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

v

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

vi

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.

vii

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.

7

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

6. REFERENCES

Abdulahi, A and Crole-Rees, A. 2001. Determinants of income diversification amongst the

rural households in Southern Mali. Food policy research, 26(4): 437-452.

Abebaw Shimeles. 2003. Dimensions of determinants of food security among rural

households in Dire Dawa, Eastern Ethiopia. MSc Thesis, Alamaya University,

Alamaya, Ethiopia.

Adekunle, D. and Henson S. J. 2007. The effect of cooperative thrift and credit societies on

personal agency belief A study of entrepreneurs in Osun State, Nigeria. African

journal of agricultural research, 2(12): 678-686.

Adugna Eneyew. 2012. Determinants of Livelihood Diversification in Pastoral Societies of

Southern Ethiopia. Journal of Agriculture and Biodiversity Research, 1(3):43-52

Adugna Enyew and Wogayehu Bekele. 2012. Determinants of livelihood strategies in

Wolaita, Southern Ethiopia. Journal of agricultural research and review, 1(5): 153

-161

AU (African Union). 2010. Policy Framework for Pastoralism in Africa: Securing,

protecting and improving the lives, livelihoods and rights of pastoralist

communities. October 2010, Addis Ababa, Ethiopia. Ayalew Yimer. 2003. Identification and intensity of food insecurity and copping strategies

of rural households in North Shoa: the case of Lalomama. MSc Thesis, Alamaya

University, Alamaya, Ethiopia.

Barrett, C. B, Reardon T. and Webb P. 2001. Non-farm income diversification and household

livelihood strategies in rural Africa: Concepts, dynamics, and policy implications.

Food Policy research, 26 (2): 315-331.

Barrett, C. B. and Reardon, T. 2000. Asset, activity and income diversification among

African agriculturalists: some practical issues. Cornell University Department of

Applied Economics and Management Working Paper No. 14734, New York: Cornell

University.

Belachew H. 2004. Livestock marketing and pastoralism. Pp. 150-165. In: Yonas (ed.),

Pastoralist forum Ethiopia, Addis Ababa, Ethiopia.

Berhanu Eshete. 2007. Livelihood strategies of smallholder farmers and income poverty in

drought prone areas. The case of Gena-Bosa woreda, Southern Ethiopia.

(Unpublished MSc thesis), Haramaya University, Haramaya, Ethiopia.

69

Biswarup, S and Ram, B. 2011. Livelihood Diversification Pursued by Farmers in West

Bengal. Indian journal of research and extension education, 11 (2): 59-64

Brown DR, Stephens EC, Okuro MJ, Murithi FM, Barrette CB. 2006. Livelihood strategies

in the rural Kenyan highland.

Bryman, A. 2004. Social research methods 2nd Edition. New York: Oxford University Press

Inc.

B. O. Bebe, J. K. Lagat and E. M. M. 2012. Evaluation of the Factors Associated with Shift

from Pastoral to Agro-pastoral Farming Systems in Trans-Mara West District of

Narok County-Kenya. Asian Journal of Agricultural Sciences 4(6): 403-410.

Carsewell, G. 2001. Livelihood diversification in Southern Ethiopia, IDS working paper 11.

Carter, M. 1997. Environment, technology, and the social articulation of risk in West African

agriculture. Economic development and cultural change, 45(3): 557-591.

Chambers, Robert and Gordon, R. Conway. 1992. Sustainable rural livelihoods: Practical

Concepts for the 21st Century. IDS Discussion Paper, No.296. Brighton: Institute of

Development Studies.

COMESA (Common Markets for East and Southern Africa). 2009. Income diversification

among pastoralists, lessons for policy makers. policy brief No. 3, March, 2009.

Coppock, D.L. 1994. The Borana plateau of Southern Ethiopia: Synthesis of pastoral

research, development and change. Addis Ababa, Ethiopia.

Crole-Rees, A. 2002. Rural household strategies in Southern Mali. Determinants and

contribution of income diversification to income level and distribution. Swiss

federal institute of technology. Zurich, Switzerland.

CSA (Central Statistical Agency). 2010. Report of the population and housing census, Addis

Ababa.

Davies, J., and R. Bennett. 2007. Livelihood adaptation to risk: constraints and opportunities

for pastoral development in Ethiopia’s Afar Region. Journal of development studies,

43 (3): 90–511.

Delgado, C., and A. Siamwalla. 1997. Rural economy and farm income diversification in

developing countries. Discussion paper No. 20. Markets and structural studies

division, International food policy research institute. Washington, D.C.

Dercon, S. and Tassew Woldehanna. 2005. Shocks and consumption in 15 Ethiopian

villages,1999-2004. African Economies, 14: 559-585.

70

Destaw Berhanu. 2003. Non-farm employment and farm production of smallholder farmers:

the case of Edja district of Ethiopia. MSc Thesis, Alamaya University, Alamaya,

Ethiopia.

Devereux, S. 2006. Vulnerable livelihoods in Somali region, Ethiopia. ODI Research Report

No. 57. Sussex, UK: Overseas Development Institute.

Doss, C.R. 1996. Testing among models of intra-household resource allocation, World

Development, Elsevier Science LTD., Great Britain, 24 (10).

Dulruba, K. and B.C. Roy. 2012. Rural livelihood diversification in West Begal:

determinants and constraints. Agricultural economics research review, 25 (1): 115-

124.

Emmanuel, K. 2011. Rural livelihood diversification and agricultural household welfare in

Ghana. Journal of Development and Agricultural Economics, 3(7): 325-334

Ellis, F. 1993. Peasant economics: Farm households and agrarian development, 2nd Edition.

Cambridge University Press.

Ellis, F. 1998. Household strategies and rural livelihood diversification. Journal of

Development Studies, 35: 1-38.

Ellis, F. 1999. Rural livelihood diversity in developing countries: Evidence and policy

implications. Overseas Development Institute.

Ellis, F. 2000. The determinants of rural livelihoods and diversity in developing countries.

Journal of agricultural economics, 51 (2): 289-302.

Ellis, F. 2001. Rural livelihoods, diversity and poverty reduction policies: Uganda, Tanzania,

Malawi and Kenya. LADDER Working Paper, No 1. Norwich: Overseas

Development Group, University of East Anglia.

Ersado, L. 2003. Income diversification in Zimbabwe: Welfare implications from urban and

rural areas. FCND Discussion Paper 152. International Food Policy Research

Institute. Washington, D.C.

Escobal, J. 2001. The determinants of non-farm income diversification in rural Peru. World

Development, 29 (3): 497–508.

Eshetu Bekele. 2000. The underlying causes of household food security and coping

strategies: The case of Legambo Wereda, South Wollo Zone, Amhara Region. MSc

Thesis, Addis Ababa University, Addis Ababa, Ethiopia.

Fikru Tesfaye. 2008. A case study non-farm rural livelihood diversification in Lume Woreda,

Oromia Regional State. Msc Thesis, Addis Ababa University, Addis Ababa,

Ethiopia.

71

Foster, J.E., J. Greer and E. Thorbecke. 1984. A class of decomposable poverty measures.

Econometrica, 52(3): 761-776.

Fredu Nega, Marysse, S.Tollense, E and Mathijsc. E. 2006. Diversification, income

inequality and social capital in Northern Ethiopia. Mekelle University, Ethiopia, and

University of Belgium.

Galab S, Fenn B, Jones N, Raju D, Wilson I, and Reddy MG. 2002. Livelihood

diversification in rural Andhra Pradesh: household asset portfolios and implications

for poverty reduction working paper no. 34.

Greene HW. 2012. Econometric analysis: 4th Edition. New York University, Macmillan

Publishing Company.

G. S. Ogato. 2013. The human ecology of disaster in Ethiopia: The quest for participatory

disaster management and sustainable livelihood improvement of pastoral

communities. American journal of human ecology, 2 (1): 21-27.

Hausman, J. and Mc Fadden, D. 1984. Specification tests for the multinomial logit model.

Econometrica, 52 (5).

Hillina Mikre. 2005. Dimensions and determinants of poverty in pastoral areas of Eastern

Ethiopia: the case of Shinile zone of Somali region. MSc Thesis, Alamaya

University, Alamaya, Ethiopia.

Idowu A.O, Awoyemi T.T, Omonona B.T and Fausi A.O. 2011. Non-farm income

diversification and poverty among rural farm households in Southwest Nigeria.

European Journal of Social Sciences, 21 (1): 163-177.

Joanne, P., Asnake Abera and Kassaye Hadgu. 2005. Livelihood/emergency assessment in

Afar region. Oxfam international.

Joshi, P. K., A. Gulati, P. S. Birthal, and L. Twari. 2002. Agricultural diversification in South

Asia: Patterns, determinants, and policy implications. Discussion Paper No. 57.

Markets and Structural Studies Division. International Food Policy Research

Institute. Washington, D.C.

Kaija Darlison. 2007. Income diversification and inequality in rural Uganda: The role of

non-farm activities. A paper prepared for the poverty reduction, equity and growth

network (PEGNeT) conference, Berlin, September 6-7.

Kakwani, N. and Silber, J. 2007. Introduction. pp. xiv-xxii. In: N. Kakwani and J. Silber

(ed.), The many dimensions of poverty. New York: Palgrave Macmillan.

72

Kassa Negussie. 2001. Among the pastoral Afar in Ethiopia: Tradition, continuity and

socioeconomic change. International Books in association with Organization for

Social Science Research in Eastern and Southern Africa (OSSREA).

Kefelegn A. 2007. Dimensions and Determinants of Agro-pastoral Household Poverty in

Kebribeyah District, Somali Regional State. MSc Thesis, Haramaya University,

Haramaya, Ethiopia.

Khan, M.H. 2000. Rural poverty in developing countries. Finance and development, 37 (4).

Krätli, Saverio, Huelsebusch, Christian, Brooks, Sally, and Kaufmann, Brigitte. 2013.

Pastoralism: A critical asset for food security under global climate change. Animal

Frontiers 3(1).

Little, P. D, Behnke R, McPeak J, Getachew G. 2010. Policy options for pastoral

development in Ethiopia. Report number 3, Pastoral economic growth and

development policy assessment. Addis Ababa, Ethiopia.

Little, P. D, Smith, K., Cellarius, B.A., Coppock, D. L., and Barrett, C.B. 2001. Avoiding

disaster: diversification and risk management among East African herders,

Development and Change. 32(3): 401-433.

Little, P.D. 2009. Income diversification among pastoralists: Lessons for policy makers.

Policy brief number 3. Tufts University, Medford, MA.

Little, P.D., McPeak, J., Barrett, C.B. and Kristjanson, P. 2008. Challenging orthodoxies:

Understanding poverty in pastoral areas of East Africa. Development and change,

39(4): 585-609.

Maxwell, S. 1999. The meaning and measurement of poverty. ODI, poverty briefing.

McPeak, J., P. D. Little, and C. R. Doss. 2011. Risk and social change in an African rural

economy: Livelihoods in pastoralist communities. New York: Routledge.

MoFED (Ministry of Finance and Economic Development). 2012. Ethiopia’s progress

towards eradicating poverty: An interim report on poverty analysis study of 2010-

2011. Addis Ababa, Ethiopia.

Mohamed Salih, M.A. 1985. Pastoralists in town: Some recent trends in pastoralism in the

North West of Omdurman district. ODI, Pastoral development network Paper 20.

Mortimore, M. 2009. Dryland opportunities: New paradigm for people, ecosystem and

development. Newyork/Gland. Available at: http://pubs.iied.org/pdfs/G02572.pdf.

Nasa’i, D. H., Atala, T. K., Akpoko, J. G. Kudi, T. M. & Habib Sani. 2010. Analysis of factors

influencing livelihood diversification among rural farmers In Giwa Local

73

Government Area of Kudana State, Nigeria. International Journal of Science and

Nature, 1(2): 161-165.

Pantuliano, S. and Mike Wekisa. 2008. Improving drought response in pastoral regions of

Ethiopia. Humanitarian Policy group, ODI, London, UK.

PFE (Pastoralist Forum Ethiopia). 2007. Millennium development goals and pastoral

development: Opportunities and challenges in the new Ethiopian millennium. Addis

Ababa, Ethiopia.

PFE (Pastoralist Forum Ethiopia). 2008. Proceedings of the fourth national conference on

pastoral development in Ethiopia. Millennium development goals and pastoral

development: Opportunities and challenges in the new Ethiopian Millennium. UN

ECA Conference Hall, Addis Ababa, Ethiopia.

PFE (Pastoralist Forum Ethiopia). 2009. Pastoralist perspectives of poverty reduction

strategy program of Ethiopia: experiences and lesson from Afar Region of Ethiopia.

PFE Research Report. Addis Ababa, Ethiopia.

Reardon, T. 1997. Using evidence of household income diversification to inform the study

of rural nonfarm labor market in Africa. World Development, 25 (5): 735-747.

Reardon, T. 2000. Interactions between the agricultural system and non-agricultural

activities of farm households in developing countries. pp. 68-93. In: J-P Colin and

E.W. Crawford (eds.), Research on Agricultural Systems: Accomplishments,

perspectives, and issues. New York: Nova Science Publishers.

Salasya B, Mwangi W, Mwabu D, Diallo A. 2007. Factors influencing adoption of stress-

tolerant maize hybrid (WH502) in Western Kenya. African journal of agricultural

research, 2(10): 544-551.

Semere Gebretsadik, 2008. Dimensions and Determinants of Rural Household Poverty: The

Case of Eastern Zone, Tigray National Regional State. M.sc Thesis in Agricultural

Economics, Alemaya University, April 2008.

Shibru Tesema, Jema Haji and Yohannes Mengesha. 2013. Dimensions and Determinants of

Agro-pastoral Households’ Poverty in Dembel District of Somali Regional State,

Ethiopia. Journal of economics and sustainable development, 4 (15): 13-20

Singh, I., Squire, L. & Strauss, J. 1986. A survey of agricultural household models: Recent

findings and policy implications. The World Bank Economic Review, 1(1): 149-179.

Smith, R.D.,Gordon, A., Meadows, K., Karen Zwick. 2001. Livelihood diversification in

Uganda: Patterns, and determinants of change across two rural districts. Food

Policy, 26 (4): 421-435.

74

Stifle, D. 2010. The rural non-farm economy, livelihood strategies and household welfare.

African journal of agricultural and resource economics, 4 (1): 82-109

Swift, J. 1988. Major issues in pastoral development with special emphasis on selected

African countries. Food and Agricultural Organization of the United Nations, (FAO),

Rome.

Swift, J. and Hamilton, K. 2001. Household food and livelihood security. pp. 67-92. In: S.

Devereux and S. Maxwell (eds.), Food Security in Sub-Saharan Africa. London:

ITDG Publishing.

Tache,B. and Oba,G. 2008. Linkage between land use changes, drought impacts and

pastoralists’ livelihood response in Borena, Southern Ethiopia. Doctoral Thesis,

Norwegian University of Life Science, Oslo, Norway.

UN OCHA–PCI (United Nations Office for Coordination of Humanitarian Affairs-Pastoral

Communication Initiative). 2007. The future of pastoralism in Ethiopia. Addis

Ababa, Ethiopia.

Virtanen, P. and Gemechu Tollosa. 2011. Global climate policies, local institutions and food

security in pastoral society of Ethiopia. Journal of sustainable development, 5 (1):

1-15.

Wassie Berhanu, Colman, D. and Fayissa, B. 2007. Diversification and livelihood

sustainability in a semi-arid environment: A case study from southern Ethiopia.

Journal of Development Studies, 43(5): 871-889.

Wassie Berhanu. 2005. Pastoralism and livelihood diversification: A case study of the Borana

pastoral system in Ethiopia. Doctoral Thesis, University of Manchester, England,

Manchester.

WB (World Bank). 2000. Entering the 21st century. World development report 1999-2000.

The World Bank. Oxford University Press. New York, NY.

Webb, P. and Coppock, D.L. 1997. Prospects for pastoralism in Semi-Arid Africa. pp.246-

260. In: S. Vosti and Thomas Reardon (eds.) Sustainability, Growth and Poverty

Alleviation. Baltimore: Johns Hopkins University Press.

Wurzinger, M., Ndumu, D., Okeyo, A. M., Solkner, J. 2008. Life style and herding practices

of Bahima pastoralists in Uganda. African Journal of Agriculture Research, 3

(8):542-548.

Yacob Aklilu. And Andy Catley. 2010. Livestock export from pastoralist areas: analysis of

benefits by wealth group and policy implication. Feinstein international center. Tufts

University.

75

Yilma Muluken. 2005. Measuring rural household food security status and its determinants

in the Benishangul-Gumuz region, Ethiopia; the case of Assosa Woreda. MSc

Thesis, Alamaya University, Alamaya, Ethiopia.

Zeray Mehari. 2008. Invasion of prosopis juliflora and rural livelihoods: the case of Afar

pastoralists at Middle Awash area of Ethiopia. MSc Thesis, Norwegian University

of Life Science, Oslo, Norway.

Zerihun Gudeta. 2012. Livelihood strategies in rural South Africa: Implication for poverty

reduction. International Association of Agricultural Economists (IAAE) Triennial

Conference, Foz do Iguacu, Brazil.

76

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

77

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

78

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

79

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. _____________________

80

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

81

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)

82

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

83

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)

84

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

85

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

86

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)

87

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)

88

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

89

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

90

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?