getinet belay wondim november 2017 haramaya university
TRANSCRIPT
LIVELIHOOD DIVERSIFICATION AMONG PASTORALISTS AND
ITS EFFECT ON POVERTY: THE CASE OF AMIBARA DISTRICT,
ZONE THREE OF AFAR NATIONAL REGIONAL STATE
MSc THESIS
GETINET BELAY WONDIM
November 2017
Haramaya University, Haramaya
Livelihood Diversification among Pastoralists and Its Effect on Poverty:
The Case of Amibara District, Zone Three of Afar National Regional
State
A Thesis Submitted to the Department of Agricultural Economics,
Postgraduate Program Directorate
HARAMAYA UNIVERSITY
In Partial Fulfillment of the Requirements for the Degree of
MASTER OF SCIENCE IN AGRICULTURAL ECONOMICS
Getinet Belay Wondim
November 2017
Haramaya University, Haramaya
ii
POSTGRADUATE PROGRAM DIRECTORATE
HARAMAYA UNIVERSITY
I hereby certify that I have read and evaluated this Thesis entitled: Livelihood Diversification among
Pastoralists and Its Effect on Poverty: The Case of Amibara District, Zone Three of Afar National
Regional State, prepared under my guidance by Getinet Belay. I recommend that it be submitted as
fulfilling the thesis requirement
Dr. Belaineh Legesse ___________ ____________
Major Advisor Signature Date
As member of the Board of examination of the MSc Thesis Open Defense Examination, I certify that
I have read and evaluated the Thesis prepared by Getinet Belay and examined the candidate. I
recommend that the thesis be accepted as fulfilling the Thesis requirement for the degree of Masters
of Science in Agricultural Economics.
___________________ _____________ ________________
Chairperson Signature Date
_____________________ _____________ _________________
Internal Examiner Signature Date
_____________________ _____________ _________________
External Examiner Signature Date
Final approval and acceptance of the Thesis is contingent up on the submission of its final
copy to the Council of Graduate Studies (CGS) through the candidate’s department or school
of graduate committee (DGC or SGC).
iii
DEDICATION
This manuscript is dedicated to my brother Anteneh Belay and my uncle Kifle Sinishaw who
passed away while I was doing this thesis work.
iv
STATEMENT OF THE AUTHOR
By my signature below, I declare and affirm that this thesis is my own work. I have followed
all ethical and technical principles of scholarship in the preparation, data collection, data
analysis and compilation of this Thesis. Any scholarly matter that is included in the Thesis
has been given recognition through citation.
This Thesis is submitted in partial fulfilment of the requirements for MSc degree at the
Haramaya University. The Thesis is deposited in the Haramaya University Library and is
made available to borrowers under rules of the Library. I solemnly declare that this Thesis is
not submitted to any other institution anywhere for the award of any academic degree,
diploma, or certificate.
Brief quotations from this Thesis may be made without special permission provided that
accurate and complete acknowledgement of source is made. Requests for permission for
extended quotation from or reproduction of this Thesis in whole or in part may be granted
by the Head of the School or Department when in his or her judgment the proposed use of
the material is in the interest of the scholarship. In all other instances, however, permission
must be obtained from the author of the Thesis.
Name: - Getinet Belay Signature: _______________
Date: _______________
School/Department: - Agricultural Economics and Agri-Business
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ACRONYMS AND ABBREVIATIONS
ASAL Arid and Semi-Arid Land
COMESA Common Markets for East and Southern Africa
CSA Central Statistics Agency
DFID Department for Foreign and International Development
GDP Gross Domestic Product
HPG Humanitarian Policy Group
IAS Invasive Alien Species
MoFED Ministry of Finance and Economic Development
ODI Overseas Development Institute
PASDEP Plan for Accelerated and Sustainable Development to End
Poverty
PFE Pastoralists Forum Ethiopia
PRA Participatory Rural Appraisal
SDPRP Sustainable Development and Poverty Reduction Program
SLF Sustainable Livelihoods Framework
SNNPR Southern Nations, Nationalities and Peoples Region
TLU Tropical Livestock Unit
TSP Transforming Structure and Processes
UNOCHA-PCI United Nations Office for Coordination of Humanitarian
Affairs Pastoral Communication Initiative
VIF Variance Inflation Factor
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BIOGRAPHICAL SKETCH
The author was born at Motta, east Gojam zone of Amhara National Regional State, Ethiopia,
on the 8th of September 1980. He attended his elementary education at Motta elementary
school. He completed his senior secondary school at Motta Senior Secondary school. Upon
the successful completion of his secondary education in 1999, he commenced his higher
education at Ambo College of Agriculture and graduated with Diploma in General
Agriculture.
After graduation, he was employed as a teacher at Tigray Regional State and served for two
years teaching Biology. After two years of service, he resigned and joined Ethiopian Institute
of Agricultural Research (the then Ethiopian Agricultural Research Organization, EARO) as
a technical assistant at Werer Agricultural Research centre in April 2004. Immediately after
joining the centre, he joined the Summer School of Haramaya University College of
Agriculture and graduated with a BSc degree in Agricultural Economics in September 2008.
After graduation, he was assigned as junior researcher in socio-economic research division.
In October 2012, he started his graduate studies in the department of Agricultural Economics
at Haramaya University.
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ACKNOWLEDGEMENTS
This work would have not been accomplished without the support of many individuals. Here,
I would like to seize this opportunity to respectfully acknowledge all those who have directly
or indirectly involved throughout the course of this study.
First and fore most I would like to thank the almighty God for His endless support and
blessing throughout my life and for giving me the patience and the courage to accomplish
this study.
My special thanks go to my major advisor Dr. Belaineh Legesse, who accepted me to be his
advisee and his encouragement, support as well as his guidance and assistance throughout
this research. He offered me very valuable comments since the development of the proposal.
I would like to thank the people without whom the field works would have not been possible,
namely the four enumerators, the supervisor, the facilitators, the household members, key
informants, and case individuals / households.
I am very much grateful to my families for their understanding during my study. Special
appreciation is extended to my wife, Tigist Tsegaye, her assistance in data entry, in addition
to the encouragement and support she offered me, was critical for the completion of this
study. I would like to thank my children, for bearing the opportunity cost of this study.
viii
TABLE OF CONTENTS
STATEMENT OF THE AUTHOR iv
ACRONYMS AND ABBREVIATIONS v
BIOGRAPHICAL SKETCH vi
ACKNOWLEDGEMENTS vii
TABLE OF CONTENTS viii
LIST OF TABLES xi
LIST OF FIGURES xii
LIST OF TABLES IN THE APPENDICES xiii
ABSTRACT xiv
1.INTRODUCTION 2
1.1. Background of the Study 2
1.2. Statement of the Problem 3
1.3. Research Questions 4
1.4. Objectives of the Study 4
1.5. Significance of the Study 4
1.6. Scope and Limitations of the Study 5
1.7. Organization of the Thesis 6
2.LITERATURE REVIEW 7
2.1. Operational Definitions of the Key Concepts 7
2.1.1. Pastoralists and livelihoods diversification 7
2.1.2. Motives of diversification 8
2.1.3. Measurements of diversification 9
2.1.4. The concept of poverty 10
2.1.5. Poverty and the pastoral context 10
2.1.6. Measurements of poverty 11
2.2. Theoretical Framework for Studying Livelihood Diversification 12
2.2.1. Sustainable livelihood framework 13
2.2.2. Components of livelihood system 13
2.2.3. Relationships between and within the livelihood components 16
2.2.4. Some strengths and limitations of the sustainable livelihood framework 16
2.3. Review of Empirical Literature 17
Continuous…
ix
2.4. Conceptual Framework 20
3. RESEARCH METHODOLOGY 22
3.1. Description of the Study Area 22
3.2. Sample Size and Sampling Techniques 23
3.3. Data Collection Methods 23
3.4. Methods of Data Analysis 24
3.4.1. Determinants of household diversification 24
3.4.2. Specification of the model 26
3.4.3. Definition of variables in the model 29
4. RESULTS AND DISCUSSION 35
4.2. Sources of Income of Pastoral Households 36
4.2.1. Income of households from pastoralism 37
4.2.2. Income of households from farming 37
4.2.3. Income of households from non-farm non-pastoral (NFNP) sources 38
4.3. Demographic and Socioeconomic Characteristics of Sample Households 40
4.3.1. Sex and marital status of the household heads 40
4.3.2. Age and educational level of the household heads 41
4.3.3. Household size of sample respondents 42
4.3.4. Livestock ownership of sample households 43
4.3.5. Access to veterinary services 45
4.3.6. Access to credit services 45
4.3.7. Access to all weather roads 46
4.4. Households’ Expenditure Pattern and Livelihood Diversification 48
4.5. Determinants of Pastoral Livelihood Diversification 49
4.6. Effect of Livelihood Diversification on Pastoral Households’ Poverty Status 54
4.6.1. Poverty measures 54
4.6.2. Demographic characteristic and poverty status of households 55
4.6.3. Physical capital of households and poverty status 57
4.6.4. Financial capital of households and poverty status 57
4.6.5. Access to all weather roads and poverty status 58
4.6.6. Households’ diversification status and poverty status 58
4.7. Determinants of Households’ Poverty Status 59
5. SUMMARY, CONCLUSION AND POLICY IMPLICATION 63
Continuous…
x
5.1. Summary 63
5.2. Conclusion and Policy Implications 65
6. REFERENCES 68
7. APPENDICES 76
xi
LIST OF TABLES
Table Page
1: Number of sample households in pastoral kebeles 23
2: Distribution of sample households with nature of diversification 36
3: Livelihood diversification by income sources 39
4: Distribution of sample households by headship 40
5: Distribution of sample households by marital status 40
6: Distribution of sample households by age 41
7: Educational level of sample household heads 42
8: Family composition of sample households 43
9: Livestock ownership of sample households (in tlu) 44
10: Access to veterinary clinic and diversification (%) 45
11: Access to credit services and diversification (%) 46
12: Access to all weather roads and diversification (%) 47
13: Summary of descriptive statistics analysis results related to livelihood diversification 47
14: Households’ food and non-food items expenditure by diversification level 48
15: Ordered probit estimates and marginal effects for determinants of pastoral livelihood
diversification 53
16: Absolute poverty indices of respondents 55
17: Sex of household heads based on poverty status 55
18: Educational level of household head by poverty status 56
19: Age, family size and dependency ratio of household by poverty status 56
20: Total livestock holding of households (tlu) by poverty status 57
21: Sources and amount of income of sample households oy poverty status 58
22: Access to all weather road and poverty status 58
23: Diversification status and households’ poverty status 59
24: Binary logit coefficient estimates, odds ratio and marginal effects for determinants of
poverty 62
xii
LIST OF FIGURES
Figure Page
1. Sustainable livelihood framework 15
2. Map of the study area 23
3. Livestock wealth status 44
xiii
LIST OF TABLES IN THE APPENDICES
Appendix Table Page
1. Simpson’s index of diversity (sid) for the sample households 76
2. Households’ average food items expenditures by diversification 76
3. Households’ average non-food items expenditures by diversification 76
4. Coefficient of correlation and variance inflation factors for continuous variables of
multinomial logit model 77
5. Contingency coefficient for discrete independent variables of multinomial logit
model 77
6. Correlation matrix for continuous explanatory variables in binary logistic model 77
7. Contingency coefficient for discrete independent variables of binary logit model 78
8. Households’ survey questionnaire 78
xiv
Livelihood Diversification among Pastoralists and Its Effect on Poverty: The Case of
Amibara District, Zone Three of Afar National Regional State
ABSTRACT
Over thousands of years, pastoralists have managed their resources and livelihoods in the
face of environmental challenges and difficult socio-economic conditions. However, since
recent decades pastoralists are challenged in maintaining their livelihoods and coping
mechanisms due to a range of ecological, demographic, economic, social, political and
climatic changes. Such changes and crises can all easily reduce large numbers of
pastoralists to destitution and sometimes cause a large-scale exodus from pastoralism. Thus,
this study was conducted to show how the Afar pastoralists are acting, reacting and
interacting with the above-mentioned factors and the effects of these activities on the poverty
status of the households. A structured questionnaire, Focus Group Discussion and Key
Informant Interview were used for data collection from 120 selected sample households in
Amibara district. The Simpson’s Index of Diversity was used to determine the livelihood
diversification level of the household and the ordered probit and binary logit models were
used to identify the determinants of livelihood diversification and poverty status of the
households, respectively. The results show that the majority of the sample households
(55.33%) were found to be diversified with average diversity index of 0.46. In terms of
diversification level, 46.67% of the respondents were non-diversified, while 37.50% and
15.83% were moderately and highly diversified, respectively. The ordered probit result shows
that age, livestock holding in tropical livestock unit and distance to the nearest market were
negatively and significantly influence livelihood diversification. Whereas, level of education,
available family labor and access to credit were positively and significantly influence
livelihood diversification. Moreover, the binary logit model shows that sex, educational level,
diversification level and total annual income were found to be significantly and negatively
influence poverty, while age and family size were positively and significantly influence
poverty status. The study concluded that households are diversifying their livelihoods and
diversification has an effect of reducing poverty in the study area. Hence, promoting
education, expanding diversification opportunities, creating market linkage and accessing
financial services are indispensable policy interventions to better livelihood.
Key words: Pastoralists, livelihood diversification, poverty, Simpson’s index of diversity,
ordered probit, Afar Regional State
1. INTRODUCTION
1.1. Background of the Study
Rural areas are the economic backbone of most developing countries. Depending on a
country’s level of advancement in the economic sphere, they contribute to overall economic
growth by creating jobs, supplying labour, food, and raw materials to other growing sectors
of the economy; and helping to generate foreign exchange (Zerihun, 2012). Despite these
significant contributions, however, the changing socio-economic, political, environmental
and climatic atmosphere in developing countries across the globe has continued to aggravate
the living conditions of most households specially those living in rural areas. They are
characterized by poverty, food insecurity, unemployment, inequality, lack of important
socio-economic services, etc. The accompanying increase in poverty levels has led residents
of these economies to diversify a number of strategies to cushion the negative effects of these
changes (Ogato, 2013).
The two broad traditional socio-economic systems, which support the livelihoods of millions
of rural populations in Ethiopia, are the traditional crop farming system and the pastoral
system. Studies over the last three decades have shown that pastoral systems are relatively
productive and represent an ecologically sustainable way of using arid and semi-arid lands
globally (Krätli et al., 2013).
The pastoral system is extremely important and is the most prevalent land use in arid and
semi-arid environments. Sixty five percent of global drylands consist of grassland used for
livestock production contributing to the livelihoods of 800 million people (Mortimore,
2009). In Africa, this system is located in the arid and semi-arid zones extending from
Mauritania to the northern parts of Mali, Niger, Chad, Sudan, Ethiopia, Kenya and Uganda.
There are also pastoral areas in the arid zones of Namibia, Botswana, South Africa and
Southern Angola. The pastoralist population in Africa is estimated at 268 million (over a
quarter of the total population), living on area representing about 43 percent of the
continent’s land mass (AU, 2010). The arid and semi-arid lowlands (ASAL) of eastern Africa
covers most of Djibouti, large areas of southern and eastern Ethiopia, the vast majority of
Kenya, Sudan and virtually all of Somalia contain the largest grouping of pastoralists in the
world.
2
Ethiopia is home of more than 15 million pastoralists who live in the peripheries of arid and
semi-arid lowlands (ASAL) of the country, which constitute around 61% of the total land
mass. The majority of pastoralists constitute the ethnic groups of the Somali (accounting
57% ), the Afar (26%), the Oromo of Borana and Karrayu (10%), while the remaining 7%
of Ethiopian pastoralists inhabit the lowlands of the Southern Nations Nationalities and
People’s Regional State (SNNPRS) and Gambella regions (Virtanen and Gemechu, 2011).
Despite the commonly held views that pastoralism fails to maximize the productive potential
of livestock production, the value of pastoralism should not be underestimated. Pastoralists
in Ethiopia own 42% of the livestock population of the country, which in turn contributes
12-16% of the gross domestic product (GDP) and 30-35% of the agricultural GDP (MoA,
2013). The system also employs about 27% of the total national population and contributes
about 90% of hard currency generated from live animal export (Kassahun et al., 2008).
Moreover, the pastoral areas are rich in biodiversity, minerals and water resources as well as
energy resources, and untapped tourist attractions.
Poverty remains particularly very intense in the pastoral regions despite the attempts to
improve poverty situation through SDPRP and PASDEP. High vulnerability coupled with
low income and food insecurity aggravates the poverty situations in those areas. Any kind of
poverty or vulnerability reduction strategy should be based on a solid understanding of their
overall life style, culture and mobility patterns. This would help in identifying the needs and
priorities of pastoral communities in the fight against poverty and overall socio economic
development of their areas (PFE, 2007).
Afar region with a population of about 1.4 million (CSA, 2011) is a low land area in North-
Eastern Ethiopia. Of the total population, 90 percent were classified as pastoralists.
Livestock rearing, mostly traditional pastoralism is the dominant activity and the biggest
occupation of the economically active population of the region. Livestock production in the
region depends on rain fed natural pasture, which its productivity is declining as the result
of recurrent drought, land degradation, encroachment of agriculture, conflict and invasion of
weeds. Moreover, livestock production is further constrained by seasonal water shortage,
livestock disease, poor infrastructure, and lack of markets. The per capita livestock holding
has declined alarmingly, which is now only about 1.2 TLU per capita, 30 percent of TLU
3
recommended for active pastoral life. As a result, livestock production is unable to support
the ever-increasing human population in the region (Joanne et al., 2005).
With these changes, pastoralism may become an inadequate means of earning a living among
the Afar pastoralists and diversifying into other livelihood strategies seems more likely if
they are to survive, improve their well-being and reduce vulnerability. This study therefore
was formulated to identify livelihood strategies that Afar pastoralist households were
adopting or had adopted as traditional pastoralism becomes difficult to pursue, the pattern of
the livelihood diversification process as well as the effects of this endeavor on poverty.
1.2. Statement of the Problem
The pastoral production system in Afar region is said to be under a critical situation in the
sense that it has become unable to support the basic needs of the people whose very survival
is strongly linked to the performance of this sector. Moreover, hence, the number of people
dropping out of the pastoral system has increased considerably.
Cognizant of the fact that traditional pastoralism is becoming unfavorable option to the Afar
people; the increasingly deteriorating living condition has forced the people to take up non-
pastoral pursuit. The involvement of the Afars in non-pastoral pursuits such as working for
wage, crop cultivation, sale of charcoal and trade is increasing from time to time. Most of
these activities are new, some are socially unacceptable and informal, and others cause
environmental degradation. For example, fuel wood and charcoal selling are informal,
against the Afar traditional rules and causes environmental degradation. Whatsoever forms
they take, however, these subsidiary activities have become important in the lives of the
pastoralists (Kassa, 2001).
In the region, along with the Awash River basin, there is are large-scale commercial farms,
that mainly produce cotton and presently sugar cane, which is competing for the area that
serves as long season grazing for livestock on the one hand and creates job opportunities for
the pastoralists on the other hand. Additionally, a large proportion of rangelands that serve
as wet season grazing for livestock have been encroached by invasive alien species (IAS)
known as Prosopis juliflora. The invasion leads to shrinkage of the rangelands and
grasslands and therefore, threaten sustained existence of the pastoral system in the area (like
seasonal herd mobility, herd composition, mutual helping institutions and others). Besides,
the invasion is making paths to water points and grazing areas inaccessible.
4
The combined effect of commercialization, climate change and invasion of alien species
contributed to increased pressure on the remaining pasture and forced the Afar pastoralists
to diversify their livelihoods. Nevertheless, the existing few researches regarding how the
Afar pastoralists are acting, reacting and interacting with the above mentioned factors and
scholarly efforts made to understand the effects of these activities on the welfare of the
households are not enough. Hence, the present study was aimed to close the existing
knowledge gap and lacuna on the relationships between livelihood diversification and
poverty in the study area.
1.3. Research Questions
The key questions of interest in studying the livelihood diversification among pastoralists
are the following:
1. What types of non-pastoral activities do pastoralists pursue?
2. What determines pastoralists’ participation in various non-pastoral economic
activities?
3. What effect would livelihood diversification have on the poverty status of
pastoralists?
1.4. Objectives of the Study
The overarching objective of this study is to measure the level of rural household
diversification and its linkage with poverty status of the pastoralists in the study area.
The study has the following specific objectives:
1. To explore the level of livelihood diversification status of the households,
2. To analyze the major determinants of household diversification status, and
3. To analyze the effects of livelihood diversification on pastoral households’ poverty.
1.5. Significance of the Study
The livelihood of most of the people in developing countries is highly dependent on
agriculture and pastoralism is the key agricultural production system in the dry lands; but the
carrying capacity of the sector is decreasing over time due to rate of increase in population
and the corresponding reduction in farm size and shrinkage of rangelands. As a result, the
participation of rural household members in a number of activities (both on and off-farms)
is increasing. It is, therefore, crucial to closely examine the cause and effect of diversification
5
to better understand the situation and explore policy options to rationally address it. It is also
important to have an understanding of households’ preferred livelihood diversification
strategies and the extent to which these strategies are feasible if appropriate interventions are
to be effective in reducing rural poverty and vulnerability to poverty. Therefore, this study
addresses the conditions facing pastoralists on their livelihoods and how to respond to
overcome such problems.
The majority of households in rural Ethiopia in general and pastoralists in particular are poor,
often face income fluctuation and fail to smoothen their consumption patterns due to price
changes, weather related shocks, pests, death and illness of family members, as well as
livestock. Hence, livelihood diversification is important for both poor and non-poor
households, but with different motivations. This study is then significant in understanding
the relationships between livelihood diversification and poverty among pastoralists.
It is also believed that the results of this study are important in providing valuable
information that can contribute to more evidence based decision making occurring across
the study area and inform policy decisions regarding poverty reduction strategies that may
be extrapolated to other districts and zones of the region.
Moreover, such an understanding of the determinants of livelihood diversification and its
effects on poverty can help government to prepare alternative livelihood development
programs in the area that can effectively reduce poverty. Furthermore, the findings of the
study will be useful to policy makers, NGOs and others in devising follow up actions for
livelihood development strategies and poverty reduction policies. Additionally, it paves the
way and gives an insight to researchers and academicians who are interested to conduct
detailed investigations of livelihoods diversification and poverty in other areas.
1.6. Scope and Limitations of the Study
The study aims at identifying the factors that determine pastoralists’ livelihood
diversification and investigating its effect on poverty status. This study is limited to one
district of zone three, Amibara district, of Afar region. This is because of limited availability
of resources to undertake on a wider scale. For the same reason, the sample size was confined
to limited number of respondents.
The present study took in to consideration cross-sectional data, which limits the wider and
an in-depth generation of information on livelihood diversification strategies over time and
6
space in the study area. The unit of analysis, like in many studies is the household, which
impose certain limitations. The household is not a homogeneous block; rather, it is internally
complex with different members (men, women and children) having different roles and
autonomy of control over resources including those crucial to diversification. The fact that
disaggregated approach to the family was not adopted is thus one important limitation.
Despite these limitations, the study can serve as a starting point to undertake further research
in other areas.
1.7. Organization of the Thesis
The entire study has been presented in six chapters. The remaining part of the thesis is
organized as follows. The second chapter deals with the review of relevant research efforts
that have a bearing on the objectives of the present study. Chapter three describes the main
features of the study area and the methodologies employed in collecting and analyzing data.
The next chapter is devoted to result and discussion of the study through a variety of tables.
Chapter five concentrates on summary and conclusions of the main findings of study along
with policy implication. The last chapter gives the references and appendices.
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2. LITERATURE REVIEW
An attempt is made in this chapter to discuss concepts used in the present study and to review
the available literatures on the subject of the study.
2.1. Operational Definitions of the Key Concepts
2.1.1. Pastoralists and livelihoods diversification
According to Swift (1988), pastoralists are households or population where more than 50%
household income/consumption is derived from livestock or livestock related activities,
either because of sales of livestock and livestock products or of direct consumption.
Pastoralism is uniquely well adapted to dry land environments. As an economic and social
system, it operates effectively in low and highly variable rainfall conditions. However, in
Ethiopia pastoralist livelihoods systems are becoming increasingly vulnerable. Human
populations are rising, the climate is changing and international markets are ever-higher
barriers for access. Infrastructure is poorly developed, education and literacy level remain
very low and competition for scarce resources is increasing (Pantuliano and Wekisa, 2008).
Pastoralist systems and related livelihoods are increasingly under pressure and caught in a
downward spiral of resource depletion, poverty and diminishing resilience against drought
related emergencies. Livelihood diversification is the key strategy assisting pastoralists to
become less dependent on livestock as their sole household assets and income generating
activity (UNOCHA-PCI, 2007).
According to Pantuliano and Wekisa (2008), one or more of the following characterize the
challenges that the population in the pastoral areas of Ethiopia faces:
1. Loss of productive assets (livestock/farming/irrigated land) due to drought, floods,
disease and livestock theft.
2. Declining sustainability as livestock holdings decrease and the human population
grows.
3. Declining livestock and agricultural productivity due to poor husbandry practices and
technologies.
4. Environmental degradation and deterioration of natural resources to the point that
production may decline below recovery levels.
5. Breakdown of traditional institutions and social relations.
8
6. Inability to access markets and achieve maximum prices for livestock products.
7. Low socio-economic empowerment of women and youth.
8. Geographical isolation in terms of infrastructure, communications and basic services.
9. Increasing impoverishment of communities and households.
Pastoral diversification is defined as “the pursuit of any non-pastoral income earning activity,
whether in rural or urban areas”. This definition includes: (1) any form of trading occupation
(e.g., selling milk, firewood, animals, or other products); (2) wage employment, both local
and outside the area, including working as a hired herder, farm worker, and migrant laborer;
(3) retail shop activities; (4) rental property ownership and sales; (5) gathering and selling
wild products (e.g., gum arabica, firewood, or medicinal plants); and (6) farming (both for
subsistence and cash income) (Little, 2009). The diversification of livelihoods can either
offer opportunities for pastoralists or if not properly managed add to the pressure on them.
Research shows that while some forms of diversification enhance welfare, others can
increase risk (COMESA, 2009).
2.1.2. Motives of diversification
Barrett et al., (2001) and Reardon (2000) discus two sets of motives for diversification of
activities by rural households; the first set of motives comprises what are termed as “push
factors”. These factors are response to diminishing factor returns in any given use (Barrett
et. al., 2001). Push factors include bad conditions or relatively worse returns in one activity,
relative to other activities. According to Reardon (2000), bad conditions include; (1)
inadequate agricultural input (e.g., a drought or land constraints), (2) missing or incomplete
agricultural insurance (which necessitate diversification to use for ex-post coping
mechanism during drought that cause loss of herd or harvest shortfalls), (3) riskiness of
agriculture (which necessitate the need to manage income and consumption risk through
diversification strategies, by undertaking activities which have returns with a low or negative
correlation with returns to agriculture, (4) absence or failure of agricultural input credit
markets (which necessitate diversification to pay for agricultural inputs such as veterinary
input or crop input). Therefore, from the push factor perspective, diversification is driven by
limited risk bearing capacity that creates strong incentives to select a portfolio of activities
in order to stabilize income flows (Barrett et al., 2001).
The second set of motives comprises “pull factors”. Pull factors include, for example, better
returns in one activity, relative to others (Barrett and Reardon, 2000). From the pull factors
9
perspective, local engines of growth such as commercial agriculture or proximity to an urban
area create opportunities for income diversification (Barrett et al., 2001). The consequence
of the presence of the above motivating factors lead to a wide spread diversification. Under
the broad framework of “push” and ‘pull” factors of motives for diversification; there are
different origins of diversifications that led individuals take different activities to earn
income.
Diversification could also emerge as ex-ante risk management strategy. It is widely
understood as a form of self-insurance in which people exchange some foregone expected
earnings for reduced income variability achieved by selecting a portfolio of assets and
activities that have low or negative correlation of incomes (Little et al., 2001; Reardon,
1997). If risk aversion is decreasing in income and wealth, then the poor will exhibit greater
demand for diversification for the purpose of ex-ante risk mitigation than do the wealthy
(Barrett and Reardon, 2000).
2.1.3. Measurements of diversification
Elements in livelihood diversification that might be used to capture and measure
diversification portfolio could be asset, activity, and income. It is difficult to aggregate
activities into a single measure that spans asset categories and it necessarily miss the income
that accrues from non-productive capital (Barrett and Reardon, 2001).
There are various indicators or indices used to measure livelihood diversification like
number of income sources and their share, Simpson’s index, Herfindel index, Ogive index,
Entropy index, modified entropy index, composite entropy index (Dulruba and Roy, 2012).
One definition of diversification is related to the number of income sources and the balance
among them. Hence, taking the number of income sources as a measure of diversification
may be criticized on several grounds. First, a household with more economically active
adults, all things being equal, will be more likely to have more income sources. This may
reflect household labor supply decision as much as a desire for diversification. Second, it
may be argued that there is discrepancy when comparing households receiving different
shares of their income from similar activities (Biswarup and Ram, 2011). Since the definition
of diversification relates the number of income sources and the balance among them, the
Simpson index of diversity is widely used to measure the diversity. Joshi et al., (2003) used
Simpson index to compare crop diversification in several South Asian countries. Aneani et
10
al., (2011) also used Simpson index to analyze the extent and determinants of crop
diversification in Ghana. Biswarup Saha and Bahal (2011) also adopted the Simpson index
to measure livelihood diversity.
2.1.4. The concept of poverty
Poverty is complex in its conception, causation, manifestation, diagnosis, and in the policies
devised and implemented in pursuit of its reduction (Kakwani and Silber, 2007). Complexity
can be discerned from what Maxwell (1999) calls the “fault lines” of the debate: the aspects
that separate different interpretative approaches adopted in advancing the conceptualization
and measurement of poverty. The relevant dichotomies include individual or household
measures; private consumption only or private consumption and publicly provided goods;
monetary only, or monetary plus non-monetary components; snapshot or timeline; actual or
potential poverty; stock or flow measures; input or output measures; absolute or relative
poverty; objective or subjective preconceptions of poverty.
In developing countries, poverty is perceived as deprivation in both physiological and
sociological aspect of the people (World Bank, 2000). The physiological deprivation
includes issues of basic necessities for life (food, shelter, basic education, and access to basic
health care). This measures the absolute poverty (cost of food and non-food) and level of
income. The second perspective is the sociological deprivation-it is the powerlessness and
voiceless-ness. This deprivation is considered from the social aspect not from individual
viewpoint. It measures encroaching poverty; thus, it looks poverty as process not a time
event.
2.1.5. Poverty and the pastoral context
Application of the concept of poverty in the agrarian systems must be distinguished from its
application to the pastoral context. In agrarian systems, the concept is built primarily around
“access to agricultural land” (Khan, 2000). Plot size and yield are measured to see if the
harvest can support the household, and the capacity to buy agricultural inputs, along with
possession of oxen, are further indicators of household wealth or poverty. Such observations
are poorly attuned to the pastoral context. As a result, the rural poverty discourse has tended
to either misdiagnose pastoral poverty or neglect it entirely (Tache and Oba, 2008).
Pastoralists are characterized by cultural and economic orientation towards livestock.
Families depend on livestock for a significant part of their income and food. Large herds
11
guarantee subsistence and income, confer status and it is regarded to provide insurance
against impact of drought (Wurzinger et al., 2008). Since pastoralists accumulate wealth in
the form of livestock, their poverty conception is built mainly on asset holding (livestock),
and poverty analysis should thus consider processes that affect asset building. Stocklessness
holds serious ramifications for pastoral households because it is economically degrading,
and thus affects human relationship among family members (Baxter, 1991). Moreover, the
lack of livestock adversely affects the capacity of local institutions.
In the context of pastoralism, poverty is viewed as lack of animal ownership. Lack of animals
is an obvious and important rural poverty indicator, more so in pastoralism since livestock
is the key asset and the primary source of such a livelihood. However, ownership of animals
or lack of them as a single criterion can hardly explain poverty in the pastoral context, since
the wider political and economic contexts are crucial in shaping present-day pastoral life
(Mohamed S, 1985).
2.1.6. Measurements of poverty
The application of inappropriate conceptions and measures of poverty will lead to its
misdiagnosis, both in terms of who the poor are and how poor they are. Misdiagnosis of
poverty will in turn lead to flawed targeting for development and relief, and ineffective
alleviation measures; an incomplete understanding of poverty may fail to utilize, and may
even marginalize, local institutions that may be crucial in its alleviation. On a broader level,
flawed targeting and inappropriate measures may be counterproductive and eventually
undermine the environment and the culture on which pastoral livelihoods depend. A critical
examination of poverty, its causes, its manifestation, and its alleviation in a pastoral context
is thus crucial from perspectives related to indigenous rights, sustainable land use, and the
achievement of international poverty reduction targets.
Research from pastoral areas of northern Kenya (Little et al., 2008) shows that when poverty
assessments in pastoral areas use indicators which are transferred from non-pastoral areas,
the result can be a misrepresentation of poverty, and a simplistic labeling of all or at least a
very high proportion of pastoralists as “poor”. The authors of the Kenya research argue
convincingly for poverty assessments in pastoral areas, which prioritize alternative measures
of poverty and specifically, livestock assets.
12
An alternative, asset-based approach to measuring poverty in pastoral areas fits well with
livelihoods analysis and related frameworks, and the ways in which pastoralists themselves
define wealth and poverty. Notably, throughout the Horn of Africa pastoralists use livestock
holdings as the basis for their descriptions of wealth. Furthermore, they explain the
relationship between livestock and wealth by reference to livestock as both financial capital
and social capital. Integral to pastoral livelihoods are the use of livestock as a form of savings
and to exchange for cash or food (financial capital), and the use of livestock as the basis for
complex social support systems, based on loans and gifts of livestock and livestock products
(social capital). In summary, while some countries measure poverty in pastoral areas using
income, market access, education and other indicators, livelihoods analysis and pastoralists
themselves tend to use livestock ownership and various aspects of social capital as the main
determinants of wealth (Yacob and Andy, 2010).
2.2. Theoretical Framework for Studying Livelihood Diversification
Over the years, various theoretical frameworks have been used in analyzing household
livelihoods. However, most were micro-economic models not adequate to cover factors
shaping household livelihoods. In the 1960s, studies on rural livelihoods took the form of
peasant studies. They examined all spheres of household economy and incorporated Marxian
concepts of surplus extraction, differentiation and class formation (Start and Johnson, 2004).
These studies were followed by the new household economics and agricultural model, which
argued that the behavior of a household is motivated by its tendency to acquire utility goods
using labor and other resources. Thus, if preferences, resources and technological
capabilities of a household were known, it would be possible to predict accurately the
behavior of a household (Start and Johnson, 2004).
The entitlement approach developed in 1980s focused on poverty and famine and tried to
understand how limited endowments were transformed into commodities. Key to this
approach was the phenomenon of entitlements, the effective command and control an
individual has over a commodity bundles that can be acquired. In late 1980, the concept of
sustainable livelihood securities was used to analyze households. It argued that tangible and
intangible stocks and capabilities are transformed into flows by livelihood activities which
contribute to the well-being of a household. This approach was later modified by including
a balance between production, exchange and consumption. Similarly, material and
immaterial assets replaced stocks and capabilities. This was further refined into a livelihood
13
entitlement framework in the mid-1990s where households were seen as balancing sources
of entitlements with utilization of the same entitlements.
An examination of these early frameworks reveals that they were not adequate to guide a
study on diversification of livelihood strategies in a pastoral community. They were
applicable to peasant agriculture households and laid more emphasis on economic aspects
thus were not holistic. Due to these limitations, this study rejected them in favor of the
sustainable livelihood framework.
2.2.1. Sustainable livelihood framework
The Sustainable Livelihood Framework (SLF) as formulated by the Department for
International Development (DFID) (1999) has underpinned this study. It argues that people
have objectives (livelihood outcomes) that they desire to achieve in their lives. In striving to
achieve them, they undertake certain activities (livelihood strategies) using certain resources
(livelihood assets) that they can access. However, this endeavor is mediated by structures
and processes, which determine access, terms of exchange and returns. The interplay of these
processes takes place in a wider external environment of vulnerability.
2.2.2. Components of livelihood system
According to the SLF, a livelihood system compromises of five components linked, related
and influencing each other in a myriad of ways (fig. 1). They include livelihood assets;
transforming structures and processes; livelihood strategies, vulnerability context and
livelihood outcomes. Each component is made up of sub-elements that influence each other
internally.
A. Livelihood assets
Livelihoods assets are resources available to an individual, household or group that form the
basis of livelihood activities. SLF distinguishes five types of assets (human, social, natural,
physical and financial) that people require to attain positive livelihood outcomes and no
single asset is sufficient to yield outcomes that people seek. Access to these assets tends to
be dynamic and limited; hence, people combine them in innovative ways to ensure survival.
The human capital refers education, skills, knowledge, capacity to work, capacity to adapt,
good health and others that enable people to pursue livelihood strategies. Social capital
includes a sense of community, family and social networks as well as membership of groups,
relationships of trust and access to wider institutions of society up on which people may
14
draw in the pursuit of their livelihood. It also includes political aspects such as rights of
participation and political empowerment. The financial capital are the financial resources
available to people. They take many forms such as savings, regular remittances and pensions,
sources of credit (bank, micro-finance) as well as wages. Financial capital provides people
with a variety of livelihood options. Physical capitals refer to the basic infrastructure and
producer goods that enable people to pursue their livelihood activities. Infrastructure
includes transport (roads, vehicles), secure shelter, and buildings, water supply and
sanitation, energy and communication as well as markets, clinics, schools and other tertiary
institutions. Producer goods entail technology, tools and equipment. Natural capital refers
the natural resource stock from which other resources flow from e.g. land, water, minerals,
air, trees and forests products, livestock, wildlife, biodiversity, etc.
B. Transforming structures and processes (TSPs)
Structures refer to organizations (private and public) that set and implement policy and
legislation, deliver services, purchase, and trade and perform functions that affect
livelihoods, while processes are the means through which structures function and includes
policies, legislations, institutions as well as culture and power relations. The two aspects
shape people’s livelihoods for they enhance or obstruct access to various types of capital,
livelihood strategies, decision making bodies and sources of influence. They also determine
the terms of exchange between different types of capital as well as returns from any
livelihood strategy. Besides these influences structures and processes also have a direct
impact on weather people are able to achieve a feeling of inclusion and well-being.
C. Vulnerability context
According to this framework, people exist in a context of vulnerability characterized by
trends, shocks and seasonality that cause direct and indirect hardship. Availability of
livelihood assets is affected by trends and changes in population, natural resources, national
and international markets and trade as well as globalization. Similarly shocks, in human
health (sickness, death of family member) in nature (drought, floods), crops and livestock
(diseases, theft) as well as conflict affect availability of assets and peoples’ livelihoods.
Furthermore, seasonality in prices, production, and health or employment opportunities also
affects availability of assets and peoples’ livelihoods. In general, people have little or no
control on the vulnerability context.
15
D. Livelihood strategies
In the SLF, this component entails the range and combination of choices that people make
as well as activities they undertake in order to attain their livelihood goals. It includes
productive activities, investment strategies and social arrangements. Peoples’ livelihood
strategies are determined by the diversity of assets that they can access taking in to account
the vulnerability context as well as transforming structures and processes. Two types of
livelihood strategies can be distinguished; namely, adaptive and coping. The later describes
short-term choices and activities that people can make and undertake because of a shock
while the former entail long-term choices and activities.
E. Livelihood outcomes
This component describes the goals that people pursue in their lives. They vary from
household to household and community to community but the SLF identifies five that it
considers common; namely, more income, increased well-being, reduced vulnerability as
well as improved food security and more sustainable use of the natural resource base. A
household or community can pursue one or more of these goals. Goals motivate people and
thus determine their behavior and priorities.
Figure 1:- Sustainable Livelihood Framework Source: Adapted from Ellis (2000)
Vulnerability context
Shocks
Trends
Seasonality
Livelihood assets
Natural
Physical
Human
Financial
Social
TSPs
Transforming
Structures and
Processes
Livelihood Strategies
Agri. Intensification/ extensification
Diversification
Migration
Livelihood outcomes
More income
Reduced vulnerability
Increased well-being
16
2.2.3. Relationships between and within the livelihood components
The SLF identifies different sets of relationships and influences between and within the five
components making up the livelihood system. Trends, shocks and seasonality can destroy or
create livelihood assets. Livelihood assets can be combined to generate positive or negative
outcomes. Depending on their nature, outcomes can be buildup or erode the asset base of a
livelihood system. Together with Transformation Structures and Processes, livelihood assets
determine the range of livelihood strategies available to people as well as their ability to
switch between different livelihood strategies. TSPs can create assets by investing in
infrastructure and technology generation; determine access through ownership rights and
institutions regulating access to common resources; and influences rates asset accumulation
through policies affecting returns from various livelihood strategies and taxation. Individuals
and groups can also influence TSPs especially if their asset endowment is immense.
Similarly, TSPs have a direct relationship with the vulnerability context specifically through
the establishment and implementation of policies, (fiscal, economic, population and health).
TSPs also influence the impact of external shocks through drought relief. Well-functioning
markets can also reduce the effects of seasonality. By formulating and implementing welfare
policies, TSPs can increase or reduce people’s sense of well-being. Various policies also
have an impact on sustainable use of natural resources. Through these mechanisms, TSPs
affect livelihood outcomes.
2.2.4. Some strengths and limitations of the sustainable livelihood framework
SLF is a flexible framework. It can be used as a set of principles to guide development or to
analyze livelihoods. Besides putting people at the center of development, it considers the
aspect of sustainability seriously, as it does not seek to facilitate human development at the
expense of the environment. Furthermore, it is holistic as it considers various sectors that
interrelate to shape the livelihoods of people. Similarly, SLF views livelihoods as dynamic
as opposed to being static. Nevertheless, there are some concerns on the SLF.
Although it argues that people are at the center of development, they are not visible in its
diagrammatic representation of its component. Thus, it is likely to be too mechanical. In
addition, by limiting assets in to five capitals it ignores other aspects such as culture that
shape peoples’ livelihoods. Similarly, it ignores the common occurrence that individuals or
groups in the society tend to manipulate rules and sanctions especially when resources are
scarce.
17
2.3. Review of Empirical Literature
Studies have found a positive correlation between households’ welfare and their involvement
in other non-farm activities (Stifel 2010; Barrett et al., 2001). These studies have also found
that rural households with the ability to diversify their income sources to other non-farm
activities tend to perform better economically than those that take up non-farm activities as
a coping strategy. It has also been observed that poor households are prevented from taking
up superior livelihood strategies due to a number of entry barriers. These barriers include
low asset endowment, access to formal credit, information or market and demographic
factors such as level of education, sex or age of the household head (Stifel, 2010). These
barriers will constrain a household form taking up more lucrative livelihood strategies.
Diversification is therefore a coping strategy that households use to maintain their level of
welfare and ensure achievement of food security.
Nasa’I et al., (2010) on their study of analysis of factors influencing livelihood
diversification among rural farmers in Ginia local government area Kudana state in Nigeria
using logistic regression, found that amount of credit received by farmers and belonging to
farmers’ organization have positive influence on farmers’ livelihood diversification. They
also found that natural resource and natural disaster have a positive influence on livelihood
diversification. This study also shows that diversified farmers are relatively food secured
than undiversified farmers. The relationship between livelihood diversification and rural
households’ food security shows that there is a strong positive association between livelihood
diversification and rural households’ food security. Therefore, multiplying food and income
sources through livelihood diversification is a positive action to run away from vicious circle
of poverty, unemployment and inequality bedeviling poor rural producers and their families.
A study of the Somali region of Ethiopia by Devereux (2006), found that almost 70 percent
of households engage in livestock rearing, but large shares also engage in cereal crop
production (43.4 percent), firewood production (17 percent), and charcoal production (14.7
percent). While smaller but not insubstantial numbers of households, engage in various
cottage industries (for example, mat making at 6.3 percent), petty trade or services, or higher
value crop production. Salaried employment is present in just 3.2 percent of households.
Similar levels of diversity are found in the Afar and Borena regions of Ethiopia, and in
Kenya, there is generally more diversification, although this also varies across space
(McPeak et al., 2011). Of course, this diversification is in some sense marginal: The capacity
18
to scale up petty cottage industries and firewood or charcoal production is limited, and in the
case of firewood and charcoal production a scaling up would be undesirable for
environmental reasons. Arguably, only gum Arabic has reasonable opportunities for scaling
up, given strong international demand.
Fikru (2008) identified dominant patterns of nonfarm rural diversification and analyzed the
key constraints and opportunities as well as the determinants and principal motivations
behind non-farm diversification in Lome district, Oromiya Regional State. The result
indicated that diversification in to low entry barriers, low return activities are predominant.
Diversification in to high value, high returns activities are virtually absent. Micro-enterprise
based diversification, while generally limited, was dominated by petty trade and household
level small-scale activities. Manufacturing comprises a negligible part of all non-farm
activities. Lack of access to sufficient fixed and working capital is a major constraint to
undertake high return non-farm activities. Poor infrastructure, especially lack of electricity,
was also found to constraint diversification. Diversification among the ‘farm-rich’ was found
to be very uncommon. The greatest extent of diversification was amongst the poor and
medium inhabitants. Although tenure security is hardly a problem, diversification in the
study sites was largely associated with negative circumstances related to landlessness,
especially among the youth.
Fredu et al., (2006) also found that diversification intensifies income inequality. A rise in
income from non-farm income and livestock, according to their study increase income
inequality. They also found that social capital is an important factor determining non-farm
income but not so for crop income. The pattern of livelihood diversification that emerged
from their study shows that livestock has an important role in diversification of livelihood in
to non-crop activities. Labour is also an important resource that has positive impact on
diversification. Size of cultivated land, cash crop production, and access to extension service
were found not to encourage diversification. They were rather important factors in enhancing
crop farming.
Wassie et al., (2007) examined the recently growing adoption of non-pastoral livelihood
strategies among the Borana pastoralists in southern Ethiopia. A large portion of the current
non-pastoral participation is in petty and natural resource-based activities. Pastoral and crop
production functions are estimated using the Cobb-Douglas model to analyze the economic
rationale behind the growing pastoralist shift to cultivation and other non-pastoral activities.
19
The low marginal return to labor in traditional pastoralism suggests the existence of surplus
labor that can gainfully be transferred to non-pastoral activities. An examination of the
pastoralist activity choices reveals that the younger households with literacy and more
exposure to the exchange system display a more diversified income portfolio preference. The
findings underscore the importance of human capital investment and related support services
for improving the pastoralist capacity to manage risk through welfare enhancing diversified
income portfolio adoption.
Zeray (2008) in his study of invasion of Prosopis juliflora and rural livelihoods the case of
Afar pastoralists at Middle Awash Area, using logistic regression has found that household
head age, level of education, change in livestock asset and location of villages are the most
determining factors to diversify livelihoods of pastoralists. According to the result, the
younger household heads the wider the chance of livelihood diversification. The level of
education was found positively and significantly affecting livelihood diversification. The
probability of diversifying livelihood is about six times higher for a household having
educated head than others. Households that lost much of livestock asset have relatively less
diversified source of income than those who did not. According to his study, households who
are nearer to towns, where governmental and non-governmental organizations are present,
have wider chance of diversifying their livelihood through employment and engage in other
activities.
Emmanuel (2011) used switching regression model on his study of analysis rural livelihood
diversification and agricultural welfare in Ghana. The finding of the study showed that
diversified households and non-diversified households differ significantly in terms of
variables related to household assets, markets and institutions. Household assets including
good health, education and household age composition mostly drove both household welfare
and rural non-farm diversification decision. According to the finding of the study, households
who live in communities, with access to fertilizers, public transport and local produce
markets are more likely to engaged in non-farm diversification and enjoy improved welfare.
B. O. Bebe et al., (2012) on their study of evaluation factors associated with shift from
pastoral to agro-pastoral farming system in Narok county of Kenya used Heckman two-step
model. They found that household decision to shift was enhanced by more frequent group
meetings and farmer trainings, declining land size, large distance to watering points, shorter
distance to market, and more income from off-farm sources.
20
Adugna and Wogayehu (2012) examined determinants of livelihood strategies in Wolaita,
Southern Ethiopia. By applying multinomial logit model, they found that age, education and
sex of household head, credit, land size, livestock and agro-ecology were the factors that
reduced the likelihood of diversification; while family size, dependency ratio, frequency of
extension contact, membership of cooperatives, input use and remittance increased the
likelihoods of diversification.
2.4. Conceptual Framework
This study conceived diversification of livelihoods as a mechanism that the pastoral Afars
had consciously adopted to ensure their survival and improve their standard of living as
livelihood components that supported their pastoral livelihood had been altered.
Specifically, alteration had occurred in the livelihood assets where by the physical asset of
technology triggered a chain of events, which lead to new transformational structures (the
government) and process (change in land tenure). These changes made traditional pastoral
livelihoods more vulnerable thus jeopardizing the realization of desired livelihood outcomes
(more income, increased well-being, reduced vulnerability). Changes in politico-economic
conditions enabled government to consolidate its power and control over the pastoral Afars.
This condition made it easy to appropriate their large areas of grazing lands for national parks
and conservation uses. Similarly, the introduction and expansion of irrigated mechanized
farming encourage crop cultivation and raise common issues of land tenure rights, restricted
access to the best traditional lands and grazing areas, pressure on natural resources and socio-
economic impact on pastoralists’ subsistence economy. In addition, advances in economic
development enable government to invest in health and educational structure as well as
facilitate the spread of a market economy. Such development has induced an increase in
human population and livestock population beyond the carrying capacity of natural
resources.
These changes have led to an increase in livestock diseases, drought, reduction in rangelands
and livestock as well as emergence of new livelihood opportunities and conflict amongst
humans or human-wildlife which made a traditional pastoral livelihood unsustainable and
insufficient to enable the realization of desired livelihood outcome.
Faced with these circumstances, the pastoral Afars must find ways of ensuring their survival
and well-being as well as realizing other livelihood outcomes that they desired hence the
21
option of diversifying their livelihood strategies. The sustainable livelihood framework was
used in the present study to think through the relationship between pastoral households’
diversification and poverty.
22
3. RESEARCH METHODOLOGY
3.1. Description of the Study Area
The study was conducted at Amibara district in zone three of Afar National Regional State.
The district has eighteen kebeles and geographically located between 09’N to 10’N latitude
and 39’45E to 40’30E longitude covering an area of 2007.2 square kilometer (Kassa, 2001).
According to CSA (2011), the total population of the district was about 78,105 of which 58%
and 42% were males and females, respectively. The majority of the district population
belongs to the Afar ethnic group who mainly depend on traditional pastoral production
system.
The average altitude of the district is 740 meter above sea level with minimum and maximum
annual temperature of 19.4 and 34.1co, respectively. The lowest temperature is between
December and February, and the highest between April and June. The average annual rainfall
is about 567 mm coming primarily in two rainy seasons, namely karma (July to September)
and gilel (March to April).
The district is one of the rich areas of Afar with fertile arable land, wealth in livestock, and
some mineral resources and it is suitable to grow crops and vegetables all year round using
the perennial Awash River (Ali, 1997). The area is mostly known for its large-scale state
owned and private irrigated cotton plantation schemes. These private and state farm schemes
make the area one of the major cotton producing locations in the country. The area has some
technical and economic advantages for irrigated agriculture. Moreover, it is easily accessible
to regional and external markets, as it is located at 250 km from Addis Ababa on the main
road to Djibouti port.
Livestock rearing, traditional pastoralism is the major economic activity in the area. People
keep camel, cattle, sheep and goat as a source of food and income. It is also an established
fact that, in addition, livestock is a measure of wealth and social status in the community.
Although traditional pastoralism is the major economic activity in the area, other means of
livelihoods have been introduced in the last few decades. Irrigated farming, wage work on
private and state owned farms, petty trading and charcoal making are among the activities
introduced. However, the intensity and dimension of such activities differ within the
23
community due to differences in factors affecting the community members to engage in such
activities.
Figure 2. Location of the Study Area Source. Afar Regional State Administration (2005)
3.2. Sample Size and Sampling Techniques This study has employed a multi stage stratified sampling technique to select sample
households. In the first stage of sampling, kebeles were stratified as pastoralists and agro-
pastoralists. Then four pastoral kebeles out of eighteen were randomly selected. Finally,
probability proportional to size random sampling technique was used to select the
representative sample of 120 households from Gelsa, Ambash, Halisomalie and Bedulalie
pastoral kebeles.
Table 1 : Number of Sample Households in Pastoral Kebeles Kebele Number of Households Sample Households % Gelsa 724 32 26 Ambash 926 41 34 Halisomalie 439 19 16 Bedulalie 650 28 24 Total 2739 120 100
Source: Own computation based on number of households received from the respective kebele administrations. 3.3. Data Collection Methods
Data collection involved both primary and secondary sources. Primary data were collected
using household survey. Semi-structured questionnaire was pre tested and revised based on
feed-back obtained. Enumerators and local language translators were recruited and trained
on basic principle of interview. Since rural livelihood diversification is multi-dimensional
24
and heterogeneous, a single approach will not provide all the answers to the research
questions. Therefore, a blend of qualitative and quantitative methods was used
complementarily in the research. The two methods were combined throughout the study in
a mixed-methods approach or triangulation. The qualitative methods of PRA approach
including, key informant interview and focus group discussions were also carried out using
a guiding checklist to complement the data collected through questionnaire. The detail of the
questionnaire is attached in appendix.
Secondary data were also collected from published and unpublished sources. Documents
from district office of pastoral and agro-pastoral, reports from regional bureaus, CSA
abstracts and others were thoroughly reviewed to get relevant information.
3.4. Methods of Data Analysis
The survey data were analyzed using descriptive and econometric methods. Descriptive
statistics such as mean, percentage and frequencies were used to summarize the data. Test
statistics like chi-square and t-tests were also used to identify the existence of significant
relationship for quantitative and qualitative data, respectively. Chi-square was used for
nominal characteristics that can be represented by non-numerical categories. For quantitative
data, a t-test that compares the means of the groups was used. With regard to econometric
model, ordered probit model was applied to analyze the determinants of household
diversification status and binomial logit model was applied to analyze the effect of
diversification on poverty status of the households. The data were analyzed using STATA
version 14 software package.
3.4.1. Determinants of household diversification
The most important determinant of diversification is the degree of diversification of a
household’s livelihood strategy or, in other words, the way in which household members
allocate their time in pursuit of various means of earning for living. Dercon & Krishnan
(1996) used income share composition to examine the relationship between income,
household characteristics and barriers to entry into higher return activities. Using a similar
methodological approach in analyzing the determinants of livelihood diversification in
Borana, Wassie (2005) used activity composition to determine the diversification level and
identified five activity categories to gain some insights as to the determinants of pastoral
household activity choice.
25
The other method of analyzing livelihood strategy is direct examination of the individual
household’s asset endowment. The amount of income earned and even the type of activity
undertaken by a household is a function of the asset it controls. Accordingly, the asset based
approach makes it possible to map a household’s asset endowment into its chosen livelihood
strategy and then into its logically subsequent stochastic income realization (Carter and
Barrett, 2006, quoted by Brown et al., 2006). Based on this method, households with similar
bundles of assets might be limited to similar livelihood strategies, but in any given period
realize quite different incomes although they are structurally identical. However, this way of
analysis does not disaggregate the household’s intention behind introducing the activity
portfolio while remaining in the same livelihood strategy with other households. Thus, it is
important to analyze the motives behind the adoption of that activity.
Diversification index was measured with the help of Simpson index of diversity. The
Simpson index of diversity is defined as:
N
iipSID
1
21 (1)
Where SID= Simpson’s Index of Diversity
N= the total number of income source
Pi= income proportion of the ith income source
The value of SID always falls between 0 and 1. If there is just one source of income, Pi=1,
so SID=0. As the number of sources increase, the shares (Pi) decline, as does the sum of the
squared shares, so that SID approaches to 1. If there are k sources of income, then SID falls
between zero and 1-1/k. Accordingly, households with most diversified incomes will have
the largest SID, and the less diversified incomes are associated with the smallest SID. For
least diversified households (i.e., those depending on a single income source) SID takes on
its minimum value of zero. The upper limit for SID is one, which depends on the number of
income sources available and their relative shares. The higher the number of income sources
as well as more evenly distributed the income shares, the higher the value of SID. The
Simpson Index of Diversity is affected both by the number of income sources as well as by
the distribution of income between different sources (balance). The more uniformly
distributed is the income from each source, the SID approaches to 1 (Biswarup and Ram,
2011).
26
3.4.2. Specification of the model
The target variable of this study, livelihood diversification status, is an ordinal categorical
variable. Ordered probit regression model, which is suitable for modeling with an ordered
categorical dependent variable, was used here to identify and analyze the determinants of
households’ diversification status. In this analysis, the level of diversification was classified
in to three categories: 1) non-diversified; which means households that have very limited
sources of income and most of their income is obtained from sell of livestock and livestock
products, 2) moderately diversified; those households having some diversified sources of
income besides livestock and livestock products, 3) highly diversified; those households that
have diversified sources of income including pastoralism.
In this study, a household was said to have diversified livelihood, when it has involved in a
number of income earning economic activities in addition to pastoralism. Both the number
of income sources and the distribution of income between different sources (income balance)
measured the level of diversification by using Simpson’s Index of Diversity (SID). Thus,
diversification by this measurement involved ordered outcome.
Following Green (2012), the model is specified as:
yi∗ = xi훽 + 휀i (2)
Where yi∗= the unobserved latent variable measures diversification status with three levels
in increasing diversification level, coded as 1= non-diversified, 2= moderately diversified
and 3= highly diversified;
xi= vector of observed non-random explanatory variables assessing the attributes of
diversification status; and
휀i= a random error term with mean 0 and variance 1.
The observed y is related to y* as specified:
yi= 1 if yi* ≤ µ1
yi= 2 if µ1 < yi* ≤ µ2 (3)
yi= 3 if µ2 < yi*
27
Where µi’s represent the thresholds or cut-points to be projected (along with the parameter
vector β). For the estimated cut-off points, µ follows the order µ1 < µ2 < µ3.
Taking the value of 3 if the household is highly diversified, 2 if the household is moderately
diversified and 1 if the household is non-diversified, the implied probabilities are obtained
as:
Pr {yi = 1| xi} = Φ (- xiβ) (4)
Pr {yi = 2| xi} = Φ (μ1 – xiβ) - Φ (μ2 – xiβ) (5)
Pr {yi = 3| xi} = 1- Φ (μ2 –xiβ) (6)
The parameters of the model specified in equations 4-6 are estimated using the maximum
likelihood method. However, there is lack of clarity in interpreting the coefficients of the
model. For example, there are three categories of the diversification variables while the
model has only one unknown threshold parameter (Greene, 2012). This necessitates for the
partial change or marginal effect, which can reveal the effects of independent variables on
the probability of three different levels of diversifications individually. A partial change in
the predicted probability of the outcome m, for continuous variable, in the interval µm-1 to µm
for a change in an explanatory variable xk at the mean value is specified as Equation 7.
= 훽 [푓(휇 − 푥훽)− 푓(휇 − 푥훽)] (7)
On the other hand, the change in the predicted probability for a discrete changes in 푥 from
initial value 푥 to the end value 푥 (e.g., a change from 푥 = 0 to 푥 = 1) is given by Equation
8:
= 푝 푦 = 푚 푥, 푥 = 푥 − 푝 (푦 = 푚 푥⁄ ,푥 = 푥 ) (8)
Where 푝 푦 = 푚 푥, 푥 states the probability that y = 푚 given x, stating a particular value
for 푥 . Thus, when 푥 changes from 푥 to 푥 , the predicted probability of outcome, 푚
changes by , holding all other variables at 푥.
In this study, total poverty indices that refer to an aggregate measure of poverty that takes
into account both the food and non-food requirements were taken. Here in determining
28
poverty line, cost of basic need approach was considered because the indicators are more
representative and the threshold is consistent with real expenditure across time, space and
groups.
According to MoFED, (2012) the level of poverty line used to calculate poverty indices is
Birr 1,985. This poverty line was estimated based on the cost of 2200 kilocalorie per day per
adult and essential non-food items. Accordingly, income of sample household was computed
per adult equivalent and Birr 1,985 was taken as a threshold for poverty line.
FGT poverty index was employed to ascertain the poverty status of the respondents and this
was then used to disaggregate them into poor and non-poor categories. It has become
customary to use the so-called Pα measures in analyzing poverty. The measures relate to
different dimensions of the incidence of poverty P0, P1 and P2 used for head count
(incidence), depth and severity of poverty, respectively. The three measures are based on a
single formula but each index puts different weights on the degree to which a household or
individual falls below the poverty line. The mathematical formulation of poverty
measurements as derived from Foster, Greer and Thorbecke (1984) is estimated as:
q
i
ija Z
YZN
P1 1
11 (9)
Where; Pα= the weighted poverty index for the ith sub group
α = Foster-Greer-Thorbecke (FGT) index and takes on the values of 0, 1 and 2 for incidence,
depth and severity of poverty measures respectively.
Z1 = the poverty line for ith sub-group
q = the number of individuals below the poverty line
N = the total number of individuals in the reference population
Yij = the per capita expenditure of household j in the sub-group i
Z1-Yij = poverty gap of the ith household
1
1
ZYZ ij
= poverty gap ratio
The quantity in bracket is the proportionate shortfall of expenditure/income below the
poverty line.
nq
Proportion of population below the poverty line
Estimation of poverty based on the FGT index was then used to disaggregate households
into poor and non-poor categories.
29
Analyzing the determinants of poverty and specifically the effects of diversification on
poverty can be captured using binary logit model. The dependent variable is the probability
of a given individual to be below or above poverty line. Dependent variable was coded as 1
if an individual is below poverty line and 0 other wise. The model specification is as follows:
ee
iZ
iZPi
1 (10)
Where: p i is 0 with the probability that the household is poor; 1 otherwise.
u i
n
iiii XaaZ
10 Where, i=1, 2… n (11)
n = the number of explanatory variables
ao= intercept term
ai= the coefficient of explanatory variables.
ui=disturbance term
Xi = explanatory variables.
The probability that the household belongs to non-poor will be (1-Pi). That is,
eP
iZi
1
11 (12)
The odds ratio can be written as:
iZi eip
p
1 (13)
In linear form by taking the natural log of odds ratio:
iZ
i
i ZeP
Pi
ln1
ln (14)
The coefficients of the logit model present the change in the log of the odds (poverty as a 0
or 1) associated with a unit change in the explanatory variables.
3.4.3. Definition of variables in the model
Ordered probit model
Dependent variable:
30
The model involved categorical ordered dependent variable, diversification status, taking the
value 1, 2 and 3 where the status is non-diversified, moderately diversified, and highly
diversified, respectively.
Independent variables:
The following are a set of variables that were thought to have an influence on diversification
status of pastoralists.
1. Livestock asset of the household (LVSTKTLU): This is a continuous variable that
indicates the number of livestock households own measured in tropical livestock unit
(TLU). Households asset ownership may accelerate or retard the diversification of a
given households. Some studies evidenced that livestock asset has positive
association with income diversification (Block and Webb, 2001), while others show
that large livestock ownership doesn’t affect households’ participation in non-
livestock income generating activities (Wassie, 2005). It was hypothesized that
households with less number of livestock try to diversify their income portfolio by
participating in other non-livestock activities and this accelerates the rate of
diversification.
2. Age of household head (AGEHHH): This is a continuous variable measured the
age of household head in years. This was expected to influences the process of
diversification. The younger the household head the more active in diversifying their
income portfolio. The study conducted by Destaw (2003) and Berhanu (2007) have
indicated that age has significant effect on diversification. It was anticipated that
older household heads resist changing their mode of life from the traditional one.
3. Dependency ratio (DEPRATO): This is a continuous variable that shows the
percentage of member of the household who are dependent and do not engage in
productive work. It refers to the proportion of economically inactive labor force (less
than 14 and above 65 years old). It is found to have positive impact on diversification
process (Block and Webb, 2001). At household level, many children combined with
few working adults imply high consumption, which also influences the well-being of
household members. Hence, this subsistence pressure tends to increase the
participation in alternative livelihood strategies (Glauben et al., 2005). It was
hypothesized that high dependency ratio affected diversification process positively.
31
4. Sex of household head (SEXHHH): This is a dummy variable that takes the value
of 1 if household head is male and 0 otherwise. Women are generally less likely to
participate in diversification activity than men. This is possibly because of social
constraints and requirements to stay at home to manage the household activities
(Stamoulis et al., 2008). It was hypothesized that male headed household have more
chance to engage in diversification.
5. Household size (FMLYSZAE): This is a continuous variable that measures the total
number of household members. It was found to affect the diversification process
(Block and Webb, 2001; Woldehana and Oskam, 2001). Hence, it was hypothesized
that Households with large family size are expected to diversify their income to
satisfy their families’ need through activity diversification.
6. Educational status of household head (EDULVL): This is a dummy variable that
takes the value of 1 if household head is literate and 0 otherwise. Better educated
members of rural households have better access to diversification more likely to
establish their own non-farm business (Stamoulis et al., 2008). It was hypothesized
that education to have positive relation with diversification of income portfolio of the
household. Households who have attended better education levels are expected to
diversify their income portfolio than the non-educated ones.
7. Available labor force (LBRFRC): This is a continuous variable representing the
number of productive age group in the household to undertake different activities.
Availability of active labor force had shown positive relation with diversification
(Abdulahi and Crole-Ress, 2001; Wassie, 2005). It was hypothesized that household
with more productive labor are engaged in non-livestock sector to diversify their
income portfolio.
8. Distance to market (DSTMRKT): This is a continuous variable that refers the
distance from the nearest market centers measured in kilometers. Proximity to the
nearest market may create opportunity of more income by providing livelihood
strategies through employment, which determine income level of rural households
(Barrett et al., 2001). Improved market access can be expected to stimulate
diversification. It was hypothesized that market access to influences the decision of
rural household to participate in diversified livelihood strategy positively.
32
9. Access to road (ACCROD): This is a dummy variable that takes the value of 1 if
the household has access to road and 0 otherwise. It has a positive effect on creating
link between households’ decision to take his/her produces such as livestock product
to the market which will accelerate the diversification process (Ellis, 2000). It was
hypothesized that households with access to road network have more chance to
diversify their income portfolio than households who do not have access to road.
10. Access to veterinary service (ACCVTSRVC): This is a dummy variable that takes
the value of 1 if the households have access to veterinary service and 0 otherwise.
Studies show that access to veterinary service has positive effect on diversification
(Smith et al., 2001). It was hypothesized that access to veterinary service to be
positively related to diversification.
11. Access to credit (ACCCRDT): This is a dummy variable that takes the value of 1 if
the household had access to credit service and 0 otherwise. Access to credit has
positive relation with participation with non-farm activity (Abdulahi and Crole-Ress,
2001; Woldehana and Oskam, 2001). It was hypothesized that households who have
access to credit service have more chance to engage in diversification of their income
by filling the gap the households face to get starting capital to engage in non-livestock
diversification activities.
12. Risk perception (RSKPRC): It is the perception about the future continuity of the
pastoralism as a way of life which may in turn be influenced by different factors such
as security issue in the area due to frequent clashes between pastoral communities
basically from conflict on resources, drought and other factors (Abdulahi and Crole-
Ress, 2001). It is hypothesized that the risk perception about production system is
reflected in the diversification of income portfolio. Households were asked to rank
their perception on the viability of the production system in order of bad, moderate
and good with the value of 1, 2, and 3, respectively.
Binary logit model
Dependent variable
Household consumption poverty (HHCONPVRTY): This is the household poverty status
measured using the poverty line. Households were classified below poverty line and above
33
poverty line using the national figure used to calculate poverty index. It is a dummy variable
that takes the value of 1 if the household is poor and 0 otherwise.
Independent variables
1.Household total income (HHTTLINCM): It is a continuous variable that shows the
households total income during the year. It includes income from all sources such as
livestock and livestock products and non-livestock activities. Different studies have
identified that household total income have positive relation in improving food
security status (Hillina, 2005; Yilma, 2005). It was hypothesized that household total
income will have negative impact on poverty status of pastoral households.
2. Livestock assets (LVSTKTLU): This is a continuous variable that shows total
number of livestock owned by household measured in TLU. Livestock asset has
positive impact on improving household food security status (Abebaw, 2003; Hillina,
2005). It was hypothesized that households with increased livestock number have
less chance to be poor.
3. Age of household head (AGEHHH): This is a continuous variable that measures
age of household head in years. With increase in age, households are supposed to
have more wealth (Abebaw, 2003; Ayalew, 2003). It was hypothesized that aged
household heads are more likely to be non-poor.
4. Sex of household head (SEXHHH): This is a dummy variable that takes the value
of 1 if household head is male and 0 otherwise. Different studies have identified that
female headed households have more chance to be poor than male headed households
(Ayalew, 2003; Yilma, 2005). It was hypothesized that male headed households have
more chance to be non-poor.
5. Dependency ratio (DEPRATO): This is a continuous variable which shows the
percentage of household members who can’t work and earn income. It has a positive
relation with being poor. The highest the dependency ratio is, the more the
households are to be poor (Ashimogo, 2000) It was hypothesized that dependency
ratio has positive impact on poverty status.
6. Level of education (EDULVL): This is a dummy variable that takes the value of 1
if the household head is literate and 0 otherwise. Some studies also identified that
34
education level of household head has negative relation with being poor (Yilma,
2005). It was hypothesized that educated households are more likely to be non-poor.
7. Household size (FMLYSZAE): This is a continuous variable that measures the
number of household members. Studies have identified that households with large
family size are more likely to be poor (Eshetu, 2000; Yilma, 2005). It was
hypothesized that households with large family size are more likely to be poor.
8. Access to road (ACCROD): This is a dummy variable that takes the value of 1 if
the household has access to road and 0 otherwise. Some studies found that the poor
are near to the road in need of non-livestock income generating activities (Coppock,
1994; Wassie, 2005). It was hypothesized that household with access to road network
has more chance to be non-poor than those Households who do not have access to
road network.
9. Access to credit service (ACCCRDT): It is a dummy variable that takes a value of
1 if the household has access to credit service and 0 otherwise. It was hypothesized
that households who have access to credit service have more chance to get engaged
in improving income and then to be non-poor. Access to credit has positive relation
with improving income (Abdulahi and Crole-Ress, 2001; Woldehana and Oskam,
2001).
10. Diversification status (DIVLVL): It is an ordered categorical variable that takes a
value of 1 for the non-diversified, 2 for the moderately diversified and 3 for the highly
diversified households. Diversification has mixed results on improving poverty
status of households. According to Carsewell (2001), diversification has improved
the income of households by generating additional income. According to other
studies, poor households do diversify as a survival strategy, and this does not improve
the poverty status (Coppock, 1994; Wassie, 2005). However, although the starting
point differs among households, diversification was expected to have a negative
effect on the poverty status of households.
35
4. RESULTS AND DISCUSSION
The main purpose of this chapter is to present results of the empirical analysis accompanied
by a discussion of major findings. The empirical analysis encompass both descriptive results
and econometric estimation of pastoral households’ engagement in the non-pastoral non-
farm economy in terms of ordered probit model. The presentation of results is preceded by
a comprehensive, but succinct, introduction that details the empirical context within which
the analysis is made. This included the nature of the data used and household level
aggregations made; the standard measures considered for some variables in the analysis; the
empirical definition and scope of pastoral livelihood diversification engagement in this
particular study. This is sought to address the possible ambiguities in making sense of the
results that may arise in subsequent sections where the main results are presented and
implications are discussed.
4.1. The Status of Livelihood Diversification
In this study, livelihood diversification refers to attempts by pastoralists and pastoral
households to find productive ways to raise incomes by setting diverse portfolio of activities
and assets in order to improve their standard of living and reduce different livelihood risks.
The definition of diversification relates to the number of source of income and the balance
among them. In this study, in order to stratify sample households into distinct diversification
status, the level of diversifying income source was compared by the share of livestock
income in total household income, the number of income sources and the relative importance
or evenness of these sources. Hence, Simpson index of diversity is widely used to measure
the diversity and used here to measure diversity.
According to the survey result, households in the study area were found to pursued livelihood
diversification, which indicate some degree of diversification with the mean value of SID,
being 0.46 (Appendix 1). The different means of livings reported by the sample households
include livestock herding, crop cultivation, off-farm wage employment, charcoal making,
petty trade, permanent employment, food aid, remittance and rent of clan land. This implies
that households in the study area are accompanied by declining importance of livestock
based livelihood strategies (pastoral livelihood strategies) and increasing reliance on non-
livestock based livelihood strategies and may depend on one or more of these activities for
survival. A household with a diverse livelihood relies on several different economic
36
activities. On the other hand, non-diversified households were depending on single economic
activity of pastoralism. As depicted below (Table 2), the majority of households i.e. 53.33
percent (64) had diversified their livelihood in different income sources and the rest, i.e.
46.67 percent (56) households maintained mainly the single source of income for their
livelihood.
Table 2: Distribution of sample households with nature of diversification
Nature of diversification SID range Frequency Mean Percentage (%) Non-diversified households 0-0.40 56 .26 46.67 Moderately-diversified households 0.41-0.69 45 .55 37.50 Highly- diversified households 0.70-0.98 19 .81 15.83 Total 120 .46 100
Source: Own computation results, 2015
4.2. Sources of Income of Pastoral Households
Pastoral household income sources may be classified into three main categories. These are
pastoralism, farming (dry land or irrigated) and non-farm non-pastoral (NFNP) activities.
Since farming and pastoralism are essentially different activities, the former was considered
as a form of pastoral income diversification; farm income is a non-pastoral income. All other
non-pastoral activities were, hence, classified as non-farm non-pastoral (NFNP) activities.
The gross income from pastoralism, on the other hand, consists of milk off-take for own
consumption and sales, livestock slaughter for own consumption and livestock sales. Income
from sales of hides and skins was not considered as none of the sample pastoralist households
sells either hides or skins for different reasons. Then pastoral net income was found by
deducting livestock expenditure from the gross pastoral income. The cost of veterinary drug
is the main item of expenditure in livestock production system of the study area.
The other components of income for pastoral households include farm income and earnings
from various non-pastoral activities. Gross farm income is the sum of values of crops
produced by households both for own consumption and sale during the survey period. Input
costs (costs of seeds, chemicals and hired labor) were deducted to find farm net income.
None of the sample households reported any use of fertilizer as farm input. Non-farm non-
pastoral (NFNP) income sources included income from leased out clan lands, income from
charcoal making, income from petty trade, income from employments as causal or
permanent basis at different private and state owned farms, and income from remittances.
37
4.2.1. Income of households from pastoralism
In this study, as mentioned above income from pastoralism includes income received by
households from sale of livestock, livestock products (only milk in this case), and livestock
and livestock products consumed at home. Actual prices as reported by the households and
average local market prices were used to value sales and consumption. From the survey, it
was found that pastoralism remains the principal source of livelihood, accounting on average
50.3 percent of the sample households’ income. The maximum income received by
households from pastoralism was Birr 34,700 while the minimum was Birr 3,700 with mean
income of Birr 13,202.08 per annum.
4.2.2. Income of households from farming
Cultivation can be seen as both a major avenue of diversification in terms of a viable risk
management strategy and an unsustainable or even destructive option that accentuate the
risks pastoralists face. This is because, there exist competition between livestock production
and farming, as it expands in a very important and productive dry season grazing lands.
Farming activity has been expanding around irrigated perimeter of Awash River with the
already established irrigation canal structure administered by Awash Basin Authority (ABA)
and establishment of large scale state owned farms since 1960s. However, it is in recent years
that the indigenous Afar pastoralists have started involving in it and over the years, crop
production has been considerably increasing in the area, as livestock production alone could
not generate sufficient income. Concerning the farming system of the study area, there are
two systems: own farming and management and joint management with sharecroppers. In
the first case, the owner performs all the farming and management activities by himself while
in the case of the second arrangement there exists a joint contribution on management and
farming but the sharecropper has a sole responsibility of providing capital and finally the
gain is equally shared after the costs have been deducted.
Among the households engaged in cultivation, the average amount of land under cultivation
was 1.1 ha, ranging from 0 ha to7 ha per household. Crops grown in the area include maize,
cotton, onion and other vegetables like tomato. According to the result of the survey data,
farming (crop production) was the second most important source of income for the sample
households in the study area contributing about 14% of the total household income. The
mean annual income of households from farming was found to be Birr 2,466.25 with a
maximum of Birr 3,239.55.
38
4.2.3. Income of households from non-farm non-pastoral (NFNP) sources
The non-farm non-pastoral income sources of pastoral households in the study area include
income from clan land rent, income from wage employments, income from petty trade,
income from remittance (earnings from relatives living outside the localities), income from
charcoal making and income from food-aids.
Income from clan land rent was obtained through the established customary rules and
principles of land administration among Afar pastoralists. The state farms handed over about
6,547 hectares of land with the entire irrigation infrastructure to local pastoralists in 1993
(MAADE unpublished documents). Accordingly, the land was partly allocated to clan
members and was partly leased-out to local investors. Using the contract agreement
established between the two parties, the pastoralists collectively earn 30 percent of the
investors’ profits in return for the use of their land, which they distribute among themselves
based on the predefined criteria. The amount each household gets depend on the size of the
land and the number of clan members who collectively claim land ownership. In the study
area, on average, the households earned about 6% of the total income and 18% of the
nonfarm non-pastoral income from clan land rent.
Income from wage employment could be seen from two aspects; permanent wage
employment in governmental and non-governmental organizations as well as private
commercial farms and casual wage employment in the existing governmental, non-
governmental and private organizations. The major permanent employment opportunity for
the local Afar people in governmental and non- governmental organization in the study area
was guarding. This is highly related with the low educational level of the pastoralists in the
study area in particular and in the region at large. The casual wage employment is highly
associated with off-farm activities that are mainly provided by the presence of large-scale
private commercial farms. Similar to the permanent employment opportunity most men
pastoralists prefer guarding to protect crop (mainly cotton) from livestock while the women
are involved in sowing, weeding and harvesting (picking) at cotton farm. Clan leaders and
some selected members of a clan with a land leased-out for investors, have also an
opportunity of earning a monthly salary all year round as facilitators of farming activities.
Generally, incomes from casual wage employment alone contributed about 9% of the total
household income and 26% of the nonfarm non-pastoral income of the household while
permanent wage employment contributed only 4% of the total household income and 11%
39
of the nonfarm non-pastoral income. All together wage employments have enabled the
pastoralists to earn on average 12% and 37% of their total and nonfarm non-pastoral income
respectively.
Petty trade, as an income source is not very common in the study area and hence contributes
a very small proportion of household income. It included small shop, livestock trade and
service like broker. It was only 1.5 and 4.3 percent of the total and nonfarm non-pastoral
incomes, respectively that came from these activities per year. Other non-farm non-pastoral
sources of income were remittance, food aid through productive safety net program (PSNP)
and charcoal making. Generally, the non-farm non-pastoral (NFNP) sources of income
together contribute about 33.7% of the total household income per year. This shows how
pastoralists diversify their sources of income apart from livestock and livestock related
activities.
Table 3: Livelihood Diversification by Income Sources
Income source Non-diversified Moderately diversified
Highly diversified F- value
Mean income (Birr)
SD Mean income (Birr)
SD Mean income (Birr)
SD
Pastoralism 12699.69 5697.56 7748.68 2825.74 6099.21 1755.59 44.07***
Farming 142.86 1069.05 3982.22 3315.69 5513.16 2711.18 9.12***
NPNF 2219.87 2704.91 6377 3513.98 10813.74 4453.16 36.66***
Total income 23122.7 4887.56 24680.1 4273.28 26505.5 2409.88 3.87*
*, *** significant at 10% and 1% probability levels, respectively Source: Own computation results, 2015
The non-diversified households derived the largest proportion of their income from
pastoralism and this was higher and significantly (p<0.01) different from what was
obtainable among moderately and highly diversified households. Income obtained from non-
pastoral non-farm activities by highly diversified households was significantly higher and
different (p<0.01) from what was obtainable among the moderately and non-diversified
households. The moderately diversified households derived a significantly (p<0.01) larger
income from farming and non-pastoral non-farm activities than the non-diversified
households.
40
4.3. Demographic and Socioeconomic Characteristics of Sample
Households
4.3.1. Sex and marital status of the household heads
Among 120 sample households 90 (75%) and 30 (25%) are male and female headed
households, respectively. Of which 4.35% of the female-headed and 18.56% of the male-
headed households were in the highly diversified group where as 73.33% of the female-
headed and 37.78% of the male-headed households were under the non-diversified group.
Table 4: Distribution of Sample Households by Headship
Sex of Household head
Non-diversified
Moderately diversified
Highly diversified
Total χ2- value
Female No. 22 7 1 30
12.18***
% 73.33 23.33 4.35 100 Male No. 34 38 18 90
% 37.78 42.22 18.56 100 Total No. 56 45 19 120
% 46.67 37.5 15.83 100 *** indicates significant at 1% probability level Source: - own computation results, 2015
Of the male headed households, 56 (62.22%) were involving at least in one non-pastoral
activity besides pastoralism, while the result for female headed households was 8 (26.67%).
A χ2 test indicates that there was a difference (P<0.01) in diversification status among sex of
household heads of sample respondents. Accordingly, male-headed households tended to
diversify their livelihoods more than female-headed households.
Considering the marital status of the sample households, 90 (75%) were married, of which
77 (85.6% were monogamous) while 13 (14.4% were polygamous) and 30 (25%) were single
(divorced or widowed). Among the married heads of households, 56 (62.22%) were
engaging at least in one non-pastoral activity on top of pastoralism whereas, 8 (26.67%) of
the single heads of households did participate at least in one non-pastoral activity.
Table 5: Distribution of Sample Households by Marital Status
Marital status Frequency Percent Married (one wife) 77 64.2 Married (more than one wife) 13 10.8 Widowed/widower 13 10.8 Divorced 17 14.2 Total 120 100
Source: own computation results, 2015
41
4.3.2. Age and educational level of the household heads
Age is an important factor determining the level of livelihood diversification. It was one of
the characteristics, which was hypothesized to influence pastoralists’ livelihood
diversification status. The age of the sample household head ranged between 24 and 68 years
with mean age of 42.76 years. The mean age of households who are engaged in any non-
pastoral activity was 35 years, while those who are involved only in pastoralism have a mean
age of 49 years. The F-value also indicates that there was statistically a strong mean age
difference (P <0.01) among diversification groups of sample households. Subsequently,
highly diversified households were younger than non-diversified households (Table 6).
Table 6: Distribution of Sample Households by Age
Age of household head Non-diversified Moderately diversified
Highly diversified
Total F_ value
Maximum 68 56 45 68 Minimum 33 24 28 24 37.14*** Mean 49 38.47 34.53 42.76
Std. Deviation 8.80 6.81 4.26 9.58 *** indicates significant at 1% probability level Source: own computation results, 2015
During the sample survey, attempts were made to classify household heads as literate and
illiterate according to their educational background. Thus, heads of households who can read
and write had either a primary or a religious education were categorized as literate while
those who did not pass through formal education and could not read and write were
categorized as illiterate. Among the sample households 85 (70.83%) were illiterate whereas,
35 (29.17%) of them were literate. Out of the literate households, 85.71% were involving
into diversification or into non-pastoral activities while; the corresponding results for
illiterate households were 40%. Compared by sex of headship, more proportion of female
headed households (96.7%) were illiterate than male headed (62.2%) ones.
The chi-square test (Table 7) shows that there was strongly significant (P<0.01) difference
in educational levels of household heads among diversification group. About 85.71% of the
literate households belong to the category of moderately and highly diversified groups. Only
14.29% of the literate households were in the category of those who did not diversify. This
shows that educated households have more chance to diversify their livelihoods through
participation of non-pastoral economic activity.
42
Table 7: Educational Level of Sample Household Heads
Level of education Non-diversified
Moderately diversified
Highly diversified Total
χ2-value
Illiterate Number 51 30 4 85
34.271***
% 60 35.3 4.7 100
Literate Number 5 15 15 35 % 14.3 42.9 42.9 100
Total Number 56 45 19 120 % 100 100 100 100
***, represents significant at 5% probability level Source: Own computation results, 2015
4.3.3. Household size of sample respondents
Household size in the pastoral context is critical as resource for herding labour. The fact is
that, for a given level of asset endowment, households with a large number of economically
active members are more likely to involve in multiple income generating strategies. This
study considered the size of a household as the sum total of a pastoralist, his spouse, off
springs and dependents present at the time of interview. The number of persons
encompassing a household was converted to adult equivalent (AE)1 based on sex and age.
The sample households comprised 6.89 members on average, which is almost similar to the
regional average; with a maximum size of 13 and minimum of 3 members.
Table 8 depicts that mean difference in total household size was not statistically significant
among the three diversifications statuses. Results hinted that composition of a household is
more important than the total size in determining whether a household is involving in a
multiple income generating activities. More specifically, adult male, total number of adults
and dependency ratio were significantly influenced diversification of households. The above
result is against the expected positive relationship between diversification and household
size. However, the result of the dependency ratio, which illustrates the number of young and
old dependent in a household shows that diversified households have higher dependency
ratio than the non-diversified households (Table 9). Households with higher dependency
1 Adult equivalent is a system for expressing a group of people in terms of standard reference adult
units, with respect to food or metabolic requirements. A reference adult is taken as an adult male:
other categories are a fraction of that adult equivalent: Adult male = 1AE; adult female = 0.9AE; M/F
10–14 years = 0.9AE; M/F/5–9 years = 0.6AE; infant/child 2–4 years = 0.52AE (Sellen, 2003).
43
ratio tend to engage in multiple income generating activities to fulfill the bare minimum
requirements of their members.
Table 8: Family Composition of Sample Households
Households composition
Non-diversified
Moderately diversified
Highly diversified
F value Mean Std.
dev. Mean Std.
dev. Mean Std.
dev. Adult male 2.41 1.12 1.98 1.29 1.68 0.82 3.49** Adult female 1.51 .74 1.33 .72 1.43 0.91 0.70 Total adult 3.93 1.45 3.31 1.70 3.12 1.28 3.04* Dependency ratio .93 .60 1.43 1.12 1.34 .74 4.61* Total household size 7.09 2.00 6.76 2.05 6.64 1.77 0.39
*, ** represent significant at 10% and 5% probability level, respectively Source: Own computation results, 2015 4.3.4. Livestock ownership of sample households
Livestock ownership is a proxy for wealth. Among Afar pastoralists, livestock asset holding
and type of species mainly determine wealth. This is because; livestock are the sources of
food, income, prestige and security in times of hardship in pastoral communities. Therefore,
in this study the number of livestock measured by tropical livestock unit (TLU2) was used
to estimate the livestock asset of individual households. This was done because households
were observed having different composition of livestock; hence, a unit of measurement for
livestock was needed to use livestock as an indicator variable to compare households. Results
portray that, about 64.2% of pastoral households in the study area have less than 4.5 TLU
per capita that is generally considered the minimum level to sustain traditional pastoral
households in East Africa according to Davies and Bennett, (2007).
The different livestock species kept by respondent households were cattle, camel, goat and
sheep with average holding of 12.27, 7.56 and 3.84 TLU, respectively while the per capita
TLU was about 3.73. A detailed analysis of livestock holdings for diversified and non-
diversified households shows that there was a significant mean difference (P<0.01) in all
species of livestock among the sample households of the diversification statuses. As of the
results of the analysis, the F-test shows that highly diversified households possessed lower
number of livestock of all species than moderately and non-diversified households (Table 9).
2 TLU refers to Tropical Livestock Unit. One TLU is equivalent to 1 camel = 0.7 cattle = 0.5 donkey = 0.1 sheep /goat = 0.8 mule/horse (ILCA, 1992).
44
Table 9: Livestock Ownership of Sample Households (in TLU)
Livestock species
Non-diversified Moderately diversified
Highly diversified
F_ value Mean Std. dev. Mean Std. dev. Mean Std. dev.
Sheep & goat 4.9 1.3 3.1 1.1 2.5 .52 45.22***
Cattle 17 7.6 8.7 4.3 5.6 2.7 41.63*** Camel 12.45 9.76 3.91 5.05 1.79 2.25 23.31*** Total- livestock 35 19 16 10 9.8 4.6 32.76***
*** represents significance at 1% probability level Source: Own computation results, 2015
Results from livestock wealth status ranking exercises revealed that the main criterion used
by community to categorize wealth is livestock holding. The criterion identified during FGD
wealth ranking exercise was similar among households for the four sample pastoral kebele
administrations given special emphasis on camel and cattle ownership. Accordingly, a better-
off household owns more than 20 camels, had at least 30 cattle and more than 60 sheep/goats
while the poor household owns less than 10 camels, 20 cattle and 35 sheep/goats. The middle
class household lies between the two groups. Applying the wealth ranking exercise based on
the ownership of livestock asset, approximately, 58%, 29% and 13% were found to be poor,
medium and better-off households, respectively.
Figure 3: Livestock Wealth Status Source: FGD in the four surveyed pastoral kebele administrations, 2015
Focus group discussion participants have also lamented that trends in livestock holding,
through time, has dramatically declined. The discussants attributed the decline in livestock
holding to recurrent drought, diseases, climate change, alien invasive plants, distress sale
and livestock raids. Of which drought and disease contributed about 88% of the decline in
livestock holding. The current unfavorable terms-of-trade (high cost of cereals than
better-off13%
Medium29%
poor58%
45
livestock) forced pastoralists to sell more livestock to purchase food crops and livestock raids
by the neighboring ‘Issa’ clan contributed to the eventual decline (12%).
4.3.5. Access to veterinary services
Access to veterinary service, which is one of the most important physical assets, was very
limited in the district. The veterinary health posts and health center were built assuming the
livestock mobility nature of the district pastoralists. According to information from the
district pastoral and agro-pastoral development office, there were only four veterinary health
posts at Andido, Allideghie, Keliat bure and Gonita birka, with very little or no health
facilities and a single veterinary health center at Haliesomalie but were not functional at time
of survey. On the other hand, livestock disease was the most frequently expressed reason of
animal death in all studied pastoral kebeles. None of the study pastoral kebeles had a
veterinary health post or health center. Households from the sample kebeles and other
pastoral administration kebeles were traveled a minimum of 5 km and a maximum of 15 km
if they have to go to any of the four health posts to acquire some advice from the health
experts. The data from the surveyed households showed that 21.43, 37.48 and 68.42 percent
of the non-diversified, moderately diversified and highly diversified households,
respectively had access to veterinary services. This result showed that there was statistically
a strong difference (P<0.01) in access of veterinary clinic among diversification groups.
Table 10: Access to Veterinary Clinic and Diversification (%)
Access to veterinary clinic
Non- Diversified
Moderately diversified
Highly diversified
Total
χ2 – value
No (44) 78.57% (28) 62.22% (6) 31.58% (78) 65% 14.015*** Yes (12) 21.43% (19) 37.48% (13) 68.42% (42) 35%
Total (56) 100% (45) 100% (19) 100% (120) 100% *** represents significant at 1% probability level Source: - Own computation results, 2015
However, the physical accesses to the veterinary clinics alone have nothing to provide for
the pastoralists. Hence, most pastoralists used indigenous knowledge and purchase medicine
from other places/districts to treat animal diseases and parasites.
4.3.6. Access to credit services
Financial institutions are not well established and developed in pastoral areas in general, and
in Afar region, in particular. Access to credit was almost none in the district in its formal
46
(bank or insurance) way. The possible sources of credit for rural households, micro-finance
institutions, were not present in the district. Working capital constraint was the major
financial problem for both diversified and non-diversified households in the study area.
However, those households who are getting involved in farming, particularly cotton, could
have access to informal credit both in kind and cash from private investors and out grower
contracts. The credit providers supply cottonseeds, chemicals and cash for payments to
wages in the form of seasonal credit while the producers did agree and/or forced to sell their
produce only to the respective credit providers.
Table 11: Access to Credit Services and Diversification (%)
Access to credit service
Non- Diversified
Moderately diversified
Highly diversified
Total
χ2-value
No (55) 98.21% (35) 77.33% (6) 31.58% (94) 78.33% 38.176*** Yes (1) 1.79% (10) 22.67% (13) 68.42% (26) 21.67%
Total (56) 100% (45) 100% (19) 100% (120) 100% *** represents significant at 1% probability level Source: Own computation results, 2015
According to the survey result of the studied pastoral kebeles, 1.79, 26.67 and 68.42 percent
of the non-diversified, moderately diversified and highly diversified households had access
to informal credit services, respectively. This shows that there was a strong difference in
access to credit and diversification status of the sample households in the study area
(P<0.01). The diversified households had more access of credit service. Those households
who had access to credit services were diversified their livelihoods more likely than those
who did not.
4.3.7. Access to all weather roads
Access to all weather roads is an important developmental parameter in rural areas specially
to promote marketing activities where agricultural products (milk and milk products,
vegetables), which are perishable in nature, are subject to transportation. The district main
town, Werer (the nearest central market place), has a geographical advantage, located
alongside the main tarmac road from Addis Ababa to Djibouti, to have access to all weather
roads. However, all the pastoral administration kebeles in the study area had no access to all
weather roads. The roads were rough but passable during the dry season but it was totally
impossible to access most rural pastoral administration kebeles in the rainy seasons. Lack of
access to all weather roads was the distinctive feature of the pastoral areas and a key factor
47
in determining the physical vulnerability, inaccessibility and marginalization from major
markets.
Table 12: Access to All Weather Roads and Diversification (%)
Access to all-weather roads
Non- Diversified
Moderately diversified
Highly diversified
Total
χ2-value
No (42) 75% (28) 62.22% (8) 42.11% (78) 65% 6.992** Yes (14) 25% (17) 37.78% (11) 57.89% (42) 35%
Total (56) 100% (45) 100% (19) 100% (120) 100% ** represents significant at 5% probability level Source: Own computation results, 2015
According to the survey result of the studied pastoral kebeles, only 25, 37.78 and 57.89
percent of the non-diversified, moderately diversified and highly diversified pastoralists had
access to road, respectively. This result reviled that, there were differences (P< 0.5) among
diversification groups in accessing all weather roads in the study area. The highly diversified
households had more access of all-weather roads than the non-diversified households did.
Table 13: Summary of Descriptive Statistics Analysis Results Related to Livelihood
Diversification
Variable Definition F/χ2-value
SEX Sex of household head 12.183**
AGE Age of household head 37.14***
EDULVL Education level of household head 34.271***
TTLFMLY Total household members of the household 0.39
DPNDRT Total dependency ratio of the household 4.61**
FMLYLBR Available labor of the household 3.04*
TTLLVSTKTLU Total livestock holding (TLU) 32.76***
ACCSVTCLNK Access to veterinary service 14.015**
ACCSCRDT Access to credit service 38.176***
ACCSROAD Access to all weather roads 6.992** ***, **, * represent significant at 1%, 5% and 10% probability levels, respectively. Source: Own computation results, 2015
48
4.4. Households’ Expenditure Pattern and Livelihood Diversification
The major types of expenditure of the pastoral households in the study area were purchased
of food items, medical care, stimulants (khat3 and ashara4), gifts (zekat), son’s circumcision
ceremony and clothes. These expenditures can be generally grouped as food items
expenditure and non-food items expenditure. The food item expenditure included the
expenses made for the purchase of food items such as grain, floors, salt, rice, sugar and the
likes from the nearby markets and the market price of own consumption like milk, meat and
grain produced and consumed by the households themselves at home. The estimated
household expenditure patterns on food items and non-food items and their diversification
level during the 2015 production year are depicted in Table 14.
There was significant mean difference of food items expenditure between diversification
groups (0.05). Highly diversified households had higher food items expenditure than non-
diversified households. Regarding the non-food items expenditures, as depicted below in
Table 14, highly diversified households tended to spent more on non-food items than non-
diversified and moderately diversified households. This was also statistically significant
(P<0.01) between diversification groups.
Table 14: Households’ Food and Non-Food Items Expenditure by Diversification Level
Expenditure type Households’ diversification level F value
Non-
diversified
Moderately
diversified
Highly
diversified
Food item Mean 7,284 8,512.51 8,084 3.20** Std. Dev. 2,578.84 4,305.25 2,801.89
Non-food item Mean 6,951.57 8,214.22 11,729.37 4.90*** Std. Dev. 4,538.94 6,297.46 7,333.24
***, ** represent significant at 1% and 5% probability levels, respectively Source: Own computation results, 2015
Comparing food and non-food item expenditure across diversification level, the large
proportion of household expenditure has gone to the food items. However, highly diversified
households had spent more of their incomes on non-food items than the non-diversified and
3 Khat is a plant grown in Ethiopia which has got a mild stimulant. 4 Ashara is a local drink in Afar produces from a mix of milk and coffee cherry,
49
moderately diversified households. On the other hand, the non-diversified households spent
higher proportion of their incomes on food items and less on non-food items.
4.5. Determinants of Pastoral Livelihood Diversification
In the preceding part, the status of livelihood diversification was analyzed using descriptive
statistics. Accordingly, different factors affecting diversification process and characteristics
of sample households were discussed. While looking at the relationships between different
factors that affect the diversification process individually, the causal relationship between
these factors and the resultant diversification and the combined effect of these factors should
be analyzed. In this section, the selected independent variables were used to estimate the
logistic regression model to examine the determinants of diversification in the selected
pastoral kebeles of Amibara district. An ordered probit model was fitted to estimate the
effects of the hypothesized variables on the status of diversification.
Ahead of model parameters estimation, it is important to check the presence of
multicollinearity among the hypothesized variables. Thus, variance inflation factor (VIF)
was used to test the degree of multicollinearity among the continuous variables and
contingency coefficient was computed to check for the degree of association among the
discrete variables. The value of VIF for continuous variables were found to be less than 10.
Thus, there is no serious problem of multicollinearity and all continuous explanatory
variables were retained and entered in to logistic regression (Appendix 4).
For the same reason, the contingency coefficient that measures the degree of association
between various discrete variables was also computed to check the degree of association
between the discrete variables. Accordingly, the computation result shows that there was no
serious problem of association among discrete explanatory variables, which was less than
0.75, which is often taken as a cut-off point (Appendix 5). Therefore, all discrete explanatory
variables were entered in to the logistic regression.
Table 15 sets out results the ordered probit regression model. The results indicate that,
collectively, all estimated coefficients are statistically significant, since the LR statistic has
a p-value less than 1%, indicating the robustness of variables used. The pseudo R2 value
indicates that 42.75% variation in the dependent variable was due to the independent
variables included in the model.
50
The model results on Table 15 indicate that household characteristics such as sex of
household head, total family size and dependency ratio as well as institutional characteristics
such as access to veterinary clinic, access to all weather roads and perception on risk were
not statistically significant determinants of pastoral livelihood diversification. While, age of
household head, level of education of the household head, available family labour in AE,
total livestock size in TLU, distance to nearest market and access to credit service were found
to be significant determinants of pastoral livelihood diversification. More specifically,
households’ livelihood diversification level varies directly with level of education of
household head, available family labour and access to credit service as the parameters of
these variables contain positive sign. On the other hand, age of household head, total
livestock size in TLU, distance to the nearest market appeared to be livelihood diversification
decreasing factors since their respective coefficients display negative sign. However, these
coefficients cannot directly reveal the effects of the regressors on each of the three different
levels of pastoral livelihood diversification. To overcome this problem, marginal effects
indicated by dy/dx were evaluated at the corresponding levels of livelihood diversification.
The model results show that age of household head has a negative effect and was statistically
significant (p<0.01) on livelihood diversification level. This result was in line with the
expected notation and suggested that young household heads were more likely to engage in
livelihood diversification strategies than aged household heads. This may be because old
pastoralists are more stuck to traditional way of life than younger ones. Indicating that
younger pastoralists are more ambitious and risk taking to try new source of income than old
pastoralists. The marginal effect estimates of age of household head reveals that an increase
in age of household head by one year increases the likelihood household being in non-
diversified group by 1.64% (p<0.01) and decreases the probability of household being in
highly diversified group by 1.03% (p<0.05). This result implies that as age of household
head increases, the household head is more likely to be found in pastoral (non-diversified)
livelihood group than in diversified livelihoods. Pursuance of pastoral livelihoods required
that an individual accumulates a sizable number of livestock and at the same time create
good networks to ensure survival of the herd. The finding for age of household head agrees
with the finding of Mohamed (2011) and Barrett et al., (2001).
Educational level of household head was noted to influence households’ livelihood
diversification level. It was found to influence diversification status of pastoral households
positively and significantly (p<0.01) in the study area. The positive sign of the coefficient
51
indicates that literate household heads had higher probability of diversifying their livelihoods
than illiterate counterparts. Meaning that increased literacy level was associated with uptake
of non-pastoral activities. Household heads engaged in pastoralism had low literacy level
compared to their counterparts pursuing non-pastoral activities. The marginal effect
estimates of level of education of household heads discloses that being literate increases the
chance of a household to be highly diversified by 24.97% (p<0.01) and reduces the
likelihood of the household being non-diversified by 25.16% (p<0.01). An increase in the
level of education thus implies that education makes individuals more versatile and enhance
the way individual perceive, understand, interpret and respond to issues. In addition, better-
educated pastoralists may consider pursuing other economic activities other than pastoralism
thus the positive effect. This finding is in line with the findings of Adugna (2012).
Available family labor in adult equivalent matters livelihood diversification status and found
to be positively and significantly (p<0.05) influence diversification in the study area. The
positive coefficient indicates that the probability of households to be non-diversified
decreases as the available family labor increases. As the number of available family labour
in adult equivalent increases by one member, the chance of a household being in non-
diversified group decreased by 24.31% (p<0.05) and on the other hand the likelihood of a
household being in moderately diversified group increased by 23.09% (p<0.1) when the
number of available family labour increased by one member. This was a good indication that
available family labors were likely to be a push factor for livelihood diversification, as large
family members could more likely be engaged in different income sources for mitigating
risk at the household level.
The number of livestock held by a household, expressed in terms of tropical livestock unit
(TLU) varied significantly between non-diversified, moderately diversified and highly
diversified households. Livestock asset ownership was found to have a significant negative
influence on pastoral livelihood diversification status (p<0.01) in the study area. This
explains that the likelihood of a household to be non-diversified increases with the size of
livestock holding. Thus, this study suggests that one unit increase in livestock ownership in
TLU increases the likelihood of households being in non- diversified group by 0.94%
(p<0.01) and reduces the probability of household being in highly diversified group by
0.59% (p<0.01). Non-diversified households therefore tended to have more livestock than
highly diversified households. As livestock and livestock products have been a prevalent
livelihood source in the area, large number of livestock enables a household to have cash
52
income that is enough for family expenses. This result is consistent with findings of Adugna
(2012).
Distance to the nearest market center in kilometers was found to have significant negative
correlation with diversification status of pastoral households (p<0.01). The negative
relationship tells that the longer the distance the higher the tendency of the households to be
non-diversified and vice versa. This result may suggest that the longer the distance to the
market, the lower the household to participate in the market and take advantage of market
incentives, which in turn increases income. The marginal effect estimates of distance to the
nearest market reveals that for a one kilometer increase in distance to the nearest market, the
likelihood of a household being non-diversified increased by 4.2% (p<0.01) while it
decreases the chance of the household being moderately diversified by 3.99% (p<0.01). This
is similar to the finding of Adugna (2012).
It is household’s access to financial sources in the form of cash and kind to do different
businesses Access to credit is found to be positive and statistically significant (p<0.01)
determinant of pastoral livelihood diversification status. It influenced pastoral livelihood
diversification in two folds. Firstly, the results show that credit access positively influences
livelihood diversification by increasing the probability of households to be highly diversified
by 27.96% (p<0.1). Secondly, credit has a probability of 23.81% (p<0.01) to influence
livelihood diversification negatively as being non-diversified. This can be explained that
credit is an important variable that could improve the livelihood diversification status when
accessed by the pastoralists who normally do not obtain it from formal institutions. It was
also hypothesized that access to credit will facilitate the process of diversification. Hence, in
line with the proposed hypothesis households with access to credit diversified their
livelihoods more than those who have no access to credit. A study by (Smith et al., 2001)
have reached similar conclusion.
53
Table 15: Ordered Probit Estimates and Marginal Effects for Determinants of Pastoral Livelihood Diversification
Variable Parameter Marginal Effects
Non-diversified Moderately diversified Highly diversified Coef. Std. Err. dy/dx Std. Err. dy/dx Std. Err. dy/dx Std. Err.
sexhh .2586 .3812 -.1026 .1509 .0982 .1456 .0044 .0065 agehh -.0570*** .0217 .0164*** .0064 -.0061 .0042 -.0103** .0042 edulvl 1.0687*** .3316 -.2516*** .0655 .0019 .0683 .2497*** .0974 ttlfmly -.1633 .1184 .0471 .0342 -.0176 .0161 -.0295 .0219 dpndrt .3873 .2827 -.1529 .1113 .1453 .1068 .0076 .0076 fmlylbr .6155** .2522 -.2431** .0991 .2309* .0974 .0121 .0095 ttllvstktlu -.0327*** .0103 .0094*** .0032 -.0035 .0024 -.0059*** .0021 dismrkt -.1064*** .0293 .0420*** .0118 -.0399*** .0119 -.0021 .0016 accsroad -.4154 .3188 .1250 .0993 -.0551 .0556 -.0699 .0509 accsvtclnk -.0872 .3139 .0345 .1243 -.0328 .1186 -.0017 .0059 accscrdt 1.1037*** .3538 -.2381*** .0592 -.0415 .0880 .2796* .1174 rskprc .2564 .2944 -.1014 .1161 .0966 .1110 .0048 .0065
/µ1 -2.7756 1.1254
/µ2 -.7112 1.0849
Number of observation 120
Log Likelihood -74.709
LR chi2 (12) 111.57
Prob > chi2 0.0000
Pseudo R2 0.4275 ***, **, * indicate significant at 1%, 5% and 10% probability levels, respectively
54
4.6. Effect of Livelihood Diversification on Pastoral Households’ Poverty
Status
4.6.1. Poverty measures
It is obvious that households may diversify their sources of income as a strategy to cope up
risks associated with deterioration of livelihoods. Livelihood deterioration in turn can be
explained through the prevalence of poverty among individuals and community. Hence, a
given household diversifies his income portfolio for his own reason and its effect on the
poverty status of the household may also differ.
In the analysis of household poverty, this study is based on aggregate expenditure measures
of both food and non-food requirement as it is better able to capture household’s
consumption capabilities. Accordingly, a household is considered as poor when household
expenditure is insufficient to meet the food and other basic needs of household members. In
this study national average household expenditure on basic needs including those on food
(2200 kcal per day per adult), clothing, housing, education and medical care estimated to be
ETB 1985.00 per adult per annum was used as poverty line according to MOFED (2012).
Poverty indices were measured using the Foster, Greer, and Thorbecke (FGT) formula.
Based on the above poverty line, from the sample households, 55% % were above poverty
line while 45% were below poverty line. Compared with the national figure, which is 30.4%,
this figure seems to be slightly higher reflecting how poverty is highly prevailing among the
pastoral community.
The resulting poverty estimates for the non-diversified, moderately-diversified and highly-
diversified households (Table 19) shows that the overall percentage of poor people measured
in absolute head count index (α= 0) is about 45%. This implies that 45% of the population
in the study area are unable to get the minimum calories required adjusted for the
requirements of non-food items expenditure. There are also significant differences between
non-diversified, moderately diversified and highly diversified households in terms of
poverty status. The proportion of people with standard of living below poverty line is 64.3%,
35.6% and 5.3% for non-diversified, moderately diversified and highly diversified
households, respectively.
55
The poverty gap index (α=1), a measure that captures the extent to which individuals fall
below poverty line across the whole diversification group is found to be 0.107 which means
that the percentage of total consumption needed to bring the entire population to the poverty
line is 10.7%. The figure for non-diversified, moderately diversified and highly diversified
is 0.147, 0.093 and 0.018, respectively. Similarly, the poverty severity index (the squared
poverty gap, α= 2) in consumption expenditure shows that 3.02% fall below the threshold
line.
Table 16: Absolute Poverty Indices of respondents
Diversification level Head count index (α= 0)
Poverty gap (α=1)
Squared poverty gap (α=2)
Non-diversified 0.643 0.147 0.042 Moderately diversified 0.356 0.093 0.026 Highly diversified 0.053 0.018 0.006 Over all 0.450 0.107 0.030
Source: own competition results, 2015
4.6.2. Demographic characteristic and poverty status of households
The demographic and human capital variables considered in the study include sex, age, level
of education of the household head, family size and dependency ratio of the household.
Among the 54 poor households, 32 (29.26%) were led by men and 22 (40.74%) by women,
which accounts about 35.56% and 73.33% of the total male headed and female-headed
households, respectively. The chi-square test also shows that there is a strong significant
difference (P<0.01) between sex of household heads and poverty status. This implies that
female-headed households in the pastoral areas are inexplicably live under poverty than male
counterparts.
Table 17: Sex of Household Heads based on Poverty Status Sex of household head
Poverty Status χ2-value
Poor (54) Non-poor (66) Total (120) Female 22 8 30
12.974***
40.74% 12.12% 25% Male 32 58 90 29.26% 87.88% 75%
*** indicates Significant at 1% probability level Source: - own computation results, 2015
Education is another human capital that has negative correlation with poverty. Access to
education is very limited in pastoral context and similarly from the sample survey, 92.59 and
56
53.03 percent of the poor and the non-poor were illiterate, respectively. Though education
does not generate remunerable employment opportunities and the difference is small in the
pastoral context, ability to read and write would have some advantage on poverty reduction.
The result of the chi-square test (Table 21) indicates that there exist differences between level
of education of household head and poverty status.
Table 18: Educational Level of Household Head by Poverty Status
Level of education Poverty Status χ2-value Poor (54) Non-poor (66) Total (120) Illiterate 50 35 85
22.501***
92.59% 53.03% 70.83% Literate 4 31 35 7.41% 46.97% 29.17%
*** indicates significant at 1% probability level Source: - own computation results, 215
Table 22 gives the demographic characteristics of sample households. The table presents the
mean and standard deviations for each variable for the poor and non-poor. The average age
of the poor household head was 46.96 years while that of the non-poor was 39.68 years.
These results indicate that the mean age of the poor household head is statistically greater
than the non-poor counterparts.
The average family size of the poor household measured in adult equivalent is 7.76 and the
corresponding figure of the non-poor is 6.21. The value of the t-statistics shows that the mean
value of family size of the poor is statistically greater than that of the non-poor. Concerning
the dependency ratio of sample households, the mean value of dependency ratio of the poor
is 1.05 while that of the non-poor is 1.13 but, not statistically different/significant.
Table 19: Age, Family Size and Dependency Ratio of Household by Poverty Status
Variable Mean SD t-value p-value
Age of household head Poor 46.96 9.04 4.548 0.000*** Non-poor 39.68 8.46
Family size in AE Poor 7.76 2.07 4.323 0.000*** Non-poor 6.21 1.84
Dependency ratio Poor 1.05 .87 -1.606 0.994 Non-poor 1.31 .87
*** indicates significant at 1% probability level Source: own computation results, 2015
57
4.6.3. Physical capital of households and poverty status
The most important physical asset in the pastoral community is livestock ownership. Almost
all respondents owned livestock though the size and composition varies. Table 23 presents
mean and standard deviations of livestock holding by households. According to the table
results, the average number of livestock per household for the poor is 29.71 while for non-
poor is 26.72. These figures show that the average number of livestock for the poor is greater
than that of the non-poor however; there is no statistical mean difference between the poor
and non-poor in livestock holding.
Table 20: Total Livestock Holding of Households (TLU) by Poverty Status
Variable Mean SD t- value P value
TTLLVSTKTLU
Poor 29.71 25.90 0.7399 0.4609
Non-poor 26.72 18.39
Source: Own computation results, 2015
4.6.4. Financial capital of households and poverty status
Financial assets are generally the scarcest capitals in pastoral areas. Availability and
accessibility to formal financial institutions by the pastoralists are very much limited. Access
to credit service has a positive relationship with improvement of household’s poverty status.
From the surveyed households who have access to credit 23.08% are poor while 51.06% of
the households who have no access to credit are poor. This shows a significant difference
among households who have access to credit and those who have no access.
The total annual income of the household, which is taken as the function of incomes from
livestock and livestock products, farming (crop) and other non-pastoral non-farm activities,
can have a direct effect on poverty status. The mean annual per capita income difference
between the poor and non-poor groups was Birr 2218.31 and is significant at 1% probability
level. Besides there is significant difference between incomes earned from the above
mentioned sources by the two groups.
58
Table 21: Sources and Amount of Income of Sample Households by Poverty Status
Source of income Mean SD t-value p_value
Pastoralism poor 8,931.25 3,192.048 -1.6923 0.0932*
Non-poor 10,507.12 6,200.35
Farming/crop Poor 559.26 1,349.006 -6.6628 0.0000***
Non-poor 3,965.91 3,551.242
NPNF Poor 2,053.13 2,305.53 -8.5056 0.000***
Non-poor 7,664.697 4,374.265 *, ***, indicate significant at 10% and 1 % probability levels, respectively Source: own computation results, 2015
4.6.5. Access to all weather roads and poverty status
Access to all weather roads can have an impact on poverty status of the household, as it is
vital to take all marketable products to the nearest market place. According to the survey
data, 72.22 % of the poor and 57.58% of the non-poor households have no access to all
weather roads while 27.78% of the poor and 42.42% of the non-poor have had access to all
weather roads. The chi-square test (χ2= 2.771) shows that there is significant difference
between the poor and the non-poor groups in accessing all weather roads (p<0.1) indicating
that the non-poor households have more road access than the poor counterparts.
Table 22: Access to All Weather Road and Poverty Status
Poverty Status
Access to all weather road Poor (N=54)
Non-poor (N=66)
Total (N=120)
χ2-value
No 39 38 77
2.771*
72.22% 57.58% 64.17%
Yes 15 28 43
27.78% 42.42% 35.83% * indicates significant at 10% probability level Source: own computation results, 2015
4.6.6. Households’ diversification status and poverty status
Diversification of income has positive effect in improving the livelihood and reducing
poverty status of the household. The other purpose of diversification is spread of risk along
different activities. However, the objective of diversification may also hamper the asset
building strategy of households. Households who diversify their income for coping strategy
59
may not have significant effect on the poverty level. As can be seen from Table 26 below,
64.29%, 37.78% and 5.26% of the non-diversified, moderately diversified and highly
diversified households of the sample respondents are poor, respectively. On the other hand,
the corresponding figures for the non-poor are 35.71%, 62.22% and 94.74% with respective
orders. The proportion of non-diversified households being poor is statistically higher than
that moderately and highly diversified households. The chi-square test (21.486) shows that there
is a strong statistical difference between the poverty status and the level of household diversification
(p<0.01); large proportion of the highly diversified households are non-poor.
Table 23: Diversification Status and Households’ Poverty Status
Poverty Status Diversification status Poor
(N=54) Non-poor (N=66)
Total (N=120)
χ2-value
Non-diversified 36 20 56 21.486***
64.29% 35.71% 100% Moderately-Diversified 17 28 45 37.78% 62.22% 100% Highly-diversified 1 18 19 5.26% 94.74% 100%
***, indicates significant at 1% probability level Source: own computation results, 2015
4.7. Determinants of Households’ Poverty Status
Wide ranges of factors can determine poverty status of pastoral households. The poverty
analysis was estimated using the binary logit model and the analysis was carried out using
STATA software version 14.
Before running the model, however, the independent variables were checked for multi-
collinearity using variance inflation factors (VIF) and contingency coefficients (C).
Accordingly, no serious problem of multi-collinearity was detected at all continuous and
discrete independent variables and hence all variables were used in the model. The various
goodness of fit measures validate that the model fits the data well. The likelihood ratio test
statistics exceeds the Chi-square critical value with 10 degree of freedom at less than 1%
level of significance, indicating that the hypothesis that all coefficients except the intercept
are equal to zero is rejected.
The dichotomous dependent variable used in the analysis is household’s poverty status with
the value of 1 indicating the probability of being poor and, 0 otherwise. Ten explanatory
60
variables, five continuous, one categorical and four dummy, were included in the logistic
regression analysis.
Among the explanatory variables considered in the model, household total income
(HHTTLINCM), sex of household head (SEXHH), age of household head (AGE), education
level of household head (EDULVL), total number of family members in the household in
adult equivalent (TTLFMLY) and households’ diversification level (DIVRSFCTN) were
found to be significant determinants of pastoral household poverty status at different levels
of probability (Table 28).
Sex of household head (SEXHH): Sex of household head was found to be one of the
determinant factors of pastoral households’ poverty status in the study area. It was
hypothesized that female headed households are more likely to be poor than male headed
counterparts. The coefficient associated with sex of household head reflects the difference in
poverty between male-headed and female-headed households and was found statistically
significant (p<0.01). The study also revealed that being a male headed household decreases
the probability of being poor by a factor of 0.172 than a female-headed household (given all
other variables). Moreover, the marginal effect results of sex suggested that the probability
of households being poor decreased by 41.35% as the household headed by male. The
possible justification here is that, females in pastoral areas are still struggling to have access
and control over resources. They have little or no access and control of resources, which are
vital to generate income and thereby reduce poverty.
Age of household head (AGEHHH): The study confirmed the hypothesis that as the age of
household head increases, the probability of being poor increases significantly (p<0.01). This
means that a household headed by old ages tend to be poor than youngsters. When the age
of head of pastoral household increases by one year, the odds ratio in favor of being poor
increases by a factor of 1.1036 (keeping all other variables constant). On the other hand, the
marginal effect result of age of household head justifies that the probability of a household
being poor increases by 0.3% as age of household head increases by one year. The reason
why households with younger heads are less probable to be poor can be explained by their
engagement in other income generating activities (livelihood diversification) as opposed to
those of older household heads. This finding is in line with Kefelegne (2007) and Ferdu
(2008).
61
Households’ total annual income (HHTTLINCM): Total annual income of a given pastoral
household is the total sum of incomes from pastoralism, farming and from non-farm non-
pastoral activities. As it was hypothesized, total household income exerts negative and
statistically significant (p<0.01) influence on poverty status of the households. The odds ratio
implies that, as the household earn one more unit of money, the probability of the household
to be poor decreases by a factor of 0.999. The average marginal effect indicates that as the
household earned one more Birr of additional income, the probability of being poor
decreases by 0.01%. The possible explanation is that total annual income can be increase as
pastoralists tend to involve in other income generating activities like farming, employment,
petty trade and the likes in addition to sell of livestock and livestock products. The result is
consistent with the findings of Hilina (2005) and Kefelegne (2007).
Level of education of household head (EDULVL): The coefficient on education reflects the
prime role that human capital plays in determining poverty. Education level of household
head was found to have negative influence on household poverty status and statistically
significant (p<0.05). Accordingly, the odds ratio of being poor decreases by a factor of
0.1637 as the household head become literate. The average marginal effect shows that the
probability of household being poor decreases by 37.56% if household heads become
literate. Although there is low level of educational background among pastoralists in general,
being able to read and write has an advantage of being employed in the existing
governmental and non-governmental as well as private investment offices in the study area.
This in turn brings an opportunity to increase income sources and hence improve livelihoods.
This result is in conformity with the finding of Shibru et al. (2013).
Total number of family members in AE (TTLFMLY): Among other demographic variables
family size has appeared to have positive and statistically significant (p<0.01) association
with poverty. The possible explanation is that, large family size implies more dependent
persons hence more burden on the households to fulfil the basic needs. The odds ratio of
1.949 for total number of family size indicates that, keeping all other factors constant, the
probability of being poor increases by a factor of 1.949 as total family size increase by one
adult equivalent. The average marginal effect, keeping all other variables at their mean,
shows that the probability of being poor increases by 16.06% if the household family size
increases by one adult equivalent. The result is consistent with Semere (2008).
62
Diversification status of household (DIVRSFCTN): Households diversify their livelihoods
either to accumulate assets or to cope risks associated with vulnerability. Through
diversification, households increase their sources of income by engaging in different income
generating activities and thereby improve their well-being. As hypothesized earlier,
diversification has negative impact on poverty status of pastoralists. It is revealed in the study
that diversification of livelihoods is statistically significant (p<0.1) with an odd ratio of 0.139
showing that as the household becomes more diversified, the probability of being poor
decreases by a factor of 0.139 keeping all variables constant. A possible interpretation of the
marginal effect result for diversification is that, keeping all other variables at their mean
values, promoting diversification in the household decreases the probability of the
households to be poor by 22.45%. The justification behind this result is that, the more the
households involve themselves in alternative livelihoods sources, the more income they
generate and the higher the probability to be out of poverty.
Table 24: Binary Logit Coefficient Estimates, Odds ratio and Marginal Effects for
Determinants of Poverty
Variables Coef. Odds Ratio Std.Err. dy/dx SEX -1.8096** .1720 .7132 -.4135
AGE .1227*** 1.1306 .0323 .0300
EDULVL -1.7541** .1637 .7691 -.3756
TTLFMLY .8449*** 1.9498 .2449 .1606
DPNDRT .2159 1.2409 .3711 .0246
TTLLVSTKTLU .0096 1.0096 .0318 .0011
ACCSROAD -1.1327 .3222 1.0224 -.1169
ACCSCRDT -.1746 .8398 .6757 -.0424
DIVRSFCTN -1.9666* .1399 1.0716 -.2245
TTLINCM -.0008*** .9992 .0002 -.0001
CONS 3.647 38.355 2.534
Log likelihood -48.698
Model chi-square 67.76***
Correctly classified 81.67%
Sample size 120
***, **, * indicate significance at 1%, 5% and 10% probability levels, respectively Source: Own computation results, 2015
63
5. SUMMARY, CONCLUSION AND POLICY IMPLICATION
5.1. Summary
The ongoing changes of social, political, economic, cultural, technological and biophysical
conditions of the world in general and the developing nations in particular including Ethiopia
necessitate households to adjust their livelihoods and adapt the inevitable change of life.
Pastoralism has been a viable mode of production since time immemorial for a significant
part of Ethiopia’s population. However, the experiences of the past two decades showed that
pastoralism as a way of life is becoming difficult due to internal and external multi-
dimensional factors. Hence, in response to these factors, pastoralists are searching for other
livelihood portfolios through diversification process.
This study was undertaken in view of examining the level of livelihood diversifications
among Afar pastoralists and factors that determine the livelihood diversification process.
Furthermore, attempts were made to assess the effects of livelihoods diversification on
poverty status of pastoralists by identifying households as poor and non-poor by examining
the incidence, depth and severity of poverty in the community.
The data for this study were collected from 120 pastoralists randomly selected from four
pastoral kebeles of Amibara district using structured questionnaires. Furthermore, the data
was supplemented by group discussions with community representatives and key
informants. Level of livelihood diversification was measured using Simpson’s Index of
Diversity (SID). Ordered probit regression model was employed to analyze determinants of
livelihood diversification among pastoralists. While the effect of livelihood diversification
on poverty status of pastoralists’ households was analyzed using binary logit model.
To analyze the level of household’s diversification, diversity index was calculated using
Simpson’s Index of Diversity (SID) and the result is 0.46 showing some degree of
diversification among households. From the sample respondents 55.33 % of the household
diversified their livelihoods while 46.67 % remained undiversified. With regard to share of
income for the households, pastoralism contributes nearly 50.3 % of the annual income of
the household while farming (crop production) and other non-pastoral non-farm activities
(NPNF) contribute 14 % and 33.7 %, respectively. Though pastoral production system is
facing different risks including recurrent drought, range land deterioration, population
pressure and expansion of mechanized crop farming, and diversification of activity portfolio
64
has been used as a means of increasing income, households diversify to accumulate asset but
also as a coping mechanism. Among the sample respondents, 64.29 %, 35.56 % and 5.26 %
of non-diversified, moderately diversified and highly diversified households are below
poverty line.
With regard to socioeconomic characteristics of the sample households, independent sample
test has shown that there is a significance difference among diversification groups with
respect to sex of household head, age of household head, level of education of household
head, dependency ratio, family labor, total livestock, access to credit and access to all
weather roads at different probability level. In a similar way, variables like sex of household
head, level of education of household head, age of household head, total family size in AE
of household and income of household show significant difference between the poor and the
non-poor in analyzing the effect of diversification on poverty status.
The output of ordered probit model further reveal that age of household head, level of
education of household head, available family labour in adult equivalent, livestock holding
in tropical livestock unit, distance to nearest market and access to credit service had
significant influence on households’ livelihood diversification level. The findings reveal that
households’ level of diversification increases with level of education of household head
(p<0.01), available family labour in AE (p<0.05) and access to credit services (p<0.01). On
the other hand, age of household head (p<0.01) and livestock holding in tropical livestock
unit (p<0.01) decline pastoral households’ livelihood diversification level.
The demographic and socio-economic characteristics of the sample pastoral households such
as sex, age of household head, educational level of household head, total family size,
households’ diversification level and total annual income were found to be important
associates with pastoral poverty. The result of binary logistic regression model showed that
among the ten explanatory variables used in the model six were found to be significant to
affect pastoral households’ poverty status. Accordingly, sex of household head (p<0.05),
level of education of the household head (p<0.05), diversification level of the household
(p<0.1) and household total annual income (p<0.01) had negative influence on households’
poverty status. On the other hand, age of household head (p<0.01) and total family size in
AE (p<0.01) were found to influence households’ poverty status positively.
65
5.2. Conclusion and Policy Implications
Pastoral households in the study area diversify their livelihoods sources to reduce the chances
of income failure by engaging in activities that confront different livelihood profiles. They
achieve this by utilizing the available resources for different economic activities such as
cultivation of crops, charcoal production, trade and wage employment. These alternative
activities ensure improved livelihoods through increased incomes, food security, and thus
reduce poverty levels among households. It is, therefore, likely that households with fewer
alternative livelihood options fall into poverty.
The positive effect of education confirms the importance of education for diversification,
showing that educational attainment leads to higher level of diversification. Since education
attainment is positively related to pastoral livelihood diversification, this may indicate a
potential ‘generational effect’ of livelihood vulnerability, mediated by schooling. Similarly,
the negative effect of education on poverty status of the households emphasizes the
importance of access to education in the study area to reduce poverty. Consequently,
improving school enrolment through implementing different practices are the possible policy
alternatives.
Age of household head was negatively related with households’ diversification level and
positively related with households’ poverty status, showing that elders are hesitant in finding
new livelihood options and are poorer than their youngster counterparts. This is highly
associated with attitudes and strong firm to keep the traditional way of life. However, this
can be broken through education and training. Therefore, the concerned bodies should exert
relentless efforts to pursue the elders to involve in other income earning activities not
necessarily out of pastoralism but also within pastoralism itself. Besides livelihood
diversification, strategies that fit elders’ interest and desire should be provided in the area.
The negative effect of livestock asset on diversification indicates that households with large
number of livestock are less likely to diversify their livelihoods. On the other hand, livestock
ownership did not guarantee a household to be non-poor. Therefore, efforts should be made
to improve livestock productivity and to promote market access through the provision of
technologies in terms of improved breeds, better management practices and forage seeds as
well as promotion of market oriented production system as it has a double advantage of
enhancing diversification and driving households out of poverty sustainably.
66
Family labour in adult equivalent was positively and significantly related with diversification
level. Households with larger family labour are able to participate in other income earning
economic activities; diversified their livelihoods. On the other hand, total family size was
found to determine household poverty status positively. Households with large family size
are more likely to be poor as they are unable to fulfil the basic needs. Hence, more
opportunities have to be created for those who are ready to involve in alternative income
generating activities.
Access to credit has a positive effect on households’ level of livelihood diversification.
Therefore, credit provision is vital to promote pastoralists to involve in other activities and
even to expand their traditional livestock production system in such a way that can be more
productive and market oriented. Pastoralists accumulate their assets in the form of livestock
due to lack of other saving institutions and they sell their animals whenever they need money.
However, by the time they bring their animals to the market, the price they receive is too
small to fulfil their needs, which in turn forced them to sell as many animals as they have.
On the other hand, due to the very nature of the pastoral community it is difficult for them
to have collaterals, which the financial institutions are requested to provide credit. Thus,
there need to be a special policy for financial institutions to be co-pastoral.
Among the factors affecting households’ livelihood diversification, distance to the nearest
market plays a negative and significant role. Market distance and related transport costs are
the major factors daunting pastoralists from using markets and related incentives. Creating
favorable transport access is therefore the best way of shortening geographic distances.
Focus should be paid on improving road access and facilitate marketing structures among
pastoralists.
The results also suggest that poverty in the study area is gender specific. Female-headed
households are more likely to be poor than their male-headed counterparts. Hence,
supporting female-headed households is pertinent to reduce households’ poverty in pastoral
areas. Policies and strategies in the pastoral communities should take in to consideration the
involvement of women either as a household head or as member of the family through
ensuring access to assets, education and participation in decision-making if poverty is to be
reduced meaningfully.
67
Livelihood diversification helps household to be non-poor as it has a negative effect on
poverty. This indicates that households with diversified livelihoods increase their income
sources and secure income for basic needs. Therefore, identifying alternative livelihoods
diversification strategies as an income generating activities need to be strengthened in the
pastoral areas as a way to get out of poverty. In order to reduce the dependency on highly
volatile and unreliable pastoral income, such income generating activities both with in
pastoralism like, small ruminant fattening and milk processing and out of pastoralism like
that of irrigation agriculture and petty trading should be considered.
Finally, given all the limitations of this study, there are some implications deserving further
researches which could possibly make some additions over the present study
Pastoral production system is wide and complex and has some variables that
cannot be captured by economic models. Moreover, diversification is a process
and not a onetime phenomenon. Hence, further researches need to be done using
time series data and considering comprehensively the social and cultural values
of the pastoral community.
The change from pastoral to agro-pastoral and from transhumant to sedentary
way of life among the communities has been implementing since the last couple
of years. However, a large proportion of the pastoral community are still insisting
in their traditional livestock production system. Thus, comparative studies
showing the positive and negative impact of sedentarization versus transhumant
way of life should have to come on board.
Poverty in the pastoral context is quite different from that of non-pastoral
system. As a result, a different poverty measurement and determination methods
should be designed for the pastoral production system.
68
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7. APPENDICES
Appendix 1: Simpson’s Index of Diversity (SID) for the Sample Households
Diversification status SID values Min. Max. Mean Std. Dev.
Non-diversified 0 .39 .26 .118 Moderately diversified .40 .69 .54 .106 Highly diversified .70 .98 .81 .118 Total 0 .98 .46 .230
Source: Own computation results, 2015
Appendix 2: Households’ Average Food Items Expenditures by Diversification
Food items Average expenditures (in Birr) Non-diversified Moderately diversified Highly-diversified
Grain foods 2228.36 2518.67 1345.26 Flours 1731.43 2092.62 2784 Rice 743.71 1059.2 881.68 Pasta/spaghetti 321.86 530.67 339.79 Edible oil 831.43 843.73 1182.95 Salt 90.937 91.16 104 Sugar 1336.29 1376.47 1446.32
Source: Own computation results, 2015
Appendix 3: Households’ Average Non-Food Items Expenditures by Diversification
Non-food items Average expenditures (in Birr) Non-diversified
Moderately diversified
Highly-diversified
Educational items 458.04 595.56 642.11 Clothe expense 1425 1537.78 1547.37 Medical items 1363.39 1431.11 1589.47 Ceremonial expense 633.93 920 2663.16 Social obligations 219.64 371.78 473.16 Kat expense 2628.21 3113.33 4621.05 Ashara expense 223.36 244.44 193.05
Source: Own computation results, 2015
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Appendix 4: Coefficient of Correlation and Variance Inflation Factors for Continuous
Variables of Multinomial Logit Model
Variable R2 VIF 1/VIF AGE 0.370 2.77 0.36 TTLFMLY 0.010 4.13 0.24 DPNDRT 0.049 3.85 0.26 FMLYLBR 0.351 7.41 0.14 TTLVSKTLU 0.329 2.21 0.45 DISMRKT 0.001 2.11 0.47
Source: Own computation results, 2015
Appendix 5: Contingency Coefficient for Discrete Independent Variables of Multinomial
Logit Model
SEX EDULVL
ACCSROAD
ACCSVTCLNK
ACCSCRDT
RISKPERC
SEX EDULVL 0.32
8
ACCSROAD 0.061
0.336
ACCSVTCLNK
0.141
0.336 0.451
ACCSCRDT 0.210
0.374 0.208 0.250
RISKPERC 0.368
0.514 0.312 0.356 0.437
Source: Own computation results, 2015
Appendix 6: Correlation Matrix for Continuous Explanatory Variables in Binary Logistic Model Variable AGE TTLFMLY DPNDRT TTLVSTKTLU HHTTLINCM
AGE 1
TTLFMLY .282 1
DPNDRT -0.375 -0.063 1
TTLVSTKTLU 0.681 0.043 -0.241 1
HHTTLINCM -0.189 0.014 0.063 0.342 1
Source: Own computation results, 2015
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Appendix 7: Contingency Coefficient for Discrete Independent Variables of Binary Logit Model SEX EDULVL ACCSROAD ACCSCRDT DIVELEVE
SEX
EDULVL 0.328
ACCSROAD 0.110 0.323
ACCSCRDT 0.210 0.375 0.239
DIVELEVE 0.311 0.524 0.245 0.556
Source: Own computation results, 2015
Appendix 8: Households’ Survey Questionnaire
I. Individual Household Questionnaire
A. Household characteristics
1. Name of household head _______________________
2. Sex of household head ____0= female 1= male
3. Age of household head ____________ years
4. Marital status 1= single 2= married (one wife) 3= married (more than one) 3=
widower/widow 4= divorced
5. Highest level of school completed for household head __ 0= no education 1= read
and write 2= grade 1-4 3= grade 5-8 4= grade 9-10 5= grade 11-12 6= above grade 12
6. Family members
6.1 Total number of family ___________
6.2 Male 0-15 years old _____________
6.3 Male 16-64 years old___________
6.4 Male 65 years and above ________
6.5 Female 0-15 years old ________
6.6 Female 16-64 years old ________
6.7 Female 65 years and above ___________
7. Do you make livelihood from?
No. Activities Response 7.1 Animal husbandry 0= no 1= yes 7.2 Crop production 0= no 1= yes 7.3 Share from leased clan land 0= no 1= yes 7.4 Wage from labor employment 0= no 1= yes 7.5 Permanent employment 0= no 1= yes 7.6 Non-farm employment 0= no 1= yes 7.7 Charcoal making 0= no 1= yes
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7.8 Petty trade 0= no 1= yes 7.9 Remittance 0= no 1= yes 7.10 Others (specify) 0= no 1= yes
B. Household’s asset holdings
1. Livestock
1.1. Livestock ownership (number) Particulars
Livestock number
Change in livestock number holding during the last one year Increment Decrement Gift from others
Purchase
Total increment
Death
Gift to others
slaughtered
sales
Total decrement
Cattle Cow Calves Heifers Bulls, Goat Sheep Camel Donkey Total
1.2. Did you own more animals in the past 10 years as compared to now? 0=no 1= yes
1.3. If yes, what are the reasons for livestock decline? 1= drought 2= disease 3= livestock
sale 4= others
1.4. List the major problems in livestock production in the area in order of importance
Problems Rank (in order of importance) 1. Recurrent drought 2. Feed problem 3. Water problem 4. Health problem 5. Lack of veterinary service 6. Lack of improved breed 7. Lack of working capitals 8. Others (specify)
1.5. What are the major livestock diseases in your locality?
a. _____________________
b. _____________________
c. _____________________
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2 Land
2.1. Do you own farm land? 0= no 1= yes
2.2. If yes, how much do you own? _______________ ha
2.3. How do you acquire the land you possess? 1= allocated by customary rule from clan
land 2= inheritance 3= by clearing clan land 4= leasing in from others 5= others
(specify)
2.4. The fertility status of the land you own? 1= fertile 2= moderately fertile 3= infertile 4=
others (specify)
2.5. What are the major crops you are growing on your own land? 1= cotton 2= maize 3=
onion 4= sesame 5= others (specify)
2.6. Do you leased/shared in farm land? 0= no 1= yes
2.7. If yes, how much do you leased/shared in? ______________ ha
2.8. What are the major crops you grow on leased/shared in land? 1= cotton 2= maize 3=
onion 4= sesame 5= others (specify)
2.9. Do you leased/shared out farm land? 0= no 1= yes
2.10. If yes, how much do you leased/shared out? _______________ ha
2.11. How much is the lease rate per ha? __________Birr/ha
2.12. Reason for leased/shared out? 1= unable to cultivate due to lack of capital 2= lack of
farming experience and knowhow about farming 3= shortage of time and labor due to
herding 4= not profitable than livestock 5= others (specify)
3. Other assets of the household
3.1. Do you have your own residential house? 0= no 1= yes
3.2. If yes, what type of house? 1= thatched roofed 2= plastic roofed hut 3= soil roofed
house 4= iron sheet roofed house 5= other
3.3. If yes, is your house permanent? 0= no 1= yes
3.4. Does the household own any one of the following item?
Asset Ownership remark Sleeping bed (wooden/metal) 0= no 1= yes Mattress 0= no 1= yes Bed sheet 0= no 1= yes Table and chair 0= no 1= yes Radio 0= no 1= yes Mobile 0= no 1= yes TV 0= no 1= yes Bicycle 0= no 1= yes
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Motor pump 0= no 1= yes Watch 0= no 1= yes Others
C. Rangeland and natural resource management and conflict
1. Where do you obtain your livestock feed? 1= private pasture land 2= community pasture
land 3= clan pasture land 4= No man’s pasture land (everywhere) 5= others (specify)
2. Do you move your animal to Halideghie site? 0= No 1= yes
3. Distance traveled to reach Halideghie site? Km ________
4. Time taken to reach to Halideghie site (days) __________
5. Number of months that animals stay at Halideghie site in a year _________
6. Who is responsible for deciding and organizing the movement? 1= household head 2=
community elders 3= clan leaders 4= other
7. Is the movement partial or involves the whole household members? 1= whole household
members 2= partial family 3= it depends on situations
8. When the movement is partial how do you manage the remaining family members?
__________________________
9. Have you or any of your family members faced any problems as the result of mobility
(moving from place to place)? 0= no 1= yes
10. If yes, what were those problems? ____________________________,
_______________________, ____________________________
11. How do you manage pastureland? 1= by shifting from pasture to pasture 2= by reserving
between dry and wet grazing/browsing area 3= using controlled grazing/browsing 4= no
organized way of management 5= other (specify)
12. Do you face feed shortage both at dry season and wet season grazing lands? 0= no 1=
yes
13. Have you ever faced conflicts in relation to resource use? 0= no 1= yes
14. If yes, what are the causes?
1. Conflict over access to irrigable land with own clan member 0= no 1= yes
2. Conflict over access to irrigable land with clan leader 0= no 1= yes
3. Conflict over land with neighbor clan 0= no 1= yes
4. Conflict over land with large investors 0= no 1= yes
5. Conflict over grazing land with neighbor clan 0= no 1= yes
6. Others (specify)
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15 How did the conflicts you have over resources resolve?
1. through the customary rules 0= no 1= yes
2. Through the local government intervention 0= no 1= yes
3. through normal court 0= no 1= yes
4. Through own negotiation with other parties 0= no 1= yes
5. Not resolved at all 0= no 1= yes
6. Others (specify)
16 If your answer for the previous question is not resolved at all, what are the reasons
you think?
1. The customary conflict management institution has been weakened than before
0= no 1= yes
2. The local government did not willing to intervene to resolve the conflict 0= no
1= yes
3. Local government officials biased to large and influential groups 0= no 1= yes
4. Others (specify)
17 What are the major constraints related to livestock feed
a. shortage of pasture due to drought 0= no 1= yes
b. deterioration of palatable grass species 0= no 1= yes
c. bush encroachment 0= no 1= yes
d. expansion of crop cultivation 0= no 1= yes
e. improper utilization of pasture 0= no 1= yes
f. Ranching 0= no 1= yes
g. Pasture underutilization due to tribal and/or boundary conflict 0= no 1= yes
h. Sale/lease of pastureland to private investors 0= no 1= yes
i. Overgrazing 0= no 1= yes
j. Others (specify)
D. Availability of Infrastructure
No. Particulars Availability 0= no 1= yes
Distance from your house (Km)
Functionalities 1= poor 2= good 3= v. good
1 Veterinary clinic 2 Human clinic 3 Formal school 4 All weather roads 5 Transport facility
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6 Protected drinking water
7 Telephone service 8 Grain mill 9 Others (specify)
E. Farming characteristics
1. Do you involve in farming (crop cultivation) activities? 0= no 1= yes
2. If yes, how long do you practice farming on your own farm land? _________ years
3. Do you have access to irrigation water? 0= no 1= yes
4. Did you face problem of access to irrigation water? 0= no 1= yes
5. If yes, what are the problems you are facing with irrigation water?
1. Uneven distribution of the water from the canal 0= no 1= yes
2. Lack of well-structured irrigation water canal to the farm land 0= no 1= yes
3. Conflict over use of the water 0= no 1= yes
4. Mismanagement of irrigation water application 0= no 1= yes
5. Others (specify)
6. The trend of crop production during the last ten years in your locality
1= decreasing 2= increasing 3= the same
7. How do you plough your land? 1= using family labor 2= using tractor /owned or
rented 3= using hired labor 4= using animal traction 5= others (specify)
8. Are your family members participate on operation on your farm activity? 0= no 1=yes
9. If yes, which members participate? 1= wife 2= male children 3= female children
4= other family members
10. For which farming activities do you use family labor and for which one hired lab our?
Farm activities Family labor 1= yes 0= no Hired labor 1= yes 0= no Land clearing Ploughing Sowing Weeding Irrigation application Hoeing Guarding Chemical application Harvesting Packaging Others (specify)
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11. If no, why they do not participate? 1= labor shortage for other activities 2= do not have
cropping activities 3= unwilling to participate 4= others (specify)
12. If you hire labor from where you hire? 1= from the local afar laborer 2= highlanders
3= both
13. Do you think that crop cultivation you involved in has contributed to your family food
security? 0= no 1= yes
14. If yes, how is the food security condition when compared to the livestock production?
1= more secured in crop cultivation
2= less secured than livestock production
3= Indifference between the two production systems
4= cannot compare the two
15. Which of the following are major problems of production for you?
1= tractor 0= no 1= yes 4= labor shortage 0= no 1= yes 7=
knowhow about farming 0= no 1= yes
2= water pump 0= no 1= yes 5= access to credit 0= no 1= yes 8= access to irrigation
water 0= no 1= yes
3= input supply 0= no 1= yes 6= access to market for products 0= no 1= yes
9= others (specify)
16. Does anyone of your family member participate on labour employment in other farm?
0= no 1= yes
17. If yes, which of them participate? 1= wife 2= female children 3= male children
4= husband 5= other family member
F. Use of Modern agricultural inputs
1. Did you use any agricultural technologies? 0=no 1= yes
2. If yes, give details
Name of agricultural technology
Quantity used
Unit price
Total price
Source 1= research 2= Office of Agri. 3= NGO 4= investors 5= other farmers 6= others (specify)
Improved seed fertilizers chemicals Improved livestock breeds
Improved forage
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3. If yes, for how many years on average have you been using these technologies?
_________years
4. The trend of households technology use in quantity and type for the past years has been.
1= increasing 2= decreasing 3= remain constant 4= specify if others
5. If you have been not using or if the use has been decreasing, would you please tell us the
reason? 1= too expensive 2= not available 3= inadequate supply 4=
others (specify)
G. Agricultural extension services
1. Is there development agent in your KAs? 0= No 1= yes
2. If yes, how many contacts did you had in the year? 1= every day 2= every week 3=
twice in a month 4= every month 5= no contact 6= others (specify)
3. What was the purpose of these visits?
1= to give advice on crop production 2= to give advice on animal production 3= to
give advice on soil conservation 4= to collect debt 5= others (specify)
4. Did you get any training from extension organization? 0= no 1=yes
5. If yes, specify the type of training _____________________________________
H. Membership of cooperative
1. Did you or member of your family participate in any formal cooperative? 0= no
1= yes
2. If yes, do you mention the name of cooperatives? _____________________________
3. What benefits did you gain by being membership of such cooperatives? 1= income
increased 2= labor shared 3= credit used 4= others (specify)
4. If no, what is the probable reason? 1= no interest 2= no cooperatives in the kebele 3=
others (specify)
I. Social Leadership Participation
1. Did you participate in any social leadership in the past 12 months? 0= no 1= yes
2. If yes, specify among the following. 1= traditional cooperative 2= religion 3= political
4= kebele administration 5= others (specify)
3. If yes, what benefit do you gained from the leadership role? 1= salaried 2= social
prestige 3= access to assets 4= others (specify)
J. Financial Capital
1. Do you face problem of working capital? 0= no 1= yes
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2. If yes, fill the following table
Type Involved/not involved
Amount Purpose Source Interest amount
Borrowing Lending Savings
Codes purpose 1= purchase of seeds 2= purchase of chemicals 3= purchase of
farm implements 4= for consumption 5= for social obligation
Codes for source 1= service cooperatives 2= micro-finance 3=
friends/relatives 4= local money lenders 5= NGOs 6= others (specify)
3. If no, why? 1= fear of ability to repay 2= lack of assets for collateral 3= no
one to give credit 4= high interest rate 5= no need of credit
K. Market access
1. Is there a nearby market place both for livestock and crop? 0=no 1= yes
2. The distance of nearby market from your residence _______________km
3. Is the market place suitable to sell all classes of livestock? 0= no 1= yes
4. Where do sell your farm produce/crop? 1= on farm (farm gate) 0= no 1= yes 2=
taking to the local market 0= no 1= yes 3= through service cooperatives 0= no 1=
yes 4= others (specify)
5. If you produce cotton, for whom did you sell your products? 1= for private ginneries
2= for nearby investors 3= for textile factories 4= for local collectors 5= at
nearby market
6. If you sold for investors only, what is the main reason? 1= forced b/c of credit other support
relation 2= no other alternatives 3= b/c of good price 4= other
(specify)
7. What means of transport do you use to transport your product/crop? 1= animal power
2= human power 3= rented truck 4= others (specify)
8. When do you sell most of your livestock? ________ month/season
9. When do you sell most part of your crop produce? ______________month
10. What are the problems in marketing your products/livestock? 1= transportation problem
2=too far from market place 3= low bargaining power 4= low price of
agricultural produces 5=others (specify)
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11. Do you think that you get fair price for your crop produce and livestock by the time you
sell? 0= no 1= yes
12. If not, what are the probable reasons? 1= low demand for the produce 2= more
supply 3= lack of access to potential markets 4= poor quality of produce due to
lack of skill 5= others (specify)
13. If you think that the price you get for your produce is low why do you sell with that
price? 1= to settle credit used 2= to pay tax 3= social obligation
4= to meet family requirement 5= others (specify)
14. Do you sell milk and milk by-products? 0= no 1= yes
15. If yes, which animals’ milk and milk by-products you sell? 1= cow 2= goat
3= camel 4= sheep
16. If yes, for what purpose you sell? 1= to meet family requirement 2= for emergency escape
3= means of livelihood 4= others
17. If not what is the reason? 1= no market access 2= used for family consumption only 3=
no demand at all 4= it is a taboo 5= others (specify)
18. Do you sell hide and skins? 0= no 1= yes
19. If no, what is the reason? 1= No market access 2= used for home material making 3=
No demand 4= it is a taboo 5= others (specify)
20. What is your basic source of market price information both for your livestock and crop
products? 1= development agents 2= friends/other pastoralists 3= mobile phone 4=
radio/TV 5= traders 6= others
21. What do you think should be done to solve these problems?
___________________________________________________________
L. Income sources
1. Income from selling animals and animal products (pastoralism)
Source quantity Price/unit income Sale of cattle Sale of camel Sale of shoat Sale of other animals Milk sale Sale of hide and skin Sale of butter Income from hired-out (draught animal) Others (specify)
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2. Income from other sources (non-pastoral purses)
Sources Days/month Wage rate/lease rate/ price per sack
Total
Income from sale of own crop Income from share cropping Income from land leased-out Income from share of clan land leased-out for investors
Farm/Causal labor Off-farm wage Employment/ permanent or contract in govt’t or private/NGO
Petty trade Sale of charcoal Remittance Others (specify)
M. Consumption expenditure
1. What type of food item is your family mostly used to eat? 1= Rice 2= milk 3= maize 4=
sorghum 5= others
2. Which one is your staple food? 1= Rice 2= milk 3= maize 4= sorghum 5= others
3. Please give details of your expenditure as per listed below
Type Quantity (kg/l) Price/unit Total amount
Food Grain Flour Rice Pasta Oil Sugar Others (specify) Sub total Non-food Education Clothing Medical Ceremonial (religious holiday, etc) Social obligation ( marriage, etc) Chat and tobacco
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Other (specify) Sub total Grand total
3. Livestock maintenance cost
Description quantity Price/unit Total cost Fodder Concentrate Medical Labor
4. Did the income from animal and animal products fairly cover the above expenses? 0= no
1= yes
5. If no, how did you manage the gap (from where did you bring the money to fill the gap)?
1= _____________________ 3= ______________________
2= _____________________ 4= _______________________
6. Crop production cost
Description Quantity Price/unit Amount 1. Labor cost
Land preparation Seeding Weeding Fertilizer application Watching Harvesting
2. Seed cost Crop1 Crop2 Crop3 Fertilizer Pesticide cost Irrigation cost
N. Risk perception
1. How often does drought occur in your locality? 1= every year 2= every two years
3= every five year 4= every ten year
2. Do you think that livestock production alone can earn enough income for your family? 1=
yes 2= no
3. Do you think that pastoralism will continue as a means of livelihood in the future give all
the conditions facing? 1= yes 0= no
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4. How do you explain your families’ living standard relatively compared with that of before
you start and participate on crop cultivation or other non-pastoral activities? 1= better
2= same 3= deteriorate 4= other (specify)
5. Is there any changes and incidences on relationships with your family members in the
following after you engaged on other activities? 1= change in division of labor
2= conflicts increased 3= failure to fulfill families obligation 4= other
(specify)
II. Discussion points with pastoralists
1. What are the common non-pastoral livelihood strategies that people are adopted in this
Kebele?
2. How do you see the situation before ten years and now?
Source of food
Source of income
Labor participation
Types of food they consume
Expenditure pattern
3. Is it changing?
4. How? And why it is changing?
5. What are the possible sources of change in livelihoods of the pastoralists?
6. Do all households undertake similar livelihood strategies? If not what makes households
differ?
7. What are the uncertainties and risks of pastoralists’ life/livelihood?
8. What are the impact of local institutions and the roles of the government in mitigating
these problems?
9. In your opinion, what should be done in order to improve the livelihoods of pastoralists?
10. What will happen to traditional pastoral livelihood strategy in the days to come? Will it
persist, decline, stop or increase? Why?