Livelihood Strategies And Food Security In Southern Ethiopia

LIVELIHOOD STRATEGIES AND FOOD SECURITY IN WOLAYTA, SOUTHERN ETHIOPIA: THE CASE OF BOLOSO SORE DISTRICT

M.Sc.Thesis

ADUGNA ENEYEW BEKELE

August 2008 HARAMAYA UNIVERSITY

LIVELIHOOD STRATEGIES AND FOOD SECURITY IN WOLAYTA, SOUTHERN ETHIOPIA: THE CASE OF BOLOSO SORE DISTRICT

A Thesis Submitted to the Department of Rural Development and Agricultural Extension, School of Graduate Studies HARAMAYA UNIVERSITY

In Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE IN AGRICULTURE (RURAL DEVELOPMENT AND AGRICULTURAL EXTENSION)

By

Adugna Eneyew Bekele

August, 2008 HARAMAYA UNIVERSITY

SCHOOL OF GRADUATE STUDIES HARAMAYA UNIVERSITY As Thesis Research advisor, I hereby certify that I have read and evaluated this thesis prepared, under my guidance, by Adugna Eneyew, entitled: Livelihood Strategies and Food Security in Wolayta, Southern Ethiopia: The Case of Boloso Sore District. I recommend that it be submitted as fulfilling the Thesis requirement.

Wagayehu Bekele (Ph. D.)

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Major Advisor

Signature

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As members of the Examining Board of the Final M.Sc. Open Defence, we certify that we have read and evaluated the thesis prepared by Adugna Eneyew and recommended that it be accepted as fulfilling the thesis requirement for the degree of Masters in Rural Development

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DEDICATION I dedicated this thesis manuscript to my father ENEYEW BEKELE, my mother NECHO HIRPA for nursing me with affections and love and for their dedicated partnership in the success of my life, and my brother DESSALEGN SIMA whose advice has always been belling in my mind in the absence of him.

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STATEMENT OF AUTHOR First, I declare that this thesis is my bonafide work and that all sources of materials used for this thesis have been duly acknowledged. This thesis has been submitted in partial fulfillment of the requirements for an advanced M Sc degree at Haramaya University and is deposited at the University Library to be 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 are allowable without special permission, provided that accurate acknowledgement of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the School of Graduate Studies when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.

Name: Adugna Eneyew

Signature: ………………

Place: Haramaya University, Haramaya Date of Submission: ……………………………………

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BIOGRAPHICAL SKETCH The author was born in 1980 in Gida Ayana town Easterm Wellega zone, Oromia Regional State, to his mother Necho Hirpa Guchu and his father Eneyew Bekele Gurmu. He attended his elementary and high school education at Gidda Junior and Senior Secondary Schools respectively. He joined Alemaya University in 1998/1999 academic year and graduated with B.Sc. degree in Agricultural Extension in July, 2002. Soon after his graduation, he was employed by the Ministry of Agriculture and Rural development and has been serving as an instructor at Wolayta Soddo ATVET College until he joined the School of Graduate Studies at Haramaya University in 2006 academic year.

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ACKNOWLEDGEMENTS Above all I would like to thank the Almighty God for his unreserved gift. I am indebted to a large number of individuals for their encouragement and help while conducting this study. First and foremost, I thank my major advisor Dr. Wagayehu Bekele to whom I am duly bound to express my gratitude. He devoted his precious time and energy to comment on and improve the progress of the study since its initiation. Without his, guidance and professional expertise the completion of this work would not have been possible.

I am deeply beholden to Wolayta Soddo ATVET College for its provision of the necessary support to let me join postgraduate studies at Haramaya University which led to the finalization of this study. My especial thanks go to Asrat Tera, for providing me all round facilitation during my research work.

The entire staffs of Boloso Sore bureau of agriculture and rural development also deserve great thanks for their support during data collection. To mention some, Deneke Derese, Woldesenbet Asrat, Safene Sana, Worku and Degife are unforgettable for their cooperativeness in all processes of data collection. I also fell great to express my thanks to the enumerators who assisted my work successfully and key informants and sample respondents who participated in the study for sparing their precious time and hospitality of the communities without which this document could have not been written.

My warmest and heartfelt thanks also extend to my colleagues Abera Habte, Wondimeneh Taye, Tigist Matusala, Feleke Assefa, Melaku W/Yohannes, Gebre kiros, Selamawit Assegid, Cheru Techane, Belete Balla, Banchayehu Yitayal, Habte Amiro, Tekeste Taddese, Wogderes Ejigu, Enku Yemane, Abebe Fekadu, Mulugeta Fekadu and Tariku Wedajo who provided me all sided helps, wishes and encouragements through emails and phone calls to accomplish my study successfully.

I also owe very much thank to Wossagn Berhane, Alebachew Dejene and Meseret Meskele who assisted me by shouldering extra course loads on behalf of me during my research work. Finally,

Siso and Zane thank you for your special advice boosting moral to work hard.

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ABBREVIATIONS AND ACRONYMS AE

Adult Equivalent

AG

Agriculture

ADLI

Agricultural Development Led Industrialization

ATVET

Agricultural Technical Vocational and Educational Training

BoARD

Bureau of Agriculture and Rural development

BoFED

Bureau of Finance and Economic Development

CSA

Central Statistical Authority

DFID

Department for International Development

ETB

Ethiopian Birr

FAO

Food and Agriculture Organization

FDRE

Federal Democratic Republic of Ethiopia

FEHHS

Female Headed Households

FI

Food Insecure

FS

Food Secure

FSCB

Food Security Coordination Bureau

GDP

Gross Domestic Product

ha

Hectare

HESs

Household Expenditure Surveys

HHs

Household Heads

HYVs

High Yielding Varieties

IDS

International Development Studies

IMCI

Inverse of Market Concentration Index

kcal

Kilocalorie

Km

kilo meter

LIMDEP

Limited Dependent Variable

LS

Livelihood Strategies

m.a.s.

meter above sea level

MDG

Millennium Development Goal

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NA

Non Agricultural

NF

Non-Farm

NGOs

Non Governmental Organizations

NR

Natural Resources

ODI

Over seas Development Institute

OFF

Off- Farm

PA

Peasant Association

PPS

Probability to Proportional Size

PRSD

Poverty Reduction and Sustainable Development

PSNP

Productive Safety Net Program

PRA

Participatory Rural Appraisal

RDA

Recommended Daily Allowance

SHHs

Sample Households

SHHHs

Sample Household Heads

SIDA

Swedish International Development Cooperation Agency

SL

Sustainable Livelihood

SLAs

Sustainable Livelihood Approaches

SNNPR

Southern Nations Nationalities Peoples Region

SPSS

Statistical Packages For Social Sciences

TLU

Tropical Livestock Unit

TOL

Tolerance Level

UN

United Nations

USD

United States Dollar

VIF

Variance Inflation Factor

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TABLE OF CONTENTS STATEMENT OF AUTHOR

IV

BIOGRAPHICAL SKETCH

V

ACKNOWLEDGEMENTS

VI

ABBREVIATIONS AND ACRONYMS

VII

LSIST OF TABLES

XII

LIST OF FIGURES

XIV

LIST OF TABLES IN THE APPENDIX

XV XVII

ABSTRACT 1. INTRODUCTION

1

1.1.

1

Background to the Study

1.2. Statement of the Problem

4

1.3. Objective of the Study

8

1.4. Research Questions

9

1.5. Significance of the Study

9

1.6. Scope and Limitation of the Study

10

1.7. Organization of the Thesis

11 12

2. LITERATURE REVIEW 2.1. Origins and Concepts of Livelihood Approaches

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2.2. Conceptual Framework for Livelihood Strategy Analysis 2.2.1. Livelihoods 2.2.2. Vulnerability Context 2.2.3. Livelihood assets 2.2.4. Mediating factors 2.2.5. Livelihood strategies 2.2.6. Livelihood outcomes

14 17 18 19 22 23 28

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2.3. Empirical Studies on Determinants of Livelihood Strategies

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2.4. Food security outcomes 2.4.1. Concepts and definition 2.4. 2. Food security indicators and measures 2.4.2.1. Food security indicators 2.4.2.2. Food security measures

36 36 37 37 38

2.5. Livelihood Strategy and Food Security Linkages

41 44

3. METHODOLOGY 3.1. Description of the Study Area

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3.2. Sampling Procedure

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3.3. Method of Data Collection

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3.4 Method of Data Analysis 3.4.1 Analytical framework 3.4.2. Descriptive analysis 3.4. 3. Econometric model 3.4.3.1. Specification of multinomial logit model 3.4. 3.2. Coefficient interpretation 3.4.4. Food security measures

49 49 50 50 51 53 61

4. RESULTS AND DISCUSSION

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4.1. Descriptive Analysis 4.1.1. Human capital 4.1.1.1. Age composition 4.1.1.2. Sex composition 4.1.1.3. Marital status 4.1.1.4. Family size 4.1.1.5. Dependency ratio 4.1.1.6. Education level 4.1.1.7. Health Status 4.1.2. Natural capital 4.1.2.1. Land size held by sample HHs 4.1.2.2. Farmers perception of soil fertility status 4.1.2.3. Agro- ecology 4.1.3. Physical capital 4.1.3.1. Livestock holding 4.1.3.2. Mean crop output harvested 4.1.3.3. Input use 4.1.3.4. Type of house owned 4.1.4. Social capital

63 64 64 65 66 67 68 69 70 72 72 73 74 75 75 80 81 82 83

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4.1. 5. Financial capital 4.1.5.1. Credit use 4.1.5.2. Saving habit 4.1.6. Institutional supports 4.2.6.1 Extension contact by the household 4.2.6.2. Access to social services 4.2.6.3. Receiving food aid

87 87 91 92 93 94 96

4.2. Livelihood Strategies 4.2.1. Income portfolio analysis 4.2.2. Diversity of livelihood strategies 4.2. 3. Specialization of livelihood strategies

97 98 103 105

4.2. Food Security Statuses as an Outcome of Livelihood Strategies 4.2.1. Causes of household food shortage 4.2.2. Months of food shortage 4.2.3. Coping strategies of SHHs during food shortage

107 111 112 113

4.3. Viability of Livelihood Strategies to Achieve Food Security

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4.4. Econometric Analysis of Determinants of Livelihood Strategies 4.4.1. Detecting multicollinearity and degree of association 4.4.2. Model results 4.4.3. Interpretation of econometric results

116 116 119 123

5. SUMMARY AND POLICY RECOMMENDATIONS

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5.1. Summary

129

5.2. Recommendations

131

6. REFERENCES

136

7. APPINDICES

146

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LSIST OF TABLES Table

Page

Table 1. Sample size distribution in the sample PAs ............................................................... 48 Table 2. Age distribution of sample HH heads by wealth categories ...................................... 65 Table 3. Sex composition of sample HH heads by wealth categories ..................................... 66 Table 4. Marital status by wealth category .............................................................................. 66 Table 5. Distribution of family size by wealth category......................................................... 68 Table 6. Dependency ratio of sample HHs by wealth category.............................................. 69 Table 7. Distribution of sample HH heads by years of education completed.......................... 70 Table 8. Summary statistics of Health status of SHHs ............................................................ 71 Table 9. Land size holding by Wealth category....................................................................... 73 Table 10. Soil fertility status as perceived by SHHs................................................................ 74 Table 11. Distribution of sample HHs in the two agro-ecologies............................................ 75 Table 12. Livestock holding by wealth category ..................................................................... 76 Table 13. Summary statistics for livestock holding by wealth category................................. 77 Table 14. Situation of Livestock production by SHHs ............................................................ 79 Table 15. Mean crop output by wealth category...................................................................... 81 Table 16. Input use by wealth category ................................................................................... 82 Table 17. Type of house owned by wealth category............................................................... 83 Table 18. Social capital access by SHHs ................................................................................. 87 Table 19. Credit use by wealth category................................................................................. 89 Table 20. Distribution of SHHs by receiving remittance......................................................... 91 Table 21. Saving habit of SHHs............................................................................................... 92 Table 22. Distribution of SHHs by extension contact.............................................................. 93 Table 23. Proximity to various services (Km) ......................................................................... 96 Table 24. Food aid distribution by wealth category................................................................ 97 Table 25. Income composition of sample HHs..................................................................... 101 Table 26. Mean income from each activity by wealth groups .............................................. 103 Table 27. Diversity indices of SHHs by wealth category ...................................................... 105 Table 28. Distribution of SHHs by income from singe source .............................................. 106 Table 29. Livelihood strategies pursued by SHHs................................................................. 107 Table 30. Food poverty for SHHs based on the lowest income quartile............................... 109 Table 31. Subsistence non-food expenditure ........................................................................ 109 Table 32. Summary statistics of food security status of SHHs ............................................. 111 Table 33. Causes of food shortage by SHHs ........................................................................ 112 Table 34. Number of food shortage months by wealth category ........................................... 113 Table 35. Mean income by food security status..................................................................... 115 Table 36. Contingency coefficients of discrete variables ...................................................... 118 Table 37. Tolerance level of continuous variables................................................................. 118 Table 38. Definition of model variables ................................................................................ 119 Table 39. Multinomial logit regression of AG + OFF livelihood strategy choice................. 120 Table 40. Multinomial logit regression of AG + NF livelihood strategy choice ................... 121

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Table 41. Multinomial logit regression of AG+OF+NF livelihood strategy choice.............. 122

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LIST OF FIGURES Figure

Page

Figure 1. Sustainable livelihoods framework........................................................................... 16 Figure 2. Location of the study area......................................................................................... 46 Figure 3. Asset-access-activities-outcome framework ........................................................... 50 Figure 4. Oxen ownership by SHHs ........................................................................................ 78 Figure 5. Livestock production problems ................................................................................ 80 Figure 7. Sources of credit used by SHHs ............................................................................... 90 Figure 8. Coping strategy of SHHS during food shortage ..................................................... 114

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LIST OF TABLES IN THE APPENDIX Appendix Table

pages

1. Conversion factors used to estimate Tropical Livestock Unit (TLU)…………. 146 2. Conversion factors used to calculate Adult Equivalent (AE)……………………146 3. Nutrient composition of Major crops grown in Areka and Gnchi……………… 146 4. Wealth ranking criteria’s set by local informants………………………………..147 5. Interview schedule for sample respondents......................................................

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148

LIST OF FIGURE IN THE APPENDIX

Figure

Page

1. Sampling procedure……………………………………………………………..147

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LIVELIHOOD STRATEGIES AND FOOD SECURITY IN WOLAYTA, SOUTHERN ETHIOPIA: THE CASE OF BOLOSO SORE DISTRICT

ABSTRACT Ethiopia is one of the least developed countries in the world and has been plagued with food insecurity for decades. Food insecurity is the result of unsatisfactory livelihood strategies and in the long run it may cause irreparable damage to livelihoods of the poor, thereby reducing self-sufficiency. Nonetheless, identification of the numerous factors that determine the abilities of rural household’s choice of livelihood strategies in Ethiopia has received little attention despite its increasing threat over the poor. This research was therefore, proposed with the aim of generating location specific data on livelihood strategies and its determinants in the context of achieving food security goal by rural households in Boloso Sore district of Wolayta, southern Ethiopia. A two stage stratified random sampling technique was employed to select 120 household heads. Data was collected using key informant interview, focus group discussion and interview schedule. Both descriptive and econometric data analysis techniques were applied. The descriptive statistics revealed that human capital variables: family size and educational status of head; natural capital variables: land size, soil fertility status and agroecology; physical capital variables: livestock holding, input use, and house type owned; social capital variables: livestock sharing, share cropping, and membership to cooperatives; financial capital variables: credit use and saving habit; and institutional variables such as extension contact and food aid were found to significantly differentiate poor, less poor and better off households at various probability levels. The wealth ranking exercise by the community showed that 42.5%, 35%, and 22.5% of the sample population were poor, less poor and better off. The income portfolio analysis revealed that agriculture still plays a leading role by contributing 64.1% of the total income of sample household. Whereas, the contribution made by off/non-farm activities accounts for 35.9%. The food security status shows that about 74.2% of sample households were food insecure. The multinomial logit model result for determinants of choices of livelihood strategies revealed that out of the 15 explanatory variables, the choice of agriculture plus off farm livelihood strategy was determined by sex of household head, years of education of household head and land size, the choice of agriculture plus non farm was affected by age of household head, years of education of household head, and dependency ratio, and choice of agriculture plus off farm plus nonfarm was influenced by, family size, agro-ecology, land size, livestock holding, input use, membership to cooperatives, credit use and remittances. The finding of this study ensured that household livelihoods are highly diverse and Policy-makers need to reflect on the most suitable ways of supporting this diversity. Any attempt to intervene the community need to target specific groups of societies such as female headed households, wage workers, petty traders, food insecure and the poor.

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1. INTRODUCTION

1.1.Background to the Study

Ethiopia with an estimated population of 76.5 million is the third populous country in Africa. According to 2007 estimate, population is growing at an estimated annual rate of 2.27 %. From the total population of the country more than 85% are rural population and the remaining is urban population (CSA, 2006). It is a multi-ethnic country with diverse geographic and climatic conditions, rich traditions and a complex history. Ethiopia is an agrarian economy based country where the agricultural sector plays an important role in the national economy, livelihood and socio-cultural system of the country. The sector supports employment of over 80% of the population, accounts for 45-50% of the national Gross Domestic Product (GDP), and makes the largest contribution to raw materials for agroindustries, food security and foreign exchange earnings. While the commercial farming subsector is limited, the dominant sub-sectors are mixed farming of the smallholder agriculture, and the pastoral livestock system. The smallholder mixed farming system is dominant in the highlands and medium altitude zones while the pastoral livestock production system prevails in most of the warmer lowland areas of the country (Berhanu, 2006).

Ethiopia is perhaps best known outside Africa as the location of some of the worst famines in the continent’s history; a contemporary symbol of African poverty and the failure of development. It is one of the most food insecure countries in the world. It suffers from both chronic and acute food insecurity (Kaluski et al., 2001; Amdissa, 2006). It has been plagued with food insecurity for decades. The problem is worsening, despite massive resources invested each year into humanitarian aid and food security programs (Frankenberger et al., 2007). That is why food security is an overriding concern for the Ethiopian Government. One of the millennium development goals of the country is to reduce by half the proportion of people suffering from hunger by 2015 (SIDA, 2003). Specifically, the goal of the Productive Safety Net Program (PSNP) is to address the basic food needs of chronically food insecure

households through multi-year predicable resources, “in a way that prevents asset depletion at the household level and creates assets at the community level” (MoFED, 2006). Although, the

struggles to achieve food security at the household level in the rural areas of Ethiopia dated back a long period, yet remained as a challenging goal (Frankenberger et al., 2007). Rural people on their side partake in a number of strategies, including agricultural intensification, and livelihood diversification, which enable them to attain food security goal, however, still unable to escape food insecurity.

Specially, the poor who generally have least access to natural resources, entitlements, employment opportunities and income, are most chronically food-insecure in the country (SIDA, 2003). Food insecurity in the long run may cause irreparable damage to livelihoods of the poor, thereby reducing self-sufficiency. It is therefore part of the process leading to malnutrition, morbidity and mortality. In addition, the state of being food insecure directly contributes to destitution and damaged livelihoods in the long term (Norton and Foster, 2001). In Ethiopia, where the subsistence agriculture and the small holder farming dominates the over all national economy, small holder farmers often face scarcity of livelihood capital and are prone to livelihood risk.

Household livelihoods and strategies they use to create them are at the centre of development and for poor people living in poor rural areas wellbeing mean just having enough to eat, shelter for their members and a basic level of security. However the livelihood strategies that they develop to ensure their livelihoods will depend on how they can combine their livelihood assets, taking in to account the vulnerability context in which they live, and the policies, institutions and processes that affect them (Ellis, 2000). Livelihood analysis, using an asset framework could help foster appreciation of the way that combination of these assets and activities are vital to secure livelihoods. The explicit linkage between food security and livelihood strategies suggest that food security will be achieved when equitable growth ensures that the poor and vulnerable have sustained livelihood (Ayalneh, 2002). In turn, this demands adequate understanding of the livelihood strategies of resource poor farmers at micro level in designing and implementation of context specific development strategies that integrate livelihoods needs of local people. Thus, a thorough understanding of alternative

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livelihood strategies of rural households and communities is indispensable in any attempt to bring improvement. This is important not to commit a limited resource available for rural development based on untested assumption about the rural poor and its livelihood strategies (Tesfaye, 2003).

In spite of the growing awareness of the seriousness of food security and its impact on the long-term livelihood of rural households’ previous studies in the country focused on determinants of food security rather than livelihood strategies. Analytical works that scrutinize poverty profile and livelihood diversification in Ethiopia are at best scanty. Even the contribution made by livelihood diversification to rural livelihoods has been ignored by policy makers who have chosen to focus on agriculture (Carswell, 2000). The importance of livelihood diversification in Ethiopia has received little attention despite its increasing importance for the poor. Moreover, the assessment of local development impact often focuses excessively or exclusively on how much cash, how much increased production, or how many jobs are generated, rather than on a broad range of livelihood issues, although, changes in the way people live their lives may be just as important as more obvious changes in what they achieve (Ashley and Hussein, 2000). Although, reducing food insecurity in the developing world continues to be a major public policy challenge, lack of information on the location, and causes of food insecurity aggravates the problem. Such information is needed to properly target assistance, evaluate whether progress is achieved, and develop appropriate interventions to help those in need (Smith et al., 2006).

In this regard, the livelihoods approach is of multiple uses. It centres on ways of understanding the practical realities and priorities of poor men and women, what they actually do to make a living, the assets that they are able to draw on and the problems that they face in doing this. The rationale is that the better this is understood, the better able those designing policies and programmes addressing food security will be to identify points of intervention and appropriate strategies. Moreover, the livelihood approaches seeks to develop an understanding of the factors that lie behind peoples choice of livelihood strategies and then reinforces factors which promote choices and flexibility because the more choice and flexibility people have in their livelihood strategies through livelihood diversification, the

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greater the ability they withstand shocks and stresses (Ayalneh, 2002; Farrington et al., 2002). Another fact is that, livelihood strategies of rural households are heterogeneous along sites and types of households due to several constraints (Tesfaye, 2003). Different households also adopt different strategies according to their particular asset status (Ellis, 2000). Therefore, it is crucial to recognize that they have their own strategies to secure their livelihoods which vary from household to household depending on numerous factors such as their socio-economic status, education and local knowledge.

In general, what distinguishes the current study from previous ones is the recognition that people have their own forms of assets and strategies. The underlying assumption behind many food insecurity mitigation strategies is that people need something to do that enables them to get access and entitlement to food. The livelihood approach employed here assumes that people are already doing a number of creative and productive activities. They have, over generations, developed strategies, including livelihood diversification, appropriate to their context and culture. Therefore, any attempt to intervene food insecurity problems should understand determinants of strategies of the rural people.

This study, therefore, attempted to see the determinants of livelihood strategy choice of rural people in their struggle to achieve food security goal in Boloso Sore district of Wolayta, Southern Ethiopia.

1.2. Statement of the Problem

There is no problem of underdevelopment that can be more serious than food insecurity that has an important implication for long term economic growth of low income countries (World Bank, 1986). Food insecurity is a pervasive problem in developing countries, undermining people’s health, productivity, and often their very survival. Therefore, much of the development agenda focuses on directing scarce resources to providing food to people in need or enabling them to acquire it themselves (Smith et al., 2006). Access to sufficient food in a sustainable manner is a fundamental human right. Realizing this, Non Governmental

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Organizations (NGOs), community organizations, research institutions and governments in Africa have been testing alternative technologies and approaches for over a decade (IIRR, 1998).

Ethiopia is among the poorest country in the world. Ethiopia’s per capita income is only 100 USD while for the rest of Africa the figure is more than 500 USD. If one has to define poverty as income of one dollar per day nearly 31.5% or 20 million Ethiopians fall below the poverty line (Medrek, 2001 cited on Astatke, 2002). It is one of the most food insecure countries in the world. It suffers from both chronic and acute food insecurity (Kaluski et al., 2001; Amdissa, 2006).

Severe food insecurity problems have been observed under almost all government regimes in Ethiopia (Beyene, 2008). Recently, 44.2% of the Ethiopian people are under absolute poverty that is unable to get the minimum required calorie (2200 Kcal. Per day per adult) adjusted for the requirement of non-food expenditure (Tassew, 2008). In the ranking of countries on the prevalence of food energy deficiency, from highest to lowest; Ethiopia is leading insecurity level by 76.4 % (Smith et al, 2006).

In order to tackle food insecurity problem the government of Ethiopia designed food security strategy in 1996 and efforts have been underway since then. However, in spite of all the effort put by the government and donors to ensure the food security of rural household in the country, it continuous to rise and a large proportion of the population faces chronic food insecurity and their livelihoods are at risk (Belayneh, 2005). The contribution of agriculture to food security both through its direct impact on food production and indirect effect on farm incomes (i.e. through improving entitlement capacity) has failed to recover even after the economic reforms of the 1990s.

The rural poor struggle to ensure food security status by participating in diversification activities. However, the contribution to be made by livelihood diversification to rural livelihoods has often been ignored by policy makers who have chosen to focus their activities on agriculture (Carswell, 2000). The rural economy is not based solely on agriculture but

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rather on a diverse array of activities and enterprises. It is crucial to recognize that rural people have their own strategies to secure their livelihoods which vary from household to household depending on numerous factors such as their socio-economic status, education and local knowledge, ethnicity, and stage in the household life cycle. Even in the same locality, there can be a big contrast between the strategies of those with different socioeconomic background, for example, for those with more land and those who are with less land or landless (Wagayehu, 2004). The extent to which farm households are able to feed themselves often depends on off/non-farm income as well as their own agricultural production. Off/nonfarm income is used by many households to purchase grain and the concept of ‘subsistence’ farmers needs to be understood in this context of diversified income sources (Chapman and Tripp, 2004). Multiple motives prompt households and individuals to diversify assets, incomes, and activities (Barrett et al., 2001).

Although livelihoods are predominantly agriculture based, labour productivity is low and most Ethiopians are net cereal buyers. Because of the primary dependence on subsistence crop production in the country, harvest failure leads to household food deficits, which in the absence of off/non-farm income opportunities leads to asset depletion and, increasing levels of destitution at the household level (FDRE, 2002). In the long run the agricultural sector is likely to face dimensioning returns to technologies and it would be impossible to bring sustained reduction in rural poverty unless the proportion of labour force employed in agriculture to declines (Tassew, 2008). In line with this view, the classical development economic theory presumed that agricultural labour could be shifted to the non agricultural sector without any reduction in total agricultural output. They called these economies “surplus labour economies”, implying that the shadow wage in agriculture is nil and that labour is immobile (Lanjouw and Lanjouw, 1995). This can be possible through diversification of livelihood strategies and incomes (Drimie et al., 2006). Additionally, the fact that, food insecurity in Ethiopia derives directly from dependence on undiversified livelihoods based on low-input, low-output rain fed agriculture, it is forcing the country to opt for diversification of the rural people modes of livelihoods that typically prevail both within and between households and across the agro ecology so as to achieve food security (Devereux, 2000).

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From the point of view of reducing poverty and food insecurity in rural Ethiopia, it is extremely important to reduce vulnerability of the poor through diversification of the sources of their livelihoods. Diversification activities can play an important role in that regard. Thus, analysis of the livelihood diversification opportunities available in rural areas, the productivity and returns offered by such activities, especially those in which the poor are engaged, and an identification of the factors that may affect the ability of the poor to raise productivity and returns in their activities or move to activities yielding higher returns (Devereux, 2000).

In spite of this fact, much of the research done so far did not focus on understanding of peoples livelihood strategies, rather it emphasized on explicit determinants of food security. Policy program interventions in Ethiopia are often planed without sufficient knowledge of farmers’ resource endowment, priority problems and felt needs (Wagayehu, 2004). Also it has been common in the past to make untested assumption about the poor (Tesfaye, 2003); this however, didn’t result in satisfying policy intervention to tackle the problem from its root. That poor people, especially in rural areas, manage a complex range of assets and activities to sustain themselves – and that development professionals and officials often fail to adequately see and understand this (Norton and Foster, 2001). Making the right choice of livelihood strategies could make the difference between successful livelihoods or returning to food insecurity among rural household (Tesfaye, 2003). Thus, unravelling the complexity and diversity of people’s life rather than relying on simplified assumption about how rural people economies work should be given a paramount importance and clustering a sample of households into a limited number of categories that pursue similar livelihood strategies (LS) may be useful to policy makers by enabling them to better target households with certain common characteristics. This implies that livelihood study would help

policy makers to understand what is really happening in people’s lives, what enable some but others, to escape from poverty (Ashely et al., 2003).

Wolayta, the study area, is well known for its fertility and population pressure – a combination that deceives people who are not familiar with the area. During times of food 7

stress, the term “green famine” is often used to describe the situation (UNDP, 2000). Wolayita’s recent history is a troubled one. Major events of widespread hunger have occurred with worrying frequency (1984; 1994; 1999/2000), (Bush, 2002). Chronic poverty is a wellestablished feature of rural life; and social indicators—from literacy levels to basic medical facilities to asset levels—are distressingly low. Specifically, in Boloso Sore, over 80 percent of the population is considered poor. The number of chronic food insecure population aided by safety net program for the past years was about 33,657 households (BoARD, 2007). The study conducted by Bush (2002) indicated that in the area 50 percent population have such a precarious “foot” in farming that they must purchase, or earn, 60 percent (or more) of their annual food needs. Frequent food insecurity is a sign of chronic poverty, and there are many indicators to support this. Asset ownership is much skewed. This implies that, farming alone does not guarantee the livelihood security of rural households in the area and livelihood diversification is mandatory.

In pursuit of the above fact, Carswell (2000) indicated that diversification activities are undertaken by a significant proportion of households in Wolayta and suggested that further study need to be undertaken in order to identify the relative importance of diversification activities in terms of their contribution households welfare.

This research, therefore, was proposed with the aim of generating location specific data on livelihood strategies and its determinants for achieving food security goal by rural households in Boloso Sore district and this would contribute to literature gap and inform policy makers at micro and macro level.

1.3. Objective of the Study

The general objective of the study was to examine the livelihood strategies pursued by rural

households and analyse determinants of choice of livelihood strategies in the context of

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achieving food security in Boloso Sore district of Wolayta zone, Southern Ethiopia. The specific objectives of the study are:

1. to assess livelihood strategies pursued by different categories of rural households in the study area, 2. to identify the determinants of rural households` choice of livelihood strategies , and 3. to determine the status of food security as an out come of different livelihood strategies pursued by rural households in the study area

1.4. Research Questions

The following research questions are answered by this study.

1. What are the livelihood strategies pursued by different categories of rural house holds in the study area? 2. What are the determinants of rural households` choice of livelihood strategies in the area? 3. What is the status of food security as out come of different livelihood strategy pursued by rural households?

1.5. Significance of the Study

Development practitioners are increasingly emphasizing the importance of understanding livelihood systems and the complexity of rural livelihoods for effective policy formulation (Deb et al., 2002). The concept of livelihood strategies has become central to development practice in recent years (Brown et al., 2006). Livelihoods approaches have the advantage of placing the poor at centre stage, and of exploring aspects of their livelihoods which are commonly neglected. These include the multidimensional nature of poverty itself, the diverse

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and dynamic nature of their ‘portfolios’, and the complexities of accessing capital assets (Farrington et al., 2002).

There fore, carrying out such empirical research would obviously have both basic (academic) and applied (practical) purposes. Academically, since literature concerning livelihood strategies and food insecurity is scarce in the study area, the findings of the study was expected to contribute toward breaching the existing literature gap on understanding the status of food security, rural households’ livelihood strategies and its determinants.

With regard to the practical purposes, the empirical findings may be utilized by planners for the formulation of new policies as well as policy reforms in the area. Thus, local as well as international NGOs interested in intervening with the aim of promoting rural development into the study area will benefit from the findings of the study. Moreover, it provides baseline information for researchers who need to undertake similar research. By recognizing and understanding this portfolio of activities and assets, policy makers can better understand points of vulnerability in poor households and understand how policy and institutional interventions can effectiv1ely reduce poverty at the household level.

1.6. Scope and Limitation of the Study

Due to time and resource constraint, the study was limited only to Boloso Sore district of Wolayta zone. Even if the livelihood strategies are diverse across ecology and context of rural people and problems of food insecurity are multi- dimensional and dynamic, this study emphasized only on household level situations. Though useful, such study does not capture the dynamic nature of livelihood strategies in the context of food security.

Studies carried out in many developing countries have pointed out that farmers are reluctant to provide accurate information on the variables such as income level, farm size, livestock number etc., due to the fact that taxes and other development contributions are distributed among them based on these factors. This study may not be free from these limitations. But to

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mitigate this problem as much as possible it was tried to convince farmers individually and collectively about the objectives of the study.

1.7. Organization of the Thesis

The rest part of this thesis is organized in to five parts. The second part deals with review of literature that includes livelihoods approaches (conceptual framework in analysis of livelihood strategies with empirical studies), and concepts and measurements of food security. The third part touches the brief description of the study area and research methodology employed in sampling, data collection and analysis. Part four goes on dealing with the results and discussions and finally part five presents summary and recommendations based on the findings of the research.

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2. LITERATURE REVIEW

The first part of this chapter selectively reviews the origins and concepts on livelihood approaches, conceptual framework for livelihood strategy analysis (livelihoods, vulnerability context, livelihood assets, mediating factors livelihood strategies, and livelihood outcomes); with empirical studies on determinants of livelihood strategies, and the second part deals with concepts of food security and its measurement and wind up with special emphasis on its linkage with livelihood strategies.

2.1. Origins and Concepts of Livelihood Approaches

The emergence of the livelihoods concept had all the qualities of a classic ‘paradigm shift’ – defined as ‘a fundamental change in approach or underlying assumptions. This shift came at a time when previous dominant theories and practices – particularly those associated with integrated rural development – were losing their intellectual and political attraction. Sustainable livelihoods offered a fresh approach (Carney et al., 1998; Solesbury, 2003). Its development has been led from the natural resources advisory group and has formed part of a cultural change within that professional group that has profound dimensions, and includes the following elements: a shift from an emphasis on natural resource issues and programmes to a people-centred approach which emphasises the goals of poverty reduction, empowerment and the promotion of increased security of livelihoods for the poor and a shift in emphasis from seeking improvements in forms of agricultural production to looking at the full diversity of strategies of poor people in rural areas ( Norton and Foster, 2001; Solesbury, 2003).

In the 1970s, many development practitioners were concerned about the famines that were taking place in Africa and Asia, and a concerted effort was made to put more resources into increasing food supplies globally (Ashley and Carney, 1999).

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In 1980s it was realized that many households were still not obtaining adequate amounts of food for a healthy life. This led to a shift from national food security to a concern with the food security and nutritional status of households and individuals (FAO, 2001).

In the mid-1980s to the early 1990s, researchers began to widen their perspective from food security to a livelihood perspective (Chambers and Conway, 1992; Solesbury, 2003). This ensured that, livelihoods approaches are based upon evolving thinking about combating food insecurity and poverty reduction, the way the poor live their lives, and the importance of structural and institutional issues. They draw on three decades of changing views of poverty. In particular, participatory approaches to development have highlighted great diversity in the goals to which people aspire, and in the livelihood strategies they adopt to achieve them (Ashley and Carney, 1999).

By the early 1990s, certain donor agencies had seen sufficient merit in livelihoods approaches to begin employing the approach in their work (Solesbury, 2003). From 1990s until the present, there has been a shift from a material perspective focused on food production to a social perspective that focuses on the enhancement of peoples’ capacities to secure their own livelihoods. Since the 1990s, there has been a shift in development studies and development policy towards more holistic views of the activities and capital assets that households draw on to make a living (Carney et al., 1998; Scoones, 1998; Ellis, 2000). Thus, it can be seen that the livelihood approaches in vogue today build on the experiences of the past (FAO, 2001).

The origination of livelihood approach as a concept is widely attributed to Robert Chambers at the Institute of Development Studies (IDS), (Solesbury, 2003). Particularly, 1992 could be named as the starting year of the livelihood focus in development cooperation. Subsequently, the previous emphasis on technologies, resources and organisations shifted to a focus on rural households and their various functionalities. It is a way of thinking about the objectives, scope and priorities for development, in order to enhance progress in poverty elimination. It aims to help poor people achieve lasting improvements against the indicators of poverty that they define (Ashley and Carney, 1999).

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The concept of livelihoods is increasingly used in development debates, in which people’s capabilities, and social as well as material assets, are recognised to be important to make a living (Kanji et al, 2005). Livelihoods approaches reflect the diverse and complex realities faced by poor people in specific contexts (Ashely et al., 2003). Unlike many ‘conventional’ approaches to poverty assessment and project design, a focus on livelihoods requires incorporating an understanding of the ways in which various contextual factors – political, institutional, environmental as well as macroeconomic –either constrain or support the efforts of poor and vulnerable people to pursue a viable living (Cahn, 2004).

The livelihoods approach also emphasises the ability of people to maintain a viable livelihood over time (Rahman et al., 2007). Another virtue of livelihoods approaches is that they attempt to build on the strengths already present in people’s existing assets, strategies and objectives, rather than ‘importing’ blueprint development models that often ignore or even undermine these positive features common features that point to strong conceptual overlaps and, at the same time, distinguish these concepts from narrower notions such as income or consumption poverty.

The strengths of the approach are that it aims to reflect the complex range of assets and activities on which people depend for their livelihoods and the importance to poor people of assets which they do not own. It provides a framework for addressing the whole range of policy issues relevant to the poor, not just access to health and education, but issues of access to finance, markets, and personal security. and the need for a people centred and participatory approach, responsive to changing circumstances, and capable of working at multiple levels from national to local, in partnership with public and private sector (Norton and Foster, 2001).

2.2. Conceptual Framework for Livelihood Strategy Analysis

The livelihoods framework provides a comprehensive, and complex, approach to understanding how people make a living. It can be used as a loose guide to a range of issues which are important for livelihoods or it can be rigorously investigated in all its aspects (Kanji

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et al, 2005). Livelihood Approaches (LA) emphasizes understanding of the context within which people live, the assets available for them, livelihood strategies they follow in the face of existing policies and institutions, and livelihood outcomes they intend to achieve (DFID, 2000).

The key question to be addressed in any analysis of livelihood is given a particular context (of policy setting, politics, history, agro ecology and socio-economic conditions), what combination of livelihood resources (different types of ‘capital’) result in the ability to follow what combination of livelihood strategies (agricultural intensification/ extensification, livelihood diversification and migration) with what outcomes? (Scoones, 1998). The framework therefore highlighted five interacting elements: contexts; resources; institutions; strategies; and outcomes (Solesbury, 2003). Understanding in a dynamic and historical context, how different livelihood resources are sequenced and combined in the pursuit of different livelihood strategies is therefore critical (Scoones, 1998).

The asset portfolio, represented by the pentagon in Figure 1 below, is a key component to understanding a household’s livelihood strategy (Jansen et al, 2004). The focus is on the conceptualization and quantification of the household’s asset portfolio as an input into the explanation of a household’s livelihood strategy. It can provide a useful starting point for household livelihood analysis, as it encourages investigators to take into account all the different kinds of assets and resources that are likely to play a role in household livelihoods. The focus on assets is appropriate given the historically stark inequalities in asset distribution (Rakodi, 1999). Identifying what livelihood resources (or combinations of ‘capitals’) required for different livelihood strategy combinations is a key step in the process of analysis (Soussan et al, 2000). For example, successful agricultural intensification may combine, in some circumstances, access to natural capital (e.g. land, water etc.) with economic capital (e.g. technology, credit etc.), while in other situations, social capital (e.g. social networks associated with drought or labour sharing arrangements) may be more significant. Thus, the livelihoods approach is concerned first and foremost with people. So an accurate and realistic understanding of people’s strengths (here called “assets” or “capital”) is crucial to analyse

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how they endeavour to convert their assets into positive livelihood outcomes (Bezmir and Lerman, 2002; Kollmair and Gamper, 2002).

Of particular interest in this framework are the institutional processes (embedded in a matrix of formal and informal institutions and organisations) which mediate the ability to carry out such strategies and achieve (or not) such outcomes, (Scoones, 1998; Kanji et al, 2005).

Among core elements of the livelihoods framework, the concept of a livelihood strategy has become central to development practice in recent years (Brown et al, 2006). The concept is increasingly important in the development debate. More attention is being paid, by policy makers, researchers, and other development practitioners, to the diverse portfolio of activities engaged in by poor households as a means to develop and engage in creative poverty reduction strategies that recognize the diversity of these activities (Jansen et al., 2004).

Figure 1. Sustainable livelihoods framework Source: Adapted from DFID, 1999.

Analysing of livelihood strategies according to assets-access-activities framework has been going on for many years (Ellis, 2000). The framework can be applied at a range of different scales – from individual, to household, to household cluster, to extended kin grouping, to village, region or even nation, with sustainable livelihood outcomes assessed at different

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levels. The specification of the scale of analysis is therefore critical, as is an analysis of the interactions between levels in terms of net livelihood effects, both positive and negative (Lovendal et al., 2004; Scoones, 1998). It should be known that the livelihoods framework is not intended to depict reality in any specific setting. Rather, it is intended as an analytical structure for coming to grips with the complexity of livelihoods, understanding influences on poverty and identifying where interventions can best be made (Kollmair and Gamper, 2002). Use of the framework as is with any tool is set by the user. The framework does not attempt to provide an exact representation of reality. It does, however, endeavour to provide a way of thinking about the livelihoods of poor people that will stimulate debate and reflection, thereby improving performance in poverty reduction (DFID, 1999). Mechanically following the framework will also yield poor result (Carney et al, 1998).

Once the brief explanation on the conceptual framework is given, the key concepts in the livelihood strategy analytical frame work will be discussed in the coming section.

2.2.1. Livelihoods

The concept of livelihood is widely used in contemporary writings on poverty and rural development, but its meaning can often appear elusive either due to vagueness or to different definitions being encountered in different sources (Ellis, 2000). Carswell, et al., (1997) also point out that definitions of livelihoods are often unclear, inconsistent and relatively narrow. That is why a precise operational definition of livelihood remains elusive (Brown et al., 2006). Moreover, a recent review of livelihoods approaches shows that definitions are far from uniform and prescriptive but are instead constantly evolving and developing. This allows for imaginative adaptations to be made as required, but also renders the concept and use of a livelihoods approach rather difficult to grasp (FAO, 2001). A popular definition is that provided by Chambers & Conway (1992) wherein a livelihood comprises the capabilities, assets (including both material and social assets) and activities required for a means of living. Briefly, one could describe a livelihood as a combination of the resources used and the activities undertaken in order to live (DFID, 1999). A livelihood is sustainable when it can

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cope with and recover from stress and shocks; maintain or enhance its capabilities and assets, while not undermining the natural resource base (Chambers and Conway, 1992). From this livelihood definition, the term capability refers to the ability of individuals to realize their potential as human beings, in the sense of both of being (to be adequately nourished, free off illness) and doing (to exercise choice, develop skills, and participate socially). Strictly, capabilities refer to the set of alternative beings and doings that a person can achieve with in or her economic, social, and personal characteristics (Derze and Sen, 1989; cited on Ellis, 2000). Scoones (1998) further disaggregated the definition to five key elements. The first three focus on livelihoods, linking concerns over work and employment with poverty reduction with broader issues of adequacy, security, well-being and capability. The last two elements add the sustainability dimension (livelihood adaptation natural resource base sustainability) (Davies, 1996).

The important feature of this livelihood definition is to direct attention to the links between assets and options people possess in practice to pursue alternative activities that can generate the income level required for survival (Ellis, 2000). Livelihoods are diverse at every level, for example, members of a household may live and work in different places engaging in various activities, either temporarily or permanently. Individuals themselves may rely on a range of different income-generating activities at the same time (Farm Africa, 2003).

2.2.2. Vulnerability Context

Vulnerability context refers to seasonality, trends, and shocks that affect people’s livelihoods. The key attribute of these factors is that they are not susceptible to control by local people themselves, at least in the short and medium term (DFID, 1999). It is the trends of change and variability in those factors that affect livelihoods, and in particular describes structural processes, that can materially disrupt different aspects of livelihood processes (Soussan et al.,

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2000). Shocks destroy assets directly. They also result in the erosion of assets indirectly, as a consequence of enforced sales and disposals made in order to buffer consumption during the sequence of responses that occur at times of disaster (Ellis, 2000). Vulnerable groups comprise people who are likely to fall or remain below a certain welfare threshold in the near future, while most of those who are presently below the threshold may face a high probability of being so also in the future (Lovendal et al, 2004).

2.2.3. Livelihood assets

In the livelihoods approach, resources are referred to as ‘assets’ or ‘capitals’ (Ellis and Allison, 2004) and the definition of each is given as:

Livelihood assets: are the resources on which people draw in order to carry out their livelihood strategies (Farrington et al., 2002). The members of a household combine their capabilities, skills and knowledge with the different resources at their disposal to create activities that will enable them to achieve the best possible livelihood for themselves. Everything that goes towards creating that livelihood can be thought of as a livelihood asset (Messer and Townsley, 2003). Synonymously, the term capital is used as livelihood assets. It refers to tangible or intangible assets that are held by a person or household for use or investment; wealth, in whatever form, capable of being used to produce more wealth; any source of benefit or assistance. Various forms of capital can be accumulated, exchanged, expended and lost, thereby affecting a household’s level of livelihood security, quality of life, and its options for coping strategies (CARE, 2001).

Different authors and organization have categorised livelihood assets (Farrington et al., 2002). For instance, Chambers and Conway (1992), classified livelihood assets into three: tangible (stores and resources); intangible (claims for material, moral or practical support); and opportunity to access resources; United Nations Development Program (UNDP,1998), grouped livelihood assets into six: human, social, natural, physical, economic and political capitals; DFID (1999) involves human, social, natural, physical, and economic capitals as

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categories of livelihood assets; CARE (2001), categorise as human, social and economic assets; and Moser, (1998) classified livelihood assets as labour, economic and social, infrastructure, housing, household relations and social capital. To have better understanding on livelihood assets, the brief review on the six often explained livelihood assets (capitals) is presented below.

Human capital (H): the skills, knowledge, ability to labour and good health important

to pursue different livelihood strategies and achieve their livelihood objectives (DFID, 2000; Scoones, 1998). A household’s human capital is comprised of those individual characteristics of its members, both qualitative and quantitative, that help them to generate income. The main characteristics of human capital are age, education, gender, health status, household size, dependency ratio and leadership potential, etc. (Bezemer and Lerman, 2003; Farrington et al., 2002; Kollmair and Gamper, 2002).

Physical capital (P): Physical capital comprises the basic infrastructure and producer goods needed to support livelihoods (DFID, 1999). Infrastructure consists of changes to the physical environment that help people to meet their basic needs and to be more productive. The following components of infrastructure are usually essential for sustainable livelihoods: affordable transport; secure shelter and buildings; adequate water supply and sanitation; irrigation machinery, clean, affordable energy; and access to information (communications) (CARE, 2001; Kollmair and Gamper, 2002, Bezemer and Lerman, 2003).

Social capital (S): There is much debate about what exactly is meant by the term ‘social capital’. In the context of the livelihoods framework it is taken to mean the social resources upon which people draw in pursuit of their livelihood objectives (Meser and Townstey, 2003). These are developed through: networks and connectedness, either vertical (patron/client) or horizontal (between individuals with shared interests) that increase people’s trust and ability to work together and expand their access to wider institutions, such as political or civic bodies; membership of more formalised groups which often entails adherence to mutuallyagreed or commonly accepted rules, norms and sanctions; and relationships of trust, reciprocity (UNDP,1998) and exchanges that facilitate co-operation, reduce transaction costs

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and may provide the basis for informal safety nets amongst the poor the social resources

(networks, membership of groups, relationships of trust, access to wider institutions of society) upon which people draw in pursuit of livelihoods (DFID, 1999). Various proxies for social capital can be used, like membership in agricultural cooperatives, incidence of mutual help in hard times, etc. (Bezemer and Lerman, 2003).

Financial capital (F): Financial capital denotes the financial resources that people use to achieve their livelihood objectives (DFID, 1999) and it comprises the important availability of cash or equivalent that enables people to adopt different livelihood strategies ( Kollmair and Gamper, 2002). Sources of financial capital include household savings, credit (borrowing), and remittances from family members working outside the home (CARE, 2001; Bezemer and Lerman, 2003).

Natural capital (N): Natural capital is the natural resource stocks from which resource flows and services useful for livelihoods are derived. There is a wide variation in the resources that make up natural capital, from intangible public goods such as the atmosphere and biodiversity to divisible assets used directly for production (trees, land, etc.). It includes, the natural resource stocks from which resource flows useful for livelihoods are derived (e.g. land, water, wildlife, biodiversity, environmental resources) (DFID, 1999; Kollmair and Gamper, 2002).

Political capital: is defined broadly as the ability to use power in support of political or economic positions and so enhance livelihoods; it refers to both the legitimate distribution of rights and power as well as the illicit operation of power which generally frustrates efforts by the poor to access and defend entitlements and use them to build up capital assets (Baumann, 2000). One way of looking at poor men and women’s access to rights is through a notion of political capital (UNDP, 1998). Political capital received attention in recent years as a key asset in accessing the other assets (Farrington et al., 2002).

This division into such six types of livelihood assets is not definitive. It is just one way of dividing up livelihood assets. Other ways may be developed depending on local circumstances. What is important here is that these are all elements of livelihoods that

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influence households directly or are potentially controlled by them (Meser and Townstey, 2003).

In practice, not all assets are owned by, or fully in the control of, men and women who are attempting to use them in their livelihood strategies – in fact some, like common property resources, cannot by definition be owned by individuals or even households, and others, such as ‘social capital’, cannot be owned, but imply a negotiated relationship (Cahn, 2004). Similarly, services supplied through targeted state programmes are officially accessible to the poor, but in practice institutional and practical barriers may limit the access of the poor to the benefits of such programmes (Farrington et al., 2002).

The livelihoods approach regards awareness of the asset status of poor individuals or households as fundamental to an understanding of the options open to them. One of its basic tenets is that poverty policy should be concerned with raising the asset status of the poor, or enabling existing assets that are idle or underemployed to be used productively (Ellis and Allison, 2004).

2.2.4. Mediating factors

Institutions, policies and processes mediate rural household’s access to and control over resources (DFID, 1999). Institutions are the social cement which link stakeholders to access to capital of different kinds to the means of exercising power and so define the gateways through which they pass on the route to positive or negative [livelihood] adaptation (Scoones, 1998). Within this broader context, these different categories of households belong to and draw support from a multiplicity of formal and informal local institutions. The latter often provide essential goods and services to the rural poor, particularly in the absence of appropriate public policies, well-functioning markets, effective local governments and official provision of safety nets for the vulnerable (Messer and Townsley, 2003).

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2.2.5. Livelihood strategies

According to DFID (1999) the term livelihood strategies are defined as the range and combination of activities and choices that people make in order to achieve their livelihood goals, including productive activities, investment strategies, reproductive choices, etc. These choices are reflected in the way that people use their assets and as such are an important part of household behavior, while determining well-being.

Livelihood strategies are composed of

activities that generate the means of household survival and are the planned activities that men and women undertake to build their livelihoods (Ellis, 2000). Livelihood strategies include: how people combine their income generating activities; the way in which they use their assets; which assets they chose to invest in; and how they manage to preserve existing assets and income (DFID 2001).

Livelihood strategies are generally understood as the strategies that people normally use in peaceful and stable times to allow them to meet basic needs and contribute to future wellbeing (Ellis, 2000). They are more than a response to contextual factors and the assets available; however they are also the result of men’s and women’s objectives and choices. These in turn are affected by individual and cultural preferences (Farrington et al., 2002).

The concept of livelihood strategies has developed through three decades of thought and study on how rural households construct their lives and income earning activities (Jansen et al, 2004). Therefore, more attention is being paid, by policymakers, researchers, and other development practitioners, to the diverse portfolio of activities that poor households engage in, as a means to develop and engage in creative poverty reduction strategies that recognize the diversity of these activities (Brown et al., 2006).

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Typologies of livelihood strategies Livelihood strategies can be classified according to different criteria. The often cited typology of livelihood strategy is given by Scoones (1998). He divided rural livelihood strategies into three broad types according to the nature of activities undertaken as agricultural intensification and extensification, livelihood diversification, and migration.

Agriculture: including crop, livestock rearing, aquaculture, forestry etc. This strategy is through processes of intensification and/ or extensification. Agricultural intensification refers to the use of a greater amount of non-land resources (labor, inputs, etc.) for a given land area, so that a higher output is produced (Hussein and Nelson, 1999). It generally focuses on the increased production of crops and agricultural commodities best suiting the agro-ecological conditions of the region and the farm and existing market outlets. Intensification often consists in the replacement of traditional crops or agricultural commodities with new high yield varieties, requiring improved technology (Warren, 2002). These strategies mainline continued or increasing dependence on agriculture. Whether households pursue this strategy or not will depend on agro-ecological potential and the implications for labour and capital (Scoones, 1998). Technical developments in agriculture may also operate as a key determinant. The availability or not of this option, and the extent to which it is undertaken by the household, will determine in major part the need for, and the household resources available to, off-farm livelihood diversification. Agricultural extensification on the other hand is bringing more land into cultivation or grazing (Scoones, 1998).

As agricultural specialization can start from an initial diversification move, also livelihoods diversification can eventually lead to some form of household specialization (ODI, 2003). For instance, in particular circumstance migratory wage labor may result so cost/effective to push the household away from conventional on-farm activities. Conversely, the identification of a particular niche commodity may lead the household to invest all its labor and other assets in it, disregarding both conventional farming activities and wage labor (Warren, 2002). The conventional wisdom for many years has been that raising output and incomes in agriculture itself are a catalyst for diverse non-farm activities in rural areas. However, in sub Saharan

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Africa this has rarely been the case, since most household diversification is not just non-farm but non-rural in character (Ellis, 2000). This leads towards the concept of diversification.

Livelihood diversification: is an increasing multiplicity of activities (regardless of the sector), or it can refer to a shift away from traditional rural sectors such as agriculture to nontraditional activities in either rural or urban space (DFID, 2001). Ellis has defined rural livelihood diversification as ‘the process by which rural households construct an increasingly diverse portfolio of activities and assets in order to survive and to improve their standards of living’ (Ellis, 2000). Thus, diversification here may be to broaden the range of on-farm activities or to diversify off/non-farm activities by taking up new jobs (Deb et al., 2002; Scoones, 1998; Start and Johnson, 2004).

Scoones (1998), further classified diversification strategies into: Natural Resource (NR) based and non NR-based activities. Natural resource based activities include; collection or gathering (e.g. from woodlands and forest), food cultivation, non-food cultivation, livestock keeping and pastoralism, and non-farm activities (e.g. brick making, weaving, thatching). Whereas non-natural resource based activities includes; rural trade (e.g. marketing of farm outputs, inputs, and consumer goods) rural manufacture, remittances (urban and international), other transfers (e.g. pensions deriving from past formal sector employment).

Diversification as a consideration notably cuts across livelihood typologies. Individuals and households may diversify on farm, off farm and non farm, including decidedly, migration as part of the diversification strategy (Ellis, 2000).

Rural livelihoods diversification has generally occurred as a result of an increased importance of off-farm wage labor in household livelihood portfolio or through the development of new forms of on-farm/on-site production of non-conventional marketable commodities. In both cases, diversification ranges from a temporary change of household livelihood portfolio (occasional diversification) to a deliberate attempt to optimize household capacity to take advantage of ever-changing opportunities and cope with unexpected constraints (strategic diversification) (Warren, 2002).

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Diversification of income sources, assets, and occupations is the norm for individuals or households in different economies, but for different reasons. Households in Sub-Saharan Africa whose livelihood heavily depend on agriculture and related activities are no exceptions to this phenomenon (Adugna, 2005). Rural dwellers of developing countries have hitherto been thought to engage only in small-scale agriculture, but this is a misnomer that is continually being disproved with emerging studies of peasant livelihoods showing highly diversified livelihoods (Rahman et al, 2007).

Migration: refers to situation when one or more family members leave the resident household for varying periods of time, and in doing so are able to make new and different contributions to its welfare, although such contributions are not guarantee by the mere fact migration (Ellis, 2000). Migration may be temporary or permanent; as a critical strategy to secure off-farm employment, or stimulate economic and social links between areas of origin and destination. Kinship structures, social and cultural norms may strongly influence who migrates. Migration will have implications for the asset status of those left behind, for the role of women and for on-farm investments in productivity. Seasonal and circular migration of labour for employment has become one of the most durable components of the livelihood strategies of people living in rural areas (Scoones, 1998; Deshingkar and Start, 2003).

Singh and Gilman (1999), and Farrington et al., (2002), have identified the principal distinctions between coping strategies, which are short-term responses to a specific shock (such as job loss of a major earner in the household, or illness), and adaptive strategies, which are a long-term change in behaviour patterns as a result of a shock or stress or in an attempt to build asset bases. The same authors distinguished between strategies that are; incomeenhancing; expenditure-reducing especially significant if the former are limited by a ceiling; based on collective support; and external representation: - negotiation with local authorities, NGOs, etc.

Another grouping of livelihood typologies based on the source of livelihood income is given by Ellis (2000). He classified livelihood strategies into three groups; farm activities (income),

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off-farm activities (income) and non-farm activities (income). Farm income refers to income generated from own account farming which includes livestock as well as crop income and comprises consumption in kind of own farm output as well as the cash income obtained from output sold. Off farm income refers to wage or exchange labour on other farms (i.e. within agriculture). It includes labour payments in kind, income obtained from local environmental resources such as firewood, charcoal, house building materials and wild plants (Hussein and Nelson; 1999; Rajadel, 2003). Non-farm income refers to non- agricultural income sources such as self employment (business), rental income from leasing land, and remittances (Ellis, 2000; Holden et al., 2004).

In line with Ellis`s classification, Frankenberger et al., (1999) categorized livelihood strategies as a range of on-farm and off-farm activities that together provide a variety of procurement strategies for food and cash. Similarly, Rao et al., (2004) pointed out that different livelihood strategies result in certain livelihood types, i.e. predominantly farm, offfarm or non-farm income sources, because of differences in resources, opportunities, and household characteristics, which help shape the comparative advantages of households.

Rakodi (1999) distinguishes between the following types of strategy: investment in securing more of an asset; substitution of one asset for another; disposal ( the sale of assets such as livestock, land or jewellery, to compensate for a consumption shortfall or to release funds for investment); sacrifice (not investing time and resources in fostering reciprocal social relations); sacrificing children’s ability to earn adequate incomes in future by withdrawing them from school because of the inability to pay fees or need for their labour. Similar to this, classification of livelihood strategies is also possible such as productive activities, investment strategies, reproductive choices (DFID, 1999).

Another categorisation of livelihood strategies by Carney et al, (1998) looks at strategies from the point of view of support activities to livelihoods that can be provided by agencies such as CARE, distinguishing between: livelihoods promotion – activities to improve households’ resilience; livelihood protection – activities to help prevent a decline in household livelihood

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security, livelihood provisioning – direct provision of basic needs, usually in emergency situations.

Tesfaye (2003) identified four typologies of livelihood strategies which include economic activities, investment strategies, reproductive choice and choice of place of residence (migration) in the analytical framework of livelihood diversification study in eastern Hararghe highlands. The same source further identified between land use strategies such as; crop land expansion and land use intensification, and livelihood diversification strategies within agriculture (diversification of crop and livestock), out of agriculture to off/non farm activities.

Drawing data from southern Ethiopia, Berhanu (2007) identified different activities both within the agricultural and non-agricultural sector. The activities in non-agricultural sectors could further take three forms as off-farm employment opportunity, non-farm income generating activities and migration, moving away of elsewhere temporarily in search of employment. The same source classified livelihood strategies into four brad groups; agriculture, agriculture plus migration, agriculture plus non-farm, and agriculture plus non farm plus off farm in order to identify determinants of livelihood strategies. The present study follows such classification in order to identify determinants of livelihood strategies.

All the above classifications of livelihood strategies are far from homogeneity. Therefore, this will guide us that it is needed to be cautious about such livelihood strategy typologies as they are prone to similar difficulties surrounding homogeneous policy domains.

2.2.6. Livelihood outcomes Livelihood outcomes are the achievements of livelihood strategies, such as more income (e.g. cash), increased well-being (e.g. non material goods, like self-esteem, health status, access to services, sense of inclusion), reduced vulnerability (e.g. better resilience through increase in asset status), improved food security (e.g. increase in financial capital in order to buy food) and a more sustainable use of natural resources (e.g. appropriate property rights) (Scoones, 1998).

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Outcomes help us to understand the ‘output’ of the current configuration of factors within the livelihood framework; they demonstrate what motivates stakeholders to act as they do and what their priorities are (Singh and Gilman, 1999; WFP, 2004). They might give us an idea of how people are likely to respond to new opportunities and which performance indicators should be used to assess support activity. Livelihood Outcomes directly influence the assets and change dynamically their level – the form of the pentagon -, offering a new starting point for other strategies and outcomes (DFID, 1999; 2000). These are the results of women and men’s livelihood strategies (Farrington et al., 2002). The present study, made use of food security measures as the outcome of livelihood strategies pursued by rural households. Before looking at these outcomes, the following section presents some empirical studies on determinants of livelihood strategies

2.3. Empirical Studies on Determinants of Livelihood Strategies

Numerous factors determine the abilities of rural households to choose among livelihood strategies and diversify their livelihood strategies away from both crop and livestock production into off- and non-farm economic activities. Different studies regarding livelihood diversification in general and determinants of livelihood diversification in particular were carried out in different countries including Ethiopia. However, scholars seem to be no consensus regarding the most important factors that drive participation in off/non-farm activities (Ellis, 2000). From these contentions, it is not simple to come up with list of major determinants that influence the decision process. Thus, the following section briefly discuss on the most important findings by giving due emphasis to the area of research. Many studies have revealed evidence of wealth differentiated barriers to entry in non-farm activities in Burkina Faso, Côte d’Ivoire, Ethiopia, Kenya, Rwanda, South Africa, and Tanzania (Holden et al., 2004). Asset poverty appeared to inhibit entry into remunerative non-farm earnings, implying a vicious self re-enforcing circle of unequal distribution of farm and non-farm earnings in areas with unequal distribution of land resources (Reardon et al., 1992). Availability

of key-assets (such as savings, land, labor, education and/or access to market or employment

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opportunities, access to common property natural resources and other public goods) is a an evident requisite in making rural households and individuals more or less capable to diversify (Warren, 2002). Yet diversification may also develop as a coping response to the loss of capital assets needed for undertaking conventional on-farm production. Decreased availability of arable land, increased producer/consumer ratio, credit delinquency, and environmental deterioration can be indeed important drives towards diversification. Economic and political shocks are often a major reason for migrate. Similarly, Meser and Townstey (2003) argued that different livelihood activities have different

requirements, but the general principle is that those who are amply endowed with assets are more likely to be able to make positive livelihood choices. That is, they will be choosing from a range of options in order to maximise their achievement of positive livelihood outcomes, rather than being forced into any given strategy. Thus, people’s access to different levels and combinations of assets is probably the major influence on their choice of livelihood strategies. Some activities require, for example: particular skills or may be very labour intensive (high levels of human capital required); start-up (financial) capital or good physical infrastructure for the transport of goods (physical capital); a certain type/level of natural capital as the basis for production; or access to a given group of people achievable only though existing social connections (social capital). Different households will have different levels of access to this range of assets. The diversity and amount of these different assets that households have at their disposal, and the balance between them, will affect what sort of livelihood they are able to create for themselves at any particular moment ( Scoones, 1998).

According to Ellis (2000), the reasons why households pursue different livelihood strategies are often divided into two overarching considerations, which are necessity or choice. Necessity refers to involuntary and distress reasons for diversifying livelihoods (such as, fragmentation of land holding on inheritance, drought, flood, and civil wars loss of the ability to continue to undertake strenuous agricultural activities due to personal accident or ill health). Choice, by contrast, refers to voluntary and proactive reasons for diversifying (seeking out seasonal wage, educating children to improve their prospects of obtaining non-

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farm jobs or trading). Barrett et al., (2001) conclude that the poor have no other option but to diversify out of farming and into unskilled off-farm labour, whether in agriculture or not.

Specifically, Ellis (2000) identified four major factors as determinants for livelihood

diversification: seasonality, risk strategies, coping strategies, as well as labor and credit market conditions. Seasonality refers to the heavy reliance of farming on weather conditions and/or fluctuations in prices as a response to changes in demand and supply conditions. Seasonality in crop production and income result in some slack seasons during which farmers may have time to engage in off-farm activities. It is also possible that households diversify activities to ameliorate the threat to its overall welfare from failure due to concentration in a single activity. Farm household diversification into non-farm activities emerges naturally from diminishing or time-varying returns to labor or land, from market failures (e.g., for credit) or frictions (e.g., for mobility or entry into high-return niches), from ex ante risk management, and from ex post coping with adverse shocks (Barrett et al., 2001).

Risk management strategies are another factor often invoked to explain diversification behavior (Reardon, 1992; Ellis 2000; Hussein and Nelson 1999). The basic logic of this argument is that previous experience of crop or market failure can provoke diversification as a means of spreading perceived risk and reducing the impact of total or partial failure on household consumption. In line with Ellis`s finding, Barrett et al., (2001) showed that from the “push factor perspective,” diversification is driven by limited risk-bearing capacity in the presence of incomplete or weak financial systems that create strong incentives to select a portfolio of activities in order to stabilize income flows and consumption, by constraints in labor and land markets, and by climatic uncertainty. From the “pull factor perspective,” local engines of growth such as commercial agriculture or proximity to an urban area create opportunities for income diversification in production and expenditure-linkage activities.

Coping strategies argument resembles that of the necessity reasoning, which states that household’s diversification is survival response to crisis or disaster (DFID, 2001). Market conditions, which in the case of rural Africa refers to market failures, leaves households to

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engage in activities to compensate for market failures, especially credit, and labor markets. The absence of such markets requires households to take advantage of the demographic composition of households to use its resources effectively and to respond to market failures (Barrett et al., 2001).

Gender relationships are also important in shaping diversification process. Social organization and culture can significantly influence the relative access of diverse gender (and age groups) to household’s capital assets (DFID, 2000).This might result in a different degree of involvement in diversification activities and/or in an unequal distribution of their benefits between genders (Warren 2002). In some cultures, migratory wage labor or off-farm enterprises are basically men business; that results in transferring to women the whole responsibility for conventional subsistence and cash cropping (the so called “feminization of agriculture”).

Transforming Structures and Processes can reinforce positive choices if they function well. However, in other cases they can act as a major constraint to choice, restricting access (e.g. in the case of rigid caste systems or state-dominated marketing systems), reducing the mobility of goods and labour and manipulating returns to given activities to make them more or less attractive (e.g. heavy-handed pricing policies) (DFID, 1999; 2000). Under such circumstances, people might be viewed as making ‘negative choices’ as to their livelihood strategies, or they may have no choice at all. In this regard, site-specific opportunities such as local market contingencies, development projects, infrastructure development (e.g. a new road), and personal contacts might play an important role in pulling rural household towards livelihood diversification (Ellis, 2000; Meser and Townstey 2003).

Rajadel (2003) attributed two general factors to be reasons of livelihood diversification by local people, local characteristics and household characteristics. Opportunities to diversify into the non agricultural (NA) sector depend on the level of development of the region, the size and dynamism of the local market and the proximity of an urban centre. Local factors

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influence the type of opportunities and incentives faced by households, but in the end, their characteristics determine their desire and capacity to diversify.

Social and cultural institutions can have a major impact on poor households’ access to resources. For instance, one cultural institution which has traditionally had a very significant impact on the access of different groups of people to a range of livelihoods assets is the construction and division of communities along lines of caste, which has strongly influenced access to employment, education, property and services (Carswell, 2000). The general stereotype of caste vis-à-vis urbanisation is that this institution is increasingly less influential in cities, as the social structure in increasingly fluid and ‘traditional’ social relationships are eroded (Farrington et al. , 2002).

According to Soussan et al., (2000), livelihoods are also influenced by a wide range of external forces, both within and outside the locality in which a household lives, that are beyond the control of the family. This includes the social, economic, political, legal, environmental and institutional dynamics of their local area, the wider region, their country and, increasingly, the world as a whole. We live in an era of increasing globalization. Its effects are felt by all, including people living in the remotest parts of the developing world (Rahman et al., 2007). These external factors are critical in defining the basic structure and the operation of livelihood systems. For example, land tenure laws are crucial in determining entitlements, and in consequence access, to land for cultivation, which in turn is a critical determinant of the overall structure of livelihoods in rural areas, whilst prices and price variability is critical (for some crops) in determining what will be grown on that land in any particular season.

Brown et al., (2006), indicated that family size, farm size, access to credit, and household heads secondary education were found significant in determining choice of livelihoods strategies.

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In the case of Ethiopia, only a few studies specifically dealt with the determinants of livelihood strategies. For instance, Devereux, (2000) found out that most Ethiopians are ‘sub-subsistence

farmers’ who have been forced to diversify into off-farm incomes to bridge their annual consumption gap, while some are effectively landless and depend entirely on non-agricultural sources of food and income, including food aid. The typical rural livelihood strategy combines crop and livestock agriculture, off-farm income-generating activities (daily labour, petty trading, and seasonal migration) and dependence on food aid.

Lautzke et al, (2003), pointed that agro-climatic zones provide diverse productive bases on which Ethiopians build their livelihoods. However, even within particular zones it should not be assumed that livelihoods are homogenous across households, or even among individuals within households. Livelihood strategies and outcomes are sensitive to combinations of age and gender, as well as to other socially constructed identities/institutions such as class, education, ethnicity, and religion. It is also clear that livelihood strategies in Ethiopia are becoming more diverse.

The study conducted by Tesfaye (2003) on the determinates of diversification of rural households into off farm and non farm sectors in eastern highlands of Harerghe revealed that in sufficient land holding, food self in sufficiency, low revenue from sales of cash crop, the number of males in a household are positively and significantly associated with participation of rural households in off farm and non farm activities.

Holden et al., (2004) identified the socio-economic and biophysical characteristics of a lessfavoured area in the Ethiopian highlands. The result indicates that land degradation, population growth, stagnant technology, and drought necessitate development of non-farm employment opportunities in the area. Access to low-wage off-farm income is also restricted by lack of employment opportunities since households otherwise would have engaged in more off farm wage employment than observed.

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Another study conducted by Adugna (2005) to explore the demographic and economic determinants of the dynamics of income diversification in Ethiopia, revealed that participation in off-farm activities is mainly driven by demographic factors, where as land and other asset ownership as well as crop income, together with demographic factors, affect intensity of offfarm activities. Initially female headed households and households with more land holdings subsequently realized less diversification into off-farm activities. On the other hand, families with larger initial crop income from main harvest season realized greater income share from off-farm activities.

The study which is similar to the current study in approach by Berehanu (2007) identified that the participation in agriculture livelihood strategy is influenced by size of arable land; sex of the household head; education level of household head; health; number of information source; distance to market place and access to credit. On the other hand, diversifying from agriculture is influenced by size of arable land; livestock ownership; age of household head; health; number of information source, and distance to market.

Specific to the study area, the study of Carswell (2000) on livelihood diversification identified a range of variables that influenced livelihood diversification on scale analysis. The result indicated that market access, differentiated access to resource, availability of land, access to transport, access to credit, ethnicity and caste, sex of household head, household size, were found to influence rural households access to resource and livelihood diversification. He also presented evidence that non-farm and off-farm activities are carried out by a significant proportion of adults and make an important contribution to livelihoods. He showed that in highland Wolayta non-farm activities (particularly trading and labouring for others) has a long history. In the case of the later, people worked as labourers with a set of arrangements that enabled them to gain access to key resources. These arrangements were deeply embedded in complex social relations. As these institutional arrangements have changed, so ‘diversification activities’ have become more visible. Consideration of the social contexts of livelihood change is thus critical for a firm understanding of livelihood change and the changing role and importance of diversification activities. In this regard, further investigation of the contribution made by the diversification activities to welfare need to be conducted (Carswell, 2000).

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2.4. Food security outcomes

To determine whether households are successful in pursuing their livelihood strategies, it is important to look at outcome measures that capture need or well-being satisfaction. Nutritional status (food security level) is often considered one of the best outcome indicators for overall livelihood security since it captures multiple dimensions such as access to food, healthcare (CARE, 2001).

2.4.1. Concepts and definition

Food security is a concept that has evolved considerably over time and there is much literature on potential household food security indicators. There are approximately 200 definitions and 450 indicators of food security (Hoddinott, 1999; 2002). Most definitions of food security vary around that proposed by the World Bank (Maxwell, 1996). Food security refers to access by all people at all times to enough food for an active, healthy life (World Bank, 1986). The essential elements in this definition are the availability (adequate supply of food); the ability to acquire it (food access through home production, purchase in the market or food transfer); stability, when availability and access are guaranteed at all times; and utilization which refers to the appropriate biophysical conditions (good health) required to adequately utilize food to meet specific dietary needs and security, as the balance between vulnerability, risk and insurance; and time (Maxwell and Frankenberger, 1992). More recently, food security has gained its link with livelihoods and vulnerability (WFP, 2004).

Food insecurity, on the other hand is a situation that exists when people lack secure access to sufficient amounts of safe and nutritious food required for normal growth and development and an active and healthy life. It may be caused by the unavailability of food, insufficient purchasing power, inappropriate distribution, or inadequate use of food at the household level. Food insecurity, poor conditions of health and sanitation and inappropriate care and feeding

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practices are the major causes of poor nutritional status. Food insecurity may be chronic, seasonal or transitory (WFP, 2004).

Household livelihoods insecurity can be defined as inadequate and unsustainable access to income and resources to meet basic needs. These needs include adequate food, health, and shelter, minimal levels of income, basic education and community participation (Devereux et al, 2004). Household livelihoods are insecure when they lack secure ownership of, or access to, resources and income earning activities, including reserves and assets, to off-set risks, ease shocks, and meet contingencies. More narrowly, livelihood strategies are undertaken essentially to facilitate food security. People enjoy food security when they have access to sufficient, nutritious food for an active and healthy life. Food insecurity exists if one or more of these conditions are not fulfilled. Further, different levels of food insecurity must also be considered if the underlying causes are to be effectively understood (Drimie et al., 2006)

2.4. 2. Food security indicators and measures

Hoddinott (1999; 2002) noted the fact that there are approximately 200 definitions of food security and 450 indicators of food security; it is difficult to measure the food security. In line to Haddinot`s argument, Maxwell ( 1995) pointed out that defining and interpreting food security, and measuring it in reliable, valid and cost effective ways, have proven to be stubborn problems facing researchers. Thus, the following section briefly reviews the most widely discussed food security indicators and the methods of food security measurement most often used by researchers.

2.4.2.1. Food security indicators According to Maxwell and Frankenberger (1992), food security indicators are generally categorized in to two main categories: ‘process’ and ‘outcome’ indicators.

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Process indicators are divided in to two: indicators that reflect food supply and indicators that reflect food access. Food supply indicators indicate the availability of food in the area for the households to obtain. A number of factors play a role in limiting food availability and the options households have for food access. These are indicators that provide information on the likelihood of a shock or disaster event that will adversely affect household food security. They include such things as inputs and measures of agricultural production, food balance sheet information, and access to natural resources, institutional development, market infrastructure, and exposure to regional conflicts or its consequences. Indicators that reflect food access: unlike supply indicators, food access indicators are relatively quite effective to monitor food security situation at a household level. Their use varies between regions, seasons, and social strata reflecting various strategies in the process of managing the diversified source of food that shift to sideline activities, diversification of enterprises and disposal of productive and non productive assets (Maxwell and Frankenberger, 1992)

Outcome indicators are used to measure the status of food security at a given point in time. Household food security outcome indicators can be grouped into direct and indirect indicators. Direct indicators of food consumption include those indicators which are closest to actual food consumption rather than to marketing channel information or medical status. Indirect indicators are generally used when direct indicators are either unavailable or too costly in terms of time and money to collect. Some of the direct indicators include: household budget and consumption surveys, household perception of food security and food frequency assessment. The indirect indicators include storage estimates, subsistence potential ration and nutritional status assessment (Alison and Slack, 1999). 2.4.2.2. Food security measures There is no fixed rule as to which method to employ due to the diversified characteristics of food insecurity and the different level of consideration. The decision to rely on a particular method usually depends on resource and time constraints, objectives of the study, availability of data, type of users and degree of accuracy required ( Debebe, 1995).

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Any commitment to improve food security and nutrition carries with it an important implication, namely the need to measure food security outcomes at household and individual levels. Measurement is necessary to characterize the severity of the food security problem and to provide a basis for measuring impact. There are four measures of household and individual food security: individual intakes, household caloric acquisition, dietary diversity, and coping indices (Hoddinott, 1999; 2002). The next section presents the reviews of merits and demerits of each method.

1) Individual intake: This is a measure of the amount of calories or nutrients consumed by individual in a given time period usually 24 hours. Methods of generating data with this method are that an enumerator resides in the household through out the entire day, measuring amount of food served to each person. The second method is recall of the previous 24 hour consumption for each household member. The advantage of this method is if implemented correctly, it produces the most accurate measures of individual caloric intake (and other nutrients) and therefore the most accurate measure of food security status of an individual. Second, because the data are collected on an individual basis, it is possible to determine whether food security status differs with in the household. Against these advantages, the disadvantages are it requires highly skilled enumerators who can observe and measure quantities quickly and accurately (Hoddinott, 2002; Migotto et al., 2005; Smith et al., 2006).

2) Household caloric acquisition: Here the person responsible for preparing meals (the most knowledgeable person in the household) is asked a set of questions regarding food prepared for meals over specific period of time usually 7 or 14 days. This measure produces a crude estimate of number of calories available for consumption in the household .The advantage is that, it produces crude estimate of the number of calorie available for consumption in the household. Therefore, the level of skill required by enumerators is less than that needed to obtain information on individual intake. The disadvantage of the method is that, the method generates a large quantity of numerical data that needs to be carefully checked both in the field and during data entry (Hoddinott, 2002; Migotto et al., 2005; Smith et al., 2006).

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3) Dietary diversity: One or more persons within the household are asked about different items they have consumed in a specified period. Where it is suspected that there may be differences in food consumption among household members. The disadvantage of this measure is that the simple form of this measure doesn’t record quantities. If it is not possible to ask about frequency of consumption of particular quantities, it is not possible to estimate the extent to which diets are inadequate in terms of caloric availability (Hoddinott, 2002; Migotto et al., 2005; Smith et al., 2006).

4) Indices of household coping strategy: This is an index based on how households adapt to the presence or threat of food shortages. The person within the household who has primary responsibility of preparing and serving meal is asked a serious of questions regarding how households are responding to food shortages. The advantage of this method is that, it is easy to train enumerators to ask these questions and individuals generally found them easy questions to answer. According to the study of Maxwell et al, (2002) there are three attractive features of this measure. First, it is easy to implement, typically taking less than three minutes per household. Second, it directly captures notions of adequacy and vulnerability. Third, the questions asked are easy to understand both by respondents and by analysts. Some disadvantages of this measure are also identified by the same study: as it is a subjective measure, different people have different ideas as to what is meant by “eating smaller portions” comparison across households or localities is problematic. Second, its simplicity makes it relatively straightforward to misreport a household’s circumstances (Smith et al., 2006).

At household or individual level the first and second methods of food security measures can be measured by Household Expenditure Survey method, which is used to measure individual or household caloric acquisition in monitory terms. This is the minimum amount of food an individual must consume to stay healthy. It can be measured in terms of the nutritional characteristics of the foods (eg calorie), the quantity of the food stuffs themselves or the monetary value of the foods. In this method, the minimum food expenditure refers to the expenditure necessary for a person with the accepted and typical regional food consumption pattern to consume a nutritionally adequate diet. Focusing on food poverty allows use of the nutrient recommended daily allowances (RDAs) as the basis for setting the food poverty line

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(Greer and Thorbecke, 1986). Setting the poverty line using the cost of calorie approach is conceptually and computationally simple, does not require an excessive sample size, and does not pre-impose a researcher’s or bureaucrat’s subjective notion of what constitutes a palatable, but inexpensive diet.

Another advantages of using household expenditure surveys (HESs) to measure food security is that the source of the food data collected is the people (adult women or men) living in surveyed households. The information comes directly from the location in which behaviour regarding food consumption takes place and from the people consuming the food. Further, compared to data on other measures of households’ resource holdings, such as income and assets, food expenditures data are not especially sensitive; people generally have little incentive to misreport how much food they acquire over a short period of time (Smith et al., 2006; Tassew, 2006). There fore, this study used expenditure approach in order to measure household food security and to calculate the cut off point (food poverty line) beyond which a household is food secure or not.

2.5. Livelihood Strategy and Food Security Linkages

Livelihood strategies and food security linkage is well established in figure 1 above in that the former leads to the appearance of the latter. Since the mid-1990s, livelihoods-based approaches have increasingly come to dominate the analysis of poverty and food insecurity, and the design of anti-poverty and famine prevention interventions, especially at the local level (Devereux et al., 2004). There is a growing consensus on the usefulness of livelihoods approaches for assessing, monitoring and mapping food insecurity and a number of analytical toolkits have been developed and adopted by development agencies that draw on the holistic nature of livelihoods-based approaches (DFID, 2001).

Analytically, household food security and the sustainable livelihoods approach each require a disaggregated analysis, as well as an analysis of livelihood diversification (agriculture and non-agricultural activities). These close linkages suggest that livelihoods approaches might

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provide a practical toolkit for linking the analysis of food insecurity with a multi-dimensional and people-centred analysis of poverty – looking beyond income and consumption levels to include an assessment of people’s strategies, assets and capabilities. The potential for a livelihoods based analytical framework to generate improved approaches to poverty and food security (Devereux et al., 2004). In effect, food security can be assessed by investigating its linkages with the resource environments and livelihoods, and looking into their determining factors using micro level data (Rao et al., 2004).

In the context of food security analysis, the most important aspects of livelihood to understand are the means by which people produce food for themselves, and the means by which they obtain income to buy food from others. Thus, the framework (Figure 1) has a number of basic elements. It answers, taking in to account of the livelihood assets at their disposal and policies and institutions around them, holds to develop the most appropriate livelihood strategies possible. These strategies may lead to more or less satisfactory livelihood outcomes (food security in this case). Food insecurity is the result of unsatisfactory livelihood strategies (Messer and Townsley, 2003). The potential for a livelihoods based analytical framework to generate improved approaches to poverty and food security measurement is very promising (Devereux et al., 2004).

As depicted in figure 1 of the analytical frame work, linkages between livelihood strategies and improved food security is one among the out comes of livelihood strategies pursued by rural households (Scoones, 1998). Thus, an analysis of the food security of different livelihood groups will lead to the identification of different interventions for each group. More over, to determine whether or not households are successful in pursuing their livelihood strategies, it is important to look at a number of outcome measures that capture need or wellbeing satisfaction (Tesfaye, 2003). Nutritional status is often considered one of the best outcome indicators for overall livelihood security since it captures multiple dimensions, such as access to food, health care and education (Ellis, 2000). Therefore, the major achievements of a livelihoods approach to food-security assessments have been a broadening of horizons. A livelihoods approach recognizes the co-existence of different risks, and consequently the need

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for simultaneously addressing life-threatening risks and the more insidious erosion of livelihoods in the longer term (Young et al., 2001).

Incorporating a livelihoods approach to the analysis of food security would have numerous advantages. Livelihoods approaches can provide an effective and practical vehicle for linking rights based approaches, measurement and action to reduce food insecurity (DFID, 1999). It would also move analysis and action from a narrow focus on agriculture towards a range of interventions to support diversified, non-agricultural livelihood strategies and the allocation of a range of resources that enhance food security (Young et al., 2001). And it would highlight the need for food security analysis to begin by understanding people’s experiences of hunger and the relationship between food insecurity and the constraints and opportunities to their existing livelihoods prior to identifying interventions (Hussien, 2002).

Literature suggest that livelihoods approaches (Carney et al., 1998) are essential for understanding the complex inter-relationships that influence food security and livelihoods approaches emphasise that food security (amount of food consumed, its nutritional quality, and the reliability of access to it over time) is only one desired outcome of household livelihood strategies alongside others such as more income. Thus an advantage of using livelihoods approaches to consider food security issues in is that they highlight the need to understand better all the various factors influencing livelihoods in order to strengthen availability, access and utilization of food successfully (Devereux et al., 2004).

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3. METHODOLOGY

This chapter starts by presenting and illustrating the location and climatic condition of the study area. It also goes through the detail methodology followed to conduct the survey such as sampling procedure, method of data collection and analysis. Finally, it presents specification of multinomial logit model used, the variables hypothesized and food security measure.

3.1. Description of the Study Area

Boloso Sore is located at about 420 km south of Addis Ababa in Southern Nations, Nationalities and Peoples’ Region (SNNPR)1 in Wolayta Zone, (Figure 2). It is connected to Wolaita Sodo town by a 30 km all weather road. There are two ecological zones in Boloso Sore, namely midland (86.4%) and highland (13.6 %). With rainfall dispersed throughout the year into two main rainy seasons and one small season. The area receives an annual rainfall of 1,551 mm and the mean maximum and minimum daily temperature are 25.40c and 13.40c.

The topography of the area includes plain lands to plateaus, hills, and rugged mountain systems. The altitude rise from 1600 m.a.s. to 3000 m.a.s. There are two main cropping seasons in the area: belg and meher. The belg season begins from late February to late March/early April where maize, haricot bean, enset, sweet potato and Irish potato are planted. The meher cropping season begins late June and continues up to end of September. Crops like tef, wheat, Irish potato, haricot bean, and sweet potato are planted in the meher season

(Endrias, 2003; BoARD, 2007).

1 SNNPRS is one of the largest regional states in Ethiopia accommodating about 112 woredas. occupy most of southwest Ethiopia and contains up to one-fifth of the country’s population 44

The total population of Boloso Sore for the year 2007 is 196,614 of which 96,341 are men and 100,273 women, with population density per square Km of 637 (next to Damot Gale district 750); Out of the total population 92 % lives in rural areas (BoFED, 2005; CSA, 2007).

Land use pattern of the district indicates that about 8954.25, 3964.75, 2280 and 1033 hectares were used for annual crops, perennial crops, forest and grazing respectively. About, 1017 hectare is degraded and not useable. Other land uses account for 32.5 ha (BoARD, 2007).

It is an area of intensive agriculture; farming systems that combines annual and perennial crops; where cereals, root crops and cash crops grow. Cropping system in Boloso Sore could be categorized into two types, intensive cereal and root crop based for the highlands and coffee and ginger cash crops dominating the midlands. Coffee, ginger and Teff are the major cash crops in the district. Even though food crops are also sold for cash there is increasing reliance on maize, sweet potato, enset and taro for food. Other sources of cash income are off farm labour sale and sale of livestock mainly Cattle and Sheep.

The livestock distribution of the district is dominated by cattle (132,678), followed by sheep (6641) and pack animals 2805 (BoFED, 2005). Livestock numbers were severely diminished during the Derg regime (Bush, 2002). Currently they are limited by a lack of grazing area, as land is ever more intensively used for arable production which provides the staple foods necessary for family subsistence. There have been a number of initiatives aimed at improving agriculture over the last 40 years.

The district comprises 22 Peasant Associations (PAs) and each PA hosts one development centre. In the district, there are one and five, senior secondary and junior schools respectively. The potential health service coverage of the district in 2007 is reported to be 90.3 % for sanitation and 81.6 percent for health. Currently there are one hospital, two health canters, and 15 clinics of different grade, 20 pharmacies, and one health post. The town has modern postal and telecommunication services including internet, fax, mobile telephone, and twenty-four hour hydroelectric power supply.

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N W

E S WOLITA ZONE

Boloso Sorie SO U TH ERN R EGI O N

Figure 2. Location of the study area

3.2. Sampling Procedure

In this study in order to capture a representative sample, multistage stratified sampling technique was used. In the first step, the district was classified in to two ecological zones highland and midland and one PA from highland and three PAs from midland were selected using PPS respectively. Second, a typology of farmers into different wealth categories was done for each PA using PRA2 tool to define socio economic classes based on local perception and criteria to get relatively homogeneous wealth groups of households who face more or less similar constraints (see Appendix Figure 1).

2 PRA can be described as a family of approaches, methods and behaviours that enable people to express and analyse the realities of their lives and conditions, to plan themselves what action to take, and to monitor and evaluate the results (chambers, 1992). This study employed wealth ranking tool of PRA

46

The procedure used in wealth breakdown by community representatives is as follows; the first action taken was team selection which involved men, women and youth representatives for each sample PA; then in group discussion, the community representatives defined three wealth groups to represent their community at large, namely better off, less poor and poor and settled criteria’s that helped them to assign each household to the pre determined wealth group.

The criteria identified during the participatory household-wealth ranking exercise were similar for the four sample PAs except some size variations. The major criteria’s considered are, the size of cultivated land, number of oxen owned, number of milking cow owned, quintals of coffee harvested annually, ability to educate all children (mainly post high school at private colleges), months of food shortage and house type. However, all criteria’s identified by the team could not easily be quantified and even difficult to differentiate two farmers with similar manner, since quantitative measurement, particularly of wealth, requires detailed information (Ashley et al., 2003). Even in some situations, the “wealthy” are those with more land; in others, they are people with more livestock or cash; and sometimes they exhibit a combination of wealth criteria. Thus, in order to address objectivity and make the PRA task manageable quantifiable criteria’s were used (Appendix Table 5). Accordingly, the distribution of wealth within sample populations is often uneven. There are usually more households at the poorer end of the scale than at the better off end. The better-off households owned at least two pairs of oxen and milking cows; owned relatively a large size of land (>0.63 ha). However, the remaining categories owned less than one pair of oxen and milking cows or none, only small or marginal land or in some cases were nearly landless, and own thatched houses, etc.

Finally, by using these criteria’s the key informants listed the name of each household head into respective wealth group and 21, 19, 27, and 53 sample respondents were randomly drawn from Yukara, D/Madalcho, Achura and Afama Mino PAs respectively

using

proportion to size sampling techniques (Table 1 ). Out of the 120 sample households 42.5%, 35% and 22.5% were poor, less poor and better off households respectively.

47

Table 1. Sample size distribution in the sample PAs

PAs Midland PAs

Household size

Poor (1)

Sample size (no) Less poor (2) Better off (3)

Sample drawn

Yukara

1046

9

8

4

21

Dangara Madalcho

968

2

10

7

19

Achura

1331

9

9

9

27

Afama Mino

2664

32

15

6

53

Total

6009

51

42

27

120

Highland PA

Source: Own survey, 2007

3.3. Method of Data Collection

Primary data on household socio-economic characteristics were collected from sample households using structured interview schedule. The interview schedule was pre-tested among eight non-sampled respondents of matching characteristics and depending on the results of the pre-test; it was revised in the lights of suggestions received. In conducting the interview, four enumerators who have knowledge about the area and acquainted with the culture and language were recruited and trained before commencing the work.

For the case of qualitative data in order to capture better the socio-economic context and type of households in the area focus groups discussion (men, women and youth groups), key informant3 interview and wealth ranking exercises at each PA were conducted. Secondary data was gathered from various sources like Boloso Sore bureau of agriculture and rural 3 Key informants are individuals who are approached for their views on livelihood issues, using a semistructured list of questions. There is no need for these informants to hold particular positions of prestige or power (DFID, 2000).

48

development, Zonal Bureau of Finance and Economic Development (BoFED), and World Bank aided project coordination office (NGO) serving in the area. 3.4 Method of Data Analysis

A wide variety of methods can be used for analyzing livelihoods. All have advantages and disadvantages. The key is to ensure that the methods chosen correspond to the questions and data needs that have been identified (ODI, 2003). The present study used analytical framework that guided the research process and employed both descriptive and econometric model to analyze the data. The coming section presents analytical framework, descriptive and econometric analysis, and food security measurement methods used.

3.4.1 Analytical framework

As indicated above, to guide the research process and serve the purpose a framework is needed (Tesfaye, 2003). As Scoones (1998) stated in work of this sort the principle of optimal ignorance must always be applied, seeking out only what is necessarily to know in order for informed action proceed. Thus, the present study in analyzing livelihood strategies involved direct examination of the individual household’s asset endowment. Specifically, the livelihood analytical framework developed by Lovendal et al., (2004) with slight modification to address the scope and objectives set forth was used (Figure 3). The key point is that understanding and being able to act on people’s survival capabilities starts of first and for most with the assets that they own, control or draw on in good and bad times.

The asset-base framework includes components: assets (human, social, physical, financial and natural), the policies and institutions (credit, market, culture and gender), livelihood strategies (agriculture, off farm and non farm), and outcomes (measures of household well-being as improved food security and increased income).

49

LH Assets H- Huma Capital S- Social capital P-Phisical Capital F-Financial capital N-Natural Capital

Policies and institutions

LH strategies Agriculture Off-farm Non- farm

Credit Market Cultural Gender

LH outcomes More income Improved food security

Source: Adapted from Lovendal et al., (2004) Figure 3. Asset-access-activities-outcome framework

3.4.2. Descriptive analysis

In order to analyse the qualitative data collected through PRA; wealth ranking, observation and key informant interview; interpretation and tabulation of data was done. The specific descriptive statistics data analysis methods used for quantitative data were one way ANOVA, mean, percentage, t-test, chi square test, and diversity indices. The descriptive data analysis was conducted using Statistical Package for Social Sciences (SPSS) version 13.

3.4. 3. Econometric model

In order to determine factors that affect choice of livelihood strategies by rural households to achieve food security goal, categorical data analysis in which the dependent variable is qualitative is deemed to be appropriate. When there are more than two alternatives among

50

which the decision maker has to choose (i.e. unordered qualitative or polytomous variables), the appropriate econometric model would be either multinomial logit or multinomial probit regression models. However, the later, is rarely used in empirical studies due to estimation difficulties imposed by the need to solve multiple integrations related to multivariate normal distributions (Greene, 2003; Senait, 2005; Chilot and Hassan, 2008). The dependent variable in this specific case, choice of livelihood strategy is a polytomous variable. Thus, a multinomial logit model when the categorical dependent outcome has more than two levels need to be employed for such study (Alwang et al., 2005; Brown et al, 2006; Jansen et al., 2004). Moreover, multinomial logit model was selected not only because of the computational ease but also multinomial logit analysis exhibits a superior ability to predict livelihood diversification and picking up the differences between the livelihoods strategies of rural households (Chan, 2005; Jansen et al., 2004). Therefore, multinomial logit model was used in this study in order to identify factors affecting rural household’s choice of livelihood strategies. The analysis of the data was conducted using LIMDEP version 7 statistical software.

3.4.3.1. Specification of multinomial logit model

Rural households make a number of decisions in their daily activities. When there are alternatives to choose from, economic theory tells that agents choose what maximizes their expected utility given the existing situation (Moti and Gardebroek, 2008). To identify the determinants behind rural household decision to engage in various livelihood strategies the assumption is that in a given period at the disposal of its asset endowment, a rational household head choose among the four mutually exclusive livelihood strategy alternatives that offers the maximum utility. Following Greene (2003), suppose for the ith respondent faced with j choices, we specify the utility choice j as:

Uij = Zij β + εij .................................... ………………………………. (1)

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If the respondent makes choice j in particular, then we assume that Uij is the maximum among the j utilities. So the statistical model is derived by the probability that choice j is made, which is:

Prob (Uij >Uik) for all other K ≠ j ………………………………………. (2) Where, Uij is the utility to the ith respondent form livelihood strategy j Uik the utility to the ith respondent from livelihood strategy k

If the household maximizes its utility defined over income realizations, then the household’s choice is simply an optimal allocation of its asset endowment to choose livelihood that maximizes its utility (Brown et al., 2006). Thus, the ith household’s decision can, therefore, be modelled as maximizing the expected utility by choosing the jth livelihood strategy among J discrete livelihood strategies, i.e, max j = E (U ij ) = f j ( xi ) + ε ij ; j = 0...J ……………………………………… (3) In general, for an outcome variable with J categories, let the jth livelihood strategy that the ith household chooses to maximize its utility could take the value 1 if the ith household choose jth livelihood strategy and 0 otherwise. The probability that a household with characteristics x chooses livelihood strategy j, Pij is modelled as:

Pij =

exp( X i' β j )

, J=0... 3............................................................ (4)

J

∑ exp( X β j =0

' i

j

)

With the requirement that ∑ j = 0 Pij = 1 for any i J

Where: Pij = probability representing the ith respondent’s chance of falling into category j X = Predictors of response probabilities

β j = Covariate effects specific to jth response category with the first category as the reference. A convenient normalization that removes an indeterminacy in the model is to assume that

β1 = 0 (this arise because probabilities sum to 1, so only J parameter vectors are needed to

52

determine the J + 1 probabilities), (Greene, 2003) so that exp( X i β1 ) = 1 , implying that the generalized equation (4) above is equivalent to

Pr( yi = j / X i ) = Pij = Pr( yi = 1 / X i ) = Pi1 =

exp( X i β j ) 1 + ∑ j =1 exp( X i' β j ) J

1 1 + ∑ j =1 exp( X i' β j ) J

, for j = 0, 2…J and

,

…………………………………. (5)

Where: y = A polytomous outcome variable with categories coded from 0… J.

Note: The probability of Pi1 is derived from the constraint that the J probabilities sum to 1. That is, pi1 = 1 − ∑ pij . Similar to binary logit model it implies that we can compute J logodds ratios which are specified as;

ln

[ ]= x (β p ij

p iJ

,

j

− β J ) = x , β j , if , J = 0 ………………………………… (6)

3.4. 3.2. Coefficient interpretation

Since multinomial logit model is the extension of the binary logit models, the interpretations resemble that of binary logit models (Gujarati, 2003). The major difference is that the reference category now no longer the other choice as in binary logit. Probability in a multinomial logit model can be calculated similarly to that in a binary logit model, with the only modification being accounting for multiple sets of β - estimates. The meaning of logit (log-odds) and odds term is identical in both models. In the binary case, the comparison is between category 1 and category 2 (or the first versus the last category). In multinomial case the comparison is between category j and J (or any category versus the last). The predicted probabilities are better interpreted using the marginal effects of the multinomial model (Greene, 2003). Therefore, every sub vector of β enters every marginal effect both through probabilities and through weighted averages that appears in δ ij . By differentiating equation (5)

53

above with respect to the covariates we can find the marginal effect of the individual characteristics on the probabilities (Greene, 2003). The marginal effects ( δ j ) of the characteristics on the probabilities are specified as

δ ij =

∂Pij ∂X i

J −     = Pij  β j − ∑ Pij β j  = Pij  β j − β  ………………………………… (7)   j =o  

Where, δ j denotes the marginal effect (the coefficient), of the explanatory variable on the probability that alternative j is chosen.

3.4. Definition of Variables and Working Hypotheses

The dependent variables of the model

The polytomous dependent variables for the determinants of rural households’ choice of livelihood strategy are specified as;

Y=0, if the choice lies in Agriculture alone Y=1, if the choice lies in Agriculture + off farm Y=2, if the choice lies in Agriculture+ non farm Y=3, if the choice lies in Agriculture + non farm+ off farm

The explanatory variables

Different variables were expected to affect livelihood strategies of rural households in the study area. Thus, in order to address the issue of how household livelihood strategies and levels of well-being are determined within heterogeneous rural areas, the analytical procedure begun by clustering the sample households into livelihood strategy groups and by regressing household livelihood strategies on basic assets controlled by the household and household’s

54

livelihood choice can be explained based on a set of pre-determined asset-based variables listed below.

Sex (SEX): Sex is dummy representing Household Head (HH) sex. It takes the value 1 if female, 0 otherwise. Men and women have different access to resources and opportunities (Ellis, 2000). For the last three decades, many women’s advocates have been arguing that women are poorer than men. On a priori grounds, there are reasons to be concerned about the welfare of FEHHs, since women are subject to discrimination in labour, credit and a variety of other markets and they own less property compared to men (Cagatay, 1998). Migratory wage labor or off-farm enterprises are basically men business, that results in transferring to women the whole responsibility for conventional subsistence and cash cropping (the so called “feminization of agriculture”), (Warren, 2002). In the context of Ethiopia, keeping in mind the great gender disparity, female headed households (FEHHs) have less chance to participate in off/ non farm activities since they invest much time in domestic roles such as child care not denying their active role in agriculture. Therefore, in this study, sex was expected to be negatively related to livelihood diversification by FEHHs.

Age of household head (AGE): Age refers to ages of the sample HH heads in years. The study conducted by Destaw (2003) and Berhanu (2007) have indicated that age has significant effect on livelihood diversification. Older household heads participate less in the agricultural wage labor market, and receive more remittance from elsewhere (Reardon et al., 1992). Thus, older farmers are expected to be less active and hence rely more on farm than off/non-farm income. In other ways, it is expected that younger farmers are more likely to be diversifiers of livelihood strategies than the older farmer that the older ones due to better possession of resources accumulation (land and livestock). Thus, it is hypothesized that older age of the household heads and diversification of livelihood strategies are negatively correlated.

Educational level of household head (EDUCAT): Education refers to the education level of HH in years. Education equips individuals with the necessary knowledge of how to make living. The education level of household head in particular and the education levels of households’ members in general affect households’ livelihood in various ways (Tesfaye,

55

2003). As livelihood is dynamic, literate people are always coming up with better off strategies. Education determines the capability of finding a job (Warren, 2002). Households in which the average level of education is higher can be expected to have more members working off-farm (often in better remunerated occupations).This variable is expected to have a positive impact on choice of diversified livelihood strategies and can be shaped by the development of human capital.

Family size (FAMILY): - Family size refers to the size of household members in Adult Equivalent (AE) which was expected to determine the households’ choice of diversified livelihood strategies positively. Family size either determines the availability of family labor or, large family size demands large amount of production to feed its members, i.e., as family size increases, the demand for food increases. This means the larger the family size the higher the probability to participate in varied income sources (Bezmer and Lerman, 2002; Berhanu, 2007).

Agro-ecology (AGROECO): Agro-ecology refers to whether the respondent is being in midland or highland. It is measured as dummy variable 1 if the respondent is in highland and 0 otherwise. Scoones, (1998), noted that whether a household pursue agricultural intensification, extensification, livelihood diversification, and migration or not depends on the agro-ecology in which it operates. The livelihood of people living in midland differs from that of highland due to different opportunity. It is, thus, an important variable that can shape livelihood strategies of the household and affects decisions of households to diversify their livelihood portfolio (Roa et al., 2004). Agro-ecology also set the limits of what is economically possible by determining soil type and rainfall levels (Davis, 2003). Therefore, this variable was expected to favour diversification of livelihoods for midlanders than highlanders.

Size of land owned (LAND): - Land size refers to the size of land owned by household in hectare. This variable is a basic asset for majority of the rural livelihoods. More land size holding means more cultivation and more possibility of production which in turn increases farm income and improves food security (Tesfaye, 2003). The same source also indicated

56

that, diminishing farm sizes and a decline in return to labor in farming under population pressure may encourage rural households to diversify their employment and sources of income. Lanjouw and Lanjouw (1995) also pointed out that landholdings per capita are negatively correlated with participation in low productivity off farm occupations. Therefore, having more land size was expected to affect livelihood diversification negatively since the farmer relay on crop production than to go for off/ non farm in order to satisfy basic needs. On the other hand, population pressure and frequent land inheritance increasingly creating farm land fragmentation. Thus, this variable was hypothesized that households who have large farm land holding would have better probability to extensify crop production than those smaller land size holders who probably participate in off/non farm activities.

Livestock holding (LIVESTOK): Livestock holding is the amount of livestock owned by HHs.

It is measured by Tropical Livestock Unit (TLU). Livestock benefit much and

perceived as the accumulation of wealth status, use for draft power, manure, income from sale of milk, butter and sale of live in times of risk to buy necessities. The household having larger size of livestock can have better chance to have better income from livestock. The more livestock owned by the household will be the less possibility of the households choice to participate in less incentive off/non farm activities. On the other hand, poor households who owe no or less livestock are likely to relay on sources of income other than livestock. Thus, off farm and non farm activities have to come form an important part of poor households’ livelihoods (Homewood et al, 2006). Galab et al., (2002) indicated that livestock linearly correlated to agricultural activities. Therefore, the possession of more size is expected to be negatively related to diversification of livelihood strategy.

Use of farm inputs (INPUT): Use of farm inputs refers to use of chemical fertilizer such as DAP, UREA and high yielding varieties (HYVs) which is dummy, 0 for non user and 1 for user. Households using fertilizer/HYVs are expected to have better food production capacity than the non-users. Use of farm input improves productivity per unit area; which is intensification of agricultural strategy and helps the household to meet food needs. The adoption of improved farm technologies such as fertilizer and improved variety can result in significant income increase for the adopters (Beyene et al, 2000). Thus, in this study, it was

57

hypothesized that to have negative impacts on decision of the household to diversify livelihoods. In other words, a household who could have used farm inputs (chemical fertilizer and HYV) is hypothesized to have negative relation with diversifying strategies.

Frequency of extension contact (EXTENS): Frequency of extension contact is the number of times the household received extension personnel contact within a year. The effort to disseminate new agricultural technologies is mainly successful if there is smooth and frequent contact between development agent and the farmer at the grass root level. Here, the frequency of contact between a farmer and development agent has the potential force to accelerate effective dissemination of adequate agricultural information that in turn enhances farmers’ decision to adopt agricultural technologies (Kidane, 2001). Therefore, agricultural extension services provided by agricultural development offices are believed to be an important source of information for improved agricultural technologies. Moreover, extension services are helpful in increasing awareness among farmers about new farming techniques. Frequent contact between the target group and development agent, and different extension services such as training, visiting and demonstration serve as the major sources of agricultural information and build decision making skill. Therefore, a household who has a frequent contact with extension personnel and service has a potential to adopt valuable extension advises and improve productivity of agriculture. Therefore, this variable was expected to determine livelihood strategy choice positively in favour of agriculture.

Membership to cooperatives (COOPER): Membership to cooperatives represents whether a household is member to cooperatives or not. It is a dummy variable of which the value is 1 if the household head is member and 0, otherwise. Co-operatives worldwide are committed to the concept of mutual self-help. This makes them natural tools for social and economic development, and provides significant additional benefit to communities and social systems co-operatives allow these people to maintain their rural lifestyles, pursue meaningful livelihoods, and contribute to rich cultural landscape, while effectively competing in a global economy. Moreover, co-operatives have the capacity to reduce social conflict by providing a means to equitably distribute resources, decision-making power and economic benefits (CCA, 2004). Formal as well as informal associations, such as indigenous cooperation groups,

58

enforcing widely agreed standards of behavior, and uniting people with bonds of community solidarity and mutual assistance. As such, they embody important forms of social capital, representing forums wherein local communities can unite and act collectively (Messer and Towensley, 2003). Membership to cooperatives also will increases households access to services that might be granted by being member. In Ethiopia, cooperatives that have been promoting by bureau of cooperative commission, including traditional cooperatives such as equb, iddir and labour sharing culture, guarantee members access to credit and labour shares (Berhanu, 2007). Various formal or informal associations relating to production and redistribution can constrain or enhance the way in which households pursue economic opportunities (Start and Johnson, 2004). Households can mobilize resources during crises; be a source of labor and credit during the year; recruit clients and customers for a business; convey important market information; and, at lower levels of income be the difference between survival and pauperization (Little, 1997). For both off and non-farm diversification strategies, it would appear that social networks that facilitate the sharing of farm equipment and labour as well as membership in community groups are important assets for the poor, (Galab et al., 2002).

Therefore, this variable is expected to be positively related to

diversification of livelihood (Warren, 2002).

Social leadership participation (LEADER): It is a dummy variable, which takes the value 1 if the farm household head participates in any of the social leadership and 0 otherwise. This variable entails socio-political role of the household within the community. The person’s affiliation and involvement in social leadership activities (such as, PA administration, equb, iddir etc) will have a higher exposure for social power and utilization than those who did not involve. It is an asset that links an individual or a group to power structures and policy outside the locality (Baumann, 2000). Therefore this variable was expected to improve household’s access to social capital and financial capital. This in turn, increases the likelihood of the household’s participation in various off/ non farm activities. There fore, this variable was expected to positively affect diversification of livelihood strategies by the rural household in to off/ non farm activities.

59

Credit use (CREDIT): Credit use refers to whether the household used credit or not. It is a dummy variable, which takes the value 1 if the farm household uses credit and 0 otherwise. Credit is considered as an important source of investment and helps to improve livelihood strategies of households and households who have better access to credit can have better investment in preferred livelihood strategies which in turn improve food security status. In this study, it was hypothesized that credit service will have a positive link with diversification of livelihood strategies in achieving food security goal of rural households (Destaw, 2003).

Distance from market centre (MKTDIST): Distance from market centre refers to the distance of the household’s residence from the nearest market place in Kilometre (Km). Access to market and other public infrastructure may create opportunities of more income by providing in diversifying livelihood strategies through non/off farm employment, easy access to input and transport facilities; household nearer to market centre have better chance to increase diversification and in turn will improve food security. Improved market access can be expected to stimulate production of cash crops; and participation in petty trading which is diversification of livelihoods. In remote areas where physical access to markets is costly and causes (household-specific) factor and product markets failures, households diversify production patterns partly to satisfy own demand for diversity in consumption (Barrett et al., 2001). There fore, this variable was expected to positively influence the decision of a rural household to participate in diversified livelihood strategy (Rao et al., 2004).

Receiving remittances (REMITA): Receiving remittance refers to relative economic support in the form of money or food to the household from abroad and within the country, mainly from urban to rural dwellers. It is a dummy which takes the value 1 if for those who receive and 0 otherwise. Remittances are playing an increasingly large role in the economies of many countries, contributing to economic growth and to the livelihoods of needy people (though generally not the poorest of the poor) (Ellis, 2000). It was expected that having relative economic support from abroad and within the country is positively related to the improvement of livelihood activities by participating in more remunerable activities such as local trading

60

for which financial capital is required (Brown et al, 2006). Thus, this variable was expected to positively affect the choice of diversified livelihood strategy by the rural household.

Dependency ratio (DEPRATIO): Dependency ratio refers to the proportion of economically inactive labour force (less than 14 and above 65 years old) to the active labour force (Between 15 and 65 years old) with in a household. A household with more economically inactive labour force compare to the active age shows a high dependency ratio and the vice versa holds true. A household with high dependency ratio is more likely indicative of labor scarcity and which may stimulate alteration of livelihood strategies. For instance, availability of a surplus of household labor (or a high producer/consumer ratio) may influence the household decision to engage in wage labor (Warren, 2002). Having more young children in the household may mean there is less labour available for new activities as it raises caregivers’ reproductive burden. More children may necessitate greater income to support their basic needs (Galab et al., 2002). Therefore, in this study dependency ratio was hypothesized to be positively stimulating diversification of livelihood strategies by the rural households to achieve food security status of households dependents (Rao et al., 2004).

3.4.4. Food security measures

In this study, it is assumed that the ultimate goal of rural households’ livelihood strategy is primarily to ensure food security of its members. Since, it is logical to assess livelihood strategies outcome food security status of the rural households as food security and livelihood strategies are inextricable phenomenon in agrarian societies like Ethiopia (Tesfaye, 2003).

There are a number of food security measurement techniques as has been discussed in literature review part of this study. Keeping in mind the objectives stated food security at the household level is measured by direct survey of income, expenditure, and consumption and comparing it with the minimum subsistence requirement. In this regard, expenses are used to compute the status of food security, i.e., food energy intake method to find a monetary value

61

of the poverty line at which “basic needs” are met was employed. To obtain the estimated cost of acquiring the calorie recommended daily allowance (RDA) that is, 2200 kcal per adult equivalent per day. The calorie intake result was calculated by using the standard food composition table prepared by Ethiopian Health and Nutrition Institute (EHNRI, 2000) and Food and Nutrition Bulletin (Tilahun et al., 2004) (see also Appendix Table 3). The value of RDA at an average price of grain in 2006/2007 local market was estimated.

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4. RESULTS AND DISCUSSION

This part deals with the results of descriptive statistics and multinomial regression output of the determinants of choices of livelihood strategies. The analysis was made in light of the objectives of the study. Section 4.1 deals with descriptive analysis and section 4.2 presents the results of the econometric analysis.

4.1. Descriptive Analysis

The livelihoods approach is concerned first and foremost with people. It seeks to gain an accurate and realistic understanding of people’s strengths (assets or capital endowments) and how they endeavour to convert these into positive livelihood outcomes. The approach is founded on a belief that people require a range of assets to achieve positive livelihood outcomes; no single category of assets on its own is sufficient to yield all the many and varied livelihood outcomes that people seek. The approach seeks to cluster households into categories with similar opportunities and constraints. This can be done by differentiating households with their asset endowment into wealth categories. This is particularly true for poor people whose access to any given category of assets tends to be very limited. As a result they have to seek ways of nurturing and combining what assets they do have in innovative ways to ensure survival (DFID, 1999). Therefore, this study employed wealth categorization and the asset approach to livelihood strategy analysis and under this section the livelihood assets that affect the wealth status and livelihood strategies pursued by rural households and its out come will be described.

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4.1.1. Human capital

Human capital represents the skills, knowledge, education, ability to labour and good health that together enable people to pursue different livelihood strategies and achieve their livelihood objectives. At a household level human capital is a factor of the amount and quality of labour available; this varies according to household size, skill levels, leadership potential, health status, etc (Carney, 1998, DFID,1999 ). Human capital is extremely low in Ethiopia, which is both a cause and a consequence of food insecurity, due to adverse synergies between poor education, health and nutrition status, and labour productivity (Devereux, 2000). In this particular study human capital assets like: age composition, sex, education level, health status of SHHHs; dependency ratio and family size of the sample households will be discussed.

4.1.1.1. Age composition It is argued that younger farmers are more likely to be poor than the older farmer that because of better possession of resources accumulation of the later (Tesfaye, 2003). Thus, age is expected to positively affect wealth status. In the survey, the average age of the respondents was 34 years with standard deviation of 9.46. This is below the national average, i.e. 44 years (MOFED, 2002). The age of sample HH heads ranged from 15 to 68 years and the majority of them were within the active labour force (99.2%). The result also shows that as age of household head increases the possibility of a household being in poor decreases as Table 2 indicates that the mean age increases from 32 to 35 and from 35 to 36 respectively for the poor, less poor and better off households (Table 2). The statistical analysis, however, revealed that there is no significant difference in the mean age of sample household heads between the three wealth categories.

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Table 2. Age distribution of sample HH heads by wealth categories Wealth category of the household (%) Age category

Poor (1)

Less poor (2)

Better off (3)

Total

(N=51)

(N=42)

(N=27)

(N=120)

15-64

42.5

35

21.7

99.2

>64

00

00

0.8

0.8

Mean

31.98

34.90

36.33

33.98

SD

9.19

7.9

11.6

9.46

F

2.218

p-value

0.113

Source: Own survey, 2007

4.1.1.2. Sex composition Women and men have different access to critical economic resources and varying power to make choices that affect their lives, as a consequence of the state of gender relations that exists in a given society. The direct result of this is seen in the unequal roles and responsibilities of women and men. Women are a critical component of the rural economy and are engaged in agricultural production. They contribute significantly, cash and food crops, subsistence farming, and reproduction of male agri-labour forces (Ellis, 2000, MoFED, 2002).

The sample survey accommodated 23.3 % female headed households and 13, 3 %, 6.7 % and 3.3 % of them were found to be poor, less poor and better off respectively. On the other hand, the survey result for men indicated that out of the total 76.7% male headed HHs 29.2%, 28.3 % and 19.2 % were found to be poor, less poor and better off respectively (Table 3). Here, more than half of female headed households are poor, whereas only one third of the men counterparts were poor. This indicates that female headed households are surely concentrated in the lowest wealth stratum. The statistical result, however, showed no significant difference between male headed HHs and female headed HHs in their wealth status.

65

Table 3. Sex composition of sample HH heads by wealth categories Sex of SHHHs

Wealth category of the household (%)

Male

Poor (1) (N=51) 29.2

Less poor (2) (N=42) 28.3

Better off (3) (N=27) 19.2

Total (N=120) 76.7

Female

13.3

6.7

3.3

23.3

χ2

3.369

P-value

0.186

Source: Own survey, 2007

4.1.1.3. Marital status The survey result indicates that from the total sample 78.3%, 2.5%, 13.3% and 7.5% are married, divorced, widow /widower and single respectively. When we compare marital status across wealth categories, out of the total sample HH heads, 30.8%, 26.7% and 20.8% are from poor, less poor and better off households respectively. Whereas among 9 single households, 5%, 1.6%, and 0.8% are from poor , less poor and better off families respectively (Table 4). The statistical test shows that there is no significant difference between poor, less poor and better off households in their marital status. Table 4. Marital status by wealth category

Wealth category of the household (%) Marital Status Married

Poor (1) (N=51) 30.8

Less poor (2) (N=42) 26.7

Better off (3) (N=27) 20.8

Total (N=120) 78.3

Divorced

0.8

1.6

0

2.4

widower/widowed

5.9

5.1

0.8

11.8

5

1.6

0.8

7.4

Single

χ2

4.273

P-value

0.64

Source: Own survey, 2007

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4.1.1.4. Family size In many developing countries a large proportion of the population lives in rural areas, and this population continues to grow at a substantial rate. Given limits to arable land, such growth rates in the rural labour force will not be productively absorbed in the agricultural sector (World Bank, 1995). Specially, population pressure in Africa is increasing dramatically. Between 2000 and 2030, population in Ethiopia, Kenya, Tanzania and Uganda is expected to double. Even though growth in rural areas will be slower than in the towns, more than half of the population will still be rural.

Obviously, the dynamic nature of population growth is the result of family size growth of each household in that country. In the present study, the overall size of the sample household members is 863, of which 391 and 472 constitutes male and female population respectively. The present study revealed that there is significant difference in the mean family size at less than 1 percent probability level between poor, less poor and better off household groups. In that the mean was in increasing order (4.9, 6.1, and 7.3) for poor, less poor and better off households, respectively. While the overall mean family size of the sample household was 5.90. This was above the national average (4.9), (Table 5). From this, we can understand that, as the mean family size increases from 5 to 6 and from 6 to 7 the probability of a household to be better off increases on contrary to expectation. Based on such surprising result, it might be worthy to argue that the better off the household will be the more incentive to have more number of children. Moreover, better off households often foster the children of their poor relatives to have additional labour (extended family). The result is in agreement with the results obtained by Berhanu (2007), Bezmer and Lerman (2003) and Tesfaye (2003). Specific to the study area, Bush (2002) identified that the better-off households are uniformly large because they are both polygamous and extended family.

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Table 5. Distribution of family size by wealth category Category of family size

Wealth category of the household (%) Poor (1) (N=51)

Better off (3) (N=27)

Total

0

12.5

1-3

10.8

Less poor (2) (N=42) 1.7

4-6

21.7

17.5

7.5

46.7

7-9

9.2

14.2

12.5

35.8

>10

0.8

1.7

2.5

5.0

Mean

4.94

6.14

7.37

5.90

SD

2.18

1.85

2.88

2.43

F-value

(120)

10.569

p-value

0.000***

***, significant at less than 1% probability level Source: Own survey, 2007

4.1.1.5. Dependency ratio Dependency ratio is defined as household members older than 65 and younger than 15 divided by the complement of this set in sampled households. Although children are often engaged in productive activities as of 7 particularly in rural Ethiopia, it is conventional to categorize children under 15 as dependents. On the other hand, old people above the age of 65 too are considered as dependants. This variable was also used as a proxy indicator for number of economically active family members since it indicates the burden over the latter.

Large ratio of dependents in a population of an area indicates the burden, which the active population should bear. Those households with proportionally more number of children under the age of 15 years and older people above the age of 65 seem particularly vulnerable to falling into poverty.

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According to the survey result the sample population has highest dependency ratio for a young population (1.167), than old age dependency ratio (0.007). This indicates that there is high fertility and probably mortality of the older group. In addition, households’ are investing more on satisfying the dependent members rather than constructing their future asset. The overall dependency ratio for the sample household is 1.164 (Table 6). Multiplied by 100 gives 116.4 which mean every 100 person within the economically active population groups supported not only themselves but also additional 116.4 economically dependent persons with all basic necessities. This figure is above the Zonal and national average, since zonal and the national dependency ratio was computed to be 92 ( BoFED, 2005) and 101 (CSA, 2001), respectively. There is no statistical difference in the value of dependency ratio across wealth categories.

Table 6. Dependency ratio of sample HHs by wealth category Wealth category of the household (%)

Dependency ratio

Poor (1) (N=51)

Less poor (2) (N=42)

Better off (3) (N=27)

Total (N=120)

<1

25.0

18.3

10.3

54.2

1-2

15.8

12.5

10.3

38.3

>2

1.7

4.2

1.7

7.5

Mean

1.068

1.249

1.212

1.164

SD

0.664

0.806

.73449

0.731

F

0.779

p-value

0.461

Source: Own survey, 2007

4.1.1.6. Education level The educational status of sampled households heads showed that 53.3 %. 15 %, 20 % and 11.8 % of them completed 0, 1-4, 5-8, and 9-12 years of schooling respectively (Table 7). The average years of schooling for the poor, less poor and better off households respectively, is 1.88, 3.33, and 3.52. Which implies as the years of schooling increases the probability of the 69

farmer to be in better off wealth category increases. The difference between the three wealth groups with regard to education was found to be statistically significant at less than 5 percent probability level. This human capital tended to mostly include households’ heads with only primary level education.

Table 7. Distribution of sample HH heads by years of education completed Head’s years of education

Wealth category of the household (%)

Total

Less poor (2) (N=42) 14.2

Better off (3) (N=27) 10.0

(N=120)

0

Poor (1) (N=51) 29.2

1-4

5.8

7.5

1.7

15.0

5-8

3.3

8.3

8.3

20.0

9-12

4.2

5.0

2.5

11.7

Mean

1.88

3.33

3.52

2.74

SD

3.4

3.7

3.6

3.6

53.3

F

4.52

p-value

0.013**

** Significant at less than 5% probability level Source: Own survey, 2007

4.1.1.7. Health Status Another important aspect of human capital is the health status of individuals in a society. Besides having a direct impact on welfare of individuals, their health status has repercussions on their potential productivity. To diversify and participate in superior livelihood strategy and gain access to livelihood asset, physical wellbeing of the rural household head is very mandatory (Scones 1998).

The survey result indicated that 95 percent of the household heads were found to be healthy for the reference year, and 5 present were found sick. The statistical analysis revealed that

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there is no significant difference among poor, less poor and better off household heads in their health condition (Table 8).

The result for the health situation of family members showed that, 46.7 % of the total sample populations are not sick. Whereas, 54 % are reported sick (this figure is double of the national average during 2000 survey (MoFED, 2002). Among them, 27 households faced sickness of family members of more than 2 in size and 12.5% of the total sample population were died. Regarding sick treatment, 1.7% of the sick did not get any medical treatment, 5% received traditional treatment and 93. 3% got health service. Table 8. Summary statistics of Health status of SHHs

Health Status of sample HH heads Sick

Wealth category of the household (%) Poor (1) Less poor (2) Better off (N=51) (N=42) (3) (N=27) 7.8 4.8 0

Not sick

92.2

95.2

100

Total (N=120) 5.0 95

χ2

2.294

p-value Number of sick family

0.318

members 0

56.9

42.9

33.3

46.7

1

21.6

38.1

37.0

30.8

2

15.7

14.3

14.8

15.0

3

3.9

2.4

11.1

5.0

4

2.0

2.4

3.7

2.5

1

33.3

41.7

25.0

10.0

2

66.7

33.3

Died family member

71

2.5

Sick treatment No treatment

2.0

2.4

0

1.7

Traditional treatment

5.9

2.4

7.4

5.0

Health service

92.2

95.2

92.6

93.3

Source: Own survey, 2007

4.1.2. Natural capital

Natural capital is the term used for the natural resource stocks from which resource flows and services useful for livelihoods are derived. None of us would survive without the help of key environmental services and food produced from natural capital (DFID, 1999). In this study natural capital comprises land size held by the HH, soil fertility status, and agro-ecology in which the HHs operates.

4.1.2.1. Land size held by sample HHs From any other productive resources land is by far the most important resource in agriculture. That is why the community wealth ranking begun with consideration of land in wealth breakdown.

Regardless of the size, all the respondents have ensured that they own land they operate. In the study area, as similar to else where in rural Ethiopia, the respondents accessed the land they own in four ways, inheritance, which is the main means (71.7 %) and it is highly challenged by the alarmingly growing population pressure resulting in land fragmentation, gifts (14.2 %), land distribution (9.2 %) and purchase (5.6 %), which, although strictly illegal as all land belongs to the government; that however has been prevail in the informal market.

For the total sample the land holding of the households vary from 0.01 ha to 2.5 hectare (ha). The average land holding being 0.45 ha. The average land holding for poor, less poor and 72

better off households is 0.27, 0.40 and 0.84 respectively. The F-test revealed that the mean difference between the three groups is statistically significant at less than 1% probability level (Table 9). This implies that land access is everywhere an acute problem, there is no longer any scope for village headmen to allocate new land to families, and farm size declines with each successive sub-division at inheritance. A comparison, of land owned would reveal that land flows from the poorer households towards the better off ones via share cropping and informal markets Table 9. Land size holding by Wealth category Land size held (in Hectares)

Wealth category of the household (%) Poor (1)

Less poor (2)

Better off (3)

Total

(N=51)

(N=42)

(N=27)

(N=120)

0.01-0.25

26.7

10.7

0

37.4

0.36-0.5

13

14.6

1.3

28.9

0.51-1

2.8

9.7

12.9

25.4

>1

0

0

8.3

8.3

Mean

0.27

0.4

0.84

0.45

SD

0.155

0.233

0.619

0.402

F

25.598

p-value

0.000***

*** Significant at less than 1% probability level Source: Own survey, 2007

4.1.2.2. Farmers perception of soil fertility status It is not only the existence of different types of natural assets that is important, but also access, quality and how various natural assets combine and vary over time (e.g. seasonal variations in value). For example, degraded land with depleted nutrients is of less value to livelihoods than high quality, fertile land, and the value of both will be much reduced if users do not have access to water and the physical capital or infrastructure that enables them to use

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irrigation (DFID, 1999). In the case of the study area, soil infertility is perceived as a major problem by farmers. Majority of the respondents said that they have soil fertility problem (75%). The comparison between wealth categories showed that 18.3, 18.5, and 4.2 percent of the poor, less poor and better off households have infertile land. The chi square test revealed that there is a statistically significant relation ship between soil fertility status and wealth category at less than 5% probability level (Table 10).

Table 10. Soil fertility status as perceived by SHHs Soil fertility status

Wealth category of the household (%)

Total

Less poor (2) (N=42) 5

Better off (3) (N=27) 10

(N=120)

Fertile

Poor (1) (N=51) 10

Moderately fertile

14.2

14.2

8.3

36.7

Infertile

18.3

15.8

4.2

38.3

25.0

χ2

9.973

p-value

0.041**

** Significant at less than 5% probability level Source: Own survey, 2007

4.1.2.3. Agro- ecology The livelihood of people living in midland differs from that of highland this difference may tie different opportunity to better wellbeing. It is, thus, an important variable that can shape the strategies of livelihood of the household. The agro-ecology in the study area is categorized into two: highland and midland. The result of the survey revealed that there is statistically significant difference among the two categories with respect to their wealth status at less than 5% probability level (Table 11).

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Table 11. Distribution of sample HHs in the two agro-ecologies

Agro-ecology Midland (0)

Wealth category of the household (%) Poor (1) Less poor (2) Better off (3) (N=51) (N=42) (N=27) 15.8 22.5 17.5

Highland (1) 26.7 12.5 χ2 p-value *** Significant at less than 1 % probability level

5.0

Total (N=120) 55.8 44.2 13.628 0.001***

Source: Own survey, 2007

4.1.3. Physical capital

Physical capital comprises capital that can be created by economic production processes, like building, irrigation cannels, tools, machineries power, and water supplies. Under this variable, livestock holding, farm input use, and house type owned will be described.

4.1.3.1. Livestock holding Livestock is one of the most important and crucial assets that farmers heavily depend on to safeguard their household from any sort of crisis. Livestock is considered as a security during crop failure and additional income for farmers in Ethiopia. The role of livestock as a source of food is critical for human kind. Livestock’s are also considered as a measure of wealth in the rural area. Farm households having more number of livestock are considered as wealthy farmers in the farm community.

The present study showed that out of the 120 sample households 108 own livestock though the numbers of livestock were not large. The mean livestock holding in Tropical Livestock Unit (TLU) for the sample households is 2.65, where as the relationship between livestock holding and wealth category is the minimum is 0.00 and the maximum is 13.3. The statistical analysis showed that it is significant at less than 1% probability level (Table 12).

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Table 12. Livestock holding by wealth category Wealth category of the household (%) TLU

Poor (1) (N=51)

Less poor (2) (N=42)

Better off (3) (N=27)

Total

<1

97.3

2.4

0.0

29.2

1-3

2.7

64.3

0.0

36.7

3.01-4

0.0

28.6

14.8

13.3

4.01-6.03

0.0

4.8

51.9

13.3

>6.03

0.0

0.0

33.3

7.5

Mean

0.773

2.676

6.160

2.651

SD

0.68

0.89

2.43

2.46

(N=120)

F

142.228

p-value

0.000***

*** Significant at less than 5% probability level Source: Own survey, 2007

As mentioned above, the fact that out of the 120 sample households 108 own (rear) livestock is misleading since it does not indicate the diversity in the number and kind of livestock held by different wealth groups. Thus, Table 13 presents summary statistics for livestock holding in kind by SHHs. Accordingly, except for Donkey, which was rarely distributed in the study area, for the rest kind of livestock holding there is significant statistical difference between poor, less poor and better off households with increasing mean values across the wealth continuum through poor to the better off households. This implies that livestock ownership is concentrated on the hands of few and there are bottlenecks that hinder the poor from participating in livestock breeding.

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Table 13. Summary statistics for livestock holding by wealth category Wealth category of the household (%)

Kind of

Poor (1)

Less poor (2)

Better off (3)

livestock

Mean

SD

Mean

SD

Mean

SD

F

p-value

Poultry

0.33

0.8

1.3

1.9

3.8

4.7

16.37

0.000***

Goat

0.02

0.14

0.12

0.45

0.3

0.87

2.726

0.070*

Sheep

0.3

0.70

0.93

1.52

1.1

1.41

5.215

0.007***

Donkey

0.02

0.14

0.07

0.26

0.07

0.27

0.854

0.428

Cows

0.23

0.38

1.1

0.44

2.3

1.4

69.802 0.000***

Oxen

0.15

0.32

0.60

0.53

1.5

0.63

66.252 0.000***

Heifer

0.17

0.33

0.45

0.59

1.04

0.80

21.622 0.000***

Calve

0.21

0.43

0.81

0.67

1.32

1.12

22.711 0.000***

***,*, Significant at less than 1 % and 10 % probability level Source: Own survey, 2007

Giving special emphasis to oxen ownership, out of the total sampled households 48.3 % did not have ox, 34.2 % have one ox and 9.2 % have two oxen (Figure 4). Moreover, the key informant interview revealed that, ox rental is not a common practice in the area. Instead, a farmer with one ox or a pair of oxen but not sufficient compared to cropland size, usually exchange the ox or oxen with another household on alternate working days. In which, except feeding the ox, no cash payment is made. Farmers with no ox get oxen from relatives or sympathetic neighbours or friends. Another alternative way of acquiring oxen is by offering human labour to ox owners to work on his farm for agreed number of days. Accordingly, the household without ox in return get the oxen for agreed number of days. Thus, of the total

households who did not have oxen 63.1% used hoe/ spade to plough their farm, 15.3% used exchange of human labours with oxen power and about 21.6 % of them were supported by the community to plough their farm plots.

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Source: Own survey, 2007 Figure 4. Oxen ownership by SHHs

The survey data also captured the situation of livestock production in the study area in the past. Out of the total livestock owners, 40% reported that they own more livestock in the past and ranked reasons for livestock decline in order of importance as 18.3, 17.5 and 5 percent respectively be disease, livestock sale and feed shortage due to draught. Again, when they rank reason for livestock sale, 15.8, 8.3, 6.7 and 5.8 percent were found to sell livestock to meet household expenditure (food, cloth, schooling etc.), to repay debt, wedding and house construction respectively (Table 14). The study also explored the reason why the poor households rent their land and it is attributed to deficiency in endowment of ox and lack of capital to access input.

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Table 14. Situation of Livestock production by SHHs No

Percent

54

44.5

To purchase input

3

2.5

Medical service

5

4.2

Household expenditure

19

15.8

To repay debt

10

8.3

House construction

7

5.8

Wedding (social obligation)

10

7.9

Feed shortage as a result of draught

8

6.70

Disease

22

18.3

Livestock sale

21

17.5

Coupling with others

40

33.3

Gift/support

52

43.3

Exchange for labour

16

13.3

Livestock sale (Yes)

Reason for Livestock sale

Reason for livestock decline

Source of oxen for non holders

Source: own survey, 2007

The survey data on livestock production further included problems related to livestock production in the study area. For the purpose respondents were asked to rank livestock related problems in order of importance. The result indicates that feed shortage/grazing (38.3%), and

livestock disease (25.8%), lack of improved breed (15.8%), shortage of water (11.6%) and market related problems (6.5%) were the major livestock constraints in the study area (Figure 5).

Thus, this study suggests that feed development, veterinary services and improve

livestock breed through expansion of artificial insemination will be priority areas of intervention in solving livestock production constraints in the area.

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Source: Own survey, 2007 Figure 5. Livestock production problems

4.1.3.2. Mean crop output harvested Rural households allocate their limited land and labour resources to crop production expecting that they will gain the maximum possible yield at the disposal of their asset endowment. In this aspect, the kind of crops produced and the amount of yield harvested make a difference between crop producers. In the study area, in terms of crop diversity, quite a number of crops were grown. The mean physical crop output harvested by each wealth group and type of crop is presented in Table 14. The mean crop output in quintal for each mentioned crops was found to be the lowest for the poor groups than the better off households. Mainly for, maize, H/bean, Pea and coffee there is a statistical mean difference at less than 10 % probability levels. This implies that the ability of the poor to harvest better quantity of yield from crop is constrained by certain potential barriers. The visible ones are that the poor with scarce land, input and oxen do not capture the advantages of crop production.

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Table 15. Mean crop output by wealth category Wealth category of the household Mean value

Poor (1)

Less poor (2)

Better off (3)

Total

F (p-value)

Teff

0.486

1.056

1.542

0.920

3.942 (0.022)

Maize

1.156

1.926

1.859

1.582

2.391 (0.096)*

Taro

6.165

8.100

12.413

8.266

1.730 (0.183)

S/Potato

8.415

6.870

15.312

9.252

1.331 (0.270)

H/Bean

0.236

0.612

0.822

0.457

4.294 (0.018)*

Pea

0.081

0.250

0.05

0.122

2.710 (0.077)*

Coffee

1.162

0.659

4.00

1.60

5.212 (0.008)**

I/ Potato

0.107

1.700

1.250

0.839

1.848 (0.178)

Ginger

11.615

15.900

20.700

15.666

0.974 (0.389)

per quintal

**,*, significant at less than 5% and 10 % probability levels respectively Source: Own survey, 2007

4.1.3.3. Input use The issue of agricultural input adoption by small-scale farmers is one of the development topics in low-income countries. This is due to its contribution to increase agricultural yields and food production, income and food security. Various studies in Ethiopia have proven that appropriate application of modern farm inputs such as, chemical fertilizers; improved seeds and herbicides increase crop yield and productivity (Degefa, 2002, cited on Tesfaye, 2005). Because of this fact, Ethiopian farmers have been encouraged to adopt utilization of modern farm inputs. However, poor farmers fail to use expensive inputs since they do not afford the cost.

In this particular study, the use of chemical fertilizer (Dap and urea), and improved verities (maize and teff) were considered. About 69.9 % of the SHHs reported that they used chemical fertilizers, and the rest 30.1% used improved varieties.

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The chi-square test of the data reveals that there is statistical difference between users and non users of farm inputs at less than 1% probability level between poor, less poor and better off households (Table 16). The decision to use or not to use new technologies at any time is influenced by various factors. At the most basic level, an economic agent is assumed to make decisions to use or not to use a new technology based on its objectives and constraints as well as cost and benefit it is accruing to it. Ranking the reason for not using technology, the survey result showed that expensiveness of the input stood first (81.2%), followed by land shortage (10.2%).

Table 16. Input use by wealth category Wealth category of the household (%) Input use

Poor (1) (N=51)

Less poor (2) (N=42)

Better off (3) (N=27)

Total

No

90.2

57.1

37

66.7

Yes

9.8

42.9

63

33.3

(N=120)

χ2

25.087

p-value

0.000***

*** Significant at less than 5% probability level Source: Own survey, 2007

4.1.3.4. Type of house owned One key area which tends to be highlighted in discussions of livelihoods is the importance of housing as an asset and housing consolidation as a key strategy for reducing vulnerability for the poor. Research indicates that housing also has major impacts on other asset bases, including social capital often based on local residential and community networks (Farrington, et al., 2001).

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About 85% of the households in Ethiopia live in low quality houses made of wood and mud and 65% of the houses are grass-roofed (MoFED, 2002). Similarly, the type of house owned by the sample respondents varies across wealth categories at 1% level of significance and the poor (96.1%) own grass roofed houses (Table 17).

Table 17. Type of house owned by wealth category Wealth category of the household (%) House type owned

Poor (1) (N=51)

Better off (3) (N=27)

Total

51.8

56.7

Mud wall with grass roofed

58.8

Less poor (2) (N=42) 57.1

Grass wall with grass roofed

37.3

21.4

0

23.3

Mud walls with corrugated iron

3.9

21.4

48.2

20.0

(N=120)

χ2

28.087

p-value

0.000***

*** Significant at less than 1% probability level Source: Own survey, 2007

4.1.4. Social capital

Social capital refers to community and wider social claims on which individuals and households can draw by virtue of their belonging to social groups of varying degrees of inclusiveness in society at large. Social capital may be defined as .the ability of actor to secure benefits by virtue of membership in social networks or social structures (Krishna, 2000). It entails reciprocity within communities and between households based on trust deriving from social ties (Moser, 1998). Economic opportunities are not taken in a vacuum, but within a specific socio-cultural context. Social and cultural institutions can have a major impact on poor households’ access to resources. In the study area, within farming, social arrangements are used to bridge resource gaps (land, labour, livestock and capital). The degree of interaction with others in the context of social networks can enable economic agents to reduce

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transaction costs and partially address access constraints arising from imperfect markets. Social capital can translate into access to relevant market information and buyers, wage employment and business opportunities, formal and informal loans, cash advances, inputs on credit, skills, shared resources for production and marketing, and migration opportunities (Davis, 1996).

The study identified different forms of social relations that mediate access to other livelihood resources. The social capital variables described under this topic are: livestock sharing, participation in share cropping, membership to cooperatives, receiving relatives support and social leadership participation.

Participation in livestock sharing

According to the key informant interview result, in the study area, livestock sharing refers to taking livestock of others (rearing others livestock) to take care and share some benefit based on negotiation made between livestock owner and caretaker. It is also mainly the job of poor households who took livestock of the better offs in pursuit of sharing some benefit. Thus, those sample households who took others livestock to their control for caretaking and benefit sharing and/ or jointly owning livestock are considered as participants in livestock sharing. This implies that, the means of accessing livestock benefit by the poor is by participating in share breeding and the reasons for livestock sharing in the study area are concentration of livestock ownership in the hands of the better off than the poor.

In line with the key informant interview result, the survey data found that, many poor households in Boloso Sore take care of livestock which are either not their own (hence they are “caretakers”), or are jointly owned (shared) with another family. Several joint-ownership and share-breeding arrangements are practiced between people of different wealth status, with a negotiated sharing of benefits. These are based on kinship ties and other social networks. This allows people to own at least a share in livestock, and to derive some benefits (food, manure, income) from them. Close observation of the data also revealed that, the majority of

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the poor participated in share breeding (since they own less or no livestock). Statistically, there is a difference across wealth groups by participation in livestock sharing at less than 5 % probability level (see Table 18).

Participation in share cropping Participation in share cropping refers to those households who shared their land for those who can afforded input and oxen to share the output based on the agreement made and vice versa or those who worked on others farm to share their labour with agreement to gain benefit. Sharecropping is found to be one of the strategies to cope with household’s food deficit situation among poorer households. Accordingly, most poor households are forced to have all or a portion of their land sharecropped. Although they may receive part of the harvest, they do not control the selection of crops, nor the amount of inputs used. As a result, benefits from sharecropping are usually very small. Here for the poor who own relatively more arable land this strategy will benefit. Comparison by wealth category in participation of share cropping indicates that there is statistical difference at less than 1 % probability level (Table 18).

Membership to cooperatives

In Boloso Sore almost every one is a member of either of the traditional local institutions such as Idiria, Shufua and Bankia4 in which the community help families (especially the poor) to cope with funerals, house construction and savings.

Membership to such institutions

increases the social network of the household and enables to obtain pooled labour and cash in credit where individual households are incapable otherwise. Membership to cooperatives in the area was found to significantly differentiate poor households from less poor and better off households at less than 1 % probability level (Table 18). Further examination of the result 4 Idiria: an institution in which community organized to support each other both financially and materially during funerals Shufua is a type of saving in which members collect money on regular basis and take the money turn by turn weekly or monthly based on lottery method or negotiation. Bankiais also other way of saving in which neighbors collect money and save so that they get back the money at the end of the year

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informs that proportion of participation increases along the wealth continuum from the poor to the better off. The observed reason for this difference is attributed to the fact that existence of some potential entry barriers that hinder the poor from participation.

Receiving relative support There are strong kinship ties, which are important alignments in arrangements for sharecropping, share-breeding, labour exchange and security during a crisis. Within larger kin groups and between households, risk can be shared by the transfer of goods between households in time of need. This strategy covers a vast range of situations and methods of transfer, but there are three basic types: gift, where food or some other item is transferred freely and without obligation from one household to another; reciprocity, where the transfer imposes an obligation on the recipient to return the goods or some other service at a later time, obligation, where the giver is obliged to relinquish some item under specified circumstances. According to the survey results out of the total respondents 21.7 % have received relatives support in the forms of money or food or livestock. The chi-square test between the wealth groups however revealed there is no statistical difference with respect to relatives support (Table 18).

Social leadership participation The involvement of heads of the household in different local administrative positions is expected to access the household to a number of information sources on different strategies to enhance access to various resources. So households who are involved in such positions are expected to be more likely to be better offs than the counterparts. Similarly, the present study showed that there is statistical difference between the three wealth categories with respect to their social leadership participation at < 1% probability level with increased share by the better off and less poor households respectively.

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Table 18. Social capital access by SHHs

Wealth category (%) Social capital

Poor (1) (N=51)

Less poor (N=42)

Better off (N=27)

Total

χ2 (p-value)

(120)

Livestock share

15

8.33

0

23.33

12.301 (0.002***)

Share cropping

32.5

20

13.3

65.8

9.48 (0.009***)

Cooperatives membership

3.33

6.7

6.7

16.7

12.786 (0.002***)

Relative support

6.7

10.8

4.2

21.7

3.366 (0.186)

Leadership participation

1.7

11.7

10

23.3

23.256 (0.000***)

***, significant at less than 1 % Source: Own survey, 2007

4.1. 5. Financial capital

Financial capital refers to stocks of money to which the household has access. This mainly involves credit use in the form of loans, saving ability and receiving remittances. The study thus analysed sample household’s use of credit, reception of remittance and saving habit in the coming section.

4.1.5.1. Credit use The most commonly reported obstacle to investment and entrepreneurship is inadequate access to capital (Davis, 2003). The availability of agricultural credit to subsistence farmers who have little or no capital or savings to invest in farming is important component of small farm development programs. Moreover, credit is an important source of earning future income. Those households who received farm credit have possibility to invest in farming activities, which is important component in small farm development programs. In line with

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this, an attempt was made to assess the number of households who had benefited from farm credit.

The study result showed that 34.5 percent of the sample households received credit while 65.5 percent of them did not due to various reasons. The comparison by wealth status disclosed that 21.6, 45.2 and 42.3 percent poor, less poor and better off households respectively received credit. Out of the non users, 28.6 % failed to use credit due to fear of repayment. Where as, 71.4 percent of them complained that they lack credit institution at their locality. The chi square test result revealed that the relation ship between credit use and wealth status is statistically significant at less than 1 percent probability level (Table 19).

In accessing credit it is not only the use of credit that differs significantly between poor and the better offs. However the amount of credit used also showed that the poor and less poor are concentrated at the bottom and there is statistical difference between wealth groups at <5% probability level (Table 19).

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Table 19. Credit use by wealth category Wealth category of the household (%) Credit use

Poor (1) (N=51)

Less poor (2) (N=42)

Better off (3) (N=27)

Total

No

78.4

54.8

57.7

65.5

Yes

21.6

45.2

42.3

34.5

(N=120)

χ2

28.087

P- value

0.000***

Amount of credit used (Birr) <100

80.4

61.9

59.3

69.2

100-1000

11.8

16.7

11

13.3

1001-2500

7.8

21.4

29.6

17.5

F 4.153 P- value 0.018** ***, ** significant at less than 1% and 5% probability levels respectively Source: Own survey, 2007

The source of agricultural credit is an important determinant of accessing credit. In Ethiopia agricultural credit is one of institutional support rendered to farmers in rural areas. This service can be given by government and non government organizations. The main governmental sources of credit in the study area are micro finance institute and bureau of cooperatives. The non governmental source is the World Bank which delivers credit in cash as well as in kind for the poorest category of the community. The loan given to rural households by the World Bank is based on prior enterprise developed by the project coordination bureau and eligibility criteria that is conducted through community wealth ranking in which the poor are eligible. Among the several enterprises being implemented by the project coordination bureau some includes fattening, dairy development, and poultry. Most people in the study area depend on the informal financial sector to meet their credit needs (38.3 %). Figure 8 below, shows that cooperatives, local money lenders including relatives, the World Bank, and

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microfinance served 42.3, 31.1, 17 and 2.5 percent of the credit users in the study area. The picture that emerges from these figures is that of a rural economy with an active, but almost exclusively informal financial market providing small interest-free and uncollateralized loans to households like what has been done by the World Bank is worth interesting to rural economy.

Source: own survey, 2007 Figure 6. Sources of credit used by SHHs

4.1.5.2. Receiving remittance Receiving remittance refers to relative economic support in the form of money or food to the household from abroad and within the country, mainly from urban to rural dwellers. Remittances contribute to economic growth and to the livelihoods of needy people worldwide (DFID, 2001). The survey result indicates that the proportion of better of households receiving remittance was more than that of the poor and less poor. Although, the situation regarding remittance is not statistically different between wealth categories; the surprising result shown in Table 20 below is that the better off households’ proportion to get remitted (18.5%) is more than that of the poor (7.9%) and less poor (9.5%). The probable justification for the result is that the better off can afford and invest in their children education and had

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good opportunity to receive remittance from educated family members who are employed in the urban areas. The finding of this study is inline with that of Tesfaye (2003) and, Bezemer and Lerman (2003).

Table 20. Distribution of SHHs by receiving remittance Wealth category of the household (%) Receiving remittance

Poor (1) (N=51)

Less poor (N=42)

Total Better off (3) (N=120) (N=27)

No

92.2

90.5

81.5

89.2

Yes

7.9

9.5

18.5

10.8

χ2

2.197

p –value

0.333

Source: Own survey, 2007

4.1.5.2. Saving habit

Saving, which was unattainable by the poor, is a basis for investment in more productive activities and improvement of future livelihood strategies. The poor spend almost all their income on food. Thus, for poor households generating savings is difficult and most often they run a debt. The present study also indicated that, there is a clear difference between poor, less poor and better off households in their saving habit at 1 % probability level of significance (Table 21).

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Table 21. Saving habit of SHHs Wealth category of the household (%) Saving habit Poor (1) Less poor Better off (3) (N=51) (N=42) (N=27) No 40.8 22.5 10 Yes 1.7 12.5 12.5 χ2 p-value ***, Significant at less than 1 % probability level

Total (N=120) 73.3 26.7 26.773 0.000***

Source: Own survey, 2007

4.1.6. Institutional supports

In many developing countries, policies and institutions discriminate against those with few assets and disadvantage poor people. Such discriminatory policies and institutions undermine development efforts to eradicate poverty and food insecurity. One of the most common problems in development is that Transforming Structures and Processes do not work to the benefit of the poor (DFID, 1999). Policies and institutions operate at all levels, and in both public and private spheres, where they influence the formation and outcomes of livelihood strategies. Institutions may influence livelihoods in many ways: fore instance, the access that poor people have to assets, the benefits they derive from them, as well as incentives for the development of assets, depend upon institutional arrangements. These in turn depend upon the institutional environment, information flows, asset characteristics and the vulnerability and power of different actors.

In the context of this study, institutional support variables included are: extension contact to the household, and proximity to various social services such as market, health, primary education and water.

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4.2.6.1 Extension contact by the household

Extension contact deliver services like advice, training, demonstration and distribution of input to rural households. A household who has a frequent contact with extension personnel and service is expected and has a potential to improve agricultural production and gain better reward from agricultural production. The survey result showed that 71.7 percent of the sample households get extension contact, which is 54.9, 81 and 88.9 percent for the poor, less poor and better off categories respectively (Table 22). The chi-square test also indicated that there is a significant relationship between extension contact and wealth status at less than 1 percent probability level.

Table 22. Distribution of SHHs by extension contact Extension contact No Yes χ2 p-value Frequency of extension contact 52 24 12 1-12 0 χ2

Poor (1) (N=51) 45.1 54.9

7.8 0.0 2.0 45.1 45.1

Wealth category of the household (%) Less poor (2) Better off (3) (N=42) (N=27) 19.0 11.1 81.0 88.9

7.1 2.4 16.7 54.8 19.0

18.5 7.4 18.5 44.4 11.1

p-value

Total (N=120) 28.3 71.7 12.786 0.002***

9.2 3.3 10.1 48.3 28.3 26.890 0.003***

***, significant at less than 1 % probability level Source: Own survey, 2007

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4.2.6.2. Access to social services An important measure of access to public services is the distance between the residence of households and the facility at hand. This measure is particularly useful for large countries like Ethiopia where the efficiency of transport network is quite low (MoFED, 2002). The Survey questionnaire recorded information on the distance between various facilities and the residence of households.

Among various social services, markets are important in determining access to assets and livelihood strategies, terms of exchange for assets, and returns to investment. In practice, the way households use markets often depends upon the ease of physical access. Distances to the markets may often be long and the travelling time substantial. In some areas, access to markets is insecure and there is a risk that goods will be stolen. It may also be difficult for people to transport heavy or bulky goods over long distances. In remote areas where physical access to markets is costly and causes (household-specific) factor and product markets failures, households diversify production patterns partly to satisfy own demand for diversity in consumption (Omamo, 1998, cited on Barrett et al, 2001).

The present study indicated that the mean distance between the sample PAs and the nearest market place in kilometre for the sample households is 2.4 km with a minimum of 0.01 km and a maximum of 8 km. The average for poor, less poor and better off households is 2.05, 2.8 and 2.5 km respectively. In relative term, the poor households have a better access to the nearby market place. However, the mean difference between the two groups with regard to distance from the market place is not statistically significant (Table 23).

Another important service which highly correlated with human capital is health service. Ethiopia is known to have one of the lowest health statuses in the world. Thus, proximity to health service can affect the wellbeing of the rural households. This is mainly due to backward socioeconomic development resulting in widespread poverty, low standard of living, poor environmental conditions and inadequate health services. The survey result showed the mean distance in kilometre to reach the nearest health centre is 2.4 Km with

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standard deviation of 2.22 for the all sample and there is no statistical difference between wealth groups (Table 23).

Distance to primary school is one of the potential determinants of rural households with scarce labour who use child labour in domestic and agricultural work. To see this difference, distance to the primary school for the SHHs was analysed and there is no statistical difference across the wealth groups (Table 23).

Access to potable drinking water is another important support for the rural poor in Ethiopia, since drinking water from protected sources is a ‘luxury’ available to only a quarter of the population (only around 15 per cent in the rural areas), (MoFED, 2002). In agreement to this fact, out of the total sample only 48.3 % were found to get access to drinking water in the study area. The rest, those who do not get access to protected drinking water, get water from springs (76%), and 21% from aquifers/river beds.

The fact that, distance to fetch water can be expected to affect livelihood of the rural poor, reasonably by diverting the labour of particularly women from agriculture, distance to fetch water was measured for the SHHs and the mean distance to fetch water for the poor, less poor and better off households is 1.25, 1.14, 0.86 Km respectively, although, there is no significant difference (Table 23).

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Table 23. Access to various services (Km)

Nearest market

Wealth category of the household (%) Poor (1) Less poor (2) Better off (3)

Total

Mean

1.97

2.86

2.60

2.42

SD

2.05

2.45

2.28

2.27 1.88 (0.157)

F (p-value) Health centre Mean

1.95

2.71

2.77

2.40

SD

1.64

2.22

2.62

2.12 2.04 (0.13 )

F (p-value) Access to 1

0

education Mean

1.48

1.36

1.53

1.45

SD

0.97

0.93

1.13

0.99 0.274 (0.761 )

F (p-value) Drinking Water Mean

1.24

1.13

.85

1.12

SD

1.10

1.37

1.13

1.20

F (p-value)

0.930 (0.398 )

Source: Own survey, 2007,

4.2.6.3. Receiving food aid Since the great famine of 1984–85 Ethiopia has received hundreds of thousands of tons of food aid per year. Even Ethiopian administration officials now speak of a dependency syndrome, a recipient mentality, among the people. For more than two decades, annual distribution of hundreds of thousands of metric tons of food aid have been channelled into safety net programs designed to alleviate the impact of food shortages in Ethiopia. Accordingly, food aid plays a role in giving relief to those households who are perceived to be most at risk of severe food insecurity

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Over the past decade, more than five million people on average have required food aid each year, even during years of seemingly normal weather and market conditions. Over the past fifteen years an average of 700,000 metric tons of food aid per annum have been imported to meet food needs (FSCB, 2004). Similar to other food insecure parts of the country in Boloso Sore food aid have been distributed since the establishment of bureau of food security section in the form of grain and food for work cash. The survey results revealed that out of the total sample HHs 39.5% respondents were reported that they have been receiving food aid in either forms. When we compare food aid across wealth categories, there is a significant difference at 1% probability level (Table 24).

Table 24. Food aid distribution by wealth category

Wealth category of the household (%) Received food aid Poor (1) (N=51) No 41.2

Total

Less poor (N=42)

Better off (3) (N=27)

(N=120)

58.5

100

60.5

58.8 41.5 0 Yes χ2 p-value ***, significant at less than 1 % probability level

39.5 44.298 0.000***

Source: own survey, 2007

4.2. Livelihood Strategies

Livelihood strategies are defined as those activities undertaken by households to provide a means of living. Livelihood Strategies are diverse at every level. Individuals themselves may rely on a range of different income generating activities at the same time, and are likely to be pursuing a variety of goals. As has been reviewed from Brown et al., (2006), several different methods of characterizing household livelihood strategies can be found in the literature. Most commonly, economists group households by shares of income earned in different sectors of

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the rural economy. For example, Barrett et al., (2005) analyzed the relationship between overall household income and the proportion of income earned in on-farm and off-farm activities in several African countries, noting how these proportions changed across income quartiles and that different income sources became dominant as one moved up the income distribution. Krishna (2000) used income share composition to examine the relationship between income, household characteristics and barriers to entry into higher return activities. Others have examined the potential determinants of diversified income portfolios for rural smallholders (Reardon et al., 1992).

Given the debate in the literature regarding appropriate methods to implement the livelihood strategy analysis, this study considered income shares of each livelihood activity as a means to conceptualize livelihood strategies. Therefore, in an effort to capture a farm household’s livelihood strategy, the household income portfolio as a critical component of the livelihoods conceptual framework, was used as a starting point to determine and define a household’s livelihood strategy. In this analysis of livelihood strategies emphasis is given to the range of income sources pursued by rural households, and the important role subsistence agriculture continue to play in maintaining household livelihood security.

A key finding of this study is that rural households pursue a diverse range of livelihood strategies in addition to agriculture. This section, therefore, discusses how these various livelihood strategies interact with basic crop and livestock production and how they contribute to household income and food security. This is important information for understanding what is occurring at the household level, and for developing appropriate interventions aimed at increasing smallholder production and productivity.

4.2.1. Income portfolio analysis This section deals with the income dimensions of livelihood outcome that sample households depend on and earn from diversifications activities. Accordingly, the annual average total income per AE earned by sample respondents was about Birr 525.24 with maximum earnings of up to Birr 4270/AE. The average total income of the poor, less poor and better off

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households was Birr 313.4, 398.4, and 1122.54 per AE respectively. The group statistical analysis showed that there is significant difference between mean incomes earned among the wealth groups at less than 1 percent probability level (Table 25).

In the study area, the major income sources for the sample households are crop sale, livestock and livestock product sale, petty trade (small business), causal wage, environmental gathering (charcoal making, fire wood selling, local furniture), farm land/ donkey hire ( rent), and remittance. Of these, the most important source of income for all households by its share was found to be crop (39.8 %), followed by livestock (24.2 %) and petty trade (12.9 %) in order of importance (Table 25). Further examination of the data showed that within groups of households where each household has the same economic opportunities, there is a large variation in both the size of income and in the relative importance of different sources of income. Farming activities (crop production and livestock rearing) were found to be dominantly pursued by all the three wealth categories with increasing share by the better off households.

To see the importance of each income source for the different wealth groups, income composition of the poor from highest to lowest showed that crop income (36.5%), petty trade (17.7%), wage income (15.7%), livestock income (11.7%), and rural craft (10.5%), (Table 25). The implication is that the poor rely more on crops, local petty trading and wage. The income of the better off households’ is composed of crop (44.1%), livestock (42.1%), remittance (6.5%) and petty trade (5.4%). This implies that the better off households’ income is mainly from crop and livestock. The fact that the better off households share of remittance overweigh that of the poor and less poor categories attributed to the abilities of the former to educate their children as a long term livelihood strategies and sent to urban areas where better employment is secured where they latter received remittance. This result is also in line with that of Tesfaye (2003).

If we compare income share by the broad livelihood activities, the share of agriculture accounts for about 64.1%, non farm for 22.8% and off farm accounts for 13.1% in decreasing

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order. Further observation of the data revealed that, off-farm5 activities (agricultural wage, land rent, and environmental gathering) are survival mechanisms pursued mainly by the poor and less poor groups but not viewed as an opportunity that farmers engage in as a choice. Non farm activities, such as rural craft is also mainly choice of the poor than the counterparts. Thus, off- farming activities seem more of a coping mechanism for the rural population than a way to accumulate wealth and reduce poverty. The poor tend to concentrate on off farm activities with low entry constraints (gathering, such as charcoal making and fire wood collection and wage). This result leads to the understanding of the challenges which prevent the poor and less poor from engaging in livestock production and more remunerative non farm activities.

If further comparison was made between own agricultural account and non- agricultural account sources, i.e. agriculture versus off/non farm shares; agriculture’s share is 64.1% and the non- agriculture is 35.9 %. The result approximates that for most of sub Saharan African countries, in which the share of non-agricultural sector accounts for 40% (Barrett et al, 2005). The important implication is that agriculture still dominates, as the most important sector of economic activity, not denying the substantial role that the other sectors play in income composition of the poor, since poor households are pushed into the off and non-farm sector due to a lack of opportunities on-farm, for example, as a result of lack of oxen or smallness of land holdings. In other words, increased role of off farm activities such as selling labour, causal wage employment, and non farm activity petty trading, especially for poor and less poor households with less access to land and other necessary resources, signify how farmers respond to a decreasing ratio of farm size to household.

5 Off-farm income refers to wage or exchange labour on other farms and income obtained from local environmental resources gathering such as fire wood, charcoal , wild plants and so on (Ellis, 2000): These are common means of livelihood for the poor in the study area.

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Table 25. Income composition of sample HHs

Cash income composition (%)

Total (N=120)

Livestock

Poor (1) (N= 51) 11.7

Wealth categories Less poor (2) Better off (3) (N=42) (N=27) 27.5 42.1

Crop

36.5

41.7

44.1

39.8

Agriculture sub total

48.2

69.2

86.3

64.1

Petty trade6

17.7

11.9

5.4

12.9

Remittance

0.94

2.3

6.5

2.9

Rural craft7

10.5

6.7

1.1

7.0

Non-farm sub total

29.14

20.9

13

22.8

Gathering

6.7

3.2

0.1

4.2

Wage

15.7

3.7

0.2

7.9

Hire/rent

0.4

2.4

0.2

1.0

Off-farm sub total

22.8

9.3

0.5

13.1

398.4

1122.5

525.2

417.3

1160.

727.9

22.04

33.71

160.4

22.04

1890

2107

4270.0

4270.0

Mean annual income 313.4 per AE SD 406.8 Minimum Maximum F

24.3

14.604

p-value

0.000***

***, significant at < 1% probability level Source Own survey, 2007

6 Petty trade items involve cereals, coffee, ginger and livestock which are bought on a market day and are sold on the same or another market day or at another place. Group discussion also revealed that there were several part-time trading farmers who bought various consumer items such as salt, soap, lamp, spices and clothes from distant areas and sold them to the local community; food items such as brad and kocho are also traded in local market during market days 7 Rural crafts in the study include pottery, bamboo work, carpentry, blacksmiths, tannery and weaving.

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To allow further understanding of income portfolios, analysis of mean income of each activity has advantages. From Table 26 below, the mean income from crop sale, livestock and livestock product sale, and own subsistence consumption values increases along the wealth continuum from poor to better off households. Whereas, that of the off farm activities increase in the opposite direction towards the poor households and the contribution made by off farm activities seems more important to the poor households. Hence crop production, livestock production and remunerative non-farm activities favour the better off households. There is also statistical difference at < 1% probability levels between poor, less poor and better off households with respect to income generated from own production (Table 26).

In addition to the various cash income streams, the data collected on incomes also included the value of food produced and directly consumed by each household. Since the subsistence income is one of the more straight forward pieces of information that provide viable insights in to differences in circumstances across wealth groups and it tells the ability to buffer households food security through self consumption (in line with the objectives of the study). Therefore, the role of subsistence in rural livelihoods in the study area can be further defined by looking at the mean value of own consumption across different wealth groups. The survey data in this regard showed that the poor groups have the lowest subsistence income than the two wealth groups (415.430<898.826 and 1438.916 Birr per HH) at < 1 % probability level (Table 26). Here less poor and better off households were distinguished by their substantially higher reliance on subsistence consumption than the lower wealth group. This further implies that the poor households are unable to buffer household food demands from own production and must diversify to off/non-farm to balance the demand.

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Table 26. Mean income from each activity by wealth groups Wealth category (Mean income in Birr per HH) Poor (1) Less poor (2) Better Off F (3)

P-value

467.627

757.000

3291.815

14.237

.000***

Livestock

154.980

456.476

2046.204

24.546

.000***

Off farm

208.667

119.750

128.148

0.539

0.585

Non farm

220.235

412.262

422.963

1.88

0157

Subsistence

415.430

898.826

1438.916

20.497

0.000***

Source of income Crop

***, **, significant at < 1 and 5% respectively Source: Own survey, 2007

4.2.2. Diversity of livelihood strategies The aforementioned income portfolio analysis proved that, households incomes normally derive from more than one source. This recognition corrects the conventional portrayal of rural households as depending on farm income, which in fact is but one of potentially many income sources. Utilizing income portfolios in order to evaluate the livelihood strategies fails to capture the relative level of participation in each activity. One possible way of getting out of such difficulty is to find a summary statistics that captures both income shares and participation shares in single figure that can be compared across wealth groups. In this regard, diversity indices are proved to be useful (Ellis, 2000).

Diversity refers to the existence, at a point in time of many different income sources. The diversity of agriculture itself, or on farm diversity, is also a dimension of importance in rural development policy, and this can be explored at an appropriate point (Ellis, 2000). In this study, rural livelihood diversification simply describes the phenomenon by which rural households take up off/non-farm activities or rely on off/non-farm income along the mainline agriculture for the overall standard of living that they are able to achieve. The extent of such diversification within or away from agriculture may be an indicator of the degree to which 103

farming operations, on its own, can provide a secure and improving livelihood. Thus, where diversification is widespread and the share of livelihood portfolios to which it corresponds is considerable, it may be supposed that farming is for one reason or another unable to satisfy those basic requirements.

In this study, diversity indices were used to come up with participation and income shares of each household from each livelihood activity, diversity index was used and then the statistics were summarised by household type using the mean and standard deviation (Table 27). Mathematically, the diversity index (inverse of market concentration index) can be formulated as

IMCI =

1 n

∑

xi 2

…………………………………………… (8)

I =1

Where, IMCI is the Inverse of Market Concentration Index xi 2 is the square of proportional contribution to total income of each activity

Income diversity is particularly relevant to developing economies. There is a growing awareness that the traditional approach equating rural areas with agriculture in much development thinking is, and probably always was, false (Bezemer and Lerman, 2002). 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. Diversification rises with wealth or income in both absolute and proportional terms in rural Africa (Barrett et al., 2001). Much as risk preferences and differential access to wealth likely contribute to

greater demand for ex ante diversification by poor people, so too are the poor more likely to diversify ex post as a coping response to shocks. They simply have less ability to self-insure through cashing in non-productive assets than do the relatively wealthy (Barrett et al., 2001). Similar to this view the diversity score for the poor, less poor, and better off households respectively, was found to be 2.51, 3.69, and 5.9, and this is inline with finding of Ellis (2000) for most sub Saharan African countries and Berhanu (2007) for southern Ethiopia. Block and Webb (2001) also found that wealthier Ethiopian households tended to have more diversified

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incomes and that those with initially more diversified incomes also had a greater increase in both income and calorie intake.

The diversification index summarized in Table 27 indicates that out of the total sample households the poor wealth category has the lowest diversity, indicating the bottlenecks hindering the poor households in diversifying their livelihood options to avert risks and it is also an indication of lack of livelihood flexibility.

Table 27. Diversity indices of SHHs by wealth category Wealth category

Diversity index Mean SD

Poor (1) 2.51 0.779 0.29

Less poor (2) 3.69 0.646 0.23

Better off (3) 5.9 0.626 0.172

Total 4.2 0.683 0.255

Source: own survey, 2007

4.2. 3. Specialization of livelihood strategies The problems identified with income portfolios and diversity indices may possibly be covered by grouping the data into more homogeneous groups, and compiling mean portfolios that describe observable group strategies. This approach classifies each household according to a typology of livelihood strategies and replaces mean income portfolios with a proportional measure of the distribution of households between different types. This method illustrates which type of strategies are being followed by most people in each group, and as such has the potential to offer better guidance for the type of support for the poorest households that the livelihoods framework seeks to address.

Thus, households were classified according to the proportion of total household income that is derived from one single source that is from, crop, livestock, and non farm and off farm

105

activities (Ellis, 2000). The cut point used was whether they obtained more than three quarters of their total income from a single activity.

Table 28, presents more accurate picture of degrees of specialization than the mean diversity indices shown above, with results that can be generated to a wider population. The table indicates that, more than 44.5% of the household receive more than 75 % of their total income from crop production alone, 31.8% from livestock, 14.6 from off farm and 13.0% from non farm sources. From these figures one can grasp that more households in the better off category than the counter parts have specialization than diversification strategies. The poor who principally depends on off farm and non farm income source are 9.8% and 5.9% respectively. Off/non-farm employment/activities are increasingly becoming important especially for low and medium social classes which have implications on rural livelihood. This result suggests that interventions to address the poor in the study area need to pay attention to off farm and non farm employment opportunities.

Table 28. Distribution of SHHs by income from singe source Wealth category (%)

Income source (> 75 % of total income)

Poor (1)

Less poor (2)

Better off (3)

Total

(N=51)

(N=42)

(N=27)

(N=120)

Principally crops

11.7

14.3

18.5

44.5

Principally livestock

0

5.9

25.9

31.8

Principally off farm

9.8

4.8

0

14.6

Principally non farm

5.9

7.1

0

13.0

Source: own survey, 2007

Based on income analysis so far, now it is possible to draw on the broad classification of livelihood strategies. Since broad categorization of livelihood strategy is important to guide policy (Scoones, 1998). The same source indicated that a household located in a particular context and economy may choose between (or be constrained from choosing) three main clusters of livelihood options agricultural intensification and extensification, income

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diversification, and migration. Accordingly, the most common livelihood strategies in the study area were agriculture, agriculture plus off farm and agriculture plus non farm activities. Out of the total SHHs, 34.5% households derive their livelihoods from agriculture alone (which encompasses crop production and livestock breeding), 19.57% HHs combined agriculture and off farm activities, 33.3% HHs combined agriculture with non farm and 13.3% HHs diversified livelihood into off farm and non farm activities (Table 29). In comparison, very few households in the sample chose the diversified livelihood strategy. The fact that households’ distribution in these livelihood strategies differ suggests the possibility of significant barriers to adoption of the most remunerative livelihood strategy.

Table 29. Livelihood strategies pursued by SHHs Livelihood Strategies8 Wealth category Poor (%) Less poor (%) Better off (%) Total

AG

AG+OFF

AG+NF

AG+OFF+NF

9.50

17.30

13.00

2.50

10.80

14.30

3.33

6.70

14.20

1.70

3.24

3.33

34.5

33.3

19.57

12.53

χ2/ p-value

22.423 (0.001***) ***, significant at < 1 % probability level Source: Own survey, 2007

4.2. Food Security Statuses as an Outcome of Livelihood Strategies

Food security, as we have heard over and over, is an issue of income: either income in the form of one's own production of food or income earned from activities that might be related to agriculture or not and used to gain access to food through the market. What all we talk about 8 Priory, the study assumed migration as one of the dominant livelihood strategies in the study area. However, both the survey data and focus group discussion didn’t contend to the expectation. The focus group discussion result informed that migration as a livelihood strategy was pursued during Derg regime where young people migrate to Awash, Arbaminch and Bilate National farms in search of wage.

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poverty in Africa, is about food security. That is why our poverty measure strongly depends on the reference of fulfilling the minimum daily requirement of food (Gervais et al., 2003). To build a complete picture of how people survive the need is to make sure that we can account for how they meet their minimal survival needs. In line with this argument, in the present study food security is defined as the extent to which a total household expenditure per AE meets its subsistence requirement. For the purpose, total expenditure/AE was used since expenditure is typically preferred over income as it reflects households’ ability to meet their basics (Tesfaye, 2003).

Thus, a food poverty line, a threshold level of consumption expenditure below which an individual is considered to be food insecure was established (establishing the poverty line starts with defining and selecting a "basket" of food items typically consumed by the lowest income quartile in the study area and the quantity of the basket is determined in such a way that the given food basket meets a predetermined level of minimum calorie requirement9 This basket is valued at local average prices to sketch the food poverty line)

The minimum expenditure for food items basically consumed by the lowest income quartile in the study area was found 395.3 Birr per AE and that of non food component was Birr 107.97 per AE (Table 30 and 31 respectively), which gives a threshold of 503.1 Birr beyond which the household is food secure. The proportion of households with an average total expenditure per AE, which is less than the minimum level, is 74.2 %. If the state of food security had been limited to attainment of the caloric requirement, only 395.3 Birr per AE would have been required per AE per year and about 65.8 % would not meet the minimum requirement.

The composition of food poverty indicated that; 78.6% of the household consumption expenditure belongs to food, which is above the national average, (67 %, MoFED 2002) and the rest 21.4 % is that of non food. A high proportion of the budget being allocated to basic food consumption is still an indication that people in rural areas are food insecure (MoFED, 2002). The important point to note is that the very poor spend almost all their income on food. 7 The pre determined level of minimum calorie requirement for use in Ethiopia is based on a basket providing 2200 kcal per adult equivalent per day (MOFED, 2002). This study made use of the same approach in order to measure the food security status of the SHHs

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Table 30. Food poverty for SHHs based on the lowest income quartile

Food items

*Mean

Gram

Kcal

Kcal per Kcal

Kcal

consume

consumed day per

per

d per AE

per day

gram

per day

per AE

AE

Mean

Value of Expen

share

price

poverty

diture

(%)

per kg

line per

share

(Birr)

year

of food Maize

3.45

302.4

1043.3

1420

64.6

1.37

205.8

52.1

Sweet potato

1.37

122.5

167.80

228.5

10.4

0.27

16.40

4.2

Enset (Kocho)

2.11

118.0

248.90

338.9

15.4

0.50

29.30

7.4

Taro

1.03

109.0

112.30

152.8

6.90

0.60

32.50

8.1

Coffee

1.10

12.00

13.200

17.90

0.80

160

95.40

24.1

Salt

1.70

18.00

30.600

41.70

1.90

1.78

15.90

4.1

2200

395.3

Source: Own survey, 2007 and * Appendix Table 3

Table 31. Subsistence non-food expenditure Expense type

Mean value of expenditure (ETB)

Health care

21.00

Clothing and foot wear

43.78

Schooling and stationary

22.15

Social and religious

10.86

Land tax

10.00

Total

107.79

Source: Own survey, 2007

The consumption expenditure analysis showed that, the mean per capita consumption expenditure of the sample households during the study period is found to be 335.00 Birr per AE that of food secure households is 606.06 per AE which is more than double of that of the food insecure which is 240.59 Birr per AE. The mean difference between annual consumption

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expenditures of the two groups is significantly different at a probability level of less than 1% (Table 32).

The food security status of SHHs for the wealth groups shows that there is statistically significant difference at less than 5 % probability level. Closer examination of the result shows that, there are members of the better off groups who have been reported to be food insecure (5%) and members of the poor group to be food secured (3.33 %), (Table 32). The observable reasons for such result were the contribution of demographic variables such as dependency ratio and family size which were reported to be large for the better off groups than the poor that have contributed for the outcome. Such out come may not be attributed to the weakness of quantitative wealth ranking since it doesn’t capture demographic variables that might affect the food security status of households in the process.

Concerning the sample PAs food security status is statistically different among the four PAs at < 5 % probability level with relatively more number of food secure households reported in Achura (> 50 % of the SHHs from that PA were food secure) which probably be due to Ginger cash crop in the area in which almost all SHHs from that PA were involved in. Where as, the largest proportion of food insecure households were found in the highland Afama Mino (> 80 % were found food insecure) which attributed to the fragmentation of land holding due to population pressure.

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Table 32. Summary statistics of food security status of SHHs

Expenditure range (AE) <200

Food security status (%) Total Food Food insecure secure 27.5 0 27.5

201-400

38.3

0

38.3

401-600

8.33

18.33

26.7

601-1434

0

7.5

7.5

Below 503.1

74.2

25.8

100

Below 395.3

65.8

34.2

100

Mean

240.59

606.06

335.00

SD

101.94

178.05

203.68

Wealth status

40.82 (0.000***)

Poor (1)

39.2

3.33

42.5

Less poor(2)

26.7

8.33

35.0

Better off (3)

5.0

17.5

22.5

Sample PAs

11.350 (0.010**)

Yukara

11.7

5.8

17.5

D/Madalcho

9.2

6.7

15.9

Achura

10.8

11.7

22.5

36.7

7.5

44.2

Afama Mino

χ2 / t (p-value) -13.93 (0.000***)

***, **, significant at < 1 %, and 5 % probability level respectively Source own survey, 2007

4.2.1. Causes of household food shortage

The food situation in sub-Saharan Africa is continuing to deteriorate as a consequence of multiple calamities such as drought, occasional flooding, and decline in soil fertility, increasing pests and diseases, land scarcity, and poor market access, coupled with discouraging policy environments. The effect is visible as recurring food shortage. The food

111

security situation in Ethiopia has been extremely precarious for some eight million people due to the combination of environmental, socio-political and developmental instabilities.

In order to identify the major perceived causes of food shortages, the sample households were asked to respond to each question set for this purpose by rating as first, second and third causes of food deficit. The farmers rated shortage of oxen (76.7 %) as the most influential of all factors under consideration followed by lack of farm input (75.8 %), and land shortage (65.8 %) as causes of food shortage (Table 33). In general, the traditional farming practice and poor performance that have greatly affected the sustainability of production and productivity coupled with the shortage and erratic rainfall have made the study area more vulnerable and food insecure. Insect and pest infestation is another important biological factor that has been negatively affecting and limiting agricultural production in the study area.

Table 33. Causes of food shortage by SHHs Food Security status (%) Causes of food shortage

Food insecure

Food secure

Total

Shortage of oxen

59.17

17.5

76.7

Lack of farm input

60

15.8

75.8

Market problems

7.5

1.7

9.2

Land shortage

63

13.3

65.8

Crop pest and diseases

52.5

5.0

30.8

Livestock disease

4.0

1.7

5.8

Source: own survey, 2007

4.2.2. Months of food shortage

The survey result ensured that the mean months of food shortage for better off is smaller than that of the poor, i.e. 5.4 for the former and 8.1 for the later. Almost all the poor face food shortage during 3-11 months of the year (Table 34). The official months of food shortage include, January, February, March, April, May, and June, and called food aid months. The

112

statistical test of chi square output showed that there is significant relation between the number of months a household faces food shortage and wealth status at less than 1% probability level.

Table 34. Number of food shortage months by wealth category Number of months

Wealth category of the household (%)

Total

of food shortage

Poor (1)

(N=120)

(N=51)

Less poor (2)

Better off (3)

(N=42)

(N=27)

0-3

12.5

25.0

62.5

6.7

4-7

28.8

36.5

34.6

43.3

8-11

58.3

35.0

6.7

50.0

Mean

8.176

7.238

5.444

7.233

SD

2.04

2.39

2.63

2.52

F

25.598

p-value

0.000***

Source: Own survey, 2007

4.2.3. Coping strategies of SHHs during food shortage

Coping strategies are short term mechanisms that households use for dealing with food shortage. The term “coping strategy” is sometimes used to describe people’s responses to shocks. In fact, most poor households already exploit to some extent all the economic opportunities open to them, and it is rare for completely new opportunities to be available. As a rule of thumb, if people are attempting an activity which they do not normally do (such as migration to another location in the hope of finding work or relief), this indicates that they are already in severe economic difficulty. Population pressures, together with the shrinkage of land size and hence of opportunities for gathering, made it much harder for poorer rural households to subsist on their own production, much less to build savings and reserves. Therefore, exploring households coping strategies would help in developing intervention 113

measures that households can easily adopt and sustain in the future. Coping strategies against the household food insecurity frequently facing farmers have potential influence on farmers' decision making on allocation and management of available resources, which will have implication on livelihood strategy choice.

In line with this, the present study identified that the food insecure households coping mechanisms were receiving relief food aid (29.2%), livestock sale (25%) and credit in kind and cash (17.5). whereas, the major coping mechanisms for the food secure households’ includes; livestock sale (12.5%) and receiving grain and cash credit (6.7%), (Figure 8). According to Frankenberger (1992) this finding will suggest that those households who used cash/grain credit, food aid and sale of livestock to cope food shortage are still highly vulnerable to food insecurity. Thus, measures to be taken involve the need for relief food aid. Whereas, households who accessed relatives support and started wage work need to strengthen their capability to reverse the situation in the long run.

35 29.2

30

25 25 20

17.5

15

Food Secure (FS) Food Insecure (FI)

12.5

10

8.3

6.7

5

1.7

0

3.3

5

0 Food aid

Cash/ Livestock Grain credit sale

Relative support

Source: Own survey, 2007 Figure 7. Coping strategy of SHHS during food shortage

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Wage work

4.3. Viability of Livelihood Strategies to Achieve Food Security

In line with the objective of the study further exploration of livelihood strategies in terms of viability in achieving livelihood outcome of the rural households is important. In order to capture these differences in returns due to different livelihood strategies further analysis is important. The most apparent livelihood outcomes considered in the analytical framework of this study were increased income and achieved food security. This would help to relate the income earned to the subsistence requirement. Analysis of the mean difference between the income source and food security status shows that the food secure and insecure groups statistically differ only in crop income and value of own product consumed (Table 35). It was observed from the survey results that crop production is the most important source of income in the study area followed by livestock production and off-farm activities, respectively. Table 35. Mean income by food security status Food secure

Food insecure

Mean income (Birr)

(N=82)

(N=38)

Overall

t-test (P-value)

Crop income

1992.7

839

1204

4.849 (0.030**)

Own consumption value

1375.99

554.88

814.90

37.209 (0.000***)

Income from livestock

860.41

605.21

686

0.869 (0.353)

Off farm income

97.43

188.20

159.43

1.074 (0.302)

Non farm income

309.71

343.90

333.06

0.098(0.754)

Average land size (ha)

0.54

0.41

0.45

2.809 (0.090*)

Livestock holding (TLU)

3.9

2.1

2.7

16.370 (0.000***)

Asset endowment

***, **, *, significant at less than 1 %, 5 % and 10 % probability level respectively. Source: Own survey, 2007

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4.4. Econometric Analysis of Determinants of Livelihood Strategies

Multinomial Logistic Regression Model was used to identify determinants of livelihood strategies. The model was selected based on the justification illustrated earlier. Therefore, in this section, procedures followed to select independent variables (continuous and dummy) and results of logistic regression analysis conducted to identify determinants of livelihood strategy choice by rural households is presented.

4.4.1. Detecting multicollinearity and degree of association

Before running the analysis, it is necessary to check for the existence of multi-collinearity among the continuous variables and verify the degree of association among discrete variables. The reason for this is that the existence of multi-colliniarity will affect the parameter estimates seriously.

Therefore, following Gujarati (2004), multicollinearity problem for continuous explanatory variables was assessed using a technique of Variance Inflation Factor (VIF) and Tolerance Level (TOL) where each continuous explanatory variable is regressed on all the other continuous explanatory variables and coefficient of determination is computed. Thus, a measure of multicollinearity associated with variance of inflation factor is defined as:

VIF (Xi) = (1-Ri2) –1---------------------------------- (9) Where R2 is the coefficient of determination when the variable Xi is regressed on the others explanatory variables. TOL (Xi) = (1-R2i ) --------------------------(10) Where, TOLi = Tolerance level of i explanatory variable R2i = Coefficient of determination of i explanatory variable

116

The larger is the value of VIF the more troublesome is the multicollinearity or collinear is the variable (Xi). If the VIF of a variable exceeds 10 (this will happen if R2 exceeds 0.90) that variable is said to be highly collinear. Similarly, TOLi approaches to one when the variable (Xi) is not correlated with other repressors

In order to see the degree of association between dummy or discrete variables contingency coefficient were computed. According to Healy (1984) contingency coefficient is a chi-square based measure of association where a value 0.75 or above indicates a stronger relationship between explanatory variables. The contingency coefficient is computed as follows:

c =

X 2 N+X

2

……………………………………. (11)

Where C= coefficient of contingency, χ2= chi-square test and N=Total sample size

Accordingly, the contingency coefficient, which measures the association between various discrete variables were computed for eight discrete variables in order to check the degree of association among the discrete explanatory variables and there were no any problems of association (Table 36). Similarly, the values of the VIF for seven continuous variables were found to be small (i.e VIF values less than 10) indicating the data have no serious problem of multicollinearity. As a result, all the seven continuous explanatory variables were retained and entered into the multinomial logistics analysis (Table 37).

117

Table 36. Contingency coefficients of discrete variables Variables

SEX

EXT ENS

AGRO ECO

INP UT

COOPE R

LEADE R

CREDIT

REMI TA

SEX

1

0.132

0.043

0.144

0.064

136

0.067

0.002

1

0.112

0.311

0.179

0.211

0.131

0.137

1

0.411

0.370

0.281

0.518

0.198

1

0.368

0.468

0.264

0.038

1

0.223

0.432

0.060

1

0.258

0.124

1

0.032

EXTENS AGROECO INPUT COOPER LEADER CREDIT

1

REMITA Source: own survey, 2007

Table 37. Tolerance level of continuous variables Variables

TOL

VIF

AGE

0.661

1.512

FAMLLY

0.665

1.505

EDUCAT

0.763

1.311

LAND

0.605

1.652

LIVESTOK

0.496

2.015

MKTDIST

0.841

1.189

DEPRATIO

0.934

1.071

Source: Own survey, 2007

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Table 38. Definition of model variables

Dependent variable Livelihood strategies

Variables definition and unit of measurement if the choice of the HH lies in

Y=0, AG

Agriculture alone

Y=1, AG+OFF

Agriculture and off farm combination

Y=2, AG+NF

Agriculture and non farm combination

Y=3, AG+OFF+NF

Agriculture, off farm and non farm

Independent variables AGE Age of Household Head in years SEX

Sex of Household Head (1= Female, 0= Male)

EDUCAT

Education level of Household Head in years

FAMILY

Family Size of the household members in number

AGROECO

Ecology of the household (0= midland, 1= high land)

LAND

Land size owned by the Household in Hectares

LIVESTOK

Livestock hold by the household in tropical livestock unit (TLU)

INPUT

Farm input use by the Household (0= No, 1= Yes)

EXTENS

Frequency of extension contact a farmer has with extension agent in a year

COOPER

Participation of the household in cooperatives (0=No, 1= Yes)

LEADER

Leadership participation of the Household Head (0=No, 1=Yes)

CREDIT

Credit use by the household (0= No, 1= Yes)

MKTDIS

Distance of the nearest market from dwelling in kilometre

REMITA

Economic support to the household (0= No, 1= Yes)

DEPRATIO

Dependency ratio of the household

4.4.2. Model results

Under this section important household capital variables and institutional factors which were hypothesized to influence rural households’ choice of livelihood strategies were identified and

119

analyzed using multinomial logit model. The analysis was made by statistical software LIMDEP version 7. The model result is presented in (Tables 39, 40 and 41).

Table 39. Multinomial logit regression of AG + OFF livelihood strategy choice

Variables

Coeff.

Std.Err.

t-ratio

P-value

Marginal effects

ONE

5.409

2.318

2.333

0.019

0.551

SEX

-1.901

1.008

-1.884

0.059*

-0.248

AGE

-0.061

0.045

-1.338

0.180

-0.003

EDUCAT

-1.002

0.384

-2.603

0.009***

-0.079

FAMILY

0.063

0.207

0.304

0.761

0.014

AGROECO

-0.489

1.048

-0.466

0.641

-0.073

LAND

-4.099

1.853

-2.212

0.026**

-0.436

LIVESTOK

-0.280

0.212

-1.319

0.186

-0.025

INPUT

1.017

1.057

0.962

0.335

0.048

EXTENS

1.553

0.912

1.702

0.088*

0.171

COOPER

1.180

1.329

0.888

0.374

0.046

LEADER

0.227

1.055

0.215

0.829

0.086

CREDIT

-1.311

1.139

-1.150

0.249

-0.156

MKTDST

-0.018

0.193

-0.093

0.925

-0.013

REMITA

0.864

1.143

0.756

0.449

0.042

DEPRATIO

0.180

1.606

0.112

0.910

-0.089

***, **,* Significant at <1%, 5% and 10% probability level respectively. Source: own survey, 2007

120

Table 40. Multinomial logit regression of AG + NF livelihood strategy choice Variables

Coeff.

Std.Err.

t-ratio

P-value

Marginal effects

ONE

2.449

1.842

1.329

0.183

0.121

SEX

-0.016

0.697

-0.023

0.981

0.156

AGE

-0.081

0.038

-2.137

0.032**

-0.014

EDUCAT

-0.831

0.336

-2.470

0.013**

-0.114

FAMILY

-0.158

0.168

-0.939

0.347

-0.054

AGROECO

0.495

0.911

0.543

0.586

0.209

LAND

-1.511

1.091

-1.383

0.166

-0.003

LIVESTOK

-0.143

0.160

-0.897

0.369

-0.005

INPUT

1.107

0.905

1.223

0.221

0.143

EXTENS

0.694

0.747

0.928

0.353

0.061

COOPER

1.353

0.985

1.373

0.169

0.171

LEADER

-0.526

0.896

-0.587

0.556

-0.091

CREDIT

-0.108

0.885

-0.122

0.902

0.106

MKTDST

0.177

0.153

1.157

0.247

0.045

REMITA

0.901

0.905

0.995

0.319

0.108

DEPRATIO

2.151

1.280

1.680

0.092*

0.550

***, **,* Significant at <1%, 5% and 10% probability level respectively. Source: own survey, 2007

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Table 41. Multinomial logit regression of AG+OF+NF livelihood strategy choice Variables

Coeff.

Std.Err.

t-ratio

P-value

Marginal effects

ONE

4.497

2.974

1.511

0.130

0.151

SEX

-1.582

1.215

-1.302

0.192

-0.074

AGE

-0.046

0.056

-0.810

0.417

-0.000

EDUCAT

-0.721

0.499

-1.443

0.148

-0.012

FAMILY

0.507

0.264

1.918

0.054*

0.033

AGROECO

-2.533

1.309

-1.934

0.053*

-0.157

LAND

-3.702

1.969

-1.879

0.060*

-0.140

LIVESTOK

-0.428

0.259

-1.653

0.098*

-0.019

INPUT

2.194

1.209

1.814

0.069*

0.093

EXTENS

0.109

1.051

0.103

0.917

-0.027

COOPER

2.977

1.337

2.226

0.025**

0.132

LEADER

-1.953

1.310

-1.490

0.136

-0.106

CREDIT

-1.945

1.170

-1.662

0.096*

-0.099

MKTDST

-0.096

0.210

-0.460

0.645

-0.010

REMITA

1.982

1.183

1.675

0.093*

0.087

DEPRATIO

-2.228

1.909

-1.166

0.243

-0.187

Maximum Likelihood Estimates Dependent variable Number of observations

Livelihood strategies 120

Log likelihood function

-117.7325

Restricted log likelihood

-158.2096

Chi-squared

80.95423

Degrees of freedom

45

Significance level

0 .000

***, **,* Significant at <1%, 5% and 10% probability level respectively. Source: own survey, 2007

122

4.4.3. Interpretation of econometric results

Sex of household head (SEX): Sex was hypothesized to affect choice of livelihood strategy since men and women have differentiated social roles in the community. Gender affects diversification options, including the choice of income-generating activities (both farm and non-farm) due to culturally defined roles, social mobility limitations and differential ownership of/access to assets (Galab et al, 2002). In the study, as expected sex of household

head is found to negatively and significantly (< 0.05) influences diversification into off farm activities. This means female-headed households (FEHHs), tend to participate less in off-farm activities. Keeping the influence of other factors constant; the likelihood of FEHHs choice of agriculture and off farm livelihood strategy decreases by 24.8 %. The opposite is true for the male counterparts. This result is in agreement with previous studies conducted by Adugna (2005) and Berhanu (2007). This implies that female headed households have difficulty of participation in off farm activities because of cultural barriers.

Age of household head (AGE): As expected, this variable was found significant (p<0.5) to negatively influence farmers decision to diversify to non farm activities while performing the livelihood domain agriculture, which implies that farmers participate in non-farm activities at a decreasing rate as they age. From Table 40, it can be seen that the likelihood of a HH simultaneous choice of agriculture and non farm activities decreases by 1.4 % with increasing age. The possible reason is that farmers whose age is relatively younger, leaving other factors constant, could be pushed to engage more in non-farm activities than agriculture alone. This is because, younger farm households cannot get enough land to support their livelihood compared to the older farm households. Therefore the younger households have to rely more on non-farm income than the older ones to support their livelihood. This result is congruent with previous studies by Barrett et al, (2001); Destaw, (2003), Rao et al., (2004); Adugna, (2005); Mulat et al., (2006), Berhanu (2007), and Khan (2007).

Educational level of household head (EDUCAT): Education increases farmers’ ability to get involved in more remunerable livelihood activities. Educational attainment proves one of the most important determinants of non farm earnings, especially in more remunerative

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salaried and skilled employment in rural Africa (Barrett et al, 2001). Education is critical since the better-paid local jobs require formal schooling, usually to completion of secondary school or beyond. Contrary to prior hypothesis, this variable has a negative and significant (p<0.01) and (p<0.05) influence on the decision of the household head participation in off and non farm activities respectively. In other words, participation in off-farm and non-farm activities and low levels of education among sample HH heads were found to be positively associated, suggesting that household heads with more years of education may have realized the low return and decided to work on agriculture. The possible explanation is that the average education achieved (which is below primary level) in by the sample households is not sufficient to be formally employed and educated farmers do not find skill demanding livelihood option in the study area. The result is in line with the findings of Galab et al, (2002), Berhanu (2007) and Khan (2007), but in contradiction with the findings of Barrett et al., (2001); Destaw (2003).

Livestock holding (LIVESTOK): This variable was expected to influence the choice of livelihood strategies by the household positively because the farmer will depend more on agriculture than diversifying since livestock can be source of both food and income. Families who are more dependents on livestock production than crop production may give less attention to off-farm activities. In line with prior expectation, livestock holding in TLU negatively influence household’s choice of AG+OFF+NF livelihood strategy at less than 10% probability level. That means the farmer with lower livestock holding would be obliged to diversify livelihoods into off and non farm in order to meet needs. In the study the likelihood of diversifying livelihoods into off and non farm activities decrease by 1.9 % for households with more livestock number in TLU. The result is in line with the findings of Tesfaye (2003) ,Berhanu (2007) and Khan (2007).

Family size (FAMILY): In line with expectation, family size was found to have positive and significant relation to diversification of livelihood strategies into AG + OFF + NF at < 10% probability level. The positive correlation between family size and diversification might be due to the relation between larger family size and household labour or corresponding higher demand for food in the household which implies that while an additional member to the

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household increases the odds to participate in agriculture plus off farm plus non- farm activities in order to meet basic needs to the family. This means, one extra person in the household increases the likelihood of diversifying livelihoods by 3.3 %. In other words, additional family member decreases the odds to work only on farming. This finding is similar to that of Bezemer and Lerman, (2003), and Khan (2007).

Agro-ecology (AGROECO): As expected, this variable has a negative and significant (P<0.10) correlation with the likelihood of choosing agriculture and off farm livelihood strategy. This means the tendency that the household diversify livelihoods into agriculture plus off farm plus non farm increases as we go from high lands to midland. Hence, the probability of diversifying into agriculture plus off farm and non farm drops by 15.7 % for highland households. The result is in line with that of Jansen et el., (2004). This might be due to differences in the quality and size of land, the amount and distribution of rainfall and population densities that influence between highlands and midlands. For instance, climatically the later is wormer than the former.

Land size owned (LAND):- As hypothesized, the area of land owned by the household has a significant (P<0.05 and p<0.10) and negative correlation with the likelihood of choosing AG+ OFF and AG+OFF+NF respectively. The results of this study suggest that rural households with more land tend to follow agricultural extensification rather than diversifying from agriculture since they draw incentives of land productivity. This implies the chances of choosing agriculture in the context of having large land size decreases the probability of diversifying to off farm and non farm activities by 43.6 % and 14.0 % respectively. On the other hand the probability of diversifying livelihoods decreases by increasing land size as farmers with more land supposed to stay on farm since land stimulates farming. Increased role of off/non farm activities such as selling labour, part-time wage employment, petty trading, especially for poor and less poor households with less land holding and other necessary resources, signify how households respond to a decreasing ratio of farm size to household. This supports the view that off-

farm and on-farm activities compete over the limited household resources. It also implies that those households who expect secured agricultural income stay on farm and lower off-farm intensity. Lanjouw and Lanjouw (1995) also found out that landholdings per capita are

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negatively correlated with participation in low productivity occupations. This result is in line with that of Berhanu (2007), Mulat et al., (2006) and Khan (2007). The implication is that farmers just switch away from off-farm activities when the farm activity is promising; and hence, this supports the necessity argument as opposed to the choice argument. Farmers consider offfarm activities as a last resort income source if crop production fails.

Frequency of extension contact (EXTENS): This variable has a positive and significant (p<0.10) correlation with the likelihood of choosing agriculture and off farm livelihood strategy instead of sustaining on agriculture alone. Keeping other factors constant; the likelihood of participation in agriculture and off farm, increases by 17.1 % for those who have gained frequent extension contact than the counterparts. The objectives of extension is to change farmers outlook towards their difficulties which assists them adapt better solution to their livelihoods (Samuel, 2001).Thus, the information obtained and the knowledge and skill gained from extension organization may influence farmers’ skill and decision making on seeking diversification. The frequent extension contact received will increase the tendency of household to participate in off farm activities. This may be also explained by the factors that the message/contents that farmer gain from extension agents help them to initiate to use risk aversion strategies that seek diversification of income within and out agriculture.

Credit use (CREDIT): Contrary to expectation, credit use is found to have a significant (p< 0.05) negative impact on the likelihood of choosing diversified livelihood strategy which combines agriculture, off farm and non farm. This implies that, the likelihood of participating in diversified livelihood strategy by the household drops by 9.9 % for a household using credit. This negative impact may be attributed to the fact that credit use allows farmers to follow agricultural intensification by accessing farm inputs which in turn improves productivity. This more implies that the formal and informal credit facilities that avail for rural farmers are a very important asset in rural livelihoods not only to finance agricultural inputs activities, but also to protect loss of crucial livelihood assets such as cattle due to seasonal food shortage, illness or death (Tesfaye, 2003). The result of the study, therefore, strongly suggest that farmers’ access and use of credit would play important role in promoting agricultural development rather than diversification. The result is also in agreement with that

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of Holden et al., (2004); Brown et al, (2006), Berhanu (2007), and Khan (2007). This implies that the incentive for accessing credit accelerates agricultural production.

Dependency Ratio (DEPRATIO):- As hypothesized, dependency ratio is found to have a significant (P<0.10) positive correlation with choice decision of agriculture and non farm livelihood strategy. This indicates that with increase in dependency ratio the ability to meet subsistence needs declines and the dependency problems make it necessary in the household to diversify their income source (Khan, 2007. Households with higher dependency ratios follow less remunerative non-farm livelihood strategies (Jansen et el., 2004). This means when the dependency ratio increase, the ability of farmers to meet family needs decrease and chance of diversifying livelihood to non farm activities increases. If the dependency ratio increases by one the probability of the household’s falling into agriculture plus non-farm livelihood strategy increases by 55%. The policy implications of this pattern seem clear, a need to address rapid population growth as well as the provision of job opportunities for adult labour. This result is inconsistent with that of Warren (2002); and Rao et al., (2004).

Inputs use (INPUT): Contrary to expectation, use of chemical fertilizer and HYVs was found to be positively and significantly affect the rural households’ decision to choose agriculture plus off farm plus non farm livelihood strategy at <10% level of significance. The probable reason for this is that due to improvement of productivity through farm input use the farmers might go for petty trading and other non farm activities. This suggests that those who are better-off can afford to buy fertilizer/ HYVs and those who are poor may not. As a result, those who use fertilizer /HYVs may produce more per unit area than non-users and can have access to large quantity of food and diversify income sources for accumulation.

Membership to cooperatives (COOPER): This variable as hypothesized was found significant (<0.05) to positively determine choice of livelihood strategy towards agriculture plus off farm plus non farm activities by 13.2 %. That means the household who participate in cooperatives will diversify livelihoods into off and non farm since cooperatives promote access to social capital in which off/ non farm options are gained. Culturally appropriate forms of social capital also appear to have the potential to aid rural income generation and reduce vulnerability to income shocks. As group discussants revealed, cooperation in the form 127

of credit unions, producer organizations, women credit association for milk and better, and churches have positive effects on the income generating capacity of their members and, through production linkages, on the wider local economy in the study area. The result is in line with that of Warren (2002) and Bezemer and Lerman (2002).

Receiving remittance (REMITA): Rremittance refers to money sent from inside and outside the country. As expected, the multinomial logit model identified this variable as it had positive contribution to the diversification of livelihood strategies apart from agriculture to off and non farm at significance of <10 % probability level. This meant that, the likelihood of a household receiving remittance increase choice of diversification into off farm and non farm activities by 8.7 %. The result is in consistent with the findings of Bezemer and Lerman, (2002) and Brown et al, (2006). Although remittances constitute only a small part of total household income on average, they appear important for keeping rural households diversify activities.

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5. SUMMARY AND POLICY RECOMMENDATIONS

This chapter is the last section of this thesis and it has two sections. In the first section, summary of the objectives, research methodology, and key findings of the model were presented. In the second section, useful policy recommendations were devised based on the finding of the study.

5.1. Summary

There is no problem of underdevelopment that can be more serious than food insecurity that has an important implication for long term economic growth of low income countries. Ethiopia has been plagued with food insecurity for decades. The problem is worsening, despite massive resources invested each year into humanitarian aid and food security programs. Food insecurity in the long run may cause irreparable damage to livelihoods of the poor, thereby reducing self-sufficiency. Rural poor on their part struggle to ensure food security status by participating in diversification activities. The contribution made by livelihood diversification to rural livelihoods has often been ignored by policy makers who have chosen to focus their activities on agriculture. For the purpose this study employed the livelihood analytical framework that guides the research process since livelihoods approaches have the advantage of placing the poor at centre stage, and of exploring aspects of their livelihoods which are commonly neglected. The study was therefore, conducted with the specific objective of examining determinants of choices of livelihood strategies in the context of achieving food security in Boloso Sore district of Wolayta zone, southern Ethiopia.

The research objectives were realized through conducting household survey on 120 randomly selected households from four PAs of the study area. Two stages stratified sampling procedure was used to select the sample households. Household livelihood asset variables, income, expenditure and other data deemed to be relevant were collected, organized, analyzed and interpreted to come with possible results. 129

The analysis employed both descriptive statistics and econometric methods. Descriptive statistics were employed to describe household livelihood asset variables across wealth categories. As implied by the wealth ranking exercise, asset holding is very unevenly distributed across households where only 22.5% were endowed with large cultivable land and livestock ownership. Thus , it is insufficient to conclude from the foregoing that raising farm output would help the poor the most; it has to be borne in mind that the poor also have the least access to land, and thus efforts directed at raising food crop yields will benefit the already well-off even more than it does the poor. Multinomial logit model was specified and estimated to identify determinants of choices of livelihood strategies by rural households. Household expenditure survey was used for the computation of food poverty line as basis to meet basic subsistence needs.

The descriptive statistics showed the existence of a significant mean difference between wealth categories in family size, educational status, land size, agro ecology, livestock holding, input use, leadership participation, cooperatives participation, credit use, and extension contact at various level of probability. The sample households were classified into food secure and food insecure groups based on expenditure value of meeting recommended daily allowance (RDA) of 2200 kcal. Accordingly, the cost of basic need poverty line which was constructed based on data from the lowest income quartile was 503.1 ETB per adult equivalent (AE) per year. This line was then used as a threshold in which above values declare success of food security and food insecurity otherwise. The proportion of households with an average total expenditure per AE, which is less than the minimum level, is 74.2 %. If the state of food security had been limited to attainment of the caloric requirement (only 395.3 Birr per AE per year would have been required), about 65.8 % would not meet the minimum requirement.

The result of the multinomial logistic regression model revealed that out of 15 variables included in the model, 13 explanatory variables are found to be significant up to less than 10% probability level. Accordingly, sex of household head (<0.05) education level of household head (< 0.01), land size (<0.05) were found to have negative association with agriculture plus off farm livelihood strategy. Where as, extension contact (<0.10) was found

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to be significant and positively influence households choice of agriculture plus off farm livelihood strategy. Meanwhile, age of household head, education level of household head negatively determine choice of agriculture plus non farm activities at < 0.05 probability level. Dependency ratios, on the other hand, positively affect the same strategy at < 0.10 probability level. In the case of diversified livelihood strategy, i. e. agriculture plus off farm plus non farm, agro-ecology (<0.10), land size (<0.10), livestock holding (<0.10), credit use (<0.10), were found significant and affect choice of this livelihood strategy negatively. Input use (<0.10), cooperatives membership (<0.05), receiving remittance (<0.10), family size (<0.10), were found to affect the choice of similar livelihood strategy positively..

Based on the present study it is possible to conclude that the constraints of the rural households in choosing livelihood strategies that will lead them achieve food security goal should not be put aside since food security problem cannot be overcome by simply concentrating on the farm sector alone; intersectoral issues and farm and non-farm linkages need to be addressed as well. Moreover, the contribution made by non-agricultural sector to rural households is a significant; although for the poor these activities are survival oriented and have little to do with wealth accumulation.

5.2. Recommendations

Understanding livelihood assets and determinants of choice of livelihood strategies would help policy makers to design and implement more effective policies and programs for the poor and there by helps to pave way to improve food security. In this respect, this study provides a base and point of departure for similar studies in the future. Therefore, the following recommendations were made in order to benefit those who need to intervene with the issue under consideration.

Household livelihoods are highly diverse. Policy-makers need to reflect on the most suitable ways of supporting this diversity. Only with more appropriate policies that recognize the importance of diversity will it be possible for more people to make positive exits from food

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security risk through diversity. The key finding of the study was that diversification across income sources helps households to combat instability in income and thereby increases the probability of their maintaining livelihood security, specially the poor and the overwhelming experience of diversification is as a coping strategy for the poor.

Any attempt to intervene the community need to target specific groups of societies such as female headed households, wage workers, petty traders, the food insecure, the poor, the midlanders or the highlanders. The intervention strategy should have a needs identification to address both the basic needs as well as the needs that arise from wealth category specific constraints. Mechanisms are needed to ensure that the concerns of the poor are reflected in public policies and required to bring these groups into the very center of policy making processes. The fact that the result of the study ensured more than 74.2% households to be food insecure demand development intervention strategies that enable immediate survival during emergency times as well as to promote disaster recovery and increase shock absorbing capacity of the food insecurity vulnerable households.

Sticking to the findings of this study, the contribution made by income from crop and the value of own consumption was found significant and substantial in achieving food security. This implies that efforts has to be made to improve income from cash crops production (Ginger and coffee) to ensure food security through promotion of input use and marketing facilities.

The poor are not merely producers but also wage labourers and consumers; extension should promote technologies not simply geared to increased production, but which are contextually sensitive to potential tradeoffs between productivity (especially labour productivity), increased employment opportunities and reduced vulnerability, doing so in ways which increase the ‘voice’ of poor people.

Family size was found to be directly related with wealth and household livelihood diversification. The main case behind is that as family size increase there is no means of accessing more land to cultivation to meet the demand of large family size. More over,

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majority of the family of the SHHs were in young age group which is an indication of high fertility rate in the area. With these scenario, having more household size aggravate the problem of meeting food leave alone education, health and other non – food demands of household that will bring future return. Thus, affirmative action based awareness creation on the impacts of population growth at the family and community level should be strongly advocated that lead to reduction in fertility and lengthen birth spacing resulted in smaller household size.

The substantial effect of education on household livelihood strategy choice for each type of livelihood strategies confirms the significant role of the variable in consideration for betterment of living condition. The fact that, the average years of education achieved by sample HH heads is below primary level it has no more incentives to involve the household head in more remunerative activities since better jobs demand more than this level. The more household head educated, the higher will be the probability of participating in more improvement in agriculture and less deemed to diversify livelihood strategies which in turn improves the welfare of that household. Therefore, strengthening both formal and informal education and vocational or skill training should be promoted to increase rural households awareness of more viable livelihood options in its locality and improve decision making skill.

Livestock sub sector plays a great role in the struggle to eliminate food insecurity. Its contribution to the household food energy requirement and total income is significant. Hence, necessary effort should be made to improve the production and productivity of the sector. This can be done through the provision of adequate veterinary services, improved water supply points, introduction of timely and effective artificial insemination services to up-grade the already existing breeds, launching sustainable and effective forage development program, provision of training for the livestock holders on how to improve their production and productivity, improving the marketing conditions, etc

The result showed that off farm and non farm incomes make an important contribution to household cash incomes (23%), and that the proportion of cash income from off farm activities is larger for poorer wealth groups. In this regard, interventions that enhance off farm

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activities in sustainable manner need to be designed. Therefore the rural development strategy should not only emphasis in increasing agricultural production but concomitant attention should be given in promoting such activities in the rural areas.

The agricultural sector of the district is characterized by land scarcity and increasing fragmentation of already very small farms, shortage of draught animals and lack of adequate grazing land. To this affect, the farming economy is not in a position to feed and sustain the increasing population of the area. This implies that the non-farm sector has to be developed to absorb more of the growing population. Thus, support to diversification away from precarious livelihood strategy (agriculture) towards sustainable alternatives whose returns are not correlated with land - possibly agro-industry, education, and ginger marketing help to shift some proportions of farmers from direct reliance on land for their livelihoods and enhancing use of technologies. To meet this goal human capacity development through training on agricultural business and expanding off-farm activities is imperative.

Culturally appropriate forms of social capital (cooperatives) also appear to have the potential to aid rural income generation and mitigate food insecurity. Support to local NGOs, credit unions, producer organizations, organizing wage labourer associations, and other groups may have positive effects on the income generating capacity of their members and, through production linkages, on the wider local economy.

The policy to promote adoption of credit to stimulate adoption of high yielding varieties and fertilizer use has not been very successful in the study area. Farmers were reporting that they failed to pose the later due to the absence of the former. Thus, enhancing and expanding rural credits to subsistence farmers in the district should be one of the primary areas of intervention and policy options. Rural credit service can help farmers in solving capital problem to buy farm oxen, modern farm inputs, use for trade, and further enhancing use of technologies etc. Therefore, access to credit on low interest must be ensured if poor people are to be afforded the chance of engaging in economic livelihood strategy.

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Technology application gap is highly influenced by the level of input price. This study has shown that an increase in input price has impeded rural households from using. Therefore, attention is needed on farmers’ financial capacity.

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Hoddinott, J., 2002. Food security in practice: Methods for rural development projects. International Food Policy Research Institute. Washington, D.C. Holden, S., Bekele, S., and Pender, J., 2004. Non –farm income, household welfare, and sustainable land management in a less favoured area in the Ethiopian highlands. Department of Economics and Resource Management Agricultural University of Norway. Homewood, K, Coast, E., Kiruswa, S., Serneel, S., Thompson, M.,and Trench, P., 2006. Maasai Pastoralist: Diversification and Poverty. A policy Research Conference, 27-28, June 2006.ILRI London. Hussein, K., and Nelson, J., 1999. Sustainable Livelihoods and Diversification. IDS Working Paper 69. London: Institute of Development Studies. Hussein, K.,2002. The Relevance of Livelihoods Approaches To Food Insecurity Measurement,ODI IIRR, 1998. Sustainable agriculture Extension manual for Eastern and Southern Africa. International Institute of Rural Reconstruction, Nairobi, Kenya. Jansen, H., Damon, P., A., John, P., Wielemaker, W., and Schipper, R., 2004. Policies for sustainable development in the hillside areas of Honduras: a quantitative livelihoods approach International Food Policy Research Institute (IFPRI), Central America Office, Washington, DC, USA Kaluski, D.N, Ophir, E and Tilahun A., 2001. Food security and nutrition – the Ethiopian case for action, Israel. Kanji, N., MacGregor, J., and Tacoli, C., 2005. Understanding market-based livelihoods in a globalising world: combining approaches and methods. International Institute for Environment and Development (IIED). Khan, M. A., 2007. Factors Affecting Employment Choices in Rural Northwest Pakistan. Conference on Internatyonal Agricultural research for Development. University of Gottingen, Germany. Kidane Gebremariam, 2001. Factors Influencing the Adoption of New Wheat Verities, in Tigray, Ethiopia: The Case of Hawizen District. MSc Thesis, Alemaya University, Alemaya. Kollmair, M., and Gamper, S., 2002. The Sustainable Livelihoods Approach: Input Paper for the Integrated Training Course of NCCR North-South Aeschiried, Switzerland.

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Krishna, A., 2000. Creating and Harnessing Social Capital. In Dasgupta, P. And I. Serageldin, Social Capital: A multifaceted perspective. The World Bank, Washington, DC. Lanjouw, J.O., and Lanjouw, P., 1995. Rural non farm employment: Policy research working paper 1463 Lautzke, S; Aklilu, Y; Raven-Roberts, R; Young, H; Kebede, G and Leaning, J., 2003: Risk and Vulnerability in Ethiopia: Learning from the Past, Responding to the Present, Preparing for the Future. A report for the US Agency for International Development (USAID). Little, P. D., 1997. Income and assts as impact indicators: University of Kentucky Washington, D.C. Lovendal, R.C., Knowles, R., and Horii, N., 2004. Understanding Vulnerability to Food Insecurity: Lessons from Vulnerable Livelihood Profiling, ESA Working Paper No. 04-18. The Food and Agriculture Organization of the United Nations.

Maxwell, S., and F. Frankenberger, 1992. Household Food Security: Conceptual indicators and measurements; A technical review. UNICEF, New York and IFAD, Rome. Maxwell, D., 1995. Measuring food insecurity: The Frequency and severity of copping strategies. International Food Policy Research Institute, USA, Washigton, D.C Maxwell, S., 1996. Food Security: A Post-Modern Perspective. Food Policy.21 Maxwell, D., B. Watkins, R. Wheeler and D. Sheikh, 2002. The coping strategy index: A tool for rapidly measuring food security and the impact of food and programs in emergencies. Field methods manual. World Food Program. Meser, N. and Townstey P., 2003.Local institutions and livelihoods: Guideline for analysis. Rural Development Divisions, FAO of the united nation, Rome Migotto, M., B. Davis, G. Carletto, and K. Beegle, 2005. Measuring Food Security Using Respondents’ Perception of Food Consumption Adequacy; Agricultural and Development Economics Division the Food and Agriculture Organization of the United Nations MoFED. 2002. Ethiopia: Sustainable Development and Poverty Reduction. Addis Ababa, Ethiopia. MoFED, 2006, Ethiopia: Building on Progress: A Plan for Accelerated and Sustained Development to End Poverty (PASDEP) (2005/06-2009/10) Volume I: Main Text

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Moser, C., 1998. ‘The asset vulnerability framework: Reassessing urban poverty reduction strategies’. World Development 26 (2). Moti Jaleta and Gardebroek, C., 2008. Crop and Market Outlet Choice Interactions at Household Level. Eth. J. Agrc. Econ. 7 (1), 29-49 Mulat Demeke, Fantu Guta, Tadele Ferede, 2006. Issues in Employment and Poverty Towards a more employment-intensive and pro-poor economic growth in Ethiopia: Discussion Paper 22. Issues and policies Employment Strategy Department, International Labour Office, Geneva. Norton, A., and, Foster, M.., 2001. The Potential of Using Sustainable Livelihoods Approaches in Poverty Reduction Strategy Papers, Working Paper 148. Centre for Aid and Public Expenditure ODI, 111 Westminster London, UK. ODI, 2003. Understanding Livelihoods in Rural India: Diversity, Change and Exclusion,UK. Rahman M. H., Firdissa T., Bwalya B., Lund T. and Ghulam R. 2007. Livelihood Diversification in Rural Unganda: Impact of Africare’s Development Activities on the Livelihoods of Nyabyumba Community. Int. J. Sustain. Crop Prod. 2(6):36-43 Rajadel, T., 2003. The Engagement in the Non-Agricultural Sector as a Risk-Mitigating Strategy in Rural Pakistan, Paris France. Rakodi, C., 1999. A capital assets framework for analysing household livelihood strategies: Implications for policy, Development Policy Review 17 (3). ODI, London. Reardon, T, Delgado, C and Matlon, P, 1992. Determinants and effects of income diversification amongst farm households in Burkina Faso; Journal of Development Studies 28. Roa, J., Niehof, A., Price, L., and Moerbeek, H., 2004. Food Security through the Livelihoods Lens: an integrative approach (the case of less favoured areas in the Philippines); /http://www:sls wau.nl/mi/response/Rao.pdf./ date accessed, December 2008. Samuel Gebre-Selassie, 2001. The Development of Integrated Management Information Systems for Agricultural Extension Institutions of Developing Countries: The case of Oromia Agricultural Development Bureau of Ethiopia, Shaker Verlag. Samuel Gebre-Sellassie, 2003. Summary report on recent economic and agricultural policy. Paper prepared for the Roles of Agriculture International Conference 20-22 October, 2003 Rome, Italy.

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Tesfaye Kumbi, 2005.household Food Insecurity in Dodota-Sire District, Arsi Zone: Coping Strategies and Policy options. A Thesis Presented to the School of Graduate Studies Alemaya University Tilahun Amede, Stroud, A., and Aune, J., 2004. Advancing Human Nutrition without degrading land resources through modelling cropping systems in the Ethiopian highlands (crop yield and nutrient coposition of major crops grown in Boloso Sore (Areka) and Ginci. Food and Nutrition Bulletin vol 25 no 4. The United Nations University. UNDP, 1998. Developing SL indicators: Reviewing lessons learned and a framework for action. (http://www.undp.org/). Date accessed 0ctober, 2006. UNDP, 2000. Update on the Current Humanitarian Situation in Ethiopia Produced by the United Nations Country Team, Office of the UN Resident Coordinator and Office of the Regional Humanitarian Coordinator. /www.telecom.net.et/~undp-eue / Date accessed August 2008. United Nations World Food Programme (WFP), 2004. Baseline Food Security Analysis in Iraq, WFP Iraq country office. Wagayehu Bekele, 2004. Analysis of Farmers’ Preferences for Development Intervention Programs: A Case Study of Subsistence Farmers from Eastern Ethiopian Highlands, African Development and Poverty Reduction, the macro-micro linkage policy paper, South Africa. Warren, P., 2002. Livelihoods Diversification and Enterprise Development: An Initial Exploration of Concepts and Issues. Rome: FAO. World Bank. 1986. Poverty and Hunger: Issues and Options for Food Security in Developing Countries. A World Bank Policy Study, Washington, D.C. World Bank, 1995. Rural Non-farm Employment: A Survey Background paper for World Development Report. Washington, D.C. Wolayta zone, BoFED, 2003. Socio- economic profile of Wolayita Zone. Wolayta Zone, BoFED, 2005. Socio- economic profile of Wolayta Zone. Young, H., Jaspars, S., Brown, R., Frize, J., and Khogali, H., 2001. Food-security assessments in emergencies: a livelihoods approach, Humanitarian Practice Network (HPN); London, UK.

145

7. APPINDICES

146

Appendix Table 1. Conversion factors used to estimate Tropical Livestock Unit (TLU) Type of livestock

TLU

Type of livestock

TLU

Chicken

0.013

Shoat (adult)

0.13

Calf

0.25

Cow and Ox

1

Weaned calf

0.34

Donkey (young)

0.35

Heifer

0.75

Donkey (adult)

0.7

Shoat (young

0.06

Source: Storck, et al. (1991) Appendix Table 2. Conversion factors used to calculate adult equivalent (AE) Age <10 10-13 14-16 17-50 >50 Source: Storck, et al. (1991)

Female 0.60 0.8 0.75 0.75 0.75

Male 0.60 0.9 1 1 1

Appendix Table 3. Crop yield and nutrient composition of Major crops grown in Areka and Gnchi Food items

Energy (kcal)

Food items

Energy (kcal)

Enset (kocho)

2111

Teff

1620

Taro

1038

wheat

2220

Pumpkin

249

Barley

2020

Sweet potato

1370

Kalea

401

Irish potato

840

Common bean

1700

Pea

2071

Sorghum

2360

*Maize

3450

Faba bean

2759

*Salt

1700

*Coffee

1103

Source: Tilahun et al., (2004); *EHNRI, (2000)

147

Appendix Table 4. Wealth ranking criteria’s set by local informants Asset owned

Wealth categories Poor

Less poor

Better off

(Duriyaa)

(Giddo Hiyeessa)

(Hiyeessa)

Oxen

0-0.5

0.5-1

1.5-3

Cows

0-0.5

0.5-1

1.5-6

Heifers

0.0.5

0.5-1

1-4

Calves

0.0.5

0.5-1

1-4

Sheep

0.0.5

1-2

3-6

Cultivated land

0.01-0.25

0.36-0.60

0.63-2.5

Source: Community wealth ranking exercise, 2007 Source: Own survey, 2007

Boloso Sore (22 PAs)

Midland From 18 PAs 3 PAs

Highland From 4 PAs 1 PA

Poor (n=32)

Less poor (n=15)

Better off (n=6)

Poor (n=19)

120 HHs

Appendix Figure 1. Sampling procedure

148

Less poor (n=27)

Better off (n=21)

Appendix Table 5. Interview schedule for sample respondents

LIVELIHOOD STRATEGIES AND FOOD SECURITY IN WOLAYTA, SOUTHERN ETHIOPIA: THE CASE OF BOLOSO SORE DISTRICT

Serial number of the questionnaire---------------Interviewers name __________ Date of interview__________ signature ______

Entry: •

Good morning/ afternoon sir/madam? I am so glad that I met you

•

Thank you very much for your commitment to meet me respecting our appointments devoting your precious time

•

The objective of this interview is all about academic. It has no any administrative values and/ or will not used for decisions that might affect your personal life. Thus, be confidential and open in your responses

• In answering to the following questions, please, stop me at any point for more clarity if need arises

1) General information

Name of the PA __________________________ Name of HH head _________________________ Sex of household head_____________________ Age of household head _____________________ Education level of household head ______________ Household size__________ Agro-ecology____________(0=midland, 1= highland)

149

2) Household Characteristics

Name No

of HH

Sex

Health

Age in Marital

Relations

Education

Main

years

hip

level

occupation

Religi

(activity)

on

status

memb

to HH

ers

head

status

1 2 3

Codes for sex: 1= male 0= female Codes for marital status: 1= Married, 2=Divorced 3= Widow 4= Widower 5=Single Codes for relationships: 1 = wife, 2 = Son, 3 = Daughter, 4 = Grand Father, 5= Grand mother Codes for main occupation: 0= no occupation, 1= daily laborer, 2= agriculture, 3= Schooling, 4= Trading, 5= handicrafts, 6= others Codes for religion: 1=orthodox, 2=protestant, 3=Muslim, 4 =Catholic 5=other_____________ Codes for health status: 1=Ok 0= Sick

2.1. Are there any absent household members? ........................................ 1= Yes; 0=No 2.2. If yes, why are they absent? 1) Seasonal labour migration 2) education 3) staying with family elsewhere 4) Start Own household 5) other specify__________________________

3) Health Status and Facilities

3.1. Do you have human health facilities in your community? 1= Yes; 0=No 3.2 How far do you travel to get the health services? ____________Km. 3.3. Do you have any sanitation facilities? 1= Yes; 0=No 3.4. Has any one in your home been seriously sick during the last one year?

150

1= Yes; 0=No 3.5. If yes, how many of your family members were got sick? ________ 3.6. What were the diseases that affect your family? ____________________ 3.7. At what time of the year is these sicknesses are worse? _____________________ 3.8. Who do mostly affected by these sicknesses in the household? 1= children; 2= wife and daughters; 3= husband; 4= elderly; 5= others(specify) _____________ 3.9 Is the problem disease changed over time? 1= less severe; 2= not changed; 3= severe; 4= got worse 3.10. Is any one died from your family members during the last one year? 1= Yes; 0=No, 3.11. If yes, indicate the age, sex and reasons for the death. Person died

Age

Sex

Causes for death

3.12 How did you manage helping the sick person? 1. Did nothing 2. Took to traditional healer 3. Took to health facility 4. Bought drugs from the shop 5. Others (specify) __________ 3.13. Do you have access to clean and protected drinking water in your vicinity? 1= Yes; 0=N 3.14. If no, what is the source of your drinking water? 1. Traditional well 2. Aquifers in the sands of riverbeds (unprotected) 3) Ponds 4) Springs 5) Others (specify)____________________ 3.15. How far you travel to fetch water? ________________ Km.

4) Farm Tools and assets ownership 4.1 What type of house do the household owned? 1) Mud walls and grass roofed 2) Grass walls and grass roofed 3) Mud walls and galvanized iron (korkoro) roofed 3) Other (Specify) ______________________________

151

5.2. List the type of implements use and value if fetched at local price No

Types of implements

Quantity

Use

5) Land use and farm characteristics

5.1 Do you own land?

1= Yes; 0=No

5.2 If yes, answer the following question

Plot Size Ownership How do

Years

No

acquired

(ha)

you

slope fertility Use Crops

Soil

grown conservation

acquired?

practice


Codes for ownership style:- 1=own 2=rented 3=shared
Codes for how did the household acquire the land? 1) Land distribution 3) Purchase


Codes for slope 1) Flat

2) Inheritance 4) Other (specify)__________________ 2) Gentle 3) Steep slope


Codes for soil fertility status 1. Fertile

2. Moderately fertile 3. Infertile

4.Other specify___________________________


Codes for land use 1) cultivation 2) grazing 3) forest 4) fallow 5) degraded/ non usable 5. 3. The trend of crop production during the last five years? 1= Decreased; 2= the same; 3= increased 5.4. Was what you produced last year enough for your family? 1= Yes; 0=No 5.5. If no, for how long could it last? ___ Months.

152

5.6.

During

which

months

is

food

shortage

severe?

________

Month

5.7. How does the household cover the food shortage? 1= Purchase of grain from market; 2= Food / cash for work ;( food aid) 4= support from relatives and friends; 5= Cash credit to be replaced in kind during harvest; 6=Grain credit to be replaced in kind during harvest; 7= others, specify________________________ 5.8. If relief food is a means to fill the deficit for how long have you been getting food aid? 1) ___ Years 2) ___ months per year 5.9. If relief food is a means to fill the deficit, indicate the amount of food aid your household received in the past five years? Type of food/aid

Unit

2002/3

2003/4

2004/5

2005/6 2006/7

1) __________________ ___________ _______ ______ ______ _____ _________ 2) __________________ ___________ _______ ______ ______ _____ _________ 3) __________________ ___________ _______ ______ _______ ____ _________ 5.10. Did any of you work on other people’s farms in exchange for food? 1= Yes; 0=No 5.11. What do you think are the main causes of food deficit in order of importance? 1) Absence of adequate rainfall 2) Insect or pest infestation 3) Shortage of cultivated land

4) Poor quality of land

6) Poor health situation of the farmers’

5) Animal disease 7) Flood

8) Shortage of oxen 9) Shortage of input supply (seed, fertilizer and animal feed) 10) Transport and marketing challenges

6) Livestock Ownership

6.1. Do you own domestic animals? ........... 1= Yes; 0=No. If ‘yes’: Go to form: 6.2. Animal form

Type

No. owned in Use the last 12 months

Reason for sale

Chickens

153

(s)

Goats Sheep Donkeys Cattle Mule Horse Cow Ox Others Use of livestock: 1) Meat 2) Manure 3) Milk 4) eggs 5) saving 6) Animal traction Reason for sale: 1) To purchase agricultural inputs

2) To pay taxes and other debts 3) To

Purchase food 4) to purchase clothes 5) Social obligations

6) To purchase farm oxen

7) To construct house 8) others (Specify) ________________________________ 6.3. Did you own more animals in the past? 1) Yes 0) No 6.4. If yes to question number 6.3 what are the reasons for livestock decline 1) Draught 2) Disease 3) livestock sale 4) other __________________ 6.5. If you do not have enough oxen, how do you get additional oxen you need? 1) Hire from someone 2) Coupling with other farmers 3) Borrow from friends 4). Exchange with labor 5) others (specify) _______________ 6.6 Do you use livestock sharing? 1=yes, 0= No 6.7 If yes, which kind of livestock do you share and for what purpose? No

Kind of livestock

Number

Purpose

shared

6.8. List the major problems in livestock production in the area in order of importance? 1= Feed problem; 2= Water problem; 3= Health problem 4= lack of veterinary service; 5=lack of improved breeds; 6= inadequate Artificial Insemination service; 7= lack of working capital; 8= others specify__________________________

154

7) Use of Modern Agricultural Inputs

7.1 Did you use any agricultural technologies for example fertilizer, high yield variety, chemicals, etc for the last 12 months? 1=Yes, 0=No 7.2 If yes, give details

Name of agricultural technologies

Quantity used

Unit price

Total price

Sources

Fertilizer: Dap Urea Improved Seed (HYVs) Maize Teff Haricot bean Chemicals

7.3. If yes for how many years on average have you been using these technologies? ______Years 7.4 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 more__________________ 7.5 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 timely 3) inadequate supply 4) lack of transport

8. Agricultural Extension Services

8.1. Is there development agent in your PAs? ____ 1= Yes; 0=No 8.2. 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) Sometimes 6) Other (specify)_____________________ 8.3 What were the purpose of this visits ___ (Multiple answer is possible). 1) To give advice on crop production 2) To give advice on animal production

155

3) To give advice on soil conservation 4) to collect taxes 5) to collect other debts 6) Other (specify) _________________________________ 8.4 Did you get any training from extension organization Yes=1; No=0 8.5 If yes, specify the kind of training________________________________

9) Membership to cooperatives 9.1 Do you or member of your family participated in any formal cooperatives? 1= Yes; 0=No 9.2 If yes, would you mention the name of the cooperatives? __________________________ _______________________________ ________________________

_______________________________

9.3 What benefits did you gain by being membership of such cooperatives? 1) Income increased 2) labour Shared 3) credit used

4) others specify________

9.4) If no, what is the probable reason 1) No information 2) No interest 3) No cooperatives in my PA 4) other specify__________________________

10) Social leadership participation

10.1 Did you participate in any social leadership in the past 12 months? 1= Yes; 0=No 10.2) If yes, specify among the following 1) traditional cooperatives like Iddir and Equb 3) Religious 4) political 4) kebele administration 5) any other 10.3 If yes, what benefit do you gained from the leadership role? 1) Salaried 2) social prestige 3) Access to assets

4) specify any

11) Credit use

11.1 Do you face problem of working capital? Yes=1, No =0 11.2 Have you received any type of credit in 2007? 1= Yes; 0=No 11.3 If yes fill the following table

156

Source borrowed

Purpose borrowed

Amount borrowed

Interest amount paid

11.4. Codes for purpose (s)

Amount Paid/returned Birr

4) Purchase of oxen

1) Purchase of seeds

5) Purchase of farm implements

2) Purchase of fertilizer

6) For consumption

3) Purchase of chemicals

7) For social obligation

11.5. Codes for the sources of credit? 1) Service cooperative

2) Commercial banks

3) Development banks

4) Friends and relatives

5) Micro finance institutes

6) Local moneylenders

7) NGOs

8) Others______________

11.6. If no why? (Multiple answers are possible) 1) Fear of ability to pay

4) High interest rate

2) Lack of asset for collateral

5) No need for credit

3) No one to give credit

6)

Others

(specify)

____________

11.7 Do you have saving habit? 1. Yes 2. No, if yes amount saved last year ___ birr or --Qt

12) Market access

12.1 Is there a nearby market place? _________ 1= Yes; 0=No 12.2 The distance of nearby market from your residence is ____________Km. 12.3. Where do you sell your farm products? (Multiple answer possible) 1) On farm (local assembler)

2) Taking to the local market

3) Through service cooperatives

4) others (specify)

157

12.4. What means of transport do you use to transport your produce? 1) Trucks

2) Animal power

3) Human power

4) Others_________

12.5. When do you sell most part of your produce? _____________ Months 12.6. What are the problems in marketing your products? 1) Transportation problem 2) Too far from market place 3) Low barging power 4) Low price of Agricultural produce 5) other specify 12.7. Do you get reasonable price for your produce at this particular time? ______ 1= Yes; 0=No 12.8 If no, what are the reasons? (Multiple answerers possible) ______ 1) No (demand) for the produce

2) More supply of the produce

3) Lack of access to potential market

4) others (specify)

12.9. Why did you sell at that particular time of lower (unreasonable) price? _____ 1) To settle debts

2) To pay tax

3) Social obligations (wedding, funeral, iddir, etc) 4) To meet family requirements

5) others (specify)

12.10. What do you think should be done to solve this problem? _______

13) Migration Strategy

13.1. Have any members of this household left the area for over a month in the past years? 1= Yes; 0=No: If ‘yes’: Go to migration form; 13.2 Migration form Name of migrant

Destination

Time interval (months)

Activity motivation

/

13.3. (If only one or several household member has left the area in the past five years, then ask :) Could you describe the household situation (food/labour/cash) in years that s/he (or you) left the area?

158

1) Increased income

2) Better employment 3) improved food access 4) food shortage

6) Low income 7) no employment 8) other___________________________________

13.4. Has the importance of migration and remittances from migrant for the household 1) Increased 2) Decreased 3) stayed the same over time 13.5. In general do you believe that migration is better alternative to escape from food shortage? 1=Yes, 0=No 13.6 If yes, justify your reason ________________________________________ ________________________________________

14) Participation in Wage Labour

14.1. Do you do casual labor work inside your PA? 1= Yes; 0= No 14.2. If yes, which types of activities you are involved in?

Activity

Where? place of

Days worked (2007)

Wage per day

Work

Male

Male

Land preparation planting Weeding and cultivation Harvesting; Domestic work in town (Araka); Construction work; Others

159

Female

Female

14.5 The access of daily labor is better 1) with in the PA 2) outside the PA

3) similar

15.6 The wage rate of Daily labourer is perceived 1) extremely low 2) low 3) medium 4) High 5) very high 15.7 Who decides the amount of wage 1) employer 2) wage labourer

3) negotiation 4)

specify if other________________________________

16) Family Networks

16.1 Do you have relatives in the village? 1= Yes; 0=No, IF Yes; 16.2 Do you help each other with farmland/ or other work? 1= Yes; 0=No 16.3 Do you give or receive food to/from these relatives? ............. 1= Yes; 0=No 16.4 Do you give or receive cash to/from these relatives ............... 1= Yes; 0=No 16.5 Have these forms of mutual aid in the past ten years 1) Increased 2) decreased 3) stayed the same over time 16.6 Do you have relatives outside the village............................... 1= Yes; 0=No 16.7 Do you help each other with farmland/ or other work?.......... 1= Yes; 0=No 16.8 Have these forms of mutual aid for the past ten years 1) Increased 2) decreased

3) stayed the same over time?

17) Income Generating Activities

17.1 What are the main sources of your income? 1) Sale of livestock 2) Sale of livestock products and by-products 3) Sale of cash crop 4) sale of staple crops 5) petty trade 6) remittance 7) handcraft 8) others (specify) ________________________________

160

17.2. Income from crop production Crops

Total harvest

Amount

Total Amount Sold

grown

(Kg/Qt)

Consumed (Kg/Qt) (Kg/

Unit

Total income in

Qt)

price

Birr

17.3 Income from livestock live sale Type of

Income generated (Birr)

No. sold in the last 12 months

livestock owned

Slaughtered During the last one year (No.)

17.4 Income from livestock product sale

Type of

Quantity sold in the last 12 months

livestock product Milk Eggs Meat Butter

161

Income generated (Birr)

Skin

17.5 Value of livestock product consumed at home (What were the quantity and type of food you produced or got from livestock you have for household consumption for the last one year?)

Livestock output

Quantity (lt/ Kg)

Value at local price (in Birr)

Milk Eggs Meat Butter

17.6. Income from Off farm activities Description of activities

Annual income (in Birr)

Remark

Sales of fire wood/ charcoal Rent of land and pack animals Sale of labour (Agricultural wage ) Others

17.7. Income from non-farm activities Description of activities

Annual income (in Birr)

Handcrafting Petty trade Remittances Other

162

Remark

17.8 Has the number of income sources for your household been 1) increased 2) decreased 3) Stayed the same over time (describe the trend)? 17.8. Which types of combination of livelihood activities do appear to you best working to bring more income to your household? (Rank them in order of importance) 1 _______________________________ 4.______________________________ 2________________________________ 5. _____________________________ 3________________________________ 6. _____________________________

18. Household Expenditures

18.1 Consumption expenditure (the quantity and type of food you purchased from market during the year 2006/7 for the household consumption ) Food stuff

Quantity (Kg/

Total expenditure in Birr

Quintal) Maize Teff Wheat Sorghum Sweet potato Irish potato Enset (Kocho) Milk Meat Eggs Oil Salt Coffee Butter Others

163

19.2 Non food expenditure

Annual expenditure(Birr)

Remark

Clothing (dress and foot wear) School and stationary fee Health care Religious& cultural expense Government tax Transport cost Sub total

Thank you very much for your cooperation!

164