The Pennsylvania State University

The Graduate School

College of Agricultural Sciences

SOCIAL NETWORKS AND THE EXCHANGE ECONOMY IN RURAL

MOZAMBIQUE: A STUDY OF OFF-FARM LABOR AND CROP MARKETING

BEHAVIORS

A Dissertation in

Agricultural, Environmental and Regional Economics & Demography

by

Luis Sevilla

© 2013 Luis Sevilla

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

December 2013

The dissertation of Luis Sevilla was reviewed and approved* by the following:

Jill L. Findeis Distinguished Professor Emeritus of Agricultural, Environmental and Regional Economics & Demography Dissertation Advisor Chair of Committee

David Abler Professor of Agricultural, Environmental and Regional Economics & Demography

Stephan Goetz Professor of Agricultural, Environmental and Regional Economics & Demography

Gary King Professor of Biobehavioral Health

Rhonda BeLue Associate Professor of Health Policy and Administration

Ann Tickamyer Professor and Head of Department of Agricultural Economics, Sociology, and Education

*Signatures on file in the Graduate School.

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ABSTRACT Of the 3 billion living in rural areas in less developed regions of the world, approximately

1.2 billion people live in extreme poverty (The Economist, 2013; World Bank, 2013), and 70% of the 1.2 billion people have some dependency on agriculture (Cleaver, 2012). In sub-Saharan

Africa, 47% of the population lives in extreme poverty (United Nations, 2012), 66% of the total population lives in rural areas, and more than 90% depend on agriculture for their livelihoods

(Asfaw et al., 2010). Unfortunately, subsistence agriculture operates as a safety net for the poor population rather than as a driver of economic growth (World Bank, 2005). To combat extreme poverty, greater economic growth and income equality will be required (Chandy et al., 2013) and this may be achieved through poverty reduction strategies that target the productivity, profitability, and sustainability of poor farm households (Asfaw et al., 2010). By promoting rural economic growth, to include farm and off-farm opportunities, households can directly benefit from increased food security and incomes (Cord, 2002).

Many economic activities in developing countries are influenced by non-market interactions with family, friends, and acquaintances. These mutually beneficial relationships require an investment of limited household resources such as time and money away from productive activities but in the long run may expand household resources (Bolin et al., 2003).

Previous studies have found that social networks affect household incomes (di Falco and Bulte,

2011; Haddad and Maluccio, 2003; Narayan and Pritchett, 1999), agricultural technology adoption (Bandiera and Rasul, 2006; Isham, 2002), employment and credit (Munshi, 2011;

Wahba and Zenou, 2005), productivity (Fafchamps and Minten, 2002), and risk sharing

(Fafchamps, 2011). This dissertation investigates the influence of social networks on economic behavior of agricultural households in rural Mozambique. Specifically, the dissertation has two research objectives: 1) to better understand the effect of social networks on male and female

iii labor allocation and off-farm work choices, and 2) to determine if social networks impact agricultural marketing behaviors of rural agricultural households.

This dissertation uses an ex ante baseline socioeconomic questionnaire administered to

Mozambican households by the Institute for Agricultural Research of Mozambique (IIAM) and

Pennsylvania State University (PSU) as part of a multidisciplinary project funded by the

McKnight Foundation. The project aims to improve food security and agro-ecosystem sustainability through the development and diffusion of common beans (Phaseolus vulgaris) bred to grow well in low phosphorous soils of Africa. Face-to-face interviews were conducted between August 2008 and August 2009 in eight villages throughout Central and Northern

Mozambique. As a baseline study, the interviews provided an initial picture of household composition, labor allocation, agricultural production and technology adoption (including beans), and social networks.

To achieve objective 1, a series of models are estimated to better understand the interrelationships between social networks and labor allocation and off-farm work decisions. A bivariate probit model is used to simultaneously estimate labor participation models for adult male and female respondents when both are present in the household. The bivariate probit model tests for jointness in decision-making between adult males and females in dual-headed households. Next, separate univariate probit models are used on the full sample to estimate models of labor participation decisions for adult male and female respondents. To test for the endogeneity of ego networks, a probit model with continuous endogenous regressors (IV probit) is included to model the off-farm labor participation decisions of adult males and females.

Lastly, a multivariate probit is used to model off-farm work choices, particularly hired farm labor, non-farm wage labor, and non-farm self-employment.

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In the bivariate probit analysis, the statistical insignificance of the cross equation correlation and the failure to reject the Wald test statistic for the hypothesis that the two equations are independent implies non-jointness in decision-making (at least for this sample).

Therefore, the labor participation decision for men and women is estimated with two separate univariate probit models.

Results from the univariate probit analysis indicate that men and women rely on different ego network types to access off-farm labor. In Mozambique, having more friends (friendship ego network size) positively affects women’s off-farm labor participation; however, kin ego network size is not a statistically significant predictor of off-farm labor. For men, the opposite is true, friendship ego networks are not statistically significant predictors of off-farm labor but larger kin ego networks positively affect the off-farm labor participation decision. Results show that, regardless of network type, both men and women benefit from knowing more people (total ego network size). However, given a total network size, an increase in the proportion of contacts who are kin has the opposite effects on men and women. For women, a more kin homogenous total ego network reduces the probability of off-farm employment whereas for males, a more kin homogenous total ego network size increases the likelihood of off-farm employment.

Additionally, results show that age is a statistically significant predictor of female off-farm labor participation. Also, education in Mozambique seems to result in different outcomes for men and women. More education among women reduces their likelihood of off-farm labor participation but increases men’s probability of working in off-farm labor.

The multivariate probit analysis extends the off-farm labor participation model to look at the different off-farm work choices for men and women. Results from the multivariate probit indicate the use of different ego network types to access high and low wage off-farm labor. The

v results show that women who are self-employed and/or have higher wage non-farm jobs also have smaller friendship ego networks. For men, friendship ego networks are not statistically significant predictors of the different off-farm work choices. However, having more relatives in the village (kin ego network) increases access to work on others’ farms. Knowing more people

(total ego network size) does not result in favorable labor outcomes for men and women; women with larger total ego network are less likely to be self-employed and both men and women with larger total ego networks are more likely to be employed in low wage work on others’ farms. A greater proportion of kin in total ego networks reduces women’s employment in hired farm labor and increases the likelihood of employment in non-farm wage labor. For men, the opposite is true; more kin homogeneous total ego networks positively affects employment on others’ farms as well as non-farm self-employment. In addition to the ego network results, the multivariate probit indicates that increased education reduces women’s dependence on low wage hired farm labor but does not result in increased access to higher wage non-farm labor or self-employment.

More than education, women benefit from improved health. Healthier women are less likely to be self-employed and more likely to participate in higher wage non-farm labor. For men, the opposite is true; improved health is not a statistically significant predictor of any of the off-farm work choices, however, increased education positively affects male participation non-farm self- employment. Other notable results include the negative effect of farm size on male participation in hired farm labor as well as the negative effect of distance to the nearest city/town on female participation in non-farm wage labor employment and self-employment.

To achieve objective 2, a double hurdle model is used to determine the effect of social networks on the market participation decision and value of sales for agricultural households.

Three separate double hurdle models are estimated to study marketing behavior of three crops:

vi beans, maize, and other crops. To test for and correct for potential endogeneity, a control function method is used. Interpretation of the results includes marginal effects of the independent variables on the probability of participating in crop markets as well as the conditional average partial effect on the expected value of sales for crops and the unconditional average partial effect on the expected value of sales.

Results from the double hurdle models indicate the importance of who you know and how many people you know for agricultural marketing behavior. Households with more relatives in the village (kin ego network size) are more likely to participate in maize and other crop markets. Furthermore, unconditional on market participation, an increase in kin ego networks positively affects sales of other crops. With regards to friendship ego networks, households with more friends 1) have lower bean sales (conditional and unconditional on market participation), 2) are less likely to participate in maize markets, and 3) have lower maize sales (conditional and unconditional on market participation). Results for total ego network size also indicate that there are advantages and disadvantages to knowing more people as kin or friends. Total ego network size has a negative influence on bean sales (both conditional and unconditional on market participation), as well as a negative effect on maize market participation and maize sales

(conditional and unconditional on market participation). With regards to other crops, knowing more people (total ego networks) positively affects participation in other crop markets and increases sales of other crops among the full sample (unconditional on market participation).

Other notable results include household characteristics, farm characteristics as well as transaction costs. Age is an important determinant of the value of sales of beans, maize, and other crops. Conditional on market participation, age of the decision-maker displays a curvilinear relationship with sales of beans and other crops, whereas, unconditional on market participation,

vii age of head has a curvilinear relationship with maize sales. The household composition variables are statistically significant predictors of agricultural marketing behavior with the only exception being the number of children (ages 14 or less). Households with a head in excellent/good health are less likely to market maize, but have greater bean and maize sales (among market participating households). Interestingly, unconditional on market participation, excellent/good health status increases bean sales but reduces maize sales. The negative effects of health on the full sample suggest that maize is marketed out of necessity (in difficult times) but is typically stored/consumed by the household. Farm characteristics are found to contribute to the marketing behavior of agricultural households. The size of land available has a positive influence on bean market participation and, unconditional on market participation, also positively affects bean sales. Households able to rely on extension services are more likely to participate in bean markets and, unconditional on market participation, have greater bean sales. For maize, once the market participation decision has been made, accessibility of extension services increases maize sales. Of the fixed transaction costs, only education is a statistically significant predictor of the market participation decision. Results show that better educated decision-makers are more likely to participate in maize and other crop markets. Lastly, variable transaction costs are found to contribute to both the market participation decision and the value of sales. A greater distance to the nearest city/town increases the likelihood of bean market participation but reduces participation in markets for other crops. Furthermore, the distance variable positively affects bean sales and negatively affects sales of other goods (conditional and unconditional on market participation). The transportation variable indicates that ownership of a bicycle, motorcycle, or cart positively affects the likelihood of participation in other crop markets as well as positively affects the value of sales of bean and maize.

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Regarding implications for policy, results indicate that there are gender differences in access to off-farm work choices. Better educated women are less likely to access higher wage off-farm jobs (self-employment and non-farm wage labor). Therefore, it is important to have policies that will facilitate (better educated) women’s access to higher wage jobs. Potentially, this may include access to credit and providing basic finance and managerial skills to promote self-employment ventures for women. With regards to social networks, results for Mozambican women indicate that friendship ego network size and total ego network size are important for accessing low wage work on others’ farms but restrict access to higher wage non-farm labor. The results suggest that Mozambican women with larger friendship ego networks or larger total ego networks are negatively affected by social norms regarding their roles in the household.

Economic empowerment programs for women could potentially change perceptions of women and their roles in the household as well as improve their off-farm work choices. Potentially, economic empowerment programs may include assistance in starting own businesses, accessing credit, as well as educating women with basic finance and managerial skills.

With regards to agricultural marketing, it is important to formulate policy conclusion based on the overall goal of the project: to improve food security through the development and diffusion of common beans (Phaseolus vulgaris) bred to grow well in low phosphorous soils of

Africa. Results suggest that, among market participating households, excellent/good health status increases sales, however, it was shown (with regards to maize) that healthier decision-makers are less likely to participate in agricultural markets and more importantly, unconditional on market participation, an improvement in health would reduce (maize) sales. Therefore, in the long run, investments in health have the potential to decrease sales of (maize) surplus and promote greater household food security. Additionally, the introduction of the new bean seeds

ix should be coupled with policies promoting greater access to land and extension services. Greater access to land for cultivation may potentially include helping smallholder farms secure land rights. On the other hand, greater access to extension services may include holding demonstrations and field days to educate more farmers at once on the new bean seeds as well as targeting extension service to organized groups. Likewise ownership of a bicycle, motorcycle, or cart reduced variable transaction costs and resulted in higher sales of agricultural goods.

Assuming households are able to produce surplus beans, it is important to support bicycle distribution programs that potentially could lower variable transaction costs and increase household incomes from bean sales. Lastly, better connected households (total ego networks) had lower maize and bean sales but greater sales of other crops suggesting that information sharing and cooperation takes place for certain crop types. Therefore, it is important to promote membership in associations and other organized groups for specific crops that may help promote trust, as well as to reduce search and transaction costs. With regards to kin ego networks, households with stronger social ties (kin ego network) were more likely to participate in maize and other crop markets as well as to report greater sales of other crops. The positive effect of kin ego networks indicates the importance of social support mechanisms for participation in agricultural markets. Therefore, programs supporting smallholder farmers market participation should mimic support mechanisms of kin networks to remove the risks associated with market participation (i.e., provide timely and accurate market information, provide assistance when faced with crises, etc.).

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Contents LIST OF TABLES ...... xiv

LIST OF FIGURES ...... xvi

ACKNOWLEDGMENTS ...... xix

Chapter 1 – INTRODUCTION ...... 1

1.1 Statement of the problem ...... 1

1.2 Research statement and motivation ...... 2

1.3 Study objectives ...... 4

1.4 Background on Mozambique ...... 5

1.5 Significance of the study ...... 8

1.6 Organization of the dissertation ...... 8

Chapter 2 – LITERATURE REVIEW ...... 10

2.1 Introduction ...... 10

2.2 Social capital defined ...... 10

2.3 Social networks defined ...... 11

2.4 Social networks and economic activity...... 13

Chapter 3 – THEORETICAL MODEL ...... 22

3.1 Introduction ...... 22

3.2 Theoretical model for labor allocation ...... 25

3.3 Model for agricultural market behavior ...... 31

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Chapter 4 – DATA ...... 38

4.1 Research trip to Mozambique ...... 38

4.2 District profiles ...... 42

4.3 Data characteristics ...... 43

Chapter 5 – METHODOLOGY ...... 69

5.1 Objective 1 and how it was achieved ...... 69

5.1.1 Model 1: Joint decision-making of off-farm labor participation ...... 69

5.1.2 Model 2: Univariate probit model for off-farm labor participation ...... 72

5.1.3 Model 3: Multivariate probit model for off-farm labor choice ...... 74

5.1.4 Correcting for endogeneity ...... 76

5.1.5 Variables used in the analyses ...... 79

5.2 Objective 2 and how it was achieved ...... 85

5.2.1 Empirical estimation ...... 86

5.2.2 Social network endogeneity ...... 88

5.2.3 Obtaining average partial effects ...... 89

5.2.4 Variables used in the analyses ...... 90

Chapter 6 – EMPIRICAL RESULTS ...... 97

6.1 Labor results – Objective 1 ...... 97

6.1.1 Results for off-farm labor market participation ...... 97

6.1.2 Results for off-farm work choice ...... 105

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6.2 Agricultural marketing behavior – Objective 2 ...... 116

6.2.1 Results for bean marketing behavior ...... 118

6.2.2 Results for maize marketing behavior ...... 124

6.2.3 Results for other crop marketing behavior ...... 131

Chapter 7 – SUMMARY AND CONCLUSIONS...... 137

7.1 Introduction ...... 137

7.2 Discussion ...... 138

7.3 Policy implications...... 143

7.4 Limitations of the study ...... 145

7.5 Future research ...... 146

REFERENCES ...... 148

Appendix A - SURVEY SITES ...... 160

Appendix B – OFF-FARM LABOR PARTICIPATION AND WORK CHOICE...... 167

Appendix C - FEMALE FRIENDSHIP AND KIN EGO NETWORKS ...... 169

Appendix D – MALE FRIENDSHIP AND KIN EGO NETWORKS ...... 176

Appendix E - HOUSEHOLD FRIENDSHIP AND KIN EGO NETWORKS ...... 183

Appendix F – IV PROBIT RESULTS FOR OFF-FARM LABOR PARTICIPATION ...... 196

Appendix G - AGRICULTURAL MARKETING BEHAVIOR ...... 199

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LIST OF TABLES

Table 4.1.1: Survey sites…………………………………………………………………… 41

Table 4.3.1: Information collected in selected modules of Mozambique baseline study….. 44

Table 4.3.2: Demographic characteristics of sample households, by province……………. 45

Table 4.3.3: Demographic characteristics for adult respondents…………………………... 47

Table 4.3.4: Participation rate in income-earning activities, by gender…………………… 48

Table 4.3.5: Experiences with common beans…………………………………………….. 53

Table 4.3.6: Decisions-making within the household for the married sample…………….. 56

Table 4.3.7: Decisions-making within the household for the full sample…………………. 56

Table 4.3.8: Participation in the marketing of crops, by gender…………………………... 57

Table 4.3.9: Household participation rates and mean sales in agricultural markets, by 58 province and crop………………………………………………………………………….. Table 4.3.10: Problems encountered and coping mechanisms, by gender and province….. 60

Table 4.3.11: Adjacency matrix illustrating ties between 6 households…………………... 63

Table 4.3.12: Ego networks, by province………………………………………………….. 68

Table 5.1.1: Variables used in bivariate probit model, by gender…………………………. 80

Table 5.1.2: Variables used in univariate and multivariate probit models, by gender…….. 81

Table 5.2.1: Variables used in double hurdle model…………………….………………… 91

Table 6.1.1: Bivariate probit results for participation in off-farm work…………………… 98

Table 6.1.2: Probit results for male off-farm labor participation………………………….. 101

Table 6.1.3: Multivariate probit results for female non-farm work choice………………... 107

Table 6.1.4: Multivariate probit results for male non-farm work choice………………….. 109

Table 6.2.1: OLS regression of factors influencing social network size…………………... 117

Table 6.2.2: Household marketing behavior for beans (endogeneity corrected)…………... 119

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Table 6.2.3: Household marketing behavior for beans (endogeneity corrected)…………... 121

Table 6.2.4: Household marketing behavior for maize (endogeneity corrected)………….. 125

Table 6.2.5: Household marketing behavior for maize (endogeneity corrected)………….. 127

Table 6.2.6: Household marketing behavior for other crops……………………………… 132

Table 6.2.7: Household marketing behavior for other crops (endogeneity corrected)…….. 134

Table B1.1: Participation rate in income-earning activities, by province…………………. 167

Table F1.1: IV probit results for male off-farm labor participation, by gender…………… 197

Table G1.1: Household marketing behavior for beans…………………………………….. 200

Table G1.2: Household marketing behavior for maize……………………………………. 202

Table G1.3: Household marketing behavior for other crops………………………………. 204

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LIST OF FIGURES

Figure 1.6.1: Map of Mozambique……………………………………………………….. 6

Figure 4.1.1: Survey sites (village) location……………………………………………… 40

Figure 4.3.1: Education, by gender and province………………………………………… 47

Figure 4.3.2: Female participation rate in different work activities, by age group……….. 50

Figure 4.3.3: Male participation rate in different work activities, by age group………… 50

Figure 4.3.4: Female participation in activities and stress, by month…………………….. 52

Figure 4.3.5: Male participation in activities and stress, by month………………………. 52

Figure 4.3.6: Source of improved bean seed varieties, by gender………………………... 54

Figure 4.3.7: Niassa-2 female friendship ego networks………………………………….. 64

Figure 4.3.8: Niassa-2 male friendship ego networks……………………………………. 64

Figure 4.3.9: Niassa-2 household friendship ego networks and household income……... 64

Figure 4.3.10: Niassa-2 female kin ego networks………………………………………... 65

Figure 4.3.11: Niassa-2 male kin ego networks………………………………………….. 65

Figure 4.3.12: Niassa-2 household kin ego networks and household income…………… 65

Figure A1.1: Tete-1 survey site…………………………………………………………… 161

Figure A1.2: Tete-2 survey site…………………………………………………………… 162

Figure A1.3: Zambezia-1 survey site……………………………………………………... 163

Figure A1.4: Zambezia-2 survey site……………………………………………………... 164

Figure A1.5: Niassa-1 survey site………………………………………………………… 165

Figure A1.6: Niassa-2 survey site………………………………………………………… 166

Figure C1.1: Tete-1 female friendship ego networks…………………………………….. 170

Figure C1.2: Tete-1 female kin ego networks……………………………………………. 170

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Figure C1.3: Tete-2 female friendship ego networks…………………………………….. 171

Figure C1.4: Tete-2 female kin ego networks……………………………………………. 171

Figure C1.5: Zambezia-1 female friendship ego networks……………………………….. 172

Figure C1.6: Zambezia-1 female kin ego networks………………………………………. 172

Figure C1.7: Zambezia-2 female friendship ego networks……………………………….. 173

Figure C1.8: Zambezia-2 female kin ego networks…………………………………….… 173

Figure C1.9: Niassa-1 female friendship ego networks………………………………...… 174

Figure C1.10: Niassa-1 female kin ego networks………………………………………… 174

Figure C1.11: Niassa-2 female friendship ego networks…………………………………. 175

Figure C1.12: Niassa-2 female kin ego networks………………………………………… 175

Figure D1.1: Tete-1 male friendship ego networks………………………………………. 177

Figure D1.2: Tete-1 male kin ego networks……………………………………………… 177

Figure D1.3: Tete-2 male friendship ego networks………………………………………. 178

Figure D1.4: Tete-2 male kin ego networks……………………………………………… 178

Figure D1.5: Zambezia-1 male friendship ego networks……………………………….. 179

Figure D1.6: Zambezia-1 male kin ego networks………………………………………… 179

Figure D1.7: Zambezia-2 male friendship ego networks…………………………………. 180

Figure D1.8: Zambezia-2 male kin ego networks…...……………………………………. 180

Figure D1.9: Niassa-1 male friendship ego networks…………………………………….. 181

Figure D1.10: Niassa-1 male kin ego networks…………………………………………... 181

Figure D1.11: Niassa-2 male friendship ego networks…………………………………… 182

Figure D1.12: Niassa-2 male kin ego networks…………………………………………... 182

Figure E1.1: Tete-1 household friendship ego networks and household income……...… 184

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Figure E1.2: Tete-1 household kin ego networks and household income……………….. 185

Figure E1.3: Tete-2 household friendship ego networks and household income……...… 186

Figure E1.4: Tete-2 household kin ego networks and household income……………..… 187

Figure E1.5: Zambezia-1 household friendship ego networks and household income..… 188

Figure E1.6: Zambezia-1 household kin ego networks and household income……….… 189

Figure E1.7: Zambezia-2 household friendship ego networks and household income..… 190

Figure E1.8: Zambezia-2 household kin ego networks and household income……….… 191

Figure E1.9: Niassa-1 household friendship ego networks and household income…...… 192

Figure E1.10: Niassa-1 household kin ego networks and household income…………… 193

Figure E1.11: Niassa-2 household friendship ego networks and household income….… 194

Figure E1.12: Niassa-2 household kin ego networks and household income…………… 195

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ACKNOWLEDGMENTS

To my advisor, Dr. Jill Findeis, I would like to thank you for supporting, encouraging, and guiding me throughout my graduate studies at Penn State. Dr. Findeis has been a mentor and a friend and has constantly pushed me to become a better researcher. I would also like to thank my committee members Stephan Goetz, David Abler, Rhonda BeLue, and Gary King for their valuable feedback and encouragement throughout the whole process.

I am grateful to Jill Findeis for my funding and supporting my research trip to

Mozambique and to Gary King who was gave me the opportunity to participate in Penn State’s

MHIRT program and incorporated me into his BBH research team. I am grateful to all the people

I have met along the way. I would like to thank my Mozambican colleagues and friends,

Magalhaes, Maria da Luz, Soares, Celestina, Mulima, Elvis, and Rinze who made Mozambique feel like home and made the research trip a success. I would also like to thank my Penn State friends, Juan, David, Rodrigo, Veronica, Vania, Hernan, and Donaldson.

I would like to thank my parents, Manuel and Maritza as well as my brothers, Manuel and Carlos, for their sacrifices and support to get me to where I am today. And, last but not least,

I would like to thank my wife, Boitumelo, for supporting and guiding me through difficult times and for bringing a smile to my day every day.

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Chapter 1 – INTRODUCTION

1.1 Statement of the problem

An estimated 3.1 billion people live in rural areas in less developed regions of the world

(International Fund for International Development, 2011; United Nations, 2006). Of the 3 billion, nearly 70% rely on agriculture as their major source of livelihood (World Bank, 2011a). As of

2010, approximately 1.2 billion people in developing countries lived in extreme poverty1 (The

Economist, 2013; World Bank, 2013), and 70% of the 1.2 billion people in extreme poverty lived in rural areas and have some dependency on agriculture (Cleaver, 2012). In sub-Saharan Africa, the proportion of the population living in extreme poverty (47%) exceeds the average for developing regions (United Nations, 2012). Furthermore, in sub-Saharan Africa, 66% of the total population live in rural areas and more than 90% depend on agriculture for their livelihoods

(Asfaw et al., 2010)2. Given the distribution of the world’s poor, agricultural growth and development should be a driver of poverty reduction and an important tool in successfully attaining the Millennium Development Goals (de Janvry and Sadoulet, 2010).

In its current state, subsistence agriculture operates as a safety net for the poor population rather than as a driver of economic growth (World Bank, 2005). Between 1990 and 2010, the global percentage of poor was reduced by 50%; at the same rate of progress, extreme poverty would be eliminated by 2030 (Chandy et al., 2013). To combat extreme poverty, greater economic growth and income equality will be required (Chandy et al., 2013) and this may be achieved through poverty reduction strategies that target the productivity, profitability, and sustainability of poor farm households (Asfaw et al., 2010). By promoting rural economic

1 Living on less than $1.25 per day 2 Statistics in Asfaw et al. (2010) refer to 2003 data. 1 growth, to include farm and off-farm opportunities, households can directly benefit from increased food security and incomes (Cord, 2002).

Rural households pursue livelihood strategies that include the diversification of farm and off-farm behavior. Many economic activities in developing countries are influenced by non- market interactions with friends, kin, and acquaintances. These mutually beneficial relationships with friends and family require an investment of scarce household resources such as time and money away from productive activities but in the long run may expand household resources

(Bolin et al., 2003; Manski, 2000). These social interactions are likely to be influential in the economic behavior of households (Fafchamps and Lund, 2003; Fafchamps and Minten, 2002;

Wahba and Zenou, 2005). This dissertation seeks to explore the influence of kin and friendship network structures on economic behavior, specifically off-farm labor decisions and agricultural marketing behaviors in rural Mozambique.

1.2 Research statement and motivation

The research undertaken in this dissertation investigates the consequences of social networks on household decisions using measures of self-reported kin and friendship networks.

Social interactions are important given that an individual’s choices and outcomes can be affected through non-market interactions with others (Topa, 2001). Previous studies have argued that social capital contributes as much if not more than human capital towards income generation

(Boxman et al., 1991; Grootaert, 1999; Narayan and Pritchett, 1999). Furthermore, social capital minimizes search costs, increases trust, and facilitates the dissemination of information

(Fukuyama, 2001). Similarly, social networks have been found to increase communication and cooperation that reduce transaction costs and results in favorable economic outcomes (Dasgupta,

2000). Given the complexities and interdependencies of household production and consumption

2 decisions, social networks are expected to be particularly important in household decision- making. Previous studies have argued that social capital affects household incomes (di Falco and

Bulte, 2011; Haddad and Maluccio, 2003; Narayan and Pritchett, 1999), agricultural technology adoption (Bandiera and Rasul, 2006; Isham, 2002), health status (Hawe and Shiell, 2000; Sirven,

2006; Szreter and Woolcock, 2004), and economic growth (Goetz et al., 2010; Gunning and

Collier, 1999; Helliwell and Putnam, 1995; Knack and Keefer, 1997; Rupasingha et al., 2000).

This dissertation proposes to explore village network structure and decision-making, including

(i) off-farm labor allocation and work choice as well as (ii) marketing of agricultural goods in

Mozambique.

First, with regards to labor allocation, previous studies have demonstrated that social interactions, especially personal contacts, are an important determinant of job status (Corcoran et al., 1980; Granovetter, 1995; Holzer, 1987; Topa, 2001; Wahba and Zenou, 2005; Wasserman and Faust, 1994). Social networks are an efficient and inexpensive job search method (Holzer,

1987). Not only does engagement in a social network improve the job search process but the size of the network also contributes to the likelihood of finding a job (Calvó-Armengol and Zenou,

2005; Calvó-Armengol, 2004; Wahba and Zenou, 2005). To date, however, relatively few studies have looked at labor allocation and social networks with regards to rural communities in developing countries. Who you know and how well you know someone may increase the likelihood of employment as well as facilitate access to certain jobs. The first section of this dissertation will examine labor allocation and work choice decisions related to off-farm work in rural villages in Mozambique.

Second, although wage labor has the potential to increase a household’s disposable income and in some cases to promote food security (Barrett et al., 2001), wage labor

3 opportunities are often limited in developing country contexts. In the absence of wage labor opportunities, incorporating households into the agricultural goods exchange economy may be an effective and alternative poverty-reducing strategy (Heltberg and Tarp, 2002; Komarek, 2010).

One of the most significant constraints for agricultural sales are transaction costs (de Janvry and

Sadoulet, 2006) that can be reduced by facilitating smallholder organization, reducing costs of inter-market commerce, and improving access to improved technologies and productive assets

(Barrett, 2008). Another effective means to increase market participation is to promote information sharing. Access to social networks may help households economize on transaction costs by reducing search costs, increasing trust, and facilitating the circulation of information

(Fafchamps and Minten, 2001; Fukuyama, 1995; Granovetter, 2000; Knack and Keefer, 1997;

Putnam et al., 1994). In the case of agricultural traders, Fafchamps and Minten (2002) show that better connected traders have significantly larger sales than less well-connected ones. Hence, the second section of this dissertation will assess the impacts of selected social network measures on farm household decisions to participate in agricultural markets and on the value of sales.

1.3 Study objectives

This research applies social networks within the framework of the agricultural household model. The agricultural household model framework is used to illustrate the impact of social networks (kin, friendship, and total ego networks) on decision-making of men and women within rural households. The overall goal is to investigate the effect of selected social network measures on economic behavior (labor participation and agricultural marketing) of agricultural households in central and northern Mozambique. Specific objectives include:

1. Objective 1: To analyze the effect of social networks on male and female labor

allocation and off-farm work choices.

4

2. Objective 2: To determine if social networks impact agricultural marketing behaviors of

rural agricultural households.

Data for this project were collected as part of a multidisciplinary project funded in China and East Africa by the McKnight Foundation. The purpose of the project is to improve food security and agro-ecosystem sustainability on low phosphorus soils in Africa through the development and diffusion of common beans (Phaseolus vulgaris) bred to grow well in low phosphorous soils (SCAU et al., 2007). The research will use cross-sectional data from

Mozambique obtained in 2008-09 by researchers from the Institute for Agricultural Research of

Mozambique (IIAM) and Pennsylvania State University. Face-to-face surveys were conducted among randomly-selected households in eight villages throughout Central and Northern

Mozambique for a total of 262 households. The six villages in the bean-growing regions of

Mozambique will comprise the dataset used for this dissertation dropping the number of households in the data set to 201, with male and female surveys for each household included, whenever possible3. The interviews gathered information on demographics, labor allocation, agricultural practices, income sources, health status, as well as community networks.

1.4 Background on Mozambique

Mozambique is located in southeastern Africa. It borders South Africa and Swaziland to the south, Zimbabwe to the west, Tanzania to the north, and Zambia and Malawi to the northwest of the country (see figure 1.6.1). The country is spread across 801,590 km2 with a population of

22.4 million inhabitants. The country is divided into 11 provinces with 129 districts. The capital city of Mozambique is Maputo. There are multiple terrains throughout the country: lowlands along the coast, uplands in the center, high plateau in the northwest, and mountains in the

3 Two of the villages are excluded because different questionnaires were administered in these villages. Furthermore, some households were excluded from the study because of incomplete surveys. 5

Figure 1.6.1: Map of Mozambique4

4 United Nations Cartographic Section (2004) 6 western part of the country (Central Intelligence Agency, 2011). The official language is

Portuguese but over 10 different languages/dialects are spoken including (% of total population):

Emakhuwa (25.3%), Portuguese (10.7%), Xichangana (10.3%), Cisena (7.5%), Elomwe (7%),

Echuwabo (5.1%), other Mozambican languages (30.1%), other foreign languages (4%) (Central

Intelligence Agency, 2011).

1.4.1 Mozambican Economy

Mozambique ended a 16-year civil war in 1992. In 1987, the government began to liberalize its economy in line with standard World Bank/IMF stabilization and structural adjustment programs (Tschirley and Benfica, 2001). The end of the civil war in 1992 coupled with the macroeconomic reforms stabilized the economy (Central Intelligence Agency, 2011).

From a macroeconomic perspective, the country has experienced low and stable inflation rates, falling interest rates, and some of the highest economic growth rates in Africa following the end of the war (Tschirley and Benfica, 2001). At present, the real GDP growth rate for 2012 is estimated to be 7.5% with a per capita GDP of $1200 (2012 USD). Of the total GDP for the country in 2012, approximately 31.8% was from the agricultural sector, 24.6% from industry, and 43.6% from services (Central Intelligence Agency, 2011).

1.4.2 Agriculture

In 2009, the agricultural sector employed nearly 80% of the economically-active population in Mozambique (Food and Agriculture Organization, 2005) but only accounted for

31.5% of Mozambique’s GDP (World Bank, 2011b). Agriculture is dominated by smallholder families as well as small and medium enterprises. Approximately 3.2 million smallholder families account for 95% of the land under production (World Bank, 2006). Smallholders farm in small rain-fed plots that on average are no more than 2 hectares, with low inputs and low yields.

7

Smallholders typically produce food crops such as maize, cassava, rice, and beans, and a small percentage (approximately 15%) also produce cash crops such as cotton and tobacco (World

Bank, 2006). On the other hand, small and medium-private enterprises produce cash crops such as cotton, cashew, other nuts, sugar cane, tobacco, and tea. As a primary source of employment for a large segment of the population, agriculture is important to Mozambican livelihoods.

Between 1996 and 2003, the rural poverty headcount decreased from 69% to 54% (World Bank,

2006).

1.5 Significance of the study

The study will contribute to the agricultural household literature by further assessing the influence of social networks on economic behavior. Specifically, the analysis includes the effect of kin, friendship, and total networks on labor allocation, work choices, and marketing behaviors.

Furthermore, it will explore if kin and friendship networks have different influences on these decisions by gender. From a policy perspective, it is important to know how and why social networks influence economic behaviors as well as the extent to which have larger kin networks, friendship networks, or total networks influence the behaviors of agricultural households. Even though the analysis is restricted to six villages in northern and central Mozambique, the results of this research will be applicable throughout the country and in other country contexts.

1.6 Organization of the dissertation

The remainder of this dissertation is organized as follows: Chapter 2 provides a review of the literature on social capital and social networks, labor allocation, and market participation;

Chapter 3 outlines the theoretical model; Chapter 4 provides an overview of the survey sites and a detailed description of the data used in this research; Chapter 5 presents the methodology,

Chapter 6 discusses the results of the analyses, and lastly, Chapter 7 summarizes the empirical

8 findings and conclusions of the studies as well as discusses the limitations of the study and future directions for research.

9

Chapter 2 – LITERATURE REVIEW

2.1 Introduction

Social capital is believed to be an important factor in the economic activity of agricultural households. Mutually beneficial relationships are especially important during periods of economic and social hardship. The purpose of the following chapter is to provide a brief overview of the definition and criticisms of social capital in Section 2.2; social network analysis in Section 2.3; and a review of recent literature on social networks and economic activity in

Section 2.4.

2.2 Social capital defined

Social capital has been called one of the most powerful and popular metaphors in current social science research (Durlauf and Fafchamps, 2004). The social capital metaphor states that people who are better off are better connected (Burt, 1992). Unfortunately, along with the popularity of the term come a plethora of definitions. Social capital has been defined as both an individual level and community level attribute. Coleman (1988, p.98) defines social capital as

“not a single entity, but a variety of different entities having two characteristics in common: they all consist of some aspect of social structure and they facilitate certain actions of individuals who are within the structure”. Portes (1998, p.6) defines social capital as “the ability of actors to secure benefits by virtue of memberships in social networks or other social structures.” On the other hand, Ostrom (2000, p.176) states that “social capital is the shared knowledge, understandings, norms, rules, and expectations, about patterns of interactions that groups of individuals bring to a recurrent activity”. According to Woolcock (2001, p.3), the basic idea of social capital is that “one’s family, friends, and associates constitute an important asset, one that can be called upon in a crisis, enjoyed for its own sake, and/or leveraged for material gain.” As a

10 community level attribute, Bourdieu (1980, p.51) defines social capital as “the aggregate of the actual or potential resources which are linked to possession of a durable network of more or less institutionalized relationships of mutual acquaintance and recognition”. Putnam (1993, p.167) describes social capital as “features of social organization, such as trust, norms and networks that can improve the efficiency of society by facilitating coordinated actions.” Finally, Narayan-

Parker (1997, p.50) writes “social capital is the rules, norms, obligations, reciprocity and trust embedded in social relations, social structures and society’s institutional arrangements which enable members to achieve their individual and community objectives.”

The major criticism of social capital is reflected in the previous paragraph. That is, there is not a widely-accepted definition of social capital. Based on the definitions, it is difficult to conclude whether social capital is an individual or collective good (Lin, 1999a). Several authors

(i.e. Haddad and Maluccio, 2003; Durlauf and Fafchamps, 2004) have been critical of the vagueness of the concept. Durlauf and Fafchamps (2004) argue that conceptual vagueness limits theoretical and empirical research. On the other hand, Haddad and Maluccio (2003) have labeled the concept as a catch-all for concepts that cannot be assigned to more tangible forms of capital.

Other authors (Arrow, 2000; Dasgupta, 2000; Solow, 2000) are critical of labeling the concept as a form of capital and advocate to rename the concept differently. They claim that other forms of capital such as economic and human capital can be identified with tangible, durable, and/or alienable objects that can be measured and assessed.

2.3 Social networks defined

Despite the criticisms of social capital, all definitions of social capital have in common that they regard social interactions as a necessary component and that the social interactions generate an externality (Haddad and Maluccio, 2003; Lin, 1999a). The focus on social

11 interactions has given increased importance to social network analysis (SNA) as a methodology for examining the impact of social interactions on economic outcomes. However, this does not imply that social networks and social capital are equivalent or interchangeable. Instead, social capital is regarded as a function of network size, relationship strength, and resources within a network (Bourdieu, 1980; Flap, 2002; Lin, 2008). Therefore, social networks help address major social capital criticisms by providing a clearly defined and more coherent conceptualization and a rigorous measurement of the social dimensions of economic life (Leavy, 2011).

Social networks are defined by a finite set of actors (individuals, groups, organizations, communities, states, or countries) and social network analysis (SNA) focuses on the patterns of relationships among network members (Jackson, 2008; Newman, 2003; Streeter and Gillespie,

1993; Wasserman and Faust, 1994). Social interactions are expressed in social network analysis via nodes and ties (Jackson, 2008; Wasserman and Faust, 1994). A node is an (individual) actor within a network of relationships and a social tie represents the relation between nodes (actors) in a network (Hanneman and Riddle, 2005; Wasserman and Faust, 1994). Relations can be directed or undirected depending on whether reciprocity in the relation exists (Jackson, 2008).

The most basic form of network data consist of binary responses that indicate whether or not a relation exists (Hanneman and Riddle, 2005; Jackson, 2008). Network data can be visualized graphically, with the visual representation of a network allowing examination of social network composition (Hanneman and Riddle, 2005; Wasserman and Faust, 1994). The shape (i.e., tree, forest, star, and circle networks) allows one to determine the importance of a network to its members (Jackson, 2008). Smaller and also more homogeneous networks may be less valuable to a network member since everyone is likely to have access to the same information, whereas

12 larger and more heterogeneous networks are preferred since weak ties (distant connections) are more likely to share novel ideas and opportunities (Granovetter, 1973)5.

2.4 Social networks and economic activity

Social networks analysis seeks to describe the structure and patterns of social interactions as well as to analyze their causes and consequences (Jackson and Watts, 2002; Jackson, 2008;

Streeter and Gillespie, 1993; Wasserman and Faust, 1994). In rural communities, formal and informal networks are present. Informal networks are relationships between individuals bound together by kinship, friendship or proximity (Angelucci et al., 2011; Fafchamps and Lund, 2003;

Rose, 2000) whereas formal networks include relationships with organizations (or individuals within organizations) such as business groups, agricultural associations, extension workers, local branches of political parties, and elected officials (Grootaert, 1999; Rose, 2000). An individual can belong to multiple networks and each network can complement, supplement, or substitute for the function of another network. Studies have explored the role of networks on economic outcomes. Below, selected empirical studies are reviewed on labor participation, off-farm work choices, labor supply, and agricultural marketing behaviors. Furthermore, the role of social networks in each of the topics will be explored.

2.4.1 Off-farm labor participation

Rural non-farm income is an important resource for rural households and increasingly non-farm activities are seen as a means out of poverty for agricultural households (Food and

Agriculture Organization, 2008). Nonfarm income contributes a significant share of 30% and

50% of household income throughout the developing world (Haggblade, 2005; Haggblade et al.,

2009). Most off-farm labor participation and labor supply studies in rural contexts have not

5 Homogeneous network may be defined by people sharing similar traits, for instance, of the same age, gender, ethnicity, relationship, employment, location, etc. whereas heterogeneous networks may be defined by dissimilar people in terms of their age, gender, ethnicity, relationship, employment, location, etc. 13 acknowledged the role of social networks relative to labor outcomes. The literature on off-farm labor in developing countries has examined aggregate off-farm employment (i.e., Jacoby, 1993;

Skoufias, 1994; Abdulai and Regmi, 2000; Abdulai and Delgado, 1999; Swaminathan et al.,

2010) as well as off-farm work choices (Babatunde and Qaim, 2010; de Janvry and Sadoulet,

2001; Ortega-Sanchez, 2001; Ruben and Van den berg, 2001; Woldenhanna and Oskam, 2001;

Yúnez-Naude and Taylor, 2001).

Two of the most influential works on off-farm labor supply are those of Jacoby (1993) and Skoufias (1994). The contributions of these papers are the methodologies for estimating structural time allocation models. These studies accounted for the non-separability of consumption and production decisions among rural households in developing countries. They first estimate a shadow wage from an agricultural production function and then implement an instrumental variable approach to estimate household labor supply for off-farm work.

More empirical analysis has included the work of Abdulai and Delgado (1999) who study nonfarm work participation decisions of married men and women in rural northern Ghana using a bivariate probit. Their findings show that individual characteristics (education and experience) and village characteristics (infrastructure, distance to the capital, population density) are significantly related to the probability of nonfarm labor market participation and the amount of nonfarm labor performed. Abdulai and Regmi (2000) conduct a similar study on the labor supply of farm households in Nepal. Estimates of the male and female shadow wages are obtained from the agricultural production function and then used in an instrumental variable approach to recover the household's structural labor supply. The study finds that male and female labor supply is sensitive to changes in shadow wages and income. Furthermore, they find that

14 education has a significant positive effect on output but no statistically significant effect on the labor supply of individuals.

Abdulai and CroleRees (2001) examine differences in the determinants of income diversification among poor and non-poor households. Their study relies on a conditional fixed effects logit using agricultural household data from southern Mali. They determine that poorer households have less diversified incomes resulting from limited opportunities in non-cropping activities such as livestock rearing and non-farm work. Furthermore, they find that the largest limitation for income diversification is a lack of capital, making it harder for poor households to diversify away from subsistence agriculture. Another study conducted by Canagarajah et al.

(2001) report that distribution of earnings differs by labor type and gender. The study relies on data from rural Ghana and Uganda. Results of the study show that off-farm income contributes to rising inequality particularly among female-headed households. Furthermore, the study finds that inequality is driven particularly by self-employed off-farm labor income whereas wage income seems to reduce inequality. Matshe and Young (2004) study small-scale agricultural households in the Shamva District of Zimbabwe. Their study implements a double hurdle model to jointly model the participation decision and the amount of hours allocated to off-farm work. Empirical analysis shows that individual, household, and farm characteristics have significant yet different effects on the participation decision as well as the labor hours supplied.

Another set of studies have expanded off-farm labor studies by looking at the different off-farm work choices available to the household. Woldenhanna and Oskam (2001) specifically looked at wage employment and self-employment in the Tigray region of Northern Ethiopia.

They use a multinomial logit to examine participation in these work choices. The study finds that the decision to pursue off-farm wage labor is driven by low farm income and surplus family

15 labor, whereas the decision to pursue off-farm self-employment is motivated by the possibility to earn an attractive return. Furthermore, high entry barriers result in relatively wealthy households occupying the most lucrative rural non-farm activities such as masonry, carpentry and small- scale trade.

Another study by Yúnez-Naude and Taylor (2001) examined the role of education on household income and activity choice in the farm and non-farm sectors. The study relies on data collected from 391 households across eight rural areas in Mexico between 1992 and 1995. Using

Lee’s generalization of Ameniya’s two step estimator to a simultaneous equation model, the study first estimates separate univariate probit regressions for the different work choices and then introduces the inverse-Mills ratios in the corresponding activity net income equation. From the first stage regressions, the authors find that education and years of schooling have an impact on the work choice of rural households. With regards to farming, completion of primary, secondary, and preparatory education have a positive effect on household income from basic grains, and completion of higher levels of education had a positive and significant impact on commercial farm incomes. With regards to off-farm work choices, a positive relation exists between completion of primary and secondary schooling and the likelihood of participation in non-farm self-employment and wage employment.

The study by de Janvry and Sadoulet (2001) examined participation in agricultural wage labor, construction work, other non-agricultural wage labor, self-employment outside of agriculture, and seasonal migration to the US. Using 1997 data from a nationwide survey, the authors examine the determinants of off-farm sources of income for 3188 individual adults in

928 households using a multinomial logit. The study concludes that education plays an important role in accessing more highly remunerated off-farm employment choices. Also, they find that

16 gender differences exist with regards to employment opportunities; women are significantly less likely than men to participate in different off-farm labor activities.

The study by Ruben and Van den berg (2001) analyzes the importance of non-farm wage employment and self-employment for rural farm households. The study relies on a national income and expenditure survey collected between 1993 and 1994 of 2584 economically active household members across 818 households in rural Honduras. The analysis relies on separate logits of the different off-farm work choices and two stage least squares regression of the calorie intake adequacy ratio on household income sources. Results indicate that hired farm labor is primarily pursued by males with little or no education whereas more highly remunerated jobs

(wage employment) are accessible to educated males in larger households. Females, on the other hand, appear to be limited to self-employment activities.

Swaminathan et al. (2010) uses a sample of 404 households from 45 villages from the

1995 Malawi Financial Markets and Household Food Security Survey to determine the effect of formal and informal access to credit by men and women on their labor allocation decisions. Their findings indicate that the women’s likelihood of participation in off-farm work is affected by formal and informal access to credit whereas men’s likelihood of participation is driven primarily by informal access to credit. Furthermore, women’s education seems to play no role in their self-employment activities suggesting that the majority of women are engaged in low-skill and low-wage activities.

It is important to consider the literature on labor outcomes from developed countries in order to account for social networks. Granovetter (1995) studied how individuals find jobs as well as the quantity and quality of contacts that job-changers had. The study used a sample of

457 men in Newton, Massachusetts. Results show that personal contacts are the most important

17 method for learning about new jobs, and that job search though personal contacts leads to better quality and higher paying jobs than job search though traditional means. Another study by Allen

(2000) used data from the Wisconsin Entrepreneurial Climate Study to analyze self-employment decisions of adults residing in Wisconsin. The author hypothesized that if social networks reduce the costs of engaging in self-employment, then households with more effective social networks are more likely to be employed in self-employment. The results of the study show that individual self-employment choice is influenced by the size and composition of social networks and that there is a gender bias within networks; women are less likely to benefit from social networks in their self-employment choices. Topa (2001) analyzes information exchange regarding jobs using census tract data for Chicago. The study finds that the likelihood of employment increases if contacts within his/her social network are also employed. Furthermore, the impact of social networks on employment status is greater in areas with less educated workers as well as areas with a higher fraction of minorities. Lastly, Wahba and Zenou (2005) develop and test a theoretical model for the study of labor outcomes. Using the 1998 Labor Market Survey for

Egypt, the authors determine that the likelihood of finding a job through social networks increases as network size increases. However, they also conclude that there is a critical network size after which the likelihood of finding a job decreases.

2.4.2 Marketing behavior

The bulk of the literature on marketing behavior does not account for the role of social networks on marketing behavior. More recent literature accounts for (i) social capital and (ii) social networks in marketing behavior. Some of the leading work in the former includes Goetz

(1992), Key et al. (2000), Heltberg and Tarp (2002), Bellemare and Barrett (2006), and Barrett

18

(2008), whereas the latter includes studies such as Fafchamps and Minten (1999), Lyon (2000)

Fafchamps and Minten (2001), Bernard and Spielman (2009), and Leavy (2011).

One of the most influential studies on agricultural marketing behavior is that of Goetz

(1992). In his study, the agricultural household's discrete decision of whether to participate in agricultural markets is separated from the continuous decision of how much to sell or buy. The study finds that fixed transaction costs are a major barrier for market participation and that improved market information increases the likelihood of market participation by sellers. On the other hand, access to coarse grain processing technologies (i.e. processing mills) increases both the quantity of goods purchased and sold.

Building off of the work of Goetz (1992), Key et al. (2000) develop a model of supply response that accounts for fixed and proportional transactions costs. Their study finds that fixed and proportional transaction costs are important determinants of marketing behavior.

Furthermore, proportional transaction costs are found to be particularly important for selling households. Their empirical analysis using Mexican data finds that lowering transaction costs, through improved transportation and marketing organizations, increases both market participation and volume of sales for market participants.

Using the framework of Key et al. (2000), Heltberg and Tarp (2002) identify causal factors behind farmers’ marketing decisions in Mozambique. They find that non-price factors such as risk, technology and transport infrastructure are important in the marketing decision.

Further, they conclude that the determinants of market participation and volume of sales do not differ by socio-economic status. Bellemare and Barrett (2006) contribute to the literature by examining market participation decisions as well as volume of sales decisions simultaneously and sequentially. Using household data from Kenyan and Ethiopian livestock markets, they find

19 that household decision-making is a sequential process. They also find that pastoralist households do not participate in livestock markets for two reasons: the first is the high fixed costs associated with market participation and the second reason is that livestock are the preferred form in which to hold assets.

Another study by Barrett (2008) examines smallholder market participation for staple food-grains in eastern and southern Africa. The purpose of the study is to determine the interventions most likely to lift smallholders out of the semi-subsistence poverty trap. Results reveal that the interventions most likely to yield success are those that facilitate smallholder organization, reduce costs of inter-market commerce, and improve access to improved technologies. Lastly, Komarek (2010) conducted a study to identify the determinants of marketing behavior for perishable crops, specifically bananas. The study relies on a double hurdle model that incorporates both fixed and variable transaction costs. Results show that improving banana-growing productivity, increasing prices and reducing the costs associated with distance to market are important determinants of marketing behavior in Uganda.

More recent literature has expanded the analysis to include social interactions.

Fafchamps and Minten have been at the forefront of this analysis. Fafchamps and Minten (1999) and Fafchamps and Minten (2001) study the social networks that agricultural traders maintain.

Fafchamps and Minten (1999) find that the transaction costs associated with agricultural trade make social networks more important than investments in physical or human capital. Both

Fafchamps and Minten (1999) and Fafchamps and Minten (2001) conclude that social networks have a large effect on firm productivity, with better connected traders having significantly larger sales and value added than less connected traders. Another study by Johnson et al. (2003) measures social capital as firm network size and the strength of their relationships. Their analysis

20 of Colombian rural agro-enterprises finds that social capital provides access to information and reduces transaction costs. They also conclude that firm-level returns to social interactions are greater than returns to physical or human capital.

Lyon (2000) documents the means through which trust is created among farmers, traders and agricultural input suppliers. Specifically, the study examined social networks composed of both formal and informal networks. The study finds that social networks are essential for business success in Ghana. Trust is necessary to operate where formal legal institutions are lacking. Lastly, a study by Leavy (2011) explores the relationship between social networks and agricultural market participation among rural households in Zambia. The study finds that economic exchange takes place via reciprocal or kinship bases. Importantly, the author finds that ego network size and ego network type are important determinants of total crop income. Larger ego networks have a negative effect on total crop income whereas a greater proportion of kin in ego network has a positive effect on agricultural income.

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Chapter 3 – THEORETICAL MODEL

3.1 Introduction

In most developing countries, agriculture is the principal source of economic activity for rural households (Singh et al., 1986). Most households are semi-subsistent, producing mainly for home consumption and selling off surplus goods in the market. Researchers interested in informing policy should take into account the complexities and interdependencies of production, consumption, and labor supply in these settings. Implementing a policy without the appropriate analysis can have positive or negative spillover effects into unintended areas.

Economists rely on agricultural household models as the basis for micro-research in rural areas (Huffman, 1991; Singh et al., 1986; Taylor and Adelman, 2003). Agricultural household models provide a theoretical framework for economists to study households as producers and consumers, making production, labor allocation, and consumption decisions either sequentially

(separable model) or simultaneously (non-separable model) (see Singh et al., 1986). In its most general form, the goal of the household model is to maximize utility through the consumption of goods and leisure subject to a set of constraints, i.e., time, budget, and production constraints

(Taylor and Adelman, 2003). Agricultural household models have evolved over time from separable to non-separable models and from unitary to collective household models.

The most basic agricultural household model is based in Becker's (1965) model of household production. The unitary household model assumes that households behave as a single entity, with one set of preferences for consumption goods and leisure, combined time, as well as a common budget constraint (Barnum and Squire, 1979; Haddad et al., 1997; Rosenzweig and

Pitt, 1984). If the model is applied to the case of no missing markets, then there exists a market for all products and factors of production and their respective prices are exogenous to the

22 household. Furthermore, perfect markets necessarily imply no transaction costs for economic activity and separability can be assumed when modeling household decisions (Swaminathan,

2002). If separability can be assumed, maximized profit levels can be used to determine optimal consumption levels.

The non-separable model is suitable for rural areas in developing countries in part because missing markets that typify these areas result in a breakdown of the separability assumption. In rural areas, missing markets exist because of geographical location and/or imperfect institutions (Singh et al., 1986). Because of imperfect markets, differences exist between purchase and sale prices of commodities. If there are missing markets, then separability is no longer a valid modeling approach and production and consumption decisions must be solved simultaneously. If the model is non-separable, then the household budget becomes endogenous and dependent on the production decisions of the farm component of the household

(Taylor and Adelman, 2003). Failure to study household consumption and production decisions under the appropriate model can result in inconsistent parameter estimates for demand and supply (Singh et al., 1986).

The unitary model has received much criticism because it ignores that individuals act in their own self-interest (i.e., they may not share) and because it oversimplifies the household economy (Browning et al., 1994; Schultz, 2001). The unitary model regards households as being composed of individuals who either share the same set of preferences or are dictated by a single decision-maker (“benevolent dictator”) (Becker, 1993). The unitary model also disregards 1) the influence of conflicting personal preferences on outcomes and 2) the impact of different sources of income (and who earns them) on intra-household allocation of resources (Schultz, 2001).

23

Given criticisms and limitations of the unitary model, subsequent models attempted to relax the assumption of the unitary model to better reflect economic theory and reality. Two alternative approaches were developed: 1) the bargaining household model (Manser and Brown,

1980; McElroy and Horney, 1981) and 2) the collective household model (Browning and

Chiappori, 1998; Chiappori, 1997, 1992, 1988a).

Bargaining household models were developed in the 1980’s and include cooperative and non-cooperative household bargaining models (Manser and Brown, 1980; McElroy and Horney,

1981). The bargaining models departed from the strict assumption of the unitary model and allowed for individual decision-making rather than a single individual (i.e., the ‘household head’) just one person making decisions on behalf of the household. Although an improvement over the strict unitary household model, the bargaining household models received criticism for unrealistic initial assumptions about the decision process (cooperative and non-cooperative behaviors). Later, Chiappori, (1988a) argued that unless preferences are known a priori, the bargaining outcomes should be treated as Pareto efficient outcomes. As a result, the collective household framework makes the assumption that households are Pareto efficient6. The collective household model assigns a utility function to each household member and Pareto efficiency implies that, regardless of how decisions are made, individual decisions result in Pareto efficient outcomes (Browning and Chiappori, 1998; Chiappori, 1997, 1992)7. Household utility is based on a sharing rule that allows for different preferences within the household (Chiappori, 1988a).

The household is assumed to maximize a weighted sum of each member’s individual utility

6 Pareto efficiency is justified given that household members are assumed to know each other and their actions fairly well and because household members are assumed to be in relatively stable and long-term relationships (Udry, 1996) 7 In a two person model this would imply that no one could be made better off without making the other person worse off. 24 function with different weights assigned to each member subject to a pooled budget constraint

(Browning and Chiappori, 1998).

3.2 Theoretical model for labor allocation

The model used here will follow the collective model outlined in Swaminathan (2002) and Zhang, (2011); additionally, social networks will be incorporated into the household model as in Katungi (2007).

Following Chiappori (1992), consider a simplified farm household with two egoistic members, male (i=1) and female (i=2). The utility function for individual i can be represented by:

(1) where represents a vector of own consumption goods for individual i (including a shared public good), represents leisure for individual i, and represents a vector of individual and household characteristics. The utility function for i is increasing and concave in own consumption and leisure (twice differentiable):

(2)

The household is assumed to maximize the weighted sum of male (i=1) and female (i=2) individual utility functions with different Pareto weights assigned to each member (Browning and Chiappori, 1998; Browning et al., 1994). The weighted sum of individual utility functions is represented as:

(3) where represents the Pareto weight associated with household members. For instance, in the extreme cases, if , the household behaves according to individual i’s preferences, whereas if , the household behaves according to individual –i’s preferences

25

(Chiappori, 1992). The Pareto weights depend on many observable and unobservable individual and household level attributes (Chiappori, 1992; Duflo and Udry, 2004)8.

The farm household faces four constraints: (i) time constraint, (ii) production constraint,

(iii) budget constraint, and (iv) non-negativity constraint. Each constraint is described in detail below.

Time constraint

Individuals must allocation their total time , between leisure , own farm labor , and

wage labor where i indexes either male (1) or female (2):

(4)

Production constraint

The farm production technology (Q) requires own farm labor and farm labor of the

other household member for production to take place on a set of farm fixed inputs

(i.e., land) and a stock of knowledge regarding agricultural production (K). The production function for farm goods (Q) is represented by:

(5)

The farm production function is continuous and twice differentiable ( ) in own labor. The stock of knowledge regarding agricultural production (K) is assumed to evolve as a function of experience with the production technology and social network capital

(Isham, 2002; Katungi, 2007). The stock of knowledge can be expressed as:

(6)

Substituting equation (6) into (5) yields the production constraint:

8 i.e., demographics, social norms, marriage laws and traditional as well as divorce laws and traditions. 26

(7)

Budget constraint

The purchase of consumption goods is financed with household income (Y). Total household income (Y) is a function of net farm income, non-farm income, and exogenous household income. Following Katungi (2007), exogenous income is composed of net transfers from private assets (i.e., rent from land or other property) and bilateral transfers from social networks (i.e., gifts, free labor, remittances, or informal credit) and can be stated as:

(8) where I represents net transfers from private assets and represents bilateral transfers from social networks.

The budget constraint states that the joint consumption of household members cannot exceed the sum of net farm income, non-farm income, and exogenous household income

. The budget constraint is represented by:

(9)

where the price of consumption goods is normalized to one, is the price of farm output and represents individual i’s non-farm market wage.

Non-negativity constraint

The number of hours allocated to own farm labor , and wage labor must be greater than or equal to zero:

(10)

Allocation of household resources

The solution to the household utility maximization problem can be expressed as:

27

(11) subject to the time constraint (4), production constraint (7), budget constraint (9), and non- negativity constraint (10). The choice variables of the utility maximization problem are

. After substituting the production constraint (7) into the budget constraint (9), the utility maximization problem facing the household can be expressed as the

Lagrangian (L) below:

(12)

The Kuhn-Tucker theorem states that there exist multipliers , all that generate the following first-order conditions (Swaminathan, 2002):

(13)

(14)

(15)

(16)

(17)

(18)

(19)

28

(20)

(21)

(22)

(23)

(24)

(25)

(26)

(27)

Equation (13) and (14) are the first order derivatives of the utility functions with respect to consumption for males (i=1) and females (i=2), respectively. Likewise, equations (15) and (16) are the first order derivatives of the utility functions with respect to leisure for males (i=1) and females (i=2), respectively. From equations (13) and (15) as well as equations (14) and (16), the following are obtained:

(28)

(29)

which implies that males (i=1) and females (i=2) will equate their marginal rate of substitution between consumption and leisure and the shadow wage (Skoufias, 1994).Rearranging equations (17), (18), (19), and (20) yields the following, respectively:

(30)

29

(31)

(32)

(33)

Substituting equation (30) into (28), equation (31) into (29), equation (32) into (28), and equation

(33) into (29), yields the following regarding the marginal rate of substitution between consumption and leisure for males (i=1) and females (i=2):

(34)

(35)

(36)

(37)

Given the latter, it is possible to explore three scenarios that can arise with regards to farm work and off-farm work (Swaminathan, 2002; Zhang, 2011):

Scenario 1: Perfect labor market ( and ).

If and then for equations (24)-(27) to hold, it must be the case that and

for . When applied to equations (34)-(37), the following is obtained:

(38)

This is a perfect labor market where farmers distribute their labor across farm labor and off-farm labor such that the marginal return across all labor types is equal to the market wage.

30

Scenario 2: No off-farm labor market ( and ).

If and then for equations (24)-(27) to hold, it must be the case that and

for . When applied to equations (34)-(37), the following is obtained:

(39)

Situation 2 is a scenario in which there is no market for off-farm labor since the marginal return of farm production is greater than the market wage :

(40)

Scenario 3: No farm labor ( and )

If and then for equations (24)-(27) to hold, it must be the case that and

. When applied to equations (34)-(37) the following relationship is obtained:

(41)

Situation 3 presents the no farm labor case resulting from the fact that the market wage is greater than the marginal return from farm labor:

(42)

3.3 Model for agricultural market behavior

Some of the earlier attempts at modeling agricultural marketing behavior include Strauss

(1984) and Goetz (1992).For this section, however, the agricultural household model developed

31 by Key et al. (2000) will be used to account for transactions costs in agricultural marketing behavior9. The model will be extended to include social networks as in Katungi (2007).

Following Key et al. (2000), a simplified model is used that ignores important components of household decision-making, particularly risk and credit constraints. Preferences can be represented by the utility function:

(43) where represents consumption good k , L represents leisure for the household, and z is a vector of individual and household characteristics. Consumption goods include self- produced agricultural goods and market commodities. The utility function is increasing and concave in own consumption and leisure. The household faces fours constraints: budget constraint, resource balance constraint, production constraint, and a non-negativity constraint.

Budget constraint

Following Katungi (2007), exogenous household income (E) is composed of net transfers from private assets (i.e., rent from land or other property) and bilateral transfers from social networks (i.e., gifts, remittances, or informal credit) and is represented by:

(44) where I represents net transfers from private assets and represents bilateral transfers from social networks. The budget constraint states that the purchase of market goods is financed with sales of farm goods and exogenous household income. The budget constraint can be expressed as:

(45)

9 Transaction costs include fixed or variable transaction costs. Fixed transaction costs are one-time costs invariant to the quantity traded whereas variable transaction costs are per unit costs (Key et al., 2000). 32 where represents the market price of good k, represents the quantity of good k marketed, and E represents exogenous household income. The marketed good variable represents a sale of good k if and a purchase of good k if

Resource balance constraint

The resource balance constraint states that for each good k , the quantity consumed and sold is equal to the sum of the initial endowment, quantity produced, and quantity purchased. The resource balance can be expressed as:

(46) where represents the quantity produced of good k, represents the initial endowment of good k, represents the quantity of good k marketed, and represents the quantity of good k consumed by the household.

Production constraint

The simplified production technology (Q) requires own farm labor for production to take place with a stock of knowledge regarding agricultural production (K) and a set of farm fixed inputs (i.e., land). The production function for farm goods (Q) is represented by:

(47)

The farm production function is continuous and twice differentiable ( ) in own labor. As in Katungi (2007), the stock of knowledge (K) is assumed to evolve as a function of experience with the production technology and social network capital . The stock of knowledge is expressed as:

(48)

Therefore, substituting equation (48) into (47) yields the production constraint:

(49)

33

Non-negativity constraint

The non-negativity constraint for the household states that the consumption and production of good k must be greater than or equal to zero:

(50)

Allocation of household resources

A farm household decides how much to consume , produce , buy and sell

of each good k . Therefore, in the absence of transaction costs, the household utility maximization problem can be expressed as:

(51) subject to the budget constraint (45), resource balance constraint (46), production constraint (49), and non-negativity constraint (50).

Introducing transaction costs

A household may face transaction costs associated with agricultural market exchange.

Transaction costs are classified as fixed transaction costs (FTCs) or variable transaction costs

(VTCs) (Heltberg and Tarp, 2002). FTCs costs are one-time costs invariant to the quantity traded and may include search costs, bargaining costs, and supervision costs associated. As one-time costs, FTCs are assumed to affect market participation and not the quantity marketed (Heltberg and Tarp, 2002; Key et al., 2000). Direct measures of FTCs are mostly unobservable, therefore,

FTCs are expressed as a function of observable exogenous characteristic, and , that affect these costs when selling and buying, respectively (Key et al., 2000). Variable transaction costs

(VTCs) are per-unit costs that may include transportation costs, storage, packaging costs, marketing costs, as well as a seller’s opportunity cost of time spent (Heltberg and Tarp, 2002).

VTCs are assumed to affect both the market participation decision and the quantity marketed

(Heltberg and Tarp, 2002; Key et al., 2000). Like FTCs, VTCs are difficult to measure and/or 34 observe and are therefore expressed as a function of observable exogenous characteristics, and

, that affect VTCs when selling and buying, respectively (Key et al., 2000).

Many households fail to participate in agricultural markets because of transaction costs associated with agricultural market exchange (Goetz, 1992). Transaction costs (FTCs and VTCs) create differences between the shadow price (price at which an agricultural household decides whether to participate in the market as a buyer/seller or not participate in the market at all) and the market price of the agricultural good (Goetz, 1992; Key et al., 2000).

To incorporate transaction costs into the household model, let FTCs on sales of

agricultural good k be defined as and FTCs on purchases be defined as .

Furthermore, let if and zero otherwise. Similarly, let if and zero otherwise. Fixed transaction costs for good k can be expressed as:

(52)

Let VTCs on sales of agricultural good k be defined as and VTCs on purchases be

defined as . Then, variable transaction costs can be expressed as:

(53)

where if (and zero otherwise) and if (and zero otherwise).

Allocation of household resources (with transaction costs)

The household utility maximization problem can be rewritten to include fixed and variable transaction costs:

(54) subject to:

35

(55)

(56)

(57)

(58)

Equations (54), (56), (57), and (58) are the same as discussed in the no transaction case. Equation

(55) is the budget constraint for the household. With transaction costs, a household will no longer purchase/sell good k at price . When sales of good k involve transaction

costs, the price received by a household will be the market price minus per-unit costs of ,

and a fixed cost of for good k. On the other hand, when purchases of good k

involve transaction costs, a household pays the market price plus a per-unit cost of on

every good k purchased and an additional fixed cost of on the purchase of good k. To derive the supply and demand equations for a household facing transaction costs, the Lagrangian is defined as (Key et al., 2000):

(59)

( ) + + ,

where , all are the Lagrangian multipliers associated with the resource balance constraint, production constraint, and budget constraint, respectively. Key et al. (2000) notes that fixed variable transaction costs create discontinuities in the Lagrangian that make the optimal solution unattainable by simply solving the first-order conditions. Instead, Key et al. (2000) suggest that the following steps be pursued: 1) solve for the optimal solution conditional on market participation regime (buyer, seller, no market participation) and then 2) choose the

36 market participation regime that yields the highest level of utility. From here, it is possible to estimate the output marketed supply, conditional on market participation.

37

Chapter 4 – DATA

4.1 Research trip to Mozambique

Between August 2008 and August 2009, an ex ante baseline socioeconomic questionnaire was administered to Mozambican households by the Institute for Agricultural Research of

Mozambique (IIAM) and Pennsylvania State University (PSU). The baseline study was administered to gain knowledge about: 1) household composition, 2) labor allocation, 3) agricultural production and technology adoption, 4) malaria knowledge, prevention and treatment, as well as 5) friendship and kin networks.

Interviews were conducted in a face-to-face format by enumerators fluent in Portuguese and in local languages. All interviewers received training before and during the data collection process, and data quality control was checked by supervisors in the field as well as at the end of the day. When conducting the interviews, a team of enumerators (a trained male and trained female) approached each household and asked to talk with the male and female decision-makers.

If either male or female decision-maker was absent during the interview process, the survey was administered to the decision-maker who was present, and enumerators attempted to survey the missing member on at least two other occasions.

The surveys were conducted in central and northern Mozambique in eight villages located in the provinces of Manica, Tete, Zambezia, and Niassa. Survey site selection was done in coordination with researchers from IIAM familiar with the country. Using Google Earth, two villages per province were selected; one village was selected that can be considered IIAM influenced (i.e., within 50 kilometers of an IIAM research station or zonal center or a village typically used as a test site by IIAM) and the other village ‘not IIAM influenced’ (50 kilometers or more from IIAM research station or zonal center or not having received IIAM support in the

38 past). This dissertation uses relies on household surveys collected in the provinces of Tete,

Zambezia, and Niassa – all regions and sites in what are considered the bean growing regions of

Mozambique . Manica is excluded since villages in this province were surveyed in the pretest pilot with a (somewhat) different survey instrument. Also, Manica is not considered a prime bean-growing region due to its lower altitude. Figure 4.1.1 provides the geographical location of the 6 research sites for the study.

For each village, two poster-size Google Earth maps were developed (for examples, see

Appendix A figures A1.1-A1.6); a full landscape map to capture the entire village and a quadrant map to show households at close proximity. The quadrant map captured households within 2 km from the village center in each direction. Household structures visible on the map were assigned household numbers. Once the interview team arrives at the village, the quadrant and full landscape maps were updated to reflect changes in the village10. For instance, updates were made to exclude household structures no longer standing but visible and intact in Google Earth as well as household structures that were recently built but not visible in Google Earth. Additional information added to the maps included identifying and labeling important landmarks such as schools, hospitals, clinics, rivers, and roads. The twin purposes of the housing and landmark labels were to allow respondents and enumerators to better understand the geographic location of friend households and kin households in the village, and also to geographically tag important

‘places’ for the research.

Using the fully populated quadrant and full-landscape maps, a random sample of 35 households was selected in each village using a table of random numbers. The 35 randomly selected households were pursued; if neither the male nor female were present after multiple

10 The Google Earth maps are not real-time maps. 39

Figure 4.1.1: Survey sites (village) location

40 visits, then, on the last day of interviews, a random household was selected using the table of random numbers. Reasons that the initial 35 randomly-selected household may not have been available include migration (i.e., migrated to another area), destruction of property (household structure may have been burnt or torn down), etc.

Table 4.1.1 provides information on the six sites (villages). Villages in Tete province are located in Angonia district and are labeled as Tete-1 and Tete-2. In Zambezia, the villages are located in different districts; Zambezia-1 is located in Gurue district whereas Zambezia-2 is located in the Alto Molocue district. The last two villages are located in Niassa province; Niassa-

1 is located in Mandimba district and Niassa-2 in Lichinga district. The useable (full) sample consists of 201 households. Of the 201 households, 88 households had both male and female present at the time of the interview. The full sample consists of 162 women and 127 men.

Table 4.1.1: Survey sites Married sample Full sample Province District Survey site Households Females Males Females Males Tete Angonia Tete-1 31 18 18 25 24 Tete Angonia Tete-2 35 16 16 33 18 Zambezia Gurue Zambezia-1 35 13 13 26 22 Alto Zambezia Zambezia-2 33 16 16 25 24 Molocue Niassa Mandimba Niassa-1 30 11 11 24 17 Niassa Lichinga Niassa-2 37 14 14 29 22 Total 201 88 88 162 127

41

4.2 District profiles

The following section provides a background on the districts where the sites (villages) are located11. Angonia has a total land area of 3,277 square kilometers and a population of approximately 360,000 (2010). Of the 3,277 square kilometers, approximately 150,000 hectares of arable land are available and only 50,000 hectares are currently being used by agricultural households, indicating that there is an abundance of farm land available. The major crops grown include: maize, cassava, beans, peanuts, and sweet potatoes (Ministério de Educação Estatal,

2005a).

Gurue has a smaller population size of 265,000 inhabitants but a larger total area of 5,688 square kilometers. The formidable climate and arable land of Gurue have led to large tea plantations visible throughout the district. Other than tea leaves, the major crops grown are: peanuts, sweet potatoes, beans, cassava, maize, rice, tobacco, sugar cane, and sunflowers

(Ministério de Educação Estatal, 2005b).

Alto Molocue has a larger land area that Angonia and Gurue districts (6,343 square kilometers) and in 2010 had a smaller population size (250,000 inhabitants) than Angonia and

Gurue. Of the 637,000 hectares of land available, 400,000 hectares are arable land. Despite the abundance of land, in 2010, only 60,000 hectares were used by farm households. The major crops grown in the district are maize, sorghum, peanuts, cassava, beans, cotton, and tobacco

(Ministério de Educação Estatal, 2005c).

Mandimba covers approximately 4,699 square kilometers and in 2010 and had a population of 132,000 inhabitants. Like Angonia and Gurue, there was an abundance of land

11 Information for districts retrieved from Ministério de Educação Estatal, 2005b; Ministério de Educação Estatal, 2005c; Ministério de Educação Estatal, 2005a; Ministério de Educação Estatal, 2005d; Ministério de Educação Estatal, 2005e. 42 available for farming. The main crops grown in the district include cassava, beans, maize, and rice (Ministério de Educação Estatal, 2005e).

Lichinga covers 5,342 square kilometers and (in 2010) had 100,000 inhabitants. A total of 22,947 hectares were being farms and the majority of farming took place in small plots. The major crops grown include maize, beans, cassava, peanuts, sweet potatoes, and potatoes

(Ministério de Educação Estatal, 2005d).

4.3 Data characteristics

Men and women were interviewed using three surveys: Profile, Plot, and Target surveys.

The Profile and Plot surveys were administered to only one member of the household to reduce survey fatigue. The Profile survey was administered to female decision-makers and was comprised of three modules: 1) household profile, 2) incidence of malaria, and 3) crop sales. The

Plot survey was administered to the male decision-maker and was comprised of three modules:

1) plots of land administered by the household, 2) total agricultural production, and 3) asset data12. Lastly, the Target survey was administered to both male and female decision-makers and comprised a total of 12 modules: 1) work activities, 2) sources of income, 3) vulnerability, 4) natural products, 5) decision-making, 6) beans, 7) alternatives for bean distribution, 8) lack of enough food to eat and malaria, 9) understanding of malaria, 10) comparing alternatives for malaria prevention, 11) networks, and 12) demographic information. Table 4.3.1 displays selected information from the modules of the survey instrument.

4.3.1 Household characteristics

Table 4.3.2 provides demographic characteristics for the full sample (201 surveyed households) as well as demographic characteristics by province. The data show that the average household size is 5.4 with a dependency rate of 0.49. The age distribution of households is left skewed; on

12 If only one household member was available, then he/she completed all three surveys. 43

Table 4.3.1: Information collected in selected modules of Mozambique baseline study Survey Respondent Module Information Collected Profile: Female 1) Household profile Name, gender, relationship to head, and gender of household members. 2) Incidence of malaria Malaria incidence in past year. 3) Crop sales Last 5 most recent crop sales, including crop sold, where sold, and to whom. Plot: Male 1) Plots of land Size of plots of land. 2) Agricultural production Quantity produced and consumed. 3) Assets Household structures and building material; ownership of agricultural inputs. Target: Female and 1) Work activities Time allocation in different work male activities. 2) Sources of income Income earned from different activities and when it was earned. 3) Vulnerability Problems experienced by household and how dealt with problems; months in which household most stressed and income

sources during stressful times.

4) Lack of enough food to Food security and malaria situation at the eat and malaria national, community, and household levels 5) Networks Network measures 6) Demographic Education, religion, health status, number information of days lost to malaria and other illnesses average, households were composed of 2.52 children (ages 14 or less), 1.43 working- age females (ages 15-59), 1.13 working-age males (ages 15-59), and 0.32 elderly adults (ages 60 or more)13.

Physical assets for the household include the total land available for cultivation, the number of structures owned, as well as ownership of a bicycle, cellular phone, radio, farm equipment and farm animals. On average, households had 2.86 hectares of land available for cultivation. Further inspection indicated that approximately 20% of surveyed households had less

13 Working age males and females (ages 15-59) was defined as in Heltberg and Tarp (2002) 44

Table 4.3.2: Demographic characteristics of sample households, by province Full Province

sample Tete Zambezia Niassa Household characteristics Household size 5.40 5.15 5.24 5.82 1 Ratio of dependents to total household size 0.49 0.50 0.47 0.50 Number of adults, 60 or older 0.32 0.48 0.15 0.34 Number of males, 15-59 1.13 1.02 1.22 1.16 Number of females, 15-59 1.43 1.59 1.29 1.40 Number of children, 14 or younger 2.52 2.06 2.57 2.91 Physical Assets Land available for cultivation (ha.) 2.86 3.09 2.45 3.06 Less than 1 hectare (%) 17.41 21.21 13.24 17.91 1-2 hectares (%) 32.34 21.21 39.71 35.82 2-3 hectares (%) 23.88 31.82 23.53 16.42 Greater than 3 hectares (%) 26.37 25.76 23.53 29.85 Buildings owned 2.42 1.98 2.71 2.57 Bicycle (% yes) 68.66 54.55 80.88 70.15 Cellular phone (% yes) 2.99 1.52 1.47 5.97 Radio (% yes) 56.22 56.06 58.82 53.73 Hoe (% yes) 92.04 98.48 97.06 80.60 Number of pigs 0.32 0.52 0.44 0.00 Number of poultry 3.30 2.98 3.53 3.39 Number of cattle 0.09 0.21 0.00 0.07 Number of goats 0.95 1.53 0.06 1.28 Socio-economic characteristics Access to electricity (% yes) 0.50 0.00 0.00 2.17 Latrine type None (%) 28.36 45.45 32.35 7.46 Open latrine (%) 42.79 15.15 27.94 85.07 Closed latrine (%) 28.86 39.39 39.71 7.46 Drinking water from pump/well (% yes) 76.62 75.76 61.76 92.54 Observations 201 66 68 67 1 Calculated as number of children (14 or less) and adults (60 or more) divided by household size.

45 than 1 hectare available for cultivation and that approximately 75% of households had 3 hectares of land or less. The average size of 2.86 hectares of land is higher than expected; although it is plausible given the abundance of land available for cultivation in these districts (see section 4.2).

The average household owned 2.42 buildings. Bicycle ownership ranged from

(approximately) 55% to 81% across the 3 provinces, indicating that bicycles are an important mode of transportation. The percentage of households that owned a cell phone was very small

(2.99% of all surveyed households). At the time of the survey, there was limited cell phone coverage in these areas.

Household assets are measured by access to electricity, ownership of a sanitary (closed) latrine, and access to drinking water from a pump/well. Most households did not have access to electricity. Less than 1% of the total population had access to electricity and the majority of households with electricity were located in Niassa province. Ownership of a closed latrine (one of the most basic forms of improved sanitation (World Health Organization, 2013)) ranged from

(approximately) 7% to 40% of households across the three provinces. Accessibility to pump/wells for drinking water ranged from 61% to 93% of surveyed households.

4.3.2 Individual characteristics

Individual characteristics of surveyed men and women across the three provinces are displayed in table 4.3.3. The average age for women was between 34 and 42 years, and for men between 36 and 44 years. Across the three provinces, women on average attended school between 1.16 and 2.10 years and men attended between 2.95 and 4.09 years (across the three provinces). Differences in years of schooling likely help to explain differences in reading and writing abilities. The full sample shows that 19% of women were able to read and 16% were able to write, whereas 58% of men were able to read and write. Figure 4.3.1 shows the grade levels

46

Table 4.3.3: Demographic characteristics for adult respondents1 Full sample Tete Zambezia Niassa Female Male Female Male Female Male Female Male Age Age (years) 37.06 39.23 42.00 44.29 34.61 37.30 34.02 36.05 Age, 16-25 29.63 12.60 20.69 7.14 33.33 21.74 35.85 7.69 Age, 26-35 26.54 40.94 25.86 30.95 25.49 32.61 28.30 61.54 Age, 36-45 18.52 14.96 22.41 19.05 21.57 17.39 11.32 7.69 Age, 46-55 11.73 17.32 5.17 19.05 11.76 19.57 18.87 12.82 Age, 56 or older 13.58 14.17 25.86 23.81 7.84 8.70 5.66 10.26 Education Years of schooling 1.56 3.68 1.16 3.90 2.10 4.09 1.47 2.95 Grade level completed 1.36 3.10 0.88 3.14 2.20 3.37 1.09 2.74 Able to read 19.14 58.27 20.69 64.29 22.00 57.78 15.09 53.85 Able to write 16.05 57.48 12.07 64.29 22.00 57.78 15.09 51.28 Portuguese: very poor 73.46 35.43 91.38 61.90 61.22 10.87 63.46 32.43 Portuguese: limited 20.99 30.71 6.90 28.57 28.57 43.48 30.77 18.92 Portuguese: good 4.94 31.50 1.72 9.52 10.20 41.30 3.85 45.95 Portuguese: excellent 0.62 2.36 0.00 0.00 0.00 4.35 1.92 2.70 Observations 162 127 58 42 51 46 53 39

Figure 4.3.1: Education, by gender and province 80 70 60 50 % 40 30 20 10 0 Female Male Female Male Female Male Tete Zambezia Niassa

No education Grades 1 or 2 Grades 3 or more

47 completed by men and women. The percentage of women with no education ranged from 35% to

69% and the percentage of men was between 16% and 31%. The percentage of women who completed grade level 3 or more ranged from 13% to 40% and more than half of men (between

56% and 60%) completed grade level 3 or more.

4.3.3 Labor allocation

Table 4.3.4 displays labor participation rates across different income-earning activities by gender for both the married sample with a male and female respondent present and the full sample14. The majority of males and females (96% or more) in the married and full samples are involved in own farm labor. With regards to off-farm labor participation, females in the married sample participate more (55.7%) in off-farm labor employment than males (45.5%); over 45% of women work as hired labor on others’ farms. On the other hand, males in the full sample participate slightly more than females (52.8% and 48.1%, respectively).

Table 4.3.4: Participation rate in income-earning activities, by gender Married households Full sample Females Males Females Males Own farm 0.977 (0.150) 0.989 (0.107) 0.963 (0.190) 0.984 (0.125) Off-farm labor1 0.557 (0.500) 0.455 (0.501) 0.481 (0.501) 0.528 (0.501) Hired farm labor 0.455 (0.501) 0.318 (0.468) 0.395 (0.490) 0.323 (0.469) Non-farm wage labor 0.068 (0.254) 0.250 (0.435) 0.068 (0.252) 0.244 (0.431) Non-farm self-employment 0.125 (0.333) 0.250 (0.435) 0.123 (0.330) 0.315 (0.466) Observations (n) 88 88 162 127 Standard deviations in parentheses 1Off-farm labor consists of hired farm labor on others' farms, working as non-farm wage labor, or working as in non-farm self-employment.

By categorizing the off-farm participation decision into different off-farm work choices, several observations can be made across gender as well as within gender. Across gender, the

14 Married sample refers to dual-headed households in which both male and female completed the surveys (all dual- headed households were married). The full sample includes the married sample as well as single/widowed/divorced households and married households in which only the male or female was able to complete the survey. 48 following observations can be made: 1) a greater percentage of females than males participate in hired farm labor on other farms (45.5% and 31.8% in married sample; 39.5% and 32.3% in the full sample); 2) a greater percentage of males than females participate in non-farm wage labor

(25% and 6.8% in the married sample; 24.4% and 6.8% in the full sample); 3) a greater percentage of males than females participate in non-farm self-employment (25% and 12.5% in the married sample; 31.5% and 12.3% in the full sample). For males, the following observations can be made: 1) among the married male sample, participation in non-farm wage labor (25%) and non-farm self-employment (25%) are equal and less than participation in participation in hired farm labor on other farms (31.8%); 2) in the full sample, male participation in hired farm labor on other farms (32.3%) and non-farm self-employment (31.5%) are roughly the same and greater than participation in non-farm wage labor (24.4%). Lastly, for females, the following is observed: 1) among the married female sample, participation is greater in hired farm labor

(45.5%) than in non-farm wage labor (6.8%) and non-farm self-employment (12.5%); 2) in the full sample, female participation is greater in hired farm labor (39.5%) than in non-farm wage labor (6.8%) and non-farm self-employment (12.3%).

Based on the observations above, it appears that it is easier for men and women to be employed in hired farm labor (in both the married and full samples) than in non-farm wage labor.

Appendix B, table B1.1 shows the labor participation rates by gender and province for both the married and full samples.

Figure 4.3.2 shows the female distribution in off-farm work opportunities by age categories. According to figure 4.3.2, 50% of young women (ages 16-25) are employed in hired farm labor, 20% are employed in non-farm self-employment, and less than 5% are employed in non-farm wage labor. Additionally, it can be observed that few women between the ages of 46

49

Figure 4.3.2: Female participation rate in different work activities, by age group 60

50

40

% 30

20

10

0 Age, 16-25 Age, 26-35 Age, 36-45 Age, 46-55 Age, 56 or older

Hired farm labor Non-farm self-employment Non-farm wage labor

Figure 4.3.3: Male participation rate in different work activities, by age group 60

50

40

% 30

20

10

0 Age, 16-25 Age, 26-35 Age, 36-45 Age, 46-55 Age, 56 or older

Hired farm labor Non-farm self-employment Non-farm wage labor

50 and 55appear to work for wages. Figure 4.3.3 shows a different pattern for men. Young men

(ages 16-25) are equally likely to participate in any of the three work opportunities

(approximately 40%). Unlike women, it appears that men’s participation in non-farm wage labor and self-employment peaks between the ages of 46-55, whereas employment in hired farm peaks in later stages (ages 56 or older).

Figures 4.3.4 and 4.3.5 provide a timeline of female and male participation in productive activities by month, respectively. Activities displayed include own farm work, production of beans, hired farm labor, non-farm wage labor, and non-farm self-employment. From the two figures, the seasonality of labor activities is evident. Hired farm labor may be regarded as temporary/seasonal and accessible to almost anyone, whereas non-farm wage labor and self- employment have high barriers to entry (particularly for females) but may be more full-time employment options15. Male and female participation rates in own farm production are highest between September and April (includes field preparation, plating, etc.). Female own farm participation reaches a maximum (approximately 70%) in October and a minimum in July (8%).

Male own farm participation is at a maximum (approximately 70% of males) in December and at a minimum in July (9%).

Interestingly, the most stressful months for the household also happen to fall between

September and April. Most men and women identified February as the most stressful month for the household. April through September was identified as the most stressful months by the lowest percentage of women. In both male and female cases, household stress follows a similar pattern to that of bean production and hired farm labor. Stress, hired farm labor, and bean production reach a maximum (greatest percentage) in the same month (February) suggesting that the periods of most farm activity are also the hardest months, since this is when food supply and

15 Examples of entry barriers may include equipment purchase or rental as well as skill acquisition. 51

Figure 4.3.4: Female participation in activities and stress, by month 70 60 50 40 % 30 20 10 0

Most stressed Own farm Produce beans Market beans Hired farm labor Non-farm wage labor Self-employment

Figure 4.3.5: Male participation in activities and stress, by month 70 60 50 40 % 30 20 10 0

Most stressed Own farm Produce beans Market beans Hired farm labor Non-farm wage labor Self-employment

52 household income are lowest. To cope with household stress, men and women complement farm work with work on others’ farms. It is also worth noting that bean marketing and stress are inversely related. As the percentage of men and women in bean marketing increases, the percentage of stressed men and women also decreases. In fact, June and July is when men and women are most involved in the sales of beans and also the period in which their households are least likely to be stressed.

4.3.4 Bean production

Beans are an important part of Mozambican diets. Table 4.3.5 displays responses to questions regarding bean production. As can be seen, the majority of men and women are involved in bean production. The data show that in both Tete and Niassa, more than 90% of males and females are involved in bean production. Surprisingly, in Zambezia both male and female participation is lower (72% and 69%, respectively) than in the other provinces.

Table 4.3.5: Experiences with common beans Full sample Tete Zambezia Niassa Female Male Female Male Female Male Female Male Bean production 86.42 88.19 93.10 97.62 68.63 71.74 96.23 97.44 Bean problems (%) Not profitable Large problem 43.95 36.89 46.55 31.71 51.06 58.14 34.62 18.42 Problem 21.02 19.67 34.48 31.71 14.89 20.93 11.54 5.26 Small problem 16.56 22.95 13.79 21.95 10.64 18.60 25.00 28.95 Not a problem 18.47 20.49 5.17 14.63 23.40 2.33 28.85 47.37 Not enough good bean seed available locally Large problem 36.94 28.69 44.83 31.71 40.43 39.53 25.00 13.16 Problem 21.02 22.95 29.31 29.27 23.40 30.23 9.62 7.89 Small problem 16.56 23.77 18.97 29.27 6.38 13.95 23.08 28.95 Not a problem 25.48 24.59 6.90 9.76 29.79 16.28 42.31 50.00 Observations 162 127 58 42 51 46 53 39

When asked about bean profitability, a greater percentage of men and women in Tete and

Zambezia identified bean profitability as a problem or large problem than men and women in

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Niassa. The percentage for women ranged from 46% to 82% and the percentage of men was between 24% and 80%. The low perceived profitability of beans may indicate why a lower percentage of men and women in Zambezia are involved in bean production (see table 4.3.5). A similar pattern is observed across provinces with regards to the availability of seeds locally.

Men and women were asked how they acquire improved bean seed varieties. Even though the question specifically asks about beans, it is possible that similar methods are used to acquire seeds for other crops. From figure 4.3.6, it can be seen that men and women rely less on formal seed distribution avenues (i.e., extension workers, farmer associations, and NGOs) and more on informal avenues to obtain improved seed varieties (friends and relatives). Combined, less than 20% of men and less than 10% of women indicated extension workers, farmer organizations, and NGOs are their sources of seeds. Most identified friends and relatives (both in the village and outside the village) as well as business contacts (traders) as their main source of improved bean seed varieties.

Figure 4.3.6: Source of improved bean seed varieties, by gender 40 35 30 25 % 20 15 10 Female 5 Male 0

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4.3.4 Household decision-making

Tables 4.3.6 and 4.3.7 display men and women’s responses (married sample and full sample respectively) to questions on household decision-making for farm production and marketing of crops. Specifically, the decision-making scenarios probe who decides: 1) what crops to plant, 2) what to feed the household, 3) what food to purchase, 4) whether to grow beans, 5) whether to try new crop varieties, 6) how and where to market beans, 7) how and where to market other crops, and 8) whether to try new ideas on the farm. The tables show that the majority of surveyed men and women (in both the married and full samples) perceive that other adults in the household do not participate in household decisions. In fact, in most scenarios, men and women stated that decisions are made only by the head of household or jointly by the head of household and the spouse of the head of household. In all scenarios, more than 55% of men and women viewed the head of household as the primary decision-maker. It is, however, interesting to note differences in male and female responses. The percentage of women who indentified the head of household as the decision-maker was greater than the percentage of males. On the other hand, a greater percentage of men than women were likely to view important household decisions as a joint decision made by the head and spouse16.

4.3.5 Agricultural sales

Agricultural sales are an important source of household income. Surveyed households identified the marketing of legumes, tubers, vegetables, grains, cash crops, and others17. A variety of means are pursued in the marketing of crops; households identified crop sales taking place

16 When building the household profile, women were asked about their relationship to the head of household (where they could identify themselves as the head); it was determined that in most instances, the husband was identified as the head of household. 17 Legumes included common beans, cowpea, fava beans, pigeon pea, and soybeans. Tubers included cassava, potatoes, and sweet potatoes. Vegetables included cabbage, collard greens, garlic, onion, peas, and peppers. Grains included maize, millet, rice, and sorghum. Cash crops included cotton and tobacco. Other crops sold included bananas, peanuts, sesame, sunflowers, and sugar cane. 55

Table 4.3.6: Decisions-making within the household for the married sample1 Adult males Head and Head only Spouse of head and/or Don’t know spouse females2 Female Male Female Male Female Male Female Male Female Male What crops to plant? 81.8 80.7 1.1 0.0 17.0 18.2 0.0 1.1 0.0 0.0 What food to feed to household? 73.9 56.8 12.5 14.8 9.1 25.0 4.5 3.4 0.0 0.0 What food to purchase? 76.1 59.1 5.7 10.2 12.5 27.3 3.4 2.3 2.3 1.1 Whether to grow beans? 67.0 58.0 4.5 3.4 22.7 34.1 5.7 4.5 0.0 0.0 Whether to try new crop varieties? 67.0 64.8 3.4 4.5 23.9 29.5 5.7 1.1 0.0 0.0 How, where to market beans? 65.9 62.5 4.5 4.5 22.7 26.1 1.1 1.1 5.7 5.7 How, where to market other crops? 67.0 59.1 8.0 4.5 18.2 33.0 1.1 1.1 5.7 2.3 Whether to try new ideas on farm? 72.7 60.2 3.4 4.5 18.2 30.7 3.4 2.3 2.3 2.3 1Based on the married sample consisting of 88 females and 88 males. 2Adult males and/or female refers to all men and women (ages 15 or more) living in the household.

Table 4.3.7: Decisions-making within the household for the full sample1 Adult males Head and Head only Spouse of head and/or Don’t know spouse females2 Female Male Female Male Female Male Female Male Female Male What crops to plant? 78.4 75.6 6.8 0.0 12.3 21.3 2.5 3.1 0.0 0.0 What food to feed to household? 69.8 55.1 13.6 13.4 8.6 26.0 5.0 5.5 1.9 0.0 What food to purchase? 76.5 58.3 5.6 9.4 11.1 28.3 3.1 3.1 3.7 0.8 Whether to grow beans? 68.5 56.7 8.6 2.4 17.3 36.2 5.6 4.7 0.0 0.0 Whether to try new crop varieties? 67.9 61.4 6.2 3.1 19.1 33.1 6.8 2.4 0.0 0.0 How, where to market beans? 65.4 63.0 6.8 3.1 17.9 26.0 2.5 3.1 7.4 4.7 How, where to market other crops? 66.0 60.6 8.6 3.1 14.2 30.7 3.1 3.1 8.0 2.4 Whether to try new ideas on farm? 70.4 59.8 6.2 3.1 16.0 32.3 4.9 3.1 2.5 1.6 1Based on the full sample consisting of 162 females and 127 males. 2Adult males and/or female refers to all men and women (ages 15 or more) living in the household.

56 through traders, cooperatives, pickup buyers, consumers in local and distant markets, as well as relying on friends and relatives to market crops.

Table 4.3.8 displays male and female participation rates in the sale of beans, maize, and other crops. Male and female participation rates in bean marketing are similar between the married and full samples. The majority of men and women (between 60% and 65% of men and women in both the married and full samples) are involved in bean sales. Differences are evident for the marketing of maize and other crops. Among the married sample, women are more involved in the marketing of maize (40.9% of surveyed women vs. 29.5% of surveyed men), whereas men are more likely to participate in the marketing of other crops. In the full sample,

36% of men and women participate in the marketing of maize (36.4% of women and 36.2% of men), but men (55.9%) appear more involved in the marketing of other crops than women

(37%). Differences in marketing participation rates suggest that men are willing to forego participation in the marketing of beans and maize in favor of more lucrative other crops (i.e., tobacco and cotton).

Table 4.3.8: Participation in the marketing of crops, by gender Married sample (%) Full sample (%) Female Male Female Male Marketing of: Beans 64.8 62.5 60.5 65.4 Maize 40.9 29.5 36.4 36.2 Other crops 37.5 54.5 37.0 55.9 Observations 88 88 162 127

Table 4.3.9 displays household participation rates in crop markets for beans, maize, and other crops as well as the mean value of annual sales (excluding non-sellers) across the three provinces. Within Tete province, the majority of households are involved in the marketing of beans (83.3%) and other crops (74.2%), and only 10.6% of households participate in maize

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Table 4.3.9: Household participation rates and mean sales in agricultural markets, by province and crop1 Tete (n=66) Zambezia (n=68) Niassa (n=67) Mean Mean Mean sales sales sales % Sellers (MZN) % Sellers (MZN) % Sellers (MZN) All Crops 93.9 1684.28 91.2 5571.45 86.6 6910.00 Beans 83.3 569.28 57.4 2246.67 82.1 4649.46 Maize 10.6 180 77.9 1304.15 52.2 1586.86 Other crops 74.2 1466.43 67.6 4101.96 32.8 4069.09 1 Mean sales are computed only for households that sell (specified) crop.

markets. The majority of income, however, is derived from the sale of other crops, with market participating households in Tete reporting average sales of 1684 MZN18. In Zambezia, participation rates in crop markets differ from those of Tete. Results indicate that households in

Zambezia are much more engaged in markets for other crops; 57.4% of households participate in bean markets, 77.9% of households participate in maize markets, and 67.6% of households participate in markets for other crops. Among market participating households, the mean value of sales in Zambezia are substantially larger than those for Tete. Market participating households reported average sales of 2,247 MZN for beans, 1,304 MZN for maize, and 4,102 MZN for other crops. Lastly, the majority of households in Niassa (82.1%) are involved in bean marketing and nearly half of surveyed households (52.2%) participate in maize markets and only 32.8% market other crops. Households involved in bean marketing report mean sales of 4,649 MZN. Even though only 32.8% of the surveyed households reported participating in markets for other crops, mean sales were 4,069 MZN, roughly equal to the value of sales for beans. Maize sales were

1,587 MZN, a value much lower than other crops within the province but considerably larger than maize sales in other provinces.

18 MZN denotes Mozambican Metical (currency). In 2010, 25 MZN was the equivalent of 1USD. 58

4.3.6 Stress and coping mechanisms:

Previously, it was shown that men and women stated that their households face stress, particularly between December and March (see previously discussed figure 4.3.4 and 4.3.5). In addition to knowing when household face stress, it is also important to know the sources of stress. Table 4.3.10 displays problems faced by men and women throughout the year and mechanisms in place to deal with these problems.

Across the three provinces, the three largest problems faced by men and women are hunger, long illness, and loss of crops. The full sample shows that 65% of women and 70% of men considered hunger to be the most pressing problem facing their household. The second largest problem for households was reported as poor health; approximately 50% of surveyed men and women stated that long illness was a major concern in their home. The third most urgent problem for households was loss of crops; 50% of women and 36% of men identified it as a major problem. It is worth noting that all three problems are interrelated.

A variety of coping mechanisms are in place to deal with such problems. A greater percentage of men and women identified more work (43% of women and 52% of men) as their method to deal with problems. However, more work is not always possible, particularly when faced with poor health and, to a certain extent, hunger. An alternative means to cope with household problems is to rely on friends and relatives. To overcome problems, 16% of women and 18% of men borrowed money from friends and relatives; 11% of women and 8% of men relied more of friends and relatives for help; 4% of women and 5% of men migrated to live with friends and family.

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Table 4.3.10: Problems encountered and coping mechanisms, by gender and province Full sample Tete Zambezia Niassa Female Male Female Male Female Male Female Male Problems in the past year (%) Hunger 64.81 70.08 82.76 90.48 49.02 58.70 60.38 61.54 Long illness 49.38 51.18 65.52 69.05 45.10 45.65 35.85 38.46 Death 32.72 16.54 36.21 11.90 39.22 17.39 22.64 20.51 Loss of job 3.09 3.15 5.17 7.14 3.92 2.17 0.00 0.00 Theft 21.60 24.41 18.97 35.71 25.49 23.91 20.75 12.82 Damage/loss of structure 24.07 11.02 27.59 19.05 29.41 10.87 15.09 2.56 Shortage of labor 8.02 6.30 6.90 0.00 17.65 13.04 0.00 5.13 Loss of crops 50.00 36.22 74.14 64.29 47.06 21.74 26.42 23.08 Coping mechanisms (%) Migrated for work 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sold physical assets, excluding livestock 6.17 15.75 8.62 28.57 3.92 13.04 5.66 5.13 Sold livestock 4.94 2.36 12.07 4.76 1.96 2.17 0.00 0.00 Relied on natural products 10.49 7.09 8.62 4.76 15.69 6.52 7.55 10.26 Sold natural products 12.96 14.96 5.17 11.90 27.45 17.39 7.55 15.38 Migrated to live with friends/relatives 3.70 4.72 6.90 9.52 1.96 0.00 1.89 5.13 Borrowed money from friends/relatives 16.67 18.11 22.41 26.19 15.69 15.22 11.32 12.82 Borrowed from moneylender/bank 0.00 1.57 0.00 2.38 0.00 2.17 0.00 0.00 Work more 42.59 51.97 53.45 66.67 56.86 58.70 16.98 28.21 Relied more on relatives/friends for help 11.11 7.87 5.17 0.00 15.69 17.39 13.21 5.13 Borrowing and saving (%) Save income for bad months in past year 18.52 37.01 22.41 21.43 13.73 41.30 18.87 48.72 Borrowed money in past year 14.20 13.39 20.69 23.81 11.76 13.04 9.43 2.56 Observations 162 127 58 42 51 46 53 39

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4.3.7 Social networks

An important result of the previous section was that men and women rely on friends and relatives in a variety of ways to overcome household problems (i.e., ask friends/relatives: 1) for more help, 2) to borrow money, 3) to live with, 4) for agricultural inputs, 5) for help marketing crops). In addition to identifying the function of friends and relatives, it is also possible to construct networks of friends and relatives within the village. Identifying and understanding variations in the social environments of men and women is important for understanding differences in economic behavior (Hanneman and Riddle, 2005).

Constructing ego networks

To analyze variations in the social environments of men and women, one must rely on ego networks. An ego network is an egocentric approach to social networks; in other words, social networks are from the perspective of a specific node (ego)19. To construct ego networks,

Google Earth satellite imagery of the survey site was shown to surveyed men and women (see discussion in section 4.1 for more details on full landscape and quadrant maps). The satellite imagery (map) contained topographic details of the area as well as the location of households within the community. After familiarizing respondents with the map, respondents were asked the following questions:

1) In this village, where do your friends live?

2) In this village, where does the rest of your family live?

The first question identified friend households and the geographic location of the house on the map. Likewise, the second question identified kin households and the geographic location of the house on the map. Responses to the questions allowed for the identification and construction of

19 A social network is composed of a set of nodes (individuals). In an ego network, an ego is the “focal” node (surveyed male/female) and the nodes identified by the ego in its network are its alters (friend households or kin households). 61 friendship ego networks, kin ego networks and total ego networks (sum of friendship and kin ego networks) for surveyed males and females. Furthermore, dividing kin ego networks by total ego networks provided the proportion of total network size that is kin. Lastly, combining male and female responses yielded household ego networks. For each survey respondent (ego), a set of alters (friend households or kin households) was identified and the ties (relationship) between them were determined.

To create and visualize ego networks, an adjacency matrix for kin and friendship households was created20. The rows of an adjacency matrix display responses for surveyed households (i.e., who the surveyed male/female identified as a friend households or kin households). In order preserve a square matrix, the rows and columns include surveyed and non- surveyed households21. The elements of the row vector for a non-surveyed household are marked as zero since they were never asked about their friend and kin networks. The columns of the adjacency matrix represent households identified as part of a surveyed households kin or friendship network. Surveyed households are also included in the columns; however, if no one identified the surveyed households as a friend or kin household, the elements of column vector are marked as zero. If a relation exists (does not exist) between households i and j, then the cell corresponding to row i, column j are labeled with a one (zero) to represent indicate that a tie is present (not present). Table 4.3.11 presents an adjacency matrix of kin ego networks for a sub- sample of 3 surveyed males in Niassa-1 village. Table 4.3.11 shows that men from household 6,

27 and 37 were interviewed (i.e., row vector for households 6, 27, and 37 are not all zero). The male respondent from household 6 stated that household 47 was a kin household ; the

20 An adjacency matrix, is a n*n binary square matrix representing relations between household i and household j (Hanneman and Riddle, 2005) 21 A non-surveyed household is one that is identified as a friend households or kin households but that was not surveyed. 62

Table 4.3.11: Adjacency matrix illustrating ties between 6 households1 Household 6 27 37 40 47 129 6 0 0 0 0 1 0 27 0 0 0 1 0 0 37 0 0 0 0 0 1 40 0 0 0 0 0 0 47 0 0 0 0 0 0 129 0 0 0 0 0 0 1 1= presence of a tie; 0= no tie.

surveyed male from household 27 identified household 40 as a kin household ; the male from household 37 identified household 129 as a kin household . The row vector for the remaining households (households 40, 47, and 129) are all equal to zero since these households were not surveyed. Likewise, the column vectors for surveyed households (6,

27, and 37) are all zero since no on identified them as a kin household. It should also be noted that the elements of column 6, 27, and 40 are all zero indicating that no one identified these households as part of their kin ego network. Adjacency matrices were constructed for friendship, kin, and total ego networks for men, women, and households in all 6 villages.

Visualizing ego networks

Using the adjacency matrices, a series of networks graphs were created using UCINET.

Figures 4.3.7 and 4.3.8 provide an example of friendship ego networks for women and men in

Niassa-2, respectively. Figure 4.3.9 combine male and female ego networks to create friendship ego networks for households in Niassa-2 and denote household income by the size of the ego.

Likewise, figures 4.3.10 and 4.3.11 provide an example of kin ego networks for women and men in Niassa-2, respectively, and figure 4.3.12 illustrates household level friendship ego networks in

Niassa-2 with household income denoted by the size of the ego. Surveyed households are

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Figure 4.3.7: Niassa-2 female friendship ego networks

Figure 4.3.8: Niassa-2 male friendship ego networks

Figure 4.3.9: Niassa-2 household friendship ego networks and household income

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Figure 4.3.10: Niassa-2 Female kin ego networks

Figure 4.3.11: Niassa-2 Male kin ego networks

Figure 4.3.12: Niassa-2 Household kin ego networks and household income

65 illustrated by the blue nodes and non-surveyed households by the red nodes. An ego (surveyed women in figure 4.3.7, surveyed men in 4.3.8 and combined male and female responses in figure

4.3.9) is always represented by a blue node (squares), whereas alters (i.e., friend households and kin households) may include other surveyed households (blue nodes) as well as non-surveyed households (red nodes).

Comparing figures 4.3.7 and 4.3.8, it can be seen that men are better connected than women. In fact, the figures shows that, in Niassa-2, all surveyed men reported having at least one friend households in their network, whereas two surveyed women reported no friend households in their network. Despite differences in friendship ego networks, figures 4.3.10 and 4.3.11 show that few differences exist in the size of kin ego networks of men and women. Furthermore, visual inspection of figures 4.3.9 shows that there are two high income households (household 95 and

359), a few middle income households (household 46, 57, 68, 81, and 111), and the majority are classified as lower income households22. It is interesting to note that higher income households did not identify many friend households, and surprisingly, no one identified these higher income households as part of their friendship ego network. In the case of kin ego networks, figure 4.3.12 shows that the higher income households reported having only one kin household in network whereas middle and lower income households had more variability in the size of their kin ego networks. Similar network diagrams were created for all 6 villages by gender (and household) and network type (see Appendix C for female friendship and kin ego networks; appendix D for male friendship and kin ego networks; appendix E for household friendship and kin ego networks).

22 Larger nodes denote higher household income relative to other surveyed households (the size of non-surveyed households is not interpreted). 66

Ego networks measures

The network diagrams help visualize the ego network size of individuals (households) in the different villages. Table 4.3.12 summarizes household, male, and female ego network variables for the full sample as well as by province. The network measures include friendship, kin networks, and total ego network size. Males had an average total ego network size of 3.25 households of which 39% of consisted of kin contacts. Separate measure of friendship and kin ego network size indicate that men had on average 2.29 friend households and 1.40 kin households. Females on the other hand had an average total ego network size of 3.25 households with approximately nearly half (46%) of all contacts being kin. Furthermore, women had a friendship ego network size of 1.69 friend households and a kin ego network size consisting of

1.56 kin households.

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Table 4.3.12: Ego networks, by province Full sample Tete Zambezia Niassa Female Male Household Female Male Household Female Male Household Female Male Household Total ego network Size 3.25 3.69 5.05 4.16 4.36 6.41 2.75 3.91 4.87 2.74 2.72 3.90 0-1 ties 9.26 7.87 3.98 6.90 7.14 3.03 7.84 2.17 2.94 13.21 15.38 5.97 2-3 ties 54.32 46.46 31.34 34.48 26.19 13.64 68.63 52.17 35.29 62.26 61.54 44.78 4-6 ties 32.10 35.43 36.82 48.28 54.76 39.39 21.57 30.43 35.29 24.53 20.51 35.82 7-11 ties 4.32 10.24 27.86 10.34 11.90 43.94 1.96 15.22 26.47 0.00 2.56 13.43 Proportion kin 0.46 0.39 0.43 0.45 0.39 0.43 0.45 0.41 0.39 0.47 0.37 0.48 Friendship ego network Size 1.69 2.29 2.88 2.12 2.67 3.55 1.53 2.48 3.03 1.38 1.67 2.07 0-1 ties 50.00 37.80 23.88 27.59 26.19 10.61 60.78 34.78 17.65 64.15 53.85 43.28 2-3 ties 45.06 37.80 45.77 60.34 38.10 40.91 37.25 36.96 51.47 35.85 38.46 44.78 4-6 ties 4.94 24.41 26.87 12.07 35.71 43.94 1.96 28.26 29.41 0.00 7.69 7.46 7-8 ties 0.00 0.00 3.48 0.00 0.00 4.55 0.00 0.00 1.47 0.00 0.00 4.48 Kin ego network Size 1.56 1.40 2.17 2.03 1.69 2.86 1.22 1.43 1.84 1.36 1.05 1.82 0-1 ties 59.88 64.57 39.70 36.21 47.62 21.88 78.43 67.39 47.06 67.92 79.49 49.25 2-3 ties 35.19 32.28 45.73 51.72 47.62 48.44 21.57 32.61 45.59 30.19 15.38 43.28 4-6 ties 4.32 2.36 14.07 10.34 2.38 28.13 0.00 0.00 7.35 1.89 5.13 7.46 7-8 ties 0.62 0.79 0.50 1.72 2.38 1.56 0.00 0.00 0.00 0.00 0.00 0.00 Observations 162 127 201 58 42 66 51 46 68 53 39 67

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Chapter 5 – METHODOLOGY

This chapter outlines the methodology used to pursue the two research objectives of the study.

This chapter also describes ego network measures use to assess friend and kin ego network size.

5.1 Objective 1 and how it was achieved

Objective 1: To analyze the effects of social networks on male and female labor allocation and off-farm work choices.

To achieve objective 1, a series of models will be estimated to provide a better understanding of the interrelationships between social networks and labor allocation and off- farm work decisions of farm households in rural Mozambique. The first set of models simultaneously estimate labor participation models for adult male and female respondents when both are present in the household. The simultaneous estimation requires that both adult male and female respondents be present in each surveyed household; therefore, a sub-sample consisting of dual-headed (married) households will be used. The next set of models include separate univariate probit models that use the full sample to estimate models of labor participation decisions for adult male and female respondents. Lastly, the analysis will be extended beyond the off-farm labor participation decision, with models of work choices (hired farm labor, non-farm wage labor, and non-farm self-employment) for males and females using the full sample. The full sample includes all surveyed households.

5.1.1 Model 1: Joint decision-making of off-farm labor participation

The off-farm labor participation decision is modeled jointly for adult male and female respondents in dual-headed (married) households (i.e., when both spouses are present in the household), based on the hypothesis that male and female respondent labor choices are interrelated (i.e., not independent). The bivariate probit model is used. If labor decisions are jointly determined, then the error terms for male and female respondents in dual-headed

69

(married) households will be correlated. If the error terms are correlated, then labor decisions are best modeled using a bivariate probit approach since the bivariate probit corrects for correlations that may exist between the errors terms (Greene, 2003). If the error terms are uncorrelated, then labor decisions can be modeled with separate univariate probits estimated for males and females.

Potential jointness in labor decision-making depends on the statistical significance of ρ (the correlation coefficient). Therefore, the following null and alternative hypothesis will be tested:

If the correlation coefficient equals zero , then estimation of male and female off-farm labor decisions can be done separately. Alternatively, if the correlation coefficient does not equal zero , a bivariate probit model should be used.

Motivation. A dual-headed (married) farm household is assumed to consist of a male spouse

(i=m) and female spouse (i=f) who make decisions regarding off-farm employment. An individual will have an economic incentive to participate in off-farm employment if the

difference between the marginal returns from off-farm work and farm work are

greater than zero. Let be the unobservable variable representing the difference between the

marginal returns from off-farm work and farm work , conditional on personal, household, geographic, as well as network characteristics. Then, the latent regression equation

for labor participation is a function of observed and unobserved determinants

(Greene, 2003; Lass and Gempesaw, 1992):

(60)

70

where is a row vector of parameters, , and . Then, the bivariate probit model for off-farm labor participation is defined as (Cameron and Trivedi, 2005;

Greene, 2003, 1998):

(61)

(62)

where and denote the observable off-farm participation choice, and are latent variables representing the difference between the marginal returns from off-farm work and

farm work , and and are the error terms which may be correlated. To estimate parameters of the equations using maximum likelihood, the bivariate normal cumulative distribution function (cdf) is defined as (Greene, 2003):

(63)

and the density function defined as (Greene, 2003):

(64)

For the bivariate probit model, the following scenarios need to be considered (Huffman and

Lange, 1989):

Probability that neither male or female spouse works off-farm (65)

Probability that only male spouse works off-farm (66)

71

Probability that only female spouse works off-farm (67)

Probability that both male and female spouses work off-farm (68)

where the subscripts m and f represent male and female spouses, respectively.

Following Greene, (2003), the log likelihood function can be constructed. Let:

(69)

(70) such that if and if . Also, let:

(71)

(72)

(73)

Then the probabilities that enter the likelihood function are (Greene, 2003):

(74) and the log-likelihood function is (Greene, 2003):

(75)

5.1.2 Model 2: Univariate probit model for off-farm labor participation

If results from the bivariate model indicate non-joint labor decision-making for adult males and females, then separate univariate probit models for male and female labor participation can be estimated. The probit is similar to the bivariate probit. An individual participates in off-farm labor if the difference between the marginal returns from off-farm work

and farm work are greater than zero. Let be the unobservable variable representing

72

the difference between the marginal returns from off-farm work and farm work , conditional on personal, household, geographic, as well as network characteristics. Then, the

latent regression equation for labor participation is a function of observed and unobserved determinants (Baum, 2006):

(76)

where is a row vector of parameters, .

As was the case in the bivariate probit, the difference between returns from off-farm and farm labor are unobservable; however, the outcome is observable and can be said to follow the following off-farm labor participation decision rule, where denotes the observable off-farm participation choice:

(77)

Then the probability of an individual making the decision to participate in off-farm labor is modeled as in Cameron and Trivedi (2009) and Baum (2006):

(78)

where is a univariate standard normal cumulative distribution function (CDF).

The parameters of the binary choice model can be estimated using maximum likelihood estimation techniques. The log-likelihood for observation j is written as in Baum (2006):

73

(79)

5.1.3 Model 3: Multivariate probit model for off-farm labor choice

The last step in the labor analysis will examine off-farm work choices (e.g., off-farm wage employment, non-farm self-employment, hired farm labor) based on the assumption that the determinants of off-farm labor participation are not uniform across work choices. Estimation of (different) off-farm work choices previously have been modeled using 1) separate univariate logit/probit models for different work choices (Isgut, 2004; Ruben and Van den berg, 2001), 2) multinomial logit (de Janvry and Sadoulet, 2001; Woldenhanna and Oskam, 2001), 3), Lee’s generalization of Ameniya’s two step estimator to a simultaneous equation model (Yúnez-Naude and Taylor, 2001), and 4) multivariate probit (Babatunde and Qaim, 2010).

The multinomial estimation procedure is not appropriate in the case of rural

Mozambique, given that the majority of males and females are engaged in multiple activities at once (i.e., work choices are not mutually exclusive). Separate univariate probit models also are not favorable given the potential correlation of error terms for the different off-farm work choices. The best option for modeling multiple off-farm work choices is to simultaneously model all choices using a multivariate probit, as in Babatunde and Qaim (2010) . The off-farm labor activities include23:

(i) Hired farm labor (k=1)

(ii) Off-farm wage labor (k=2)

(iii) Non-farm self-employment (k=3)

23 Own-farm labor could have been a possible choice in the multivariate probit analysis; however, it is not included due to the fact that more than 95% of males and females who responded to the Mozambique survey are employed in this sector. 74

To model off-farm labor choice, let the preference of individual j for activity k be a function of observed and unobserved determinants. Then, the propensity of individual j to select off-farm employment in choice k is represented by the latent variable, :

(80)

An individual is assumed to participate in work choice k when . Let denote the observable off-farm labor participation decision in choice k. The decision rule is defined:

(81)

The model for off-farm labor choice using a multivariate probit is an extension of the bivariate probit model.

A challenge in the estimation of multivariate probit models using maximum likelihood techniques is the computation of multivariate normal integrals (Greene, 2003); in fact, STATA does not have the computing capabilities for the trivariate normal CDF (Cappellari and Jenkins,

2003; Terracol, 2002). For this reason, the multivariate probit model is estimated using the simulated maximum likelihood (SML) method (Greene, 2003), specifically, the Geweke-

Hajivassiliou-Keane (GHK) smooth recursive conditioning simulator. In theory, the GHK

75 simulator is said to be consistent as the number of observations and draws tend to infinity, and asymptotically equivalent to the true maximum likelihood estimator as the ratio of the square root of the sample size to the number of draws tends to zero (Cappellari and Jenkins, 2003). In practice, Cappellari and Jenkins (2003) suggest that accurate results from the SML estimator can be obtained if the number of draws are greater than or equal to the square root of the sample size.

Therefore, for accuracy of results, the number of draws is set to 30 for males and females . Estimation is conducted in STATA using the user-written program mvprobit by Cappellari and Jenkins (2003).

5.1.4 Correcting for endogeneity

The next section examines the issues of endogeneity which may be a problem with the inclusion of network measures as exogenous variables. An explanatory variable is said to be endogenous if it is correlated with the error term; correlation with the error term is said to violate the zero conditional mean assumption (Wooldridge, 2003). Endogeneity can result from any of the following: 1) reverse causality, 2) omitted variable bias, and 3) measurement error (Bascle,

2008; Cameron and Trivedi, 2005). Each is discussed below.

Reverse causality implies that the dependent variable and explanatory variable are jointly determined. Social networks are constantly evolving: “depending on the circumstances, people may switch focus between networks, and may also react to a deterioration of their position in one network by joining another or by creating new networks” (Leavy, 2011, p. 169). Therefore, the direction of causality is uncertain. It may be the case that better connected individuals are more likely to find off-farm employment but it also may be the case that individuals with off-farm employment opportunities are better able to network than individuals working primarily in their

76 own farm/home. If reverse causality is present, the endogenous regressor and the error term are likely to be correlated, resulting in biased and inconsistent parameter estimates (Bascle, 2008).

Omitted variable bias results from excluding a variable that may affect the dependent variable for which data are available or excluding a variable because of data unavailability

(Cameron and Trivedi, 2005). In the case of social networks, important omitted and unobservable variables include the characteristics of other farmers in the network as well as their participation in off-farm labor markets. If the omitted variable is correlated with the other explanatory variables, then the omission of this key variable means that the error term is correlated with the explanatory variable (Wooldridge, 2002). Omitted variable bias therefore can result in biased and inconsistent estimates.

Finally, measurement error results when the true value of a regressor is not reflected and, instead, an imprecise measure is included in the analysis. For example, Holland and Leinhardt

(1973) illustrates that network data collection may be subject to measurement error if individuals are instructed to identify a set number of individuals as part of their network. Inclusion of an imprecise variable introduces measurement error and results in correlation between the error term and the (incorrectly measured) explanatory variable (Bascle, 2008; Wooldridge, 2003).

Thus, concern regarding the possible endogeneity of social networks and off-farm labor participation is an important issue that must be assessed. Like OLS, probit estimates are inconsistent if any variable is endogenous (Cameron and Trivedi, 2009). To test for endogeneity, the ivprobit procedure in STATA is used. The ivprobit procedure fits a probit model when an endogenous regressor is present. The procedure can be explained using a two-step approach; however, the ivprobit is done in one step (simultaneously). In the first step, the potentially endogenous network variable is regressed on the set of independent variables and instruments,

77 and the residuals from this equation are used in the second stage probit model. An endogeneity test is included through the Wald test of exogeneity of the instrumented variables. If the test statistic is not significant, the null hypothesis that there is no endogeneity present cannot be rejected and a regular probit regression is suitable.

As in Leavy (2011), kin ego networks are regarded as exogenous variables since they are beyond an individual’s control (i.e., cannot choose who you are related to). Friendship ego network size, total ego network size, and the proportion of network contacts who are kin are treated as potentially endogenous variables24. If friendship ego networks and total ego networks are potentially endogenous, it must be the case that the proportion of network members who are kin (a function of kin ego networks and total ego networks) is also a potentially endogenous variable25. Of course, one of the biggest challenges in estimating an IV model is the identification of instruments that are highly correlated with an individual’s friendship ego network size, total ego network size, and the proportion of network members who are kin but not correlated with the off-farm labor participation decision. The proposed instruments follow the work of Leavy (2011): friendship ego network size is instrumented with the non-self cluster mean measure of friendship ego network size, total ego network size in instrumented with the non-self cluster mean measure of total ego network size, and similarly the proportion of network contacts who are kin are instrumented with the non-self cluster mean measure of the proportion of network contacts who are kin. The non-self cluster mean instruments are the mean network size measure calculated for all other respondents in the village and exclude the respondent and his/her spouse’s ego network. Rather than test first for endogeneity, IV probit regressions are included since they automatically report a test of exogeneity.

24 An exception to the endogeneity of total ego network size would be the case in which the total ego network is composed entirely of kin. 25 Leavy (2011) treats the variable for the proportion of network contacts who are kin as exogenous. 78

5.1.5 Variables used in the analyses

Descriptive statistics of the variables used in the estimation of the off-farm participation models are included in table 5.1.1 for the married sample and in table 5.1.2 for the full sample.

The tables show means by gender and standard deviations.

The dependent variables for the bivariate probit and univariate probit analyses are male and female off-farm labor participation. The dependent variable is a binary outcome, indicating whether or not the male/female participated in off-farm labor. The dependent variable for the multivariate probit analysis of off-farm work choice consists of three binary outcome variables measuring employment in hired farm labor, non-farm wage labor, and non-farm self- employment. The independent variables include individual, household, farm, and network characteristics provided by survey respondents. Table 5.1.2 indicates that individual characteristics in the probits include age and age squared, educational attainment, and health status of both respondents. Table 5.1.3, on the other hand, uses age categories instead of age and age squared of the respondent. Each independent variable or set of variables is discussed below:

Age: The age and age-squared variables are self-explanatory. The categorical age variable is introduced as a series of binary variables in the analyses, with age category 15-25 being the reference category. The categories included in the analyses are ages 26-35, 36-45, 46-55, and 56 or older. The purpose of the age variables is to explore the impact of a person’s life cycle on labor participation. The age variables are expected to reveal an inverse U relationship with off- farm participation, as has been demonstrated in many studies, at least for males.

79

Table 5.1.2: Variables used in bivariate probit model, by gender

Males (n1=88) Females (n2=88) Variable Definition of variable Mean Std. Dev. Mean Std. Dev. Off-farm labor Dummy for off-farm labor participation (1=yes, 0=no) 0.455 0.501 0.557 0.500 Age Age of respondent (years) 37.966 14.322 34.614 13.183 Age squared Age2 of respondent 1644.193 1321.423 1369.909 1105.832 Last grade level completed Last grade level completed by respondent 3.239 2.464 1.375 1.834 Health status Dummy for health status (1=excellent/good, 0=fair/bad) 0.739 0.442 0.602 0.492 Proportion aged 14 or less Number of household members age 14 or less / household size 0.462 0.206 0.462 0.206 Household size Number of household members 5.920 2.041 5.920 2.041 Land (ha.) Area available for cultivation by household (ha.) 3.060 2.327 3.060 2.327 Distance (km.) Distance to nearest city/town (km.) 23.878 11.145 23.878 11.145 Friendship ego network size Number of friend-identified households in village 2.170 1.383 1.807 0.969 Kin ego network size Number of kin-identified households in village 1.420 1.069 1.591 1.068 Total ego network size Sum of friend and kin ego networks 3.591 1.879 3.398 1.712 Proportion ego network kin Kin ego network size / total ego network size 0.408 0.235 0.445 0.184

80

Table 5.1.3: Variables used in univariate and multivariate probit models, by gender

Males (n1=127) Females (n2=162) Variable Definition of variable Std. Std. Mean Mean Dev. Dev. Off-farm employment Dummy for off-farm labor participation (1=yes, 0=no) 0.528 0.501 0.481 0.501 Hired farm labor Dummy for hired farm labor (1=yes, 0=no) 0.323 0.469 0.395 0.490 Non-farm wage labor Dummy for off-farm wage labor (1=yes, 0=no) 0.244 0.431 0.068 0.252 Off-farm self-employment Dummy for self-employment (1=yes, 0=no) 0.315 0.466 0.123 0.330 Age, 16-25 Dummy if aged between 16-25 (1=yes, 0=no) 0.126 0.333 0.296 0.458 Age, 26-35 Dummy if aged between 26-35 (1=yes, 0=no) 0.409 0.494 0.265 0.443 Age, 36-45 Dummy if aged between 36-45 (1=yes, 0=no) 0.150 0.358 0.185 0.390 Age, 46-55 Dummy if aged between 46-55 (1=yes, 0=no) 0.173 0.380 0.117 0.323 Age, 56 or older Dummy if aged 56+ (1=yes, 0=no) 0.142 0.350 0.136 0.344 Education: No education Dummy if did not attend school (1=yes, 0=no) 0.213 0.411 0.562 0.498 Education: Grades 1 or 2 Dummy if completed grade level 1 or 2 (1=yes, 0=no) 0.205 0.405 0.198 0.399 Education: Grades 3+ Dummy if completed grade level 3+ (1=yes, 0=no) 0.583 0.495 0.241 0.429 Marital status Dummy for marital status (1=married, 0=single/divorced/widowed) 0.819 0.387 0.698 0.461 Health status Dummy for health status (1=excellent/good, 0=fair/bad) 0.685 0.466 0.605 0.490 Number of adults, 60 or older Number of adults, age 60 or more 0.291 0.668 0.309 0.681 Number of males, 15-59 Number of males, age 15-59 1.291 0.691 1.056 0.689 Number of females, 15-59 Number of females, age 15-59 1.449 0.923 1.512 0.967 Number of children, 14 or Number of children, age 14 or less 2.654 1.673 2.586 1.757 younger Land (ha.)1 Area available for cultivation by household (ha.) 3.153 2.418 2.743 2.341 Distance (km.) Distance to the nearest city/town (km.) 23.957 11.621 24.123 11.624 Friendship ego network size Number of friend-identified households in village 2.291 1.512 1.691 0.961 Kin ego network size Number of kin-identified households in village 1.402 1.041 1.556 1.120 Total ego network size Sum of friend and kin households in village 3.693 2.018 3.247 1.705 Proportion ego network kin Kin ego network size / Total ego network size 0.390 0.233 0.457 0.214 19 males and 7 females reported land size greater than or equal to 7 hectares.

81

Education: In the bivariate probit analysis, education is measured as the last grade level completed (i.e., educational attainment), whereas the univariate and multivariate probit use binary education variables. The effect of the education variable on the off-farm labor participation decision is ambiguous since the off-farm labor participation variable includes different off-farm work choices. When disaggregated, it is expected that education will have different effects on the different off-farm work choices. Relative to individuals with no education, respondents with some education are more likely to have better reading, writing, and math proficiency and therefore are more likely to be entrepreneurial and have better management skills. These two skills could be important for success in off-farm wage labor and self- employment. In the hired farm labor case, education is not expected to have more than a negligible impact since this is a low-skill job.

Health and marital status: Health status is also included in tables 5.1.2 and 5.1.3. The health status variable is a binary variable with self-assessed health being excellent/good or fair/bad. Marital status was included only in the univariate/multivariate analyses given that the sample used in the bivariate probit consists of married individuals. Marital status was coded as a binary variable indicating if the respondent was not married (single, widowed, or divorced) or was married.

Household composition: Household composition is reflected in the bivariate models by two variables: 1) the proportion of household members aged 14 or less and 2) household size

(table 5.1.2). The proportion of household members aged 14 or less is the ratio of household members aged 14 or less to total household size. Household composition for the univariate/multivariate probit models is reflected by the following variables: 1) number of adults aged 60 or more, 2) number of males aged 15-59, 3) number of females aged of 15-59, and 4) the

82 number of children aged 14 or less. The household composition variables for these models are count variables. The respondent is excluded from the variables above.

Land assets: The land variable measures the total land available for cultivation and is measured in hectares. It is expected that having more land to cultivate will reduce the likelihood of off-farm participation. Having more land to cultivate implies more time required to work on own farm. With regards to off-farm work choices, it is expected that land scarce individuals will be more likely to pursue off-farm opportunities, specifically in hired farm labor which is generally accessible.

Distance: The distance variable is a measure of kilometers to the nearest city/town. The effect of distance on the off-farm labor participation is ambiguous. Distance is expected to have a different effect on the off-farm work choices. With regards to hired farm labor, distance is expected to have a negligible effect since most hired farm labor opportunities take place in the village. On the other hand, non-farm wage labor opportunities are mainly outside of the village and the marketing of goods from self-employment is outside of the village. Therefore, an increase in the distance to the nearest city/town is likely to negatively affect the likelihood of employment in non-farm wage labor and self-employment.

Ego networks: Network characteristics at the village level are captured by ego-network measures. The measures include friendship ego network size, kin ego network size, total ego network size, as well as the proportion of total contacts who are kin26. Friendship ego network size provides a count of friend households in the village. Kin ego network size provides a count of kin households in the village. Total ego network size is an aggregate count measure consisting

26 It is recognized that other types of networks exist beyond friendship and kin. Examples include networks of affiliations (church, farmer associations, etc.). 83 of the sum of kin and friendship contacts. The proportion of network contacts who are kin is the ratio of kin households to total ego network size.

Previous studies have indicated that larger networks facilitate access to and exchange of information (Bourdieu, 1986; Burt, 1992; Fafchamps and Minten, 2002). It is hypothesized that individuals with larger ego networks (regardless of type) will be more likely to participate in off- farm employment given their access to a greater set of resources. With regards to the separate off-farm work choices, larger total ego networks will have an ambiguous effect on hired farm labor and a positive effect on the likelihood of employment in non-farm self-employment and non-farm wage labor.

The proportion of network contacts that are kin is intended to capture homophily of total ego networks. McPherson et al. (2001, p. 416) defined homophily as “the principle that a contact between similar people occurs at a higher rate than among dissimilar people.” Homophily of total ego networks means that total networks are more kin homogeneous and that contact with kin (similar contacts) is more likely to take place than with friends (dissimilar contacts)

(McPherson et al., 2001)27. Previous research has argued that homophily may facilitate trust and increase obligations among network members (Coleman, 1988) but also discourage alternative ways of thinking or behaving among network members (Burt, 2004). Therefore, with regard to off-farm labor participation, the effect of the proportion of network contacts is ambiguous. On the other hand, homophily of total ego networks (an increase in the proportion of total network contacts that are kin) is expected to have a positive effect on the likelihood of hired farm labor

27 One could argue that with only two network types (friendship and kin ego networks) there exist both kin homogeneous total ego networks as well as friendship homogeneous total ego networks. However, since friendship ego networks consist of friends, acquaintances, and business contacts, it is assumed that a friendship homogenous network implies diversity. 84 and an ambiguous effect on the likelihood of employment in non-farm self-employment and non- farm wage labor.

Different network types may provide different kinds of support. Emotional aid, material aid, information, and companionship is available from different network types but most likely, not entirely from a specific network type (Walker et al., 1993). Friendship and kin ego networks are expected to have an ambiguous effect on the off-farm labor participation. When disaggregated into different off-farm work choices, kin ego networks are expected to have a positive effect on hired farm labor due labor pooling arrangements favoring work amongst kin.

The effect of kin ego networks on non-farm self-employment and non-farm wage labor is ambiguous. The effect of friendship ego network is ambiguous for all three work choices.

5.2 Objective 2 and how it was achieved

Objective 2: To determine if social networks impact agricultural marketing behaviors of rural agricultural households.

To achieve objective 2, models of crop marketing behavior are estimated for different crops to determine the effect of village-level social networks on the market participation decision and value of sales for agricultural households. The analysis is conducted on the useable sample of 201 households and focuses on marketing behaviors for beans, maize, and other crops. For each crop, two models are presented that examine the impacts of kin ego network size, friendship ego network size, and total ego network size. Given data limitations, household purchasing behaviors cannot be included in the analysis. Instead, two household regimes are modeled: Seller and non-seller28. Following the analytical framework developed by Strauss (1984) and Goetz

(1992), let:

28 The non-seller regime includes households that do not sell any agricultural goods in the market but may possibly include households that rely on markets to purchase the commodity. 85

(82)

(83)

where is the production of crop k by household h, is consumption of crop k by household h, is a vector of prices, F is a set of fixed factors related to agricultural production

(i.e., land, household characteristics, and other factors affecting production of good k), z refers to household characteristics affecting consumption, E represents exogenous household income and is a function of net transfers from private assets (I) and bilateral transfers from social networks , and represents farm profits. The marketed surplus of crop h by

household k is then defined as:

(84) and is said to depend on all exogenous variables from the production and consumption decisions such that:

(85)

5.2.1 Empirical estimation

Agricultural marketing behavior is modeled using Cragg’s double hurdle model (Cragg,

1971). The model assumes that a household decides:

(i) Whether or not to participate (sell) in agricultural markets for crop k

(ii) Value of crop k sold to market.

The double hurdle model estimates market participation and the value of sales as two separate decisions. If a positive value of sales is reported, a two-stage decision process has been completed: in the first step, the household has decided whether or not to participate in the crop market, and in the second stage, a (market participating) household has determined the extent of participation. At each step, a household weighs the utility difference of each course of action

86

(Matshe and Young, 2004). Since the utility difference is unobservable, it is assumed that two

latent variables, and , exist, (h=1,2,…,n). The latent variable is associated with the

decision to participate (sell) in crop markets and is associated with the decision of the degree of market participation (Matshe and Young, 2004). The latent variables are expressed as linear functions of and :

(86)

(87) where represents variables used to explain the participation (sell) decision and represents variables used to explain the value of sales. Denoting the market participation index variable as:

(88)

the first hurdle (market participation) can be estimated as a probit model. Next, conditional on market participation, the degree of market participation (second hurdle) can be defined as:

(89)

OLS regression analysis of the second stage will yield inconsistent estimates if a large number of households report no income from agricultural sales (Burke, 2009). The degree of market participation can be regarded as censored from below and therefore best estimated as a truncated regression.

The two-tiered model suggested by Cragg (1971) uses the truncated normal distribution and has a density of y given x as (Burke, 2009; Wooldridge, 2002):

(90)

87 where w is a binary indicator equal to 1 if y is positive and 0 otherwise. Cragg’s double hurdle model is suitable for the study of agricultural marketing behavior given that it does not require that variables that determine the participation decision and variables that determine the value of agricultural sales (given market participation) be the same (Burke, 2009; Matshe and Young,

2004).

As previously discussed in Chapter 3, marketing behavior is affected by fixed transaction costs (FTCs) and variable transaction costs (VTCs). FTCs are one-time costs invariant to the quantity traded and are assumed to affect market participation and not the quantity marketed

(Heltberg and Tarp, 2002; Key et al., 2000). VTCs are per-unit costs and are assumed to affect both the market participation decision and the quantity marketed (Heltberg and Tarp, 2002; Key et al., 2000). Therefore, in equation (86), it can be said that the variables used to explain the participation decision will include FTCs, VTCs, as well as other variables that affect the participation (sell) decision. On the other hand, in equation (87), the variables used to explain the value of sales will not include FTCs which do not affect the quantity marketed.

5.2.2 Social network endogeneity

The possible endogeneity of social networks and marketing behavior also must be tested.

Failure to address possible endogeneity of social networks can result in inconsistent estimates.

Kin ego networks are assumed exogenous. Potential endogeneity can arise from friendship ego network size and total network size (Leavy, 2011)29. Reverse causality is of particular concern given that households that participate in the market have more exposure to other people and are more likely to have a larger network size than someone who chooses not to participate in the

29 Endogeneity of the proportion of total contacts who are kin was also considered; however, the proportion variable was not included in the analysis since 1) there was no suitable instrument for the proportion of total contacts who are kin, 2) the control function approach cannot handle more than one endogenous variable at a time, and 3) the proportion of total contacts who are kin yields similar results to that of kin ego networks. 88 market. Likewise, a household with a larger network is likely to have access to more market information and, therefore, be more likely to participate in the market than someone who has a smaller network size. To test for and correct for endogeneity, the control function (CF) method is used (Wooldridge, 2002). The control function approach relies on an OLS regression of the endogenous variable on the set of independent variables and instrumental variables. Then the residuals from the first step regression are saved and included as an additional regressor in the probit model of interest. The significance of the coefficient on residuals will test and control for correlation between social networks and the error term (Imbens and Wooldridge, 2007; Lewbel,

2004; Ricker-Gilbert et al., 2011). Like before, the instrumental variable for friendship ego network size is the (village-level) non-self cluster mean measure of friendship ego network size, and the instrument variable for total network size is the non-self cluster mean measure of total ego network size.

5.2.3 Obtaining average partial effects

Parameter estimates of the first and second hurdles are estimated simultaneously using the craggit command in STATA (Burke, 2009). To facilitate interpretation of the regression coefficients, post-estimation analysis includes the marginal effects of the probability of market participation as well as the conditional and unconditional average partial effect on the expected value of sales. Results presented in Chapter 6 include coefficient estimates of the probit and truncated regression in column 1 and column 2, respectively. Column 3 presents the marginal effects of the independent variables on the probability of market participation (tier 1), column 4 presents the conditional average partial effect (CAPE) of the independent variables on the value of sales only for households that participate in the market for that specific crop (tier 2), and column 5 displays the unconditional average partial effect (UAPE) which is a function of both

89 the probit (tier 1) and truncated regressions (tier 2) and measures the expected effect of the independent variables on the value of sales, regardless of the participation decision. The standard errors for the marginal effects and average partial effects are calculated via bootstrapping using

250 iterations30 31.

5.2.4 Variables used in the analyses

Descriptive statistics of the variables used in the estimation of the double hurdle model are included in table 5.2.2. The table displays means at the individual level (head of household) and household level for the 201 surveyed households. Variables include demographic variables, farm characteristics, fixed and variable transaction cost variables, and network variables. Based on the decision-making variable, men are treated as the primary decision-maker. Women are treated as the decision-maker if the male was not surveyed or if she lived alone32.

Demographics: Demographic characteristics include age and age squared of decision- maker, marital status, health status of decision-maker, and household composition. The likelihood of having access to important market information is expected to increase with age, therefore, age is expected to have a positive effect on market participation. On the other hand, age is expected to have a parabolic (inverse U) relationship with the value of sales. In early stages of the life cycle, a decision-maker may be willing and capable of selling large quantities of crops; however in later stages of the life cycle, old age may result in reduced ability to transport goods or less time available for marketing crops. Marital status was coded as a binary

30 See Burke (2009) for further details on bootstrapping standard errors for average partial effects (APE). 31 Bootstrapping is intended to provide a measure of accuracy to statistical estimates (Efron and Tibshirani, 1993; Efron, 1979). It assumes that the sample is representative of the population and therefore approximates the distribution of a statistic by repeatedly resampling from the data with replacement (Angrist and Pischke, 2008; Cameron and Trivedi, 2005). 32 Based on this assumption, 127 households have a male head of household and 74 households have a female head of household. 90

Table 5.2.1: Variables used in double hurdle model (n=201 households) Std. Variable Definition of variable Mean Dev. Households characteristics Age Age of decision-maker 39.502 15.945 Marital status Dummy for marital status (1=married, 0=single/divorced/widowed) 0.706 0.457 Health status Dummy for health status of decision-maker (1=excellent/good, 0=fair/bad) 0.657 0.476 Number of adults, 60 or older Number of adults, age 60 or more 0.323 0.707 Number of males, 15-59 Number of males, age 15-59 1.134 0.746 Number of females, 15-59 Number of females, age 15-59 1.428 0.936 Number of children, 14 or younger Number of children, age 14 or less 2.517 1.772 Farm characteristics Wage labor Dummy if decision-maker employed in non-farm wage labor (1=yes, 0=no) 0.179 0.384 Land (ha.) Area available for cultivation by household (ha.) 2.863 2.406 Extension Dummy if decision-maker able to ask extension for help (1=yes, 0=no) 0.632 0.484 Fixed transaction costs Own radio/cell phone (communication) Dummy if household owns radio/cell phone (1=yes, 0=no) 0.567 0.497 Education: No education Dummy if decision-maker did not attend school (1=yes, 0=no) 0.358 0.481 Education: Grades 1 or 2 Dummy if decision-maker completed grade level 1 or 2 (1=yes, 0=no) 0.189 0.393 Education: Grades 3+ Dummy if decision-maker completed grade level 3+ (1=yes, 0=no) 0.453 0.499 Variable transaction costs Distance (km.) Distance to the nearest city/town (km.) 24.125 11.824 Improved transportation Dummy if household owns cart/bicycle/motorcycle (1=yes, 0=no) 0.687 0.465 Network characteristics Friendship ego network size Number of friend-identified households in village 2.020 1.375 Kin ego network size Number of kin-identified households in village 1.443 1.095 Total ego network size Sum of friendship and kin ego networks 3.463 1.924 Instrumental variables Non-self cluster mean friendship network Mean friendship ego network size for all other households (village-level) 1.945 0.536 Non-self cluster mean total network Mean total ego network size for all other households (village-level) 3.432 0.833

91 variable to indicate if the respondent was not married (single, widowed, or divorced) or was married. Marital status was meant to control for one or two person households. The expected effect of marital status is ambiguous. Health status is a binary variable with self-assessed health being coded as excellent/good or fair/bad. Decision-makers in excellent/good health are expected to be more willing and able to participate in markets, and also better able to transport larger quantities of goods to sell. Household composition is captured by the number of adults age 60 or more, number of males aged 15-59, number of females aged 15-59, and number of children ages

14 years or less. The number of adults, age 60 or more, as well as the number of males and females, ages 15-59, are expected to have a positive effect on market participation since more adults or working age males/females increases the number of individuals available to market goods. More adults (age 60 or more) or more males/ females (ages 15-59) may facilitate the transport of large quantities of goods to be sold but may reduce the quantity marketed because there of greater household consumption associated with larger households. For this reason, the variables for the number of adults age 60, the number of males aged 15-59, and the number of females aged 15-59 are expected to have an ambiguous effect on the value of sales. On the other hand, the number of children is expected to have a negative effect on market participation since children are likely to keep adults from marketing activities. Likewise, the number of children is expected to have a negative effect on the value of sales since having more children present in the household implies having more mouths to feed.

Farm characteristics: Farm characteristics are captured by farm size, relationship with extension workers, and participation in non-farm wage labor. Farm size is the total land available for cultivation by the household and is measured in hectares. Households with more land available are more likely to produce more than they consume (marketable surplus). Therefore,

92 farm size is expected to have a positive relationship with the value of sales. The relationship with extension is a binary variable indicating whether the decision-maker can rely on (or not) extension for help to farm better. Extension services are expected to provide valuable farming information to households and additionally, Extension workers may provide marketing information. For this reason, having a good relationship with extension is expected to positively impact both the participation decision and the value of sales. Non-farm wage labor is a binary variable indicating whether the decision-maker is involved in wage labor. Non-farm wage labor is expected to have a negative impact on marketing probability and the value of sales for the following reasons: 1) being employed in wage labor may result in less time for farm work and the marketing of crops and 2) households with wage labor employment are more likely to higher incomes and therefore a lower dependence on crop markets for household income.

Transaction costs: Transaction costs are costs associated with market transactions and may be indicative of why household participate (or not) in the marketing of crops as well as the degree of market participation by households (Key et al., 2000). Transaction costs include fixed transaction costs (FTCs) and variable transaction costs (VTCs). Fixed transaction costs (FTCs) are lump sum (one-time) costs that affect market participation and have no effect on the quantity marketed (Goetz, 1992) and may include search costs associated with identifying customers, traders, and/or markets, negotiating and bargaining costs, and supervision costs (Heltberg and

Tarp, 2002; Key et al., 2000). Variable transaction costs (VTCs) are per-unit costs that lower the price received by the seller of a crop (Key et al., 2000). VTCs impact the participation decision and the degree of market participation by agricultural households (Key et al., 2000) and may include storage, packaging, transportation, and marketing costs (Key et al., 2000). Unfortunately, most transaction costs (FTCs and VTCs) are difficult to observe in survey work. Previous

93 research has relied on observable exogenous variables that may affect the magnitude of transaction cost as proxies for FTCs and VTCs.

Information access variables may be indicative of the size of the fixed transaction costs faced (Heltberg and Tarp, 2002). FTCs are therefore proxied with the education of the decision- maker and ownership of a radio or cell phone. Education is measured via a set of binary variables, with no education being the reference category (see table 5.2.2). Better educated individuals are expected to access quicker and understand important market information better than their uneducated counterparts. Therefore, education is expected to be positively associated with market participation. The communication variable is a binary variable indicating whether the household owns a radio or cell phone33. Access to a radio or cell phone provides access to important market information that other households may have difficult accessing. The communication variable is expected to have a positive effect on market participation.

Variable transaction costs: Variable transaction costs are proxied with the distance (in kilometers) to the nearest city/town as well as ownership of an improved means of transportation. Both variables are expected to determine the size of variable transaction costs

(Heltberg and Tarp, 2002). An increase in the distance to the nearest city/town is expected to have an ambiguous effect on the participation decision. Distance to the nearest city/town may deter households from selling in city/town markets but may increase the likelihood of selling directly to consumers, traders, or in local markets. Likewise, the distance variable is expected to have an ambiguous effect on market sales. An increase in the distance to the city/town may affect transportation costs to city/town markets but may have no effect on transport costs associated with sales to local consumers, traders, or markets. Ownership of an improved means

33 Few household own a cell phone but it was important to include them since cell phones provide instant access to market information. 94 of transportation is a binary variable indicating if a household owns a bicycle, motorcycle, or cart. Ownership of a bicycle, motorcycle, or cart is expected to positively affect market participation and value of sales. Household with a bicycle, motorcycle, or cart can travel to different selling points and are therefore more likely to access market information quicker than households without an improved means of transport. Likewise, households with a bicycle, motorcycle, or cart are able to transport more goods to sell (per trip or per person) than households without an improved means of transport.

Ego Networks: Network characteristics at the village level are captured by ego-network measures. The measures include friendship ego network size, kin ego network size, total ego network size34. Model (1) includes kin and friendship ego networks size and model (2) includes total ego network size. Friendship ego network size provides a count of friend households in the village. Kin ego network size provides a count of kin households in the village. Total ego network size is an aggregate count measure consisting of the sum of kin and friendship contacts.

The research on marketing behavior is exploratory because the effects of kin and/or friend networks on market participation and sales in rural and resource poor contexts has been minimally explored.

Previous studies have indicated that larger networks facilitate access to and exchange of information (Bourdieu, 1986; Burt, 1992; Fafchamps and Minten, 2002). As was previously discussed, information access variables may be indicative of the size of the fixed transaction costs faced (Heltberg and Tarp, 2002). A larger total ego network will facilitate access to market information and reduce the size of the fixed transaction costs faced by the household (Heltberg and Tarp, 2002). Therefore, total ego network size is expected to have a positive effect on the

34 It is recognized that other types of networks exist beyond friendship and kin. Examples include networks of affiliations (church, farmer associations, etc.). 95 likelihood of market participation. On the other hand, the effect of total ego networks on the sales of beans, maize, and other crops is ambiguous.

As was previously discussed, different network types may provide different kinds of support, however little is known about how support and obligations will affect market participation and the value of sales. Friendship ego networks may increase access to market information, and, therefore increase the likelihood of market participation. However, the effect of friendship network on the value of sales is ambiguous. On the other hand, the effects of kin ego networks on market participation and the value of sales are ambiguous.

The next chapter provides the results of the model estimations for the labor allocation and agricultural marketing models.

96

Chapter 6 – EMPIRICAL RESULTS

6.1 Labor results – Objective 1

6.1.1 Results for off-farm labor market participation

The following section presents the estimated off-farm labor participation results for adult males and adult females in dual-headed households (married sample). Bivariate probits are estimated to account for possible jointness in off-farm labor decision-making. The bivariate probit model is an appropriate model if male and female off-farm labor participation decisions are correlated. Two models are presented in table 6.1.1. The first includes measures of friendship ego network size and kin ego network size, whereas the second model includes measures of total ego network size and the proportion of total ego network contacts who are kin.

The Wald test was used to test the hypothesis that all the parameters in the regression equation are jointly zero (except the constant) (Greene, 2003)35. The hypothesis is rejected at the

1% level of significance. As a result, it can be concluded that at least some variables in the bivariate probit are statistically significant.

The estimated cross-equation correlation (athrho in table 6.1.1) summarizes the direction of correlation between the error terms of the off-farm labor participation equations for males and females. The estimated cross-equation correlation is positive (0.204 in model (1) and 0.262 in model (2)) but statistically insignificant in both instances. The insignificance of the cross equation correlation and the failure to reject the Wald test statistic (2nd Wald in table 6.1.1) for the hypothesis that the two equations are independent (ρ=0) imply non-jointness in decision- making, at least for this sample.

Previous research on the jointness of male and female off-farm labor participation decisions has yielded inconclusive evidence; several studies have indicated jointness in decision-

35 Displayed as 1st Wald in Table 6.1.1 97

Table 6.1.1: Bivariate probit results for participation in off-farm work Model (1) Model (2) Female Male Female Male Marginal Marginal Marginal Marginal Coefficient Coefficient Coefficient Coefficient Effects Effects Effects Effects Age (male) 0.178 0.061 0.116 0.038 0.173 0.056 0.131 0.042 (0.133) (0.051) (0.117) (0.048) (0.119) (0.046) (0.113) (0.047) Age2/1000 (male) -0.002 -0.001 -0.001 -0.000 -0.002* -0.001 -0.001 -0.000 (0.002) (0.001) (0.001) (0.000) (0.001) (0.001) (0.001) (0.000) Age (female) -0.342*** -0.119** -0.208* -0.068 -0.307*** -0.100** -0.219* -0.070 (0.123) (0.051) (0.109) (0.046) (0.117) (0.047) (0.118) (0.050) Age2/1000 (female) 0.004*** 0.001** 0.002 0.001 0.004*** 0.001** 0.002 0.001 (0.002) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Grade level completed (male) -0.044 -0.017 0.000 0.002 -0.058 -0.022 0.002 0.004 (0.065) (0.024) (0.070) (0.027) (0.067) (0.024) (0.072) (0.029) Grade level completed (female) -0.151 -0.060 0.054 0.029 -0.128 -0.053 0.075 0.038 (0.100) (0.038) (0.092) (0.037) (0.101) (0.037) (0.093) (0.037) Health status (male) 0.309 0.064 0.981** 0.357*** 0.184 -0.009 1.121*** 0.409*** (0.368) (0.155) (0.382) (0.128) (0.376) (0.152) (0.395) (0.125) Health status (female) -0.989*** -0.335*** -0.301 -0.076 -0.961*** -0.312*** -0.363 -0.091 (0.375) (0.123) (0.356) (0.145) (0.358) (0.118) (0.358) (0.147) Proportion age 14 or less -0.589 -0.258 0.683 0.304 -0.654 -0.279 0.544 0.262 (0.808) (0.311) (0.816) (0.332) (0.798) (0.302) (0.819) (0.336) Household size 0.083 0.025 0.128 0.048 0.087 0.027 0.090 0.031 (0.100) (0.038) (0.096) (0.039) (0.097) (0.036) (0.099) (0.040) Land (ha.) 0.118* 0.046* -0.021 -0.014 0.132* 0.049* -0.006 -0.011 (0.066) (0.025) (0.066) (0.027) (0.070) (0.026) (0.072) (0.029) Distance (km.) -0.026* -0.010* -0.002 0.000 -0.025* -0.009 -0.001 0.001 (0.015) (0.006) (0.013) (0.006) (0.015) (0.006) (0.014) (0.006)

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Table 6.1.1 (cont): Bivariate probit results for participation in off-farm work Model (1) Model (2) Female Male Male Female Marginal Marginal Marginal Marginal Coefficient Coefficient Coefficient Coefficient Effects Effects Effects Effects Friendship ego network size (male) 0.033 0.012 0.005 0.000 - - - - (0.136) (0.050) (0.132) (0.052) Kin ego network size (male) 0.095 0.023 0.253 0.098 - - - - (0.162) (0.059) (0.172) (0.066) Friendship ego network size (female) 0.402** 0.141* 0.213 0.067 - - - - (0.204) (0.077) (0.194) (0.078) Kin ego network size (female) -0.390** -0.135** -0.240 -0.078 - - - - (0.175) (0.069) (0.174) (0.072) Total ego network size (male) - - - - 0.048 0.010 0.118 0.045 (0.093) (0.033) (0.101) (0.039) Proportion ego network kin (male) - - - - -0.347 -0.237 1.645** 0.691** (0.767) (0.279) (0.749) (0.293) Total ego network size (female) - - - - 0.052 0.018 0.015 0.003 (0.106) (0.038) (0.103) (0.040) Proportion ego network kin (female) - - - - -2.394*** -0.831** -0.944 -0.235 (0.918) (0.359) (0.979) (0.411) Constant 3.902** - -0.691 - 4.591*** - -0.948 - (1.703) (1.788) (1.642) (1.757) Athrho 0.204 0.262 (0.194) (0.197) Observations 88 88 88 88 Wald chi2(32) (1st Wald) 66.40*** 66.40*** 71.01*** 71.01*** Wald test of rho=0: (2nd Wald) 1.11 1.11 1.78 1.78 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

99 making (Abdulai and Delgado, 1999; Ahearn et al., 2006; Benjamin et al., 1996; Bharadwaj,

2007; Kimhi, 2004; Kwon et al., 2006; Unni, 1993), whereas another set of studies (Brick and

Garvey, 2005; Corsi and Findeis, 2000; Huffman and Lange, 1989; Lass and Gempesaw, 1992;

Lass et al., 1989; Mishra and Goodwin, 1997; Pandit et al., 2013; Sahn and Alderman, 1996) has yielded an insignificant correlation coefficient. For this sample (married sample), it is concluded that work decisions are independently made and therefore, the labor participation decisions for men and women in Mozambique is estimated with two separate univariate probit models.

Estimating separate univariate probit models of male and female labor participation decisions imply that the model can include the full sample of 162 females and 127 males. This allows for greater numbers of observations in the estimates.

The potential endogeneity of friendship ego network size and total ego network size is addressed through testing of instrumental variables (IV). The instrument for friendship network size in model (1) is the (village-level) non-self cluster mean friendship ego network size and the instrument for total ego network size in model (2) is the (village-level) non-self cluster mean total ego network size. The results are included in appendix F, table F1.1. The Wald test of exogeneity fails to reject the null hypothesis of exogeneity of the regressors; therefore, friendship ego network size and total ego network size can be treated as exogenous variables,. Furthermore since both friendship ego network size and total ego network size are exogenous, it can be concluded that the proportion of total ego network that is kin is also exogenous (the proportion variable is a function of two exogenous variables). For this reason, the remainder of the analysis treats friendship network size and total network size as exogenous variables.

Table 6.1.2 present univariate probit results for the off-farm labor participation of men and women in the 6 villages of Mozambique. The pseudo R2, an indicator of goodness of fit, is

100

Table 6.1.2: Probit results for male off-farm labor participation Females Males Model (1) Model (2) Model (1) Model (2) Marginal Marginal Marginal Marginal Coefficient Coefficient Coefficient Coefficient Effects Effects Effects Effects Age: 26-35 -0.824*** -0.311*** -0.803*** -0.303*** -0.018 -0.007 0.007 0.003 (0.308) (0.105) (0.308) (0.105) (0.404) (0.160) (0.405) (0.161) Age: 36-45 -0.740** -0.277** -0.713** -0.268** 0.108 0.043 0.084 0.033 (0.344) (0.115) (0.343) (0.116) (0.487) (0.191) (0.489) (0.193) Age: 46-55 -1.724*** -0.501*** -1.714*** -0.498*** 0.404 0.156 0.499 0.191 (0.399) (0.063) (0.397) (0.064) (0.480) (0.177) (0.486) (0.175) Age: 56+ -0.979** -0.347*** -0.906** -0.326** -0.338 -0.134 -0.277 -0.110 (0.424) (0.121) (0.429) (0.127) (0.565) (0.222) (0.573) (0.226) Education: Grades 1 or 2 0.290 0.115 0.314 0.125 0.677* 0.254* 0.768** 0.285** (0.307) (0.121) (0.308) (0.121) (0.390) (0.133) (0.392) (0.129) Education: Grades 3+ -0.470 -0.183* -0.426 -0.166 0.137 0.055 0.185 0.074 (0.291) (0.109) (0.290) (0.109) (0.339) (0.135) (0.338) (0.134) Marital status -0.180 -0.072 -0.159 -0.063 -0.217 -0.085 -0.175 -0.069 (0.262) (0.104) (0.262) (0.104) (0.352) (0.136) (0.356) (0.139) Health status -0.345 -0.137 -0.373* -0.148* 0.297 0.118 0.419 0.166 (0.222) (0.087) (0.223) (0.087) (0.269) (0.106) (0.274) (0.107) Number of adults, 60 or older -0.021 -0.009 -0.071 -0.028 0.340 0.135 0.328 0.130 (0.181) (0.072) (0.179) (0.071) (0.242) (0.096) (0.254) (0.101) Number of males, 15-59 0.129 0.051 0.107 0.043 -0.390** -0.155** -0.390** -0.155** (0.183) (0.073) (0.179) (0.071) (0.175) (0.070) (0.175) (0.070) Number of females, 15-59 0.094 0.037 0.099 0.039 0.199 0.079 0.158 0.063 (0.120) (0.048) (0.121) (0.048) (0.148) (0.059) (0.149) (0.059) Number of children, 14 or younger 0.070 0.028 0.064 0.026 -0.009 -0.004 -0.022 -0.009 (0.066) (0.026) (0.066) (0.026) (0.073) (0.029) (0.075) (0.030)

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Table 6.1.2 (cont.): Probit results for male off-farm labor participation Females Males Model (1) Model (2) Model (1) Model (2) Marginal Marginal Marginal Marginal Coefficient Coefficient Coefficient Coefficient Effects Effects Effects Effects Land (ha.) 0.077 0.031 0.074 0.030 -0.047 -0.019 -0.043 -0.017 (0.048) (0.019) (0.048) (0.019) (0.055) (0.022) (0.056) (0.022) Distance (km.) -0.003 -0.001 -0.003 -0.001 0.002 0.001 0.002 0.001 (0.010) (0.004) (0.010) (0.004) (0.011) (0.004) (0.011) (0.004) Friendship ego network size 0.336*** 0.134*** - - 0.056 0.022 - - (0.125) (0.050) (0.088) (0.035) Kin ego network size -0.009 -0.003 - - 0.313** 0.124** - - (0.104) (0.042) (0.139) (0.055) Total ego network size - - 0.171*** 0.068*** - - 0.153** 0.061** (0.065) (0.026) (0.070) (0.028) Proportion ego network kin - - -0.925* -0.368* - - 1.395*** 0.554*** (0.528) (0.210) (0.516) (0.205) Constant -0.114 - 0.307 - -0.506 - -1.161 - (0.508) (0.528) (0.748) (0.780) Observations 162 162 127 127.00 Wald chi2(16) 40.12*** 40.57*** 22.60 31.05*** Pseudo R2 0.15 0.15 0.12 0.14 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

102

0.12 in model (1) and 0.14 in model (2) for males; in the female model, the pseudo R2 is 0.15 in both models. The Wald test statistic for the hypothesis that all regression coefficients are jointly zero is rejected at the 1% level of significance for females (in both models) and for males in model (2). This indicates that the proposed models for females and males perform better than the alternative (constant-only) models.

For women, there is a negative relationship between age and off-farm labor participation.

In model (1), results show that relative to younger women (age category 15-25), women in age category 26- 35 have a 31% lower probability of off-farm labor participation (statistically significant at the 1% level), women aged 36-45 have a 27% lower probability (statistically significant at the 5% level), middle-aged woman (age category 46-55) have a 50% lower probability (statistically significant at the 1% level), and elderly women (56 or older) have a 37% lower probability of off-farm labor participation (statistically significant at the 1% level).

Typically for women, participation in off-farm work decreases with age during child bearing years, but eventually increases. In this case, a similar pattern emerges, except that participation in off-farm work does not increase in later age categories. This may possibly be due to the fact that the labor participation variable aggregates several off-farm work choices. For males, none of the age categories are statistically significant indicating that there is no relationship between male off-farm labor participation and age.

Education variables in model (1) show that better educated Mozambican women (grade level 3 or more) are less likely to engage in off-farm labor than women with no education

(statistically significant at the 10% level). This finding seems counter-intuitive; however, it may be due to fact that the majority of women in the six villages in Mozambique are employed in

103 hired farm labor, typically a job accessible to less-educated individuals36. Mozambican men exhibit the opposite behavior. Relative to males with no education, males who completed grade level 1 or 2 have a 25% higher probability of being employed in off-farm labor (statistically significant at the 10% level).

The coefficient for male health status was statistically insignificant indicating that there is no relationship between male off-farm labor and health status. On the other hand, for a woman in fair/bad health, a change in health status (where she is in excellent/good health) would decrease the probability of off-farm labor by about 0.148 (statistically significant at the 10% level in model (2)). The negative effect of health on female off-farm labor participation suggests that healthier women are more likely to work on their own farm since better health may allow them to be more productive on their own farm.

Surprisingly, none of the household composition variables are statistically significant predictors of female off-farm labor participation. However, for Mozambican men, a marginal change in the number of working age adult males from the average of 1.29 is associated with a

15.5% decrease in off-farm labor participation (statistically significant at the 5% level). It was expected that an increase in working age males would diversify non-farm work activities, however, result suggest that as the number of working age males increase, Mozambican men may allocate more time to leisure or own farm work.

Results from model (1) indicate that men and women rely on different network types to access off-farm labor. For Mozambican women, the statistically insignificant coefficient for kin ego network size indicates that there is no relationship between kin ego networks and off-farm labor participation. However, a marginal change in friendship ego network size from the average

36 Results from table B1.1 show that in each province, female participation in off-farm labor was greater in hired farm labor than in any other work category. 51.7% of females in Tete province employed in hired farm labor, whereas 33.3% of females in Zambezia and 32.1% of females in Niassa pursued work in hired farm labor. 104 of 1.69 is associated with a 13.4% increase in the probability of female off-farm labor participation (statistically significant at the 1% level). The opposite is true for men. A marginal change in kin network ego size from the average of 1.40 is associated with 12.4% increase in the likelihood of off-farm labor participation (statistically significant at the 5% level). On the other hand, results indicate that that there is no relationship between friendship ego networks and male off-farm labor participation. The results indicate that men and women rely on different ego network types to access off-farm labor.

In model (2), a marginal increase in female total ego network size from the average of

3.25 is associated with a 6.8% increase in the probability of off-farm labor participation

(significant at the 1% level). For men, a marginal increase in total ego network size from the average of 3.69 is associated with a 6.1% increase in the probability of off-farm labor participation (statistically significant at the 5% level). For men and women, being better connected increases access to information and opportunities in off-farm labor. The proportion of total ego network that is kin has the opposite effect on men and women. A marginal increase in the proportion of contacts who are kin from the female average of 0.457 is associated with a

0.368 reduction in the probability of off-farm employment (statistically significant at the 10% level) whereas for men, a marginal increase in the proportion of contacts who are kin from the average of 0.390 is associated with a 0.554 increase in the likelihood of off-farm employment

(statistically significant at the 1% level).

6.1.2 Results for off-farm work choice

Having already explored the off-farm labor participation decision for men and women in

Mozambique, the next step in the analysis is to explore the determinants of off-farm labor participation by sector. Estimation results from the multivariate probit model are presented for

105 women in table 6.1.3 and for men in table 6.1.4. Given the independence of male and female off- farm labor decision-making established in section 6.1.1, the participation decision of off-farm work choices is modeled separately for males and females and is based on the full sample of 162 females and 127 males. This allows for greater numbers of observations in the estimates.

The Wald chi-square statistic that tests the hypothesis that all regression coefficients are equal to zero is rejected at the 1% significance level, in model (1) and model (2) for both females and males (see table 6.1.3 and 6.1.4, respectively). This indicates that 1) the proposed models perform better than the alternative (constant-only) models and 2) removing all the variables would reduce the fit of the models. Furthermore, the likelihood ratio test statistic (for rho21 = rho31 = rho32) rejects the independence of hired farm labor, non-farm wage labor, and non-farm self employment at the 1% level for males in both model (1) and model (2). Rejection of the hypothesis implies that the use of a multivariate probit is favored over three independent probits for off-farm work choice. In the case of females, the likelihood ratio test statistic cannot reject the independence of the different off-farm work choices. Despite the statistically insignificant likelihood ratio test statistic (for rho21 = rho31 = rho32), a multivariate probit is justified in the female case given that there is a negative and statistically significant correlation of the error terms for employment in hired farm labor and non-farm wage labor (atrho21).

Female off-farm work choice

Typically, female participation in off-farm work decreases with age during child bearing years, but eventually increases. Results for the different off-farm work choices show that a similar pattern emerges for hired farm labor and self-employment. In model (1), women across all age categories (except age category 56 or older which is statistically insignificant) are less likely to be employed in hired farm labor than women in the age category 15-26. With regards to

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Table 6.1.3: Multivariate probit results for female non-farm work choice Model (1) Model (2) Self- Self- Hired farm Non-farm Hired farm Non-farm employment employment Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Age, 26-35 -0.801** 0.985** -0.058 -0.784** 0.949** -0.108 (0.317) (0.477) (0.389) (0.315) (0.465) (0.388) Age, 36-45 -0.629* 1.238** -0.807* -0.605* 1.234** -0.914* (0.366) (0.522) (0.463) (0.362) (0.496) (0.479) Age, 46-55 -1.271*** -4.127*** -5.858*** -1.274*** -4.407*** -6.193*** (0.417) (0.671) (0.574) (0.415) (0.898) (0.712) Age, 56 or older -0.536 0.098 -1.922*** -0.453 0.271 -2.051*** (0.442) (0.782) (0.649) (0.445) (0.814) (0.659) Education: Grades 1 or 2 0.337 -0.103 -0.377 0.370 -0.142 -0.422 (0.304) (0.463) (0.390) (0.306) (0.467) (0.398) Education: Grades 3+ -0.757** 0.025 0.295 -0.691** 0.041 0.186 (0.302) (0.426) (0.356) (0.297) (0.435) (0.354) Marital status 0.013 -0.309 -0.428 0.022 -0.308 -0.437 (0.273) (0.405) (0.344) (0.269) (0.383) (0.329) Health status -0.171 0.508 -0.877*** -0.198 0.593* -0.800*** (0.229) (0.360) (0.296) (0.228) (0.351) (0.301) Number of adults, 60 or older -0.012 -0.974** 0.454* -0.075 -0.930** 0.596** (0.195) (0.409) (0.253) (0.197) (0.464) (0.251) Number of males, 15-59 0.186 -0.181 0.370* 0.160 -0.100 0.411* (0.185) (0.244) (0.214) (0.178) (0.224) (0.210) Number of females, 15-59 0.044 -0.372 0.224 0.051 -0.385* 0.227 (0.119) (0.235) (0.171) (0.119) (0.229) (0.175) Number of children, 14 or younger 0.076 -0.259* -0.008 0.067 -0.276* -0.013 (0.069) (0.151) (0.102) (0.069) (0.144) (0.102)

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Table 6.1.3 (cont.: Multivariate probit results for female non-farm work choice Model (1) Model (2) Self- Self- Hired farm Non-farm Hired farm Non-farm employment employment Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Land (ha.) 0.002 -0.114 0.051 0.002 -0.099 0.048 (0.040) (0.127) (0.056) (0.040) (0.131) (0.052) Distance (km.) 0.003 -0.044** -0.027** 0.004 -0.046** -0.031** (0.010) (0.019) (0.012) (0.010) (0.020) (0.013) Friendship ego network size 0.411*** -0.510** -0.457** - - - (0.124) (0.246) (0.199) Kin ego network size 0.016 0.385* 0.075 - - - (0.105) (0.211) (0.164) Total ego network size - - - 0.226*** -0.035 -0.181* (0.065) (0.114) (0.108) Proportion ego network kin - - - -1.040* 1.920** 0.687 (0.543) (0.956) (0.621) Constant -0.815 0.517 0.259 -0.340 -0.546 -0.027 (0.517) (0.980) (0.674) (0.541) (1.029) (0.706) atrho21 -0.399* -0.459** (0.233) (0.220) atrho31 0.175 0.116 (0.185) (0.187) atrho32 0.034 0.112 (0.236) (0.262) Observations 162 162 Wald chi2(48) 962.74*** 801.00*** Likelihood ratio test of rho21 = rho31 = rho32 = 0: 3.21 3.88 Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1

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Table 6.1.4: Multivariate probit results for male non-farm work choice Model (1) Model (2) Self- Self- Hired farm Non-farm Hired farm Non-farm employment employment Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Age, 26-35 -0.219 -0.648 -0.342 -0.241 -0.654 -0.339 (0.404) (0.397) (0.388) (0.404) (0.407) (0.389) Age, 36-45 -0.440 -0.637 -0.816 -0.459 -0.696 -0.855 (0.476) (0.504) (0.547) (0.482) (0.504) (0.556) Age, 46-55 0.247 -0.072 0.222 0.306 -0.039 0.253 (0.493) (0.457) (0.469) (0.487) (0.477) (0.468) Age, 56 or older -0.097 -1.299* -0.983 -0.098 -1.297* -0.975 (0.578) (0.688) (0.636) (0.578) (0.712) (0.648) Education: Grades 1 or 2 0.121 0.325 0.799** 0.132 0.403 0.859** (0.388) (0.406) (0.401) (0.392) (0.403) (0.404) Education: Grades 3+ 0.024 0.267 0.272 0.009 0.317 0.303 (0.342) (0.351) (0.369) (0.341) (0.354) (0.364) Marital status -0.127 0.211 -0.596* -0.072 0.235 -0.579* (0.359) (0.362) (0.350) (0.356) (0.358) (0.350) Health status 0.318 -0.266 0.167 0.475 -0.155 0.267 (0.284) (0.279) (0.259) (0.308) (0.284) (0.279) Number of adults, 60 or older 0.386* 0.054 0.285 0.396* 0.045 0.271 (0.233) (0.235) (0.210) (0.237) (0.247) (0.214) Number of males, 15-59 -0.238 -0.211 -0.151 -0.231 -0.193 -0.143 (0.188) (0.203) (0.185) (0.186) (0.203) (0.186) Number of females, 15-59 0.003 0.173 0.294** -0.042 0.143 0.276* (0.152) (0.157) (0.146) (0.159) (0.159) (0.145) Number of children, 14 or younger 0.048 0.078 -0.011 0.034 0.068 -0.024 (0.080) (0.083) (0.076) (0.079) (0.083) (0.077)

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Table 6.1.4 (cont.) - Multivariate probit results for male non-farm work choice Model (1) Model (2) Self- Self- Hired farm Non-farm Hired farm Non-farm employment employment Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Land (ha.) -0.205*** 0.059 0.011 -0.205*** 0.063 0.014 (0.077) (0.045) (0.058) (0.076) (0.044) (0.056) Distance (km.) 0.001 -0.015 -0.002 0.000 -0.015 -0.003 (0.011) (0.013) (0.012) (0.011) (0.013) (0.012) Friendship ego network size -0.068 -0.051 -0.011 - - - (0.094) (0.099) (0.087) Kin ego network size 0.360*** 0.110 0.147 - - - (0.131) (0.136) (0.126) Total ego network size - - - 0.109* 0.016 0.057 (0.066) (0.068) (0.065) Proportion ego network kin - - - 1.880*** 0.989 0.922* (0.631) (0.650) (0.516) Constant -0.251 -0.528 -0.567 -1.096 -1.032 -1.023 (0.757) (0.727) (0.771) (0.802) (0.837) (0.829) atrho21 0.241 0.216 (0.182) (0.184) atrho31 0.217 0.199 (0.172) (0.172) atrho32 0.702*** 0.688*** (0.195) (0.196) Observations 127 127 Wald chi2(48) 79.40*** 89.55*** Likelihood ratio test of rho21 = rho31 = rho32 = 0: 17.41*** 16.36*** Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

110 self-employment, the coefficient for females in age category 26-35 is statistically insignificant, however, females in age categories 36-45, 46-55, and 55 or older are less likely to participate in self-employment than younger females (ages 15-26). The age categories have the opposite effect on participation in non-farm wage labor. Females in age categories 26-35 and 36-45 (both statistically significant at the 5% level) are more likely than females aged 15-26 to participate in non-farm wage labor. In later stages, women between the ages 46-55 are less likely than their younger counterparts to be employed in non-farm wage labor (statistically significant at the 1% level). Employment in non-farm wage labor may require skills and experience that older women

(between the ages of 26 and 45) have relative to younger women, however in later stages, women between the ages of 46 and 55 may be less willing and/or able to work regular hours and be away from home (i.e. non-farm wage labor).

With regards to education, results show that better educated females (completed grade level 3 or more) are less likely than females with no education (statistically significant at the 5% level in model (1)) to participate in hired farm labor. The negative effect of education on hired farm labor suggests that, given the lack of opportunities in other off-farm work choices, better educated women may opt to work on their own farm instead of on other’s farms. Surprisingly, there is no relationship between education and female employment in non-farm wage labor and self-employment.

Female health status has different effects on off-farm work choices. In model (2), results indicate that for a woman in fair/bad health, a change in health status (where she is in excellent/good health) decreases the likelihood of being self-employed (statistically significant at the 1% level) but increases the likelihood of non-farm wage labor (statistically significant at the

10% level). The opposite effect of health status on self-employment and non- farm wage labor

111 suggests that unhealthy women are more likely to pursue employment with flexible hours (i.e., self-employment) whereas healthy women are more able to be employed in a job that could require regular hours and perhaps be away from home (i.e. non-farm wage labor).

With regards to household composition, results from model (2) indicate that, as expected, an increase in the number of elderly adults (60 or older) as well as an increase in the number of children (14 or younger) decrease the likelihood of female participation in non-farm wage labor.

Also, an increase in the number of working-age females (ages 15-49) reduces the likelihood of participation in non-farm wage labor (statistically significant at the 10% in model (2)). This finding appears to be counter-intuitive since an increase in working age females would be expected to free up labor for more off-farm work. Female participation in self-employment is positively related to the number of elderly adults (statistically significant at the 5% level in model (2)) and to the number of working-age males (statistically significant at the 10% level). In both cases, an increase in the number of dependents or an increase in working age males may require women to allocate more time towards own farm work and household activities.

The distance to the nearest city/town influences the off-farm work choice of women.

Results from model (1) show that a one kilometer increase in the distance to the nearest city/town is associated with a decrease in the likelihood of non-farm self-employment and non- farm wage labor employment (both statistically significant at the 5% level). This result makes sense given that longer distances typically result in larger transportation costs (in the case of self- employment, output from small businesses are often marketed in cities/towns).

Ego networks types are statistically significant predictors of female off-farm work choices. In model (1), friendship ego network size positively affects the likelihood of work on others’ farms (as well as on their own, in most instances) (statistically significant at the 1% level)

112 but negatively affects participation in jobs that provide more income (non-farm wage labor and self-employment) (both statistically significant at the 5% level). In fact, women who are self- employed and/or have higher wage non-farm jobs also have fewer friends. On the other hand, results show that kin ego networks are statistically insignificant predictors of female participation in hired farm labor and participation in self-employment. However, an increase in kin ego network size positively affects the likelihood of employment in non-farm wage labor.

In model (2), total ego networks have a positive effect on the likelihood of employment in hired farm labor (statistically significant at the 1% level) and a negative effect on the probability of self-employment (statistically significant at the 10% level). Also, women with more kin homogeneous total ego networks have a lower likelihood of employment in hired farm labor (statistically significant at the 10% level) but a higher likelihood of participation in non- farm wage labor (statistically significant at the 5% level). In summary, results from model (1) and model (2) suggest that friendship ego network size and total ego network size are important for accessing low wage work on others’ farms but restrict access to higher wage non-farm labor.

In fact, results suggest that Mozambican women with larger friendship ego networks or larger total ego networks are negatively affected by social norms regarding their roles in the household.

With regards to kin ego networks and the proportion of total contacts who are kin, results contradict the claim that kin networks are associated with lower incomes (di Falco and Bulte,

2011). In this case, the positive effect of kin ego networks and a higher proportion of kin in total ego networks on higher wage non-farm jobs may be attributed to a stronger sense of reciprocity among kin.

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Male off-farm work choice

For males, none of the different age categories are statistically significant predictors of the likelihood of employment in hired farm labor and non-farm self-employment. On the other hand, results show that, as expected, older males are less likely to be employed in non-farm wage labor than younger males (age 15-25) (statistically significant at the 10% level in model (1)).

Higher levels of education were expected to increase access to non-farm wage labor.

However, results show that the there is no relationship between the education and participation in non-farm wage labor and between education and participation in hired farm labor. Education did influence self-employment; in model (1), the coefficient for completion of grade level 1 or 2 is statistically significant and indicates that better educated Mozambican men (completed grades 1 or 2) are more likely to be employed in non-farm self-employment than their male counterparts with no education (statistically significant at the 5% level). A similar result is documented in

Yúnez-Naude and Taylor (2001).

The coefficient on marital status is statistically significant and indicates that for a single/widowed/divorced male, a change in marital status (where he is married) would lower the likelihood of being self-employed (statistically significant at the 10% level in model (1)). This is plausible since single/divorced/widowed men may require a flexible off-farm job in order to work in own farm and household activities.

Household composition variables, in some cases, influence off-farm work choices for men. An increase in the number of adult dependents (aged 60 or older) positively affects the likelihood of male employment in hired farm labor work (statistically significant at the 10% level in model (1)) and suggest that an increase in elderly adult may free up own farm labor to work on other’s farms. On the other hand, an increase in the number of working-age women (ages 15-

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59) increases the probability of male participation in self-employment (statistically significant at the 5% level in model (1)) suggesting that an increase in the number of working-age women may provide greater flexibility for men to pursue work in own farm and self-employment.

The statistically significant coefficient for land shows that a one hectare increase in land available for cultivation lowered the probability of male employment in hired farm labor

(statistically significant at the 1% level in both model (1)). In Mozambique, greater access to land for cultivation may reduce the need to work in low wage work on others’ farms.

Results from model (1) indicate that there is no relationship between friendship ego network size and any of the off-farm work choices for men. Furthermore, model (1) shows that there is no relationship between kin ego network size and participation in non-farm self- employment and non-farm wage labor. However, an increase in kin ego network size is associated with an increase in the likelihood of employment in hired farm labor (statistically significant at the 1% level). The positive effect of kin ego networks on employment in hired farm labor suggests that work on others’ farms may be taking place on the farm land of kin ego networks.

In model (2), total ego network size has a positive effect on the likelihood of employment in hired farm labor (statistically significant at the 10% level) indicating that a larger ego network size facilitates access to hired farm labor in network contacts’ farms and/or that larger ego networks facilitate the exchange of employment information related to hired farm labor.

Additionally, the coefficient for the proportion of total ego networks that is kin is statistically significant and has a positive effect on the likelihood of employment in hired farm labor

(statistically significant at the 1% level) and self-employment (statistically significant at the 10% level). As with kin ego network size, results show that having a greater proportion of kin in total

115 ego networks can facilitate access to hired farm labor. Additionally, a greater proportion of kin can also increase the likelihood of being self-employed (a more desirable labor outcome than hired farm labor).

6.2 Agricultural marketing behavior – Objective 2

Two models are presented for each crop type; the first includes separate network measures for friendship and kin ego networks and the second model includes total ego network size. Column 1 (tier 1) and column 2 (tier 2) in each of the tables display the regression coefficients of the probit and truncated regressions, respectively. Interpretation of results is based on columns 3, 4, and 5.

Table 6.2.1 displays results from the first step of the control function method to test and correct (if necessary) for endogenous social network measures. The OLS results indicate factors affecting potentially endogenous variables of friendship ego network size and total ego network size. The residual from the first step is included in the double hurdle model as an additional covariate to test and control for possible endogeneity. The independent variables used in the OLS regression include the covariates from the double hurdle model as well as the instrumental variables (village-level non-self cluster mean friendship ego network size for friendship ego network model and village-level non-self cluster mean total ego network size for total ego network model). As in Ricker-Gilbert et al. (2011), IV strength in both models is tested by the partial correlation of village-level non-self cluster mean friendship ego network size and village- level non-self cluster mean total ego network size in table 6.2.1. Results show that the coefficient of the IV in both models has the expected positive effect and is statistically significant at the 1% level in both instances. The non-self cluster mean friendship ego network size and non-self cluster mean total ego network size can be considered potentially strong instruments since they

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Table 6.2.1: OLS regression of factors influencing social network size Friendship ego network Total ego network size size Coefficient SE Coefficient SE Own radio/cell phone 0.123 (0.194) 0.094 (0.271) Education: Grades 1 or 2 0.091 (0.265) -0.128 (0.370) Education: Grades 3+ 0.399* (0.218) 0.158 (0.305) Age 0.046 (0.030) 0.095** (0.042) Age2/1000 -0.424 (0.321) -1.019** (0.445) Marital status -0.472** (0.216) -0.778** (0.302) Health status 0.226 (0.198) 0.332 (0.279) Number of adults, 60 or older -0.103 (0.150) 0.263 (0.207) Number of males, 15-59 0.203 (0.126) 0.178 (0.177) Number of females, 15-59 -0.245** (0.098) -0.269* (0.137) Number of children, 14 or younger -0.078 (0.053) -0.103 (0.074) Wage labor -0.227 (0.235) 0.028 (0.329) Land (ha.) 0.018 (0.038) 0.016 (0.053) Distance (km.) -0.001 (0.008) -0.008 (0.011) Extension 0.099 (0.192) 0.244 (0.271) Transportation 0.246 (0.216) 0.495 (0.303) Kin ego network size 0.115 (0.085) - - Non-self cluster mean friendship ego network size 0.967*** (0.178) - - Non-self cluster mean total ego network size - - 0.916*** (0.159) Constant -1.049 (0.722) -1.366 (1.032) Observations 201 201 R-squared 0.293 0.284 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

117 are statistically significant predictors of the potentially endogenous variables (friendship ego network size and total ego network size, respectively) but not correlated with the error term in the model for household marketing behavior. Additional results show that education, age, and marital status are also statistically significant predictors.

6.2.1 Results for bean marketing behavior

The second step of the control function methods requires the inclusion of the residual variable from the first stage OLS regression in the double hurdle model. Results from table 6.2.2 and table 6.2.3 indicate that the residual variable is statistically significant at the 1% level in tier

2, indicating that friendship ego network size and total ego network size are endogenous with regards to the value of bean sales (tier 2)37. Statistical insignificance of the residual in the market participation decision (tier 1) suggests that friendship ego network size and total ego network size can be treated as exogenous in the market participation model for beans (and excluded from tier 1).

Table 6.2.3 indicates that age of the decision-maker has a curvilinear relationship with regards to bean sales. Bean sales increase as age increases (farming ability and marketing experience), but decreases in later stages of life as the decision-maker becomes older (physically incapable of marketing larger quantities of beans). The health status coefficients for CAPE

(column 4) and UAPE (column 5) are both statistically significant in table 6.2.3 and indicate that

(conditional and unconditional on market participation), household with a decision-maker in excellent/good health have greater sales than a household with a decision-maker in poor health

(CAPE: 0.679 and statistically significant at the 1% level; UAPE: 0.919 and statistically significant effect at the 10% level). In fact, it can be said that bean sales will be nearly double

(97%) for households with a decision-maker in excellent/good health than for a households with

37 See appendix G, table G1.1-G1.3 for bean, maize, and other crop models without CF approach. 118

Table 6.2.2: Household marketing behavior for beans (endogeneity corrected) Marginal Effects1 Tier 1 Tier 2 Probit CAPE UAPE Own radio/cell phone -0.045 - -0.011 - - (0.249) (0.067) Education: Grades 1 or 2 0.359 - 0.094 - - (0.331) (0.092) Education: Grades 3+ -0.047 - -0.012 - - (0.276) (0.074) Age 0.025 0.080 0.006 0.080 0.102 (0.035) (0.051) (0.010) (0.051) (0.092) Age2/1000 -0.261 -0.886 -0.068 -0.885 -1.106 (0.374) (0.599) (0.114) (0.713) (1.069) Marital status 0.221 0.306 0.058 0.306 0.607 (0.250) (0.274) (0.072) (0.293) (0.507) Health status 0.226 0.501** 0.059 0.500** 0.760* (0.228) (0.228) (0.060) (0.226) (0.449) Number of adults, 60 or older -0.182 -0.177 -0.047 -0.176 -0.445 (0.200) (0.125) (0.075) (0.174) (0.433) Number of males, 15-59 -0.093 0.310*** -0.024 0.309** 0.068 (0.139) (0.113) (0.044) (0.137) (0.271) Number of females, 15-59 -0.099 -0.190* -0.026 -0.189 -0.311 (0.105) (0.114) (0.031) (0.128) (0.234) Number of children, 14 or younger 0.013 -0.030 0.003 -0.029 0.000 (0.066) (0.070) (0.020) (0.079) (0.137) Wage labor -0.255 -0.317 -0.067 -0.316 -0.674 (0.254) (0.259) (0.076) (0.270) (0.512) Land (ha.) 0.202** 0.035 0.053** 0.034 0.373** (0.083) (0.056) (0.024) (0.056) (0.169) Distance (km.) 0.035*** 0.028*** 0.009** 0.028*** 0.081*** (0.012) (0.007) (0.003) (0.008) (0.022) Extension 0.626*** -0.041 0.164*** -0.040 1.048** (0.233) (0.206) (0.061) (0.218) (0.436) Transportation 0.164 0.663*** 0.043 0.663*** 0.773 (0.253) (0.206) (0.079) (0.221) (0.521)

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Table 6.2.2 (cont.): Household marketing behavior for beans (endogeneity corrected) Marginal Effects1 Tier 1 Tier 2 Probit CAPE UAPE Residual - 0.704*** - 0.703*** - (0.190) (0.200) Friendship ego network size 0.014 -0.795*** 0.003 -0.795*** -0.564** (0.082) (0.175) (0.024) (0.184) (0.221) Kin ego network size -0.155 0.082 -0.040 0.082 -0.206 (0.104) (0.102) (0.030) (0.120) (0.225) Constant -1.421* 5.032*** - - - (0.800) (0.884) Sigma 1.149*** (0.073) Observations 201 Wald chi2(18) 35.95*** Robust standard errors in parentheses 1 Bootstrap standard error obtained via bootstrapping at 250 repetitions *** p<0.01, ** p<0.05, * p<0.1

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Table 6.2.3: Household marketing behavior for beans (endogeneity corrected) Marginal Effects1 Tier 1 Tier 2 Probit CAPE UAPE Own radio/cell phone -0.031 - -0.008 - - (0.246) (0.069) Education: Grades 1 or 2 0.375 - 0.100 - - (0.325) (0.100) Education: Grades 3+ 0.001 - 0.000 - - (0.271) (0.075) Age 0.026 0.137*** 0.007 0.137** 0.147 (0.035) (0.046) (0.110) (0.055) (0.094) - Age2/1000 -0.264 1.514*** -0.070 -1.514** -1.579 (0.371) (0.532) (0.106) (0.617) (1.063) Marital status 0.188 -0.050 0.050 -0.050 0.291 (0.251) (0.244) (0.073) (0.285) (0.453) Health status 0.239 0.679*** 0.064 0.679*** 0.919** (0.224) (0.222) (0.063) (0.211) (0.427) Number of adults, 60 or older -0.221 0.171 -0.059 0.171 -0.259 (0.187) (0.117) (0.680) (0.189) (0.471) Number of males, 15-59 -0.075 0.249** -0.020 0.249** 0.054 (0.139) (0.110) (0.044) (0.125) (0.276) Number of females, 15-59 -0.114 -0.186 -0.030 -0.186 -0.335 (0.104) (0.115) (0.031) (0.118) (0.214) Number of children, 14 or younger 0.008 -0.075 0.002 -0.075 -0.041 (0.067) (0.066) (0.020) (0.075) (0.128) Wage labor -0.288 -0.057 -0.077 -0.057 -0.544 (0.257) (0.253) (0.071) (0.275) (0.493) Land (ha.) 0.194** 0.027 0.051** 0.027 0.357** (0.083) (0.051) (0.023) (0.055) (0.172) Distance (km.) 0.034*** 0.025*** 0.009*** 0.025*** 0.078*** (0.012) (0.007) (0.004) (0.008) (0.025) Extension 0.606*** 0.202 0.161** 0.202 1.205*** (0.233) (0.209) (0.069) (0.224) (0.435) Transportation 0.173 0.788*** 0.046 0.788*** 0.883* (0.252) (0.200) (0.073) (0.225) (0.514)

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Table 6.2.3 (cont.): Household marketing behavior for beans (endogeneity corrected) Marginal Effects1 Tier 1 Tier 2 Probit CAPE UAPE Residual - 0.807*** - 0.807*** - (0.141) (0.157) Total ego network size -0.059 -0.814*** -0.016 -0.814 -0.706*** (0.060) (0.126) (0.017) (0.131) (0.160) Constant -1.416* 5.312*** - - - (0.789) (0.794) Sigma 1.088*** (0.064) Observations 201 Wald chi2(17) 31.87** Robust standard errors in parentheses 1 Bootstrap standard error obtained via bootstrapping at 250 repetitions *** p<0.01, ** p<0.05, * p<0.1

a decision-maker in fair/bad health. In table 6.2.3, an increase in the number of working age men

(ages 15-59) is associated with an increase in bean sales (0.249 and statistically significant at the

5% level).

The coefficients for farm characteristics, in some instances, influence the likelihood of market participation as well as bean sales. In table 6.2.3, all else equal, a one hectare increase in the size of land available for cultivation positively affects the likelihood of bean market participation. Furthermore, prior to the market participation decision (UAPE), a one hectare increase in land available for cultivation positively affects bean sales (statistically significant at the 5% level). Like land, the coefficient on the extension variable is statistically significant and increases the probability of bean market participation (table 6.2.3). Furthermore, unconditional on market participation, being able to rely on extension is statistically significant at the 1% level and positively affects bean sales suggesting that extension can play a critical role in increasing bean sales among seller and non-seller households.

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Of the variable transaction costs, only the distance coefficient was a statistically significant predictor of the market participation decision, whereas both distance and transportation are statistically significant predictors of the value of bean sales (table 6.2.3). A one kilometer increase in distance to the nearest city/town increases the likelihood of market participation (statistically significant at the 1% level) and positively affects bean sales (both distance coefficients in CAPE and UAPE are positive and statistically significant at the 1% level). In fact, a one kilometer increase in distance to the nearest city/town increases bean sales by 3%. The positive effect suggests that in Mozambique, increased remoteness drives households to participate in bean markets and to sell a greater share of their bean output, possibly as an alternative livelihood strategy. With regards to transportation, table 6.2.3 shows that among market participation households (CAPE), and more importantly, among sellers and non-sellers

(UAPE), ownership of an improved means of transportation has a positive effect on bean sales

(CAPE: 0.788 and statistically significant at the 1% level; UAPE: 0.883 and statistically significant at the 10% level). Among market participating households, bean sales are expected to be more than double (120%) for households with a bicycle, motorcycle, or cart than for households without an improved means of transportation.

Table 6.2.2 examines the role of different ego network types. Conditional on market participation, friendship ego networks have a negative effect on bean sales (statistically significant at the 1% level). In fact, for a one unit increase in friendship ego network size, bean sales are expected to decrease by 55%. Also, the coefficient for friendship ego networks among the full sample (UAPE) indicates that an increase in friendship ego networks also has a negative effect on bean sales (statistically significant at the 5% level). Results from table 6.2.3 indicate a similar result for total ego networks. Among market participation households, the coefficient for

123 total ego network size is statistically insignificant indicating that there is no relationship between total ego networks and bean sales. However, unconditional on market participation, a one unit increase in total ego network size has a negative and statistically significant effect on bean sales.

The negative effect of friendship and total ego networks suggests that there is little cooperation among larger and more diverse networks and that larger friendship and total ego networks have the opposite effect (increase) on search and transaction costs.

6.2.2 Results for maize marketing behavior

Table 6.2.4 and 6.2.5 present results of the double hurdle model for maize marketing behavior. In both tables, the residual variable is positive in tier 1 and tier 2, indicating that friendship ego network in table 6.2.4 and total ego network in table 6.2.5 are endogenous in the model for market participation and maize sales.

In table 6.2.5, conditional on market participation, an increase in the age of the decision- maker is positively related to the value of maize sales (statistically significant at the 5% level).

Furthermore, unconditional on market participation, age has a curvilinear relationship with maize sales; age is positive and age squared is negative (both statistically significant at the 5% level).

Unlike the bean model, marital status is a statistically significant predictor of the value of maize sales (table 6.2.5). Married households are expected to have 46% lower maize sales than unmarried households. A possible explanation for the negative effect of marriage is that male and female decision-makers in married households may operate with different income pools; in other words, the household may have a shared income pool where a portion of maize sales are included as well as a separate and unreported income pool where they set aside a portion of the earnings for maize sales. In table 6.2.4, the health status variable indicates that excellent/good health in a decision-maker reduces the likelihood of market participation (statistically significant

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Table 6.2.4: Household marketing behavior for maize (endogeneity corrected) Marginal Effects1 Tier 1 Tier 2 Probit CAPE UAPE Own radio/cell phone 0.317 - 0.092 - - (0.235) (0.076) Education: Grades 1 or 2 1.029*** - 0.300*** - - (0.305) (0.094) Education: Grades 3+ 0.908*** - 0.264*** - - (0.285) (0.083) Age 0.042 0.093** 0.012 0.093 0.123 (0.037) (0.041) (0.013) (0.071) (0.096) Age2/1000 -0.415 -0.661 -0.120 -0.661 -1.096 (0.383) (0.411) (0.128) (0.656) (0.987) Marital status 0.188 -0.399 0.054 -0.399 0.167 (0.269) (0.317) (0.090) (0.370) (0.565) Health status -0.667*** 0.736*** -0.194*** 0.735** -0.918* (0.245) (0.250) (0.072) (0.298) (0.497) Number of adults, 60 or older -0.431** -0.043 -0.125* -0.042 -0.836* (0.167) (0.275) (0.064) (0.342) (0.486) Number of males, 15-59 0.186 0.176 0.054 0.176 0.435 (0.156) (0.171) (0.055) (0.214) (0.322) Number of females, 15-59 -0.303** -0.360* -0.088** -0.359 -0.743** (0.134) (0.206) (0.041) (0.256) (0.313) Number of children, 14 or younger 0.019 -0.048 0.005 -0.047 0.013 (0.069) (0.068) (0.023) (0.089) (0.135) Wage labor -0.979*** 0.398 -0.285*** 0.398 -1.669*** (0.262) (0.394) (0.081) (0.449) (0.558) Land (ha.) 0.033 0.007 0.009 0.006 0.065 (0.040) (0.068) (0.014) (0.098) (0.099) Distance (km.) -0.011 -0.003 -0.003 -0.003 -0.022 (0.009) (0.009) (0.002) (0.010) (0.019) Extension -0.137 0.441* -0.039 0.441* -0.052 (0.236) (0.245) (0.076) (0.259) (0.503) Transportation 0.236 0.690** 0.068 0.689* 0.772 (0.252) (0.336) (0.079) (0.386) (0.522)

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Table 6.2.4 (cont.): Household marketing behavior for maize (endogeneity corrected) Marginal Effects1 Tier 1 Tier 2 Probit CAPE UAPE Residual 0.709*** 0.552* 0.206*** 0.551* 1.603*** (0.227) (0.291) (0.072) (0.319) (0.497) Friendship ego network size -0.767*** -0.855*** -0.223*** -0.855*** -1.855*** (0.201) (0.262) (0.058) (0.327) (0.377) Kin ego network size 0.203** -0.122 0.059* -0.122 0.326 (0.094) (0.155) (0.030) (0.195) (0.209) Constant 0.357 5.361*** - - - (0.750) (0.767) Sigma 1.058*** (0.087) Observations 201 Wald chi2(19) 60.13*** Robust standard errors in parentheses 1 Bootstrap standard error obtained via bootstrapping at 250 repetitions *** p<0.01, ** p<0.05, * p<0.1

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Table 6.2.5: Household marketing behavior for maize (endogeneity corrected) Marginal Effects1 Tier 1 Tier 2 Probit CAPE UAPE Own radio/cell phone 0.273 - 0.078 - - (0.233) (0.076) Education: Grades 1 or 2 0.889*** - 0.254*** - - (0.305) (0.087) Education: Grades 3+ 0.687*** - 0.196** - - (0.263) (0.080) Age 0.068* 0.145*** 0.020 0.145** 0.193** (0.039) (0.049) (0.013) (0.069) (0.091) Age2/1000 -0.753* -1.272** -0.215 -1.272 -1.971** (0.404) (0.514) (0.145) (0.777) (1.002) Marital status 0.025 -0.613* 0.007 -0.613* -0.242 (0.275) (0.319) (0.079) (0.352) (0.541) Health status -0.597** 0.764*** -0.170** 0.764*** -0.733 (0.249) (0.231) (0.079) (0.265) (0.488) Number of adults, 60 or older -0.127 0.285 -0.036 0.285 -0.099 (0.166) (0.289) (0.070) (0.396) (0.487) Number of males, 15-59 0.122 0.151 0.035 0.151 0.294 (0.149) (0.157) (0.051) (0.199) (0.351) Number of females, 15-59 -0.262** -0.363* -0.075* -0.363 -0.649** (0.130) (0.192) (0.039) (0.226) (0.277) Number of children, 14 or younger 0.009 -0.076 0.003 -0.076 -0.020 (0.071) (0.066) (0.023) (0.088) (0.155) Wage labor -0.725*** 0.474 -0.207** 0.474 -1.103** (0.264) (0.389) (0.084) (0.440) (0.496) Land (ha.) 0.032 0.004 0.009 0.004 0.061 (0.039) (0.061) (0.014) (0.089) (0.092) Distance (km.) -0.013 -0.006 -0.004 -0.006 -0.027 (0.009) (0.009) (0.003) (0.010) (0.018) Extension 0.028 0.577** 0.008 0.577** 0.320 (0.245) (0.259) (0.084) (0.284) (0.467) Transportation 0.320 0.839** 0.091 0.839** 0.977* (0.255) (0.353) (0.072) (0.428) (0.501)

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Table 6.2.5 (cont.): Household marketing behavior for maize (endogeneity corrected) Marginal Effects1 Tier 1 Tier 2 Probit CAPE UAPE Residual 0.627*** 0.734*** 0.179*** 0.734*** 1.488*** (0.158) (0.240) (0.043) (0.263) (0.325) Total ego network size -0.597*** -0.943*** -0.171*** -0.943*** -1.532*** (0.142) (0.211) (0.039) (0.255) (0.276) Constant 0.725 5.649*** - - - (0.735) (0.696) Sigma 1.024*** (0.078) Observations 201 Wald chi2(18) 63.46*** Robust standard errors in parentheses 1 Bootstrap standard error obtained via bootstrapping at 250 repetitions *** p<0.01, ** p<0.05, * p<0.1

at the 1% level). Interestingly, despite the negative effect of health on market participation, results show that, among market participating households, excellent/good health in a decision- maker increases higher maize sales relative to a households with a decision-maker in fair/bad health (0.735 and statistically significant at the 5% level). However, unconditional on market participation (among the full sample), health has a negative effect on maize sales (statistically significant at the 10% level). The positive effect of health among market participating households and the negative effect among the full sample suggest that under normal circumstances (i.e. good health), Mozambican households may favor storage and consumption over the marketing of maize38.

Of the household composition variables in table 6.2.4, few are statistically significant predictors of the market participation decision and the value of maize sales. An increase in the number of elderly adults (ages 60 or more) reduces the likelihood of market participation

38 In fact, many of the surveyed households stated that, after selling their maize, food shortages required them to purchase commercial maize flour at a higher price. 128

(statistically significant at the 10% level) and similarly, an increase in the number of working age females (ages of 15-59) reduces the likelihood of market participation (statistically significant at the 5% level). More importantly, unconditional on market participation, the number of elderly adults and the number of working age females (UAPE) negatively affects maize sales.

The coefficient for non-farm wage labor is statistically significant at the 1% level and indicates that employment in higher wage non-farm wage labor by the decision-maker reduces the likelihood of maize market participation (table 6.2.4). The negative effect of employment in non-farm wage labor suggests that households with alternative income sources (i.e., higher wage non-farm labor) are less willing to sell maize in markets (two possible explanations are: 1) their time is consumed by non-farm job or 2) higher income from wage labor does not require them to sell maize). Access to extension is very important; results show that the extension variable is positively related with maize sales (in table 6.2.5). In fact, maize sales will be 78% higher for households able to rely on extension than for households unable to rely on extension suggesting that access to extension is crucial for households to participate in crop markets.

In the bean model, none of the measures for fixed transaction costs were statistically significant. In the case of maize, education is a statistically significant predictor of market participation. Table 6.2.4 indicates that the likelihood of maize market participation increases in a household with a decision-maker that completed grades 1 or 2 (statistically significant at the

1% level) relative to a household with an uneducated decision-maker. Likewise, households with a decision-maker that completed grade 3 or more are also more likely (0.264 and statistically significant at the 1% level) than households with an uneducated decision-maker to participate in maize markets. The positive effect of education in both instances suggests that education

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(possibly the ability to read and write) can help decision-makers process information better and make more informed decisions regarding market participation.

Of the VTCs, only the coefficient for ownership of a bicycle, motorcycle, or cart was statistically significant. Results from table 6.2.5, show that ownership of an improved means of transportation increases sales (CAPE: 0.839 and statistically significant at the 5% level; UAPE:

0.977 and statistically significant at the 10% level). As was previously discussed, the positive effect in both CAPE and UAPE suggest that improved transportation reduces transport costs and has the potential to increase household income from crop markets.

In table 6.2.4, the coefficient for friendship ego network size is statistically significant at the 1% level for both market participation and the value of sales. An increase in friendship ego networks reduces the likelihood of market participation and reduces maize sales (among market participating households, a one unit increase in friendship ego networks is expected to reduce maize sales by 57%). Also, unconditional on market participation, an increase in friendship ego networks reduces maize sales. Conversely, a one unit increase in kin ego network size increases the likelihood of maize market participation (statistically significant at the 10% level). Results from table 6.2.5 are similar to that for the bean model; a one unit increase in the total ego network size reduces the likelihood of market participation (statistically significant at the 1% level) and has a negative effect on maize sales (both conditional and unconditional on market participation). Once the market participation decision has been made, an increase in total ego network size reduces maize sales by 61%. As was previously discussed, it appears that larger total ego networks and more friend households (friendship ego networks) reduce access to novel market information (reduce market participation) and reduce maize sales. However, results

130 suggest that unlike beans, cooperation and information sharing is more common among kin networks.

6.2.3 Results for other crop marketing behavior

Results for the marketing behavior of other crops are presented in tables 6.2.6 and 6.2.7.

The residual variable from the first stage OLS regression is statistically insignificant and therefore excluded from the analysis in table 6.2.6. Statistical insignificance of the residual variable in tier 1 and tier 2 of table 6.2.6 indicates that friendship ego networks are exogenous in the model for market participation and the value of sales of other crops. On the other hand, the residual variable in table 6.2.7 is statistically significant in tier 1 indicating that total ego networks are endogenous in the market participation decision for other crops.

As was the case with beans, the coefficients of age and age squared in table 6.2.7 are statistically significant predictors of sales of other crops (curvilinear relationship between age and sales of other crops). In table 6.2.6, a one unit increase in the number of elderly adults (aged

60 or more) and an increase in the number of working wage males and working age females increase the likelihood of participation in other crops markets. Among market participating households, the number of elderly adults positively affects sales of other crops (statistically significant at the 5% level). In fact, for a one unit increase in the number of elderly, a 52% increase in sales of other crops is expected. Furthermore, unconditional on market participation, an increase in the number of elderly adults as well as an increase in the number of working age males has a positive and statistically significant effect on sales of other crops.

Of the FTCs, only education was a statistically significant predictor of the market participation decision (table 6.2.6). A decision-maker that completed grade level 1 or 2 by was more likely to participate in other crop markets relative to a decision-maker with no education.

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Table 6.2.6: Household marketing behavior for other crops Marginal Effects1 Tier 1 Tier 2 Probit CAPE UAPE Own radio/cell phone -0.066 - -0.020 - - (0.218) (0.073) Education: Grades 1 or 2 0.937*** - 0.292*** - - (0.308) (0.106) Education: Grades 3+ 0.449* - 0.140* - - (0.232) (0.074) Age 0.006 0.136*** 0.001 0.135*** 0.091 (0.037) (0.041) (0.013) (0.049) (0.086) Age2/1000 -0.125 -1.610*** -0.039 -1.609*** -1.195 (0.402) (0.451) (0.146) (0.578) (1.059) Marital status -0.093 -0.208 -0.029 -0.207 -0.312 (0.251) (0.263) (0.083) (0.290) (0.613) Health status 0.185 0.281 0.057 0.280 0.543 (0.227) (0.300) (0.066) (0.337) (0.565) Number of adults, 60 or older 0.453** 0.418*** 0.141* 0.417** 1.174** (0.207) (0.133) (0.073) (0.198) (0.477) Number of males, 15-59 0.302** 0.050 0.094** 0.050 0.649** (0.133) (0.187) (0.042) (0.219) (0.317) Number of females, 15-59 -0.011 -0.174 -0.003 -0.173 -0.124 (0.109) (0.146) (0.037) (0.157) (0.259) Number of children, 14 or younger 0.011 0.129* 0.003 0.128 0.098 (0.059) (0.071) (0.019) (0.088) (0.146) Wage labor 0.237 0.593* 0.073 0.593 0.832 (0.266) (0.349) (0.094) (0.385) (0.668) Land (ha.) 0.033 -0.038 0.010 -0.038 0.046 (0.055) (0.047) (0.026) (0.062) (0.151) Distance (km.) -0.031*** -0.061*** -0.009*** -0.061*** -0.100*** (0.010) (0.014) (0.003) (0.016) (0.023) Extension 0.302 -0.309 0.094 -0.308 0.441 (0.212) (0.299) (0.064) (0.353) (0.504) Transportation 0.141 0.651** 0.044 0.650** 0.668 (0.235) (0.285) (0.075) (0.315) (0.547)

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Table 6.2.6: Household marketing behavior for other crops Marginal Effects1 Tier 1 Tier 2 Probit CAPE UAPE Friendship ego network size 0.097 -0.048 0.030 -0.048 0.171 (0.075) (0.094) (0.025) (0.103) (0.170) Kin ego network size 0.223** 0.002 0.069** 0.001 0.459** (0.096) (0.107) (0.033) (0.130) (0.229) Constant -0.829 5.178*** - - - (0.807) (0.859) Sigma 1.327*** (0.094) Observations 201 Wald chi2(18) 39.72*** Robust standard errors in parentheses 1 Bootstrap standard error obtained via bootstrapping at 250 repetitions *** p<0.01, ** p<0.05, * p<0.1

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Table 6.2.7: Household marketing behavior for other crops (endogeneity corrected) Marginal Effects1 Tier 1 Tier 2 Probit CAPE UAPE Own radio/cell phone -0.108 - -0.033 - - (0.215) (0.078) Education: Grades 1or 2 0.889*** - 0.275*** - - (0.309) (0.105) Education: Grades 3+ 0.337 - 0.104 - - (0.232) (0.080) Age -0.028 0.137*** -0.009 0.137*** 0.023 (0.040) (0.041) (0.014) (0.049) (0.093) Age2/1000 0.235 -1.626*** 0.073 -1.625*** -0.472 (0.430) (0.448) (0.151) (0.539) (1.011) Marital status 0.134 -0.214 0.042 -0.214 0.149 (0.289) (0.259) (0.098) (0.259) (0.654) Health status 0.073 0.276 0.023 0.276 0.310 (0.238) (0.300) (0.072) (0.294) (0.582) Number of adults, 60 or older 0.356* 0.432*** 0.110 0.432** 0.978* (0.212) (0.114) (0.074) (0.175) (0.508) Number of males, 15-59 0.280** 0.043 0.087* 0.043 0.596* (0.134) (0.188) (0.048) (0.216) (0.346) Number of females, 15-59 0.048 -0.168 0.015 -0.168 0.000 (0.111) (0.146) (0.039) (0.169) (0.278) Number of children, 14 or younger 0.055 0.128* 0.017 0.128 0.186 (0.062) (0.071) (0.021) (0.085) (0.138) Wage labor 0.226 0.596* 0.070 0.596 0.809 (0.260) (0.349) (0.085) (0.366) (0.677) Land (ha.) 0.035 -0.037 0.011 -0.037 0.049 (0.058) (0.048) (0.025) (0.061) (0.152) Distance (km.) -0.028*** -0.061*** -0.009*** -0.061*** -0.093*** (0.010) (0.014) (0.003) (0.016) (0.025) Extension 0.167 -0.314 0.052 -0.314 0.158 (0.230) (0.298) (0.077) (0.308) (0.584) Transportation 0.046 0.654** 0.014 0.654** 0.476 (0.234) (0.285) (0.072) (0.310) (0.522)

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Table 6.2.7 (cont.): Household marketing behavior for other crops (endogeneity corrected) Marginal Effects1 Tier 1 Tier 2 Probit CAPE UAPE Residual -0.282* - -0.087* - - (0.153) (0.051) Total ego network size 0.378*** -0.028 0.117*** -0.028 0.753** (0.142) (0.068) (0.045) (0.073) (0.296) Constant -0.998 5.170*** - - - (0.825) (0.861) Sigma 1.327*** (0.095) Observations 201 Wald chi2(18) 40.57*** Robust standard errors in parentheses 1 Bootstrap standard error obtained via bootstrapping at 250 repetitions *** p<0.01, ** p<0.05, * p<0.1

Similarly, a decision-maker that completed grade level 3 or more had a higher likelihood of market participation relative to households with an uneducated decision-maker (statistically significant at the 10% level).

Ownership of a bicycle, motorcycle, or cart is statistically significant at the 5% level and increases the probability of market participation. On the other hand, a one kilometer increase in the distance to the nearest city/town (statistically significant at the 1% level) reduces the likelihood of market participation and has a negative effect on sales of other goods (CAPE: -

0.061 and statistically significant at the 1% level; UAPE: -0.100 and statistically significant at the 1% level). Among market participating households, a one kilometer increase in the distance to city/town is expected to reduce other crop sales by 6%.

Unlike the bean and maize model, results from table 6.2.6 show that there is no relationship between friendship ego networks and agricultural marketing behavior for other crops. However, ceteris paribus, a one unit increase in size of kin ego networks increases the

135 likelihood of market participation (statistically significant at the 5% level), and among the full sample (UAPE), positively affects sales of other crops (statistically significant at the 5% level).

Furthermore, results from table 6.2.7 show that total ego network are statistically significant but have the opposite effect on other crops than for beans and maize. All else equal, a one unit increase in the size of total ego networks increases the probability of market participation

(statistically significant at the 1% level) and, unconditional on market participation, a larger total ego network has a positive effect on sales of other crops (statistically significant at the 5% level).

The positive effect of total ego networks suggests cooperative behaviors among households may result in greater household income from sales of other crops possibly due to the fact that marketing of other crops includes cash crops which are more lucrative when sold in greater numbers.

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Chapter 7 – SUMMARY AND CONCLUSIONS

7.1 Introduction

Of the 3 billion living in rural areas in less developed regions of the world, approximately

1.2 billion people live in extreme poverty (The Economist, 2013; World Bank, 2013), and 70% of the 1.2 billion people have some dependency on agriculture (Cleaver, 2012). In sub-Saharan

Africa, 47% of the population lives in extreme poverty (United Nations, 2012), 66% of the total population lives in rural areas, and more than 90% depend on agriculture for their livelihoods

(Asfaw et al., 2010). Unfortunately, subsistence agriculture operates as a safety net for the poor population rather than as a driver of economic growth (World Bank, 2005). To combat extreme poverty, greater economic growth and income equality will be required (Chandy et al., 2013) and this will be achieved through poverty reduction strategies that target the productivity, profitability, and sustainability of poor farm households (Asfaw et al., 2010). By promoting rural economic growth, to include farm and off-farm opportunities, households can directly benefit from increased food security and incomes (Cord, 2002).

Many economic activities in developing countries are influenced by non-market interactions with family, friends, and acquaintances. These mutually beneficial relationships require an investment of limited household resources such as time and money away from productive activities but in the long run may provide emotional, instrumental, and informative support that expands household resources (Bolin et al., 2003). Previous studies have found that social networks affect household incomes (di Falco and Bulte, 2011; Haddad and Maluccio,

2003; Narayan and Pritchett, 1999), agricultural technology adoption (Bandiera and Rasul, 2006;

Isham, 2002), employment and credit (Munshi, 2011; Wahba and Zenou, 2005), productivity

(Fafchamps and Minten, 2002), and risk sharing (Fafchamps, 2011). This dissertation

137 investigated the influence of social networks on economic behavior of agricultural households in rural Mozambique. Specifically, the dissertation had two research objectives: 1) to better understand the effect of social networks on male and female labor allocation and off-farm work choices, and 2) to determine if social networks impact agricultural marketing behaviors of rural agricultural households.

This dissertation used an ex ante baseline socioeconomic questionnaire administered to

Mozambican households by the Institute for Agricultural Research of Mozambique (IIAM) and

Pennsylvania State University (PSU) as part of a multidisciplinary project funded by the

McKnight Foundation. The project aims to improve food security and agro-ecosystem sustainability through the development and diffusion of common beans (Phaseolus vulgaris) bred to grow well in low phosphorous soils of Africa. Face-to-face interviews were conducted between August 2008 and August 2009 in eight villages throughout Central and Northern

Mozambique. As a baseline study, the interviews provided an initial picture of household composition, labor allocation, agricultural production and technology adoption (including beans), and social networks.

7.2 Discussion

Off-farm labor models: To achieve objective 1, a series of models were estimated to better understand the interrelationships between social networks and labor allocation and off- farm work decisions. A bivariate probit model was used to simultaneously estimate labor participation models for adult male and female respondents when both were present in the household. The bivariate probit model tested for jointness in decision-making between adult males and females in dual-headed households. Next, separate univariate probit models were used on the full sample to estimate models of labor participation decisions for adult male and female

138 respondents. To test the potential endogeneity of ego networks, a probit model with continuous endogenous regressors (IV probit) was included to model the off-farm labor participation decisions of adult males and females. Lastly, a multivariate probit was used to model off-farm work choices of adult males and females in hired farm labor, non-farm wage labor, and non-farm self-employment.

In the bivariate probit analysis, the statistical insignificance of the cross equation correlation and the failure to reject the Wald test statistic for the hypothesis that the two equations are independent implied non-jointness in decision-making (at least for this sample).

Therefore, the labor participation decision for men and women was estimated with two separate univariate probit models. Results from the univariate probit analysis indicated that men and women rely on different ego network types to access off-farm labor. In Mozambique, having more friends (friendship ego network size) positively affected women’s off-farm labor participation; however, kin ego network size was not a statistically significant predictor of off- farm labor. For men, the opposite is true, friendship ego networks were not statistically significant predictors of off-farm labor but larger kin ego networks positively affected the off- farm labor participation decision. Results show that, regardless of network type, both men and women benefitted from knowing more people (total ego network size). However, given a total network size, an increase in the proportion of contacts who are kin had the opposite effects on men and women. For women, a more kin homogenous total ego network reduced the probability of off-farm employment whereas for males, a more kin homogenous total ego network size increased the likelihood of off-farm employment. Additionally, results show that age was a statistically significant predictor of female off-farm labor participation. Also, education in

Mozambique seems to result in different outcomes for men and women. More education among

139 women reduced their likelihood of off-farm labor participation but increased men’s probability of working in off-farm labor.

The multivariate probit analysis extended the off-farm labor participation model to look at the different off-farm work choices for men and women. Results from the multivariate probit indicated the use of different ego network types to access high and low wage off-farm labor. The results show that women who were self-employed and/or had higher wage non-farm jobs also had smaller friendship ego networks. For men, friendship ego network size was not a statistically significant predictor of the different off-farm work choices. However, having more relatives in the village (kin ego network size) increased access to work on other’s farms. Knowing more people (total ego network size) did not result in favorable labor outcomes for men and women; women with larger total ego network were less likely to be self-employed and both men and women with larger total ego networks were more likely to be employed in low wage work on other’s farms. A greater proportion of kin in total ego networks reduced women’s employment in hired farm labor and increased the likelihood of employment in non-farm wage labor. For men, the opposite was true; more kin homogeneous total ego networks positively affected employment on others’ farms as well as non-farm self-employment. In addition to the ego network results, the multivariate probit indicated that increased education reduced women’s dependence on low wage hired farm labor but did not result in increased access to higher wage non-farm labor or self- employment. Women benefitted from improved health; healthier women were less likely to be self-employed and more likely to participate in higher wage non-farm labor. For men, the opposite was true; improved health was not a statistically significant predictor of any of the off- farm work choices, however, increased education positively affected male participation in non- farm self-employment. Other notable results included the negative effect of farm size on male

140 participation in hired farm labor as well as the negative effect of distance to the nearest city/town on female participation in non-farm wage labor employment and self-employment.

Agricultural marketing behavior: To achieve objective 2, a double hurdle model was used to determine the effect of social networks on the market participation decision and value of sales for agricultural households. Three separate double hurdle models were estimated to study marketing behavior of three crops: beans, maize, and other crops. To test for and correct for potential endogeneity, a control function method was used. Interpretation of the results included marginal effects of the independent variables on the probability of participating in crop markets as well as the conditional average partial effect on the expected value of sales for crops and the unconditional average partial effect on the expected value of sales.

Results from the double hurdle models indicated the importance of who you know and how many people you know for agricultural marketing behavior. Households with more relatives in the village (kin ego network size) were more likely to participate in maize and other crop markets. Furthermore, unconditional on market participation, an increase in kin ego network size positively affected sales of other crops. With regards to friendship ego network size, households with more friends 1) had lower bean sales (conditional and unconditional on market participation), 2) were less likely to participate in maize markets, and 3) had lower maize sales

(conditional and unconditional on market participation). Results for total ego network size also indicated that there were advantages and disadvantages to knowing more people as kin or friends. Total ego network size had a negative influence on bean sales (both conditional and unconditional on market participation), as well as a negative effect on maize market participation and maize sales (conditional and unconditional on market participation). With regards to other crops, knowing more people (total ego network size) positively affected participation in other

141 crop markets and increased sales of other crops among the full sample (unconditional on market participation).

Other notable results included household characteristics, farm characteristics as well as transaction costs. Age was an important determinant of the value of sales of beans, maize, and other crops. Conditional on market participation, age of the decision-maker displayed a curvilinear relationship with sales of beans and other crops, whereas, unconditional on market participation, age of head had a curvilinear relationship with maize sales. The household composition variables were statistically significant predictors of agricultural marketing behavior with the only exception being the number of children (ages 14 or less). Households with a head in excellent/good health were less likely to market maize, but had greater bean and maize sales

(among market participating households). Interestingly, unconditional on market participation, excellent/good health status increased bean sales but reduced maize sales. The negative effect of health on the full sample suggests that maize was marketed out of necessity (in difficult times) but typically stored/consumed by the household. Farm characteristics also contributed to the marketing behavior of agricultural households. The size of land available had a positive influence on bean market participation and, unconditional on market participation, also positively affected bean sales. Households able to rely on extension services were more likely to participate in bean markets and, unconditional on market participation, had greater bean sales.

For maize, once the market participation decision was made, accessibility of extension services increased maize sales. Of the fixed transaction costs, only education was a statistically significant predictor of the market participation decision. Results show that better educated decision-makers were more likely to participate in maize and other crop markets. Lastly, variable transaction costs were found to contribute to the market participation decision and the value of

142 sales. A greater distance to the nearest city/town increased the likelihood of bean market participation but reduced participation in markets for other crops. Furthermore, the distance variable positively affected bean sales and negatively affected sales of other goods (conditional and unconditional on market participation). The transportation variable indicated that ownership of a bicycle, motorcycle, or cart positively affected the likelihood of participation in other crop markets as well as positively affected the value of sales of bean and maize.

7.3 Policy implications

Regarding implications for policy, results indicated that there are gender differences in access to off-farm work choices. Female off-farm opportunities were greater in hired farm labor than in non-farm wage labor and non-farm self-employment. Furthermore, relative to men, women had a lack of opportunities in off-farm work; a greater percentage of women participated in hired farm labor than men, but fewer women participated in non-farm wage labor and non- farm self employment than men. When designing policies, it is important to promote women’s off-farm work opportunities in rural environments. Also, the analysis shows that better educated women were less likely to access higher wage off-farm jobs (self-employment and non-farm wage labor); policies should facilitate (better educated) women’s access to higher wage jobs.

Potentially, this may include access to credit and providing basic finance and managerial skills to promote self-employment ventures for women. With regards to social networks, results for

Mozambican women indicated that friendship ego network size and total ego network size were important for accessing low wage work on others’ farms but restricted access to higher wage non-farm labor. The results suggest that Mozambican women with larger friendship ego networks or larger total ego networks were negatively affected by social norms regarding their roles in the household. Economic empowerment programs for women could potentially change

143 perceptions of women and their roles in the household as well as improve their off-farm work choices. Potentially, economic empowerment programs may include assistance in starting a business, accessing credit, as well as educating women with basic finance and managerial skills.

With regards to agricultural marketing, it is important to formulate policy conclusion based on the overall goal of the project: to improve food security through the development and diffusion of common beans (Phaseolus vulgaris) bred to grow well in low phosphorous soils of

Africa. Results show that, among market participating households, excellent/good health status increased sales, however, (with regards to maize) healthier decision-makers were less likely to participate in agricultural markets and more importantly, unconditional on market participation, an improvement in health reduced (maize) sales. Therefore, in the long run, investments in health have the potential to decrease sales of (maize) surplus and promote greater household food security. Additionally, the introduction of the new bean seeds should be coupled with policies promoting greater access to land and extension services. Greater access to land for cultivation may potentially include helping smallholder farms secure land rights. On the other hand, greater access to extension services may include holding demonstrations and field days to educate more farmers at once on the new bean seeds as well as targeting extension service to organized groups.

Ownership of a bicycle, motorcycle, or cart reduced variable transaction costs and resulted in higher sales of agricultural goods. Assuming households are able to produce surplus beans, it is important to support bicycle distribution programs that potentially could lower variable transaction costs and increase household incomes from bean sales. Lastly, better connected households (total ego networks) had lower maize and bean sales but greater sales of other crops suggesting that information sharing and cooperation takes place for certain crop types. Therefore, it would be possible to promote membership in associations and other organized groups for

144 specific crops to further promote trust, as well as to reduce search and transaction costs. With regards to kin ego networks, households with stronger social ties (kin ego network) were more likely to participate in maize and other crop markets and had greater sales of other crops. The positive effect of kin ego networks indicates the importance of social support mechanisms for participation in agricultural markets. Therefore, programs supporting smallholder farmers market participation should mimic support mechanisms of kin networks to remove the risks associated with market participation (i.e., provide timely and accurate market information, provide assistance when faced with crises, etc.).

7.4 Limitations of the study

The dataset, although rich in data, consists of a relatively small sample size. In the future, a larger dataset covering multiple survey periods will allow for a more complete and comprehensive econometric analysis. The labor study would have be strengthened if data on hours worked was available. Data on hours worked would expand the analysis to include the estimation of the labor participation decision and labor supply of men and women in hired farm labor, non-farm self-employment, and non-farm wage labor. For the study of agricultural marketing behavior, results would have been improved if more information was known about the contents of the other crop variable. Interpretation of other crops was complicated by the fact that it was an aggregate variable that included cash crops, vegetables, grains, etc. Another limitation in the study of agricultural marketing behavior was the lack of data on crop purchasing behavior.

Including information on the value of beans, maize, and other crops purchased would have provided a more complete picture of marketing behavior and yielded interesting results with regards to the influence of ego networks on households participating in markets as buyers and/or sellers.

145

7.5 Future research

Future research should further disaggregate off-farm work choices. For instance, it would be interesting to examine the influence of ego networks on the type of self-employment or the type of non-farm wage labor. Similarly, the study could be extended by differentiating between wage payments and in-kind payments, particularly for hired farm labor.

Future research on agricultural marketing behavior should explore the role of individual ego networks on agricultural sales by men and women (as opposed to household crop sales). The descriptive tables indicated that men and women were both involved in the marketing of crops; therefore, it is likely that individual ego networks will also influence male and female participation in crop markets as well as the value of sales. Furthermore, rather than examine marketing behavior for individual crops (beans, maize, and other crops), the study would be strengthened if crops were aggregated into different crop types. For instance, it would be interesting to examine marketing behaviors for (i) cash crops and staple crops, (ii) legumes, grains, tubers, and vegetable crops, or (iii) perishable versus non-perishable crops. This would eliminate the need to include an aggregate ‘other crop’ variable that is difficult to interpret.

Lastly, future research should expand on network measures. The current analysis relies only on friendship and kin ego network size. Future research should include characteristics of network members. To incorporate this, the next round of interviews is constructing full village networks. Prior to administering the survey, a roster of every village member is obtained with the help of community leaders. The full roster facilitates the identification of ego networks and allows additional network information to be pursued, for instance, the gender, education, off- farm work choices, and agricultural production and marketing of network members. Detailed network data would mean that future research could examine the impact of quality and quantity

146 of ego networks on off-farm labor and off-farm work choices as well as agricultural marketing behavior.

147

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Appendix A - SURVEY SITES

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Figure A1.1: Tete-1 survey site

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Figure A1.2: Tete-2 survey site

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Figure A1.3: Zambezia-1 survey site

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Figure A1.4: Zambezia-2 survey site

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Figure A1.5: Niassa-1 survey site

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Figure A1.6: Niassa-2 survey site

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Appendix B – OFF-FARM LABOR PARTICIPATION AND WORK CHOICE

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Table B1.1: Participation rate in income-earning activities, by province Province Tete Zambezia Niassa Female Male Female Male Female Male Own farm labor 0.983 (0.131) 1.000 (0.00) 0.980 (0.140) 0.978 (0.147) 0.925 (0.267) 0.974 (0.160) Off-farm labor1 0.569 (0.500) 0.667 (0.477) 0.471 (0.504) 0.478 (0.505) 0.396 (0.494) 0.436 (0.502) Hired farm labor 0.517 (0.504) 0.476 (0.505) 0.333 (0.476) 0.217 (0.417) 0.321 (0.471) 0.282 (0.456) Non-farm wage labor 0.086 (0.283) 0.310 (0.468) 0.078 (0.272) 0.283 (0.455) 0.038 (0.192) 0.128 (0.339) Non-farm self- 0.172 (0.381) 0.381 (0.492) 0.137 (0.348) 0.326 (0.474) 0.057 (0.233) 0.231 (0.427) employment Observations (n) 58 42 51 46 53 39 Standard deviations in parentheses 1Off-farm labor consists of working as hired farm labor on others' farms, working on non-farm wage labor, or working on non-farm self-employment.

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Appendix C - FEMALE FRIENDSHIP AND KIN EGO NETWORKS

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Figure C1.1: Tete-1 female friendship ego networks

Figure C1.2: Tete-1 female kin ego networks

Legend Surveyed household Non-surveyed household

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Figure C1.3: Tete-2 female friendship ego networks

Figure C1.4: Tete-2 female kin ego networks

Legend Surveyed household Non-surveyed household

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Figure C1.5: Zambezia-1 female friendship ego networks

Figure C1.6: Zambezia-1 female kin ego networks

Legend Surveyed household Non-surveyed household

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Figure C1.7: Zambezia-2 female friendship ego networks

Figure C1.8: Zambezia-2 female kin ego networks

Legend Surveyed household Non-surveyed household

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Figure C1.9: Niassa-1 female friendship ego networks

Figure C1.10: Niassa-1 female kin ego networks

Legend Surveyed household Non-surveyed household

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Figure C1.11: Niassa-2 female friendship ego networks

Figure C1.12: Niassa-2 female kin ego networks

Legend Surveyed household Non-surveyed household

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Appendix D – MALE FRIENDSHIP AND KIN EGO NETWORKS

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Figure D1.1: Tete-1 male friendship ego networks

Figure D1.2: Tete-1 male kin ego networks

Legend Surveyed household Non-surveyed household

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Figure D1.3: Tete-2 male friendship ego networks

Figure D1.4: Tete-2 male kin ego networks

Legend Surveyed household Non-surveyed household

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Figure D1.5: Zambezia-1 male friendship ego networks

Figure D1.6: Zambezia-1 male kin ego networks

Legend Surveyed household Non-surveyed household

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Figure D1.7: Zambezia-2 male friendship ego networks

Figure D1.8: Zambezia-2 male kin ego networks

Legend Surveyed household Non-surveyed household

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Figure D1.9: Niassa-1 male friendship ego networks

Figure D1.10: Niassa-1 male kin ego networks

Legend Surveyed household Non-surveyed household

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Figure D1.11: Niassa-2 male friendship ego networks

Figure D1.12: Niassa-2 male kin ego networks

Legend Surveyed household Non-surveyed household

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Appendix E - HOUSEHOLD FRIENDSHIP AND KIN EGO NETWORKS

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Figure E1.1: Tete-1 household friendship ego networks and household income

Legend Surveyed household Non-surveyed household

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Figure E1.2: Tete-1 household kin ego networks and household income

Legend Surveyed household Non-surveyed household

185

Figure E1.3: Tete-2 household friendship ego networks and household income

Legend Surveyed household Non-surveyed household

186

Figure E1.4: Tete-2 household kin ego networks and household income

Legend Surveyed household Non-surveyed household

187

Figure E1.5: Zambezia-1 household friendship ego networks and household income

Legend Surveyed household

Non-surveyed household

188

Figure E1.6: Zambezia-1 household kin ego networks and household income

Legend Surveyed household Non-surveyed household

189

Figure E1.7: Zambezia-2 household friendship ego networks and household income

Legend Surveyed household Non-surveyed household

190

Figure E1.8: Zambezia-2 household kin ego networks and household income

Legend Surveyed household Non-surveyed household

191

Figure E1.9: Niassa-1 household friendship ego networks and household income

Legend Surveyed household Non-surveyed household

192

Figure E1.10: Niassa-1 household kin ego networks and household income

Legend Surveyed household Non-surveyed household

193

Figure E1.11: Niassa-2 household friendship ego networks and household income

Legend Surveyed household Non-surveyed household

194

Figure E1.12: Niassa-2 household kin ego networks and household income

Legend Surveyed household Non-surveyed household

195

Appendix F – IV PROBIT RESULTS FOR OFF-FARM LABOR PARTICIPATION

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Table F1.1: IV probit results for male off-farm labor participation, by gender Females Males Model (1) Model (2) Model (1) Model (2) Marginal Marginal Marginal Coefficient Coefficient Marginal Effects Coefficient Coefficient Effects Effects Effects Age: 26-35 -0.835*** -0.314*** -0.814*** -0.307*** 0.033 0.013 0.034 0.014 (0.311) (0.106) (0.310) (0.105) (0.419) (0.166) (0.408) (0.162) Age: 36-45 -0.748** -0.280** -0.690** -0.260** 0.105 0.041 0.117 0.046 (0.346) (0.116) (0.345) (0.118) (0.478) (0.188) (0.489) (0.192) Age: 46-55 -1.693*** -0.497*** -1.664*** -0.491*** 0.502 0.191 0.546 0.208 (0.437) (0.070) (0.409) (0.068) (0.519) (0.186) (0.493) (0.174) Age: 56+ -0.995** -0.352*** -0.897** -0.323*** -0.275 -0.110 -0.255 -0.101 (0.434) (0.122) (0.418) (0.125) (0.582) (0.230) (0.577) (0.228) Education: Grades 1 or 2 0.309 0.123 0.336 0.133 0.709* 0.264** 0.740* 0.276** (0.317) (0.124) (0.310) (0.121) (0.390) (0.132) (0.400) (0.133) Education: Grades 3+ -0.477 -0.186* -0.414 -0.162 0.171 0.068 0.165 0.065 (0.293) (0.109) (0.289) (0.109) (0.345) (0.137) (0.344) (0.137) Marital status -0.165 -0.066 -0.134 -0.053 -0.311 -0.121 -0.254 -0.100 (0.274) (0.109) (0.269) (0.107) (0.431) (0.164) (0.429) (0.165) Health status -0.337 -0.134 -0.366 -0.145* 0.337 0.134 0.438 0.173 (0.226) (0.089) (0.223) (0.088) (0.274) (0.108) (0.277) (0.108) Number of adults, 60 or older -0.013 -0.005 -0.111 -0.044 0.313 0.124 0.317 0.126 (0.191) (0.076) (0.180) (0.072) (0.263) (0.104) (0.264) (0.105) Number of males, 15-59 0.133 0.053 0.106 0.042 -0.374** -0.148** -0.384** -0.152** (0.184) (0.073) (0.174) (0.069) (0.180) (0.072) (0.177) (0.071) Number of females, 15-59 0.081 0.032 0.077 0.031 0.178 0.070 0.156 0.062 (0.136) (0.054) (0.128) (0.051) (0.152) (0.060) (0.150) (0.059) Number of children, 14 or younger 0.071 0.028 0.058 0.023 -0.018 -0.007 -0.027 -0.011 (0.066) (0.026) (0.068) (0.027) (0.077) (0.031) (0.077) (0.031)

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Table F1.1 (cont.): IV probit results for male off-farm labor participation, by gender Females Males Model (1) Model (2) Model (1) Model (2) Marginal Marginal Marginal Coefficient Coefficient Marginal Effects Coefficient Coefficient Effects Effects Effects Land (ha.) 0.078 0.031 0.078 0.031 -0.055 -0.022 -0.046 -0.018 (0.049) (0.019) (0.048) (0.019) (0.059) (0.023) (0.057) (0.023) Distance (km.) -0.003 -0.001 -0.003 -0.001 0.002 0.001 0.002 0.001 (0.010) (0.004) (0.009) (0.004) (0.011) (0.004) (0.011) (0.004) Friendship ego network size1 0.412 0.164 - - -0.042 -0.017 - - (0.434) (0.173) (0.261) (0.104) Kin ego network size -0.033 -0.013 - - 0.341** 0.135** - - (0.166) (0.066) (0.155) (0.061) Total ego network size1 - - 0.241 0.096 - - 0.105 0.042 (0.170) (0.068) (0.154) (0.061) Proportion ego network kin - - -1.027* -0.409* - - 1.370*** 0.544*** (0.571) (0.227) (0.513) (0.204) Constant -0.217 - 0.144 - -0.278 - -0.904 - (0.749) (0.639) (0.940) (1.068) athrho -0.070 -0.125 0.139 0.097 (0.372) (0.279) (0.335) (0.268) lnsigma -0.202*** 0.383*** 0.192*** 0.486*** (0.058) (0.052) (0.071) (0.074) Observations 162 162 127 127 Wald chi2(14) (1st Wald) 36.33*** 37.90*** 23.24 26.35** Wald test of exogeneity (2nd Wald) 0.04 0.20 0.17 0.13 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 1 Endogenous variable

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Appendix G - AGRICULTURAL MARKETING BEHAVIOR

199

Table G1.1: Household marketing behavior for beans Model 1 Model 2 Tier 1 Tier 2 Tier 1 Tier 2 Own radio/cell phone -0.045 - -0.031 (0.249) (0.246) Education: Grades 1 or 2 0.359 - 0.375 (0.331) (0.325) Education: Grades 3+ -0.047 - 0.001 (0.276) (0.271) Age 0.025 0.038 0.026 0.040 (0.035) (0.050) (0.035) (0.049) Age2/1000 -0.261 -0.492 -0.264 -0.523 (0.374) (0.592) (0.371) (0.584) Marital status 0.221 0.554** 0.188 0.502** (0.250) (0.260) (0.251) (0.255) Health status 0.226 0.222 0.239 0.214 (0.228) (0.221) (0.224) (0.223) Number of adults, 60 or older -0.182 -0.100 -0.221 -0.041 (0.200) (0.123) (0.187) (0.114) Number of males, 15-59 -0.093 0.231** -0.075 0.202* (0.139) (0.112) (0.139) (0.112) Number of females, 15-59 -0.099 -0.095 -0.114 -0.074 (0.105) (0.108) (0.104) (0.108) Number of children, 14 or younger 0.013 0.017 0.008 0.023 (0.066) (0.072) (0.067) (0.074) Wage labor -0.255 -0.314 -0.288 -0.299 (0.254) (0.275) (0.257) (0.279) Land (ha.) 0.202** 0.047 0.194** 0.048 (0.083) (0.057) (0.083) (0.058) Distance (km.) 0.035*** 0.034*** 0.034*** 0.034*** (0.012) (0.007) (0.012) (0.007) Extension 0.626*** -0.169 0.606*** -0.180 (0.233) (0.216) (0.233) (0.216) Transportation 0.164 0.498** 0.173 0.505** (0.253) (0.207) (0.252) (0.207)

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Table G1.1 (cont.): Household marketing behavior for beans Model 1 Model 2 Tier 1 Tier 2 Tier 1 Tier 2 Friendship ego network size 0.014 -0.223*** - - (0.082) (0.071) Kin ego network size -0.155 -0.023 - - (0.104) (0.097) Total ego network size - - -0.059 -0.145** (0.060) (0.058) Constant -1.421* 4.806*** -1.416* 4.823*** (0.800) (0.913) (0.789) (0.904) Sigma 1.193*** 1.203*** (0.076) (0.079) Observations 201 201 Wald chi2(18) 35.95*** - Wald chi2(17) - 31.87** Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

201

Table G1.2: Household marketing behavior for maize Model 1 Model 2 Tier 1 Tier 2 Tier 1 Tier 2 Own radio/cell phone 0.204 - 0.190 (0.230) (0.228) Education: Grades 1 or 2 0.818*** - 0.775*** (0.298) (0.296) Education: Grades 3+ 0.588** - 0.529** (0.248) (0.241) Age -0.002 0.069* -0.004 0.072* (0.034) (0.038) (0.034) (0.038) Age2/1000 0.001 -0.423 0.008 -0.454 (0.352) (0.383) (0.351) (0.384) Marital status 0.493** -0.202 0.500** -0.178 (0.243) (0.279) (0.243) (0.282) Health status -0.807*** 0.660*** -0.805*** 0.609*** (0.238) (0.253) (0.237) (0.234) Number of adults, 60 or older -0.358** 0.032 -0.310* 0.060 (0.167) (0.266) (0.174) (0.259) Number of males, 15-59 0.113 0.103 0.095 0.083 (0.152) (0.172) (0.150) (0.174) Number of females, 15-59 -0.174 -0.274 -0.150 -0.266 (0.129) (0.210) (0.126) (0.206) Number of children, 14 or younger 0.078 -0.009 0.084 -0.004 (0.062) (0.071) (0.062) (0.072) Wage labor -0.849*** 0.564 -0.792*** 0.583 (0.265) (0.374) (0.263) (0.376) Land (ha.) 0.039 0.012 0.040 0.011 (0.040) (0.069) (0.039) (0.070) Distance (km.) -0.008 0.002 -0.008 0.002 (0.009) (0.008) (0.009) (0.008) Extension -0.289 0.449* -0.294 0.437* (0.230) (0.252) (0.229) (0.253) Transportation 0.142 0.572* 0.142 0.541* (0.245) (0.324) (0.245) (0.324)

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Table G1.2 (cont.): Household marketing behavior for maize Model 1 Model 2 Tier 1 Tier 2 Tier 1 Tier 2 Friendship ego network size -0.141* -0.383*** - - (0.075) (0.099) Kin ego network size 0.069 -0.212 - - (0.087) (0.151) Total ego network size - - -0.059 -0.328*** (0.054) (0.070) Constant 0.182 4.760*** 0.242 4.793*** (0.767) (0.747) (0.762) (0.745) Sigma 1.074*** 1.078*** (0.089) (0.087) Observations 201 201 Wald chi2(18) 41.88*** - Wald chi2(17) - 41.89*** Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Table G1.3: Household marketing behavior for other crops Model 1 (control function) Model 2 Tier 1 Tier 2 Tier 1 Tier 2 Own radio/cell phone -0.114 - -0.071 (0.216) (0.218) Education: Grades 1 or 2 0.870*** - 0.901*** (0.318) (0.307) Education: Grades 3+ 0.315 - 0.406* (0.256) (0.228) Age -0.013 0.133*** 0.006 0.137*** (0.039) (0.042) (0.037) (0.041) Age2/1000 0.060 -1.585*** -0.122 -1.626*** (0.414) (0.448) (0.402) (0.448) Marital status 0.029 -0.194 -0.082 -0.214 (0.282) (0.275) (0.247) (0.259) Health status 0.123 0.259 0.182 0.276 (0.234) (0.316) (0.227) (0.300) Number of adults, 60 or older 0.469** 0.428*** 0.449** 0.432*** (0.210) (0.136) (0.204) (0.114) Number of males, 15-59 0.273** 0.045 0.291** 0.043 (0.134) (0.194) (0.135) (0.188) Number of females, 15-59 0.043 -0.170 -0.005 -0.168 (0.114) (0.144) (0.109) (0.146) Number of children, 14 or younger 0.038 0.133* 0.014 0.128* (0.061) (0.078) (0.059) (0.071) Wage labor 0.283 0.597* 0.269 0.596* (0.261) (0.349) (0.262) (0.349) Land (ha.) 0.036 -0.037 0.032 -0.037 (0.056) (0.049) (0.056) (0.048) Distance (km.) -0.030*** -0.061*** -0.031*** -0.061*** (0.010) (0.014) (0.010) (0.014) Extension 0.240 -0.316 0.305 -0.314 (0.221) (0.304) (0.212) (0.298) Transportation 0.095 0.637** 0.141 0.654** (0.232) (0.304) (0.235) (0.285)

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Table G1.3 (cont.): Household marketing behavior for other crops Model 1 (control function) Model 2 Tier 1 Tier 2 Tier 1 Tier 2 Residual -0.289 -0.054 - - (0.221) (0.261) Friendship ego network size 0.344* -0.006 - - (0.205) (0.224) Kin ego network size 0.164 -0.007 - - (0.103) (0.107) Total ego network size - - 0.145*** -0.028 (0.055) (0.068) Constant -0.859 5.168*** -0.807 5.170*** (0.818) (0.857) (0.805) (0.861) Sigma 1.326*** 1.327*** (0.095) (0.095) Observations 201 201 Wald chi2(19) 41.24*** - Wald chi2(17) - 37.54*** Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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VITA - Luis Sevilla

ACADEMICS Doctor of Philosophy, Agricultural Economics and Demography 08/06 – 12/13 The Pennsylvania State University, University Park, Pennsylvania

Bachelor of Arts, Economics and International Studies 08/01 – 05/05 University of Wisconsin, Madison, Wisconsin

PROFESSIONAL EXPERIENCE Research Associate, University of Missouri 08/11 – 02/13 Research Assistant, Pennsylvania State University 08/06 – 08/11 Teaching Assistant, Pennsylvania State University 2007, 2010, 2011

AWARDS International Comparative Rural Policy Summer Institute. As, Norway 2011 Minority Health Research Training Grant. Penn State University 2008 Bunton-Waller Graduate Fellowship. Pennsylvania State University. 2006 Gerald G. Somers Undergraduate Essay Award. Economics Dept. University of Wisconsin 2002

LANGUAGES English: Fluent and fully proficient Spanish: Fluent and fully proficient Portuguese: Advanced level conversation and grammar French: Five year training with intermediate level conversation and grammar

SELECTED PROFESSIONAL PAPERS AND PRESENTATIONS Conserve D., Sevilla L., Mbwambo J., and King G.; Determinants of Previous HIV Testing and Knowledge of Partner's HIV Status Among Men Attending a Voluntary Counseling and Testing Clinic in Dar es Salaam, Tanzania; American Journal of Men’s Health; December 2012

Findeis, J., Sevilla, L., Quinhentos M.; Network Barriers and Broad Disseminations; Poster Presentation; Pulse CRPS Global Research Meetings on “Transforming Grain Legume Systems to Enhance Nutrition and Livelihoods ; Kigali, Rwanda; 2012

Conserve D., Sevilla L., Younge S., Mbwambo J., King G.; Condom Use among HIV-Positive Sexually Active Adults and Partner's HIV Status in Dar es Salaam, Tanzania; Journal of Health Care for the Poor and Underserved; May 2012.

Conserve D., Sevilla L., Younge S., Mbwambo J., King G.; Safer Sexual Practices Among HIV Positive Individuals in Tanzania. Poster Presentation. American Public Health Association Annual Conference. 2010.

Findeis, J.L.,Snyder, A., Jensen, L., Sevilla, L., Bedford, J., Salcedo Du Bois, R., Tello Sucre. M., Osorio, Carriazo, F. (2008). The Well-Being of Youth in Migrant or Seasonal Farm Worker Families: Final Report to the National Institutes of Health (NIH). 2008.