Smallholder Farmers’ Participation In Off-Farm Activities. The Case Of Bambasi Wereda, Benishangul Gumuz Regional State, Ethiopia


Master's Thesis, 2020

88 Pages, Grade: 4.00


Excerpt

TABLE OF CONTENTS

Contents Pages

DEDICATION

ACKNOWLEDGMENTS

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

LIST OF ACRONYMS AND ABBREVATIONS

ABSTRACT

CHAPTER ONE
INTRODUCTION
1.1. Background of the Study
1.2. Statement of the Problem
1.3. Research Questions
1.4. Objectives of the Study
1.5. Significance of the Study
1.6. Scope of the Study
1.7. Organization of the Study

CHAPTER TWO
LITERETURE REVIEW
2.1. Theoretical Review
2.1.1. Definition of Terms
2.1.2. Motives of Livelihood Diversification
2.1.3. Overview of Livelihood Diversification in Africa
2.1.2.Theoretical Framework of Modeling Selection Bias
2.2. Empirical Studies
2.3. Conceptual Framework

CHAPTER THREE
METHODOLOGY OF THE STUDY
3.1. Description of the Study Area
3.2. Sampling Design
3.2. Sample Size Determination
3.3. Method of Data Collection
3.4. Methods of Data Analysis
3.4.1. Models Specification
3.5. Assumption in Treatment Effects Estimation

CHAPTER FOUR
RESULTS AND DISCUSSION
4.1. DESCRIPTIVE STATISTICS
4.1.1. Demographic Characteristics of Off-farm Activities Participants
4.1.2. Gender and Marital Status Distribution of Respondents
4.1.3. Distribution of Assets
4.1.4. Farm Land Possession and Market Distance
4.1.5. Types of Off-farm Activities in Bambasi Wereda
4.1.6. Work Places of Off-farm Activities and its Motives
4.1.7. Distribution of Income Among Participants and Non-participants
4.1.8. Empirical Estimation Procedure and Hypothesis Testing
4.2. ECONOMETRIC ANALYSIS
4.2.1. Heckman Sample Selection Model Estimation
4.2.2. Determinants of Off-Farm Participation Decision
4.2.3. Intensity of Off-Farm Participation
4.2.3. Impact of Off-farm Participation on Income of Smallholder Farmers
4.2.4. Measure of impact of off-farm participation on income

CHAPTER FIVE
CONCLUSIONS AND POLICY IMPLICATIONS
5.1. SUMMARY
5.2. CONCLUSIONS
5.3. RECOMMENDATIONS AND POLICY IMPLICATIONS

REFERENCE

APPENDICES

DEDICATION

This thesis is dedicated to my father, Misgana Gaddissa, and mother, Sili Dhaba for their assistance and continues prayer; and my wife Abaynesh Melaku including my whole family for their financial and mental support and also their encouragement to accomplish this paper successfully. Keeping this in heart, I truly appreciate all of them for supporting me towards attaining the Master of Science in Development Economics.

ACKNOWLEDGMENTS

First of all, I acknowledge the Almighty God through his son Jesus Christ, who saved and inspired me to pursue a master’s degree in Developmental Economics as well as giving me endurance to withstand every challenges of life. I would like to acknowledge the support and guidance rendered to me from my main adviser Etana Ayeru (PhD Candidate) and co-adviser Terefe Admaw(MSc) as my supervisors during the development and timely accomplishment of this thesis. It would have been very difficult without expertise and generous contribution of them from the very beginning of the research design to the final write up. I would also like to acknowledge Mr. Abiy Abebe for his efforts in providing me necessary materials needed for the study.

It is my pleasure to sincerely thank all my Lecturers and staffs in the Department of Development Economics for the mentorship and suppor I would like to extend my gratitude to Bambasi District administration office for providing me all necessary information available on hand, and for their unreserved assistance in accomplishing this paper. At the end I would also like to acknowledge my brother; Mathewos Misgana for his endless advises and support from the development to the final write up of this study.

LIST OF TABLES

Table 1. Selected PAs and survey respondents from each

Table 2. Variables hypothesized to affect off-farm participation decision

Table 3. The pre-treatment (Confounder) variables included in the study

Table 4. Summary of continues variables

Table 5. Summary of categorical variables

Table 6. Summary of off-farm participants & non-participants in gender

Table 7. Summary of off-farm participants & non-participants in race

Table 8. Summary of off-farm participants & non-participants in credit access

Table 9. Demographic characteristics of off-farm activities participants

Table 10. Gender and Marital status Distribution the respondents

Table 11: The distribution of Farm land possession and market distance

Table 12:Types of Off-farm activities in Bambasi Wereda

Table 13: Work places of Off-farm activities and its Motives

Table 14:Distribution of income variations

Table 15: Breusch Pagan test of heteroscedasticity

Table 16: Result of Two Step Heckman selection model

Table 17: Determinants of off-farm participation Decision

Table 18: The Tobit Estimates of Intensity of off-farm participation

Table 19: Variables related to off-farm participation by Logistic regression

Table 20: propensity score percentage in the treated and control groups

Table 21: Hosmer-Lemeshow Goodness-of-fit test

Table 22: Estimate of average treatment effect

Table 23: Estimation of the ATET

Table 24: Two-sample Wilcoxon rank-sum (Mann-Whitney) test.

LIST OF FIGURES

Figure 1: Conceptual Framework of the Study

Figure 2: Geographical Map of Bambasi Wereda

Figure 3: Relationship between off-farm income and age of respondents

Figure 4: Relationship of off-farm participation and educational levels

Figure 5. Distribution of age versus normal probability of off-farm income

Figure 6: Distribution of education level vs off-farm participation

Figure 7: Distribution of Log of odds of propensity score.

LIST OF ACRONYMS AND ABBREVATIONS

ATE Average Treatment Effects

ATET Average Treatment for Treated

CI Conditional Independence

DA Development Agent

HH Household

i.i. d Independent and Identically Distributed

IMR Inverse Mills Ratio

NGO Non-Governmental Organization

NNM Nearest Neighbors Matching

PA Peasant Association

PASCAP Program for Accelerated Sustainable Development to End Poverty

PS match Propensity Score Match

Teffects Treatment Effects

UNRDP United Nations Rural Development Program

ABSTRACT

Majority of the world’s poor live in rural areas of developing countries are depending on agriculture and its related activities as a source of livelihood. But agriculture alone cannot provide sufficient livelihood opportunities to sustain life. There are other methods to supplement agriculture like, rural off-farm activities. The study focuses on the analysis of off-farm participation of smallholder farmer’s of the study area. A cross sectional sampling procedure was employed to draw data from a random sample of 248 respondents. A two-stage Heckman model was used to investigate determinants of off-farm participation decision and intensity of off-farm participation. The assessment of impact of participation of off-farm activities on income of smallholder farmers have been addressed through estimating the average treatment effect by using the estimated propensity score matching. The result of two-stage Heckman model and estimate of average treatment effect (ATE) revealed variables that affect off-farm participation and intensity of off-farm participation. Also the estimate of average treatment effect indicated that off-farm participation have a significant and positive impact on income of smallholder farmers of the study area. In the policy implications, it was recommended that implementation of Strategies that direct on the improvement of educational status of rural farmers, Policies that focus on increasing the farm incomes of the smallholders through intensive farming systems and raising their market bargaining power, the creation of conductive environment for the development of off-farm activities and implementation of Policies that encourage more participation of youth age than the old ages on off-farm activities.

Key Words: Off-farm activities, Participation Decision, intensity of participation, impact

CHAPTER ONE

INTRODUCTION

1.1. Background of the Study

Majority of the world’s poor live in rural areas in developing countries are depending on agriculture and its related activities as a source of livelihood. Farm households in developing countries diversify their income sources by allocating productive resources among diverse income generating activities including farm and off-farm work. Diversification may be a deliberate household strategy or a spontaneous response to crisis. Also, it may be attributed to the dropping and uncertain farm incomes, rising poverty, and emerging opportunities for off-farm work. Income diversification by farm households has gained the attention of governments, policy makers, and researchers because of its commonness and contribution to socioeconomic development especially in these countries (Benjamin et al, 2019).

In Africa, off-farm economic activities, as a means of income diversification, are very much indispensable for improving the livelihood of rural poor (Asenso-Okyere and Samson, 2012). Besides, it can serve as source of input supply for agricultural production and employment opportunity for those who do not have arable land and do not further want to rely on agriculture. Despite its vitality, in Africa, off-farm activities participation is low; and 37% of the rural households’ income is really extracted from off-farm activities where surprisingly not more than 20% of the labor force is being participated (Adewunmi et a l. 2011). It involves participation in remunerative work outside the participant’s own farm and has been recognized to play an increasingly essential role in sustainable development and poverty reduction particularly in rural areas leading potential to increase farm investment, higher productivity and reduce income uncertainty (Benjamin et al, 2019).

Various studies have shown that most rural households in Africa are involved in agricultural activities as their main source of livelihood, however, they also engage in other income generating activities to augment the main source of income (Adepoju and Obayelu, 2013). According to Ovwigho (2014), off-farm activities are supplementary or complimentary activities that farmers engage in either off- season or on-season to support themselves such as in casual labor, transportation business, traditional dancing, wine taping, petty trading etc. The off-farm income is the sum of rural on-farm income and wage earning in agriculture. On the other hand, off-farm refers to all income-generating activities except crop and livestock production. Off-farming income generating activities ostensibly obviate the seasonality of primary agricultural production and create a continuous stream of income to cater for exigencies of life (Ovwigho,2014).

As part of sub-Saharan Africa, Ethiopian farmers tend to participate in off-farm activities to satisfy consumption needs by supplementing the sluggish agricultural income caused by erratic and seasonal rain fall; and due to crop failure and the resultant abundant labor force. Besides, farmers are vulnerable both for natural and manmade disasters and shocks that could be weather related, pests, death of livestock and others. As a means of relief, rural farm households prefer to participate in off-farm activities and diversify their income thereby (Berg and Kumbi, 2006). Hence, a certain farm household is tempted to participate in off-farm activities, largely due to push factors, like drought. They further inferred that, Ethiopian rural poors prefer to participate in off-farm activities with less entry costs like firewood collection and charcoal production, while the most lucrative off-farm activities are left aside. Contrary to this, some rural farm households tend to participate in off-farm activities basically to widen their income sources given their ample agricultural production and the resultant accumulated wealth, as a pull factor (Merima and Peerlings, 2012)

The study carried out by Seid S., (2017) In Benishangul Gumuz Regional state, Assosa Zone, same as other parts of Ethiopia, indicated that Poverty and low income are pertinent problems, as the economy is mainly dependent on agriculture. Agriculture is also facing various difficulties to provide full resources for the community in Africa, such as seasonal and erratic rain fall, drought, pests, crop failure, population pressure and dependency on nature. As a reason, the agricultural sector is a declineing sector in carrying capacity and is vulnerabile to different kinds of shocks. To sustain life rural households of the area engage in different types of off-farm activities to generate income for the family. It was indicated that majority the households were engaged in off-farm activities to cover life sustaining necessities for their households. Furthermore, out of the total households 41.2%, 22.7%, 19.33% and 8.4% were found engaged in unskilled wage employment, employed in the government sector, casual agricultural employment, and private sector, respectively. Moreover, the study by Seid S., (2017) also pointed out that about 35.3%, 32.8%, 26.05%, 24.4% and 14.3% of the households in the study area were engaged in petty trade, collecting and selling of firewood and charcoal, mining, handicraft and others, respectively.

Being part of the zone, the study area is characterized by rural HHs who engages in off-farm activities which are undertaken to generate additional income and improve their wellbeing. But, there was no empirical research that supports the existing off-farm and non-farm employment opportunities practiced by the farmers in the area. Gebrehiwot and Fekadu (2012) argued that, intervention which can motivate households’ participation to be effective there needs to critically investigate factors that determine rural households’ engagement in off-farm and non-farm activities. Because well-designed policies and strategies that promote rural households, especially poorer ones, access to off-farm and non-farm income earning opportunities, which in turn improves their income and well-being, depends on location specific knowledge (Babatunde et al., 2010).

Therefore, this study was attempted to came to identify some location specific knowledge on determinants of off-farm participation and impact of engagement on participants income, in Benishangul Gumuz Regional State, Bambasi Wereda; and make it help to develop a well-designed policies and strategies that promote rural households access to off-farm income earning opportunities, which in turn improves their income and well-being.

1.2. Statement of the Problem

In many rural areas of Africa and elsewhere, people engage in off-farm activities to supplement their scrimpy agricultural economy or as a primary means of livelihood. While the rural off-farm economy is usually neglected in policy debates, it has grown too large to ignore. The off-farm economy accounts for about 37 percent of rural incomes in Africa while it goes even beyond 50 percent in Asia and Latin America (Haggblade, et al 2009). Compositionally, the rural off-farm economy consists of a highly heterogeneous collection of trading, agro-processing, manufacturing, commercial, and mining. The type of activities, the scale of operation, their production mix, and the way of production vary across locations and among households, and thus, impacts on livelihoods vary in the same manner (Bekele, 2016).

Ethiopia is an agrarian country in which the majority of the community depends on this sector as a primary means of livelihood. The sector contributes about 43% of GDP, creates employment opportunities for more than 80% of the population and accounts for more than 83% of foreign exchange earnings of the country (UNDP, 2014). On the other hand, one can see that the contribution of off-farm activities to the economy of rural population is high in Ethiopia.

Rising rural population resulting in shrinking farm size and reduced access to land, declining soil fertility, and limited capacities of urban areas to absorb rural job seekers underscore the importance of the off-farm economy as a route to expand households’ income in Ethiopia (MoFED, 2012). In addition to this, according to FAO, 2012, rapid population growth in the country forced households to produce and make their living on the small size of land. Due to the decline in carrying capacity of agriculture as well as fragmentation of their holding and low farming income, the majority of rural households are exposed to food insecurity and chronic poverty (Seid et al., 2016).

A study by Amsalu et al. (2014) find that, rural households diversify their activities into off-farm and non-farm activities to reduce the diverse forms of risks and uncertainties associated with farming; create a way of smoothing their income over the years and seasons; and reduce their vulnerability to different kinds of shocks, seasonality, and trends. In addition to this, off-farm employment has the potential to improve the income and well-being of rural and helps to reduce income uncertainty through smoothing income by spreading risk across several activities in rural areas. Moreover, according to Bedemo et al. (2013), off-farm activities play key role in alleviating the problems of low agricultural productivity and the resulting low income.

The study carried out by Seid S., (2017) in Benishangul Gumuz Regional state, Assosa Zone, indicated that Poverty and low income are pertinent problems of the area. According to the study, rural households in Assosa Zone, the zone in which the study area is found, are mainly dependent on subsistence farming activity as a major means of livelihood. The researcher identified that the farming practice of the area has been and is facing challenges such as insects, pests and land degradations which results in poor quality of land, decline in agricultural production, animal diseases and low agricultural income in the area (Benishangul Gumuz Region Development Gap Assessment), 2010). To cope up with these challenges, rural households in the area engage in off farm activities which are undertaken to generate additional income and improve their wellbeing. But there was no empirical research that supports the existing off farm and non-farm employment opportunities practiced by the farmers in the area.

Gebrehiwot and Fekadu, (2012) argued that, intervention which can motivate households’ participation to be effective there needs a critically investigation of factors that determine rural households’ engagement in off-farm and non-farm activities. Because well-designed policies and strategies that promote rural households, especially poorer ones, access to off-farm income earning activities, which in turn improves their income and wellbeing (Babatunde et al., 2010). Therefore, analysis of participation in off-farm activities by smallholder farmers in the study area and factors affecting households’ participation in off-farm is very important to improve the response mechanisms related to agricultural productivity, poverty and income and livelihood wellbeing.

In general, from the ideas discussed with respect to the study area, this study intended to fill the gap of location specific knowledge on determinants of off –farm participation disicion and intensity of off-farm participation. It also investigated the impact of participation on different off-farm activities and clarify the benefits of participation for the smallholder farmers to increase the status of current participation level and create a base line information from the study to enable other researchers carry out further study on it and government develop a clear and sound policy which accelerate farm households off-farm participation so as to diversify their income and cope up with livelihood challenges.

1.3. Research Questions

- What are the determinants of the participation decisions of smallholder farmers in off-farm activities in the study area?
- What are the factors that affect intensity of off-farm participation of smallholder farmers in the study area?
- What is the impact of taking part in off-farm activities on the income of the participants in the study area?

1.4. Objectives of the Study

The general objective of the study is to analyze smallholder farmer’s participation in off-farm activities in the study area.

Specific Objectives of the Study are:

- To assess determinants of smallholder farmers’ participation decision in off-farm activities in the study area.
- To find out factors that affect smallholder farmers intensity of participation in off-farm activities in the study area.
- To assess the impact of participation in off-farm activities on income of smallholder farmers in the study area.

1.5. Significance of the Study

This study is a base line study that provided useful economic information, for government, governmental, non- governmental organizations and others whoever wants to intervene in promotion of community-based development strategies and provide useful economic information that contribute to literature on households’ decisions to engage in off-farm activities by smallholder farmers. The study has focused on identification of the significant factors that affect households’ decision to participate on off-farm activities by using off-farm income as dependent variable that would help the Benishangul Gumuz Regional state in designing effective programs to boost households’ income in the short term and the economy of Ethiopia in the long term through policy adjustment. It would also important to create awareness about the use of off-farm activities in livelihood diversification strategies of the smallholders to increase income and improve living conditions.

1.6. Scope of the Study

The scope of the study was limited to analysis of smallholder Farmers’ Participation in off-Farm Activities in Bambasi Wereda, Benishangul Gumuz Regional State, Ethiopia. In the study it was used 8 peasant associations from the total of 36 rural PAs in the study area. The study mainly focused on the assessment of household level determinant factors which affect off-farm participation decision, intensity of articipation; and impact of participation on off-farm participation on income of the participants of the study area. . the study also focused mainly on the off-farm activities which are widely performed by household heads of the study area, next to agricultural activities to avoid the complexity of analysis in the case of multi off-farm activities that may be performed by a single household heads.

1.7. Organization of the Study

This thesis is organized into five chapters. It included the introductory part, which constitutes the background, problem statement, objectives, research questions, as well as the significance of the study in chapter one; literature review in chapter two, research methodology in chapter three, results and discussion in chapter four and conclusions and policy implications in chapter five.

CHAPTER TWO

LITERETURE REVIEW

To put this study in context, this section provides an overview of the relevant theoretical framework and empirical literatures on sample selection model for handling limited dependent variables to assist the analysis related to off-farm participation and the available constraints and opportunities to engage in off-farm activities for smallholder farmers of Bambasi Wereda Benishangul Gumuz Regional State, Ethiopia.

2.1. Theoretical Review

2.1.1. Definition of Terms

Off- farm participation entails farmers being able to engage in different livelihood diversification activities other than the farm activity. Intensity of off-farm participation is defined as the amount of off-farm income that would be derived from participating on off-farm activities. Impact of participation implies the effect that participating in off-farm activities may pose on the life of the smallholder farmers of the study area. This study considers determinants of off-farm participation and intensity of off-farm participation and also impact of off-farm participation from the view of farmers being able to participate in a variety of off-farm activities. Therefore, literature was reviewed from these perspectives.

2.1.2. Motives of Livelihood Diversification

It is often stated that “distress-pushed” diversification factors that push farmers into a variety of low-return activities, leading to more stable but lower household income generating activities. In this light, diversification is seen as an involuntary reversion of the process of specialization, brought on by crises such that the multiplication of activities is an adaptation necessary to ensure survival (Cinner, McClanahan, & Wamukota, 2010). On the other hand, progressive success and wealth, which in turn lead to increased access to resources, may lead to increased livelihood diversification as although they may have lower risk incentives than the poor, the non-poor may be more capable of financing this diversification if it is costly, have high entry barriers, and is initially risky. From this point of view, diversification can be seen as a deliberate strategy adopted by proactive households with greater opportunities (Martin &Lorenz, 2016). These factors are negative factors that may force farm households to seek additional livelihood activities within or outside the farm. These factors tend to dominate in high-risk and low-potential agricultural environments, subject to drought, flooding and environmental degradation (Haggblade, 2010). The most common push factors are related to different forms of risk, such as seasonality and climatic uncertainty and others include land constraints driven by population pressure and fragmented (Kassie et al., 2017).

On the other hand, pull factors are positive and these may attract farm households to pursue additional livelihood activities to improve their living standards. These factors provide incentives for farmers to expand their range of income activities outside farming by increasing the returns from nonfarm activities. Such factors tend to dominate in less risky and more dynamic agricultural environments. Alternatively, diversification resulting from a push or pull factors have been categorized by some scholars as either “survival-led” or “opportunity-led” respectively. Survival-led diversification, which is driven by push factors, mainly occurs when poorer rural households engage in low-return nonfarm activities by necessity to ensure survival, to reduce vulnerability or to avoid falling deeper into poverty. They are pushed into low-return nonfarm activities because they have low endowments of assets such as land, capital, livestock and credit, making them less resistant to seasonal and other risk factors (Kassie et al., 2017).

Opportunity-led diversification is mainly driven by pull factors. It occurs when wealthier rural households engage in high-return non-farm activities, with accumulation objectives, in order to increase household income by maximizing returns from their assets (Loison&Loison,2016). Income diversification has been shown to be positively associated not only with wealth accumulation but with an increased ability to withstand exogenous shocks, at least in terms of partial consumption smoothing (Saha & Bahal, 2012).

Livelihood diversification is a key rural household survival strategy and plays a considerable role in reducing vulnerability to economic changes which enhance adaptation options of adverse conditions. Engagement in non-farm activities, besides its contribution in absorbing rural surplus labor could enable to reduce income uncertainties, increasing agricultural productivity and could also be among the plausible adaptation strategies. The rural non-farm sector has been considered as a low-productivity sector which produces low-quality goods and was often expected to diminish as a country develops. But appreciation of the role of rural non-farm sectors in their contribution to economic growth, rural employment, poverty reduction, sustainable natural resource management, climate change adaptation strategy and a more spatially balanced population distribution. (Yaro, 2013).

Non-farm income generating activities provide an important source of primary employment in the rural areas of most developing countries’, and it is assumed that, as farm size becomes smaller due to population pressure, the percentage of non-farm income becomes larger (Hilson, 2016). Non-farm activities have the potential to play a crucial role in reducing vulnerability to poverty by providing households with a form of insurance against the risks of farming and reducing reliance on natural resources (Simtowe et al., 2016).In developing countries, farm households allocate their labor to off-farm income diversification activities for the following reasons: to reduce income risk by diversifying ex ante; to maintain food security (income and consumption) in the face of low farm productivity and income shocks such as drought, by diversifying ex post, in the face of insurance market failure; and to earn cash income to finance farm investments, in the face of credit market failure (Kassie, 2017). Agricultural product processing and input requirements, which is determined by the agricultural product mix, create derived demand for nonfarm labor. Forces outside agriculture, mainly in the cities and in the mining sector also affect labor use in the rural nonfarm economy.

2.1.3. Overview of Livelihood Diversification in Africa

Africa as a continent is identified by subsistence farmers, nonfarm income sources already account for as much as 40–45% of average household income (Hilson, 2016; Saha & Bahal, 2012). High population growth resulting from high fertility rates, shrinking farm sizes and growing landlessness in sub Saharan Africa could have potentially negative impact on rural welfare and food security and by default pushing unskilled farm labor into mainly low-return nonfarm sectors (Headey, D. D.2014). Secondly, urbanization in SSA is taking place without industrialization (Anderson Djurfeldt, 2015). Urbanization and emerging industries gradually allow rural people to leave agriculture and enter to nonfarm employment and rewarded investments in education and migration (Jayne &Headey, 2014). In the absence of manufacturing industries and high-return service sectors to provide skilled nonfarm opportunities, prospects for increased employment and rising incomes in urban areas of SSA remain limited. This leaves smallholder farming as the primary option for gainful employment for SSA’s growing young labor force (Losch et al., 2012).

However, rapid growth in nonfarm sectors fueled by improvements in education and infrastructure can potentially alter this situation (Haggblade2010). Thirdly, persistent low agricultural productivity coupled with chronic food insecurity and severe poverty characterizes the smallholder rural economy in SSA (Loison S.A., 2016).

Rural households in sub-Sahara African countries usually have to cope with both poverty and income variability. There are two points to be considered here, the first being diminished farm productivity in sub-Sahara African. Second, in sub-Sahara African, under adjustment, support for agriculture has virtually disappeared, particular subsidies on crucial inputs such as fertilizers. These drivers have been divided along the spectrum of “necessity versus choice”: they make up a typology featuring, on the one extreme, “push factors”, and on the other extreme, “pull factors”. In sub-Saharan Africa, correspond to the two sets of drivers identified above “pull factors” (“demand pull”) diversification and “push factors” (“distress push”) diversification, respectively have been identified in parallel direction (Makita, 2016).

In Ethiopia, the complex interlinkages among poverty, population pressure, institutional failure and environmental degradation cause shrinkage of land holdings that led to farm fragmentation, landlessness and expansion of farming to steeper and marginal lands (Belay & Bewket, 2015). There are many empirical studies in Ethiopia which investigated determinant factors of livelihood diversification. The studies consider a variety of household characteristics such as age, gender, farm size, education and asset, along with other environmental characteristics such as credit access, distance to the nearby market and location (Bezabih, Gebreegziabher, Gebremedhin, & Köhlin, 2010; Bezu & Barrett, 2010; Sisay, 2010).These studies help us to understand factor determinants of livelihood diversification in Ethiopia by providing a lot of compelling and insightful results(Kassie et al., 2017).

Lanjouw (2000) observed that while the rural non-farm sector was traditionally viewed as a low-productivity sector producing low quality goods that are expected to wither away as a country develops, recent years have seen a shift away from this position towards recognition of the fact that the rural non-farm sector can, and often does, contribute to economic growth, rural employment, poverty reduction, and a more spatially balanced population distribution (Shittu, Adebayo M. 2014).

In one of its report, the World Bank gave its testimony that millions of rural people worldwide have enabled to leapfrog from poverty through better incomes and employment in rural non-farm enterprises and hence contributed to better livelihood (World Bank, 2008). Since rural non-farm economies are mostly small-scale, require low entry capital, and its seasonality and amenability are suitable to home-based activity; they can play an important role in the economic transformation of developing countries and as a viable adaptation strategy (Mwamba, 2013).

2.1.4. Household Level Benefits of Off-farm Activities

The benefits of rural households to participate in off-farm activities may vary among social systems, across geographical locations, and over time. Landless and near-landless households in many developing countries of Africa, Asia and Latin America depend heavily on off-farm activities as a primary source of income while those who are better-off are engaged in such activities to supplement agriculture or expand it (Demurger et al, 2010). Multiple motives (push factors and the pull factors) prompt households and individuals to diversify assets, incomes and activities. While some diversify because they have little choice, better-off households may diversify because they have a lot of choices (Ellis 2000; Barrett, Reardon, and Webb 2001). Diversification may occur either as a deliberate household strategy or as an involuntary response to the crisis; it can act both as a safety valve for the rural poor (survival) and as a means of accumulation for the rural rich (Adi, 2007). Likewise, the reasons behind diversification as a livelihood strategy, according to Ellis (2000), are often divided into two principal considerations: necessity (involuntary and desperation reasons) or choice (voluntary and proactive reasons) (Amogne A. et.al, 2017).

The limited availability of non-farm employment opportunities would make the effort of household’s in securing income diversification. Knowing the existing livelihood strategies and pointing out the determinant factors affecting smallholder farmers in practicing non-farm sources of livelihood is unquestionably important in the provision of information to formulate an appropriate strategy for the development of the sector. Focusing on the impacts of off-farm work, available evidence suggests that increased participation in off-farm work among members of farm households is associated with higher incomes as well as improved food consumption, nutrition and food security.

Despite the common evidences that income from non-farm sources helps in relaxing financial constraints on farm households and enhancing farm investment, evidences on the impacts on domestic food supply, production efficiency and household welfare, in general, remain quite conflicting. For example, while Lien et al., (2010) reported that off-farm income had a positive effect on farm output but no systematic effect on farm technical efficiency, Pfeiffer, et al. (2009) reported that off-farm income has negative effect on agricultural output and the use of family labor on the farm, but positive impact on use of purchased inputs and confer a slight efficiency gain on farm households participating in off-farm activities. Shi et al. (2011), however, found that the negative lost-labor effect is much stronger than the (small) positive income effect while Holden et al. (2004) reported that access to non-farm income in less favored Ethiopian highlands reduces farm households’ incentives to invest in conservation and this leads to more overall soil erosion and more rapid land degradation even though intensity of production is reduced. (Shittu, Adebayo M. 2014)

Livelihood diversity helps to reduce households’ dependence on environmental resources, thereby helping environment restoration. Off-farm employment causes the outflow of the rural population and reduces regional population and environmental pressures, which is beneficial for maintaining rural development achievements. This is indicated by the results of the study on ecological restoration in the Wuyi Mountain Area of Fujian, China as well as the studies on combating desertification in Asia, Africa and the rest of the world (Heshmati & Squires, 2013). The reduction in households’ engagement to the environment is mainly caused by less dependence on environmental resources after the improvement of livelihood diversity. As an external “pull” factor, the rapid development of China’s overall economy effectively promotes the non-agricultural transfer of the rural labor force and improvement of household livelihoods, so as to indirectly facilitate the restoration of the ecological environment (Wang et al., 2016).

The significance of participating in of off-farm activities is based on the premise that off-farm incomes of smallholders and thus, livelihoods of smallholders are likely to improve. (World Bank, 2008). As income of the farmers increase, they could afford different agricultural inputs which in turn could accelerate the increased production. Increased production contribute for productivity improvement through increased household income from the sale of surplus products on the market. In this way it can assist food security, and could igly contribute the nations agenda of poverty reduction and hence sustainable development. So in line with high importance of the rural off-farm sector, due attention should be given to this sector. Rural livelihoods should not be equated with agriculture, and rural development should focus on off-farm activities beside agriculture as a way of reducing poverty and achieving sustainable growth in rural areas.

2.1.2.Theoretical Framework of Modeling Selection Bias

A two stage Heckman sample selection model was employed in assessment of determinants of off-farm participation decision and intencity of participation among smallolder farmers of the study area. Heckman’s (1974, 1978, 1979) sample selection model was developed using an econometric framework for handling limited dependent variables. It was designed to address the problem of estimating the average wage of women using data collected from a population of women in which women who stayed at home, who were not in the labor market were excluded by self-selection. Based on this data set, Heckman’s original model focused on the incidental truncation of a dependent variable. Maddala (1983) extended the sample selection perspective to the evaluation of treatment effectiveness.

Heckman’s sample selection model was a seminal contribution to 20th-century program evaluation. The sample selection model triggered both a rich theoretical discussion on modeling selection bias and the development of new statistical procedures to address selection effects. Heckman’s key contributions to program evaluation include the following: (a) He provided a theoretical framework that emphasized the importance of modeling the dummy endogenous variable; (b) His model was the first attempt to estimate the probability (i.e., the propensity score) of a participant being in one of the two conditions indicated by the endogenous dummy variable, and then used the estimated propensity score model to estimate coefficients of the regression model; (c) He treated the unobserved selection factors as a problem of specification error or a problem of omitted variables and corrected for bias in the estimation of the outcome equation by explicitly using information gained from the model of sample selection; and (d) He developed a creative two-step procedure by using the simple least squares algorithm. To understand Heckman’s model, it needs to review the concepts related to the handling of limited dependent variables.

Due to censoring and truncation, limited dependent variables are common in social and health data. Truncation, which is an effect of data gathering rather than data generation, occurs when sample data are drawn from a subset of a larger population of interest. Thus, a truncated distribution is part of a larger, untruncated distribution. Censoring occurs when all values in a certain range of a dependent variable are transformed to a single value. Under this condition, researchers may estimate a regression model for a larger population using both the censored and the uncensored data.

The central task of analyzing limited dependent variables is to use the truncated distribution or censored data to infer the untruncated or uncensored distribution for the entire population. In the context of regression analysis, we typically assume that the dependent variable follows a normal distribution. The challenge then is to develop moments (mean and variance) of the truncated or censored normal distribution. Theorems of such moments have been developed and can be found in textbooks on the analysis of limited dependent variables. In these theorems, moments of truncated or censored normal distributions involve a key factor called the inverse Mills ratio, or hazard function, which is commonly denoted as λ. Heckman’s sample selection model uses the inverse Mills ratio to estimate the outcome regression.

A concept closely related to truncation and censoring, or a combination of the two concepts, is incidental truncation. Indeed, it is often used interchangeably with the term sample selection.

Thus, sample selection or incidental truncation refers to a sample that is not randomly selected. It is in situations of incidental truncation that we encounter the key challenge to the entire process of evaluation, that is, the departure of evaluation data from the classic statistical model that assumes a randomized experiment. This challenge underscores the need to model the sample selection process explicitly. We encounter these problems explicitly and implicitly in many data situations.

2.2. Empirical Studies

A few studies have been undertaken to examine the determinant factors that affect smallholder farmers to engage in off-farm activities which are empirical in nature (Berihun K.et al., 2016, Gebre-Egziabher et al., 2000, Kassie et al., 2017, MoFED, 2010, Sisay, 2010). These studies look engagement of smallholder farmers in to off-farm activities at different aspects, and have found mixed evidence for the direction of off-farm work effects on household income and farm production.

In a study carried out by Kassie et al., 2017 on investigation of the determinant factors of livelihood diversification in Gozamin district, Ethiopia, through applying the logit model to the survey data indicated that, as the age of the farm household increases, he/she could not be capable of diversifying as many livelihood activities as possible. Thus, old farmers are more likely to concentrate on on-farm agricultural activities for the purpose of maintaining their subsistence consumption need. With limited resources, new young farmers have to work with meager rural resource endowment, which pushes them to diversify their livelihood activities to earn enough income for smoothing out yearly consumption. Also, recently service and construction sectors are growing significantly faster than the industrial and agricultural sectors in Ethiopia. This provided a better opportunity for younger rural farmers to engage in the service and construction sectors than the older farmers who like to be paid better in other sectors as their productivity is much higher than the agricultural sector (MoFED, 2010).

The study carried out by Kassie et al., 2017 identified that Location of the household is another determinant factor for livelihood diversification. As we move away from capital city Addis Ababa /towns/, the probability of participation in livelihood diversification found to decline, that is households located in far villages found to be less likely to engage in livelihood diversification activities compared to the farm merchandising who live in the far villages. Resource endowment differences between the villages were found also creates variations in diversification incidences among villages. Educational level of the farm household head had found to have a negative impact on the livelihood diversification decisions of farm households. Farms households who attended secondary school and higher educational level had shown a lower probability of diversifying livelihood activities compared to whom who do not have any formal educational backgrounds (Woldehanna,2000). On the other hand, other studies reported education has a positive effect (Gebre-Egziabher et al., 2000; Sisay, 2010).

On the other hand, the study by Kassie et al., 2017 indicated that the distance of farm households from the nearest market place found to have the greatest influence on the diversification. Markets promote the rural-urban linkages like vertical linkages, in which the farm household can supply the rural resources and products to the nearest marketplace where small-scale agro-processing industries are located that use predominantly raw materials from rural areas. Backward-linkages may also be facilitated in that some farm households may have been involved in the merchandising processes of buying urban products for their rural villagers. This indicates that urbanization and access to market will facilitate the diversification process of farm households (Kassie et al., 2017).

Most literatures in development economics have identified two main factors that drive diversification into off-farm activities among farm households in developing countries. These factors are broadly classified into “pull factors” and “push factors”. Reasons why a farm household can be pulled into the off-farm sector include higher returns to labor and or capital and the less risky nature of investment in the off-farm sector (Kilic et al., 2009). The push factors that may drive off-farm income diversification includes, the need to increase family income when farm income alone cannot provide sufficient livelihood (Minot et al., 2006); the desire to manage agricultural production and market risks in the face of a missing insurance market (Barrett et al., 2001) and the need to earn income to finance farm investment in the absence of a functioning credit market (Kilic et al., 2009; Oseni and Winter, 2009).

As cited in Bezabih, et’al (2010) a study conducted by Honduras, et’al (2001) on off farm participation is showed that educated and wealthier households tend to participate in off farm activities, indicating the importance of human and physical capital. Besides, Deininger and Olinto (2001), in their study of off farm employment in Columbia Showed that investment in a single income source is the most beneficial to capital constrained households with limited education/human capital.

Bhatata Bp. and Arethun, T. (2013) On their study found that households’ decision to enter into a labor market significantly depends on the characteristics of the households such as sex, age of the household heads and labor endowments in the households. Moreover, Bezabih M. et,al (2010) on their study also confirmed that the off-farm activity choice of households is also influenced by climatic factors or weather conditions. A study conducted by Destaw (2003), on non-farm employment and farm production of small holder farmers, using logit model also showed that Age, education, credit use, distance from road and distance from market were found to be highly important variables influencing participation in non-farm activities.

Abdulai and Delgado (1999) jointly estimated the determinants of the decision of husbands and wives to participate in cash- income-oriented off-farm work in Northern Ghana by using a bivariate probit model. The result of the analysis suggests that the variable age has a positive effect on the probability of labor supply to the off-farm sector at younger ages and at older ages the probability of participating in off-farm work decreases as age increases. Human capital, as embodied in education and experience, is essential in increasing off-farm earnings and time allocation of rural families and to diversify the rural economy away from agriculture. A husband or wife who had more schooling (as measured by years of schooling) had a significantly higher probability of engaging in off-farm activities. The other variables nonlabour income and distance to the regional capital are found to have a negative influence on the participation decisions of farm households. Unlike other studies the presence of children had no significant effect on the participation decision of women in non-farm work and on the labor supply of husbands and wives. This is similar with findings of Rosenzweig (1980) and Skoufias (1994). Jacoby (1993) also indicated that the number of children 5 years old and younger does not lessen women’s hours worked, which includes housework, though not child care per se. But a well-developed infrastructure and population density had positive significant effects on the probability of off-farm work by both males and females.

In light of the above discussed theoretical and empirical literature, this tudy intended to conduct the assessment of determinants of off-farm participation decision, intensity of off-farm participation and impact of off-farm participation on income of the participants in Bambasi Wereda, Benishangul gumauz Regional State, Ethiopia to generate location specific knowledge on the the objectives of the study.

2.3. Conceptual Framework

Rural farm HHs participation in off-farm activities is affected by numbers of variables which may consequently depend on the nature of the individual farmer’s characteristics, family characteristics and farm characteristics. The conceptualization of this study is given in figure 1. It identifies factors that influence farmer’s decision to participate in off-farm activities. The study conceptualizes that farmer’s participation is influenced by socio-economic and institutional factors. Socio-economic factors include; household size, farm land size, age and gender of the household head, education level, household’s wealth and occupation and institutional factors which include access to credit which may influence household’s extent of participation.

Abbildung in dieser Leseprobe nicht enthalten

Figure 1: Conceptual Framework of the Study

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Details

Title
Smallholder Farmers’ Participation In Off-Farm Activities. The Case Of Bambasi Wereda, Benishangul Gumuz Regional State, Ethiopia
Course
Developmental Economics
Grade
4.00
Author
Year
2020
Pages
88
Catalog Number
V984842
ISBN (eBook)
9783346349194
ISBN (Book)
9783346349200
Language
English
Tags
smallholder, farmers’, participation, off-farm, activities, case, bambasi, wereda, benishangul, gumuz, regional, state, ethiopia
Quote paper
Amanuel Misgana (Author), 2020, Smallholder Farmers’ Participation In Off-Farm Activities. The Case Of Bambasi Wereda, Benishangul Gumuz Regional State, Ethiopia, Munich, GRIN Verlag, https://www.grin.com/document/984842

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Title: Smallholder Farmers’ Participation In Off-Farm Activities. The Case Of Bambasi Wereda, Benishangul Gumuz Regional State, Ethiopia



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