chapter vii determinants and linkages of rural...

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CHAPTER VII

DETERMINANTS AND LINKAGES OF RURAL NON-FARMSECTOR

A number of pre-requisites and compatible socio-economic environment are

must for the development of rural non-farm sector (RNFS). In other words, the

development of social and physical infrastructure is a sine qua non for the

development of this sector. There are also chances that the RNFS may develop

because of stagnant agriculture or an imbalanced growth strategy. Moreover, the

numerous linkages between the rural non-farm sector and other sectors of the

economy, mainly agriculture and urban based industrial sector are needed to be

developed for any sustainable and productive development of this sector. The

literature suggests a number of factors and processes which have a bearing on the

development of this sector. However, the debate regarding their relative importance

and significance has not yet settled. The discussion on a number of important factors

responsible for the development of RNFS is divided into two sections. Section I deals

with the growth linkages between agricultural development and RNFS on one side,

and other determinants influencing the development of RNFS on the other. And, the

empirical evidence generated on RNFS by the present study has been presented in

Section II.

I

7.1.1 Agriculture led growth linkages of RNFS

There is a considerable body of literature which maintains that agriculture is

the prime mover behind the emergence, sustenance and growth of rural non-farm

sector. The processes through which the stimuli of agricultural advancements are

transmitted to RNFS are popularly known as agricultural growth linkages in the

literature. The dominant view before 1970s was that the agriculture has week

stimulating linkages (Hirschman, 1958). This view was based on the impression that

the traditional agriculture used few capital goods purchased from other sectors. Mellor

and Lele (1973) asserted that even the traditional agricultural systems purchased non-

agricultural goods. However, modern agriculture, based on high yielding and cost

reducing techniques, really creates dynamic forward and backward linkages with non-

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agricultural sectors. They argued that agriculture cannot be denied its role in the

process of economic development on the misplaced belief of its having devoid of or

having weak linkages with other sectors.

The basic premise of Mellor (1976) is that increases in agricultural production

through cost reducing technological innovations normally distribute the initial

benefits largely to the already more prosperous and better endowed farmers. These

incremental increases in the hands of relatively better-off farmers stimulate

consumption of non-food goods which in turn gives rise to numerous non-farm local

occupations; which are local, labour intensive and requiring low capital in the

production process. Increased agricultural production also creates a demand for agro

processing, marketing and transport facilities through forward linkages with non-farm

sector and at the same time increased demand for inputs like pump sets, mechanical

ploughs, seed drillers, etc. through backward linkages give rise to numerous local

firms producing, supplying/distributing and repairing these inputs.

Agricultural growth linkages are normally classified into following five

categories:

(i) Production linkages

Production linkages of agriculture with rural non-farm sector are of two types:

(a) forward linkages reflect the need to process, transport and trade, distribute and

sale, etc. of agricultural produce. Many agro-processing units like rice-shellers, flour

mills, fruit processing units, and the like develop in the course of development; (b)

backward linkages are generated by the demand of agricultural production process

itself. Johnston and Kilby's analysis (1975) of farm equipment in India, Pakistan and

Taiwan stresses that traditional tools are most often made by rural artisans, while

improved implements and irrigation pumps and motors are likely to be fabricated by

light engineering workshops located in rural towns. Tractors, combine harvesters,

chemical fertilizers and other large items with high performance characteristics, tend

to be produced abroad or in urban areas having little potential for promoting

local/rural non-farm linkages. Consequently, the nature of agricultural technology

adopted has important effects on rural non-farm linkages. Even when the above said

high capital intensive items are produced in metropolitan towns or even in foreign

locations, a wide spread network of dealership, spare-parts and repair shops are

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generally set up in large villages or in mandi towns where agriculture produce is

normally marketed (Chadha, 1986). The market-led and market-linked agricultural

development in many states of India have experienced small linkages (Gill and

Ghuman, 2001)

(ii) Consumption linkages

Consumption linkages manifest themselves when the farm households spend

their earnings from agriculture on non-farm goods and services. As their incomes

increase, farm households increase the share of their expenditure devoted to non-food

items (Mellor and Lele, 1973; Hossain, 2004). It has also been found that

consumption linkages are more important than production linkages (Haggblade et al.,

1989; Hazell and Haggblade, 1990; Bell et al., 1982; Hazell and Ramasamy,1986;

Shukla, 1992).

(iii) Factor market linkages

In rural labour markets, a strong seasonality of demand in agriculture

generates corresponding surges in rural non-farm activity (Anderson and Leiserson,

1980; Fabella, 1986; Leons and Feldman, 1998) Tightening of labour markets caused

by the expansion of RNF employment has been widely acknowledged (Bhalla, 1993).

Similarly, cash surpluses from agricultural sale frequently finance non-farm

investments, while reciprocal reverse flows from rural non-farm activities finance the

purchase of agricultural inputs (Evans and Ngau, 1991). Rural non-agricultural

activities may affect agriculture directly, through a reallocation of resources, and

indirectly though investment (Ho, 1986b).

(iv) Productivity linkages

Productivity linkages of agriculture with non-farm economy have emerged

only recently in the growth linkage discussions (Haggblade et al., 2007). Increase in

agricultural productivity may lower the price of foodstuffs and the resulting increase

in absorption may increase the productivity of poor manual workers in non-farm

sector.

(v) Reverse linkages

Rural non-agricultural sector also impacts agricultural production and

productivity. Evans and Ngau (1991) note down that at the level of household, rural

non-farm incomes have a positive impact on agricultural productivity. Others have

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observed that increased participation of rural households in non-farm occupations led

to a fall in family's incomes from agriculture (Kada, 1986; Ho, 1986b).

7.1.2 Quantitative estimates of agricultural led growth linkages

Quantitative estimates of the production and consumption linkages at the level

of regional economy were pioneered by Bell et al. (1982) in their evaluation of Muda

irrigation project in northern Malaysia. They estimated that $ 1 of the value added in

agricultural income generated from the project stimulated an additional $ 0.83 in the

region's non-agricultural economy. About 40 per cent of it was due to production

linkages (both backward and forward), and 60 per cent to consumption linkages.

Subsequent analysis of consumption linkages by Hazell and Roell (1983) claim that

large farmers exhibit highly multiplicative patterns of demand for regional non-

tradable commodities and that their consumption behaviour is, therefore, key source

of growth in the regional economy. Hazell and Ramasamy (1991) have estimated

agricultural growths multipliers over the 1970s for the North Arcot district of Tamil

Nadu in India. They estimated that as a result of production and consumption

linkages, every 100 rupees increase in agricultural income induced an additional 82

rupees income in other sectors of the rural economy. Production linkages accounted

for half of the income, the other one-half of the increase was due to consumption

linkages. Haggblade et al., (1989) estimate that African regional growth multipliers

are approximately 60 per cent those in Asia.

Haggblade et al. (2007) provided a much useful survey of the studies on farm-

non-farm linkages. A cross sectional econometric study by Hazell and Haggblade

(1990) across 85 different districts in India analyzed the relationship between

agricultural growth and growth in rural non-farm economy. It estimates agriculture to

rural non-farm income multiplier of the order of 0.64, distributed as 0.39 in rural

towns and 0.25 in rural areas. But these multipliers are not invariant. Their values

increase with the level and growth of agricultural development. The value of

multiplier is 0.93 in Punjab and Haryana and 0.46 in low productivity states such as

Bihar and M.P. From this evidence, the study claims that rural non-farm sector is

driven primarily by agricultural growth.

Agriculture influences not only the level but also the composition of rural non-

farm employment. When demand for agricultural labour goes up as it does initially in

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most green revolution settings, it pulls the agricultural wage rate upwards and the

opportunity cost of labour in non-farm pursuits, thereby rendering low-return non-

farm activities uneconomic. This leads to the demise of many low-productivity craft

and household manufacturing activities and agricultural supporting services

(Haggblade and Liedholm, 1991). The artisans who earlier used to make a living by

producing and selling these products start disappearing from the scene and a large

proportion of these join the ranks of agricultural labor (Gill, 1980). But,

simultaneously, there is a growth of higher-return non-farm activity such as

mechanical milling, transport, commerce, and personal, health and educational

services (Bhalla, 1981; Hossain, 2004). Advancements in agricultural productivity,

thus, change the very nature, structure and organization of rural non-farm enterprises

(Bhalla, 2004).

Many studies from rural India show a positive correlation between earnings

per worker in agriculture and development of rural non-farm activity (Chadha, 1986;

Papola, 1987; Vaidyanathan 1986; Bhatt, 1998; Shukla, 1991, 1992; Rosegrant and

Hazell, 2000). Although cross-section comparisons of agricultural and rural non-farm

income typically yield positive correlation, one cannot necessarily infer causality

from these associations. Favourably endowed zones may simultaneously attract high-

productivity agriculture, infrastructure investments, improved agricultural extension

and technology adoption, public investment in schools, roads and development,

offices, as well as external non-farm investment (Haggblade et al., 2007).

Study results from slow-growing agricultural regions suggest that a sluggish

agriculture gives rise to anemic non-farm employment and wage rates. Though rural

non-farm employment may actually increase in lackluster agricultural zones

(Haggblade and Liedholm, 1991; Bhalla, 1994), it normally emerges in low wage-last

resort activities such as basket weaving, embroidery and gathering activities. It means

that level of rural non-farm employment is not always a true indicator of significance

of these activities towards, households' living standards. Productivity or returns from

them certainly is a better criterion for judging their economic importance.

In Latin America, rural linkages appear to be low because of the extreme

inequality in land and income distribution, absentee ownership of large land holdings,

and the consequently feeble rural consumption and input linkages emanating from the

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often urban-based and urban-supplied Hacienda owners (Haggblade et al., 2007). In

South Africa increasing non-farm activity in some rural communities has been

accompanied by a decline in agricultural production and income (Low, 1981). In

Kenya, regions with higher non-farm incomes tend to have higher agricultural income

levels (Evans and Ngau, 1991). In Bicol, a resource-poor region of the Philippines, a

rapid increase in non-farm income in absolute and relative terms accompanied a

decline in both agricultural and total income in the late 1970s and 1980s (Reyes,

1987). These results prompted the researcher to assert that "the direction and causality

of growth in non-farm and farm activity is far from clear" (Leons and Feldman, 1998).

Some scholars (Vyas and Mathai, 1978; Islam, 1986; Ray, 1994) argue that

unless the structure of rural demand is broad based, compassing the demand generated

by all the sections of rural society and not merely of richer farmers, rural non-farm

sector would not hold much prospect for absorbing the underutilized/unutilized rural

labour force and for pulling the workers participating in it out of the poverty trap.

These scholars therefore recommended that unless rural assets, especially the land are

not equitably distributed the virtuous interaction of farming and non-farming will not

play to its fullest potential.

Forces other than local agriculture may strongly influence the growth of non-

agricultural activities (Hariss and Hariss, 1984; Hariss, 1987, Amsden, 1991; Shih,

1983; Hart, 1998; Bhalla, 1993b). In the Indian context, the recent closure orders of

Supreme Court of India of the household-based/small non-household manufacturing

units operated from the residential areas of Delhi, pushed many of these units into the

rural regions of neighbouring UP, Haryana and Rajasthan apart from rural areas of

Delhi itself. Rural industrialization, thus, should not be treated as an adjunct to

agricultural growth; but rather as a development which needs to be fostered

independently (Papola, 1987).

In the Indian Punjab, the epicenter of green revolution, high agricultural

incomes did not result in industrial development in general and rural industrialization

in particular. There was a drain of investible surpluses to far flung regions via banking

mechanisms (Chadha, 1986; Gill, 1994). Policies biased against industrial bourgeois

(Chadha, 1986) or the interactions of majority nationalism and minority nationalism

(Singh, 2008) are said to be important reasons for industrial backwardness of the

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state. Thus, the assertions of agriculture-led employment strategy of Mellor and

associates, based on a mechanical interpretation of agricultural and non-agricultural

equations, in disregard to social, political and institutional contexts, seem to have a

limited relevance for the policies and programmes of non-farm employment

generation in rural areas of many a developing countries of the world. Agricultural

growth may be a necessary condition for rural diversification, but certainly not a

sufficient condition (Hariss, 1991).

7.1.3 Pull and push factors in RNF employment

The extent, growth and variation in rural non-farm involvement have also

been explained by the conceptual framework of 'pull' and 'push' factors. 'Pull' factors

are those which lure the rural workers towards high productive activities/areas where

relative returns to labour are higher. A vibrant non-farm sector is a major 'pull' factor.

On the other hand, when rural workers are joining the non-farm sector even when

wages here either are equal to or lower than agricultural wages, they are said to be

pushed by the farm sector. It may be due to non-availability/access to land,

fragmentation, marginalization and unviable farm holdings or may be due to

incapacity of agricultural sector to absorb increments of labor force. There is no

unanimity as to whether it is the operation of 'pull' factors which is behind the

observed movement of rural workforce from agriculture to non-agricultural activities

or they are pushed out of agriculture.

Agriculture-led-employment school of thought propagates that the most

important 'pull' factor in luring the rural workers towards non-farm pursuits is the

emergence of a vibrant and productive rural non-farm sector caused by dynamic

agriculture through a variety of production and consumption linkages. The study has

already referred to the studies by Hossain (2004), Chadha (1986), Bhalla (1981), Raj

(1976), Ho (1986b), Oshima (1986b), Haggblade et al. (1989), Hazell and Haggblade

(1990) and various others cited in support of this contention.

There are other studies which emphasize that it is the rather operation of 'push

factors' which is the main reason for the observed increase in rural workers'

engagement in non-farm occupations. Vaidyanathan (1986) emphasizes that there is a

significant and positive relation between rural unemployment rate and the incidence

of non-agricultural employment and this association is much stronger than in the case

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of all other explanatory variables on which he estimated an importance of rural non-

agricultural employment in total employment in rural India. This gives credibility to

the notion that non-agricultural activities in rural areas may be acting to some extent

as a residual sector absorbing labour which cannot find work in agriculture. Similar

findings have been reported by Basu and Kashyap (1992), Simmons and Supri (1995),

Parthasarthy et al. (1998), Ghuman (2005) and Ranjan (2009) which emphasize that it

is mainly the increased helplessness and marginalization of rural workers in the wake

of increased landlessness and fragmentation of land holdings, grossly unequal land

holdings and sinking growth rates of agricultural output and employment. Under these

circumstances, rural non-farm occupations are nothing but refuse activities towards

which workers are pushed to avoid deprivation.

The actual reality always lies in the middle of these two contrasted situations.

Ho (1986a) and Kada (1983) demonstrate that it is the simultaneous and complex

interaction of both push and pull factors which are the reason behind observed

expansion of rural non-farm employment. Urbanization, especially the preponderance

of small and medium typed towns, also pulls the rural workforce for joining the rural

non-farm sector. White (1991) argues that the conceptual frame work of "pull versus

push" factors, in fact, does not explain much and the debate on the relative

importance of pull and push factors is not very productive as different groups of rural

society and households enter into different kinds of rural non-farm pursuits for

different reasons, producing all-together different outcomes.

7.1.4 Other determinants of RNFS

(i) Infrastructure

Almost all the studies on the theme of rural non-farm employment, in all the

regions, point to the development of infrastructure as one of the basic factor in

promoting rural non-farm employment. Fabella (1985) has emphasized population

density, education, electrification, irrigation and high yielding varieties of rice as the

main modernizing factors determining the development of RNF employment in

Philippines. Oshima (1986a), while contrasting the development of rural non-farm

sector in Japan and Taiwan with South Korea and these countries with that of South-

East Asian ones, argues that the 'transport indexes' are approximately correlated with

the share of off-farm income in Japan, Taiwan, South Korea and Philippines. Rural

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electrification and telecommunication are again very important for the growth of z-

goods component of rural non-farm sector (Ranis and Steward, 1993; Lanjouw,

1998). Similarly, Park (1986), Kada (1986), Ho (1986b) and Saith (1987). All these

studies have underlined the importance of infrastructural facilities for promoting rural

non-farm employment in East Asian countries. Hazell and Haggblade (1990) have

also identified the importance of rural infrastructure like roads, electrification,

banking services etc. in enhancing the size of the agricultural growth multipliers.

Shukla (1992) has estimated that infrastructural facilities of roads, electricity, posts

and telegraphs have statistically positive influences on the level and density of rural

non-farm employment.

It is not only physical infrastructure such as transport, communication, electric

power which is important; equally important is social infrastructure such as

educational, medical and recreational facilities (Oshima, 1986a). Since services will

be among the most rapidly growing rural non-farm activities, investment in human

capital will be essential for realizing the potential gains. But there is another side to

infrastructural development. The development of transport and communication

facilities tend to lift the barriers between cities and rural areas leading to an easy and

cost-effective availability of goods and services produced at urban locations to rural

consumers. More often than not the result is demise of rural manufacturers and

artisanal products though services have been reported to be less affected by this

increased confrontation. Also, this development affects the tastes, preferences and

attitudes of rural households and orients them towards urban products again hitting

the rurally produced goods from demand side.

(ii) Education

Education not only improves an individual's qualifications for non-agricultural

jobs, it also increases his ability to allocate his work time efficiently among

alternative income-producing activities (Huffman, 1980). Education contributes to

higher productivity in trading, construction, service and manufacturing activities.

Secondary education stimulates entrepreneurial capacity, whereas primary education

enhances the productivity of the workforce, including foremen, supervisors, and other

middle-level personnel, makes it easier to impart on-the-job training. Education is an

important factor in the choice of non-farm activity and it raises productivity in the

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non-farm sector (Khandker, 1995). In fact, a non-linear relationship between

educational level and income has been found (Vijverberg, 1995). Lanjouw (1998)

also notes that returns to education are quite substantial in the non-agricultural sector.

A review by Reardon et al. (2001) pertaining to Latin American countries showed

very vividly that education determines participation and success in RNF employment

and income. More and higher level education tended to mean more non-farm wage

employment in high productivity, well paying jobs. The more educated tend to avoid

farm wage labour and gravitate towards non-farm wage employment and only

secondarily towards non-farm self employment.

(iii) Urbanization

Urbanization is another major pull force for the expansion of rural non-farm

activity. Ho (1986a) and Kada (1983) argue that proximity to urban areas is an

important determinant of both the extent of rural involvement in non-agricultural

activities and the quality of that involvement (as measured, for example, by their

average income earned from non-agricultural sources). In India, the most dynamic

growth areas in the rural non-farm sectors rely heavily on urban and export demand

and possess negligible linkages with agriculture (Fisher et al. 1997).

The linkages between rural based small scale units and urban based larger

units (in manufacturing as well in trade) have been noted widely (Ho, 1986b). Otsuka

(2007) contends that these linkages are rather the defining characteristics of mature

phase of rural industrialization of many countries like Japan, Taiwan and South

Korea. Rural industry in many countries seems to develop most vigorously near an

urban industrial nexus either on the peri-urban fringes of major cities or within a 25-

30 mile radius of a major economic centre (Yusuf and Kumar, 1996; Fafchamps and

Shilpi, 2003). There is evidence that productivity is greater for those located near

large towns and cities (Lanjouw and Lanjouw, 1995).

In the Indian context, Jayaraj (1994) found the factor of urbanization

interacting in a complex way in explaining the extent and variation of rural non-farm

activity in the state of Tamil Nadu in India. Micro level studies reveal that temporary

migration of labour force from rural to urban area, particularly of commuting variety,

account for a sizable portion of workforce in various economic activities of the urban

centres. It also forms a major share of off-season employment of agricultural labour

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and small farmers (Basu and Kashyap, 1992). It is being increasingly recognized that

the employment linkages generated by investment in urban areas could be stronger

than those from agriculture (Basant, 1994). In the mature phase of agricultural

growth, rural demand for non- agricultural goods produced in urban areas may

increase faster than for goods produced locally (Chandrasekhar, 1993). However,

there is no one to one or monotonic relationship between increase in urban population

and activity in an economy and the level of its rural non-farm employment because

the processes of urbanization may have a differential impact on different types and

forms of non-agricultural activities. Different sized towns can influence the structure

of rural non-farm employment in a different manner. Other things being the same, a

more equitable pattern of urbanization biased towards small towns will probably be

more conducive to the growth of rural non-agricultural sector than a less equitable

one (Shukla, 1992; Basant, 1994).

(iv) Inequality in land distribution

Oshima (1986a) lists greater inequality in the distribution of arable land

ownership accompanied by a much larger share of non-cultivating land owners and

tenants (including the landless) as against owner-cultivators as one of the reasons for

much larger share of off-farm employment in the Philippines than in South Korea.

Unni (1991), too, found that unequal land distribution was positively and significantly

associated with the percentage of male non-agricultural workers. This could occur, he

reasoned, because of a positive stimulus of land concentration (via surplus generation)

or due to distress diversification. Dev (1990) also confirmed the factor of inequalities

in asset structure as an important variable explaining rural non-farm employment.

Vaidyanathan (1986), however, found that other things being equal, higher inequality

of operational holdings seem to go with lower incidence of non-agricultural

employment. The importance of redistributive land reforms enhancing access to land

to a large mass of rural populace in promoting localized rural non-farm activities has

been emphasized by several studies (Islam, 1986; Saith, 1991, 1992; Ray, 1994). The

experience of development of rural non-farm sector in West Bengal testifies to it. It is

claimed that after the land reform measures, the demand for agricultural inputs by

marginal and small farmers has greatly increased. With the result that 40 per cent of

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rural workers in the state are engaged in non-farm activities (West Bengal Human

Development Report, 2004).

(v) Public Investment

Direct public investment in rural areas is also one of the main sources of rural

non-farm employment. Public investment in agricultural marketing, rural

infrastructure, roads and bridges, and irrigation facilities has generally spurred RNF

employment (Shukla, 1992). Visaria (1995) highlights the contribution of investment

in public utilities resulting in higher involvement of rural labour force in non-

agricultural employment. Men are the main takers of the jobs that arise from such

investment. Fisher et al. (1997) also note that almost 20 per cent of total non-farm

employment in rural India was generated by public sector services. The contraction in

agricultural investment in particular and in rural investment in general in India is said

to be the main reason behind the wide spread decline in the rate of growth of

employment in rural trade which was linked to slowdowns in agricultural growth

through lower agricultural investment since the mid-1980s and specially after the

'reforms' period (Chadha and Sahu, 2002; Sen and Jha, 2005). But contraction in

public investments under the dictates of Structural Adjustment Programmes, followed

by many African Sub-Saharan countries since the early 1990s, have been shown to

have triggered a huge unplanned income diversification in African rural areas

(Bryceson, 1999).

(vi) Commercialization of Agriculture

The higher percentage of commercial crops in the cropping pattern is said to

create opportunities of tertiary employment (Dantwala, 1953). Increased

commercialization and specialization in agriculture lead to general commercialization

of rural economy also which, other things being equal, is likely to reduce rural non-

farm activity as a part time or secondary occupation but to encourage rural industry as

a specialized activity. Sharma (2005) emphasizes the diversification of agriculture

into commercial off-season vegetables and fruits in Himachal Pradesh as the main

reason for the increase in rural non-farm employment in the state in recent period.

Vaidyanathan (1986) also notes a significant positive association of percentage of

area under non-food grain crops and census estimates of rural non-farm activities in

India.

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7.1.5 Determinants of household participation in RNFS

(i) Land holding size

As agricultural land becomes scarce, households must seek compensating

earnings in non-farm economy. For this reason, landless households typically depend

most heavily on non-farm earnings (Anderson and Leiserson, op.cit). However, the

effect of land holding on participation and earning from RNF activity is complex. A

household with a larger land holding may be more committed to agriculture, thus,

exhibiting a negative relationship of land holdings with non-farm activity. But large

land holding may also mean more wealth and contacts which are important for getting

entry into high-end non-farm activities. Households with large land holdings are, thus,

better placed in search of and engaging in rural non-farm endeavours. A study by

Kijima and Lanjouw (2005) of rural India suggests that the probability of employment

in regular non-farm activities and in non-farm self employment (relevant to

agricultural labour) is significantly higher for those with more per capita land

holdings.

(ii) Education

Less educated households rely on low-paying farm wage employment or very

low productivity non-farm pursuits (Lanjouw and Shariff, 2004). In a study of

Ecuador, Elbers and Lanjouw, (2001) found that the least educated were found to do

low-paying non-farm work in manufacturers or services. Those with basic education

were found managing small enterprises mainly in manufacturers and those with more

education work in the higher paying jobs such as teachers or manage large local

enterprises.

(iii) Seasonal nature of income streams from agriculture

Risk and uncertainty in agriculture also forces the households into rural non-

farm employment. Ellis (1998) asserts that the prime motive and consequences of

successful household diversification is to reduce vulnerability. He distinguishes

"rational risk-management" from default "coping strategies". Both seasonal

smoothing and risk diversification can be very important factors in environment

where agricultural output varies greatly over the year and across years and where

mechanisms for smoothing income such as credit and transfers are costly or absent

(Lanjouw and Lanjouw, 1995).

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(iv) Credit markets

The failures of credit or insurance markets drive households to self-ensured

and self-funded input purchases (Evans and Ngau, 1991). Where credit and insurance

markets are missing, rural non-farm activity becomes a vehicle of self insurance and

for financing agricultural inputs and assets. Migration and remittances tend to help the

households in coping with the missing credit markets. The household left behind

becomes less inclined to engage in rural non-farm activities. Out-migration, thus,

becomes an important factor which discourages the household from joining the non-

farm activities (Simmons and Supri, 1995). Foster and Rosenzweig (2008), however,

found that in rural Pakistan, migration has positive impact on rural non-farm sector

both by altering the size and composition of the local labour force and also by directly

affecting incomes, given the size of the local labour force.

(v) Household size and structure

Household size and structure is positively and highly significantly related to

the incidence of RNF activities. Where household size is large, it is more likely to

participate in RNF activities (Simmons and Supri, 1995). This study also found

number of male workers in a household to be significantly related with the incidence

of RNF employment, but they termed the 'household size' a superior predictor than the

number of male workers. Reardon (1997) also provides evidence showing positive

association of household size and participation in non-farm occupations.

(vi) Gender composition

Gender is another important factor influencing participation patterns and

trends in the RNF sector. The role of gender enabling or restricting access to

economic activity also varies from country to country. Women tend to have lower

participation in non-farm sector than men (Coppard, 2001). They earn lower than men

per unit of time of work done. They are the main victims of the process of evolution

of rural non-farm economy (Haggblade and Liedholm, 1991; Som et al., 2002). It is

only in regions where new labour intensive industries are located near farms and farm

steads that a large number of women tend to join non-farm wage work (Ho, 1986b).

(vii) Ethnicity and caste

Ethnicity is an important determinant of participation in rural non-farm

activities. Scheduled Castes (SCs) in India, for example, are more likely to be found

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in agricultural wage labour than in other lucrative segments of rural non-farm activity.

They are more in casual wage segment than in regular wage employment as compared

with non-SC sections of the workforce (Lanjouw and Shariff, 2004; Thorat and

Sabharwal, 2005).

II

7.2 Empirical evidence from present study

7.2.1 Education and RNF activities

In order to examine whether education has any influence on household

participation in rural non-farm employment, the study first examined the association

of educational attainments of household heads and the extent of their participation in

non-farm activities. It is evident from Table 7.1 that there is positive relation between

the level of education and the employment in non-farm activities.

Table 7.1: Educational attainment of household heads and household participation infarming and non-farming occupations in sampled villages

Type of householdEducational slabRNF Farming Total

Percentage oftotal*

Illiterate 1943(51.62)

1821(48.38)

3764(100.00)

46.37

Up to Primary 613(62.30)

371(37.70)

984(100.00)

12.12

Beyond primary butup to high

1834(64.49)

1010(35.51)

2844(100.00)

35.03

Beyond high but upto senior secondary

180(70.31)

76(29.69)

256(100.00)

3.15

Graduate and above 158(74.88)

53(25.12)

211(100.00)

2.60

Others (skilled) 53(89.83)

06(10.17)

59(100.00)

0.73

Total 4781(58.89)

3337(41.11)

8118(100.00)

100.00

Notes: 1. Due to rounding up percentage shares may not total to 1002. Figures in brackets denote percentage share.3. * denotes the percentage of total households in a particular education slab.

Source: Field Survey.

The data show that among 3764 illiterate household heads, 51.62 per cent are

in RNF and 48.38 per cent in farming. In the category of household heads which had

studied up to class fifth, one finds that out of 984 households, 62.30 per cent are in

RNF and 37.70 per cent in farming. Similarly, in the educational slab of more than

primary but up to matriculation, the percentage of RNF households has gone further

to 64.49 and that of farming decreased to 35.51 pr cent. Amongst the rural households

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whose heads were either graduates or above, about three-fourth (74.88 per cent) were

in RNF. Similarly, of the technically qualified heads as high as 89.93 per cent of the

households had chosen rural non-farm activities as their major source of livelihood (in

terms of employment) and only 10 per cent remained dependent solely in farming.

It is obvious that formal education of household heads is very important for

the engagement of households in non-farm pursuits. Furthermore, even primary

education increases the households’ participation in RNF by as much as 11 percentage

points. It seems that education empowers household heads in making informed

choices about livelihood sources. It further revealed that though less than 1 per cent of

households were having their heads as technically qualified, yet about 90 per cent of

them preferred RNF as primary activity.

Across the different zones, the less developed zone (zone-III) has a lower

percentage of educated and skilled persons (Table 7.2). However, the relationship

between education and skill level with the employment in non-farm sector is almost

similar to the aggregated data. However, the illiteracy is higher in the least developed

zone as compared to the developed zones. Significantly, the proportion of household

heads, having education above primary level, is lower in the least developed zone

than that in the developed zones.

Table 7.2: Education attainments of household heads and household participation innon-farming occupations by different zones (Percentage share)

RNF households Percentage of total non-farminghouseholds in each education slabEducation slab

Zone I Zone II Zone III Zone I Zone II Zone IIIIlliterate 806

(64.27)452

(51.89)685

(41.79)40.04 32.22 50.18

Up to primary 243(65.15)

178(69.80)

192(53.93)

12.07 12.69 14.07

Beyond primarybut up to high

788(65.02)

647(72.94)

399(53.56)

39.15 46.12 29.23

Beyond high but upto senior secondary

91(73.98)

48(71.64)

41(62.12)

4.52 3.42 3.00


60(76.92)

33(67.35)

3.23 4.28 2.42


18(90.00)

15(88.24)

0.99 1.28 1.10

Total 2013(65.61)

1403(64.42)

1365(47.53)

100.00 100.00 100.00

Note: Figures in brackets denote percentage share out of RNF and agricultural households together inrespective categories.

Source: Field survey.

146

Table 7.3 shows that like the household heads, the percentage share of RNF

workers also increases with the increase in their education level. Though 33.58 per

cent of all the workers in study villages are illiterate, only 34.79 per cent are illiterate

in RNF and the rest, i.e., 65.21 per cent are in pure agricultural sector. Of the workers

who are read up to primary level, almost 52 per cent are in RNF and the remaining 48

per cent in farming. Of all the workers who have studied from class 10th up to senior

secondary, graduation and above and had any technical education, 68.25 per cent,

80.00 per cent, and 90.98 per cent respectively are in RNF. It means that educated

workers prefer non-farming over farming as a means of livelihood. Beside, there is a

positive correlation between the level of education and participation in non-farm

employment.

Thus, analysis of household and workers' participation in rural non-farm

occupations and their educational attainments confirms the findings of many other

research studies (Huffman, 1980; Singh, and Prabhakar, 2000) that education enables

a worker to make better choices over livelihood options available to him and also that

with the spread of education more and more of rural workers are joining non-farm

vocations as compared to agriculture. Further, the probability of participation in RNF

is lower if a worker is illiterate; he/she is more likely to be in farming. Of all the

uneducated workers, only about 35 per cent were in RNF (Table 7.3). Nevertheless,

the illiterate and lowly educated workers settle at low-productivity low-earning non-

farm activities.

It has been seen earlier that 58.55 per cent of rural workers in Zone I was in

RNF. Participation of workers in RNF activities was low at 39.78 per cent in zone III

(Table 7.4). Table 7.4 shows that only 27.61 per cent of rural workers in Zone I

(developed zone) are illiterate as compared to 43.16 per cent in Zone III (less

developed zone). Secondly, similar level of education in Zone III as in Zone I is

producing lower shares of RNF workers in the former zone. For example, share of

workers who have education up to primary is 11.93 per cent of the total in Zone III

and 10.57 per cent in Zone I. But in Zone III share of these workers in RNF activities

is only 42.95 per cent as compared to 55.72 per cent in Zone I.

147

Table 7.3: Educational attainment and distribution of workers in agricultural and non-agricultural occupations in study villages

Educational slab RNFworkers Agri. workers Total

Percentageshare of each

in total*Illiterate 1725

(37.03)3234

(69.41)4659

(100.00)31.55


785(48.10)

1632(100.00)

11.05

Beyond primary but upto high

3554(58.50)

2521(41.50)

6075(100.00)

41.14

Beyond high but up toSenior Secondary

935(68.25)

435(31.75)

1370(100.00)

9.28


122(20.00)

610(100.00)

4.13


11(9.02)

122(100.00)

0.83

Total 7660(51.87)

7108(48.13)

14768(100.00)

100.00

Notes: 1. Figures in brackets indicate percentage share.2. * indicates column wise shares in total


Table 7.4: Educational attainment and RNF workers in different zonesRNF workers Percentage of total workers in

each education slabEducation slab

Zone I Zone II Zone III Zone I Zone II Zone III

Illiterate 771(48.95)

419(35.60)

535(24.24)

23.08 18.33 26.30


249(59.43)

262(42.95)

10.06 10.89 12.88

Beyond primarybut up to high

1547(59.50)

1148(65.75)

859(49.68)

46.32 50.22 42.23

Beyond high but upto SeniorSecondary

435(69.27)

271(73.24)

229(61.56) 13.02 11.85 11.26


167(83.08)

106(71.62)

6.44 7.31 5.21


32(86.49)

43(91.49)

1.08 1.40 2.11

Total 3340(58.55)

2286(57.87)

2034(39.78)

100.00 100.00 100.00


Source: Field Survey.

Educational facilities in all their forms – from physical infrastructure to absenteeism

of teachers - are far less developed in these villages than in villages of developed blocks (and

of moderately developed blocks) of which Zones I and II are, respectively, constituted. Thus,

148

it is not only the level of education which is important, equally important, for participation in

rural non-farm activities is the quality of education. The latter assertion is proved from the fact

that though in Zone I, percentage of workers with technical education (0.67 per cent) is less

than that in Zone II (0.94 per cent), 94.74 per cent of these workers are in RNF activities in

Zone I as compared to only 86.49 per cent in Zone II.

7.2.2 Land holding size and RNF activities

Land holding size, too, has an influence on participation of workers and households

in rural non-farm pursuits. Table 7.5 reveals that more than three-fifth of all the rural

households (62.50 per cent) in the study villages are landless. Of all the landless

households, 70.63 per cent participate in RNF activities and only 29.37 per cent are pure

farming households. As the size of land holding increases, the percentage share of RNF

households declines and the decline is very neat. For the farm households' tendency is

otherwise. With the increase in land holding size, the share of pure farm households

increases. The data, thus, confirms the fact of an inverse relationship between land holding

size and household participation in RNF activities.

Table 7.5: Land holdings size, RNF and agricultural households in study villagesType of householdLand holding size

classRNF Farming Total Percentage share of

total households*Landless 3585

(70.63)1491

(29.37)5076

(100.00)62.53

Marginal 491(55.42)

395(44.58)

886(100.00)

10.91

Small 271(40.57)

397(59.43)

668(100.00)

8.23

Semi-Medium 245(30.82)

550(69.18)

795(100.00)

9.79

Medium 160(27.63)

419(72.37)

579(100.00)

7.13

Large 29(25.44)

85(74.56)

114(100.00)

1.40

Total 4781(58.89)

3337(41.11)

8118(100.00) 100.00

Notes: 1. Figures in brackets indicate percentage shares.2. *indicates column wise shares in total.


By looking at the distribution of rural households in terms of land holding

sizes in three zones, one can easily find that percentage of RNF households in Zone I

is higher (65.61 per cent) as compared to Zone II (64.42 per cent) and Zone III (47.53

149

per cent). One of the reasons is the distribution of land holding in these zones. In Zone

I, about 65 per cent of the households are land less. In zone III, the percentage of

landless households is less (57.21 per cent). This is shown in Table 7.6.

Further, in Zone III, the share of medium and other large holders is about

10.73 per cent as compared to 8.76 per cent in Zone I and 5.32 per cent in Zone II.

Given the inverse relationship between land holding size and household participation

in RNF, the participation of households in RNF is bound to be less in zone III than

that in Zone I and Zone II. Secondly, per household availability of land is more in

Zone III as compared to Zone I and II. It has been held that in regions where per

household land availability is large, household participation in RNF is less (Reardon

et al., 2007).

Table 7.6: Land holdings and RNF households in different zonesRNF households Percentage share of total households in

each land holding size classLandholding sizeclass Zone I Zone II Zone III Zone I Zone II Zone IIILandless 1600

(80.56)1014

(70.07)971

(59.10)79.48 72.27 71.14

Marginal 132(49.44)

183(68.80)

176(49.86)

6.56 13.04 12.89

Small 92(37.55)

92(57.5)

87(33.08)

4.57 6.56 6.37

Semi-Medium

102(33.89)

76(40.21)

67 5.07 5.42 4.91

Medium 74(31.22)

31(35.63)

55(21.57)

3.68 2.21 4.03

Large 13(40.63)

7(24.14)

9(16.98)

0.65 0.50 0.66

Total 2013(65.61)

1403(64.42)

1365(47.53) 100.00 100.00 100.00



At the household level, it has been found that size of land holding and

participation of rural households in non-farm occupations is inversely related. Now,

question arises how rural workers react to changes in land holding size classes. An

assessment of detain Table 7.7 shows that out of 14768 rural main workers, 7660

workers (51.87 per cent) were engaged in non-farm occupation. Further, 57.83 per

cent of all rural workers in all the study villages do not own any land. They are

landless. But of all these workers 68.07 per cent are in RNF employment and 31.93

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per cent in agriculture and related activities. Thus more landless workers tend to join

non-farming than farming occupations. In fact, in the absence of any land, majority of

these workers have joined non-farm employment.

Table 7.7: Land holdings, non-farm and farm workers in study villagesLand holdingsize class

RNF workers Agri. workers Total workers Percentageshare of total

workers*Landless 5814

(68.07)2727

(31.93)8541

(100.00)57.83

Marginal 798(49.44)

816(50.56)

1614(100.00)

10.93

Small 409(30.66)

925(69.34)

1334(100.00)

9.03


1291(78.58)

1643(100.00)

11.13

Medium 243(18.13)

1097(81.87)

1340(100.00)

9.07

Large 44(14.86)

252(85.14)

296(100.00)

2.00

Total 7660(51.87)

7108(48.13)

14768(100.00)

100.00

Notes: 1. Figures in brackets indicate percentage shares.2. *indicates column wise shares in total.


It is also clear from Table 7.7 that nearly one-tenth of rural workers (10.93 per

cent) belong to the land size class of marginal farmers. In this class, the share of RNF

(49.44 per cent) is slightly less than that of farm activities (50.56 per cent) in

employment. With increase in land holding size, the participation of rural workers in

non-farm operations further declines and that of farming increases. In fact the

percentage share of workers in RNF decreases from 68.07 per cent for landless class

to 14.86 per cent in the size class of 25 acres or more. Consequently, the percentage

share of farming in employment increases from 31.93 per cent for landless workers to

85.14 per cent for workers having 25 acres or more. Thus, the existence of inverse

relationship between land holding size and participation of family workers in non-

farm occupations is confirmed by data. About 18.13 per cent of medium farmers (with

land between 10-25 acres) and slightly less than 15 per cent of large farmers engage

in rural non-farm pursuits. Taking together, 11.13 per cent of rural workers belong to

the land size classes of 10 acres or more. Of these workers, 21.42 per cent work in

non-farm occupations and the remaining 78.58 per cent in farming. From this, it can

151

be inferred that slightly more than one-sixth of workers belonging to better-off

households engage in various non-farm activities. It has been emphasized that this

class of workers join RNF sector not out of any compulsion, but as a matter of a well-

thought of strategy of accumulation (White, 1991; Saith, 1992).

With regard to different zones, the landless workers in RNF activities varied

from 54.89 per cent in Zone III to almost 80 per cent in Zone I as is evident from

Table 7.8. Among the land owners, the data show inverse relationship between land

holding size and the workers in RNF sector across the zones.

The higher percentage of RNF workers amongst large farmers in zone I may

be due to higher level of infrastructural development as compared to other zones. It is

clear from the foregoing discussion that the participation of land owning households

in RNFS and that of RNF workers from such households has an inverse relationship

with the size of land holding. A vast majority of landless households and workers

prefer RNF to farming.

Table 7.8: Land holdings and non-farm workers in different zonesRNF workers Percentage share of total

workers*Land holdingsize class

Zone I Zone II Zone III Zone I Zone II Zone IIILandless 2733

(79.66)1634

(66.05)1447

(54.89)81.83 71.48 71.14

Marginal 200(43.67)

325(65.79)

273(41.24)

5.99 14.22 13.42

Small 120(25.48)

163(44.05)

126(25.56)

3.59 7.13 6.19


114(29.46)

94(15.72)

4.31 4.99 4.62

Medium 121(20.30)

41(24.40)

81(14.06)

3.62 1.79 3.98

Large 22(24.18)

9(15.79)

13(8.78)

0.66 0.39 0.64

Total 3340(58.55)

2286(57.87)

2034(39.78) 100.00 100.00 100.00

Note: 1. Figures in brackets denote percentage share out of RNF and agricultural households togetherin respective categories.

2. * indicates percentage shares of each land holding size in total.Source: Field survey.

From the above tabular analysis, it is clear that education level of the

household heads as well as of workers exert a positive influence on the participation

in non-farm activities. The size of land holding with the household, however, has a

152

negative relationship with the extent of participation. The study further explored these

relationships with the help of probit analysis.

7.2.3 Village level factors and RNF activities

It has been seen earlier that the availability of and access to land is one of the

important determinants of household participation in RNF activities. If one looks at

the village level engagement of workers in RNF and per capita land holding in the

village, it is found that in those villages where per capita land holding is more, the

extent of participation of workers in RNF is less than in the villages where per capita

land holding is less. In 10 villages out of 24 study villages, the extent of RNF workers

is 68 per cent or more. In all of these villages, per capita land holding is less than 2

acres. At the other extreme, there are four villages where per capita land holding is

between 4 to 5 acres. In 3 of these villages, extent of RNF workers is less than 25 per

cent.

(i) Proximity to towns/mandi towns

The study has also reviewed a number of studies which established that rural-

urban linkages are perhaps as important as agriculture and non-agricultural linkages in

the development of rural non-farm employment. In 6 out of 24 study villages the

share of RNF workers in total workforce varied between 71 per cent and 91 per cent.

What is common in these villages? None of these villages, except Dhupsari, is close

to a large town of the state. Four villages i.e. Behnangal, Udassian, Chunni Kalan and

Purkhali, are located either on a state highway or on a main district road. These

villages are situated in such a way that villagers from surrounding 6 to 7 villages

come here for taking a bus to nearby towns. These villages are rather located on mid-

way between two large towns linked by the road. All these villages are connected

with hinterland by a network of all-weather metalled roads. These villages, over the

years and without any conscious effort by the state government, have evolved into

market-villages.

Across 13 villages having RNF share more than 60 per cent of workforce, the

study finds that four villages, namely, Dhupsari (77), Assal (69), Chakmirpur (69) and

Udassian (79) fall within 3 kilometers of an urban town or a rural town (mandi town).

Another set of four other villages, namely, Sidhwan Khurd (67), Randhawa (64),

Dhandour (68) and Gobindpura (71) fall within 8 kilometers of an urban town or a

153

mandi town. The mandi towns have grain markets, offices of the local market

committees, parking lots for trucks and taxies where the operators wait for their turn,

and so on. Round the clock electricity and clean drinking water promote many

manufacturing and repair activities. These small towns are host to many private

schools, hospitals/nursing homes, cinemas and other entertainment sources. During

the marketing seasons of wheat and paddy much of the interaction amongst farmers,

grain cleaners and loaders, local commission agents, workers of procurement agencies

and of transporters takes place here. These towns, especially during the marketing

seasons become very vibrant and attracting manual labour from the hinter land

villages. Taxi stands, barber shops, liquor vends and many other service providers

jostle for space near the mandis. Thus, 8 out of 13 villages having RNF workers more

than 60 per cent can be said to have urban influence. Remaining 5 villages, namely,

Chunni Kalan, Purkhali, Behnangal, Simbro (64) and Ammonangal (68) all fall on the

main roads and are located favourably. One of the factors common in all these 5

villages was their ability to cater to a cluster of villages falling in the hinter land.

At the other extreme are four villages having RNF share between 20 per cent

and 30 per cent. These villages are 10 or more kilometers away from the nearest town.

Surprisingly, the villages which are more than 15 kilometers from the nearest town

show larger participation of workers in RNF than those villages which are located at a

distance of 10 to 15 kilometers. From the above discussion, it can be safely concluded

that closeness to an urban township or a mandi town along with the location of the

village explain much of the variations in the shares of RNF workers in the village

workforce. However, some exceptions are always there.

(ii) Infrastructure facilities

Some of the villages showing high shares of RNF in their total workforce have

all the infrastructural facilities. For example, four villages, namely, Chunni Kalan,

Beh Nangal, Purkhali and Dhadour have regulated purchase centres for marketing

agricultural produce, focal points, telephone exchanges, clean drinking water supply,

primary health and veterinary centres. But there is no one-to-one relationship. Some

of the villages having small shares of RNF like Badalgarh and Chakkar also have

purchase centres. Further more, some of the villages which are closer to urban towns,

154

though have high shares of RNF, but do not have these facilities located in the

villages; they use the facilities located in the towns.

In rural Punjab of today, none of the villages is without all weather metalled

link road. All the houses, even in remotest of the villages have electric power.

However, the quality of service delivery deteriorates as one moves away from villages

on the main roads to ones in the remote locations. In the latter set of villages,

absenteeism amongst teachers, pharmacists, doctors, operators of water supply,

electricity supply etc. is much more.

Table 7.9: Impact of village level factors on RNF employment in village

RNF workers

As percentage of population As percentage of totalworkers

Profile

Mean S.D. Mean S.D.Connectivity with national/state highwayConnected (5) 20.75 2.38 65.20 7.24Not Connected (19) 17.46 6.58 54.76 10.54t-Value 1.08 2.07**Connectivity with railway stationConnected (5) 20.95 3.19 68.15 12.40Not Connected (19) 17.41 6.48 53.98 13.35t-Value 1.17 2.14**Technical education institutions (TEI)Villages with TEI (6) 21.76 4.09 69.24 12.86Villages without TEI (18) 16.95 6.22 52.83 14.10t-value 1.76* 2.52**Distance from nearby city/townUp to 5 km (4) 22.07 2.32 73.68 5.615-10 km (10) 17.98 4.59 55.76 11.4510-15 km (4) 14.07 9.28 41.92 16.82>15 km (6) 18.54 7.11 57.72 12.73r-value -0.170 -0.411*# -0.200 -0.472**#Operational areaUp to 500 acres (9) 22.69 2.28 74.10 8.53500-1000 acres (8) 18.76 5.61 57.78 16.40> 1000 acres (7) 11.61 4.00 33.88 11.34r-value -0.594*** -0.659***PopulationUp to 1000 (2) 20.97 0.22 65.56 2.451001-2000 (17) 19.00 6.47 59.76 13.64Above 2000 (5) 14.11 4.05 43.85 11.60r-value -0.320 -0.360

Notes: 1 ***, ** and * denote significance at 1%, 5% and 10%, respectively. # denotes r-values from 1km to 15 km.

2 Figures in parentheses indicate number of villages.

155

The study has also statistically tested the significance of influence of various

village level factors on RNF employment in the village. RNF employment was taken

in two forms; (i) RNF workers as percentage of total population of the village; and (ii)

RNF workers as percentage of total workers in the village. To compare the average

level of RNF employment between two categories, student's t-test was applied to see

the relationship between two parametric variables i.e. between RNF employment and

distance from the nearest city/town; RNF employment and operational area; and RNF

employment and population size of the village, coefficient of correlation (r-value) was

worked out. The results so obtained are presented in Table 7.9.

(iii) Connectivity with national/state highway

The proportion of RNF workers out of total workers was significantly higher

in connected villages (65.20 per cent) as compared to that in other villages (54.76 per

cent). This shows that village connectivity to a state/national highway has a

significant impact on village level RNF employment. It is also clear from the

statistical significance (Table 7.9)

(iv) Connectivity with railway station

The proportion of RNF workers in total workers in connected villages is

significantly higher than in those villages which are not connected with a railway

station. This indicates that connectivity to railway station also exerts significantly

positive effect on RNF employment.

(v) Presence of technical education institutions

It is clear from Table 7.9 that the RNF workers as proportion of total workers

was 69.24 per cent in the villages which enjoyed the facility of technical education

institutions, while this proportion was 52.83 per cent in other villages. This difference

of proportion of RNF workers' in total workers between the two categories of villages

was significant at 5 per cent level.

(vi) Distance of villages from nearby city/town

The RNF workers as a proportion of the total workers were the highest in the

villages which fall within 5 kms from the nearest town. This proportion declines with

the increase in distance from 5 to 15 kms. After that, it again shows an increase in

156

proportion. Though no definite trends are discernible, yet the results support a study

by Fafchamps and Shilpi (2003) which had showed that rural non-farm wage

employment falls away quickly as one leaves peri-urban areas for the hinterland. But

there was a U-shaped pattern for self employment because some rural non-farm

activities in the hinterland had to come up to support local needs not met by supply

from urban areas. The results also support the contention that the distantly located

regions/villages are insulated from the onslaught of cheap supplies from urban based

industrial units.

(vii) Size of operational land area

To see the effect of operational area of the villages on RNF employment, the

area was grouped into three categories of up to 500 acres, 500-1000 acres and above

1000 acres. The proportion of RNF workers to total workers declined significantly

from 74.10 per cent in village having up to 500 acres of operational land to 57.78 per

cent with 500 to 1000 acres and further to 33.88 per cent in the village having more

than 1000 acres of operational land. The decline was significant as indicated by the

coefficient of correlation (- 0.659***) significant at one per cent level of probability.

It is significant to note that there is a negative correlation between the land mass

available for cultivation in the village and the proportion of workers in RNF

employment. As the operational area increases the proportion of RNF workers goes

on declining.

(viii) Population size of village

The RNF workers as a proportion of total workers in the village showed a

declining trend with the increase in population of the villages under study. The

proportionate share of RNF workers in total workers was 65.56 per cent in villages

having population up to 1000. It decreased to 59.76 per cent and further to 43.65 per

cent with the increase in village population from up to 1000, to 1001-2000, and above

2000 respectively. In this case, coefficient of correlation estimated at -0.360, is

significant at 10 per cent level. This indicates that proportion of RNF workers out of

total workers was inversely related to the population size of the study villages. This

result is contrary to many studies which claim that the amount of non-farm activity

tends to vary directly and positively with the size of rural settlements.

157

7.2.4 Linkages of RNF sector with other sectors

(i) Linkages with agriculture

A number of studies reviewed have shown that it is mainly the agriculture

sectors' production and consumption linkages with the local rural economy which

cause the emergence and growth of rural non-farm sector. One way to look at the

issue is to examine the nature of RNF activities. In a sample of 300 rural non-farm

households, 65 households were self-employed households, 198 were wage labour

households and another 37 were mixed households. In all 452 workers were found to

be engaged in 86 non-farm activities. Of these, 49 were in self employment and the

rest were wage labour activities.

Of the 49 self employment activities, 13 were closely related with agriculture. Thus,

almost 27 per cent of self employment activities had direct linkages with the agriculture. Out

of these, 9 were found to have forward production linkage with agriculture. These are flour

mill, fruit shop, juice shop, quilt filling, vegetable vending, livestock trading, sweets making,

ration depot and commission agency. All these activities use agricultural produce as input for

transformation into new products or value addition of the same product. Four activities were

found to have backward linkage with agriculture. These are diesel engine repair, land

leveling, combine harvesting and wheat reaper trading. Here rural non-farm activities provide

valuable inputs to agricultural production processes. Thus, out of all the agricultural related

self-employed activities, 69 per cent were found to have grown because of RNF sectors'

forward linkage with agriculture and 31 per cent were the result of backward linkages.

New agricultural technologies by increasing the agricultural productivities not only

release the labour force into the non-farm markets but increased agricultural production also

provides funds for redeployment into non-farm pursuits. From this investment angle, sources

of funding of RNF self employment activities were examined. It was found that as much as

36.71 per cent of start-up capital came from surplus generated in agriculture. Table 7.10 gives

an idea how different RNF activities used the agricultural surplus to start-up their ventures.

Looking at the zonal level view, the study found that it was the most

developed zone I where the contribution of agricultural surplus to start-up capital was

the highest (Table 7.11). In certain lines, e.g., mining and quarrying, manufacturing

related activities, other services, and trade and commerce, the share of agricultural

savings as a part of total initial outlay is more than 40 per cent. RNF sector in the

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study villages, thus, has become a vehicle to mop up unutilized idle savings and their

re-deployment in productive activities of different kinds.

Table 7.10: Nature of activities and amount of savings generated by agriculture forsetting up different RNF enterprises (per unit)

Activities No. of units Amount which came fromagricultural savings

Total start-upCapital

Mining and quarrying 1 1000,000(41.67)

2400,000(100.00)

Manufacturing, processing,services and repairs

15 25,143(41.85)

60,084(100.00)

Construction 7 13,000(29.09)

44,686(100.00)

Trade and commerce 33 133,837(43.13)

310,336(100.00)

Transport, storage andcommunications

21 106,381(25.46)

417,786(100.00)

Other services 26 78,926(40.44)

195,160(100.00)

Total 103 98,746(36.71)

268,959(100.00)

Notes: 1. Besides, agricultural savings, other sources of funding included funds raised fromrelatives/friends, loans from institutional sources and loans from non-institutional sources

2. Figures in brackets denote percentage of the total.3. Amount per unit


Table 7.11: Percentage of funds generated by agriculture to start up differentRNF activities in different zones

Amountwhich camefromagriculturalsavings

Totalstart-upcapital

No. ofunits

Amountwhichcamefromagriculturalsavings


No. ofunits

Amountwhichcamefromagriculturalsavings


Nature ofactivity

No. ofunits

Zone I Zone II Zone III

Mining andquarrying 1 1000,000

(41.67)2400000(100.00)

00

(0.00)0

(100.00)0

0(0.00)

0(100.00)

Manufacturing,processing,services andrepairs

6 40,000(51.02)

78,400(100.00)

10

(0.00)0

(100.00)8

17143(31.83)

53,857(100.00)

Construction 2 29,000(96.67)

30,000(100.00)

31000

(13.16)7600

(100.00)2

15,000(13.04)

115,000(100.00)

Trade andcommerce 10 168,333

(74.85)224,892(100.00)

10211,000(34.52)

611,280(100.00)

1347,944(33.20)

144,400(100.00)

Transport,storage andcommunications

9 205,200(29.31)

700,000(100.00)

658,333(28.08)

207,750(100.00)

66200(3.03)

204,500(100.00)

Other services 7 253,333(63.47)

399,167(100.00)

913,750(27.50)

50,000(100.00)

1015,500(8.47)

183,000(100.00)

Total 35 188,613(46.25)

407,814(100.00)

2989,198(33.02)

270,147(100.00)

3925,195(17.56)

143,463(100.00)

Note: Figures in brackets indicate percentage shares.Source: Field survey.

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(ii) Rural/Urban linkages

(a) Employment linkages

Saith (1992) argued that location approach alone to rural non-farm sector does

not capture it entirely. Enterprises can be located in urban or peri-urban areas. The

only condition is that they must have some linkages with the rural sector. To be able

to generate employment for rural labour is an important criteria which can indicate

whether an enterprise based in urban areas has linkages with rural hinter land or no.

Table 7.12 shows where the rural workers are engaged. Mining and Quarrying

is an activity which is located in rural areas. Employment is completely generated in

rural areas, but the activity has urban links as well because its output is not entirely

used in rural areas. From the employment angle, of all the RNF workers in

manufacturing related activities, 80.51 per cent were working in units located within

the villages. Similarly, in other services related activities, as high as 96.97 per cent of

workers found work in villages. Construction activities are such attracted 71.43 per

cent of workers employed in these have to travel to urban areas for getting

employment.

Table 7.12: Distribution of RNF self-employed workers in different activities inrural and urban areas

Nature of activity Rural Urban Rural + Urban TotalMining and quarrying 1

(100.00)0

(0.00)0

(0.00)1

(100.00)Manufacturing, processing,services and repairs

16(80.00)

4(20.00)

0(0.00)

20(100.00)

Construction 5(71.43)

2(28.57)

0(0.00)

7(100.00)

Trade and commerce 26(76.47)

4(11.76)

4(11.76)

34(100.00)


15(65.22)

8(34.78)

0(0.00)

23(100.00)

Other services 32(96.97)

1(3.03)

0(0.00)

33(100.00)

All 95(80.51)

19(16.10)

4(3.39)

118(100.00)

Note: Figures in brackets indicate percentage of workers in total workers.Source: Field survey.

From the employment angle, one can say that 15.53 per cent of rural workers

engaged in self employment activities find non-farm work in urban areas (Table 7.13).

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2.91 per cent are located in rural and urban area. The remaining 81.55 per cent are

located in rural areas.

In no way, thus, rural areas can be considered as completely bounded spheres.

Rather, rural non-farm activity grows in a continuum of rural-urban space. It calls for

infrastructural development of roads, transport, communication etc.

Table 7.13: Location of RNF self-employed enterprises/activitiesNature of activity Rural Urban Rural + Urban TotalMining and quarrying 1

(100.00)0

(0.00)0

(0.00)1


11 (73.33)

4(26.67)

0(0.00)

15(100.00)


2(28.57)

0(0.00)

7(100.00)


4(12.12)

3(9.09)

33(100.00)


15(71.43)

6(28.57)

0(0.00)

21(100.00)


0(0.00)

0(0.00)

26(100.00)

Total 84(81.55)

16(15.53)

3(2.91)

103(100.00)


(b) Market linkages

In this context, the study calculated from where the self-employed ventures

bought these inputs and where did they sell their end-products. The study makes the

result interesting (Table 7.14). For example, a mining and quarrying unit, after paying

a certain annual license fee, mines sand and gravel in rural areas and end-users are in

urban as well as rural areas. The study found that 70 per cent of sand and gravel was

extracted for use in rural areas. It speaks about the construction boom in rural areas –

both by private as well as government development agencies. In trade and commerce,

there are 33 units. In terms of purchase of inputs, 7 units purchase their raw material,

etc. from purely rural areas and remaining 26 were found to depend on urban areas for

the purchase of inputs. For selling the products, 25 units sold their output in rural

areas and the 8 units sold it in urban locations. Thus, as revealed by the data in Table

7.14, rural self employment enterprises have both rural-urban linkages in terms of the

purchase of inputs as well as sale of their products and services.

161

Table 7.14: Input output destinations of RNF self-employed enterprisesPurchasing from (%) Selling to (%)Nature of activity

Rural Urban Rural UrbanMining and quarrying 1

(100.00)0

(0.00)1

(70.00)0


3(28.57)

12(71.43)

13(86.67)

2(13.33)


7(100.00)

7(100.00)

0(0.00)


26(76.67)

25(75.76)

8(24.24)


2(9.09)

19(90.91)

13(61.90)

8(38.10)


16(62.50)

24(92.31)

2(7.69)

Total 23(25.88)

80(74.12)

82(79.61)

21(20.39)


7.2.5 Probit analysis predicting participation of RNF employment

In order to predict the probabilities of households’ participation in rural non-

farm activities in sampled villages, Probit analysis was done with the following

function:

Involvement of households in RNF activities = f (Male members in working

age, family size, operational area, leased-in land, leased-out land)

The results of the analysis are presented in Table 7.15. The results are tested

by value of Chi-Square at 1 per cent level of significance. This indicates that the

variables taken for the Probit model are significantly associated with the dependent

variable. It called for the goodness of the data to proceed for the analysis.

Table 7.15: Probit model for predicting participation in RNF employment in PunjabParameter Estimate t-value Significance levelMale 0.035 3.353 0.01Family Size -0.027 -0.504 NSOperational Area -0.182 -2.414 0.05Leased in 0.027 0.272 NSLeased Out 0.006 3.026 0.01

PROBITa

Intercept -1.154 -3.611 0.01a. PROBIT Model: PROBIT(p) = Intercept + bX

b. NS stands for Non –SignificantChi-Square Tests Chi-SquarePearson Goodness-of-Fit Test 18.223Significance 0.01

162

On the basis of intercept term and different Probit coefficients in Probit

model, classification of households into RNF and agriculture sector was tested for

accuracy. The model predicted that 283 households involved in RNF activities

(against the actual number of 300 households). Thus 83.33 per cent outcomes were

correctly classified, while the remaining 16.67 per cent of the RNF households were

misclassified. Among agricultural households, 68.00 per cent were correctly classified

and the remaining 32.00 per cent were misclassified. The classification suggested that

there is a probable shift of 16.67 per cent households from RNF to agriculture and

32.00 per cent from agriculture to RNF. Hence, there is a probability of net shift of

15.33 per cent households towards RNF activities (Table 7.16).

Table 7.16: Number of households in farm and non-farm activities as per Probit Modeland Actual number in respective activities

Predicted ActualType of Household

Yes No. TotalProbable shift

RNF 251(83.33)

49(16.33)

300

Farm 32(32.00)

68(68.00)

100

Total 283(70.75)

117(29.25)

400

From Agri. to RNF = 32.00 per cent

From RNF to Agri. = 16.67 per cent

Net Shift to RNF = 15.33 per cent

Note: Figures in brackets indicate percentage shares.

The Chi-square test rejected the hypothesis that the pattern of prediction was

at random at well beyond 1 per cent level of significance. The significant calculated

value of chi-square implied that the predictive pattern was not random, but based on

the significant interaction of explanatory variables with the dependent variable.

The probit coefficients indicate that the increased number of male members in the

working age group increased the probability of involvement of rural households in the

non-farm activities. Similar was the role of leased-out agricultural land. The

operational area and leased-in area exerted inverse impact on probability of

engagement of rural households in non-farm activities. In the previously tabular

analysis, we had observed that larger the land holding which a household has smaller

was its participation in non-farm activities The Probit analysis also supports it.

Leasing-in land is an addition to the operational holding; hence its relationship also

came to be negative with the involvement of rural households in non-farm activities.

Leasing out land pushed the household towards non-farm activities, as with the

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leasing out of the land the size of the operational holding with the household declines.

In a study from Punjab (Singh et al., 2007), it was seen that a large majority of

farmers who left farming and joined other activities in rural areas have leased out their

land. Out of the total 2 lakh farmers who left farming during 1991 to 2007, about 64

per cent farmers had leased out their land, while the remaining farmers had sold off

their land, totally or partially. This shows that farmers in Punjab generally participate

in RNF activities after leasing out their land.

The foregoing discussion has amply displayed that social and physical

infrastructures are the significant determinants for the development of RNF sector.

The participation of households in RNF activities, however, depends on the level of

education and skill. The land holding size also significantly impacts participation of

households in non-farm activities.

chapter vii determinants and linkages of rural...

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