent a ‘rational’ response to a situation where one has more
land than can be cultivated by family labour, and the other less” (p. 220). The rationale
for why the sharecropper has the higher probability of becoming a non-farm entrepreneur
lies in the same argument.
Let us expose it more explicitly. We can see the possibility of the sharecroppers to be
engaged in non-farm production at least from two angles. (1) Since the sharecropper has
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
258
a larger family size (than the wealthier landowner) which exceeds the number of workers
needed to cultivate their own land, he occupies an extra force of free manpower that can
be initially used in some labour-intensive non-farm business enterprise (in which
relatively less amount of capital is required) if he partially ventures so at the expense of a
part of sharecropping. At the initial stage, such partial engagement may give the
sharecropping family the opportunity to operate both the activities, i.e. farming and non-
farm business. At the later stage, if the non-farm activity flourishes greater than farm
business then the sharecropper may finally lean towards full devotion in his non-farm
manufacturing unit. (2) To meet the economic needs of his large family, the sharecropper
leases in and cultivate other’s land based on agreement of sharing output. This indicates
that the economic needs of the sharecropping families may insist them to start a non-farm
business for earning a better living. It is true that their investment strength is much
weaker than the landowning farmers, but at the same time it is also true they are not as
distressed as landless labourers. Moreover, the sharecroppers are expected to make use of
their farm business experiences in non-farm business.
Seen in this perspective, we hypothesize that the sharecropper is likely to turn into non-
farm entrepreneurship for earning a greater income, since he has an incentive to support
his family members.
6.4.11 Types of crops (CROP)
Hypothesis 10: The farmer has a higher likelihood to be a non-farm entrepreneur if he
produces three crops a year such as aman rice, boro rice, and potato, than the other who
produces less than these three.
There are three principal crop seasons in West Bengal: (1) the spring season, roughly
from April to July, during which the main crop is aus rice; (2) the rainy season, during
which aman rice is grown almost exclusively, beginning with the commencement of the
monsoon in June and ending with the aman harvest in October and November; and (3)
the winter or rabi season, roughly from November to March, during which a variety of
crops is grown, including boro rice, wheat, potato, pulses, oilseeds, etc.
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Aman is common rice which is produced by the most farmers all over Bengal. One of the
main reasons of producing this crop in enormous volume is availability of sufficient rain
water. In the spring and winter seasons, shortage of water is the key problem. The farmer
faces great difficulty in rabi (winter) season. Boyce (1987) observes that, owing to
insufficient soil moisture and lack of irrigation, much land remain fallow in the winter
season. This means that producing crops in winter heavily depends on the farmer’s
private irrigation system. So, the farmer, who can afford to irrigate land in winter on his
own and produce crop, is financially stronger than the other who cannot. Naturally, the
former has a stronger capital base and deserves to be a non-farm entrepreneur. Therefore,
in the formulation of hypothesis we stress on the production of boro rice and potato
which are produced in winter (rabi) season and accordingly we hypothesize that the
farmer, who is producing three crops a year such as aman rice (which is a common one),
boro rice, and potato, has a higher likelihood to be a non-farm entrepreneur.
The counterargument may support the alternative hypothesis, i.e. the farmer has a higher
likelihood to be a non-farm entrepreneur if he produces less than three crops such as
aman rice, boro rice, and potato, than the other who produces these three crops a year.
The producer of three crops may be financially stronger than the other producer who is
not engaged in the production of the above mentioned three crops in a year. It may be
assumed that the rich farmers are engaged in farming throughout the year. And since they
are rich and happy with their traditional occupation they do not have urge to
occupationally deviate. At the same time, it is also true that the producer of the three
crops may have lack of time to be engaged in some activities other than farming.
Producing three crops, along with the other minor crops, in different seasons requires
much time from the farmer who may not find considerable amount of extra time to think
of diversification in non-agriculture. Hence, we hypothesize that the farmer has a higher
likelihood to be a non-farm entrepreneur if he produces less than three crops mentioned
above.
6.4.12 Wealth (WEALT)
Hypothesis 11: The farmer’s probability of being a non-farm entrepreneur increases with
the increase in his wealth.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
260
Income could be an important variable for determining non-agricultural entrepreneurship
of the farmer. But, with anticipation that the farmer may hide information relating to his
actual income during interview, which is a common phenomenon experienced in such
kinds of field studies, the farmer’s wealth (which is verifiable) has been selected as a
substitute. That means, instead of income, wealth of the farmer has been taken as a
variable for this model. The wealth of the farmer may further be assessed by four
indicators mentioned below:7
• Size of farm (Landc)
• Homestead (Landh)
• Whether or not the farmer has access to irrigation (Irri)
• Harvesting method used in farming (Harvest)
The above (four) indicators are considered as observable variables in the model. They are
important to explain the farmer’s economic position in the society. Especially, land and
gold are two prestigious items which can indicate the farmer’s status in the village. The
farmer feels pride in holding them in enormous volume. But the information of gold,
which the farmer may have provided us, is not verifiable. So we have refrained ourselves
from obtaining such information. Rather the farmers’ assets can be compared by their
size of landholdings which are verifiable. Landholding—both homestead and farm-size—
is a crucial factor to indicate the farmer’s financial strength. Other two indicators
mentioned above are also similarly important in this regard. The farmer who has access
to irrigation system is expected to have been better off than the other who doesn’t have
access to irrigation. The farmer who uses mechanical harvesting method is expected to
have been better off than the other who harvests manually. Thus the four indicators
directly or indirectly reflect the farmer’s income which is further accumulated as his
wealth.
7 Please note that, in the present study, the variables ‘involvement in agriculture’ (AGRI) and ‘types of crops’ (CROP) are treated separately rather than as indicators of wealth. This is because of the fact that although these two variables indicate wealth, they involve the entrepreneurial qualities of the farmer needed to run their agricultural activities, whereas the actual four indicators of ‘wealth’ (WEALT) indicate only the assets of the farmer.
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What is the significance of wealth of the farmer in determining non-farm
entrepreneurship? The farmer who is wealthier has greater probability of becoming a
non-farm entrepreneur, because the wealthier section can afford the start-up investment
cost required for a non-farm manufacturing unit. The start-up cost of a non-farm
manufacturing business includes the purchase of land, plant and equipments, and raw
materials. Hired human capital, such as skilled labourers and other staff, may also be
required (Ray, 1998). In order to invest money for employing such physical and human
capitals in the non-farm business the farmer may need to be wealthier. That is, the
wealthier the farmer is, the greater is the possibility of being a non-farm entrepreneur. In
this perspective, we may conclude that wealthier farmer has a higher likelihood to
become a non-farm entrepreneur.
Some counter argument may be furnished here. In such case, the farmer’s probability of
being a non-farm entrepreneur may decrease with the increase in his wealth which is
reflected by four indicators. The farmers of the higher socioeconomic status, or the
wealthier farmers, have always typically been recognised as agriculturists and, moreover,
their economic condition has never forced them to put a parallel footstep in non-
agriculture. Temporary slumps in agri-business may not affect them too much. On the
other hand, farmers in the lower level of wealth may be driven by their economic
condition to move towards an alternative source of income, as a result of which this
section of farmers may try to find out an avenue in the non-agricultural sector. Thus, the
farmer of relatively lower socioeconomic status has the probability of being a non-farm
entrepreneur for his survival.
6.4.13 Innovation (INNOV)
Hypothesis 12: The likelihood of the farmer’s being a non-farm entrepreneur is higher if
he has an innovative bent of mind with regard to non-farm business.
Schumpeter has convinced us that entrepreneurship was essential to development, but
hadn’t told us how to create entrepreneurship where it didn’t exist, except to make sure
that the “climate” was appropriate for entrepreneurial endeavour (see Higgins, 1997: 1-
2). What “climate” had Schumpeter referred to? Can we assume it to be a situation where
innovations gain momentum to emerge? According to Peter Drucker (1986),
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
262
entrepreneurs are those who can create new satisfaction for the customers or new
consumer demand. This “new” thing in the business world can only occur through
innovation. Therefore, entrepreneurship of a person is related with his innovativeness.
But, again a question arises: how innovation takes birth. Let us start with a remark of
Theodore W. Schultz as to how innovation evolves. Innovation requires a particular type
of ability of a person. In analyzing the equilibrating activities of people, according to
Schultz (1975: 833-834), it is postulated that “there are economic incentives to reallocate
resources, that people respond to these incentives to the best of their ability, and that the
difference in their performance is a measure of the difference among people with respect
to the particular type of ability that is required. In accordance with this postulate, there is
a type of ability that is useful and whose value is some function of the demand for and the
supply of that ability. This particular ability, as noted at the outset, represents the
competence of people to perceive a given disequilibrium and to evaluate its attributes
properly in determining whether it is worthwhile to act, and if it is worthwhile, people
respond by reallocating their resources. The realized gains from such reallocations are the
observable rewards.” Thus, the expected gains are the economic incentives to enter the
equilibrating activities and the gains that are realized represent an improvement in
income. Examples can illustrate it better. In agriculture, these gains are exemplified by
the profitability of the adoption of hybrid corn; in industry, these gains may be
exemplified by the profitability of the adoption of capital intensive technology rather than
labour intensive technology. Innovation not only means bringing changes in the existing
products or services but also indicates bringing changes in techniques of production.
Developing a new packaging design of a product is also a kind of innovation. It could
relate to the changes in organisation too. We are not going into detail with regard to the
definition and kinds of innovation. Our discussion centres the phenomena of
innovativeness and non-farm entrepreneurship of the farmer.
However, entrepreneurs innovate. Innovation is the specific instrument of
entrepreneurship (Drucker, 1986). Can an innovative farm entrepreneur be an innovative
non-farm entrepreneur? We need to distinguish between innovativeness of a farmer in the
farm activities and that in non-farm activities. An innovative farm entrepreneur does not
necessarily prove himself to be a potential innovative non-farm entrepreneur. The reason
is as follows. To the most farmers, farming is a traditional business. That is, most of the
farmers are born farm entrepreneurs. Whether or not they were willing to be farmers does
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not matter, they have become farmers by birth. Naturally, we may postulate that many (or
at least some) of them are inherent innovators. Can most of those inherent innovators in
farming be interested in non-farm manufacturing activities? Let us take an example from
Punjab where the agricultural sector is very rich and the farmers are known to have been
very innovative. In a research paper, Dunham (1989: 86) concludes that the ‘new
capitalist farmers’ are unlikely to become involved in the industrial sector to any
significant degree. He suggests instead that they tend to invest in economic activities
which are closely related to agriculture—putting them at a comparative advantage and
making monitoring easier—such as trading, seed distribution, dealerships, local transport
businesses and repair shops. This is empirically confirmed by Bhalla and Chadha (1983:
161), who in their study on the farming-community in Punjab conclude that:
“large and very large farmers are recording substantial savings in the rest
of Punjab. Strangely enough, only a fraction of these are now being used
in capital formation in agriculture or even in house construction. Having
reached a plateau in farm investment, the rich farmers seem to be
squandering their surpluses on conspicuous consumption, including
purchase of jeeps, cars and television sets, excessive indulgence in
alcoholism and demonstrative expenditure on social ceremonies, etc. A
microscopic fraction of them have also invested on houses or shops in
urban areas and mandis and on goods transport and cold storages.”
In Punjab, due to rise in rural incomes through agricultural development, an effective
consumer demand for the non-farm goods was present in the economy. But the farmers
were not interested in investing money in the production of non-farm goods due to the
fact that perhaps they might have lacked the ability “to perceive a given disequilibrium”
in the non-farm economy and to “respond by reallocating their resources.” Thus, the
farmer who is innovative in the farm sector may not come up as a non-farm entrepreneur.
The Punjab experience showed us that the capitalist farmers were interested in investing
money in economic activities which were “closely related to agriculture” and the amount
of innovation required in running these trading businesses (mentioned above) is generally
very nominal. Also, trading businesses can be imitated very easily. Some argue against
the compatibility of imitation with entrepreneurship. If the manufacturing businesses are
imitated in a greater volume by the new-comers then the growth of the business may be
inhibited by early market saturation which may result in a very nominal profit margin
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
264
earned by the owners. So, in the next phase, an industry requires innovative ventures for
its growth by creating new consumer demand. In this context, entrepreneurship is often
judged by the person’s innovativeness. Real or healthy competitiveness grow in a
particular industry when the entrepreneurs exhibit their innovativeness through launching
their products. Therefore, the prospect of rural industrialisation depends on whether or
not the farmers are interested in innovative diversification in manufacturing business, not
in imitative diversification, of course. Imitation is ruled out as a quality of an
entrepreneur. Drucker (1986: 21) maintains strict view regarding this: “…not every small
business is entrepreneurial or represents entrepreneurship,” even if it is bearing some risk.
He emphasizes innovative ventures so far as small entrepreneurship is concerned. But the
question is: how can we measure innovativeness of a person? It is difficult to measure
indeed. However, we can get some indication about one’s innovativeness if the person is
simply interested to do something new or something different in his own field,
irrespective of his age, education, or anything else.
Seen in such perspective, we may hypothesize that if the farmer has an innovative (not
imitative) bent of mind with regard to the non-farm manufacturing business, i.e. if the
farmer has an intension to create new consumer demand in the non-farm manufacturing
sector then he has higher likelihood of becoming a non-farm entrepreneur. Please note
that, for this study, we have measured innovativeness of the farmer by asking the
question: if you start a business, would that be one that is being run by the others, or
would it be one which is new/innovative? (See APPENDICES 5.1 and 5.2 for the
questionnaires). If the farmer likes to start a new kind of business then he is innovative,
otherwise not.
6.4.14 Risk (RISK)
Hypothesis 13: The farmer’s likelihood of being a non-farm entrepreneur is higher if he
is a risk loving person so far as diversification is concerned.
Risk is an important part of entrepreneurship. What determines a person’s attitude toward
risk? Partly, it is a matter of taste. Some people simply tend to be more cautious than
others. To a large extent, however, it is also shaped by the economic circumstances of a
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person (Ray, 1998). Therefore, personal taste and the economic condition determine a
person’s risk taking attitude.
How is risk measured? We can try to understand it by an example. Suppose that a farmer
tosses a coin and the farmer wins Rs. 100 if it shows a head and the farmer loses Rs. 100
if it shows a tail. That is, the farmer expects to win Rs. 100 with probability ½ and the
farmer expects to lose Rs. 100 with probability ½. The farmer’s expected return is:
½(100) + ½(-100) = 0.
This is called a fair gamble. A fair gamble is a gamble whose expected return is 0. A risk
lover will pay some positive price to play this game or gamble. A risk-neutral individual
will pay a zero price; that is, he will play if it is free. An individual afraid of taking any
risks will demand some money to play this game. We now have the following definitions:
• Individuals are called to be risk averse if they are not willing to undertake a fair
gamble;
• Individuals are called to be risk neutral if they are indifferent between accepting
and rejecting a fair gamble;
• Individuals are called to be risk loving if they are eager to undertake a fair
gamble.
Now let us come back to the discussion on our variable. Starting a business involves
financial risks. Similarly, diversification of economic activities is a matter which is very
much embedded in a person’s risk taking propensity. The farmer interested in starting a
new non-farm manufacturing enterprise must have to be a risk loving person. Those
farmers who are not risk loving may find it difficult to diversify in non-farm
manufacturing business. Please note that, for this study, we have measured risk attitude of
the farmer by asking the question: if you are offered a large amount of money, would you
start a business, or do other things? (See APPENDIXES 5.1 and 5.2 for the
questionnaires). If the farmer likes to start a business then he is a risk loving person,
otherwise not.
6.4.15 Summary of the explanatory variables and descriptive statistics
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
266
The discussion presented in the preceding sections is summarized in Table 6.1. We
present an overview of the explanatory variables, their definitions, measurement scales,
and the labels used to represent them in subsequent tables. Moreover, in Table 6.2 we
present descriptive statistics for NFE and its explanatory variables.
6.4.16 Simultaneity bias
Above we have described the expected impacts of the explanatory variables. However,
for several explanatory variables a reverse impact is likely to hold. For instance, non-
farm entrepreneurship (NFE) of a farmer is not only supposed to be influenced by his
children and wealth but, on the other hand, a farmer’s non-farm entrepreneurship may
influence his number of children and wealth. The other explanatory variables that are
likely to be influenced by NFE are marital status of the farmer, types of crops produced
by the farmer, political position of the farmer, financial family support, marriage relation,
farmer’s risk taking propensity, and farmer’s innovativeness. In order to avoid
simultaneity bias remedial action has to be taken. Well-known procedures are limited
information estimators such as 2SLS or full information estimators such as simultaneous
equations method. We opt for the latter, particularly the LISREL approach which does
not only make it possible to deal with simultaneity bias but simultaneously with latent
and observable variables as well. A brief summary of the LISREL approach is presented
in section 6.5.
Table 6.1: Independent variables of the empirical model and their hypothesized influence on the dependent variable
Independent Description Measurement Variable Hypothesized
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267
Variables Names Influence on the Farmer’s Non-farm Manufac-turing Entrepre-neurship (NFE)
Personal characteristics
Age and age2 Age and age2 of the farmer
Years AGE, AGE2
+/- -
Marital status Marital status of the farmer
Married/otherwise: 1/0 MARS +/-
Children Children of the farmer
Number CHIL +
Education Education level of the farmer
Years EDU +/-
Political factor
Political position of the farmer
Whether or not the farmer holds office in panchayat
Yes/no: 1/0 POLIT +/-
Financial factor
Wealth Wealth of the farmer
A latent variable measured by four indicators: Landc (size of the farmer’s cultivable land), Landh (size of the farmer’s homestead land), Irri (whether or not the farmer has access to irrigation), and Harvest (harvesting method the farmer uses)
WEALT +/-
Financial family support
Household’s financial support to the non-farm venture
Yes/no: 1/0 FSUP +
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
268
Agricultural factors
Involvement in agriculture
Whether the farmer is independent owner or sharecropper
Independent farmer / sharecropper: 1/0
AGRI +/-
Types of crops produced
Whether the farmer produces three crops or less a year
three/less than three: 1/0 CROP +/-
Psycho-socio-cultural factor
Marriage relationship
Whether or not the farmer is willing to arrange marriage for his daughter/sister with a non-farm business family
Yes/no: 1/0 MAREL +
Work-effort or fate?
Does the farmer believe in work-effort or fate?
Work-effort/fate: 1/0 FATE +/-
Innovation and risk factors
Innovation Whether or not the farmer is interested in innovative business8
Yes/no: 1/0 INNOV +
Risk Whether or not the farmer is willing to take risk9
Yes/no: 1/0 RISK +
8 We have measured innovativeness of the farmer by asking the question: if you start a business, would that be one that is being run by the others (0), or one which is new/innovative (1)? (See APPENDICES 5.1 and 5.2 for the questionnaires). 9 We have measured risk attitude of the farmer by asking the question: if you are offered a large amount of money, would you start a business (1), or do other things (0)? (See APPENDICES 5.1 and 5.2 for the questionnaires).
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Table 6.2: Descriptive Statistics for NFE and Explanatory Variables (Sample Size: 290)
Variable Mean St. Dev. Skewness
Kurtosis Minimum Freq. Maximum Freq.
NFE 0.583 0.494 -0.337 -1.899 0.000 121 1.000 169AGE 45.234 15.583 0.295 -0.841 17.000 1 90.000 1AGE2 2288.138 1501.955 0.834 0.058 289.000 1 8100.000 1MARS 0.859 0.349 -2.069 2.298 0.000 41 1.000 249EDU 9.131 4.147 -0.162 -0.370 0.000 14 17.000 11CHIL 1.828 1.485 1.110 2.227 0.000 58 9.000 1AGRI 0.955 0.207 -4.422 17.679 0.000 13 1.000 277CROP 0.431 0.496 0.280 -1.935 0.000 165 1.000 125POLIT 0.021 0.143 6.770 44.132 0.000 284 1.000 6FSUP 0.634 0.482 -0.561 -1.697 0.000 106 1.000 184MAREL 0.666 0.473 -0.705 -1.513 0.000 97 1.000 193FATE 0.831 0.375 -1.776 1.162 0.000 49 1.000 241RISK 0.710 0.454 -0.932 -1.139 0.000 84 1.000 206INNOV 0.207 0.406 1.455 0.117 0.000 230 1.000 60WEALT* 0.000 1.000 2.859 18.220 -1.138 1 8.525 1Landc 17.121 14.277 3.551 26.142 1.000 1 150.000 1Landh 7.169 6.540 2.348 7.190 1.000 12 40.000 3Irri 0.862 0.345 -2.111 2.473 0.000 40 1.000 250Harvest 0.293 0.456 0.914 -1.173 0.000 205 1.000 85
*Four indicators have been used for the latent variable wealth (WEALT). The last four rows of this table show the descriptive statistics about them.
6.5 The LISREL model10
As indicated above, the LISREL approach will be applied to estimate the model outlined
above. In this section, we present a brief summary of the LISREL model. In section 6.5.1
we discuss the measurement model and in section 6.5.2 the structural model. Some
submodels are dealt with in section 6.5.3. Section 6.5.4 discusses the theoretical and the
sample covariance matrices. Sections 6.5.5 and 6.5.6 deal with identification and
estimation of the model, respectively. Model judgement and model modification are
discussed in section 6.5.7.
In order to deal simultaneously with both the measurement and the main theory a
LISREL model is made up of two related submodels:
- a latent variables measurement model, which represents the relationships between
the latent variables and their observable indicators. 10 The full form of LISREL is LInear Structural RELations.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
270
- a structural model, representing the relationships between the latent variables.
6.5.1 The measurement model
Let Tpyyyy ),...,,( 21= and T
qxxxx ),...,,( 21= be vectors of observable endogenous and
exogenous variables, respectively.11 Furthermore, let Tm ),...,,( 21 ηηηη = be a vector of
latent endogenous variables and Tn ),...,,( 21 ξξξξ = a vector of latent exogenous
variables. Finally, Tp ),...,,( 21 εεεε = and T
q ),...,,( 21 δδδδ = are defined as vectors of
measurement errors of y and x, respectively. The relationships between the observed and
latent variables are given in the latent variables measurement models (1) and (2):
εη +Λ= yy (1)
and
δξ +Λ= xx (2)
where yΛ and xΛ are (p x m) and (q x n) matrices of regression coefficients (also called
factor loadings).
6.5.2 The structural model
The structural model consists of a set of relationships among the latent variables:
ςξηη +Γ+Β= ~ (3)
or
ςξη +Γ=Β (4)
where Β~ is an m x m coefficient matrix with ijβ representing the effect of the j-th
endogenous variable on the i-th endogenous variables; Γ is a m x n coefficient matrix
with ijγ representing the effect of the j-th exogenous variable on the i-th endogenous
variable; ς is a random vector of residuals; Β−Ι=Β ~ , where I is the identity matrix.
Chapter 6
271
In connection with model (1) – (4), the following notation is introduced. The covariance
matrices of ε and δ , which need not be diagonal in LISREL, will be denoted by
)( pxpεΘ and )(qxqδΘ and the covariance matrices of ξ and ς by )(nxnΦ and
)(mxmΨ .
The following remarks are in order here. First, for reasons of simplicity but without loss
of generality, it is assumed that B is non-singular. Thus, dependent equations are assumed
to have been removed from the system of equations. Secondly, it is possible to estimate
intercept terms of the equations (1) – (4). Such parameters may be of interest in the
comparison of different, mutually exclusive, sets of observations. In the present kind of
study, however, attention will only be paid to the analyses of a single sample. In such
analyses, the intercept terms hardly provide any information. Therefore, the assumption is
made here, that both the observed and the latent variables are centralized. Formally:
E(y) = 0; E(x) = 0; E(η ) = 0; E(ξ ) = 0 (5)
Thirdly, the following standard assumptions are made:
E(ε ) = 0; E(δ ) = 0; E(ς ) = 0
E( Tηε ) = 0; E( Tξδ ) = 0; E( Tηδ ) = 0; E( Tξε ) = 0; E( Tεδ ) = 0 (6)
E( Tςξ ) = 0; E( Tςδ ) = 0; E( Tςε ) = 0
In (5) and (6), “0” denotes a vector or matrix of appropriate order.
Fourthly, multiple observable variables for a latent variable are often preferable and
necessary so as to provide a tool for identification (see, among others, Goldberger 1972,
1973). Besides, one single observable variable may be an indicator of more than one
latent variable. Finally, as described by, among others, Theil (1971), the problem of
multicollinearity arises as a consequence of the occurrence of (highly) correlated
explanatory variables. It usually leads to the increase of the estimated variances of the
estimators of the coefficients of the collinear explanatory variables, so that one may be 11 The superscript ‘T’ denotes the transposed vector of matrix.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
272
led to drop variables incorrectly from an equation. By means of the possibility to handle
observable and latent variables simultaneously within one model framework, as in the
LISREL case, the consequences of multicollinearity can be mitigated. This can be seen as
follows. Collinear explanatory variables, which are indicators of a given latent variable,
are dependent variables in one of the latent variables measurement models (1) and (2) and
therefore are not removed from one of these models because of their collinear nature.
Furthermore, in the structural model the latent variables appear instead of their
corresponding observable variables. So, collinear variables are neither removed from the
structural model in spite of the fact that they are collinear.
6.5.3 Submodels
Model (1) to (4) is a general framework in which several specific models are contained.
The most common of these models are first- and second- order factor analysis models,
structural equation models for directly observable variables, and various types of
regression models. The specifications for the various models mentioned above are given
below.
- Suppose the following specifications are made: 0=Β , 0=Γ , 0=Λ y , 0=Θε , 0=Ψ ,
where “0” denotes a zero matrix of appropriate order. Then the well known factor
analysis model is obtained:
δξ +Λ= xx (7)
- If only the x-variables are removed from the model, i.e. 0=Λ x , 0=Θδ , we have:
εη +Λ= yy (8)
ςξη +Γ=Β−Ι )~( (9)
or
)()~( 1 ςξη +ΓΒ−Ι= − (10)
Putting Β~ = 0 and substituting (10) in (8) gives:
εςξ ++ΓΛ= )(yy (11)
Model (11) is a second-order factor analysis model.
Chapter 6
273
- Remove all latent variables from the model by specifying identity relationships between
y and η and between x and ξ . This is done by defining xΛ and yΛ as identity matrices
and εΘ and δΘ as zero matrices. This results in:
ς+Γ=Β xy (12)
which is a simultaneous equation model with observables only.
- If (12) is written as
ς+Γ+Β= xyy ~ (12a)
and Β~ is specified as a zero matrix then the “classical” linear model is obtained:
ς+Γ= xy (13)
If (13) consists of one equation only, we have the standard linear model.
- If only the latent exogenous variables are removed from the general model i.e., x≡ξ ,
so that Ι=Λ x , the identity matrix, and 0=δθ , we get:
εη +Λ= yy (14)
ςη +Γ=Β x (15)
A special case of model (14), (15) is the so-called fixed-x model. In that case the
conditional distribution of the y variables for given x is studied. This type of model is
frequently met in traditional econometrics (see, for instance, Johnston, 1972). It should be
noted that in both the fixed-x and the random-x cases xxS=Φ , where xxS is the sample
covariance matrix of the x-variables.
We are now in a position to present the model we are going to estimate. The
measurement model reads as follows:12
12 It should be observed that, for the latent variables 1η - 9η , only one indicator for each is available. Hence, they are identical to the corresponding 1y - 9y variables.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
274
y = ⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
13
12
11
10
yyyy
, ⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
=Λ
13
12
11
10
λλλλ
y , [ ]10ηη = , ⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
=
13
12
11
10
εεεε
ε
The structural equation matrices are:
⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟
⎠
⎞
⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜
⎝
⎛
=Β
000000000000000
00000000000000000000000000000000000000000000
0
105104101
9891
8108987868581
710787671
6106867646361
51051
410464341
31031
21021
1101918171615141312
βββββ
βββββββββββββββββββββββββββββββββββ
⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟
⎠
⎞
⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜
⎝
⎛
=Γ
00
00
00
0
101
92
8281
72
61
5251
42
3231
21
1211
γγγγγ
γγγγγγ
γγγ
6.5.4 The theoretical and the sample covariance matrices
When topics of identification estimation and judgement of LISREL models are discussed,
the theoretical covariance matrix and the corresponding sample matrix play essential
roles. As will be explained below, the sample covariance matrix should preferably be
Chapter 6
275
analyzed. The sample covariance matrix of TTT Xyz ),(= will be denoted as S and the
theoretical covariance matrix as Σ .
Let us first pay attention to the structure of Σ . The matrix Σ can be expressed in terms of
the eight model matrices εΘΨΦΓΒΛΛ ,,,,,, xy and δΘ . This can be seen as follows.
Because 1−Β exists, equation (4.4) can be written as:
ςξη 11 −− Β+ΓΒ= (16)
Substitution of (16) in (1) gives
εςξ +Β+ΓΒΛ= −− )( 11yy (17)
Calculation of the covariance matrix of y , i.e. )( TyyE , using (17) and the assumptions
(6) gives: T
yyT EyyE ))()()(()( 1111 εςξεςξ +Β+ΓΒΛ+Β+ΓΒΛ= −−−−
εΘ+ΛΒΨΒ+ΒΓΦΓΒΛ= −−−− Ty
TTTy ))()(( 1111 (18)
In similar ways )( TxxE and )( TyxE are calculated. This gives:
⎥⎥⎦
⎤
⎢⎢⎣
⎡
Θ+ΦΛΛΛΒΦΓΛΓΦΛΒΛΘ+ΛΒΨ+ΓΦΓΒΛ
=Σ −
−−−
δ
εTxx
Ty
TTx
Txy
Ty
TTy
)())((
1
111
(19)
On the basis of prior information (expectations, theoretical considerations, etc.), the
elements in the parameter matrices, and thus in Σ , may be regarded either as free, fixed
or constrained (see among others, Johnston, 1972). A constrained parameter is unknown
but assumed to be equal to one or more other parameters.13 All independent, free and
constrained parameters contained in the matrices xΛ , yΛ , Β , Γ , Φ , Ψ , εΘ and δΘ
will be denoted by the vector π . It is obvious that a specific structure of π , i.e. a specific
configuration of free, fixed and constrained parameters, determines a specific structure of
Σ .14 Moreover, the determination of the value of π forms the core of the estimation
problem.
13 In addition to these constraints various other equality and inequality constraints on various parameters, such as variances, correlations, factor loadings and structural coefficients, as well as ordered inequalities can be imposed. 14 It should be noted that when Σ must explicitly be expressed as a function of π , we will write )(πΣ ; otherwise the argument will be omitted.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
276
Let us now turn to the sample covariance matrix S. Let Z be a M x (p+q) matrix of M
observations of the y and x vectors and TTT xyz ),(= the sample mean vector. Then:
)(1
1 TT zzMZZM
S −−
= (20)
When there are ordinal or nominal variables among the observable variables, (20) cannot
be used in general. When there are ordinal variables among the y-variables or among the
x-variables, which may not be considered as fixed, the LISREL program can estimate and
analyze the matrices or polychoric, tetrachoric and polyserial correlation coefficients. In
all three cases the ordinal z variables are regarded as crude measurements of an
underlying unobservable continuous variable, say z*, which is assumed to be standard
normally distributed. The polychoric correlation coefficient is the correlation between
two underlying z* variables.15 The tetrachoric correlation coefficient is a special case
when both observables are dichotomous. The correlation between a z* variable and a
normally distributed observed variable is called the polyserial correlation coefficient. By
way of the observable variables the various correlations mentioned above are estimated
(for details see, among others, Olsson 1979, Olsson et al 1981 and Muthén 1978, 1979,
1981).
It should be noted that not only the sample covariance matrix can be analyzed by the
LISREL program, the sample matrix of moments about zero and the sample correlation
matrix can also be used to estimate their theoretical counterparts. The sample matrix of
moments about zero, defined as:
ZZM
T1 (21)
has to be used when intercept terms and means of the latent variables are required.
15 In the case of a large number of distinct categories the ordinal variable is treated as a continuous variable.
Chapter 6
277
When the measurement scales are very different the correlation matrix could be analyzed
for numerical expediency. Then each variable is expressed in units of its standard
deviation. The correlation matrix is defined as:
11 −− SDD (22)
with
2/1)( SdiagD = , (23)
i.e. a diagonal matrix of standard deviations.
Now that the most important features and assumptions of LISREL models have been
described, we can pay attention to the identification problem in connection with this type
of models.
6.5.5 Identification
In order to be able to draw inferences for the vector η from the variance-covariance
matrix of the observable variables, the structure of Σ has to be such as to allow a unique
solution of η from Σ . Thus, the vector η has to be uniquely determined by Σ ; in other
words, the model has to be identified.
A necessary condition for identification is that the number of distinct elements in Σ is at
least as large as the number of independent parameters to be estimated. A second
necessary condition for identification is that each individual parameter can be separated
from the other parameters. This condition is often difficult to test. Furthermore, it is not a
sufficient condition. However, the LISREL program gives hints about identification
problems. It calculates an estimate of the matrix of second-order derivatives of the fitting
function used to estimate the model. Rothenberg (1971) has shown that under quite weak
regularity conditions local identifiability is equivalent to non-singularity of the
information matrix. Furthermore, the rank of the matrix indicates which parameters are
not identified.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
278
In the case of models with latent variables, the model is not identified if the latent
variables have not been assigned measurement scales. The easiest way to fix the
measurement scales of the latent variables is to set one λ -coefficient equal to 1 for each
latent variable. It is usually possible to fix or to constrain unidentified parameters on the
basis of theoretical knowledge or ad hoc reasoning so as to render the model identified.
6.5.6 Estimation of the model
As mentioned above, estimation of LISREL models consists of fitting the theoretical
matrix to the sample matrix of a set of M observations on z = (yT, xT)T by minimizing the
distance between both matrices in some metric. The exposition below will be in terms of
the covariance matrices Σ and S but it has already been remarked that the matrix of
moments about zero and the sample correlation matrix can also be analyzed. However,
the estimates based on each of these matrices are usually not the same.
The LISREL program can provide any of seven estimators:
• Instrumental variables (IV)
• Two-stage least squares (TSLS)
• Unweighted least squares (ULS)
• Generalised least squares (GLS)
• Maximum likelihood (ML)
• Generally weighted least squares (WLS)
• Diagonally weighted least squares (DWLS)
The maximum likelihood estimator is described below in detail.
Maximum likelihood has been the “traditional” estimator of LISREL models in the sense
that the other estimators were introduced at a later stage. The maximum likelihood
procedure is based on minimization with respect to the unknown parameters of the non-
negative function:
Chapter 6
279
( ) ( )[ ]qpSStrF +−−Σ+Σ= − loglog21 1 (24)
by means of a modification of the Fletcher-Powell algorithm. In (24) | . | stands for the
determinant and tr ( . ) for the trace of the matrix concerned. When δεςξ ,,, are
multinormally distributed, and thus z, then:
( ) ( )[ ]1log2log21 −Σ+Σ++−=′ StrqpMF π (25)
is the log-likelihood function of the sample in the case of independent observations.
From (24) and (25) it follows that under the assumptions of normality of z and
independence of the observations, minimization of F (which gives the same parameter
estimates as maximization of F ′ ), results in ‘genuine’ maximum-likelihood estimators.
Under the usual regularity conditions, which are satisfied in the case of normality, the
maximum likelihood estimator of π is asymptotically normally distributed with mean π
and covariance matrix [ ] 1)(1 −πJM
.16 Furthermore, this estimator is consistent and
asymptotically efficient.
It is obvious from (24) that S has to be positive definite. This condition is satisfied when
there exists no exact linear relationship between any of the z variables, and if M ≥ p+q.
Furthermore, the starting values needed for minimization algorithm, say π ′ , should be
such that ( )π ′Σ , is also positive definite. The initial estimates provided by LISREL
program usually satisfy this condition (see below).
The maximum likelihood procedure also produces an estimate of the covariance or
correlation matrix of the estimators, which can be used for model judgement purposes.
However, it should be noted that although an estimate of the covariance or correlation
matrix of the estimator is produced whatever sample matrix has been analyzed, the
covariance or correlation matrix of the estimators is only valid when a sample covariance
matrix has been analyzed.
16 ( )πJ is defined as ( ) ( ) ( )⎥⎥⎦
⎤
⎢⎢⎣
⎡= π
δπδπ
δπδπ π ;log.;log zpzpEJ
jiij with F ′ substituted for
( )π;log zp , where ( )π;zp is the likelihood function.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
280
As mentioned above, a necessary condition for the maximum likelihood procedure to
give “genuine” maximum likelihood estimates is the normal distribution of the
observable variables. However, the distribution of the observables is seldomly known in
practice. It also rarely happens that the theory at hand suggests a definite distribution.17
However, when the range of the variables is in principle (- ∞∞, ) and second order
moments exist, the assumption of multivariate normality can be justified as a first
working hypothesis on the basis of a central limit theorem or maximum entropy. The
latter means that the normal distribution reflects the lack of knowledge about the
distribution more completely than other distributions (see, among others, Rao, 1965).
Maximum likelihood under normality (i.e. application of maximum likelihood under the
assumption of normality whereas the distribution actually deviates from normality) may
also be defended on the basis of the fact that it usually leads to a reasonable fitting
function and to estimators with acceptable properties for a rather wide class of
distributions. Under quite weak distributional assumptions, maximum likelihood under
normality is consistent and asymptotically normal. However, in the case of deviation
from normality the standard errors produced by the LISREL program should be
interpreted very cautiously. The same applies to various other judgement statistics to be
described below.
The maximum likelihood fitting function can also be used without the assumption of
normality. In that case it is similar to the unweighted least squares estimator. Under these
circumstances the resulting estimator is still consistent. However, the judgement statistics
are no longer valid.
6.5.7 Model judgement and model modification
The purpose of model judgement is to judge how well an estimated model fits to the
sample data. Various aspects of a LISREL model may be considered in this connection,
such as the model as a whole, the various submodels and the individual parameters.
Individual parameters 17 For an exception, see the theory of rational consumer behaviour (cf. Theil, 1975, 1976).
Chapter 6
281
The statistics which relate to the individual parameters are parameter estimates and, when
maximum likelihood has been used, standard errors and correlations of the estimators of
the individual parameters.
Separate equations
For the equation of each observed variable in each latent variables measurement model
the squared multiple correlation is given.
The latent variables measurement models
The coefficient of determination for the latent variables measurement model (i.e. for the
endogenous and exogenous latent variables jointly) shows how well the observed
variables serve jointly as indicators of the endogenous and exogenous latent variables.
The structural model
The coefficient of determination for all structural equations jointly shows what
proportion of the variation in the endogenous variables is accounted for by the variation
in the systematic part of the model.
The overall fit
For the model as a whole several statistics are provided. First, there is the 2χ -measure
which is given if maximum likelihood is used. Another measure for the overall fit, when
maximum likelihood is used, is the goodness of fit index (GFM) defined as:
21
21
)ˆ()ˆ(1
StrIStrGFM
−
−
Σ−Σ
−= (26)
This measure, adjusted for degrees of freedom (AFGM), is defined as:
)1(2
)1)((1 GFMh
qpqpAGFM −+++
−= (27)
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
282
Measures similar to (26) and (27) are given for unweighted least squares. Then GFM is
replaced by GFU defined as:
)()ˆ(1 2
2
StrStrGFU Σ−
−= (28)
All measures (26) – (28) are expressions of the relative share of variances and
covariances accounted for by the model. They usually fall between zero and one. A good
fit corresponds to values close to one.
A measure of the average of the residual variances and covariances is the root mean
square residual. A small value of this statistic in relation to the sizes of the elements in S
is an indication of a good fit. Finally, the LISREL program gives normalized residuals
which are approximately standard normal variates. As a rule of thumb, a normalized
residual larger than 2, is an indication of specification errors.
For further details, the reader is referred to LISREL manual (see Jöreskog and Sörbom,
2001).
6.6 The Results
6.6.1 The measurement model
The estimation results of the measurement model are presented in Table 6.3 (see
APPENDIX 6.1 for the detailed results). The coefficient of the variable Landc (i.e. the
farmer’s farm size) has been fixed to 1 in order to fix the measurement scale of the latent
variable 10η . From Table 6.3 it follows that Landc and Landh (farmer’s homestead land)
are the main determinants followed by Harvest (harvesting method the farmer follows)
and Irri (whether or not the farmer has access to irrigation). The R2 values of the first two
indicators are low whereas for the latter two indicators they are very low. This implies
that improvement of the model is needed.
Chapter 6
283
Table 6.3: The results of the measurement model
Observable variable
Coefficient
Standard error t-value R2
Landc 1.00000 - - - - 0.30729 Landh 0.30224 0.05287 5.71667 0.13380 Irri 0.00812 0.00264 3.07509 0.03459 Harvest 0.01539 0.00356 4.32247 0.07136
6.6.2 The structural model
Goodness of Fit Statistics
The goodness of fit statistics in the box below show that the model fits the data
reasonably well. Minimum fit function chi-square value is 167.05504 and normal theory
weighted least squares chi-square value is 164.43435. Both of them are nearly double of
the value of degrees of freedom, which is 84. This is usually interpreted as a reasonable
fit. Also, goodness of fit index (GFI) is found to be very high, which is 0.92949. This also
indicates that the model fits the data well.
Interpretation of the results
We shall discuss the main results of the structural model in the following. See Table 6.4
for the results and for the detailed results see APPENDIX 6.1. Our main emphasis is on
equation 1 (see Table 6.4) since that equation relates to the main objectives of this study,
i.e. identification of determinants of non-farm entrepreneurship among farmers. We have
incorporated the other equations in the structural model in order to control for
simultaneity bias as well as to examine the interdependence between some variables. We
find from the results presented in Table 6.4 that the values of R2 for a few dependent
variables (see, for example, equations 4 and 8) are very low, which implies that further
development of these equations is needed.
As indicated earlier, during data collection we did not find any female entrepreneur in
rural non-farm manufacturing sector in our study area and, consequently, the variable
‘sex’ has not been incorporated into the model. So, no further discussion is presented
here.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
284
Goodness of Fit Statistics Degrees of Freedom = 84 Minimum Fit Function Chi-Square = 167.05504 (P = 0.00000) Normal Theory Weighted Least Squares Chi-Square = 164.43435 (P = 0.00000) Estimated Non-centrality Parameter (NCP) = 80.43435 90 Percent Confidence Interval for NCP = (47.87108 ; 120.79258) Minimum Fit Function Value = 0.57805 Population Discrepancy Function Value (F0) = 0.27832 90 Percent Confidence Interval for F0 = (0.16564 ; 0.41797) Root Mean Square Error of Approximation (RMSEA) = 0.057562 90 Percent Confidence Interval for RMSEA = (0.044407 ; 0.070539) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.16397 Expected Cross-Validation Index (ECVI) = 0.81811 90 Percent Confidence Interval for ECVI = (0.70544 ; 0.95776) ECVI for Saturated Model = 0.83045 ECVI for Independence Model = 5.39569 Chi-Square for Independence Model with 105 Degrees of Freedom = 1529.35478 Independence AIC = 1559.35478 Model AIC = 236.43435 Saturated AIC = 240.00000 Independence CAIC = 1629.40300 Model CAIC = 404.55007 Saturated CAIC = 800.38571 Normed Fit Index (NFI) = 0.89077 Non-Normed Fit Index (NNFI) = 0.92711 Parsimony Normed Fit Index (PNFI) = 0.71261 Comparative Fit Index (CFI) = 0.94169 Incremental Fit Index (IFI) = 0.94254 Relative Fit Index (RFI) = 0.86346 Critical N (CN) = 203.51118 Root Mean Square Residual (RMR) = 1.51949 Standardized RMR = 0.065117 Goodness of Fit Index (GFI) = 0.92949 Adjusted Goodness of Fit Index (AGFI) = 0.89927 Parsimony Goodness of Fit Index (PGFI) = 0.65064
Chapter 6
285
R2
0.90
966
0.22
868
0.88
578
0 .0
7588
- -
0.65
739
- -
0.09
458
0.14
990
0.56
638
EDU
-0.0
0803
(0
.002
8)
-2.8
3695
- -
- -
- -
- -
- -
- -
- -
0.01
994
(0.0
053)
3.
7522
1
- -
AG
E - -
0.01
071
(0.0
011)
9.
2564
7
- -
- -
- -
- -
- -
-0.0
0897
(0
.001
6)
-5.4
9441
- -
0.38
224
(0.0
403)
9.
4636
5
WEA
LT
-0.0
6190
(0
.005
6)
-10.
8792
- -
0.50
441
(0.1
177)
4.
2850
2
- -
- -
- -
- -
- -
- -
- -
INN
OV
- -
- -
- -
- -
- -
- -
- -
- -
- -
- -
RIS
K
0.06
804
(0.0
267)
2.
5436
7
- -
- -
- -
- -
- -
- -
- -
0.29
794
(0.0
485)
6.
1430
0
- -
MA
REL
- -
- -
- -
- -
- -
-0.1
2417
(0
.035
6)
-3.4
8123
- -
- -
- -
- -
FSU
P
- -
- -
- -
- -
- -
- -
- -
- -
- -
- -
POLI
T
- -
- -
- -
- -
- -
- -
- -
- -
- -
- -
CR
OP
0.06
002
(0.0
250)
2.
3933
6
- -
- -
- -
- -
- -
- -
- -
- -
- -
CH
IL
- -
- -
- -
- -
- -
- -
- -
- -
- -
- -
MA
RS
0.18
676
(0.0
473)
3.
9457
6
- -
- -
- -
- -
- -
- -
- -
- -
- -
Exp
lana
tory
var
iabl
es
NFE
- -
- -
6.78
127
(1.6
94)
4.00
165
-.336
29
(0.0
61)
-5.5
055
- -
0.79
354
(0.0
34)
23.2
898
- -
- -
- -
- -
NFE
M
AR
S C
HIL
C
RO
P PO
LIT
FSU
P M
AR
EL
RIS
K
INN
OV
W
EALT
Eq. 1
Eq. 2
Eq. 3
Eq. 4
Eq. 5
Eq. 6
Eq. 7
Eq. 8
Eq. 9
Eq. 1
0
Tab
le 6
.4: T
he re
sults
of t
he st
ruct
ural
mod
el (b
eta
and
gam
ma
resu
lts)
Endo
-
geno
us
varia
bles
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
286
From the results it is found that the sign of the coefficient of the explanatory variable
MARS in equation 1 is positive. It depicts that the probability of a farmer to be a non-
farm entrepreneur (NFE) is higher if he is married. Marriage may be an incentive for the
farmer to go for non-farm entrepreneurship in order to support his wife. In Indian society,
like many other societies, marriage is such a tie that the wife usually takes care of home
and the husband finds moral obligation to provide his wife with financial security. The
housework that the wife provides bears high value to the family. In turn, this may ease
the burden of the farmer-husband and stimulate him to devote greater attention into work,
e.g. into some risky non-farm venture, for the betterment of the family. In addition, the
farmer can find himself in a better social position in his own community if he can offer
his wife financial affluence. Therefore, the married farmer has higher likelihood of
becoming a non-farm entrepreneur than the unmarried farmer.
In contrast to expectation, the number of children of the farmer (CHIL) does not have an
impact on starting up a non-farm enterprise (NFE). This indicates that the farmer who has
children does not have an incentive to diversify in non-farm manufacturing activity. This
may be because of the fact that children heavily bring the sense of responsibility to the
mind of the farmer. This is such a responsibility which may obstruct the farmer to enter
into a new risky venture. Thinking of the future of the children, the farmer may not want
to gamble in a new business. But it is interesting to observe that the inverse effect holds,
i.e. NFE has a positive impact on the farmer’s number of children. This indicates that the
number of children of the farmer increases as the farmer has diversified into non-farm
entrepreneurship. Let us go into little detail. Once the farmer has started a non-farm
manufacturing activity he is likely to have been stimulated in having children through
gaining additional income as well as ensuring higher financial security in the family.
Perhaps, this farmer is not frightened of losing money for raising children since he has
already diversified and has become familiar with his new non-farm business.
We find that the variable CROP has a positive impact on NFE. This indicates that as the
farmer, who is engaged in the production of three crops—aman rice, boro rice, and
potato—in a year, does have higher probability of being a non-farm manufacturing
entrepreneur and the farmer who produces less than three crops in a year is less likely to
be a non-farm entrepreneur. The farmer who grows three crops a year is more active than
the one who produces less than the major three. Growing three crops a year implies that
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287
the farmer likes engagement and activity in his profession. He does not want to keep
himself idle and may further like to have more engagements and may want to diversify
into non-farm businesses. Equation 4 shows us that there is a negative impact of NFE on
CROP. Non-farm activities are time consuming which depresses his farming activities.
Political position of the farmer (POLIT) is a variable which is not found to have any
impact on NFE. Involvement in politics leaves little time for other activity except farming
(which is his primary occupation). Doing politics is an activity which demands
substantial amount of time from the person concerned. Along with doing politics, the
farmer may not find time to run non-farm activity.
The variable FSUP (financial family support) has also been found to have no significant
impact on NFE. A possible explanation is that the family considers NFE as risky.
Moreover, the traditional farm family may not support the farmer to put a footstep
outside agriculture. This may be viewed as a traditional cultural blockage on the road to
rural industrialisation. The culture of modern thinking through building industrial unit
(since agriculture is considered as traditional sector) is probably absent in the soul of
farm family. Farm family as a unit is perhaps not motivated to break the tradition. On the
other hand, in equation 6 we find that NFE has a positive impact on FSUP. This implies
that the farmer who diversifies into non-farm manufacturing entrepreneurship is likely to
support the family financially.
The variable MAREL does also have no impact on NFE. This implies that the farmer
who is willing to arrange his daughter’s or sister’s marriage with a non-farm businessman
does not increase the possibility of becoming a non-farm entrepreneur.
The positive coefficient of the variable RISK suggests that the farmer’s probability of
being a non-farm entrepreneur is higher if the farmer is prepared to take risk for non-farm
manufacturing activities. The farmer who is prepared to bear risk may not keep his
surplus idle (either at home or in bank) and may look for challenging, non-traditional
business projects. The farmer of this category is more likely to be a non-farm
entrepreneur. This clearly supports our hypothesis.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
288
INNOV is another variable which has been found to have no impact on NFE. The fact
that whether or not the farmer has an innovative bent of mind does not influence the
probability of his being a non-farm entrepreneur.
The coefficient of the variable WEALT (wealth of the farmer) has a negative sign, the
implication of which is that there is a negative relationship between wealth of the farmer
and the probability of the farmer to be a non-farm entrepreneur. That is, the more the
farmer has wealth, the lower the possibility of the farmer to be a non-farm entrepreneur.
This simply does not support our hypothesis. A possible explanation is the following. The
greater the farmer has wealth, the happier the farmer is. The wealthy farmer does not
have an urge to look for an alternative source of income possibly because he is leading a
smooth life with his existing properties. On the contrary, the farmer who has relatively
little wealth may want to diversify in non-farm activity in search of greater earning.
The variable AGE has no direct impact on NFE, but it has several indirect impacts on
NFE. First, we observe that AGE has a positive impact on NFE via MARS. To be
explicit, in the second equation AGE has a positive impact on MARS and then in the first
equation MARS has a positive impact on NFE. As age of the farmer increases, the
probability of the farmer to be married is higher, and as the farmer gets married, the
probability of being a non-farm entrepreneur is higher. Similarly, we observe an indirect
effect of AGE on NFE via RISK and this effect is negative. Let us take a closer look at it.
AGE has a negative effect on RISK and RISK has a positive effect on NFE. As age of the
farmer increases, his attitude to risk decreases, and as attitude to risk decreases, the
probability of the farmer’s being a non-farm entrepreneur decreases. In the similar way,
we observe that AGE has a negative impact on NFE via WEALT. To be more explicit,
AGE has a positive impact on WEALT, whereas WEALT has a negative impact on NFE.
As age of the farmer increases, his wealth increases, and as wealth of the farmer
increases, his probability of being a non-farm entrepreneur decreases.
The variable EDU, educational achievement of the farmer, has a direct, negative impact
on NFE. This indicates that the more the farmer is educated, the lower the probability of
the farmer’s being a non-farm entrepreneur. With increase in educational achievement,
the farmer may prefer more secure job in government for side income. Additionally, if
some one has achieved higher education and then starts a non-farm business, he may not
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be able to enjoy social dignity as, for instance, a school teacher (employed in a
government aided school) or other government employee enjoys. So, achieving higher
education may disqualify the farmer to be a non-farm entrepreneur. On the other hand,
the farmer with relatively less education may weigh his social position in terms of money
and, accordingly, he may drive for non-farm entrepreneurship in search of a better
income.
As regards the other three exogenous variables, i.e. age squared (AGE2), farmer’s
involvement in agriculture (AGRI), and farmer’s faith in fate or work-effort (FATE) have
been found to be insignificant and consequently have been removed from the structural
model.
6.7 Conclusions
This chapter is the most crucial chapter of this book since it contains theoretical
considerations and empirical findings of our model. Our objective was to identify the
determinants of non-farm entrepreneurship among the farmers. In the theoretical
considerations, we have formulated the following hypotheses:
1. The combined impact of age and age2 of the farmer on his entry into non-farm
manufacturing entrepreneurship follows an inverted U-curve.
2. The probability of being a non-farm entrepreneur among married farmers is higher
than that among unmarried farmers.
3. The probability of being a non-farm entrepreneur among farmers who have children is
higher than that among farmers who do not have children.
4. The probability of being a non-farm entrepreneur among farmers increases with their
educational achievements.
5. The farmer has a higher likelihood to become a non-farm entrepreneur if he is
occupying a position in the local government body i.e. panchayat.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
290
6. The farmer has a higher probability of becoming a non-farm entrepreneur if he enjoys
financial support from his family.
7. The farmer has a higher likelihood to become a non-farm entrepreneur if he believes in
work-effort, not in fate.
8. The farmer has a higher likelihood to become a non-farm entrepreneur if he is willing
to arrange his daughter’s or sister’s marriage with a non-farm businessman.
9. The farmer has a higher probability of becoming a non-farm entrepreneur if he is an
independent (wealthier) farmer, not a sharecropper.
10. The farmer has a higher likelihood to be a non-farm entrepreneur if he produces three
crops a year such as aman rice, boro rice, and potato, than the other who produces less
than these three.
11. The farmer’s probability of being a non-farm entrepreneur increases with the increase
in his wealth.
12. The likelihood of the farmer’s being a non-farm entrepreneur is higher if he has an
innovative bent of mind with regard to non-farm business.
13. The farmer’s likelihood of being a non-farm entrepreneur is higher if he is a risk
loving person so far as diversification is concerned.
The LISREL approach has been applied to estimate the model including the above
mentioned hypotheses. The LISREL approach makes it possible to deal with simultaneity
bias. Another important feature is that it allows the simultaneous estimation of a latent
variables measurement model and a structural model. More specifically, a LISREL model
is made up of two related submodels:
i) a latent variables measurement model, which represents the relationships
between the latent variables and their observable indicators.
ii) a structural model, representing the relationships between the latent variables.
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The LISREL programmme can provide any of the following seven estimators:
Instrumental variables (IV) Two-stage least squares (TSLS) Unweighted least squares (ULS) Generalized least squares (GLS) Maximum likelihood (ML) Generally weighted least squares (WLS) Diagonally weighted least squares (DWLS)
In our case, the full information maximum-likelihood-estimator has been used.
Let us now turn to the results of the model. The measurement model is made up of one
endogenous variable, i.e. wealth of the farmer (WEALT), and four exogenous variables,
i.e. farmer’s farm size (Landc), farmer’s homestead land (Landh), farmer’s access to
irrigation (Irri), and the harvesting method farmer uses (Harvest). These four indicators
indicate the wealth of the farmer. Each of the indicators was found to be significant. The
most important indicators turned out to be farmer’s farm size (Landc) followed by
farmer’s homestead land (Landh), the harvesting method farmer uses (Harvest), and
farmer’s access to irrigation (Irri). We observe that the overall fit of the measurement
model is rather low which means that more indicators would be desirable. The structural
model consists of 10 equations, amongst which the first equation is the most important
one. The first equation is formulated to examine the influence of various factors, as
indicated in the above mentioned hypotheses, on farmer’s non-farm manufacturing
entrepreneurship. To control for simultaneity bias (that is, interdependencies between
dependent and explanatory variables), we have formulated a simultaneous equations
system of 10 equations. During estimation, we had to remove three exogenous variables
(AGE2, AGRI, and FATE) from our structural model since they were found to be highly
insignificant.
A farmer’s marital status (MARS), types of crops produced by a farmer in a year
(CROP), a farmer’s risk taking propensity (RISK), a farmer’s wealth (WEALT) and a
farmer’s education (EDU) have been found to have direct impacts on a farmer’s non-farm
entrepreneurship (NFE). But, among these variables, WEALT and EDU have been found
to have negative impacts on NFE, whereas the other variables (MARS, CROP and RISK)
have been found to have positive impacts on NFE. The positive sign of the coefficient of
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
292
the variable MARS suggests that the probability of a farmer to be a non-farm
entrepreneur (NFE) is higher if he is married. The positive sign of the variable CROP
suggests that the farmer, who is engaged in the production of three crops (aman rice,
boro rice, and potato) in a year, does have higher probability of being a non-farm
manufacturing entrepreneur and the farmer, who produces less than three crops in a year,
is less likely to be a non-farm entrepreneur. The positive coefficient of the variable RISK
indicates that the probability of being a non-farm entrepreneur is higher if the farmer is
prepared to take risk. The negative sign of the coefficient of the variable WEALT depicts
that more the farmer has wealth the lower the possibility to be a non-farm entrepreneur.
Similarly, the negative sign of the coefficient of the variable EDU indicates that the more
the farmer is educated the lower the probability of the farmer’s being a non-farm
entrepreneur. Age of a farmer (AGE) has been found to have indirect effect on non-farm
entrepreneurship via marital status, risk attitude and wealth. The explanatory variables
political affiliation (POLIT), financial family support (FSUP), marriage relation
(MAREL), and innovativeness (INNOV) have been found to have insignificant impacts
on NFE. The variable -- number of children (CHIL) -- has also been found to have an
insignificant effect on NFE but interestingly NFE has been found to have a positive
impact on the number of children.
The results we have obtained in the present study can hardly be compared with those of
other studies because the present study manifests itself in the field of rural
industrialisation as a very different kind. Several studies pertaining to rural
industrialisation have been carried out in order to examine the linkage between
agriculture and non-agriculture among other issues (see, for example, Chandrasekhar
1993, Eapen 1999, among others). Some are non-empirical, qualitative, policy oriented,
studies (for example, Saith 1992). Unlike them, our emphasis was on non-farm
entrepreneurship of farmer, which does not resemble with other studies. Yet, at least from
one point of view, we can relate our result with Mellor’s (1976) thesis. Mellor argued that
additional income of farmers caused by increased agricultural based on cost decreasing
technology can generate demand for rural non-agricultural goods and consequently
promote rural industries. If we have good faith in Mellor’s argument then we can imagine
that the wealthy farmers will come forward with their investible surplus to produce and
supply the non-farm goods in order to meet the new demand. During 1980s and 1990s
West Bengal experienced high growth in agricultural production. But our result shows
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293
that wealthy farmers are not likely to be non-farm entrepreneurs. This simply contrasts
with Mellor’s arguments. Entrepreneurship is such an issue which is often tied with
cultural tradition. For example, orthodox, rich, farmers of West Bengal might not have
liked to break their professional tradition by investing their money out of agriculture and
thus might have inhibited growth in rural industrial sector.
To summarize the results of this study we may state that farmers who are married,
engaged in producing three crops a year, and risk takers have a relatively high probability
to become non-farm entrepreneurs. The reverse holds for wealthy farmers and those who
have higher levels of education. Finally, age has indirect positive impact on farmer’s non-
farm entrepreneurship via marriage, and indirect negative impact on farmer’s non-farm
entrepreneurship via risk attitude and wealth.
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
294
APPENDIX 6.1: The Full Results DATE: 3/18/2004 TIME: 10:21 L I S R E L 8.54 BY Karl G. Jöreskog & Dag Sörbom This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue, Suite 100 Lincolnwood, IL 60712, U.S.A. Phone: (800)247-6113, (847)675-0720, Fax: (847)675-2140 Copyright by Scientific Software International, Inc., 1981-2002 Use of this program is subject to the terms specified in the Universal Copyright Convention. Website: www.ssicentral.com The following lines were read from file C:\han3\lisrel850\Subroto\LISRELL13.lpj: DA NI=23 NO=290 LA FI=Strucequat.PSF RA FI=Strucequat.PSF SE 1 3 5 7 8 9 10 12 13 14 15 16 18 2 4 / MO NY=13 NE=10 NX=2 NK=2 BE=FU GA=FU PS=SY,FI IT=5000 TD=ZE LE 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP 7-MAREL 8-RISK 9-INNOV 10-WEALTH LK 1-AGE 2-EDU VA 1 LY 1 1 LY 2 2 LY 3 3 LY 4 4 LY 5 5 LY 6 6 LY 7 7 LY 8 8 LY 9 9 LY 10 10 FR LY 11 10 LY 12 10 LY 13 10 VA 1 LX 1 1 LX 2 2 FI TE 1 - TE 9 FR TE 10 - TE 13 FI TD 1 - TD 2 FR BE 1 2 BE 1 4 BE 1 8 - BE 1 10 FR BE 3 1 BE 3 10 BE 4 1 FR BE 6 1 BE 6 7 FR BE 9 8 FI GA 1 1 GA 2 2 GA 3 1 GA 3 2 GA 4 1 - GA 4 2 FI GA 5 1 GA 5 2 GA 6 1 GA 6 2 GA 7 1 - GA 7 2 FI GA 8 2 GA 9 1 GA 10 2 FR PS 1 1 PS 2 2 PS 3 3 PS 4 4 PS 5 5 PS 6 6 PS 7 7 PS 8 8 PS 9 9 PS 10 10 !FI PS 1 1 PS 1 3 - PS 1 7 PS 2 2 PS 2 3 - PS 2 5 PS 2 8 PS 2 9 PS 3 2 PS 3 3 PS 3 5 - !PS 3 9 !FI PS 4 4 PS 4 5 PS 4 7 - PS 4 9 PS 5 2 - PS 5 4 PS 5 5 PS 5 7 - PS 5 9 PS 6 2 PS 6 3 !PS 6 5 !PS 6 6 PS 6 7 - PS 6 9 !FI PS 7 2 - PS 7 6 PS 7 7 PS 8 1 - PS 8 5 PS 8 8 PS 9 1 PS 9 3 PS 9 4 PS 9 6 PS 9 8 PS !9 9 !PS 9 10 PS 10 2 PS 10 3 !FI PS 10 7 PS 10 9 PS 10 10 !Path Diagram OU ND=5 ADD=OFF MI SC RS EF BE=BE1 GA=GA1 PS=PS1 TE=TE1 TD=TD1
Chapter 6
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DA NI=23 NO=290 Number of Input Variables 23 Number of Y - Variables 13 Number of X - Variables 2 Number of ETA - Variables 10 Number of KSI - Variables 2 Number of Observations 290 DA NI=23 NO=290 Covariance Matrix NFE MARS CHIL CROP POLIT FSUP -------- -------- -------- -------- -------- -------- NFE 0.24399 MARS -0.04881 0.12181 CHIL -0.20367 0.23852 2.20546 CROP -0.06867 0.01617 0.17838 0.24609 POLIT -0.00518 -0.00399 0.00704 0.00143 0.02033 FSUP 0.18952 -0.02763 -0.13936 -0.06336 -0.00625 0.23272 MAREL 0.03297 -0.01285 -0.03711 -0.02142 -0.00344 -0.00157 RISK 0.06904 -0.02725 -0.01205 -0.00966 -0.00437 0.04255 INNOV 0.01742 -0.01563 -0.00919 -0.00298 0.00262 0.00668 Landc -3.77819 0.95311 5.73887 2.23327 0.00441 -3.07857 Landh -1.09535 0.35788 2.08806 0.50823 0.09857 -0.78405 Irri -0.03007 0.02195 0.04534 0.01814 -0.00406 -0.02983 Harvest -0.05721 0.01736 0.09569 0.04624 0.00084 -0.04820 AGE -5.28591 2.60074 11.07171 1.32765 0.00205 -3.70292 EDU -0.30500 0.09471 -0.60709 -0.09128 -0.00964 -0.23222 Covariance Matrix MAREL RISK INNOV Landc Landh Irri -------- -------- -------- -------- -------- -------- MAREL 0.22337 RISK 0.00313 0.20647 INNOV 0.01408 0.06014 0.16466 Landc -0.84184 -1.35592 -0.13751 203.84525 Landh -0.51941 -0.03912 0.02547 30.32296 42.76720 Irri 0.01945 -0.01587 -0.03019 1.02189 0.28117 0.11932 Harvest -0.02273 -0.03245 0.01181 2.15481 0.53335 0.02327 AGE -0.91784 -2.17752 -0.63692 101.10482 29.05367 0.87675 EDU -0.22938 -0.06918 0.32228 8.74019 4.78747 -0.04761 Covariance Matrix Harvest AGE EDU -------- -------- -------- Harvest 0.20791 AGE 1.00716 242.81680 EDU 0.21406 3.98647 17.19384 DA NI=23 NO=290 Parameter Specifications LAMBDA-Y 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- NFE 0 0 0 0 0 0 MARS 0 0 0 0 0 0 CHIL 0 0 0 0 0 0 CROP 0 0 0 0 0 0 POLIT 0 0 0 0 0 0
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
296
FSUP 0 0 0 0 0 0 MAREL 0 0 0 0 0 0 RISK 0 0 0 0 0 0 INNOV 0 0 0 0 0 0 Landc 0 0 0 0 0 0 Landh 0 0 0 0 0 0 Irri 0 0 0 0 0 0 Harvest 0 0 0 0 0 0 LAMBDA-Y 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- NFE 0 0 0 0 MARS 0 0 0 0 CHIL 0 0 0 0 CROP 0 0 0 0 POLIT 0 0 0 0 FSUP 0 0 0 0 MAREL 0 0 0 0 RISK 0 0 0 0 INNOV 0 0 0 0 Landc 0 0 0 0 Landh 0 0 0 1 Irri 0 0 0 2 Harvest 0 0 0 3 BETA 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE 0 4 0 5 0 0 2-MARS 0 0 0 0 0 0 3-CHIL 9 0 0 0 0 0 4-CROP 11 0 0 0 0 0 5-POLIT 0 0 0 0 0 0 6-FSUP 12 0 0 0 0 0 7-MAREL 0 0 0 0 0 0 8-RISK 0 0 0 0 0 0 9-INNOV 0 0 0 0 0 0 10-WEALT 0 0 0 0 0 0 BETA 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 1-NFE 0 6 7 8 2-MARS 0 0 0 0 3-CHIL 0 0 0 10 4-CROP 0 0 0 0 5-POLIT 0 0 0 0 6-FSUP 13 0 0 0 7-MAREL 0 0 0 0 8-RISK 0 0 0 0 9-INNOV 0 14 0 0 10-WEALT 0 0 0 0 GAMMA 1-AGE 2-EDU
Chapter 6
297
-------- -------- 1-NFE 0 15 2-MARS 16 0 3-CHIL 0 0 4-CROP 0 0 5-POLIT 0 0 6-FSUP 0 0 7-MAREL 0 0 8-RISK 17 0 9-INNOV 0 18 10-WEALT 19 0 PHI 1-AGE 2-EDU -------- -------- 1-AGE 20 2-EDU 21 22 PSI 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 23 24 25 26 27 28 PSI 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 29 30 31 32 THETA-EPS NFE MARS CHIL CROP POLIT FSUP -------- -------- -------- -------- -------- -------- 0 0 0 0 0 0 THETA-EPS MAREL RISK INNOV Landc Landh Irri -------- -------- -------- -------- -------- -------- 0 0 0 33 34 35 THETA-EPS Harvest -------- 36 DA NI=23 NO=290 Number of Iterations = 27 LISREL Estimates (Maximum Likelihood) LAMBDA-Y 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- NFE 1.00000 - - - - - - - - - - MARS - - 1.00000 - - - - - - - - CHIL - - - - 1.00000 - - - - - - CROP - - - - - - 1.00000 - - - - POLIT - - - - - - - - 1.00000 - -
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
298
FSUP - - - - - - - - - - 1.00000 MAREL - - - - - - - - - - - - RISK - - - - - - - - - - - - INNOV - - - - - - - - - - - - Landc - - - - - - - - - - - - Landh - - - - - - - - - - - - Irri - - - - - - - - - - - - Harvest - - - - - - - - - - - - LAMBDA-Y 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- NFE - - - - - - - - MARS - - - - - - - - CHIL - - - - - - - - CROP - - - - - - - - POLIT - - - - - - - - FSUP - - - - - - - - MAREL 1.00000 - - - - - - RISK - - 1.00000 - - - - INNOV - - - - 1.00000 - - Landc - - - - - - 1.00000 Landh - - - - - - 0.30224 (0.05287) 5.71667 Irri - - - - - - 0.00812 (0.00264) 3.07509 Harvest - - - - - - 0.01539 (0.00356) 4.32247 LAMBDA-X 1-AGE 2-EDU -------- -------- AGE 1.00000 - - EDU - - 1.00000 BETA
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299
1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE - - 0.18676 - - 0.06002 - - - - (0.04733) (0.02508) 3.94576 2.39336 2-MARS - - - - - - - - - - - - 3-CHIL 6.78127 - - - - - - - - - - (1.69462) 4.00165 4-CROP -0.33629 - - - - - - - - - - (0.06108) -5.50550 5-POLIT - - - - - - - - - - - - 6-FSUP 0.79354 - - - - - - - - - - (0.03407) 23.28980 7-MAREL - - - - - - - - - - - - 8-RISK - - - - - - - - - - - - 9-INNOV - - - - - - - - - - - - 10-WEALT - - - - - - - - - - - - BETA 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 1-NFE - - 0.06804 0.02104 -0.06190 (0.02675) (0.02553) (0.00569) 2.54367 0.82410 -10.87925 2-MARS - - - - - - - - 3-CHIL - - - - - - 0.50441 (0.11771) 4.28502 4-CROP - - - - - - - - 5-POLIT - - - - - - - - 6-FSUP -0.12417 - - - - - - (0.03567) -3.48123 7-MAREL - - - - - - - - 8-RISK - - - - - - - - 9-INNOV - - 0.29794 - - - - (0.04850) 6.14300 10-WEALT - - - - - - - - GAMMA 1-AGE 2-EDU -------- -------- 1-NFE - - -0.00803
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
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(0.00283) -2.83695 2-MARS 0.01071 - - (0.00116) 9.25647 3-CHIL - - - - 4-CROP - - - - 5-POLIT - - - - 6-FSUP - - - - 7-MAREL - - - - 8-RISK -0.00897 - - (0.00163) -5.49441 9-INNOV - - 0.01994 (0.00531) 3.75221 10-WEALT 0.38224 - - (0.04039) 9.46365 Covariance Matrix of ETA and KSI 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE 0.24478 2-MARS -0.04003 0.12181 3-CHIL -0.20162 0.22995 2.19691 4-CROP -0.06893 0.01346 0.15856 0.24618 5-POLIT - - - - - - - - 0.02033 6-FSUP 0.19425 -0.03177 -0.16000 -0.05470 - - 0.23971 7-MAREL - - - - - - - - - - -0.02774 8-RISK 0.06154 -0.02332 -0.00253 -0.02069 - - 0.04883 9-INNOV 0.01693 -0.00610 0.00504 -0.00569 - - 0.01343 10-WEALT -3.69060 0.99410 6.56858 1.24110 - - -2.92863 1-AGE -5.34370 2.60074 10.57899 1.79701 - - -4.24044 2-EDU -0.21559 0.04270 -0.69335 0.07250 - - -0.17108 Covariance Matrix of ETA and KSI 7-MAREL 8-RISK 9-INNOV 10-WEALT 1-AGE 2-EDU -------- -------- -------- -------- -------- -------- 7-MAREL 0.22337 8-RISK - - 0.20647 9-INNOV - - 0.06080 0.16506 10-WEALT - - -0.83233 -0.21760 62.63881 1-AGE - - -2.17752 -0.56928 92.81395 242.81680 2-EDU - - -0.03575 0.33224 1.52378 3.98647 17.19384 PHI 1-AGE 2-EDU -------- -------- 1-AGE 242.81680 (20.19969) 12.02082
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2-EDU 3.98647 17.19384 (3.80804) (1.43034) 1.04685 12.02082 PSI Note: This matrix is diagonal. 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 0.02211 0.09396 0.25093 0.22750 0.02033 0.08213 (0.00631) (0.00782) (0.36215) (0.01900) (0.00169) (0.00683) 3.50593 12.02082 0.69289 11.97620 12.02082 12.02082 PSI Note: This matrix is diagonal. 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 0.22337 0.18694 0.14031 27.16174 (0.01858) (0.01555) (0.01167) (5.37150) 12.02082 12.02082 12.02082 5.05664 Squared Multiple Correlations for Structural Equations 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 0.90966 0.22868 0.88578 0.07588 - - 0.65739 Squared Multiple Correlations for Structural Equations 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- - - 0.09458 0.14990 0.56638 Squared Multiple Correlations for Reduced Form 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 0.48432 0.22868 0.22977 0.05446 - - 0.31143 Squared Multiple Correlations for Reduced Form 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- - - 0.09458 0.04936 0.56638 Reduced Form 1-AGE 2-EDU -------- -------- 1-NFE -0.02188 -0.00746 (0.00133) (0.00267) -16.50978 -2.79896 2-MARS 0.01071 - - (0.00116) 9.25647 3-CHIL 0.04440 -0.05062 (0.00462) (0.01542) 9.61976 -3.28201 4-CROP 0.00736 0.00251 (0.00138) (0.00104)
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5.32880 2.40335 5-POLIT - - - - 6-FSUP -0.01737 -0.00592 (0.00129) (0.00213) -13.46888 -2.77896 7-MAREL - - - - 8-RISK -0.00897 - - (0.00163) -5.49441 9-INNOV -0.00267 0.01994 (0.00065) (0.00531) -4.09531 3.75221 10-WEALT 0.38224 - - (0.04039) 9.46365 THETA-EPS NFE MARS CHIL CROP POLIT FSUP -------- -------- -------- -------- -------- -------- - - - - - - - - - - - - THETA-EPS MAREL RISK INNOV Landc Landh Irri -------- -------- -------- -------- -------- -------- - - - - - - 141.20643 37.04507 0.11519 (11.87944) (3.09001) (0.00959) 11.88663 11.98867 12.01476 THETA-EPS Harvest -------- 0.19307 (0.01608) 12.00678 Squared Multiple Correlations for Y - Variables NFE MARS CHIL CROP POLIT FSUP -------- -------- -------- -------- -------- -------- 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 Squared Multiple Correlations for Y - Variables MAREL RISK INNOV Landc Landh Irri -------- -------- -------- -------- -------- -------- 1.00000 1.00000 1.00000 0.30729 0.13380 0.03459 Squared Multiple Correlations for Y - Variables Harvest -------- 0.07136 BE was written to file C:\han3\lisrel850\Subroto\BE1
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GA was written to file C:\han3\lisrel850\Subroto\GA1 PS was written to file C:\han3\lisrel850\Subroto\PS1 TE was written to file C:\han3\lisrel850\Subroto\TE1 TD was written to file C:\han3\lisrel850\Subroto\TD1 Goodness of Fit Statistics Degrees of Freedom = 84 Minimum Fit Function Chi-Square = 167.05504 (P = 0.00000) Normal Theory Weighted Least Squares Chi-Square = 164.43435 (P = 0.00000) Estimated Non-centrality Parameter (NCP) = 80.43435 90 Percent Confidence Interval for NCP = (47.87108 ; 120.79258) Minimum Fit Function Value = 0.57805 Population Discrepancy Function Value (F0) = 0.27832 90 Percent Confidence Interval for F0 = (0.16564 ; 0.41797) Root Mean Square Error of Approximation (RMSEA) = 0.057562 90 Percent Confidence Interval for RMSEA = (0.044407 ; 0.070539) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.16397 Expected Cross-Validation Index (ECVI) = 0.81811 90 Percent Confidence Interval for ECVI = (0.70544 ; 0.95776) ECVI for Saturated Model = 0.83045 ECVI for Independence Model = 5.39569 Chi-Square for Independence Model with 105 Degrees of Freedom = 1529.35478 Independence AIC = 1559.35478 Model AIC = 236.43435 Saturated AIC = 240.00000 Independence CAIC = 1629.40300 Model CAIC = 404.55007 Saturated CAIC = 800.38571 Normed Fit Index (NFI) = 0.89077 Non-Normed Fit Index (NNFI) = 0.92711 Parsimony Normed Fit Index (PNFI) = 0.71261 Comparative Fit Index (CFI) = 0.94169 Incremental Fit Index (IFI) = 0.94254 Relative Fit Index (RFI) = 0.86346 Critical N (CN) = 203.51118 Root Mean Square Residual (RMR) = 1.51949 Standardized RMR = 0.065117 Goodness of Fit Index (GFI) = 0.92949 Adjusted Goodness of Fit Index (AGFI) = 0.89927 Parsimony Goodness of Fit Index (PGFI) = 0.65064 DA NI=23 NO=290 Fitted Covariance Matrix
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NFE MARS CHIL CROP POLIT FSUP -------- -------- -------- -------- -------- -------- NFE 0.24478 MARS -0.04003 0.12181 CHIL -0.20162 0.22995 2.19691 CROP -0.06893 0.01346 0.15856 0.24618 POLIT - - - - - - - - 0.02033 FSUP 0.19425 -0.03177 -0.16000 -0.05470 - - 0.23971 MAREL - - - - - - - - - - -0.02774 RISK 0.06154 -0.02332 -0.00253 -0.02069 - - 0.04883 INNOV 0.01693 -0.00610 0.00504 -0.00569 - - 0.01343 Landc -3.69060 0.99410 6.56858 1.24110 - - -2.92863 Landh -1.11546 0.30046 1.98531 0.37511 - - -0.88516 Irri -0.02996 0.00807 0.05332 0.01007 - - -0.02377 Harvest -0.05680 0.01530 0.10109 0.01910 - - -0.04507 AGE -5.34370 2.60074 10.57899 1.79701 - - -4.24044 EDU -0.21559 0.04270 -0.69335 0.07250 - - -0.17108 Fitted Covariance Matrix MAREL RISK INNOV Landc Landh Irri -------- -------- -------- -------- -------- -------- MAREL 0.22337 RISK - - 0.20647 INNOV - - 0.06080 0.16506 Landc - - -0.83233 -0.21760 203.84525 Landh - - -0.25157 -0.06577 18.93219 42.76720 Irri - - -0.00676 -0.00177 0.50843 0.15367 0.11932 Harvest - - -0.01281 -0.00335 0.96403 0.29137 0.00782 AGE - - -2.17752 -0.56928 92.81395 28.05243 0.75336 EDU - - -0.03575 0.33224 1.52378 0.46055 0.01237 Fitted Covariance Matrix Harvest AGE EDU -------- -------- -------- Harvest 0.20791 AGE 1.42844 242.81680 EDU 0.02345 3.98647 17.19384 Fitted Residuals NFE MARS CHIL CROP POLIT FSUP -------- -------- -------- -------- -------- -------- NFE -0.00079 MARS -0.00878 - - CHIL -0.00205 0.00857 0.00856 CROP 0.00027 0.00270 0.01982 -0.00009 POLIT -0.00518 -0.00399 0.00704 0.00143 - - FSUP -0.00472 0.00414 0.02063 -0.00866 -0.00625 -0.00699 MAREL 0.03297 -0.01285 -0.03711 -0.02142 -0.00344 0.02616 RISK 0.00750 -0.00393 -0.00952 0.01103 -0.00437 -0.00628 INNOV 0.00049 -0.00953 -0.01423 0.00271 0.00262 -0.00675 Landc -0.08759 -0.04099 -0.82971 0.99217 0.00441 -0.14994 Landh 0.02011 0.05742 0.10274 0.13312 0.09857 0.10111 Irri -0.00011 0.01389 -0.00798 0.00806 -0.00406 -0.00606 Harvest -0.00041 0.00206 -0.00540 0.02713 0.00084 -0.00313 AGE 0.05780 0.00000 0.49272 -0.46937 0.00205 0.53751 EDU -0.08941 0.05202 0.08626 -0.16378 -0.00964 -0.06114 Fitted Residuals MAREL RISK INNOV Landc Landh Irri -------- -------- -------- -------- -------- -------- MAREL - - RISK 0.00313 - - INNOV 0.01408 -0.00067 -0.00040
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Landc -0.84184 -0.52359 0.08009 0.00000 Landh -0.51941 0.21244 0.09124 11.39078 0.00000 Irri 0.01945 -0.00911 -0.02842 0.51346 0.12750 0.00000 Harvest -0.02273 -0.01964 0.01516 1.19078 0.24198 0.01544 AGE -0.91784 0.00000 -0.06764 8.29087 1.00124 0.12338 EDU -0.22938 -0.03343 -0.00996 7.21640 4.32692 -0.05998 Fitted Residuals Harvest AGE EDU -------- -------- -------- Harvest 0.00000 AGE -0.42128 0.00000 EDU 0.19060 0.00000 - - Summary Statistics for Fitted Residuals Smallest Fitted Residual = -0.91784 Median Fitted Residual = 0.00000 Largest Fitted Residual = 11.39078 Stemleaf Plot - 0|988555422111111000000000000000000000000000000000000000000000000000000000+23 0|111111111111222555 1|002 2| 3| 4|3 5| 6| 7|2 8|3 9| 10| 11|4 Standardized Residuals NFE MARS CHIL CROP POLIT FSUP -------- -------- -------- -------- -------- -------- NFE -0.45277 MARS -1.91454 - - CHIL -0.40406 0.81623 0.31542 CROP 0.45277 0.28539 1.16083 -0.45276 POLIT -1.24786 -1.36135 0.56626 0.34405 - - FSUP -2.14628 0.60036 0.82900 -1.05290 -1.52248 -2.39112 MAREL 2.39677 -1.32437 -0.90052 -1.55265 -0.86685 2.39677 RISK 0.97816 -0.50403 -0.52505 0.90701 -1.14583 -0.64898 INNOV 0.04812 -1.17856 -0.61329 0.23289 0.77032 -0.63206 Landc -0.85792 -0.16746 -1.05764 3.03720 0.03687 -0.66323 Landh 0.36726 0.47988 0.24120 0.79221 1.79698 0.88319 Irri -0.03597 2.12434 -0.32842 0.85895 -1.40021 -0.95344
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Harvest -0.10328 0.24181 -0.17306 2.23426 0.21839 -0.38007 AGE 1.12400 - - 1.20558 -1.64154 0.01570 2.67388 EDU -1.21669 0.69706 0.49501 -1.41478 -0.27720 -0.66559 Standardized Residuals MAREL RISK INNOV Landc Landh Irri -------- -------- -------- -------- -------- -------- MAREL - - RISK 0.24746 - - INNOV 1.24653 -0.31760 -0.31760 Landc -2.12087 -1.56070 0.24020 - - Landh -2.85686 1.30267 0.59042 2.69830 - - Irri 2.02522 -1.02559 -3.45882 2.17307 1.05090 - - Harvest -1.79305 -1.69379 1.40121 3.89767 1.54148 1.76184 AGE -2.11865 - - -0.20738 1.23214 0.28001 0.61006 EDU -1.98973 -0.31760 -0.31760 2.28305 2.82492 -0.71973 Standardized Residuals Harvest AGE EDU -------- -------- -------- Harvest - - AGE -1.61678 - - EDU 1.75149 - - - - Summary Statistics for Standardized Residuals Smallest Standardized Residual = -3.45882 Median Standardized Residual = 0.00000 Largest Standardized Residual = 3.89767 Stemleaf Plot - 3|5 - 3| - 2|9 - 2|41110 - 1|98766665 - 1|444322211100 - 0|9997776665555 - 0|44333333222100000000000000000 0|22222233334 0|555666678888999 1|011222234 1|5888 2|0122344 2|778 3|0 3|9 Largest Negative Standardized Residuals Residual for Landh and MAREL -2.85686 Residual for Irri and INNOV -3.45882 Largest Positive Standardized Residuals Residual for Landc and CROP 3.03720 Residual for Landh and Landc 2.69830 Residual for Harvest and Landc 3.89767 Residual for AGE and FSUP 2.67388 Residual for EDU and Landh 2.82492 DA NI=23 NO=290 Qplot of Standardized Residuals 3.5.......................................................................... . .. . . . . . . . . . . . .
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. . . . . . . . x . . x . . . x . . . x . . . x . . . * . . . x* . N . . * xx . o . . xx x x . r . . x* . m . .*** . a . .*xx . l . x**x . . ** . Q . x * . u . *xx . a . *. . n . x*x . t . xx*. . i . x*x . . l . **x . . e . xx . . s . xx . . . xxx . . . x x . . . x . . . x . . . x . . . x . . x . . . . . . . . . . . . . . . . . . . . -3.5.......................................................................... -3.5 3.5 Standardized Residuals DA NI=23 NO=290 Modification Indices and Expected Change Modification Indices for LAMBDA-Y 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- NFE - - 5.64299 0.48291 2.05724 0.15489 0.18601 MARS 4.02985 - - 0.11346 0.83024 2.63414 0.16798 CHIL - - 3.49897 - - 0.25982 1.69311 0.09522 CROP - - 0.00068 1.24485 - - 0.02473 1.78070 POLIT 1.55715 1.85327 0.32065 0.11837 - - 2.31796 FSUP - - 2.75837 0.54001 2.06182 1.14287 - - MAREL 5.74451 0.94943 0.54996 3.24149 1.13144 5.74451 RISK 0.84979 0.00056 0.00031 0.19962 2.61183 0.00621 INNOV 0.00002 1.52814 0.34176 0.06188 1.65529 0.14491 Landc 0.95147 0.64576 1.34855 8.58982 0.56300 1.33957 Landh 0.11342 0.02521 0.04503 0.52502 2.14509 0.38631 Irri 0.00284 3.94102 0.11850 0.68269 2.72470 1.18199 Harvest 0.01680 0.00005 0.03849 4.78432 0.01066 0.33012 Modification Indices for LAMBDA-Y 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- NFE 0.05424 2.34439 0.56376 0.38113 MARS 0.26414 0.25405 1.45781 3.07968
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CHIL 0.12673 0.08634 0.05479 - - CROP 0.74158 1.23747 0.06416 1.38292 POLIT 0.75142 1.31294 0.59340 1.43427 FSUP - - 2.58579 0.62467 0.59161 MAREL - - 0.00462 1.13071 5.42100 RISK 1.06625 - - 0.12508 0.58588 INNOV 2.80034 - - - - 0.02472 Landc 0.94897 1.74801 0.16241 - - Landh 4.58486 2.47322 0.48969 - - Irri 6.29955 0.85525 12.10714 - - Harvest 1.45193 2.44190 2.24286 - - Expected Change for LAMBDA-Y 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- NFE - - -0.12303 -0.01029 0.04954 -0.03814 0.03394 MARS -0.13942 - - 0.01012 0.03491 -0.19329 -0.01983 CHIL - - 1.23469 - - -0.18862 0.63514 0.07342 CROP - - -0.00217 0.05051 - - -0.03046 -0.12600 POLIT -0.02115 -0.03272 0.00320 0.00582 - - -0.02608 FSUP - - 0.08247 0.00869 -0.05083 -0.12639 - - MAREL 0.13468 -0.07612 -0.01365 -0.09899 -0.20318 0.16972 RISK 0.06869 -0.00183 0.00055 0.02245 -0.26858 -0.00484 INNOV 0.00020 -0.07893 -0.01271 0.01117 0.19858 -0.01827 Landc -4.93723 -1.84391 -0.75368 4.39897 -3.68888 -2.59642 Landh 0.80200 0.17859 0.06465 0.55340 3.68118 0.70499 Irri -0.00686 0.12244 -0.00567 0.03510 -0.23117 -0.06841 Harvest -0.02183 0.00056 -0.00423 0.12039 -0.01873 -0.04689 Expected Change for LAMBDA-Y 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- NFE 0.00897 0.05240 0.02698 -0.00523 MARS -0.01847 -0.02102 -0.05380 0.00769 CHIL 0.05242 -0.09978 -0.08062 - - CROP -0.05033 0.07188 0.01756 0.02702 POLIT -0.01538 -0.02115 0.01590 0.00128 FSUP - - -0.06203 -0.03291 0.00521 MAREL - - -0.00408 0.07130 -0.00821 RISK -0.05177 - - -0.06354 -0.00352 INNOV 0.07792 - - - - -0.00045 Landc -1.44489 -2.10684 0.69774 - - Landh -1.62367 1.27746 0.61896 - - Irri 0.10605 -0.04181 -0.17144 - - Harvest -0.06593 -0.09152 0.09557 - - Standardized Expected Change for LAMBDA-Y 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- NFE - - -0.04294 -0.01525 0.02458 -0.00544 0.01662 MARS -0.06898 - - 0.01501 0.01732 -0.02756 -0.00971 CHIL - - 0.43092 - - -0.09359 0.09056 0.03595 CROP - - -0.00076 0.07487 - - -0.00434 -0.06169 POLIT -0.01047 -0.01142 0.00475 0.00289 - - -0.01277 FSUP - - 0.02878 0.01288 -0.02522 -0.01802 - - MAREL 0.06663 -0.02657 -0.02023 -0.04911 -0.02897 0.08310 RISK 0.03399 -0.00064 0.00082 0.01114 -0.03830 -0.00237 INNOV 0.00010 -0.02755 -0.01884 0.00554 0.02831 -0.00894 Landc -2.44273 -0.64355 -1.11711 2.18263 -0.52599 -1.27121 Landh 0.39679 0.06233 0.09583 0.27458 0.52490 0.34517 Irri -0.00340 0.04273 -0.00840 0.01741 -0.03296 -0.03349
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Harvest -0.01080 0.00020 -0.00626 0.05974 -0.00267 -0.02296 Standardized Expected Change for LAMBDA-Y 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- NFE 0.00424 0.02381 0.01096 -0.04141 MARS -0.00873 -0.00955 -0.02186 0.06090 CHIL 0.02478 -0.04534 -0.03276 - - CROP -0.02379 0.03266 0.00714 0.21386 POLIT -0.00727 -0.00961 0.00646 0.01011 FSUP - - -0.02818 -0.01337 0.04126 MAREL - - -0.00185 0.02897 -0.06502 RISK -0.02447 - - -0.02582 -0.02784 INNOV 0.03683 - - - - -0.00360 Landc -0.68289 -0.95732 0.28347 - - Landh -0.76739 0.58046 0.25147 - - Irri 0.05012 -0.01900 -0.06965 - - Harvest -0.03116 -0.04159 0.03883 - - Completely Standardized Expected Change for LAMBDA-Y 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- NFE - - -0.08679 -0.03083 0.04968 -0.01099 0.03359 MARS -0.19764 - - 0.04299 0.04963 -0.07897 -0.02781 CHIL - - 0.29073 - - -0.06314 0.06110 0.02425 CROP - - -0.00152 0.15090 - - -0.00875 -0.12433 POLIT -0.07340 -0.08008 0.03331 0.02024 - - -0.08956 FSUP - - 0.05879 0.02632 -0.05151 -0.03681 - - MAREL 0.14099 -0.05621 -0.04280 -0.10392 -0.06130 0.17582 RISK 0.07480 -0.00141 0.00180 0.02451 -0.08428 -0.00521 INNOV 0.00024 -0.06781 -0.04637 0.01364 0.06969 -0.02202 Landc -0.17109 -0.04507 -0.07824 0.15287 -0.03684 -0.08904 Landh 0.06067 0.00953 0.01465 0.04199 0.08026 0.05278 Irri -0.00983 0.12371 -0.02431 0.05041 -0.09543 -0.09696 Harvest -0.02369 0.00043 -0.01373 0.13101 -0.00586 -0.05035 Completely Standardized Expected Change for LAMBDA-Y 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- NFE 0.00857 0.04813 0.02215 -0.08370 MARS -0.02501 -0.02737 -0.06263 0.17449 CHIL 0.01672 -0.03059 -0.02210 - - CROP -0.04794 0.06583 0.01438 0.43102 POLIT -0.05099 -0.06740 0.04531 0.07091 FSUP - - -0.05756 -0.02731 0.08427 MAREL - - -0.00392 0.06129 -0.13756 RISK -0.05385 - - -0.05682 -0.06127 INNOV 0.09065 - - - - -0.00886 Landc -0.04783 -0.06705 0.01985 - - Landh -0.11734 0.08876 0.03845 - - Irri 0.14510 -0.05500 -0.20164 - - Harvest -0.06834 -0.09120 0.08515 - - Modification Indices for LAMBDA-X 1-AGE 2-EDU -------- -------- AGE - - 1.99757 EDU 0.69261 - -
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Expected Change for LAMBDA-X 1-AGE 2-EDU -------- -------- AGE - - -0.25346 EDU 0.05794 - - Standardized Expected Change for LAMBDA-X 1-AGE 2-EDU -------- -------- AGE - - -1.05100 EDU 0.90283 - - Completely Standardized Expected Change for LAMBDA-X 1-AGE 2-EDU -------- -------- AGE - - -0.06745 EDU 0.21773 - - Modification Indices for BETA 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE - - - - 8.29762 - - 0.21284 0.40065 2-MARS 4.02985 - - 0.09294 0.83024 2.42930 0.06373 3-CHIL - - 3.49897 - - 0.25982 1.69311 0.09522 4-CROP - - 0.00068 1.21017 - - 0.00599 1.56271 5-POLIT 1.55715 1.85327 0.32065 0.11837 - - 2.31796 6-FSUP - - 2.75837 0.54001 2.06182 1.14287 - - 7-MAREL 5.74451 1.75395 0.81093 2.41072 0.75142 5.74451 8-RISK 1.09645 0.25405 0.07541 0.31642 1.43789 0.02327 9-INNOV 0.00002 1.52814 0.34488 0.06188 1.71821 0.13107 10-WEALT 0.52543 3.07968 0.73604 4.34347 3.29866 2.16132 Modification Indices for BETA 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 1-NFE 0.28735 - - - - - - 2-MARS 0.12557 0.25405 1.45781 3.07968 3-CHIL 0.12673 0.08634 0.05479 - - 4-CROP 0.60697 1.23747 0.06416 1.38292 5-POLIT 0.75142 1.31294 0.59340 1.43427 6-FSUP - - 2.58579 0.62467 0.59161 7-MAREL - - 0.06124 1.55384 5.67872 8-RISK 0.18035 - - 0.12508 0.92873 9-INNOV 2.89606 - - - - 0.02472 10-WEALT 1.42071 0.92873 0.00414 - - Expected Change for BETA 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE - - - - -1.43186 - - 0.03052 0.02148 2-MARS -0.13942 - - 0.00918 0.03491 -0.19709 -0.01278 3-CHIL - - 1.23469 - - -0.18862 0.63514 0.07342 4-CROP - - -0.00217 0.04981 - - -0.01523 -0.12009 5-POLIT -0.02115 -0.03272 0.00320 0.00582 - - -0.02608 6-FSUP - - 0.08247 0.00869 -0.05083 -0.12639 - - 7-MAREL 0.13468 -0.10550 -0.01689 -0.08700 -0.16901 0.16972 8-RISK 0.08846 -0.04182 -0.00956 0.03001 -0.21388 -0.01028 9-INNOV 0.00020 -0.07893 -0.01277 0.01117 0.20256 -0.01738 10-WEALT -3.18102 2.22442 -0.48106 1.87696 3.96444 -1.96618 Expected Change for BETA
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7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 1-NFE 0.01070 - - - - - - 2-MARS -0.01352 -0.02102 -0.05380 0.00769 3-CHIL 0.05242 -0.09978 -0.08062 - - 4-CROP -0.04625 0.07188 0.01756 0.02702 5-POLIT -0.01538 -0.02115 0.01590 0.00128 6-FSUP - - -0.06203 -0.03291 0.00521 7-MAREL - - 0.01514 0.08530 -0.00843 8-RISK -0.02285 - - -0.06354 -0.00508 9-INNOV 0.07934 - - - - -0.00045 10-WEALT -0.78494 -0.73843 -0.04996 - - Standardized Expected Change for BETA 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE - - - - -1.95256 - - 0.43268 0.08868 2-MARS -0.80740 - - 0.01775 0.20160 -3.96036 -0.07476 3-CHIL - - 2.38675 - - -0.25648 3.00524 0.10117 4-CROP - - -0.01252 0.06773 - - -0.21524 -0.49433 5-POLIT -0.29987 -0.65740 0.01516 0.08221 - - -0.37361 6-FSUP - - 0.48260 0.01198 -0.20924 -1.81039 - - 7-MAREL 0.57596 -0.63955 -0.02411 -0.37100 -2.50795 0.73346 8-RISK 0.39348 -0.26371 -0.01420 0.13311 -3.30115 -0.04623 9-INNOV 0.00099 -0.55666 -0.02120 0.05540 3.49664 -0.08739 10-WEALT -0.81237 0.80529 -0.04101 0.47798 3.51296 -0.50741 Standardized Expected Change for BETA 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 1-NFE 0.04576 - - - - - - 2-MARS -0.08196 -0.13254 -0.37945 0.00279 3-CHIL 0.07484 -0.14815 -0.13389 - - 4-CROP -0.19723 0.31884 0.08714 0.00688 5-POLIT -0.22828 -0.32645 0.27453 0.00113 6-FSUP - - -0.27881 -0.16546 0.00135 7-MAREL - - 0.07050 0.44425 -0.00225 8-RISK -0.10642 - - -0.34422 -0.00141 9-INNOV 0.41320 - - - - -0.00014 10-WEALT -0.20984 -0.20534 -0.01554 - - Modification Indices for GAMMA 1-AGE 2-EDU -------- -------- 1-NFE 3.13493 - - 2-MARS - - 0.48589 3-CHIL 2.20024 0.37852 4-CROP 2.39270 2.81222 5-POLIT 0.00025 0.07684 6-FSUP 3.97872 0.07212 7-MAREL 4.48867 3.95902 8-RISK - - 0.10087 9-INNOV 0.04301 - - 10-WEALT - - 1.41949 Expected Change for GAMMA 1-AGE 2-EDU -------- -------- 1-NFE 0.00275 - - 2-MARS - - 0.00304 3-CHIL 0.01895 -0.02215 4-CROP -0.00419 -0.01142 5-POLIT 0.00001 -0.00056 6-FSUP 0.00299 -0.00110
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7-MAREL -0.00378 -0.01334 8-RISK - - -0.00195 9-INNOV -0.00031 - - 10-WEALT - - 0.09512 Standardized Expected Change for GAMMA 1-AGE 2-EDU -------- -------- 1-NFE 0.08646 - - 2-MARS - - 0.03608 3-CHIL 0.19927 -0.06196 4-CROP -0.13147 -0.09544 5-POLIT 0.00092 -0.01631 6-FSUP 0.09528 -0.00930 7-MAREL -0.12463 -0.11704 8-RISK - - -0.01781 9-INNOV -0.01184 - - 10-WEALT - - 0.04984 No Non-Zero Modification Indices for PHI Modification Indices for PSI 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE - - 2-MARS 3.13493 - - 3-CHIL 8.29762 0.84106 - - 4-CROP - - 0.28685 0.25982 - - 5-POLIT 0.21284 2.42930 1.69311 0.00599 - - 6-FSUP 0.58268 1.13111 0.15045 2.06182 1.14287 - - 7-MAREL 0.28735 0.12557 0.12673 0.60697 0.75142 - - 8-RISK 3.13494 0.25405 0.00346 0.61834 1.43789 1.35270 9-INNOV - - 1.64988 0.00090 0.06619 1.71821 0.05582 10-WEALT 3.13493 3.07968 2.20024 4.10474 3.29866 2.89926 Modification Indices for PSI 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 7-MAREL - - 8-RISK 0.18035 - - 9-INNOV 2.89606 0.04301 - - 10-WEALT 1.42071 0.92873 0.00002 - - Expected Change for PSI 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE - - 2-MARS -0.02408 - - 3-CHIL -0.35930 0.04968 - - 4-CROP - - 0.00464 -0.04291 - - 5-POLIT 0.00062 -0.00401 0.01291 -0.00031 - - 6-FSUP 0.00218 0.00553 0.00774 -0.01156 -0.00257 - - 7-MAREL 0.00239 -0.00302 0.01171 -0.01033 -0.00344 - - 8-RISK 0.05722 -0.00393 0.00368 0.00956 -0.00435 -0.00849 9-INNOV - - -0.00868 -0.00156 0.00270 0.00412 -0.00149 10-WEALT -0.19506 0.20900 -1.34686 0.41347 0.08060 -0.19960
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Expected Change for PSI 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 7-MAREL - - 8-RISK -0.00510 - - 9-INNOV 0.01772 -0.00644 - - 10-WEALT -0.17534 -0.13804 -0.00056 - - Standardized Expected Change for PSI 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE - - 2-MARS -0.13945 - - 3-CHIL -0.48995 0.09604 - - 4-CROP - - 0.02680 -0.05835 - - 5-POLIT 0.00880 -0.08052 0.06110 -0.00438 - - 6-FSUP 0.00899 0.03237 0.01066 -0.04760 -0.03681 - - 7-MAREL 0.01022 -0.01831 0.01672 -0.04406 -0.05099 - - 8-RISK 0.25454 -0.02478 0.00546 0.04241 -0.06712 -0.03818 9-INNOV - - -0.06118 -0.00259 0.01342 0.07109 -0.00750 10-WEALT -0.04982 0.07566 -0.11481 0.10529 0.07142 -0.05151 Standardized Expected Change for PSI 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 7-MAREL - - 8-RISK -0.02377 - - 9-INNOV 0.09230 -0.03486 - - 10-WEALT -0.04687 -0.03839 -0.00018 - - Modification Indices for THETA-EPS NFE MARS CHIL CROP POLIT FSUP -------- -------- -------- -------- -------- -------- NFE 0.69656 MARS 3.25588 3.13493 CHIL 0.99397 1.26482 - - CROP 2.08001 0.36336 0.15273 - - POLIT 0.15489 2.63414 1.69311 0.02473 - - FSUP 0.25704 0.56531 0.15045 2.36687 1.14287 - - MAREL 0.09609 0.12437 0.18196 1.32776 1.13144 - - RISK 1.28724 0.00477 0.02409 0.38620 2.61183 1.27567 INNOV 0.05466 1.60252 0.00002 0.06619 1.65529 0.07370 Landc 9.13000 1.04116 3.74235 8.12394 0.56300 1.18255 Landh 1.91680 0.00527 0.23247 0.44755 2.14509 0.01133 Irri 2.91721 4.31713 3.16711 0.90907 2.72470 0.44508 Harvest 3.04388 0.28699 0.25193 4.43187 0.01066 0.68386 Modification Indices for THETA-EPS MAREL RISK INNOV Landc Landh Irri -------- -------- -------- -------- -------- -------- MAREL - - RISK 1.53311 0.03121 INNOV 2.51203 0.05910 - - Landc 1.37477 1.19984 0.22896 - - Landh 4.31120 2.58160 0.15630 7.28083 - - Irri 5.40574 0.18751 10.25394 4.72223 1.10440 - - Harvest 1.81269 5.73810 3.30238 15.19180 2.37616 3.10408 Modification Indices for THETA-EPS Harvest -------- Harvest - -
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
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Expected Change for THETA-EPS NFE MARS CHIL CROP POLIT FSUP -------- -------- -------- -------- -------- -------- NFE 0.00632 MARS -0.00866 0.12894 CHIL -0.02451 0.05954 - - CROP 0.01119 0.00522 -0.03271 - - POLIT -0.00078 -0.00393 0.01291 -0.00062 - - FSUP 0.00339 0.00366 0.00774 -0.01218 -0.00257 - - MAREL 0.00263 -0.00277 0.01375 -0.01474 -0.00413 - - RISK 0.00685 -0.00051 0.00898 0.00714 -0.00546 -0.00767 INNOV 0.00127 -0.00855 -0.00025 0.00270 0.00404 -0.00171 Landc 0.58071 -0.21513 -1.94651 0.94878 -0.07500 -0.21846 Landh 0.13017 -0.00762 -0.22224 0.11301 0.07484 0.01093 Irri 0.00878 0.01202 -0.04389 0.00895 -0.00470 -0.00382 Harvest 0.01169 0.00403 -0.01625 0.02561 -0.00038 -0.00613 Expected Change for THETA-EPS MAREL RISK INNOV Landc Landh Irri -------- -------- -------- -------- -------- -------- MAREL - - RISK -0.01359 -0.01797 INNOV 0.01615 -0.00754 - - Landc -0.38057 -0.31246 0.12566 - - Landh -0.34454 0.23277 -0.05303 11.56950 - - Irri 0.02150 0.00349 -0.02393 0.51762 0.12790 - - Harvest -0.01612 -0.02500 0.01759 1.20353 0.24303 0.01547 Expected Change for THETA-EPS Harvest -------- Harvest - - Completely Standardized Expected Change for THETA-EPS NFE MARS CHIL CROP POLIT FSUP -------- -------- -------- -------- -------- -------- NFE 0.02582 MARS -0.05014 1.05848 CHIL -0.03342 0.11510 - - CROP 0.04558 0.03014 -0.04448 - - POLIT -0.01099 -0.07897 0.06110 -0.00875 - - FSUP 0.01399 0.02141 0.01066 -0.05013 -0.03681 - - MAREL 0.01126 -0.01681 0.01962 -0.06284 -0.06130 - - RISK 0.03048 -0.00321 0.01333 0.03167 -0.08428 -0.03448 INNOV 0.00632 -0.06030 -0.00041 0.01342 0.06969 -0.00861 Landc 0.08221 -0.04317 -0.09198 0.13393 -0.03684 -0.03125 Landh 0.04023 -0.00334 -0.02293 0.03483 0.08026 0.00341 Irri 0.05139 0.09968 -0.08572 0.05221 -0.09543 -0.02257 Harvest 0.05183 0.02530 -0.02405 0.11321 -0.00586 -0.02745 Completely Standardized Expected Change for THETA-EPS MAREL RISK INNOV Landc Landh Irri -------- -------- -------- -------- -------- -------- MAREL - - RISK -0.06326 -0.08705 INNOV 0.08411 -0.04086 - - Landc -0.05640 -0.04816 0.02166 - - Landh -0.11147 0.07833 -0.01996 0.12391 - - Irri 0.13168 0.02221 -0.17049 0.10496 0.05662 - -
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Harvest -0.07481 -0.12065 0.09493 0.18487 0.08150 0.09822 Completely Standardized Expected Change for THETA-EPS Harvest -------- Harvest - - Modification Indices for THETA-DELTA-EPS NFE MARS CHIL CROP POLIT FSUP -------- -------- -------- -------- -------- -------- AGE 0.07071 5.36483 1.09116 3.37250 0.89972 1.44918 EDU 0.07470 0.80147 0.59430 2.62418 0.20398 0.02182 Modification Indices for THETA-DELTA-EPS MAREL RISK INNOV Landc Landh Irri -------- -------- -------- -------- -------- -------- AGE 0.01482 0.35675 0.05844 1.50654 0.21654 0.03811 EDU 4.09467 0.06410 0.05910 2.93580 7.20751 0.11713 Modification Indices for THETA-DELTA-EPS Harvest -------- AGE 4.51233 EDU 1.44160 Expected Change for THETA-DELTA-EPS NFE MARS CHIL CROP POLIT FSUP -------- -------- -------- -------- -------- -------- AGE -0.04310 -0.61334 1.15125 -0.49824 -0.07584 0.19345 EDU 0.01643 0.06520 -0.46319 -0.18414 -0.01501 -0.00986 Expected Change for THETA-DELTA-EPS MAREL RISK INNOV Landc Landh Irri -------- -------- -------- -------- -------- -------- AGE -0.03160 0.17626 0.05089 8.45611 1.60635 -0.03725 EDU -0.21832 -0.02661 0.37748 4.82405 3.82945 -0.02710 Expected Change for THETA-DELTA-EPS Harvest -------- AGE -0.52637 EDU 0.12329 Completely Standardized Expected Change for THETA-DELTA-EPS NFE MARS CHIL CROP POLIT FSUP -------- -------- -------- -------- -------- -------- AGE -0.00559 -0.11278 0.04985 -0.06444 -0.03413 0.02536 EDU 0.00801 0.04505 -0.07536 -0.08950 -0.02538 -0.00486 Completely Standardized Expected Change for THETA-DELTA-EPS MAREL RISK INNOV Landc Landh Irri -------- -------- -------- -------- -------- -------- AGE -0.00429 0.02489 0.00804 0.03801 0.01576 -0.00692 EDU -0.11140 -0.01412 0.22407 0.08148 0.14122 -0.01892 Completely Standardized Expected Change for THETA-DELTA-EPS Harvest --------
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316
AGE -0.07408 EDU 0.06521 Maximum Modification Index is 15.19 for Element (13,10) of THETA-EPS DA NI=23 NO=290 Standardized Solution LAMBDA-Y 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- NFE 0.49476 - - - - - - - - - - MARS - - 0.34901 - - - - - - - - CHIL - - - - 1.48220 - - - - - - CROP - - - - - - 0.49617 - - - - POLIT - - - - - - - - 0.14259 - - FSUP - - - - - - - - - - 0.48960 MAREL - - - - - - - - - - - - RISK - - - - - - - - - - - - INNOV - - - - - - - - - - - - Landc - - - - - - - - - - - - Landh - - - - - - - - - - - - Irri - - - - - - - - - - - - Harvest - - - - - - - - - - - - LAMBDA-Y 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- NFE - - - - - - - - MARS - - - - - - - - CHIL - - - - - - - - CROP - - - - - - - - POLIT - - - - - - - - FSUP - - - - - - - - MAREL 0.47262 - - - - - - RISK - - 0.45439 - - - - INNOV - - - - 0.40627 - - Landc - - - - - - 7.91447 Landh - - - - - - 2.39210 Irri - - - - - - 0.06424 Harvest - - - - - - 0.12181 LAMBDA-X 1-AGE 2-EDU -------- -------- AGE 15.58258 - - EDU - - 4.14655 BETA 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE - - 0.13175 - - 0.06019 - - - - 2-MARS - - - - - - - - - - - - 3-CHIL 2.26358 - - - - - - - - - - 4-CROP -0.33533 - - - - - - - - - - 5-POLIT - - - - - - - - - - - - 6-FSUP 0.80189 - - - - - - - - - - 7-MAREL - - - - - - - - - - - - 8-RISK - - - - - - - - - - - - 9-INNOV - - - - - - - - - - - - 10-WEALT - - - - - - - - - - - - BETA
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7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 1-NFE - - 0.06249 0.01728 -0.99018 2-MARS - - - - - - - - 3-CHIL - - - - - - 2.69338 4-CROP - - - - - - - - 5-POLIT - - - - - - - - 6-FSUP -0.11986 - - - - - - 7-MAREL - - - - - - - - 8-RISK - - - - - - - - 9-INNOV - - 0.33323 - - - - 10-WEALT - - - - - - - - GAMMA 1-AGE 2-EDU -------- -------- 1-NFE - - -0.06734 2-MARS 0.47820 - - 3-CHIL - - - - 4-CROP - - - - 5-POLIT - - - - 6-FSUP - - - - 7-MAREL - - - - 8-RISK -0.30754 - - 9-INNOV - - 0.20354 10-WEALT 0.75258 - - Correlation Matrix of ETA and KSI 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE 1.00000 2-MARS -0.23185 1.00000 3-CHIL -0.27495 0.44451 1.00000 4-CROP -0.28081 0.07775 0.21561 1.00000 5-POLIT - - - - - - - - 1.00000 6-FSUP 0.80189 -0.18592 -0.22048 -0.22518 - - 1.00000 7-MAREL - - - - - - - - - - -0.11986 8-RISK 0.27373 -0.14707 -0.00376 -0.09179 - - 0.21950 9-INNOV 0.08422 -0.04300 0.00837 -0.02824 - - 0.06754 10-WEALT -0.94251 0.35989 0.55994 0.31605 - - -0.75579 1-AGE -0.69313 0.47820 0.45804 0.23243 - - -0.55581 2-EDU -0.10509 0.02950 -0.11281 0.03524 - - -0.08427 Correlation Matrix of ETA and KSI 7-MAREL 8-RISK 9-INNOV 10-WEALT 1-AGE 2-EDU -------- -------- -------- -------- -------- -------- 7-MAREL 1.00000 8-RISK - - 1.00000 9-INNOV - - 0.32937 1.00000 10-WEALT - - -0.23145 -0.06767 1.00000 1-AGE - - -0.30754 -0.08992 0.75258 1.00000 2-EDU - - -0.01897 0.19722 0.04643 0.06170 1.00000 PSI Note: This matrix is diagonal.
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1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 0.09034 0.77132 0.11422 0.92412 1.00000 0.34261 PSI Note: This matrix is diagonal. 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 1.00000 0.90542 0.85010 0.43362 Regression Matrix ETA on KSI (Standardized) 1-AGE 2-EDU -------- -------- 1-NFE -0.68927 -0.06256 2-MARS 0.47820 - - 3-CHIL 0.46677 -0.14161 4-CROP 0.23113 0.02098 5-POLIT - - - - 6-FSUP -0.55272 -0.05017 7-MAREL - - - - 8-RISK -0.30754 - - 9-INNOV -0.10248 0.20354 10-WEALT 0.75258 - - DA NI=23 NO=290 Completely Standardized Solution LAMBDA-Y 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- NFE 1.00000 - - - - - - - - - - MARS - - 1.00000 - - - - - - - - CHIL - - - - 1.00000 - - - - - - CROP - - - - - - 1.00000 - - - - POLIT - - - - - - - - 1.00000 - - FSUP - - - - - - - - - - 1.00000 MAREL - - - - - - - - - - - - RISK - - - - - - - - - - - - INNOV - - - - - - - - - - - - Landc - - - - - - - - - - - - Landh - - - - - - - - - - - - Irri - - - - - - - - - - - - Harvest - - - - - - - - - - - - LAMBDA-Y 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- NFE - - - - - - - - MARS - - - - - - - - CHIL - - - - - - - - CROP - - - - - - - - POLIT - - - - - - - - FSUP - - - - - - - - MAREL 1.00000 - - - - - - RISK - - 1.00000 - - - - INNOV - - - - 1.00000 - - Landc - - - - - - 0.55433
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Landh - - - - - - 0.36578 Irri - - - - - - 0.18598 Harvest - - - - - - 0.26714 LAMBDA-X 1-AGE 2-EDU -------- -------- AGE 1.00000 - - EDU - - 1.00000 BETA 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE - - 0.13175 - - 0.06019 - - - - 2-MARS - - - - - - - - - - - - 3-CHIL 2.26358 - - - - - - - - - - 4-CROP -0.33533 - - - - - - - - - - 5-POLIT - - - - - - - - - - - - 6-FSUP 0.80189 - - - - - - - - - - 7-MAREL - - - - - - - - - - - - 8-RISK - - - - - - - - - - - - 9-INNOV - - - - - - - - - - - - 10-WEALT - - - - - - - - - - - - BETA 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 1-NFE - - 0.06249 0.01728 -0.99018 2-MARS - - - - - - - - 3-CHIL - - - - - - 2.69338 4-CROP - - - - - - - - 5-POLIT - - - - - - - - 6-FSUP -0.11986 - - - - - - 7-MAREL - - - - - - - - 8-RISK - - - - - - - - 9-INNOV - - 0.33323 - - - - 10-WEALT - - - - - - - - GAMMA 1-AGE 2-EDU -------- -------- 1-NFE - - -0.06734 2-MARS 0.47820 - - 3-CHIL - - - - 4-CROP - - - - 5-POLIT - - - - 6-FSUP - - - - 7-MAREL - - - - 8-RISK -0.30754 - - 9-INNOV - - 0.20354 10-WEALT 0.75258 - - Correlation Matrix of ETA and KSI 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE 1.00000 2-MARS -0.23185 1.00000 3-CHIL -0.27495 0.44451 1.00000 4-CROP -0.28081 0.07775 0.21561 1.00000 5-POLIT - - - - - - - - 1.00000 6-FSUP 0.80189 -0.18592 -0.22048 -0.22518 - - 1.00000 7-MAREL - - - - - - - - - - -0.11986 8-RISK 0.27373 -0.14707 -0.00376 -0.09179 - - 0.21950 9-INNOV 0.08422 -0.04300 0.00837 -0.02824 - - 0.06754 10-WEALT -0.94251 0.35989 0.55994 0.31605 - - -0.75579
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1-AGE -0.69313 0.47820 0.45804 0.23243 - - -0.55581 2-EDU -0.10509 0.02950 -0.11281 0.03524 - - -0.08427 Correlation Matrix of ETA and KSI 7-MAREL 8-RISK 9-INNOV 10-WEALT 1-AGE 2-EDU -------- -------- -------- -------- -------- -------- 7-MAREL 1.00000 8-RISK - - 1.00000 9-INNOV - - 0.32937 1.00000 10-WEALT - - -0.23145 -0.06767 1.00000 1-AGE - - -0.30754 -0.08992 0.75258 1.00000 2-EDU - - -0.01897 0.19722 0.04643 0.06170 1.00000 PSI Note: This matrix is diagonal. 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 0.09034 0.77132 0.11422 0.92412 1.00000 0.34261 PSI Note: This matrix is diagonal. 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 1.00000 0.90542 0.85010 0.43362 THETA-EPS NFE MARS CHIL CROP POLIT FSUP -------- -------- -------- -------- -------- -------- - - - - - - - - - - - - THETA-EPS MAREL RISK INNOV Landc Landh Irri -------- -------- -------- -------- -------- -------- - - - - - - 0.69271 0.86620 0.96541 THETA-EPS Harvest -------- 0.92864 Regression Matrix ETA on KSI (Standardized) 1-AGE 2-EDU -------- -------- 1-NFE -0.68927 -0.06256 2-MARS 0.47820 - - 3-CHIL 0.46677 -0.14161 4-CROP 0.23113 0.02098 5-POLIT - - - - 6-FSUP -0.55272 -0.05017 7-MAREL - - - - 8-RISK -0.30754 - - 9-INNOV -0.10248 0.20354 10-WEALT 0.75258 - - DA NI=23 NO=290
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Total and Indirect Effects Total Effects of KSI on ETA 1-AGE 2-EDU -------- -------- 1-NFE -0.02188 -0.00746 (0.00133) (0.00267) -16.50978 -2.79896 2-MARS 0.01071 - - (0.00116) 9.25647 3-CHIL 0.04440 -0.05062 (0.00462) (0.01542) 9.61976 -3.28201 4-CROP 0.00736 0.00251 (0.00138) (0.00104) 5.32880 2.40335 5-POLIT - - - - 6-FSUP -0.01737 -0.00592 (0.00129) (0.00213) -13.46888 -2.77896 7-MAREL - - - - 8-RISK -0.00897 - - (0.00163) -5.49441 9-INNOV -0.00267 0.01994 (0.00065) (0.00531) -4.09531 3.75221 10-WEALT 0.38224 - - (0.04039) 9.46365 Indirect Effects of KSI on ETA 1-AGE 2-EDU -------- -------- 1-NFE -0.02188 0.00057 (0.00133) (0.00054) -16.50978 1.05162 2-MARS - - - - 3-CHIL 0.04440 -0.05062 (0.00462) (0.01542) 9.61976 -3.28201 4-CROP 0.00736 0.00251 (0.00138) (0.00104) 5.32880 2.40335 5-POLIT - - - - 6-FSUP -0.01737 -0.00592 (0.00129) (0.00213) -13.46888 -2.77896 7-MAREL - - - - 8-RISK - - - -
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9-INNOV -0.00267 - - (0.00065) -4.09531 10-WEALT - - - - Total Effects of ETA on ETA 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE -0.01978 0.18307 - - 0.05883 - - - - (0.00995) (0.04567) (0.02402) -1.98782 4.00848 2.44949 2-MARS - - - - - - - - - - - - 3-CHIL 6.64711 1.24142 - - 0.39895 - - - - (1.69580) (0.21306) (0.13891) 3.91976 5.82655 2.87192 4-CROP -0.32963 -0.06156 - - -0.01978 - - - - (0.05774) (0.02018) (0.00995) -5.70869 -3.05095 -1.98782 5-POLIT - - - - - - - - - - - - 6-FSUP 0.77784 0.14527 - - 0.04668 - - - - (0.03432) (0.03677) (0.01916) 22.66473 3.95039 2.43606 7-MAREL - - - - - - - - - - - - 8-RISK - - - - - - - - - - - - 9-INNOV - - - - - - - - - - - - 10-WEALT - - - - - - - - - - - - Total Effects of ETA on ETA 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 1-NFE - - 0.07284 0.02062 -0.06067 (0.02565) (0.02501) (0.00560) 2.83949 0.82467 -10.83005 2-MARS - - - - - - - - 3-CHIL - - 0.49394 0.13986 0.09296 (0.14635) (0.16760) (0.01465) 3.37509 0.83451 6.34472 4-CROP - - -0.02449 -0.00694 0.02040 (0.01009) (0.00854) (0.00404) -2.42741 -0.81222 5.04497 5-POLIT - - - - - - - - 6-FSUP -0.12417 0.05780 0.01637 -0.04815 (0.03567) (0.02051) (0.01986) (0.00490) -3.48123 2.81861 0.82415 -9.82023 7-MAREL - - - - - - - -
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8-RISK - - - - - - - - 9-INNOV - - 0.29794 - - - - (0.04850) 6.14300 10-WEALT - - - - - - - - Largest Eigenvalue of B*B' (Stability Index) is 46.979 Indirect Effects of ETA on ETA 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE -0.01978 -0.00369 - - -0.00119 - - - - (0.00995) (0.00240) (0.00108) -1.98782 -1.54081 -1.10122 2-MARS - - - - - - - - - - - - 3-CHIL -0.13416 1.24142 - - 0.39895 - - - - (0.05848) (0.21306) (0.13891) -2.29418 5.82655 2.87192 4-CROP 0.00665 -0.06156 - - -0.01978 - - - - (0.00424) (0.02018) (0.00995) 1.57045 -3.05095 -1.98782 5-POLIT - - - - - - - - - - - - 6-FSUP -0.01570 0.14527 - - 0.04668 - - - - (0.00793) (0.03677) (0.01916) -1.98062 3.95039 2.43606 7-MAREL - - - - - - - - - - - - 8-RISK - - - - - - - - - - - - 9-INNOV - - - - - - - - - - - - 10-WEALT - - - - - - - - - - - - Indirect Effects of ETA on ETA 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 1-NFE - - 0.00480 -0.00042 0.00122 (0.00763) (0.00056) (0.00063) 0.62902 -0.73899 1.95062 2-MARS - - - - - - - - 3-CHIL - - 0.49394 0.13986 -0.41145 (0.14635) (0.16760) (0.11480) 3.37509 0.83451 -3.58406 4-CROP - - -0.02449 -0.00694 0.02040 (0.01009) (0.00854) (0.00404) -2.42741 -0.81222 5.04497 5-POLIT - - - - - - - - 6-FSUP - - 0.05780 0.01637 -0.04815 (0.02051) (0.01986) (0.00490) 2.81861 0.82415 -9.82023
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7-MAREL - - - - - - - - 8-RISK - - - - - - - - 9-INNOV - - - - - - - - 10-WEALT - - - - - - - - Total Effects of ETA on Y 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- NFE 0.98022 0.18307 - - 0.05883 - - - - (0.00995) (0.04567) (0.02402) 98.48886 4.00848 2.44949 MARS - - 1.00000 - - - - - - - - CHIL 6.64711 1.24142 1.00000 0.39895 - - - - (1.69580) (0.21306) (0.13891) 3.91976 5.82655 2.87192 CROP -0.32963 -0.06156 - - 0.98022 - - - - (0.05774) (0.02018) (0.00995) -5.70869 -3.05095 98.48886 POLIT - - - - - - - - 1.00000 - - FSUP 0.77784 0.14527 - - 0.04668 - - 1.00000 (0.03432) (0.03677) (0.01916) 22.66473 3.95039 2.43606 MAREL - - - - - - - - - - - - RISK - - - - - - - - - - - - INNOV - - - - - - - - - - - - Landc - - - - - - - - - - - - Landh - - - - - - - - - - - - Irri - - - - - - - - - - - - Harvest - - - - - - - - - - - - Total Effects of ETA on Y 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- NFE - - 0.07284 0.02062 -0.06067 (0.02565) (0.02501) (0.00560) 2.83949 0.82467 -10.83005 MARS - - - - - - - - CHIL - - 0.49394 0.13986 0.09296 (0.14635) (0.16760) (0.01465)
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3.37509 0.83451 6.34472 CROP - - -0.02449 -0.00694 0.02040 (0.01009) (0.00854) (0.00404) -2.42741 -0.81222 5.04497 POLIT - - - - - - - - FSUP -0.12417 0.05780 0.01637 -0.04815 (0.03567) (0.02051) (0.01986) (0.00490) -3.48123 2.81861 0.82415 -9.82023 MAREL 1.00000 - - - - - - RISK - - 1.00000 - - - - INNOV - - 0.29794 1.00000 - - (0.04850) 6.14300 Landc - - - - - - 1.00000 Landh - - - - - - 0.30224 (0.05287) 5.71667 Irri - - - - - - 0.00812 (0.00264) 3.07509 Harvest - - - - - - 0.01539 (0.00356) 4.32247 Indirect Effects of ETA on Y 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- NFE -0.01978 0.18307 - - 0.05883 - - - - (0.00995) (0.04567) (0.02402) -1.98782 4.00848 2.44949 MARS - - - - - - - - - - - - CHIL 6.64711 1.24142 - - 0.39895 - - - - (1.69580) (0.21306) (0.13891) 3.91976 5.82655 2.87192 CROP -0.32963 -0.06156 - - -0.01978 - - - - (0.05774) (0.02018) (0.00995) -5.70869 -3.05095 -1.98782 POLIT - - - - - - - - - - - - FSUP 0.77784 0.14527 - - 0.04668 - - - - (0.03432) (0.03677) (0.01916) 22.66473 3.95039 2.43606 MAREL - - - - - - - - - - - - RISK - - - - - - - - - - - - INNOV - - - - - - - - - - - - Landc - - - - - - - - - - - - Landh - - - - - - - - - - - -
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
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Irri - - - - - - - - - - - - Harvest - - - - - - - - - - - - Indirect Effects of ETA on Y 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- NFE - - 0.07284 0.02062 -0.06067 (0.02565) (0.02501) (0.00560) 2.83949 0.82467 -10.83005 MARS - - - - - - - - CHIL - - 0.49394 0.13986 0.09296 (0.14635) (0.16760) (0.01465) 3.37509 0.83451 6.34472 CROP - - -0.02449 -0.00694 0.02040 (0.01009) (0.00854) (0.00404) -2.42741 -0.81222 5.04497 POLIT - - - - - - - - FSUP -0.12417 0.05780 0.01637 -0.04815 (0.03567) (0.02051) (0.01986) (0.00490) -3.48123 2.81861 0.82415 -9.82023 MAREL - - - - - - - - RISK - - - - - - - - INNOV - - 0.29794 - - - - (0.04850) 6.14300 Landc - - - - - - - - Landh - - - - - - - - Irri - - - - - - - - Harvest - - - - - - - - Total Effects of KSI on Y 1-AGE 2-EDU -------- -------- NFE -0.02188 -0.00746 (0.00133) (0.00267) -16.50978 -2.79896 MARS 0.01071 - - (0.00116) 9.25647 CHIL 0.04440 -0.05062 (0.00462) (0.01542) 9.61976 -3.28201 CROP 0.00736 0.00251 (0.00138) (0.00104) 5.32880 2.40335
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POLIT - - - - FSUP -0.01737 -0.00592 (0.00129) (0.00213) -13.46888 -2.77896 MAREL - - - - RISK -0.00897 - - (0.00163) -5.49441 INNOV -0.00267 0.01994 (0.00065) (0.00531) -4.09531 3.75221 Landc 0.38224 - - (0.04039) 9.46365 Landh 0.11553 - - (0.01861) 6.20720 Irri 0.00310 - - (0.00099) 3.14496 Harvest 0.00588 - - (0.00130) 4.52325 DA NI=23 NO=290 Standardized Total and Indirect Effects Standardized Total Effects of KSI on ETA 1-AGE 2-EDU -------- -------- 1-NFE -0.68927 -0.06256 2-MARS 0.47820 - - 3-CHIL 0.46677 -0.14161 4-CROP 0.23113 0.02098 5-POLIT - - - - 6-FSUP -0.55272 -0.05017 7-MAREL - - - - 8-RISK -0.30754 - - 9-INNOV -0.10248 0.20354 10-WEALT 0.75258 - - Standardized Indirect Effects of KSI on ETA 1-AGE 2-EDU -------- -------- 1-NFE -0.68927 0.00478 2-MARS - - - - 3-CHIL 0.46677 -0.14161 4-CROP 0.23113 0.02098 5-POLIT - - - - 6-FSUP -0.55272 -0.05017 7-MAREL - - - - 8-RISK - - - - 9-INNOV -0.10248 - - 10-WEALT - - - -
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
328
Standardized Total Effects of ETA on ETA 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE -0.01978 0.12914 - - 0.05900 - - - - 2-MARS - - - - - - - - - - - - 3-CHIL 2.21880 0.29232 - - 0.13355 - - - - 4-CROP -0.32870 -0.04330 - - -0.01978 - - - - 5-POLIT - - - - - - - - - - - - 6-FSUP 0.78603 0.10356 - - 0.04731 - - - - 7-MAREL - - - - - - - - - - - - 8-RISK - - - - - - - - - - - - 9-INNOV - - - - - - - - - - - - 10-WEALT - - - - - - - - - - - - Standardized Total Effects of ETA on ETA 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 1-NFE - - 0.06690 0.01694 -0.97059 2-MARS - - - - - - - - 3-CHIL - - 0.15142 0.03834 0.49636 4-CROP - - -0.02243 -0.00568 0.32547 5-POLIT - - - - - - - - 6-FSUP -0.11986 0.05364 0.01358 -0.77831 7-MAREL - - - - - - - - 8-RISK - - - - - - - - 9-INNOV - - 0.33323 - - - - 10-WEALT - - - - - - - - Standardized Indirect Effects of ETA on ETA 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- 1-NFE -0.01978 -0.00261 - - -0.00119 - - - - 2-MARS - - - - - - - - - - - - 3-CHIL -0.04478 0.29232 - - 0.13355 - - - - 4-CROP 0.00663 -0.04330 - - -0.01978 - - - - 5-POLIT - - - - - - - - - - - - 6-FSUP -0.01586 0.10356 - - 0.04731 - - - - 7-MAREL - - - - - - - - - - - - 8-RISK - - - - - - - - - - - - 9-INNOV - - - - - - - - - - - - 10-WEALT - - - - - - - - - - - - Standardized Indirect Effects of ETA on ETA 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- 1-NFE - - 0.00441 -0.00034 0.01959 2-MARS - - - - - - - - 3-CHIL - - 0.15142 0.03834 -2.19702 4-CROP - - -0.02243 -0.00568 0.32547 5-POLIT - - - - - - - - 6-FSUP - - 0.05364 0.01358 -0.77831 7-MAREL - - - - - - - - 8-RISK - - - - - - - - 9-INNOV - - - - - - - - 10-WEALT - - - - - - - - Standardized Total Effects of ETA on Y 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- NFE 0.48497 0.06389 - - 0.02919 - - - - MARS - - 0.34901 - - - - - - - - CHIL 3.28870 0.43327 1.48220 0.19794 - - - - CROP -0.16309 -0.02149 - - 0.48635 - - - -
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POLIT - - - - - - - - 0.14259 - - FSUP 0.38484 0.05070 - - 0.02316 - - 0.48960 MAREL - - - - - - - - - - - - RISK - - - - - - - - - - - - INNOV - - - - - - - - - - - - Landc - - - - - - - - - - - - Landh - - - - - - - - - - - - Irri - - - - - - - - - - - - Harvest - - - - - - - - - - - - Standardized Total Effects of ETA on Y 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- NFE - - 0.03310 0.00838 -0.48021 MARS - - - - - - - - CHIL - - 0.22444 0.05682 0.73571 CROP - - -0.01113 -0.00282 0.16149 POLIT - - - - - - - - FSUP -0.05868 0.02626 0.00665 -0.38106 MAREL 0.47262 - - - - - - RISK - - 0.45439 - - - - INNOV - - 0.13538 0.40627 - - Landc - - - - - - 7.91447 Landh - - - - - - 2.39210 Irri - - - - - - 0.06424 Harvest - - - - - - 0.12181 Completely Standardized Total Effects of ETA on Y 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- NFE 0.98022 0.12914 - - 0.05900 - - - - MARS - - 1.00000 - - - - - - - - CHIL 2.21880 0.29232 1.00000 0.13355 - - - - CROP -0.32870 -0.04330 - - 0.98022 - - - - POLIT - - - - - - - - 1.00000 - - FSUP 0.78603 0.10356 - - 0.04731 - - 1.00000 MAREL - - - - - - - - - - - - RISK - - - - - - - - - - - - INNOV - - - - - - - - - - - - Landc - - - - - - - - - - - - Landh - - - - - - - - - - - - Irri - - - - - - - - - - - - Harvest - - - - - - - - - - - - Completely Standardized Total Effects of ETA on Y 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- NFE - - 0.06690 0.01694 -0.97059 MARS - - - - - - - - CHIL - - 0.15142 0.03834 0.49636 CROP - - -0.02243 -0.00568 0.32547 POLIT - - - - - - - - FSUP -0.11986 0.05364 0.01358 -0.77831 MAREL 1.00000 - - - - - - RISK - - 1.00000 - - - - INNOV - - 0.33323 1.00000 - - Landc - - - - - - 0.55433 Landh - - - - - - 0.36578 Irri - - - - - - 0.18598 Harvest - - - - - - 0.26714 Standardized Indirect Effects of ETA on Y 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- NFE -0.00979 0.06389 - - 0.02919 - - - - MARS - - - - - - - - - - - - CHIL 3.28870 0.43327 - - 0.19794 - - - -
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
330
CROP -0.16309 -0.02149 - - -0.00982 - - - - POLIT - - - - - - - - - - - - FSUP 0.38484 0.05070 - - 0.02316 - - - - MAREL - - - - - - - - - - - - RISK - - - - - - - - - - - - INNOV - - - - - - - - - - - - Landc - - - - - - - - - - - - Landh - - - - - - - - - - - - Irri - - - - - - - - - - - - Harvest - - - - - - - - - - - - Standardized Indirect Effects of ETA on Y 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- NFE - - 0.03310 0.00838 -0.48021 MARS - - - - - - - - CHIL - - 0.22444 0.05682 0.73571 CROP - - -0.01113 -0.00282 0.16149 POLIT - - - - - - - - FSUP -0.05868 0.02626 0.00665 -0.38106 MAREL - - - - - - - - RISK - - - - - - - - INNOV - - 0.13538 - - - - Landc - - - - - - - - Landh - - - - - - - - Irri - - - - - - - - Harvest - - - - - - - - Completely Standardized Indirect Effects of ETA on Y 1-NFE 2-MARS 3-CHIL 4-CROP 5-POLIT 6-FSUP -------- -------- -------- -------- -------- -------- NFE -0.01978 0.12914 - - 0.05900 - - - - MARS - - - - - - - - - - - - CHIL 2.21880 0.29232 - - 0.13355 - - - - CROP -0.32870 -0.04330 - - -0.01978 - - - - POLIT - - - - - - - - - - - - FSUP 0.78603 0.10356 - - 0.04731 - - - - MAREL - - - - - - - - - - - - RISK - - - - - - - - - - - - INNOV - - - - - - - - - - - - Landc - - - - - - - - - - - - Landh - - - - - - - - - - - - Irri - - - - - - - - - - - - Harvest - - - - - - - - - - - - Completely Standardized Indirect Effects of ETA on Y 7-MAREL 8-RISK 9-INNOV 10-WEALT -------- -------- -------- -------- NFE - - 0.06690 0.01694 -0.97059 MARS - - - - - - - - CHIL - - 0.15142 0.03834 0.49636 CROP - - -0.02243 -0.00568 0.32547 POLIT - - - - - - - - FSUP -0.11986 0.05364 0.01358 -0.77831 MAREL - - - - - - - - RISK - - - - - - - - INNOV - - 0.33323 - - - - Landc - - - - - - - - Landh - - - - - - - - Irri - - - - - - - -
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Harvest - - - - - - - - Standardized Total Effects of KSI on Y 1-AGE 2-EDU -------- -------- NFE -0.34102 -0.03095 MARS 0.16690 - - CHIL 0.69185 -0.20990 CROP 0.11468 0.01041 POLIT - - - - FSUP -0.27061 -0.02456 MAREL - - - - RISK -0.13974 - - INNOV -0.04163 0.08269 Landc 5.95626 - - Landh 1.80024 - - Irri 0.04835 - - Harvest 0.09167 - - Completely Standardized Total Effects of KSI on Y 1-AGE 2-EDU -------- -------- NFE -0.68927 -0.06256 MARS 0.47820 - - CHIL 0.46677 -0.14161 CROP 0.23113 0.02098 POLIT - - - - FSUP -0.55272 -0.05017 MAREL - - - - RISK -0.30754 - - INNOV -0.10248 0.20354 Landc 0.41718 - - Landh 0.27528 - - Irri 0.13996 - - Harvest 0.20104 - - Time used: 0.300 Seconds
Determinants of Non-farm Manufacturing Entrepreneurship of Farmers
332
Chapter 7
Conclusions
7.1 Summary of the study
In the introductory chapter of this book, we have discussed rural poverty as a problem
area. Rural poverty is the real threat to a country’s development. Rural deprivation is
usually described as of two types: the core poverty of the landless or near landless in
accessible areas with flat, fertile, and often irrigated lands; and the peripheral poverty of
those in remoter hinterlands, usually with undulating and less fertile lands and rainfed
agriculture. In India, core poverty is concentrated especially in the Eastern Gangetic basin
and peripheral poverty especially in Madhya Pradesh and neighbouring regions. Unless
and until rural poverty is eradicated through possible policy measures, the country will
not be able to prosper rapidly. This study gives importance to rural development through
rural industrialisation. The issue of rural industrialisation has been studied from
entrepreneurial point of view. The study attempts to identify the factors that influence
non-farm entrepreneurship among farmers. We focus on farmers (not the agricultural
labourers) because in a country like India the rural economy is dominated by agriculture
and land-owning farmers do have investible surplus generated from agriculture.
This study holds special importance at least from two angles among others. First,
although several studies have been conducted in the field of rural industry, one can hardly
find studies that emphasize rural diversification from entrepreneurial point of view. In
other words, most of the existing literature in the field of rural non-farm economy have
shown little interest in studying farmer’s attitude towards non-farm entrepreneurship. In
this context, the present study contributes additional dimension to the literature of rural
industrialisation. Secondly, This study has a unique feature, since it analyzes data through
the LISREL approach which helps control for simultaneity bias in the model. Moreover,
Conclusions
334
it can handle observable and unobservable latent variables within one framework. We
will return to LISREL when we summarize chapter 6 later in this section.
Chapter 2 has presented an overall description of the study area. It has described the state
of West Bengal and one of its districts called Bardhaman. The first part of the chapter is
devoted to West Bengal whereas the second part is devoted to Bardhaman. We started the
chapter with description of a place (situated in West Bengal) called Murshidabad where
the colonial regime had commenced from. The chapter points out the fact that the town of
Murshidabad which was comparable to London in the past has gradually turned into a
rural village during the British rule. A similar discussion on account of whole Bengal, i.e.
Bengal in retrospect, has also been presented. The chapter has given a brief history of
entrepreneurial background of the Bengalis. The Bengali businessmen were not favoured
by the British rulers in getting business contracts. So, the uneven competition ruined the
strength of the Bengali business class. Moreover, it is true that the Bengalis put low
esteem on business occupations—especially on the profession of traditional businessman
such as petty trader. This lack of participation in traditional business virtually excluded
the Bengalis from the upward mobility to modern business. As a result of which, the
Marwaris and other non-Bengali business castes have gradually occupied the dominating
positions in modern manufacturing and trades in Bengal.
The chapter has provided description of the agricultural and industrial background of the
study area. In agriculture, West Bengal experienced a slow growth between 1950s and
end of 1970s. Since early 1980s the state agriculture started experiencing high growth.
Although there is much debate with regard to the methods of measuring actual growth
rate and reasons for higher growth, there is a consensus among scholars that since early
1980s West Bengal agriculture has experienced higher output growth than what it has
witnessed in the first three decades of the post-independence period. In industry, Bengal
was the leading state in British India. After independence, the industrial performance of
West Bengal was also good. The share of income earned from registered industry in West
Bengal to that of India started going down after 1963 – initially at slow pace, but
afterwards rapidly.
Chapter 7
335
In addition to the above discussions, chapter 2 has emphasized the linkage between
urbanisation and rural industrialisation in West Bengal. It showed that significant
diffusion of urban centres has not occurred in West Bengal. Strengthening the major
primate city Kolkata and neglecting the rural sector in terms of building infrastructure
hampered rural industrial sector grow in the state. Not so many small industries have
flourished in those districts of the state which are less urbanised. With respect to this
issue, the chapter suggests that the investment projects in rural infrastructure can be
nothing but worthwhile in order to promote rural industry.
Chapter 2 also paid attention to Bardhaman district in particular. Bardhaman is mainly
identified as an agricultural district of West Bengal, although a part of the district is
covered with heavy industries. Especially in rice production, Bardhaman is traced as an
important district in the state. Selecting such a district for the present study is justified
since we have examined farmers' participation in non-agricultural industrial activities
through investment of their surplus generated from agriculture.
Chapter 3 has discussed several issues relating to rural industries. It has focused on the
linkage between agriculture and rural industry. The chapter starts discussion from the
Physiocrats and includes the views of Adam Smith and Karl Marx while presenting the
classical political economy framework with regard to agriculture/non-agriculture linkage.
The basic difference between the Physiocrats and Adam Smith is that the Physiocrats put
emphasis on agriculture while Adam Smith gives high importance to industry. But the
similarity between them is that both of them recognized interlinkage between agriculture
and industry. From Marx’s point of view, agriculture and domestic rural industry live
together in the sense that the peasant family’s demand for manufacturing goods is met by
their own community as they themselves often supply different raw materials required for
the domestic industrial production and produce goods. But Marx traces out capitalistic
system of production as the destructive force of such rural domestic industry since
capitalists look for a big market and consequently intend to destroy the small production
units.
Conclusions
336
Next, the chapter turns towards the neo-classical views pertaining to agriculture/non-
agriculture linkage and presents Kuznets’s growth theory. Kuznets recognizes
agricultural growth as an engine of non-agricultural growth and shows that with the
increase in non-agricultural income over time the agriculture’s share to gross domestic
product goes down. But Kuznets’s theory remains silent about the separate existence of
rural non-agricultural sector as a subset of the whole industrial sector.
Chapter 3 has also presented an econometric model based on the linkage between
agricultural and industrial growth and identified the shortfalls of the model from
econometric modelling point of view. This model also reserved no place for rural
industrial sector as a vital player in the process of development at the initial stage.
Further, the chapter presents the theories as well as the theoretical debates on the linkage
issue.
The theoretical discussions first start with Hirschman’s general concepts of backward and
forward production linkages and then turn towards specific areas of agriculture/rural
industry linkage. It was John W. Mellor who first empirically specified the linkages
between agriculture and rural, small-scale, industry. Mellor’s argument is that increased
food production based on cost decreasing technology can give large farmers an additional
income which may generate/increase demand for rural non-agricultural goods. Following
him many literature supported the argument that agricultural growth is a precondition for
non-agricultural growth in rural areas, i.e. for the development of rural non-agricultural
sector self-sufficiency in agriculture has to be earned first. Since people in poor countries
are for the most part agrarian and pastoral folk (Dasgupta, 1993), agricultural growth can
play a crucial role to break the vicious circle of poverty by raising the rural income level
in the first place. But, at the same time, it has also been argued that agricultural growth is
necessary but certainly not a sufficient condition for rural non-agricultural growth. There
is a chance that demand for non-farm goods, generated from agricultural growth, may
shift from rural choice to urban choice. Also, from the supply side, the larger volume of
agricultural produce available for processing may tend to shift the processing industries
Chapter 7
337
to town in order to tap larger urban demand (since size of rural market is relatively small
due to remarkable uneven income distribution). So, there may be other factors as well,
which are responsible for the growth of rural industrial sector. Agriculture, of course,
may be placed at the centre, but the other factors should be placed at the immediate
periphery. The other factors like urbanisation and development of rural infrastructure,
which require a good amount of government expenditure, can help the rural economy.
Chapter 3 theoretically analyzed how urbanisation or development of rural infrastructure
can play important role in rural industrialisation. This apart, the chapter presents Hymer-
Resnick model which takes up rural non-farm economy into consideration but fails to
define the particular developmental role of this sector; rather it gives importance to the
linkage between rural agricultural sector and urban industrial sector. The shortcomings of
Hymer-Resnick have later been covered in a series of new models developed by Ranis-
Stewart who showed us the important transitional role (gradual shift of traditional
household/village products to modernizing non-agricultural products) of rural non-farm
sector during transformation of a developing economy from an unfavourable low income
situation to a favourable high income situation.
Chapter 4 has discussed the very important issue of culture with regard to development of
a country in general and entrepreneurship development in particular. It has emphasized
the role of psychological, sociological and cultural factors in the development of a region.
It first presents a brief analytical history regarding the disappearance of entrepreneurship
from the mainstream economics. This part of the chapter includes the discussion with
arguments offered by the Austrian school of thought in favour of entrepreneurship. In this
chapter, the main emphasis has been given to examine, within the context of the existing
literature, whether or not cultural values play any role in influencing entrepreneurship.
We observe that entrepreneurship which has virtually deserved an important place in
economic development occurred through technological change has always been neglected
by mainstream economic studies. The other important matter is that the scholars who
talked in favour of the significant role of the entrepreneurs in economic development kept
themselves silent in talking about culture or environment or value in the society. The
Conclusions
338
non-economic factors are entirely ignored in the concerned literature. But it is interesting
to note that entrepreneurship is such an economic issue which somehow maintains links
with the non-economic factors.
Chapter 4 presented David McClelland’s theory of achievement motive that tried to
understand economic growth from psychological point of view. Rapid economic growth
is usually explained in terms of “external” factors like favourable opportunities for trade,
unusual natural resources, or conquests that opens up new markets or produced internal
political stability etc. On the contrary, McClelland was interested in looking at economic
prosperity through the “internal” factors, i.e. people’s values and motives that lead them
to extract opportunities or to take advantage of favourable trade conditions. McClelland
examined correlation between achievement motive of people (manifested in literature)
and economic development (measured in terms of electricity produced, which is the form
of energy and which is essential to modern economic development) for several modern
nations and found the results which confirmed the hypothesis of his thesis. In this
connection, Hagen’s concept of “innovational personality” has also been discussed in the
chapter. Family environment is another important issue. In the same chapter, Ghosh’s
(1989) model of entrepreneurship has been presented in which Ghosh has shown how
entrepreneurship is crucially dependent on environment.
We show how Bengali family culture differs from that of Marwari and Gujarati families
who are known as business castes in India. Although one may find some dedicated
Bengali businessmen, Bengalis are generally risk averse. This may be the result of the
fact that Bengali children grow up in an environment where the parents teach them to put
low esteem to business profession and go for other profession, whereas the environment
is very different in Marwari or Gujarati families. The chapter elaborately discussed
culture of development in Indian perspective. We picked up religious and sociological
issues and analyzed how culture influences development of a region.
In chapter 4, we discussed Darwinian principles of evolutionary change and technical
culture and explained how an entrepreneur generated from an environment of technical
Chapter 7
339
culture plays a crucial role in economic evolution. For instance, a society where technical
culture is regarded as marginal culture and is not accepted into mainstream culture may
lack industrial culture and consequently fall far behind the advanced nations. Finally, the
chapter ends up with presentation of some interesting models of entrepreneurship which
are culturally sensitive.
Chapter 5 has discussed the procedure and other facts relating to data collection. It has
described sampling procedures of administrative blocks, panchayats (cluster of villages)
and farm households. It has described obstacles faced during data collection and reducing
of errors during interviews, among many other things. Bardhaman district is divided into
31 administrative blocks. The western part of the district is dominated by heavy
industries whereas the eastern part is known as agricultural area. We have eliminated the
western part from our study and confined ourselves to the eastern part, i.e. the agrarian
area that roughly counts 20 administrative blocks. Of 20 blocks, five have been randomly
selected for this study. From each block, six panchayats have been randomly selected.
For the names of the selected blocks and panchayats, see Table 5.1 in chapter 5. Finally,
ten farmers have been randomly selected from each panchayat. So, 300 farmers were
selected for interviews. But, ultimately, we could reach 290 farm households, since ten
households remained non-respondents. Of 290 respondents, we interviewed 169 farmers
who were found to have been engaged in non-farm manufacturing activities and 121 were
found to be engaged in farming only. For two kinds of farmers we formulated two types
of questionnaires – one was for the former and the other was for the latter. Some practical
problems were encountered during fieldwork, but they were handled carefully so as to
make the fieldwork to be least hampered.
Chapter 6 is the crucial chapter of this book which contains the theoretical considerations
and the empirical model of the study. Totally 13 hypotheses were formulated to
determine the factors that may influence non-farm entrepreneurship of farmers. The
model that included the thirteen hypotheses has been estimated by the LISREL approach
(LInear Structural RELations approach). By using the LISREL approach, one can control
for simultaneity bias in the model. Also, LISREL can simultaneously deal with latent and
Conclusions
340
observable variables of a model. To be more explicit, a LISREL model is made up of two
related submodels. One is a latent variables measurement model, which represents the
relationships between the latent variables and their observable indicators. The other is a
structural model that represents the relationships between the latent variables.
The LISREL programme can provide several estimators. We have used the full
information maximum-likelihood-estimator for estimating our model. In the same
chapter, we have presented the results of the estimation. The construction of the
measurement model holds one endogenous latent variable, i.e. wealth of the farmer
(WEALT), and four exogenous observable variables, i.e. farmer’s farm size (Landc),
farmer’s homestead land (Landh), farmer’s access to irrigation (Irri), and the harvesting
method farmer uses (Harvest). Farmer’s wealth is indicated by these four indicators
which are all found to be statistically significant in the estimation results. The overall fit
of the measurement model was found to be rather low which indicates that some other
indicators need to be included in the model.
In order to control for simultaneity bias in the structural model (i.e., interdependencies
between endogenous and explanatory variables), a structural simultaneous-equations-
system of 10 equations has been formulated. During estimation, we removed three
exogenous variables – age squared (AGE2), farmer’s primary involvement in agriculture
either as a landowning farmer or as a sharecropper (AGRI), and farmer’s faith in work-
effort or fate (FATE) – from our structural model since they were found to be highly
insignificant. The variables marital status of farmer (MARS), types of crops farmer
produces in a year (CROP), farmer’s risk attitude (RISK), farmer’s wealth (WEALT),
and education of farmer (EDU) have been found to have direct impacts on farmer’s non-
farm entrepreneurship (NFE). Of these, marital status, number of crop, and risk attitude
have been found to have positive impacts on non-farm entrepreneurship, whereas
farmer’s wealth and education have been found to have negative impacts. This means that
farmers who are married, engaged in producing three crops a year, and risk takers have a
relatively high probability to become non-farm entrepreneurs. On the other hand, farmers
who are relatively wealthy and have higher levels of education are less likely to become
Chapter 7
341
non-farm entrepreneurs. Lastly, age of farmer (AGE) has been found to have indirect
positive impact on his non-farm entrepreneurship via marriage, and indirect negative
impact on his non-farm entrepreneurship via risk attitude and wealth. The policy
implications of these findings are presented in the following section.
7.2 Policy recommendations
Most of the explanatory variables of this study do not involve direct policy implications.
Still there is scope of government intervention which may be required to make the
farmers less risk averse by creating facilities and influencing the culture.
The results show that the married farmer has a higher probability of being a non-farm
entrepreneur. It can be imagined that usually the unmarried farmer is younger than the
married farmer. Since success in non-farm manufacturing business is often subject to
lengthy pay-back period (i.e. considerable instant monetary return at the initial stage may
not be expected), the younger farmer may not be psychologically motivated in non-farm
entrepreneurship at the cost of prolonged patience; rather they are interested in an
alternative source of income. For instance, they may look for permanent job in
government in order to ensure instant income and security in professional life. So, the
unmarried, younger farmer needs some incentives to become interested in non-farm
manufacturing businesses.
The government policy makers should think of launching some incentive measures with
regard to the promotion of small-scale industries in the rural areas in order to attract the
unmarried young farmers into the field of manufacturing business. For example, the
government may think of building industrial estates in the rural and remote areas for the
young generation. In the industrial estate, the young farmer may be offered free space and
other free infrastructural facilities at the initial stage, say, for a 2-year period. Publicity is
also needed to attract the young unmarried generation. The approach of publicity should
be in such a way that it emphasizes the theme that self employment is much better than
employment in public and wage jobs and that the degree of self dignity in self
Conclusions
342
employment is higher than that in other employment. Publicity programme should focus
on the fact that in self employment financial gain is possible whereas wage employment
provides limited salary to the employee.
Wealth has been found to have a negative impact on non-farm entrepreneurship. The
government policy should incorporate some measures to attract the wealthier section of
the farmers towards non-farm manufacturing entrepreneurship. This section could play a
vital role in rural industrialisation once they are interested, because they are the affluent
class. At least they do not have dearth of money to invest. What is lacking in the policy
now is that there is no programme from the side of the government to convince this
section of farmer to start a business in the industrial sector. The existing rural industrial
sector in the villages is suffering from lack of capital investment. A part of the rural
economy is belonging to the vicious circle of poverty. It is rolling between poor producer
and poor consumer. To break this tradition, heavy investment is required. If we look at
the problem from such an angle, then we observe that only the rich farmer can bring
about change in the rural economy with his investible surplus. But the unfortunate thing
is this that the richer section of the farmers shows relatively little interest in non-farm
sector. The policy makers should take steps to tap this section which can be playing a
pioneering role in rural industrialisation in the economy of rural India since financial
resource transfer from agriculture to non-agriculture is often considered one of the pre-
conditions of the development and modernisation of rural non-farm economy. The
government has never thought of improving the condition of villages through this
possible line. As soon as they take steps in order to channel rural agricultural surplus
towards rural non-farm economy, it is better for the rural society.
The results also show that education of the farmer has a negative impact on non-farm
entrepreneurship. Here the concept of education mainly reflects general education.
General education hardly stimulates people to take risky business ventures. The education
policy in the state of West Bengal has not given serious attention towards
entrepreneurship. It should have incorporated entrepreneurship as a major subject into the
syllabi right from early stage to higher education. A major section of rural people is risk
Chapter 7
343
averse. They are afraid of taking risk so far as starting up a new business is concerned. If
the government is to influence such culture in order to generate awareness about positive
risk attitude among people then intervention in the education policy is needed and
entrepreneurial education should start from early stage. Training programmes like
entrepreneurship development programme and creativity generation programme may also
be useful. Currently, such programmes are mainly concentrated in the core urban areas
and in the peripheries, but these programmes should reach the remotest places of the
region.
7.3 Further research recommendations
Most part of the rural areas of the developing countries is suffering from acute poverty.
Rural policy oriented development research needs to be substantially undertaken by the
future researchers. The variables we have taken up in our study may be applied to the
same kind of work based on other geographical regions to examine any interesting
difference in the results.
Several of the operational definitions of the variables could be improved upon by using a
set of questions. For instance, we have measured innovativeness of a farmer by asking a
question: if you start a business, would that be one that is being run by the others, or
would it be one which is new/innovative? Future researchers could think of improving
such operational definitions.
The variable wealth has been taken in this study as a substitute of income, predicting that
the farmer may hide his actual income during interviews. The wealth of the farmer has
been assessed by four indicators, viz. size of farm, homestead, whether or not the farmer
has access to irrigation, and harvesting method used in farming. This operational
definition of wealth can also be improved upon in further study by taking a more perfect
set of indictors of wealth.
Conclusions
344
Moreover, the cultural and sociological factors that may affect economic situation have
not been fully explored yet. To what extent and under what conditions is development
related with the cultural factors? Answer to this question has not been intensively sought
yet. Since the cultural issues differ from region to region, intensive studies are
recommended to be undertaken based on various parts of the globe. In our study, the
cultural factors covered a part only. Broader thoughts with regard to cultural issues may
be recommended in further rural development oriented research.
345
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Samenvatting en Conclusies (Summary and Conclusions in Dutch)
Samenvatting van het onderzoek
In het inleidende hoofdstuk hebben we armoede op het platteland besproken als een
probleem. Rurale armoede is een reële bedreiging voor de ontwikkeling van een land.
Men onderscheidt gewoonlijk twee types in de plattelandsachterstelling: de kernarmoede
van de (bijna) landlozen in toegankelijke gebieden met vlak, vruchtbaar en vaak
beregende grond; en de perifere armoede van hen in het afgelegen achterland, gewoonlijk
bergachtig en minder vruchtbaar terrein en regengevoede landbouw. In India is de
kernarmoede voornamelijk gelegen in het Oostelijk stroomgebied van de Ganges en de
perifere armoede voornamelijk in MadPradesh en omgeving. Tenzij, en tot wanneer,
armoede op het platteland zal verdwijnen door politieke maatregelen, zal het land
economisch gezien niet snel kunnen bloeien. Deze studie hecht belang aan ontwikkeling
op het platteland door middel van rurale industrialisatie. De kwestie van rurale
industrialisatie is bestudeerd vanuit het standpunt van ondernemerschap. De studie tracht
de factoren vast te stellen die het niet-agrarisch ondernemerschap van boeren
beïnvloeden. We richten ons op boeren (niet de landarbeiders) omdat in een land als India
de rurale economie wordt gedomineerd door landbouw en landbezittende boeren met een
beleggingsoverschot, gegenereerd uit de landbouw.
In hoofdstuk 2 is een algemene beschrijving gepresenteerd van het onderzoeksgebied. De
situatie van West-Bengalen en een der districten, Bardhaman, is beschreven. Het eerste
deel van het hoofdstuk is gewijd aan West-Bengalen en het tweede aan Bardhaman. Er is
een korte beschrijving gegeven van de geschiedenis van het ondernemerschap in het
onderzoeksgebied, alsmede een beschrijving van de landbouw- en industriële
achtergrond. De nadruk is gelegd op het verband tussen urbanisatie en rurale
industrialisatie in het onderzoeksgebied.
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Hoofdstuk 3 beschrijft verschillende kwesties gerelateerd aan rurale industrieën. Veel
literatuur steunt de redenering dat groei van de landbouw een voorwaarde is voor niet-
agrarisch gerichte groei in rurale gebieden. Tegelijkertijd wordt er ook gesuggereerd dat
groei van de landbouw nodig is, maar het zeker niet genoeg is. Er zijn ook ‘andere
factoren’ die verantwoordelijk zijn voor de groei van de rurale industriële sector. Er is
ook benadrukt dat de landbouw in het centrum moet worden geplaatst en andere factoren
in de periferie. Aangezien mensen in arme landen voornamelijk agrarisch en pastorale
mensen zijn (Dasgupta, 1993), kan agrarische groei een cruciale rol spelen in het
doorbreken van de vicieuze cirkel van armoede door in de eerste plaats het rurale
inkomensniveau te verhogen. De ‘andere factoren’ zoals urbanisatie en ontwikkeling van
de rurale infrastructuur, die een behoorlijk bedrag aan overheidsuitgaven vereisen,
kunnen de rurale economie helpen.
Hoofdstuk 4 behandelt het zeer belangrijke aspect van cultuur met betrekking tot de
ontwikkeling van een land in het algemeen en van het ondernemerschap in het bijzonder.
Het legt de nadruk op de rol van psychologische, sociologische en culturele factoren in de
ontwikkeling van een regio. Allereerst wordt er een korte analytische historie
gepresenteerd betreffende het verdwijnen van het ondernemerschap in de hoofdstroom
economie. Het hoofdstuk combineert de discussie met argumenten vanuit de Oostenrijkse
school, die ondernemerschap prefereert. In dit hoofdstuk wordt de meeste nadruk gelegd
op het onderzoeken, binnen de context van de bestaande literatuur, of culturele waarden
een rol spelen in de beïnvloeding van het ondernemerschap.
Hoofdstuk 5 behandelt de procedure en andere feiten met betrekking tot het verzamelen
van gegevens. Het beschrijft onder anderen de belemmeringen die zich voordeden tijdens
de verzameling van gegevens en het verminderen van fouten tijdens de interviews.
Hoofdstuk 6 is het cruciale hoofdstuk en bevat de theoretische overwegingen en het
empirisch onderzoeksmodel. Een analyse is gemaakt van de gegevens door middel van
het LISREL model (LInear Structural RELations model). De LISREL benadering maakt
het niet alleen mogelijk om simultaneïteit bias te behandelen, maar tegelijkertijd latente
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en waarneembare variabelen. In hoofdstuk 6 worden de resultaten van het onderzoek
gepresenteerd. De variabelen huwelijkse staat van de boer, type gewassen dat de boer
produceert, risicogeneigdheid, bezit en opleiding van de boer blijken een directe invloed
te hebben op niet-agrarisch ondernemerschap. Van deze hebben huwelijk, aantal
gewassen en een positieve houding t.o.v. risico een positieve invloed op het niet-
agrarisch ondernemerschap, terwijl bezit en opleiding een negatieve invloed hebben.
Leeftijd van de boer blijkt een indirect effect te hebben op niet-agrarisch
ondernemerschap via huwelijkse staat, risicohouding en bezit. De beleidsimplicaties van
deze resultaten worden gepresenteerd in de volgende paragraaf.
Aanbevelingen voor het beleid
De meeste verklarende variabelen van dit onderzoek brengen geen directe beleids-
implicaties met zich mee. Daarom moet overheidsinterventie gericht zijn op het
verminderen van risicomijdend gedrag van boeren door het creëren van voorzieningen en
beïnvloeden van de cultuur.
De resultaten geven aan dat getrouwde boeren een grotere kans hebben op niet agrarisch
ondernemerschap. Men mag aannemen dat de ongetrouwde boer jonger is dan de
getrouwde. Aangezien succes in de niet-agrarische productie-industrie vaak onderworpen
is aan terugbetaalperioden die lang op zich laten wachten, zijn de jongere boeren
mogelijk niet gemotiveerd tot niet-agrarisch ondernemerschap waarbij hun geduld te veel
op de proef wordt gesteld. Als ze al geïnteresseerd zijn in een alternatieve bron van
inkomsten, zoeken ze eerder een vaste baan bij de overheid om verzekerd te zijn van
onmiddellijk inkomen en de zekerheid van een professioneel leven. Dus de jongere
ongetrouwde boer heeft enige prikkels nodig om geïnteresseerd te raken in niet-agrarisch
ondernemen. De beleidsmakers moeten denken aan het lanceren van prikkelende
maatregelen met betrekking tot het bevorderen van kleinschalige industrieën in the rurale
gebieden om jonge ongetrouwde boeren aan te sporen op het gebied van de productie-
industrie. Bijvoorbeeld: de overheid kan industrieterreinen bouwen in rurale en afgelegen
gebieden voor de jonge generatie. Op deze industrieterreinen kan de jonge boer in de
beginperiode (bijvoorbeeld voor 2 jaar) gratis ruimte en andere infrastructurele
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faciliteiten geboden worden. Ook is publiciteit nodig om de jonge ongetrouwde generatie
aan te trekken. De publiciteitsaanpak moet zo zijn dat de nadruk komt te liggen op het
feit dat zelfstandig ondernemen veel beter is dan overheids- en particuliere functies en dat
de eigen waardigheid in zelfstandige beroepen hoger is dan in een ander beroep. De
publiciteitsprogramma’s dienen gericht te zijn op het feit dat in zelfstandige beroepen
financieel gewin mogelijk is, terwijl loonwerk slechts een beperkt salaris oplevert.
Gebleken is dat bezit een negatief effect heeft op niet-agrarisch ondernemerschap.
Overheidsbeleid moet maatregelen bevatten om het meer welvarende deel van de boeren
aan te trekken tot de niet-agrarische productie-industrie. Deze boeren, eenmaal
geïnteresseerd, kunnen een vitale rol spelen in rurale industrialisatie, omdat zij de
welvarende klasse vertegenwoordigen. Zij hebben tenminste geen gebrek aan
investeringsgeld. Waarin het beleid nu tekortschiet is dat er geen programma is vanuit de
overheid om deze boeren te overtuigen een bedrijf te starten in de industriële sector. De
bestaande rurale industriële sector in de dorpen lijdt aan het gebrek aan
investeringskapitaal. Een deel van de rurale economie behoort tot de vicieuze cirkel van
armoede. Het beweegt zich van arme producent naar arme consument. Om deze traditie
te doorbreken, is een stevige investering nodig. Als we het probleem zo benaderen, dan
zien we dat alleen de rijke boer verandering te weeg kan brengen vanwege zijn
investeringsoverschot. Maar jammer genoeg heeft het rijkere deel van de boeren relatief
weinig interesse voor de niet-agrarische sector. De beleidsmakers moeten stappen zetten
om dat deel aan te spreken, welke een pioniersrol kan vervullen in de rurale
industrialisatie in de economie van ruraal India, aangezien de transfer van financiële
bronnen van agrarisch naar niet-agrarisch vaak wordt beschouwd als één van de
voorwaarden voor de ontwikkeling en modernisering van de rurale niet-agrarische
economie. De overheid heeft nooit overwogen via deze lijn de situatie van de dorpen te
verbeteren. Zodra ze stappen zetten om het rurale agrarische overschot te kanaliseren
richting de niet-agrarische economie, zal het beter worden voor de rurale samenleving.
De resultaten tonen ook aan dat opleiding van de boer een negatief effect heeft op niet-
agrarisch ondernemerschap. Met opleiding wordt hier algemene opleiding bedoeld.
Algemene opleiding stimuleert mensen nauwelijks om iets te wagen. Opleidingen in de
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staat West Bengalen besteden geen serieuze aandacht aan ondernemerschap. Het zou een
belangrijk onderwerp moeten zijn in het leerprogramma vanaf het allereerste begin tot en
met de hogere opleidingen.
Een belangrijk deel van de rurale bevolking is risicomijdend. Ze zijn bang om risico’s te
lopen wat betreft het starten van een onderneming. Als de overheid zo’n cultuur moet
beïnvloeden om bewustwording ten aanzien van een positieve risicohouding onder de
mensen te creëren, dan is wederom interventie in het onderwijsbeleid nodig, zodat
onderwijs gericht op ondernemerschap op een jonge leeftijd kan beginnen.
Trainingsprogramma’s gericht op het ontwikkelen van ondernemerschap en creativiteit
zijn tevens nuttig. Op dit moment zijn deze programma’s hoofdzakelijk geconcentreerd in
de kern stedelijke gebieden en omgeving, maar deze programma’s zouden de meest
afgelegen plaatsen van de regio moeten bereiken.
Aanbevelingen voor vervolgonderzoek
In het grootste deel van de rurale gebieden van ontwikkelingslanden heerst sterke
armoede. Toekomstige onderzoekers moeten zich met name bezighouden met ruraal
beleidsgericht ontwikkelingsonderzoek. De variabelen gebruikt in dit onderzoek kunnen
toegepast worden voor eenzelfde soort studie gebaseerd op andere geografische regio’s,
om zo mogelijke verschillen in resultaten te bestuderen.
Verscheidene operationele definities van de variabelen zijn voor verbeteringen vatbaar
door middel van vragen. Bijvoorbeeld, we hebben vernieuwingsgezindheid van de boer
gemeten door de vraag te stellen: als u een onderneming zou starten, zou dat één zijn die
ook door anderen wordt gedreven of zou het iets nieuws/innovatiefs zijn? Toekomstige
onderzoekers zouden kunnen denken aan het verbeteren van dit soort vragen.
De variabele bezit is in dit onderzoek gebruikt als een vervanger voor inkomen, omdat de
boer mogelijk zijn eigenlijke inkomen niet zou prijsgeven tijdens de interviews. Het bezit
van de boer is gemeten met behulp van vier indicatoren: bedrijfsgrootte, boerderij, wel of
geen toegang tot irrigatie, en toegepaste oogstmethode. Deze operationele definitie van
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bezit kan ook verbeterd worden in een vervolgstudie, door betere indicatoren voor bezit
te creëren.
Bovendien zijn de culturele en sociologische factoren die van invloed kunnen zijn op de
economische situatie nog niet onderzocht. Staat ontwikkeling in verband met culturele
factoren? Naar een antwoord op deze vraag is nog niet uitputtend gezocht. Aangezien de
culturele kwesties verschillen van regio tot regio, moeten er intensieve studies worden
gedaan, gebaseerd op verschillende delen van de wereld. In onze studie besloegen de
culturele factoren slechts een deel. Ruimere aandacht voor culturele kwesties is
aanbevolen voor volgend ruraal ontwikkelingsonderzoek.
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About the Author
Subrata Dutta was born on 12 June 1967 at Barasat, West Bengal, India. He followed his
secondary education at Duttapukur Mahesh Vidyapith and passed Madhyamik
Examination conducted by the West Bengal Board of Secondary Education (WBBSE) in
1983. After finishing his higher secondary education, conducted by the West Bengal
Council for Higher Secondary Education (WBCHSE), in 1985 from Nebadhai High
School, Duttapukur, North 24-parganas, West Bengal, he completed his Bachelors and
Masters in economics at Rabindra Bharati University, Kolkata, in 1988 and 1990
respectively. From 1991 to 1994 he worked for the Human Resource Development
Research Institute, Kolkata. From 1994 to 1996 he worked for the Indian Council of
Small Industries, Kolkata. In the mean time, in1995, he attended a short course on Small
Entrepreneurship Promotion and Industrial Assistance at the Maastrciht School of
Management, the Netherlands, and earned a post-graduate diploma. From 1996 to 1999
he worked as a University Junior Research Fellow at the department of economics at
Rabindra Bharati University, Kolkata. In 1999, he started his Ph.D. study at the
Maastricht School of Management, the Netherlands. In 2000, he moved to Wageningen
University in the same country for his Ph.D. study.
He has published an academic paper in Economic and Political Weekly (EPW) in 2002 on
linkage between urbanisation and development of rural small enterprises.
His interest includes directorial work in theatre and film.
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