Farm/non-farm linkages in smallholder agriculture ...

Katholieke Universiteit Leuven

Faculteit Bio-ingenieurswetenschappen

Farm/non-farm linkages in smallholder agriculture: Evidence from Tigray, Northern Ethiopia (De link tussen landbouwbedrijf en niet-landbouwbedrijf gerelateerde activiteiten in kleinschalige landbouw: bewijs uit Tigray, Noord-Ethiopië)

Promotor: Prof. Erik Mathijs Copromotor: Prof. Miet Maertens

Masterproef voorgedragen tot het behalen van het diploma van Master in de bio-ingenieurswetenschappen: land- en bosbeheer Vandercasteelen Joachim

Departement Aard- en Omgevingswetenschappen Afdeling Landbouw- en Voedseleconomie Juli 2011

“Dit proefschrift is een examendocument dat na de verdediging niet meer werd gecorrigeerd voor eventueel vastgestelde fouten. In publicaties mag naar dit proefwerk verwezen worden mits schriftelijke toelating van de promotor, vermeld op de titelpagina.”

Acknowledgements

ACKNOWLEDGEMENTS I would like to put a note of thanks to all the people who helped me with realizing my master thesis. First of all, I would like to thank all the people involved in my Ethiopian research. I would like to express my gratitude to the VLIR (Vlaamse Interuniversitaire Raad) for awarding me a travel grant, the MU-IUC (Mekelle University – Institutional University Cooperation) for providing me with an office at the University of Economics & Business and the Afdeling Landbouw- en Voedseleconomie for giving me the opportunity to do research in a developing country. Special message of thanks to professors Erik Mathijs and Miet Maertens for guiding, supporting and assisting me during my research and writing this thesis process. This study wouldn‟t be possible without the help and supervision of Kidane. He introduced me to the research area, methodology and the country. Further, I owe many thanks to all the other supervisors, Alemtsehay, Abebe and Berhanu because they helped me to make my stay in Mekelle as nice as possible and introduced me to the incredible Ethiopian food. I would also like to thank Daan, who helped me with statistical issues and treated me with a lot of coffees. Also a big thank you to the extension agents Mebratu en Tesfai for assisting me during the field trips. Although we have spent only a short time together, they have become real friends. I‟m also very thankful for all the people that lived with us in Mekelle. Thanks to all the faranji‟s that stayed in our house, Lutgart and Daan and his wife Anita. Next to this, I want to thank all the Ethiopians I have met at university and beyond: the drivers of the VLIR, Kidanemariam Hailu, Jemael, Aklilu and Temesgen. Thanks for your help, kindness and laughter. Special gratitude to Tsegay, my dear friend in Mekelle. He introduced me to Mekelle and the Ethiopian culture and he taught me so many things. He made my stay in Mekelle unforgettable and he will always remain a special friend of mine. Last but not least, I would like to express my gratitude to all the people around me. Especially my parents and sister, without them it wouldn‟t be possible for me to come this far. Thank you for your support and the opportunities you gave me. I also like to thank my girlfriend Hanne, who has always been there for me. Also thanks to my close friends who made it possible for me to relax after work. Finally, I want to thank my roommates Sebastian, Mattias, Jeroen and Jan for creating a nice working atmosphere at home.

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Abstract

ABSTRACT The rural non-farm employment sector has become an important component of the rural economy in developing countries. The (historical) dominance of the agricultural sector raises questions how the two sectors are inter-linked in a synergistic rural development approach. Literature has identified several mechanisms of farm/non-farm linkages. This dissertation focuses on the complementary investment linkages between farm and non-farm activities as suggested in the recent literature. Investment linkages imply that non-farm income is used by farm households as an additional income source to finance farm activities. They occur when these households face credit constraints, which hamper their farm investments. The ultimate objective of the study is to examine whether households that participate in non-farm activities spend more money on farm investments and expenditures. We differentiated the impact for wage and selfemployment non-farm income, farm input use and the different types of farm investments. We used cross-sectional data from a household survey conducted in the Tigray region of Ethiopia in 2009. An instrumental variable regression was used in order to overcome the endogeneity problem caused by omitted variable bias. The instrumental variable estimation results showed that non-farm income is significant and positively related with farm expenditures and investments and in particular with livestock and equipment investments. We derived similar findings when we estimated the relationship separately for wage income; while self-employment activities did not have a significant impact on agricultural activities. Hence, our results supported the hypothesis that access to non-farm income has alleviated farmers‟ credit constraints. However, access to non-farm did not increase the farm input use because of unfavorable local conditions. Finally, the estimation of the level of non-farm income showed that the family size (both dependent and labor forces) and low agricultural wealth (especially of low livestock assets) are major determinants, indicating that households with sufficient labor supply and poor resources are pushed into non-farm activities. The results did not provide exclusive evidence that entry barriers are present in Tigray. This might indicate the low level of skill and capital requirement to participate in non-farm activities. Hence, a virtuous circle could possibly exist, in which access to non-farm income increases farm expenditure and hence modernize agricultural production. This would suggest that increase in availability and access of non-farm activities will have a positive impact on farm activities. These findings support policies in rural parts of Ethiopia that effectively target less endowed and marginal farm households.

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Nederlandstalige Samenvatting

NEDERLANDSTALIGE SAMENVATTING De landelijke niet-agrarische sector is uitgegroeid tot een belangrijk onderdeel van de plattelandseconomie in ontwikkelingslanden. De dominantie van landbouw doet vragen rijzen hoe de twee sectoren verbonden zijn. De literatuur identificeert een aantal mechanismen van agriculturele/niet-agriculturele verbanden. Deze studie richt zich op complementaire investeringsverbanden, die impliceren dat niet-agrarisch inkomen gebruikt wordt als extra bron van inkomsten om agrarische activiteiten te financieren. Ze komen voor wanneer huishoudens tegen kredietbeperkingen aanlopen, die hen belemmeren om te investeren in landbouw. Het uiteindelijke doel van deze studie is om te onderzoeken of huishoudens die actief zijn in niet-agrarische activiteiten meer geld investeren in hun boerderij. We onderscheidden de effecten voor het loon en zelfstandige niet-agrarische inkomsten, landbouwinput gebruik en de verschillende soorten investeringen. We maakten gebruik van cross-section gegevens uit een onderzoek onder huishoudens in 2009 uitgevoerd in de Tigray regio van Ethiopië. Een instrumentele variabele regressie werd gebruikt om het endogeniteit probleem, veroorzaakt door ongemerkte heterogeniteit, te overwinnen. De resultaten van deze schatting toonden aan dat niet-agrarisch inkomen significant en positief gerelateerd is met landbouw uitgaven, en in het bijzonder met investeringen in vee en uitrusting. We bekwamen gelijkaardige bevindingen wanneer we deze relatie afzonderlijk schatten voor inkomen uit loon, terwijl zelfstandige activiteiten geen significante invloed hebben op agrarische activiteiten. Onze resultaten ondersteunden de hypothese dat de toegang tot niet-agrarisch inkomen de kredietbeperkingen van boeren versoepelt. Dit inkomen verhoogde het gebruik van landbouwinput echter niet omwille van plaatselijke ongunstige omstandigheden. Uit de schatting van het niveau van niet-agrarisch inkomen bleek ten slotte dat de gezinsgrootte en beperkt agrarische rijkdom belangrijke determinanten zijn, waaruit volgt dat huishoudens met arbeid overschot en weinig middelen „geduwd‟ worden in niet-agrarische activiteiten. De resultaten boden geen exclusief bewijs dat toetredingsdrempels aanwezig waren in Tigray. Niet agrarische activiteiten vereisen dus slechts weinig kennis of kapitaal. Bijgevolg is het mogelijk dat een deugdzame cirkel bestaat waarin de toegang tot nietagrarisch

inkomen

de

investeringen

(en

dus

ook

de

modernisering)

in

de

landbouwproductie doet stijgen. Een stijging van de beschikbaarheid van en toegang tot niet-agrarische activiteiten zal dus een positieve invloed hebben op de landbouw activiteiten. Deze bevindingen ondersteunen het plattelandsbeleid in Ethiopië om minderbedeelde en marginale boerderij huishoudens effectief te bereiken.

ii

List of abbreviations and symbols

LIST OF ABBREVIATIONS AND SYMBOLS 2SLS

: Two Stage Least Squares

BoANRD

: Bureau of Agriculture and Natural Resource Development

CSA

: Central Statistical Agency of Ethiopia

ETB

: Ethiopian birr

IV

: Instrumental Variables

REST

: Relief Society of Tigray

RNFE

: Rural Non-Farm Employment

TDA

: Tigray Development Agency

masl

: meters above sea level

MU-IUC

: Mekelle University – Institutional University Cooperation

OLS

: Ordinary Least Squares

VLIR

: Vlaamse Interuniversitaire Raad

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List of tables

LIST OF TABLES Table 4.1:

Households‟ total farm expenditures, input use and investments in ETB .. 37

Table 4.2:

Households' income composition in ETB .............................................. 40

Table 4.3:

The nature of wage and self-employment jobs ..................................... 42

Table 4.4:

Wage employment requirements and duration (in percentages) ............. 43

Table 4.5:

Attitude towards additional non-farm employment ................................ 44

Table 4.6:

Comparison of households with and without access to non-farm income .. 45

Table 4.7:

Summary statistics of individual and household characteristics ............... 47

Table 4.8:

Summery statistics of the instruments used in the 2SLS ........................ 48

Table 4.9:

First stage OLS regression results ....................................................... 51

Table 4.10: OLS and 2SLS estimations results of total farm expenditures ................. 56 Table 4.11: OLS and 2SLS estimations results of farm investments ......................... 58 Table 4.12: IV

estimation

results

of

investments

in

water&land,

livestock,

equipment and building ..................................................................... 60 Table 4.13: OLS and 2SLS estimations results of farm input use ............................. 61 Table 4.14: OLS and 2SLS estimations results of farm investments with wage income ............................................................................................ 63 Table 4.15: OLS and 2SLS estimates results of farm investments with business income ............................................................................................ 64 Table 4.16: Summary of the impact of non-farm income and control variables .......... 69

iv

List of figures

LIST OF FIGURES Figure 3.1: Map of Ethiopia (small) and Tigray Regional state ................................. 25 Figure 3.2: Research area .................................................................................. 26

v

Table of Contents

TABLE OF CONTENTS ACKNOWLEDGEMENTS ........................................................................................ I ABSTRACT ......................................................................................................... II NEDERLANDSTALIGE SAMENVATTING ............................................................... II LIST OF ABBREVIATIONS AND SYMBOLS ......................................................... III LIST OF TABLES ................................................................................................ IV LIST OF FIGURES ............................................................................................... V TABLE OF CONTENTS ......................................................................................... VI 1

2

INTRODUCTION .......................................................................................... 1 1.1

Background ............................................................................................ 1

1.2

Problem statement ................................................................................. 2

1.3

Research questions and hypothesis ........................................................ 4

1.4

Significance of the study ........................................................................ 4

LITERATURE REVIEW ................................................................................. 6 2.1

Farm/non-farm linkages ........................................................................ 6 2.1.1

Definitions .....................................................................................6

2.1.2

Evidence .......................................................................................7

2.2

The impact of farm activities on the RNFE sector ................................... 8

2.3

The impact of the RNFE sector on farm activities ................................... 9

2.4

2.3.1

Underestimation of the RNFE sector ................................................ 10

2.3.2

Importance of the RNFE sector ....................................................... 11

2.3.3

Participation in non-farm activities .................................................. 13

2.3.4

The nature of the impact of non-farm activities ................................ 15

2.3.4.1

Competing linkages ........................................................................... 16

2.3.4.2

Complementary linkages .................................................................... 17

Investment linkages ............................................................................. 19 2.4.1

Definition..................................................................................... 19

2.4.2

Liquidity and credit constraints ....................................................... 19

2.4.3

Evidence ..................................................................................... 21

vi

Table of Contents

2.5 3

4

Virtuous circle ...................................................................................... 23

METHODOLOGY ........................................................................................ 25 3.1

Study and survey area .......................................................................... 25

3.2

Survey design ....................................................................................... 28

3.3

Data analysis ........................................................................................ 29 3.3.1

Ordinary Least Squares estimation ................................................. 30

3.3.2

Omitted variable bias .................................................................... 31

3.3.3

Two Stage Least Squares estimation ............................................... 32

3.3.4

Instrumental Variables .................................................................. 34

RESULTS AND DISCUSSION ...................................................................... 37 4.1

Descriptive statistics ............................................................................ 37 4.1.1

Farm expenditure, input use and investments .................................. 37

4.1.2

Farm and non-farm activities ......................................................... 39

4.1.3

Households with non-farm income vs. households without non-farm income ........................................................................................ 44

4.1.4 4.2

Multivariate analysis ............................................................................ 49 4.2.1

First Stage Results ........................................................................ 50

4.2.2

Second Stage Results .................................................................... 54

4.2.2.1

The impact of non-farm income on farm expenditures ............................ 55

4.2.2.2

The impact of non-farm income on durables investments ....................... 57

4.2.2.3

The effect on different types of farm investments .................................. 59

4.2.2.4

The impact of non-farm income on farm inputs ..................................... 60

4.2.2.5

The effect of different types of non-farm income ................................... 62

4.2.3

5

Control and instrumental variables.................................................. 46

Discussion ................................................................................... 64

4.2.3.1

The impact of non-farm income ........................................................... 64

4.2.3.2

The effect of control variables ............................................................. 68

CONCLUSIONS AND RECOMMENDATIONS ................................................ 71 5.1

Conclusion ............................................................................................ 71

5.2

Policy Recommendations ...................................................................... 73

REFERENCES .................................................................................................... 75 APPENDIX 1 ..................................................................................................... 81 APPENDIX 2 ..................................................................................................... 83

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Chapter 1: Introduction

1

INTRODUCTION

1.1

Background

Agriculture has historically been dominating the rural economy of Ethiopia. The leading role of the agricultural sector was reflected in earlier government policies as development programs and interventions focused mainly on the agricultural sector. As observed in other developing countries, most efforts of the local government to develop rural growth, corresponded with policies to enhance farm productivity (Escobal, 2001) or increase employment in agriculture (Woldenhanna, 2000) and they thereby neglected the importance of the rural non-farm sector (Woldenhanna, 2002; Adams, 2002). Despite the importance of agriculture in the Ethiopian economy, the country has been facing structural food insecurity since the early 1970s (Belay and Abebaw, 2004; Shimelis and Bogale, 2007). This makes it doubtful whether the agricultural sector is capable to support rural development on its own and questions the leading role of agriculture in overall economic processes. Two major factors enhanced this recognition, making the traditional view of agriculture being the sole engine of rural growth in rural Ethiopia to be superseded. First, the agricultural productivity in Sub-Saharan Africa is the lowest in de world (Ehui and Pender, 2005). Agriculture is not able to provide sufficient food for its human population because of the stagnating, subsistence-oriented, small-scaled agricultural sector; the low utilization of modern inputs; crucial dependence on rainfall, poor resources

endowment;

poor

policy

environment

and

poor

public

investments

(Woldenhanna, 2002; Belay and Abebaw, 2004; Ehui and Pender, 2005; Pender et al., 2005). Despite the importance of agriculture, Ethiopia has been facing structural food insecurity since the early 1970s (Belay & Abebaw 2004, Shimelis & Bogale 2007). Moreover, the OECD (2010) believes that Ethiopia will continue to face food insecurity. Population pressure has resulted in land fragmentation, decreased farm size and environmental degradation. It is assumed that further labor absorption in agriculture will be difficult due to these natural and human induced problems (Woldenhanna, 2002; Jayne et al., 2003). This will only be possible through the intensification of agricultural production and irrigation use, which is unlikely in the short term (Woldenhanna, 2002). The initial policy of the Ethiopian government to promote access to credit, high yielding crop varieties and fertilizer to achieve productivity increase has not been successful in the majority of Ethiopia (Holden et al., 2004; Ashworth, 2005).

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Second, the rural development literature has pointed out that rural households make up their livelihood based on complex strategies and not just on agricultural production (Ellis, 1998; Woldenhanna, 2000; Barrett et al., 2001; Adams, 2002; Reardon et al., 2006; Shimelis and Bogale, 2007; Kilic et al., 2009). It is recognized that non-farm income sources are becoming increasingly important for rural households in developing countries (Islam, 1997; Reardon et al., 1998; Escobal, 2001; Lanjouw et al., 2001). The livelihood of rural households is the result of the interaction between complex strategies and multiple income generating activities (Kilic et al., 2009). The livelihood concept should therefore be adopted in development programs (Islam, 1997; Farrington et al., 2000). Although agriculture remains the most important sector, the study of diversification behavior offers insights necessary for broader development strategies (Barrett et al., 2001). The recognition of the importance of the rural nonfarm sector in the rural development process has promoted

Rural Non-Farm

Employment (RNFE) as a policy and gained the support from a broad range of development agencies. This trend occurs especially in countries like Ethiopia, which suffer from consumption and income shocks (Block and Webb, 2001). These considerations make it clear that policy makers are obligated to look for wider, alternative development strategies, in order to successfully tackle poverty and food insecurity in the East African highlands (Block and Webb, 2001; Jayne et al., 2003; Holden et al., 2004). Employing rural people in non-farm activities has several advantages: it offers an alternative remunerative allocation of resources, provides a socially cost-effective expansion of the economy, diminishes land pressure and degradation and reduces rural outmigration. Moreover, non-farm income provides farmers additional income and is less risky and fluctuating (Islam, 1997; Lanjouw and Lanjouw, 2001; Ruben and van den Berg, 2001; Woldenhanna, 2002). Development policies should therefore focus on an alternative synergistic approach in which agriculture is combined with other sources of employment and the different sectors receive equal emphasis. The role of RNFE in overall rural development gave researchers incentives to study the non-farm sector and its components (Islam, 1997).

1.2

Problem statement

The awareness of the importance of the RNFE sector and the need for a synergistic development approach produces an economy where both the agricultural and nonagricultural sector are emphasized. In such economies, the linkages between these two sectors become important. While the impact of agriculture on RNFE has been a major 2


object of study, the study of the impact of non-farm activities on agriculture is only recent. The impact of RNFE on farm activities is, however, not clear a priori: RNFE could be complementary or competing with agricultural production. The RNFE literature provides evidence of both effects of non-farm income on agriculture, making the overall impact of RNFE not ambiguously defined. Complementarity implies that RNFE provides non-labor variable inputs, credit or capital to farmers which can be used to increase agricultural productivity, consumption or intensification. Competing implies that RNFE withdrawals resources from the farm and thus decreases farm agricultural productivity. We therefore want to examine the direction of the impact of RNFE on agricultural activities. One particularly important type of complementary linkage are investment linkages. These linkages imply that income gathered from RNFE is used to finance investments in farm durables or input use. By this, non-farm income is spent on their farm. Investment linkages are likely to occur in the presence of liquidity constraints. Households lack credit or liquidity to invest in their farming activities, and therefore look for other sources of credit. Households are willing to participate in non-farm activities, as the latter result in additional cash which can be reinvested in the farm. The objective of this study is to investigate whether positive investment effects outweigh the loss in family farm labor availability. This is done by regressing farm investments on non-farm income and other household and individual characteristics. Finding a substantial and positive impact of the RNFE on the purchased farm inputs and capital investments will have important implications. RNFE could be a potentially important determinant of farm investment when farm households face credit market failures. Davies et al. (2009) hypothesize the existence of a virtuous circle. Promoting the RNFE, through access to microcredit or training, enhances the access to the RNFE and facilities the vanquish of the associated entry restrictions. Households could provide themselves with non-farm income and increase their total budget. Farmers are able to modernize, commercialize or intensify agricultural production via investment linkages. It is assumed that this will either increase the demand for agricultural wage labor or create a surplus of capital to invest in education, migration and higher skilled RNFE. This will in turn promote the RNFE sector. By this, a circle of self-reinforcing mechanisms is set up. If the existence of such a virtuous circle is likely to occur, potential opportunities for policymakers and development agents will be created.

3


1.3

Research questions and hypothesis

The possible existence of such a virtuous circle poses some interesting research questions. It hypothesizes that households who participate in non-farm activities will use the non-farm income to finance investments in their farm. As the farm becomes modernized, non-farm wage labor demand will be increased. In this way, a selfreinforcing effect will be launched. To indicate whether such virtuous circle is realistic to occur, this dissertation will investigate the linkages between the RNFE and agricultural sector. More specific, we investigate whether non-farm and farm activities are complementary or not. We therefore look at the impact of non-farm income on farm investments and expenditures. We hypothesize that households use non-farm income as an additional capital source to finance farm activities. We thus assume a positive relation between farm investments and non-farm income. This favorable relation is suggested to be driven by credit restraints. We therefore suppose that households are mainly pushed into RNFE because they face credit constraints, and use non-farm income to overcome these restrictions. We will also distinguish different types of non-farm income and farm expenditures. By analyzing the impact of non-farm income on farm expenditures, not only the impact of non-farm activities on agricultural production will be evaluated, but also the influence of human capital, household characteristics and regional factors will be examined. However, if such virtuous circle exists, the existence of factors that constrain the participation in RNFE will hinder the modernization or diversification of farm households. Several studies have investigated the existence of such „entry barriers‟ by studying the concentration in RNFE over households (Dercon and Krishnan, 1996; Dercon, 1998; Barrett et al., 2001; Davies et al. 2002). These studies indicate the existence of entry barriers to participate in the more lucrative RNFE. For example, Woldenhanna and Oskam (2001) find entry barriers in the Tigray region of Ethiopia. This study will therefore also pay attention to the determinants of non-farm income to examine exactly which factors hinder or enhance participation in non-farm activities.

1.4

Significance of the study

Our research focuses on Ethiopia, a poor country in the Horn of Africa. The linkages between the agricultural and RNFE sector are believed to be very important in developing countries. In the African context, Davies et al. (2002) conclude that forward

4


and backward production linkages are generally limited because of low agricultural inputs. Instead, they hypothesize that agricultural growth affects the non-farm sector mainly through expenditure linkages in the form of both consumption and investment linkages. This finding is consistent with the results of Woldenhanna (2002) in Ethiopia and Hazell and Hojjati (1995) in Zambia. Hence, this study will add to the empirical strand of literature on complementary farm/non-farm linkages in developing countries. Our results will provide evidence of the existence of investment linkages between agricultural activities and RNFE. By this, the study will contribute to a better understanding of the impact of non-farm income on agricultural production and the role of the RNFE in the rural development process. If our results indicate that the RNFE is complementary to farm activities and is used to overcome credit constraints, promoting the RNFE will be important, if not necessary, to modernize farm activities in rural areas of developing countries. As a result, some policy recommendations to promote the access to non-farm activities and hence farm investments can be formulated. Recent literature has focused on the RNFE sector and studied its role in rural development. We are therefore interested in the importance of non-farm income (and its different sources) in rural parts of Ethiopia. We will study the nature of the RNFE and which factors may be important in determining the level of non-farm income. Simple descriptive statistics will indicate the significance of non-farm activities in rural areas and will contribute to literature focusing on the importance the non-farm sector. Next to this, we will try to distinguish between the different types of non-farm activities. The access, nature and requirements of the various types of non-farm activities are different and they hence have a different effect on farm agricultural activities. Most studies of farm/non-farm linkages do not differentiate between wage and self-employment activities (except for, among others, Woldenhanna and Oskam, 2001; Maertens, 2009). Finally, our study is well-founded by a robust empirical model. This is done for two reasons. First, the deviation of the normality assumption of the error term must be dealt with. Second, it is recognized that, for several reasons, non-farm activities are endogenously related with agricultural production and simple regressions will be therefore biased. An advanced empirical model will be used to solve the possible endogenous effects of non-farm income. This methodology might serve as an example for other empirical research that deals with the endogeneity problem.

5

Chapter 2: Literature review

2

LITERATURE REVIEW

2.1

Farm/non-farm linkages

2.1.1

Definitions

The relationship between the farm and non-farm sector is mostly described by using the concept of farm/non-farm linkages (Reardon et al., 1998). Linkages are financial transactions between the two sectors over time. A distinction between production and expenditure linkages is made. Production linkages can be further divided into upwards and downwards production linkages. Upward production linkages are found in the nonfarm sector when agricultural output is used as input. Raw agricultural outputs are processed and distributed by non-farm enterprises (Woldenhanna, 2000). Growth in farming stimulates agricultural productivity and hence the capacity to supply inputs and services to the non-farm sector. Downward production linkages refer to non-farm activities that provide inputs to agricultural production, such as agrochemicals, water pumps or fertilizers. The non-farm sector is encouraged to invest in supply capacity of agroprocessing and distribution services (Reardon et al., 1998; Davies et al., 2002). The characteristics of the local agriculture will determine whether the production linkages will be upward or downward. For example, farm size determines the profitability of markets for tractors (Reardon et al., 1998). Expenditure linkages occur when households finance spendings in one sector by the money earned from another sector. Farmers can for example purchase non-farm products with income generated from farm activities. On the contrary, people that have access to RNFE buy food and other agricultural output with the income derived from that non-farm activity. When these expenditures are related to household consumption, consumption linkages establish. Farm income increases the demand of basic goods and services and results in diversification of consumption (Woldenhanna, 2000). Investment linkages include expenditures used to finance non-farm or farm activities, which are mainly important within households. Returns from non-farm activities can be used to make investments in farm activities and thereby enhance agricultural productivity (Davies et al., 2002). The profitability of these expenditure linkages depends on the level and distribution of the income. Poor households will spend more on local goods and services in the RNFE, while richer households are more likely to invest in goods from the modern and urban manufacturing sectors or in imports.

6


The structure of the agricultural sector and the type of growth determines the type of linkage that will occur. Davies et al. (2002) illustrates this with several examples. If significant external inputs are needed for agricultural production, it is expected that backward production linkages will occur. Agricultural output that requires processing before selling induces forward production linkages. If growth in the agricultural sector is capable of inducing rural income growth, consumption and potential investments will be enhanced by expenditure linkages.

2.1.2

Evidence

There exists numerous literature that reviews the inter-sectoral linkages in rural areas. Refer to Islam (1997) and Lanjouw and Lanjouw (2001), who reviewes the earlier work done on inter-sectoral linkages. Cross-sectional linkages seem to be crucial for the initial development of the RNFE. Studies proof that non-farm activities positively influence the farm activities and vice versa mainly through production and consumption linkages. Islam (1997) states that growth in farm income stimulates the consumption of goods and inputs, while agricultural raw material is processed in the rural non-farm sector. The growth pattern in agricultural income and the technology used in production determines the strength of the production and consumption linkages. AndreossoO‟Callaghan and Yue (2004) did a comparative analysis of the traditional and modern methods in linkage analysis in China between 1987 and 1997. They conclude that forward and backward linkages have generally increased, indicating an increased intersectoral dependency. Blunch and Verner (2006) observe two-way spillover effects between industrial and agricultural growth in three African countries, indicating interdependence in sectoral growth. Davies et al. (2002) discuss several case studies of farm/non-farm linkages in Africa and Latin America where spin-off activities already exist. In low–income countries such as Ghana, Kenya or Ethiopia, limited local demand and limited investments oblige for spin-off activities. In Kenya, contract farming acts as the primary means of interaction between farmers and the agro-industry. This creates an income surplus which can be spent in local markets. In Ghana, substantial forward linkages are noticed in the lowinput cassava sector because processing is necessary and transportation is required. Other research in Africa has been conducted by Hazell and Hojjati (1995), who examined different types of inter-sectoral linkages in Zambia. First, they observe that credit linkages might occur since agricultural income could be used to finance and invest in easy-entry non-farm activities. Second, it is concluded that seasonal labor

7


shortage affects both farm and non-farm activities. The authors suggest the existence of a shift of labor depending on the seasonal requirement of the sectors. This effect is uncertain since the opposite is also observed. Third, it is noted that production linkages are most pronounced in the agricultural growth of small farms. Long-term investments in the farm are assumed to create demand linkages to both the farm and non-farm sector. Finally, consumption linkages are considered to be the most dominant linkages in Zambia. In Ethiopia, research on farm/non-farm linkages in the Tigray region has been done by Woldenhanna (2002). First, it is observed that production linkages, both backward and forward, are limited because the households‟ purchase of fertilizer and pesticides was very low. The agricultural sector is unable to support the processing industry due to the fact that households consume most of their own farm products. Farm households only sell 15% or less of their production. These limited production linkages imply that the correlation between RNFE and farm activities is rather weak. Second, the significant use of wholesale and retail trades by farmers indicates that consumption linkages are more important than production linkages. These consumption linkages are the strongest expressed in locally produced food. The author also concludes a positive relation between the demand for consumption goods and agricultural income. Third, it is suggested that agricultural production has the potential to enhance the demand for non-food goods. The elasticities of local non-food expenditures are high, implying that when households‟ income increases, their importance in the household budget also rises. However, this effect is only valid in the short term, as the elasticities of imported non-food expenditures are even higher. Finally, non-farm activities absorb labor that cannot be allocated on agricultural activities. This is suggested by the negative relation between agricultural output and small enterprises.

2.2

The impact of farm activities on the RNFE sector

The previous section shows that the rural development literature attributed attention to inter-sectoral linkages. Furthermore, the existence of farm/non-farm linkages in rural areas is suggested. Given the historical dominant role of agriculture and the fact that the majority of the rural population is engaged in it, the rural development literature initially emphasized the fostering role of agriculture in the overall economy. It was believed that the rural sector in developing countries was entirely driven by agriculture and that agricultural growth would stimulate overall economic growth by promoting other sectors via production and consumption linkages (Islam, 1997; Reardon et al., 8


1998; Woldenhanna, 2000; Escobal, 2001; Adams, 2002). Rural development policies and programs in developing countries prioritized the income derived from agricultural production, while the activities related to the non-farm sector received little attention to date (Reardon et al., 1998; Woldenhanna, 2000; Escobal, 2001). As a consequence, it was believed that agricultural growth would be the main stimulus for overall economic growth by promoting other sectors (Islam, 1996; Lanjouw et al., 2001; Ashworth, 2005). Hill (1982), as cited by Reardon (1997), mentions that until the early 1980s the widespread view persisted that rural African farmers mainly undertook farm activities and less non-farm activities, except when they migrated out of the rural areas. According to Mills, this resulted in an initial neglect and underestimation of the rural non-farm sector. In developing countries, the impact of agriculture on RNFE occurs mainly through both production and consumption linkages (Haggblade et al., 1989; Islam, 1997; Lanjouw and Lanjouw, 2001; Blunch and Verner, 2006). Haggblade et al. (1989) provide evidence suggesting that consumption and production linkages explain the positive effect of agricultural growth on RNFE and non-farm income. They indicate that higher agricultural income increases households‟ demand multiplier effect across the rest of the rural economy, especially in closed economies. Many developing countries face closed economies due to high transaction costs, but this result was more pronounced in Asia compared to Africa. Ellis (1998) reviews earlier work done on rural growth linkages in developing countries and finds that agricultural growth provided the stimulus for the growth in non-farm activities mainly due to consumption linkages. Reardon et al. (2001) discuss the growth engines for the development of RNFE and notice the positive relation between the rise in farm output (and income) and demand for non-farm products through consumption linkages. Ashworth (2005) states that the initial development programs in Ethiopia expected agricultural growth to simulate non-farm sector development and RNFE creation as well as overall economic growth. The number of people directly dependent on agriculture for their income and subsistence reduces as employment in the non-farm sector is created.

2.3

The impact of the RNFE sector on farm activities

The impact of non-farm activities on agricultural production has not been a major topic in literature of developing economies. However, the RNFE literature has increasingly paid attention to the impact of non-farm activities on agricultural production during the last decades. Moreover, this switch in focus of literature is only recent, both because of 9


the

mastery

of

the

agricultural

sector

and

the

historical

disregarding

and

underestimation of the RNFE sector. Drivers behind the change in focus are the increasing acknowledgement of the significant role and importance of the non-farm sector in rural areas and the stagnation of the agricultural sector (Islam, 1997; Woldenhanna, 2002). Therefore, the rural non-farm sector has recently received increasing attention from both analysts and policymakers.

2.3.1

Underestimation of the RNFE sector

Previously, the common view persisted that rural growth in developing countries is entirely driven by agriculture and that non-agricultural activities are not significant. Davies et al. (2009) and Pfeifer et al. (2009) point out that previous literature assumed that farm households mainly rely on farm activities to attain their food security and that the contribution of the RNFE in rural development was mostly ignored. Only if the farm activities were not able to fulfill the household‟s needs, households‟ members would work on other farmers‟ land for wage or would be sent out as migrants for remittances. The bias against the rural non-farm sector has been further enhanced by the fact that the sector was initially a poorly understood component of the rural economy of developing countries and little was known about its role in the broader development process. Further, it was wrongly assumed that the non-farm sector was characterized by low productivity and production of low quality goods; and that it will vanish as a country develops (Lanjouw and Lanjouw, 2001). As a consequence, most government policies were did not support the non-farm sector, nor did the governments devote resources to upgrade the non-farm sector (Islam, 1997; Lanjouw and Lanjouw, 2001). In Ethiopia, Woldenhanna (2000) confirms that the non-farm sector has been ignored because of the power given to the agricultural sector. He states that “while substantial resources have been spent on agricultural research and extension to alleviate food shortage in the nation, no research and extension have been done on the issue of non-farm employment versus farm employment. Despite this fact, farmers are engaged in a variety of non-farm activities to diversify their income and to feed themselves during crop failures” (Woldenhanna 2000, p. 94). As a result, the impact of the RNFE on agricultural production and activities has been mostly ignored. Pfeiffer et al. (2009, p. 139) state that “as rural households become increasingly involved in non-farm activities, their on-farm production changes. The

10


ways in which non-farm activities transform agricultural production have not been a major subject of inquiry in the development literature”. Also Davies et al. (2009, p. 120) state “that the RNFE impact on agriculture, especially on micro level, has received relative little attention, although the importance of the RNFE has been proven and attention has been given to agriculture as a determinant of RNFE”. Stampini and Davis (2009, p. 177) review the previous literature and find that “the existing empirical literature on household-level linkages between rural non-farm activities and farming is limited and inconclusive”.

2.3.2

Importance of the RNFE sector

The literature and empirical research of the significance of the RNFE sector has increased, since the late 1980s (Davies et al., 2009). This literature provides evidence of the importance of the RNFE sector and thereby removes the bias against it. In this way, the significance of non-farm sector has been revealed and, to some extent, the attitude towards the RNFE has changed. A review of this literature can be found in Ellis (1998), Escobal (2001) and Lanjouw and Lanjouw (2001). In developed countries like the U.S., Fernandez-Cornejo et al. (2007) state that non-farm work has risen steadily and has become the most important component of farm household income, far more important than farm income. However, the impact of non-farm income is not that straightforward in developing countries (Reardon, 1997). It is assumed that farm activities remain important in rural households as they provide the main source of employment and income in rural areas of developing countries (van den Berg and Kumbi, 2006). For example, Reardon (1997) finds that there was little known about why or how much farm households engage in RNFE, especially in Africa. Carswell (2002) concludes that it is not the importance of the RNFE that is increasing, but the interest and recognition given to it. Nevertheless, it is a widely observed phenomenon that the rural non-farm sector in African developing countries is growing rapidly (Islam, 1997; Lanjouw et al., 2001; Reardon et al., 2001; Escobal, 2001). Reardon et al. (1994) are among the firsts to provide evidence from Burkina Faso that show that income from RNFE can be an important source of household cash. Islam (1997) reviews earlier studies and published data on the RNFE in developing countries and states that globally 23% to 50% of total households‟ income is made up by non-farm earnings. In Latin America, Asia and Africa respectively 26%, 28% and 14% of the rural labor force is employed in the non-farm sector. Reardon (1997) reviews earlier studies on income diversification, small

11


enterprises and inter-sectoral linkages in sub-Saharan Africa. He notices that local wage labor earnings in the non-farm sector were dominant in the majority of the case studies. The average share of non-farm income in total household income for the different case studies was 45%, ranging from 22% (Burkina Faso) to 93% (Namibia). Reardon et al. (1998) study the nature, importance and determinants of the RNFE in developing countries based on a review of about 100 household surveys. They show that the non-farm sector accounts for 25% of employment and that it generates 40% of household income in rural Latin America. The income shares for the RNFE accounts for 32% in Asia and 42% in Africa. Escobal (2001) concludes that in rural Peru almost 35% of the labor is allocated to non-farm activities and 51% of the income comes from the RNFE. He therefore suggests that the RNFE no longer has a marginal contribution. Lanjouw and Lanjouw (2001) review conceptual and empirical literature concerning the RNFE and focus on the experience in developing countries. They made a review of aggregate data of the RNFE based on than 60 surveys within the RNFE literature on developing countries. They conclude that the RNFE sector is substantial in many countries and has been growing over time. They stress that the percentage of RNFE may be underestimated because of temporary non-farm jobs during slack periods in agriculture or unremunerated work (especially done by women). De Janvry et al. (2005) use a detailed household survey dataset from Hubei province in China to analyze the impact of economic reforms. They state that farming remains the main income source for rural households. However, non-farm income is becoming more important. The study shows that 72% of the rural households have a non-farm income that accounts on average for 36% of total household income. The average income of households that only participate in farm activities is lower than the average income of households that have access to non-farm activities. Next to this, the authors find that non-farm income decreased income inequality and reduced rural poverty. Ruben and van den Berg (2001) use the national income expenditure survey from 1993 to 1994 in Honduras to study the role of non-farm income of rural farm households. They estimate that 68% of the rural

adults are somewhat involved in non-farm activities.

Furthermore, the income from non-farm wage en self-employment accounts for 1625% of the household‟s income. Kilic et al. (2009) review empirical evidence on the RNFE in a number of developing countries. They came across literature stating that RNFE income accounts for 50% and 35% of the total income in respectively Latin America and Africa. Comparing this with other literature, RNFE income accounts for 58% of the total income on a global scale. 12


Refer to Jayne et al. (2003), Otsuka and Yamano (2006) and Reardon et al. (2006) for more recent data on the increased importance of non-farm income and patterns of RNFE in case studies from Asia, Latin America and Africa. Carswell (2002) finds evidence that non-farm and off-farm activities are carried out by a significant proportion of adults in Wolayat, southern Ethiopia. Trading, a form of self-employment, seems to be the most important activity, carried out by 14% of the adults. 84% of the participants in non-farm activities does this at least once a week, while for more than 50% the job is active throughout the year.

2.3.3

Participation in non-farm activities

The actual participation of households in non-farm activities depends on the incentive and capacity to participate (Reardon, 1997) and the occurrence of entry barriers (Dercon and Krishnan, 1996). Two opposite forces will determine the households‟ motif to diversify its income sources. Push factors (or necessity) are the involuntary and sometimes desperate reasons to diversify; they include income risk management, coping mechanisms, diminishing or time-varying returns to productive assets, longterm constraints or smoothing household consumption (Ellis, 1998; Ellis, 2000b; Barrett et al., 2001; Reardon et al., 2001). Voluntary diversification is opted for accumulation objectives (Reardon et al., 2006) or with the goal to maximize profits (Kilic et al., 2009). Reardon et al. (2000) suggest that poor households will be attracted to low-risk RNFE in order to decrease income variability, even though they might have low returns (Reardon et al., 2006). Wealthier households will be less diversified in their income sources because risk aversion motivation declines as wealth increases under perfect market conditions. Pull factors will attract households to the non-farm sector when the non-farm activities offer higher returns compared to farming. For the poor, their relative return

is higher because they have a

lower

reservation

wage

(Woldenhanna and Oskam, 2001; Reardon, 1997). However, rich households can be pulled into the RNFE to maximize their profits (Kilic et al., 2009). According to Davies et al. (2009) the ability of households to act on these incentives depends on a set of capacity variables. On the micro level, these variables include the vector of assets such as physical, social, human, and organizational capital, and liquidity from sources such as cash cropping. On the meso level, these variables contain access to local assets such as hard infrastructure (roads) and soft infrastructure (financial services). The access to credit and financial markets proofs to play a crucial

13


role (Dercon and Krisnan, 1996; Barrett et al., 2001b; Reardon et al., 2006; Davies et al., 2009). However, high return non-farm activities have certain requisites to enter these activities. These requirements include among others education, skills and investments. Activities with no or low entry barriers generally offer low returns, while activities with significant entry restrictions have higher returns. Participation in these lucrative nonfarm activities is thus conditioned by the possibility to overcome the required entry barriers. When entering a non-farm activity requires substantial investments, liquidity constraints will hamper households with restricted assets to enter these activities (Dercon and Krishnan, 1996; Woldenhanna, 2000; Barrett et al., 2001; van den Berg and Kumbi, 2006). The ability of households to overcome these entry barriers depends on their capacity variables (Davies et al., 2009). Collateral requirements, market imperfections and differences in repayment capacity make credit constraints more severe for poor households than for rich (Woldenhanna, 2000). As a consequence, poor households will be unable to overcome these entry barriers while wealthier households will have less problems in diversifying their income. Not everything is decided based on motivation only. Participation is also influenced by capacity and entry barriers. As a result, the opposite outcome of what would seem logical could be observed. Poorer households might be motivated to diversify their income for risk aversion, but entry barriers prevent this (Dercon and Krishnan, 1996; Dercon, 1998; Barrett et al., 2001; Escobal, 2001; Davies et al., 2002) resulting in participation in non-farm activities with lower returns and higher risks (Sumberg et al., 2004) and thus less diversified income sources. On the contrary, rich households may have less incentive to diversify, but they find themselves in a superior position to make investments to overcome entry barriers (Reardon et al., 2000). Even though the marginal value of non-farm activities exceeds the reservation wage, entry barriers will prevent poor households to enter. They are forced to participate in low-return non-farm activities for which the entry barriers are low, most probably wage employment. Wealthier households will face less binding credit constraints and will engage in highreturn activities with higher entry barriers, mostly self-employment (Woldenhanna and Oskam, 2001). Rich households have a great freedom to choose among a wider range of non-farm activities, while poor households are relying on unskilled labor and activities with low barriers and therefore low returns (Sumberg et al., 2004).

14


2.3.4

The nature of the impact of non-farm activities

As it is suggested that the RNFE has a substantial impact on agriculture, the question emerges how participation in non-farm activities influences the households‟ farm decisions. Ellis (1998) finds that possible adverse (competition for labor and credit) and beneficial (reinvestments and insurance) impacts of non-farm income on household level are suggested in the literature. The net impact of non-farm activities is however highly specific in time and space. Reardon et al. (1994) did research in Burkina Faso and suggest that non-farm activities influence farm activities indirectly through capital investment and input acquisition. They hypothesize that non-farm activities can either draw resources away from agricultural production or stimulate reinvestments in farm activity. Whether non-farm activities are complementary or competing to farm activities depends on both physical, economic and institutional factors and determinants of households‟ allocation choice of resources over farm and non-farm activities. On the one hand, Woldenhanna (2000) states that productivity can be decreased due to lack of specialization, management inefficiency and competition for inputs. On the other hand, productivity can be increased because non-farm activities increase manager skills, reduce land pressure and provide credit for farm investments in case of credit or capital constraints. It is concluded that income diversification can have both a positive and a negative impact on farm income and its net impact cannot be determined a priori. Pfeiffer et al. (2009) suggest that as market imperfections occur, agricultural income can be affected by non-farm income in different ways. A first direct impact is through the loss of labor, because agricultural inputs must be sacrificed when household members are participating in non-farm activities. Under perfect labor market conditions, households are able to hire perfect substitutes for missing labor on the farm and agricultural production can be maintained. However, developing countries face labor market constraints and non-farm activities will reduce the labor input in agricultural activities. The non-farm income affects agricultural production in a direct and indirect way when credit and liquidity markets imperfections occur. The direct effect of non-farm income is the relaxation of household budget constraints and the increase of the purchase of normal goods. The indirect effects of non-farm are more complex: non-farm activities provide households capital, security and liquidity to invest in technology or farm inputs.

15


2.3.4.1

Competing linkages

Many authors recognize that the impact of the RNFE on the agricultural sector can be either complementary or competing. Competing linkages occur if households‟ decisions about non-farm and farm activities are made jointly and households face limited resources and inputs such as capital and labor (Reardon et al., 1994). Participation in non-farm activities requires reallocation of those limited resources and results in an inevitable withdrawal from the farm. For example, RNFE consumes agricultural labor so that the labor availability on the farm decreases. Another consequence of the competing nature is that the factor bias of farm technology can be affected (Reardon et al., 1998). If non-farm activities have a higher return and agricultural investments are risky, investments in land conservation and technology could be impeded (Reardon et al., 2000). According to Reardon et al. (2001), the high productivity and higher returns of non-farm activities lead to concentration of resources in the RNFE. As a consequence, participation by households in non-farm activities can hamper their own farm productivity (Ellis and Freeman, 2004; Phimister and Roberts, 2006). Agricultural production

and

farm

income

will

decrease

and

thereby

hampers

agricultural

commercialization or modernization (Ruben and van den Berg, 2001). The amount of non-farm activities performed and the importance of each activity depends on the relative returns to farm versus non-farm activities and their input requirements. There is empirical evidence of competing linkages between farm and non-farm activities. Goodwin and Mishra (2004) studiy the relationship between farm efficiency and non-farm labor supply in the U.S. They report that high involvement in non-farm activities decreases farm efficiency. They suggest that this inverse relationship can be explained by the hypothesis of Smith (2002, as cited in Goodwin and Mishra, 2004) “Does off-farm work hinder smart farming”. Smith suggests that non-farm activities have strong implications for the efficiency of farming, because less attention can be devoted to issues important for farm productivity such as adoption of best management practices. As Goodwin and Mishra (2004) find evidence that non-farm labor allocation and farming efficiency are jointly determined, their results show that farmers who are more efficient on the farm (this is a higher implicit farm wage) tend to allocate less labor to non-farm activities. This is in line with the results of Chang and Wen (2010) in Taiwan. They suggest that farmers without RNFE are likely to have better knowledge of and pay more attention to farm management. Therefore, their use of inputs is more productive than farmers with access to RNFE. Huang et al. (2009) study the linkages between non-farm labor markets and the on-farm labor allocation to production of fruit crops in the Shandong Province of China. Among other things, the authors conclude

16


that the intensity of fruit production is reduced with increased RNFE. The fruit production sector is associated with lower entry barriers than the RNFE, attracting individuals that are unable to engage in the RNFE because of high age or low education. Kilic et al. (2009) use data from the 2005 Albania Living Standards Measurement Survey to explore the overall impact of the RNFE on agricultural expenditures and technical efficiency of rural households. They find that Albanian households do not use non-farm

earnings

to

invest

in

time-saving,

efficiency

increasing

agricultural

technologies. Less expenditures are made in productivity enhancing crop input investments. The authors suggest that the existence of sectoral problems is driving income diversification out of crop production but towards livestock production. In the Ethiopian context, Holden et al. (2004) find that non-farm activities negatively influence farm production in two ways. First, better access to the RNFE reduces total agricultural production and farm inputs expenditure. Second, rural non-farm income reduces households‟ incentive to invest in conservation measures, increasing land degradation and soil erosion. Not only agricultural intensity is decreased, a drop in farm productivity is suggested. This has important consequences for rural development, as the need to import food in the area increases.

2.3.4.2

Complementary linkages

Complementary linkages indicate that non-farm income acts as an important source of capital and cash in the households‟ total budget. This extra budget could be used by the household to invest, hire labor, purchase inputs or finance consumption. Reardon et al. (1994) suggest that consumption and investments compete for the use of household income and therefore household decisions affect the nature of the consumption and investment linkages. It is not clear how the households allocate their resources over consumption and investments. According to Woldenhanna (2000), Ruben and van den Berg (2001) and Pfeifer et al. (2009), non-farm income is important to satisfy consumption requirements when agricultural production cannot provide food security. It can, however, also be used to finance farm activities. Non-farm income might have a positive impact on agriculture as it can be used to buy food which frees up other resources that can be invested in farm activities (Kilic et al., 2009; Woldenhanna, 2000) or to buy farm inputs (Ruben and van den Berg, 2001). However, non-farm income might also be used outside the farm because of complex agricultural sectoral problems that cannot easily be surmounted. In such case, finance investments in the RNFE, education and training or to migrate out of the rural sector (Kilic et al., 2009).

17


Complementary linkages have been suggested earlier. Reardon et al. (1994) show that RNFE can provide an important source of cash for households in Burkina Faso. Reardon et al. (1998) find that income from agroindustrial activities influences farm households‟ capacity to invest in farm input, capital and appropriate technology. It is suggested that non-farm activities are potentially important for long-term food security because it stimulates spending on farm inputs and thereby farm productivity. The authors provide evidence from Burkina Faso, the Niger and Senegal, indicating that non-farm income is usually the main source of cash for the purchase farm inputs. The authors suggest a dynamic effect as non-farm income surplus is invested in farm inputs, creating capital that substitutes for labor and thus reduces farm labor demand. Lanjouw and Lanjouw (2001) suggest that non-farm income leads to higher average income from agriculture in two different ways. First, technologies or crop varieties with higher productivity are often related to higher variability. Access to an alternative stable income source will facilitate the adoption of these technologies or varieties. Second, if access to low cost credit is absent, additional income sources can stimulate the investments in farm inputs. Davies et al. (2002) suggest that the growth linkages that arise from a first round of agricultural boom could be reinvested in capitalizing agriculture. Moreover, the RNFE sector can provide opportunities for households to reduce agricultural risks and stabilize their income (Phimister and Roberts, 2006). Chang and Wen (2010) study the impact of non-farm wages on agricultural efficiency and production risks in Taiwan. They use a nationwide survey of rice farmers to investigate the efficiency and yield difference between households that participate in RNFE and households that do not. It is noticed that the resource use for both households is different and that RNFE does not necessarily induce technical inefficiency. In the lower percentiles of the efficiency distribution, farmers with RNFE are more efficient than farmers without. Lien et al. (2010) analyze the determinants of the nonfarm work decision and its influence on farm performance, based on panel data from Norwegian grain farms from 1991 to 2005. They did not observe a negative impact of non-farm work on farm efficiency or production. They show evidence that households that participate in RNFE could increase production to some extent. This positive effect first increases but then decreases with increasing hours spend on non-farm work.

18


2.4 2.4.1

Investment linkages Definition

While the previous section suggests competing and complementary linkages possible to occur, we now turn our focus to investment linkages. In the context of investment linkages, farm households selectively engage in the rural non-farm market to earn an additional income. This supplementary income is a source of liquidity and credit for the household. Moreover, Reardon et al. (1994, p. 1175) state that “non-farm income can also serve as collateral and thus facilitate access to credit”. The additional source of credit and liquidity can be used by the household to finance risky investments in agriculture (Barrett et al., 2001; Kilic et al., 2009; Pfeifer et al., 2009). Households use their non-farm income sources to finance farm investments, to self insure or purchase cash inputs for agricultural production (Reardon et al., 1994; Ellis, 1998; Barrett et al., 2001; Davies et al., 2002; Reardon et al., 2006). Oseni and Winters (2009) and Pfeifer et al. (2009) expect the surplus of cash generated by non-farm income to directly influence the purchase of agricultural inputs. Under the condition that the additional source of credit is invested in durable investments and farm input purchase, households are in the position to increase their farm expenditures and expand investments (Reardon et al., 1994; Oseni and Winters, 2009). The non-farm income thereby improves farm productivity and boosts the agricultural production and farm income in the long or short term (Pfeiffer et al., 2009). The former refers to the purchase of inputs like fertilizer, while the latter refers to the adoption of productivity increasing technologies like improved seeds. The ultimate result is that RNFE could be an important driver to foster farm commercialization, modernization or agricultural diversification into higher value activities or agricultural production intensification (Davies et al., 2009; Oseni and Winters, 2009).

2.4.2

Liquidity and credit constraints

Investment linkages are likely to occur when households are pushed into non-farm activities because they face credit and liquidity constraints or access to other sources of cash is not available (Oseni and Winters, 2009; Pfeifer et al., 2009). These credit constraints hamper households‟ investment in production and productivity increasing technologies and inputs. As a result, credit restrictions drive households to self-insure or finance input expenditure with their own credit (Reardon et al., 2006; Oseni and Winters, 2009). Participation in non-farm activities could be an important way to 19


generates cash and substitute for the absence of credit or the high cost linked with borrowing credit. It implies that households diversify their livelihood into non-farm activities to overcome some of the credit and insurance constraints. How reasonable is it to expect that credit constraints determine the linkage between farm and non-farm activities? Households‟ engagement in RNFE is conditioned by their motives (push or pull), capacities and the existence of entry barriers, as explained in section 2.2.3. While it is viable that a combination of motives drive participation in the RNFE, some authors suggest that the most important reason for households in rural areas of developing countries to participate in non-farm activities is the relaxation of constraints (Oseni and Winters, 2009; Pfeifer et al., 2009; Stampini and Davis, 2009). In Tigray, Woldenhanna (2000) suggests that if a binding liquidity constraint is present, these constraints will induce farm households to participate in non-farm activities to help them finance farm input and hire farm labor. The constrained accessibility of credit is driven by market imperfections (Oseni and Winters, 2009; Pfeifer et al., 2009; Stampini and Davis, 2009). Credit or insurance market failures imply low availability and access to credit which hampers households‟ investment in production and productivity. Reardon et al. (2004) already suggested that the RNFE is likely to have a positive impact on farm activities in cases where the rural markets do not function properly. This is in line with the arguments given by Kilic et al. (2009), who suggest that “given the imperfect covariance between farm and nonfarm income, non-farm earnings may help households overcome credit and insurance market constraints by providing liquidity that can be utilized for productivity enhancing input purchase and long-term investments in agriculture (Kilic et al., 2009, p. 140)”. Oseni and Winters (2009) and Pfeifer et al. (2009) state that market imperfections are likely to occur. Moreover, several studies suggest the existence of imperfect markets in developing countries (Ellis, 2000b; Barrett et al., 2001). De Janvry and Sadoulet (2003) review literature dealing with market imperfections and study households‟ behavior under market imperfections. They conclude that market imperfections are present. Pfeifer et al. (2009) find evidence that labor and credit markets are imperfect in rural Mexico. They suggest that either liquidity constraints or the presence of transaction costs in markets impede access for small farmers to hired labor. Oseni and Winters (2009) state that in rural areas of developing countries, credit and insurance markets do not function properly or even non-existent in some cases. The lack of access to credit hinders households‟ investing possibilities and consumption smoothing. In conclusion, market imperfections exist, and merely occur in credit markets. 20


Woldenhanna (2000) assumes that farm households in Tigray are not fully integrated into the market. The survey respondents stated that their demand for credit is not fully satisfied. Moreover, private supply of credit and consumption credit is almost absent. The available credit is supplied by public organizations and linked with participation in extension activities. Pender et al. (2006) state that credit is practically absent for smallholders in the East African highlands. However, there is limited credit available through cooperatives, private firms and government support programs, but there are almost no opportunities to borrow from formal financial institutions such as commercial, insurance or construction banks. Moreover, these organizations require collateral and involve time consuming screening processes (Woldenhanna, 2000). Households are therefore left with uncertain loans provided by credit schemes or small traders.

2.4.3

Evidence

In response to the awareness that the impact of the RNFE on agricultural production could be mainly through investment linkages, the evidence proving the existence of investment linkages is growing. Phimister and Roberts (2006) investigate the extent to which the RNFE changes the intensity of agricultural input for 2,419 farmers in England. Their results suggest that fertility intensity declines while conservation inputs increase with RNFE. Ellis and Freeman (2004) make a comparison of the rural livelihoods in Uganda, Kenya, Tanzania and Malawi. One of their findings is the positive relation between non-farm employment and agricultural production, which can be explained by the availability of cash to invest in farm inputs or conservation practices and to hire labor. De Janvry et al. (2005) find that participation in RNFE has a positive spillover effect on household agricultural production. They use a household survey dataset from the Hubei province in China and study the influence of the RNFE on households‟ income, poverty and inequality. Deficient rural credit markets force households to engage in RNFE, enhancing on-farm investment capacity, mitigate income fluctuations and function as insurance system. Ruben and van den Berg (2001) analyze the role of the RNFE of farm households, using a national expenditure and income survey from 1993 to 1994 in Honduras. Among other things, they conclude that rural non-farm income can be used as a capital source to finance investments used to optimize yield and labor productivity. This linkage is most pronounced if credit constraints occur as non-farm activities can be considered the collateral for borrowing. Anriquez and Daidone (2009) explore the effect of the

21


growing RNFE on farm diversification, household input demands and production efficiency in Ghana. The linkages between agricultural and RNFE are measured by a household level input distance function. They conclude that the expansion of the RNFE increases investments in most agricultural inputs. Stampini and Davis (2009) examine the use of agricultural inputs as a result of the relationship between participation in non-agricultural labor activities and farming production decisions. The authors use a longitudinal survey from 1993 to 1998 in Vietnam. They state that households that engage in non-agricultural labor activities spend significantly more on agricultural inputs. In case of incomplete credit markets, participation in RNFE relaxes credit constraints through credit provision for purchases of agricultural input. Also, the share of the RNFE in total rural income increases over the same period. Maertens (2009) mentions that access to low-skilled RNFE has alleviated farmers‟ liquidity constraints, resulting in the increase in smallholder agricultural production. The author uses a household survey dataset in the main horticulture region in Senegal. The results prove that the income from employment in the horticulture export industry is used to invest in the farm, resulting in higher expenditures, higher farm incomes and larger farm sizes. Entry barriers in credit and input markets can be tackled by the RNFE. Hertz (2009) documents the relationship between non-farm income and agricultural investments in Bulgaria. More specifically, the influence of non-farm wage employment and pensions on expenditures, working capital and investments in livestock is studied. Farm expenditure is regressed against RNFE and other variables in a two part model. This research points out that farmers fund farm expenses from nonfarm income and use credit for consumption. The latter implies that if credit is available or the access to credit would be enhanced, the borrowed funds are not used to finance agricultural investments but used for consumption purposes. Pfeiffer et al. (2009) use the 2003 National Rural Household Survey Dataset from Mexico to explore the effect of the RNFE on agricultural production activities. Instrumented variable models were used to study whether households with and without RNFE differ in farm decisions. The authors state that RNFE negatively influences the family labor in crop production and farm output, but increases the use of purchased inputs. As access to RNFE is increased, households will earn more and this induces a shift out of cropping. This effect is caused by both the relaxation of the credit constraint but and the effort by households to seek for family labor substitutes when the returns of non-farm work increase. Finally, Woldenhanna (2000) investigates the impact of non-farm employment and income on farm households and agricultural production. His analysis uses a farm household model with liquidity constraints build upon a farm 22


household survey from Tigray, northern Ethiopia. He finds that farm households with more diversified sources of income have a higher agricultural productivity. Non-farm income surplus helps households to finance farming activities such as the investment in labor and inputs (seeds, fertilizer and pesticides). When non-farm income increases by 1% expenditures on variable input will increase by 0.43%. Hence, the author concludes that non-farm income can be complementary to farm income if households face borrowing constraints.

2.5

Virtuous circle

The existence of a virtuous circle is important for rural development. When households face liquidity constraints, they will be pushed into non-farm activities. Promoting the latter makes access to non-farm employment easier for households. In this way, households increase their income and diversify their income sources. This income surplus will then be reinvested in the farm, facilitating farm modernization and commercialization. It is assumed that this will increase agricultural productivity and demand for agricultural labor which ends the virtuous circle (Davies et al., 2009). Investment linkages should enhance rural modernization, commercialization and specialization, because the increase in agricultural productivity enables households to participate in the RNFE without lowering agricultural production (Kilic et al., 2009). The hypothesis of such a virtuous circle is however not new. John Mellor, (undated) cited by Lanjouw and Lanjouw (2001), observes a virtuous cycle emerging whereby increasing agricultural productivity and farm income would be magnified by multiple linkages with the RNFE. Increased agricultural income would enlarge demand for goods and services, but also potential linkages through the supply of capital and labor were assumed to occur. The increased agricultural productivity will reallocate labor or increase income so that the new agricultural surplus could be used for investment in the RNFE. The assumed growth in the RNFE would in turn stimulate further growth in agricultural productivity via lower input costs and the fact that profits are invested back in agriculture and technological improvements. Mellor believes the income and employment in the agricultural and non-farm sector are mutually reinforced when the both sectors grow. If the virtuous circle occurs, it is important to promote the access to non-farm activities and to make sure households can enter the RNFE sector. The existence of entry barriers hinder poor households to participate in certain non-farm activities. These 23


entry barriers can be present in the form of abilities or credit (Dercon and Krishnan, 1996). Some non-farm activities require better education and specialized skills. Credit constraints hinder poor households to make the required investments in capital and equipment necessary for the participation in

the RNFE. As a result, income

diversification in high return niches within the RNFE will be more difficult to obtain for poor households than for rich households. More educated, skilled and better endowed households will have better access to high-return non-farm activities (Dercon and Krishnan, 1996; Barrett et al., 2001; Woldenhanna and Oskam, 2001). The existence of entry barriers hinders households to participate in non-farm activities, and as a result the gained non-farm income cannot be used as a liquidity source for farm investments. The occurrence of entry barriers determines whether investment linkages are likely to occur. Barrett et al. (2001, p. 324) suggest that “those with the least agricultural assets and income are typically also least able to make up this deficiency through non-farm earnings because they cannot meet the investment requirements for entry into remunerative non-farm activities”. Davies et al. (2002) point out that the lack of access to credit can hamper the linkage between RNFE and agriculture in several ways. First, households‟ capacities to expend their current activities

may

be

limited,

undermining

the

households‟

ability to

exploit

the

opportunities for selling to agribusiness. Credit is necessary to enter in these activities, which will foster the development and expansion of the RNFE. Access to credit will not be a guarantee for this, but it will facilitate the process. Second, credit is necessary to adopt new crops and technology in order to produce the required processing quality. An interesting study in the Tigray region of Ethiopia has been conducted by Woldenhanna and Oskam (2001). They prove that increasing access to RNFE can expand the economic activity in Tigray, but due to entry barriers, relative wealthy households will dominate the most profitable RNFE such as petty trade, masonry and carpentry. They conclude that constrained access to credit and liquidity is the most important underlying factor that hinders participation in RNFE. However, van den Berg and Kumbi (2006) present evidence that entry barriers for the poor to participate in non-farm sector are low and they therefore participate actively in non-farm activities in the Oromia state. The non-farm sector provides an alternative to use labor excess from agricultural production. They conclude that further growth in the non-farm sector will not increase income inequality, as is sometimes assumed.

24

Chapter 3: Methodology

3

METHODOLOGY

3.1

Study and survey area

The survey area is located in Tigray, the most northern Federal State of Ethiopia (Figure 3.1). Tigray is divided into six zones, which are further subdivided into 34 woredas. Each woreda contains a number of tabias which are regarded as the lowest administrative hierarchy (Negash, 2008). According to the Central Statistical Agency of Ethiopia (CSA), Tigray covers an area of more than 50 thousand km² and has an estimated population of 4.6 million with an average annual population growth rate of above 2.6% (CSA, 2008; cited by Negash, 2008). The altitude in the region ranges between 300 meters above sea level (masl) in the east and more than 3,000 masl in the north and central part, covering three agro-climatic zones: lowland (kolla: below 1,500 masl), medium highland (woina dega: between 1,500 and 2,3000 masl) and upper highland (douga: between 2,300 and 3,200 masl) (Kidane, undated).

Figure 3.1: Map of Ethiopia (small) and Tigray Regional state source: Figure created by the local organizing committee of the International Congress Water 2011 [online], available at http://ees.kuleuven.be/water2011/excursions/index.html [date of search: 13/04/2011]

25


The analysis of the farm/non-farm linkage is based on the survey of rural households conduced in 2009 at a sample of 734 households in the Geba catchment. The research site covers 4600 km² and contains 8 woreda and 168 tabias in the Tigray region. Using a structured questionnaire, a survey was conducted in Tanqua, Samre, Atsbi and Wukro woreda (Figure 3.2). The woredas were not chosen at random as it would be impossible to survey the extensive area and the available time and budget were limited. Moreover, it is in line with the research areas of the MU-IUC Collaboration Program. The woredas in the Geba Catchment were stratified into three agro-climatic zones. Based on population, four woredas were picked randomly: one from the highland, two from the middle-highland and one from the lowland. By this, the survey was designed to be representative for the whole region, as the contrasting socio-economic (access to market in woreda and Mekelle) and agro-climatic (altitude, temperature and rainfall) zones were reflected. From each woreda, samples were drawn randomly to constitute sample research sites. As a result, from each Woreda two Tabias were selected randomly (so 8 Tabias in total): Rubafeleg, Barka, Negash, Adiqsanded, Addisalem, Andewoyane, Lemlem and Hadnet. Households were drawn from each tabia based on their respective population size and participation in agricultural extension service, with densely populated tabias getting a relative higher number of households quota.

Figure 3.2: Research area Source: BoANRD 2004 (Bureau of Agriculture and Natural Resource Development)

26


Next to the variability of rainfall and susceptibility to droughts, the fragile Ethiopian highlands still suffer from natural soil erosion depletion and other land degrading processes, low soil fertility and aridification (Pender, 2000; Block and Webb, 2001; Holden et al., 2004; Ehui and Pender, 2005). These natural problems are enhanced by population pressure, intensive land (over)use by livestock grazing and cultivation, crop/animal pests, cultivation of marginal lands on steep slopes and deforestation (Woldenhanna, 2000; Negash, 2008; Tesfay, 2009). Furthermore, the region has suffered from droughts, famines, food insecurities, civil wars, political conflicts, border conflicts, poor governance, underdeveloped infrastructure, restricted access to formal financial institutions and underdeveloped education or training (Pender, 2000; Woldenhanna, 2002; Negash, 2008; Tesfay, 2009). This all resulted in environmental and

ecological

problems,

degraded

and

fragmented

land

and

poor

resource

management and hence even poorer performance of agriculture (Woldenhanna, 2000). Farm households participate in a wide variety of both farm and non-farm activities. Subsistence agriculture1 remains the main occupation of the rural people in Tigray which is highly dependable from variability in rainfall and recurrent droughts (Tesfay, 2009). Teff is the main staple food and occupies the largest share of cultivate land, but maize production is increasing in importance (Pender et al., 2006). Woldenhanna (2002) finds that in 1996, 78% of the households was engaged in mixed farming, while only 19% was engaged in cropping only and 3% in livestock husbandry only. Next to this, 81% of the rural households participated in various non-farm activities. He also shows that, on average, farm production accounted for 57% of the total income and hence non-farm activities 43%. In 1996, the average farm size was 0.97 ha and 70% of the households owned less than one ha (Woldenhanna, 2002). The average farm size now ranges from 0.6 ha in the eastern part to 1.2 ha in the western part and average landholdings generally decrease with altitude. Agricultural production is below the natural average, on average below one ton per hectare, even in good years (Woldenhanna, 2002). Yields have increased only marginally because of increased maize yields and expansion of the fertilized area (Dercon and Christiaensen, 2010). As a result, agricultural production is not able to support farm households of five to six members for longer than six months (TBORAD, 2008; as cited in Kidane, undated). Deforestation, continuous cultivation,

1

mixed farming, subsistence oxen plough single cropping cereal crop dominated combined with livestock

rearing production is the typical farming system (Kidane, undated)

27


soil nutrient depletion, using dung and crop residues as fuel and severe soil erosion have resulted in low fertilizer application use (below 15kg/ha). Farming systems in Tigray are characterized by traditional technology use, mainly rain-fed land and animal traction. During drought years, people become fully dependent on food aid (Kidane, undated). It is assumed that further increase in agricultural employment is difficult because of the above mentioned constraints (Woldenhanna, 2002). In 2003, the Agricultural Sample Enumeration (CSA, 2003; as sited in Kidane, undated) reported the total livestock in Tigray to be 10.8 million animals of which 2.8 million cattle, 1.8 million goats and 0.7 million sheep. Households on average own 3.2 units of cattle. This makes the Ethiopian herd to be the largest in Sub-Saharan Africa (Pender et al., 2006). The average milk production is low, about 1.5 liters per day per cow. Woldenhanna (2002) states that livestock plays a secondary role, but oxen plough stays very important. Also, the lack of pasture, fodder and scarcity of veterinary clinic constrain livestock development. Moreover, after a drought, the revival of livestock farming is difficult because a significant number of the livestock dies. There are almost 230,000 beehives in Tigray. The annual honey production is estimate at 19,000 ton of which 98% comes from traditional beehives.

3.2

Survey design

The survey was conducted under the supervision of Kidane M.G. Egziabher, as part of his Ph.D. to study the impact of agricultural extension on household income and income diversification in 2009. Refer to Kidane (undated) for a description of the methodology used in this research. Both qualitative and quantitative methods were used to collect data. The qualitative method included in-depth semi-structured interviews and focus group

discussions

with

policy

makers,

agricultural

researchers,

development

practitioners, farmers and development agents. The quantitative method included 500 rural household surveys and 200 household choice experimentation surveys using questionnaires. The dataset contains detailed information collected using questionnaires about the households‟ head individual characteristics (age, gender, education, …), households‟ characteristics (family size, access to irrigation, …), households‟ access to capital (finance, landholdings, livestock, fixed, …), types of households‟ occupation (migration, transfer, non-farm and farm), local conditions (regional variables, access to markets and capital, …), investments (durables and inputs) and consumption (food and non28


food). The questionnaire designed for the household survey was planned to be collected in

two rounds and was administered for each of the selected head of household to

collect the relevant information by deploying trained enumerators. However, this research only uses first round data (cross-sectional data set). Separate checklists were developed to gather secondary information. Focus group approach (administered by the researcher) was used to collect in-depth information through discussion on a wide range of issues related to households‟ recruitment criteria to extension package programs, technology selection, farms and policy makers perception regarding programs performance and possible perspectives as how to improve the lives of farming communities. Qualitative

data

included

information

about

extension

(institutional

linkages,

coordination, research outputs and priority setting processes), farmers‟ satisfaction level (extension, marketing and credit systems), development agents‟ skills and farmers‟ attitude. This qualitative information was gathered through desk review of government policy documents, previous studies and related information available in the study area. Relevant information for the desk review was acquired from the Bureau of Rural Development and Agriculture, Regional Agricultural Research Institute, Regional Marketing Agencies, Food security Office, Micro-Finance Institutions, Local Government administrators and Cooperative Development Offices and ngo‟s active in the area. The validity of the research questionnaires and checklists was verified through pilot tests. Based on the results of pilot test, to revise and modify the questionnaires were revised and modified. To improve the quality of the survey, qualified enumerators were recruited and trained. Despite this careful data collection it is reasonable to expect some underestimation of the actual income, expenditures and assets. This is due to the sensitive nature of the information on income and expenditure, the limited capacity to recall on all the transactions by the respondents and unwillingness of the respondents to disclose to outsiders. These are however natural shortcomings and the data will give a reasonable picture of the research sites.

3.3

Data analysis

Our primary interest lied on the analysis of the impact of non-farm income (x) on farm investments (y). We First defined these investments as the total household expenditure on farm activities. These expenditures included both investments in durables and farm input use. Afterwards, we narrowed our analysis to both types of investments. 29


Households‟ durables included land and water conservation measures, animals, buildings and equipment. We expanded our analysis to these different types of farm investments. Farm input use included local and improved seeds, fertilizer and labor. In the empirical specification of the econometric model used, we used the general term „investments‟ and the latter might refer to either of both investments. Our most important independent variable was non-farm income. This was defined as the income earned by farm households by participating in the RNFE sector. Next to this, we included a large set of observable variable in the model. These control variables include (1) household head age, sex and schooling; (2) households landholding, animal assets and fixed assets; (3) distance to Mekelle and (4) edir membership, access to irrigation and number of adult working labor forces. Non-farm activities were defined as rural non-farm labor activities and therefore consisted only of wage income and self-employment. We thus not included migration and transfer income in the non-farm income. The rationale behind this definition is that transfer income is not a real non-farm labor activity as it is support received from the government or ngo‟s. As migration income is not influencing households‟ decisions during the time of production season, we prefered not to include it in our analysis. Migratory activities contrast with rural activities because households‟ allocation of labor resources during the production season does not affect migratory activities. It is assumed that wage and self-employment activities are important for farm households. In order to avoid sample selection bias we added a positive constant to the non-farm income variable and take logarithms: ln(x+c). The same is done for farm investments, because the logarithmic form is also necessary to correct for the clear positive skewness at the right side of the income and investment variable. This prevented outliers to influence our estimates, especially in small samples.

3.3.1

Ordinary Least Squares estimation

The effect of non-farm income on farm investments was analyzed by comparing households that have access to non-farm income with those households that do not, while controlling for a set of observable factors. The systematic differences between these two groups of households were captured through the inclusion of these observable individual and household characteristics. The following regression equation estimates the effect of non-farm income on farm investments, and is referred as the structural equation: y=β0 +β1 x+βi ni + u

(eq. 1)

30


where β0 is the intercept, β1 is the parameter associated with non-farm income, βi the parameter of the ith explanatory variable ni and u the error term. Assume that the vector Ni of explanatory variables is exogenous, but x is correlated with u and is therefore an endogenous explanatory variable, this is Cov(x,u)≠0. The latter condition causes the Ordinary Least Squares (OLS) estimation of eq. 1 to be inconsistent (Wooldridge, 2002b).

3.3.2

Omitted variable bias

Cov(x,u)≠0 is caused by the fact that u contains omitted variables that are correlated with x, but not with Ni. This means that unobserved variables are present that influence both our dependent variable farm investments and our most important explanatory variable non-farm income. Excluding them from the regression analysis makes x correlated with u and thus the zero conditional mean assumption to fail. As a result, the coefficients in a simple OLS regression will be biased. This is termed omitted variable bias or unobserved heterogeneity. The most optimal solution to this problem is the use of panel data to control for time-invariant unobserved households-level fixed effects, find a suitable proxy variable for these unobserved variables or use the instrumental variable (IV) method. In the absence of the first two possibilities, we will use the IV regression model. As many researchers dealing with this problem have pointed out: the solution is to cure the disease rather than prevent it (Wooldridge 2002b). It is most likely that non-farm income is correlated with unobserved variables that also influence farm decisions. Several different possible sources of omitted variable bias are found in the literature, such as Hertz (2009), Kilic et al. (2009), Oseni and Winters (2009) and Maertens (2009). This literature has pointed out that it is impossible to determine a-priori the direction and magnitude of the omitted variable bias. Farm and non-farm income activities are complex related and our data set includes many sources of non-farm income and farm activities. Many potential effects could therefore be present and it is impossible to assume one mechanism to be dominant. It is more likely that several factors will have a plausible effect. Entrepreneurship and general ability are assumed to have a positive effect on most economic activities, including both farm investments and non-farm activities. Economic motivation might drive household members to seek for non-farm employment as well as to invest in their farming activities. Both unobserved variables would lead to an upward bias in the estimated effect of non-farm income on farm investments. However,

31


economic motivation can also force households to gradually leave farm activities as non-farm activities have a better pay-off and therefore spend less money on farm activities. Also risk aversion might have such ambiguous effect. On the one hand, it may stimulate households to participate in non-farm activities in order to finance farm investments. By this, they reduce the risks associated with farm activities. On the other hand, risk aversion might lead to a downward effect: households are avoiding costly investments in farm activities and focus on non-farm activities to diversify their income sources. Households that face more constraints to agricultural production might allocate more labor to non-farm activities.

3.3.3

Two Stage Least Squares estimation

The bias in the estimation can be reduced by including as much as theoretically relevant control variables as possible in our analysis. These control variables should be correlated with the unobservable variables. It is however impossible to exclude all variation in the error term and a more specialized method is necessary. To overcome the problem of omitted variables here, we use the IV approach in order to isolate exogenous variation in non-farm income. This approach tries to solve the omitted variable bias problem by leaving the unobserved variables in the error term and using estimation methods that recognize the presence of omitted variables (Wooldridge 2002, Wooldridge 2002b). By finding appropriate IV we isolate the movements in x that are uncorrelated with y. We will describe the Two Stage Least Squares (2SLS) estimation model following Wooldridge (2002, 2002b). Recall the structural equation eq. 1 where farm investments is the endogenous dependent variable, non-farm income x is the main explanatory variable of interest, that is likely endogenous, and Ni is a set of exogenous variables. Assume that zj is a set of exogenous variables that are not included in eq. 1 and which are assumed to be a set of valid instruments. An appropriate IV candidate for x must satisfy two econometric assumptions. First, the instrument z j should be uncorrelated with the error term u: Cov(zj,u)=0. This is often referred as the orthogonality assumption and indicates that the instruments zj should have no partial effect on investments y and zj is uncorrelated with other factors that affect y. Second, the instrument must be correlated with non-farm income, this is Cov(zj,x)≠0, meaning that zj must be related to the endogenous non-farm income (Wooldridge, 2002b).

32


The regression equation (eq. 1) is solved using a two stage least squares (2SLS) model. In the first stage of the 2SLS model, x is written as a linear function of the exogenous variables (instruments and control variables) and an error term v. This equation is the reduced form equation for x: x=η0 +ηj zj +ηi ni +v

(eq. 2)

Out of all possible linear combinations of the exogenous variables that can be used as instruments for x, 2SLS will choose that which is the most correlated with x. The key identification assumption is that there is at least one of the πj or ≠ 0. The best IV for x is the linear combination of zj and ni which is called x* , x* =η0 +ηj zj +ηi ni

(eq. 3)

and is uncorrelated with u. By this, we can see that eq. 2 consist of two parts: x* and v. x* can be interpreted as the part of x that is uncorrelated with u while v is possible related to u. To obtain the fitted value x (this is the estimated version of x* ), a regression on eq. 2 is run: x=η0 +ηj zj +ηi ni

(eq. 4)

Therefore, the first stage of 2SLS purifies x of its correlation with u before doing an OLS regression. It estimates the relationship between on one side the control variables and the IV and on the other side non-farm income. By this, the component of non-farm income that is uncorrelated with the error term is isolated. The second stage uses this component to estimate the coefficient of non-farm income in the farm investment regression. The second stage is the OLS regression, but with x instead of x, which gives us the reduced form for y: y=β0 +β1 x* +βi ni + u +β1 v

(eq. 5)

The composite error u + β1v has zero mean and is uncorrelated with all explanatory variables x* and ni. the OLS will give unbiased results for the reduced form parameters because of the latter feature. Another advantage of IV methods is that it is a general solution to isolate exogenous variation in non-farm income (Wooldridge, 2002b). Simultaneity bias could occur when the decision to allocate labor (to farming, but also to non-farm activities or leisure) are made simultaneously with decisions related to expenditures on farm investments. The non-farm income is therefore related with the error term. If large investments in farm activities free up labor time devoted to farming, this labor time could be allocated to non-farm activities. This would create a positive relation between non-farm income and

33


the error term, leading to an upward bias in our estimates on its effect. It is however also possible that farm investments intensify farm activities and thereby drawing labor away from farm activities. This would reduce non-farm income and create a downward bias (Hertz, 2009). Also Stampini and Davies (2009) suggest the OLS regression to be downward biased because farming and non-farm labor decisions are made jointly. They suggest that the more households farm, the less likely they are to participate in nonfarm activities. The use of 2SLS methods brings however also some limitations. First, the IV technique produces less precise estimates as it exploits only a part of the correlation between the dependent variable and the exogenous regressors. This correlation is determined by the correlation between the exogenous regressors and the instruments (Stampini and Davis, 2009; Oseni and Winters, 2009). Moreover, the calculated causal effect places greater weight on those households most influenced by the instruments and thereby capturing the effects of a subpopulation (Oseni and Winters, 2009). Next to this, Wooldridge (2002) states that an important cost related to the IV estimation is the larger asymptotic variance of the IV estimator which is sometimes much larger than the asymptotic variance of the OLS estimator. This implies that 2SLS are inherently biased as their standard errors are larger than those from OLS. The magnitude of the standard errors of 2SLS depends on the quality of the IV used in estimation. Therefore, solving the omitted variable bias always comes with the cost of efficiency.

3.3.4

Instrumental Variables

As stated before, appropriate instruments must satisfy two assumptions. Good instruments should be both valid and relevant, this is orthogonal to the errors and correlated with the endogenous covariate non-farm income (Baum et al., 2003). The orthogonality assumption can never be checked as the error term u is unobservable. However, if an equation is overidentified, we can test whether the instruments are uncorrelated with the error process.

Roughly speaking, we test whether the

instruments are correlated with farm investments or not. This is what is called instrumental exogeneity and can be tested through the overidentifying restriction test. As we only can suggest instrumental exogeneity instead of proving it, we must be sure that the instruments have strong theoretical grounds. The second assumption is often referred as the relevance of instruments and can be tested by the significance of the instruments in the first stage IV regression, the underidentification test and weak instrument identification test. Variation in relevant instruments causes variation in non-

34


farm income which is uncorrelated with the error term. Underidentification test is a test whether the regression is identified, meaning correlated with the non-farm income, and is essentially a test of the full column rank of a matrix. Weak identification refers to instruments that are weakly correlated to non-farm income, causing estimators to perform poor (Wooldridge, 2002b; Baum, 2006). These tests will be discussed together with the results. Finding appropriate instruments is not obvious. Ideally, a set of instruments or alternative sets of instruments should be used to avoid focusing on a subpopulation (Oseni and Winters, 2009). The best instruments are those that reflect push and pull factors driving households into non-farm activities but have no direct impact on farm activities. Such factors could include distance to nearby a factory, literacy in English and so on. During the time of survey, not much attention was given to collect information related to push and pull variables. We therefore lack information about distinct push and pull factors, so alternative sets of instruments were not found. We were however able to construct one push and one pull variables that might serve as strong instruments. Our first instrument is the number of dependents in the household (dependents). This variable includes all children younger than 14 years and all elderly older than 65 years. It is assumed that these members of the household are no working forces at the time of survey. They must therefore be supported by the labor forces within the household. Households are pushed into non-farm activities because they must find means to maintain all their dependents. It is hypothesized that the number of dependent stimulate households to allocate labor forces to non-farm activities for additional income and that it does not affect farm investments directly. Its effect is indirect, through its effect on non-farm income. However, it might be unrealistic to think that the number of dependents has no influence on farming decisions in any way. The more dependent people in the family, the more effort that needs to be devoted to farming and food production. The tests of instruments validity and weakness will decide whether the number of dependents is an appropriate instrument. The second instrument is the non-farm income share in the total household income at tabia level (non-farmshare_tabia). This variable is calculated as the mean of the households‟ non-farm income share for each tabia. The latter is constructed by dividing the non-farm income by the total household income for each household in the tabia. This non-farm income share is a proxy for the non-farm employment rate at the tabia level. It indicates the households‟ potential to diversify their income sources and to 35


increase their non-farm income if they are willing to do so. This variable can thus be used as an indicator of non-farm employment opportunities and a predictor for nonfarm income. The existence of non-farm activities on its turn is a proxy for the demand for non-farm labor. We hypothesize that the non-farm income share positively influences non-farm income and has no direct effect on farm investments. The fact that the instrument is defined at tabia level and we control for regional effects (through the distance to Mekelle) makes it unlikely that the instrument is correlated with other local factors that affect farm activities (Kilic et al., 2009).

36

Chapter 4: Results and discussion

4

RESULTS AND DISCUSSION

4.1

Descriptive statistics

4.1.1

Farm expenditure, input use and investments

Our data contain information about the value of households‟ investments in farming activities. As described in section 3.3, the latter consist of investments in durables as well as expenditures on farm input. All investments are expressed in Ethiopian Birr2 (ETB). The total value of all investments made in farm activities is defined as the total farm expenditure and hence includes all money spent by households on durable investments and farm input use. Table 4.1 shows that the value of farm expenditures is on average 3,247 ETB. 96% of the households in the Geba catchment spends at least some money on farm expenditure and this value ranges from zero to 44,000 ETB. More importantly, Table 4.1 shows that investments in farm durables make up the major part (79%) and farm input use only a minor part (21%) in the total farm expenditures. Table 4.1: Households’ total farm expenditures, input use and investments in ETB

Variable

Mean

Std. Dev.

Min

Max

Share

Access

3,246.78

4,099.92

0

44,000

1.00

0.96

693.56

580.47

0

3,684

0.21

0.90

2,553.22

3,956.55

0

43,250

0.79

0.86

Fertilizer

209.96

245.05

0

1,620

0.26

0.62

Improved seeds

109.27

206.87

0

1,600

0.14

0.37

Local seeds

361.29

346.78

0

2,634

0.57

0.88

10.42

33.41

0

368

0.02

0.13

2.62

7.95

0

87

0.01

0.18

557.77

1,567.56

0

22,750

0.29

0.50

Livestock

1,039.22

1,750.35

0

23,400

0.39

0.53

Buildings

718.94

2,470.19

0

31,000

0.15

0.22

Equipment

237.29

873.47

0

15,950

0.17

0.59

Farm expenditures Farm input use Farm investments Input use types

Other inputs Labor Investments types Water and Land

Notes: observations=733, shares calculated if value > 0 (input use= 661 obs; investments= 663 obs)

2

One ETB = 0,06397 € (National Bank of Ethiopia, http://www.nbe.gov.et, 08/06/2009)

37


To get a view of the farming system, households were asked about their usage of fertilizer, improved seeds, local seeds, other inputs and labor usage. 86% of the households has used any of these inputs during the last production season. The total value of the agricultural input use is on average 694 ETB, with a maximum amount of 3,684 ETB. Almost nine out of ten households has access to local seeds which makes up the biggest part of their spending (361 ETB). Out of the total sample households, 62% of them has used some fertilizers on their fields, which on average has a value of 210 ETB. One on three households has used improved seeds during the last production season, with an estimated value of only 110 ETB on average. 13% of the households has used other inputs, but the value of these inputs is low (10 ETB). Also the use of farm labor seems to be rather low, only 18% of the households has actually spent some money on labor during the last production season. The local seeds have the highest share in total input use (57%), followed by fertilizer(26%) and improved seeds (14%). The contribution of both labor and other inputs is very small, respectively 1% and 2%. As we expected, the values of the fertilizer and improved seeds used by households are low and indicate that farm input use is restricted. Households may hence face liquidity problems to buy farm inputs and rely on local seeds. Other studies have found that household farm input use is rather low in Ethiopia. Pender et al. (2006) find that households‟ in Tigray on average cover only 27% and 2% of their plots with respectively fertilizer and improved seeds. The total use of seeds amounts to 118 kg/ha. Dercon and Christiaensen (2010) report that on average 22% of the households use fertilizer in Ethiopia. They note that fertilizer use in Ethiopia has remained limited because of limited knowledge and education, risk preferences, credit constraints, low profitability of fertilizer use, lack of market access as well as limited or untimely availability of inputs. The low use of farm input is in contradiction with the efforts done by the government in Ethiopia to promote the adoption of modern varieties and accompanying inputs in order to boost productivity. The Ethiopian government supports two holding companies who control the market for fertilizer. In addition, spot markets are very thin. Fertilizer use is heavy promoted by the government extension and credit program (Pender et al., 2006; Dercon and Christiaensen, 2010). Herweg (1993), as cited by Pender et al. (2006), notes that fertilizer use is more risky and less profitable in low-rainfall areas due to the fact that the uptake of nutrients may be limited by inadequate soil moisture. Contrary, adoption of soil and water conservation measures might be less risky and more profitable here because they have a larger impact on yields in the short term. Also Ehui and Pender (2005) and Pender (2004) state that in areas with lower agricultural potential, intensification of agriculture 38


through fertilizer use and improved seed use is more limited. Investments in irrigation, water harvesting or water and soil conservations to overcome soil moisture constraints have more potential. Table 4.1 describes the value of expenditures for households‟ investment on durables during the last production season. We see that 86% of households in the Geba catchment makes farm investments of any kind, and spends on average 2,553 ETB on farm investments. Following Table 4.1, the households‟ investments can be divided in four big groups. First, households make investments in water and land which include investments in terracing, water pond, water well and water diversion projects. At least half of the households spends some money on these investments, with an average amount spent representing 558 ETB. Second, investments are made in the households‟ livestock resources. More than half of the households has made at least some investments in cows, oxen, sheep and goats, poultry and/or equine (donkey, horse and mule). Households spend on average 1,039 ETB on animal related investment. Third, investments in buildings include investments or renovation in respectively new and old houses for both human and animal purpose. Only one out of five of the households spends money on their housing and invests on average 719 ETB. Finally, investments in treadle pump, generator, plow, axe and spade and beehive and bees are collected in investments in equipments. 59% of the households spends money on equipment, which represents an average value of 237 ETB. Table 4.1 concludes that the share of livestock investments in the total households‟ investments (39%) is the highest, followed by the share of investments in water and land (29%). The shares of investments in buildings and equipment are lower, both close to 15%.

4.1.2

Farm and non-farm activities

Table 4.2 summarizes the different farm and non-farm activities undertaken by households in the Geba catchment. Of the total 734 households, one household has reported to have no income of any kind, and is therefore excluded from our analysis. The total household income is on average 8,301 ETB ranges from 315 ETB up to 213,425 ETB. 97% of the households in the survey has access to farm income, which is the major share of the households‟ income (65%). Farm activities refer to the production or gathering of unprocessed crops, livestock, forest or fish products from natural resources (Barrett et al., 2001). Farm income can be broken down into crop income and livestock income. Crop income is derived from selling crops during the three different seasons (Kiremt, Belg and Irrigation). Households cultivate different

39


crops, mainly teff, barley, wheat, maize and sorghum. Livestock income is generated from selling livestock such as oxen, cows, heifers, calves, sheep, goats, horses, camels, mules, donkeys, beehives and poultry. Table 4.2: Households' income composition in ETB

Income

Obs

Mean

Std. Dev.

Min

Max

Share

Access

Total

733

8,302.58

10,841.39

315

213,425

1.00

1.00

Farm

733

5,639.93

6,273.28

0

67,410

0.65

0.97

Crop

733

4,883.96

6,025.16

0

67,200

0.55

0.94

Livestock

733

755.97

1,181.34

0

13,877

0.10

0.66

733

1,873.39

4,358.04

0

97,000

0.27

0.80

Wage

733

1,439.41

3,316.94

0

80,000

0.22

0.74

Business

733

433.98

2,070.61

0

44,000

0.05

0.22

Transfer

731

596.41

7,427.93

0

200,000

0.06

0.29

Migration

730

190.84

821.21

0

10,500

0.02

0.13

Non-Farm

Note: missing values are interpreted as zeros in the calculation of income shares

Table 4.2 describes the different types of non-farm activities available in the dataset. Non-farm activities are defined as the opposite of farm activities: all activities undertaken by rural households that are not related with agricultural production on the farm (Barrett et al., 2001). Distinction is made based on the nature of the non-farm activities. First, distinction is made between labor and non-labor non-farm activities. This distinction is crucial because non-labor activities do not have an impact on the labor availability of the farm household, and thus require no farm labor input. The nonlabor activities are present in the form of transfers from governments, ngo‟s (food aid, cash or in kind), friends or relatives. 29% of the households has access to transfer income which is on average 596 ETB. It only has a minor share in the total household income (6%). Labor non-farm activities can be further divided into self-employment, wage activities and migration. Self-employment (or business) activities include any activities run and owned by the household located in their home or nearby towns. Self-employment activities include weaving, milling, handicraft, trade in grain, trade in livestock, traditional healer (or religious teacher), transport by pack animal, selling cactus, selling wood and charcoal, selling food and/or drinks and others. Wage activities include all types of agricultural and non-agricultural wage employment such as farm worker, traditional labor sharing, professional (teacher, government worker, administration, health worker, clerical), skilled laborer (builder, thatcher, barber), trader, soldier,

40


driver/mechanic, unskilled worker, domestic servant, food for work and others. Migration remittance is the income sent back or brought back by a migrant labor members living in other parts of Ethiopia or in foreign countries. Migratory refers to domestic urban and foreign activities and therefore does not come to terms with rural activities. Several authors (Dercon, 1998; Barrett et al., 2001; Woldenhanna and Oskam, 2001; Maertens, 2009) stress the importance to make a distinction between the different nonfarm activities. Dercon (1998) states that it is incorrect to treat all non-farm activities as the same, because possible entry and returns are likely to be different. The nature, structure and input requirements for non-farm activities are different and pose different demands on the household asset base. Non-farm self employment requires more capital and skills than wage employment. If credit constraints occur, the participation in wage employment will be easier than in self employment. Wage employment is more likely to be available in areas near to towns and commercialized agriculture. Farm households can participate in petty trade activities in areas far from urban centers. Contrary, near to urban areas, non-farm employment may face serious competition (Dercon, 1998; Woldenhanna, 2000). Table 4.2 shows that the majority (80%) of the sample households earns non-farm income. Hence, four out of five households has a member who is active in a non-farm activity of any kind. Although non-farm activities have a lower share in the total household income than farm activities, their contribution is far from marginal (27%). Non-farm income is mainly gained from participation in wage activities (72%) while only one on five households are engaged in self-employment activities. The mean income generated from wage activities is also higher than the income derived from business activities, respectively 1,439 ETB and 434 ETB. It seems as if wage income activities are easy accessible for the households and also have a higher payoff in the Geba catchment. This is in contrast with the conventional idea that self-employment activities offer a higher income than wage activities. Only 13% of the households has access to migration income which has only a share of 2% in the total household income. The average amount of money sent back to the household by migrant members is on average 191 ETB. These numbers are in line with the findings of Woldenhanna and Oskam (2001) and Woldenhanna (2002) in Tigray. The survey contains specific information about wage and self-employment activities. The household head and his or her spouse were inquired after the nature of their nonfarm jobs. We constructed the participation of households in the different types of self41


employment, based on the mean total days worked by the household head and his/her spouse on the specific type of business activity, because there was no other information available. Table 4.3 shows that households worked most of the days on trade (general and livestock), handicraft and others. As Woldenhanna (2000) notes, farm households need to invest some level of working capital to get started in self-employment activities such as petty trade, handicraft, etc. Table 4.3: The nature of wage and self-employment jobs

Percentage of total amount wage

Percentage of total amount of self-

employment jobs

employment jobs

Farm worker

7.22

Weaving

2.90

Trad. labor sharing

1.71

Milling

6.47

Professional

0.01

Handicraft

10.42

Laborer

5.12

General trade

21.86

Trader

2.62

Livestock trade

11.78

Soldier

0

Trad. healer/rel. teacher

2.36

Driver/mechanic

0

Transport

0.87

Unskilled worker

18.18

Selling cactus

4.05

Domestic servant

60.09

Selling wood

1.40

Food for work

3.41

Selling food

10.57

others

27.32

In total, 762 wage jobs are performed by the household head an his or her spouse. Table 4.3 describes the contribution of each type of wage employment in the total amount of wage jobs. „Food for work‟ is the most important wage employment, which is clearly the dominant wage job (60%). Food for work is followed by unskilled work (18%) and farm work (7%). Household members participating in wage employment were also asked if they needed qualification, experience or educational training to get the wage job. The responses are reported in Table 4.4. It is clear that more than half of the wage employments did not need any qualification or training. If such thing was required, experiences seemed the most appropriate. Next to this, Table 4.4 shows that most wage employment are of temporary nature. Baumeister (1996) cited by Ruben & van den Berg (2001) notes that permanent wage labor is mainly engaged by landless farmers, while small farmers will seek temporary employment during the off-season on other farms in their neighborhood.

42


Table 4.4: Wage employment requirements and duration (in percentages)

Does the job require qualification, experience or educational training? Experience only

24.15

Training only

10.37

Qualification/training only Nothing

0.78 64.70

Temporary or permanent work?* Permanent

9.82

Temporary

90.18

Note*: information on the duration of the wage employment was missing for 39 of the in total 762 wage-jobs

Woldenhanna (2000) finds very similar results in Tigray. Food for work is the dominant wage type because it does not require experience, skill and initial capital investment. It wage rate is however the lowest of all types of wage employment. Poorer households are given priority when there are not enough jobs in the program. Unskilled wage employment requires some purchase of equipment and although experience and skill are not required, households spend a lot of time in finding such a job. Skilled wage jobs require the most investments, skills and experience, but the wage rate is the highest. Ehui and Pender (2005) find that during 1998-1998 food for work projects accounted for 40% of the average non-farm income and more than 10% of the average total household income. Households that participate in such projects also earn higher incomes than other households (Pender et al., 2006). Next to this, Woldenhanna (2002) states that most non-farm work in Tigray is of temporary nature and does not require skilled labor. As Woldenhanna and Oskam (2001) point out, most farmers in Tigray have difficulties with acquiring skills, startup capital and jobs in non-farm activities. Until 1998, non-farm workers had to obtain their skills and experience without training and could only acquire them during employment. There are no schools for building, carpentry or other technical training, except some limited training given by the Tigray Development Agency (TDA). The only organization which provides credit to small farmers is the Relief Society of Tigray (REST). Access to jobs in urban areas is only possible after a long process of searching, via relatives and family. In summary the descriptive statistics show that non-farm income is an important part of the total household income and is accessible for most households in the survey. In addition, some interesting information about the attitude of the households towards the RNFE is available. Table 4.5 describes the attitude towards additional non-farm work.

43


Three quarter of the households has a member willing to work for additional wage if a non-farm job would be offered to that member. Even 40% of the households has reported to have a household member that is willing to work during planting, weeding, harvesting or threshing season. Table 4.5 shows that agriculture is unable to absorb all the available household labor and could indicate that households have a positive attitude towards non-farm activities and are willing to work more non-farm if they would have the opportunity. Similar, Woldenhanna and Oskam (2001) find that 66% of the farmers in their sample did not work more hours in non-farm activities because they could not get non-farm employment. They suggest that this response indicates the fact that not all available labor could be allocated to agriculture. Table 4.5: Attitude towards additional non-farm employment

Attitude towards additional non-farm work work for wages during planting weeding harvesting and threshing

Mean

Std. Dev.

Min

Max

0.75

0.43

0

1

0.40

0.49

0

1

Note: observations=733

4.1.3

Households with non-farm income vs. households without non-farm income

Table 4.6 describes the differences in characteristics between households that have access to non-farm activities and those that do not. The mean difference between these two groups is calculated and we will check if this difference is significant using a t-test3. As 80% of the households has access to the RNFE (Table 4.2), we can see that 150 households do not participate in non-farm activities, while 583 households do. Households with access to non-farm activities have on average a significant lower total household income (8,035 ETB vs. 9,344 ETB), farm income (5,170 ETB vs. 7,470 ETB) and households‟ resources. Households with non-farm income have lower landholdings, number of animals and fixed assets than households that do not participate in non-farm activities. It seems as if the participation in non-farm activities is not conditioned by the households‟ resource base. This could indicate that non-farm activities are especially important for less endowed households.

3

Although the normality assumption for most variables is violated, the t-statistic has approximately a t

distributions, at least in large sample sizes (Wooldridge 2000b)

44


Table 4.6: Comparison of households with and without access to non-farm income

Variable

Mean (150 obs) Only farm

Mean (583 obs) Non-farm

income=0 9,344.16

income > 0 8,034.59

Farm

7,469.99

5,169.08

2,300.91***

Fm1age

52.34

41.77

10.57***

Fm1sex

0.77

0.73

Fm1schooled

0.27

0.39

- 0.12***

hhlandholdsize

5.23

4.17

1.06***

8,060.20

5,921.62

2,138.58***

21,695.56

11,086.02

10,609.55**

66.58

71.13

-4.55**

edirm

0.24

0.23

0.01

havei

0.26

0.21

0.05*

adults

3.03

2.91

0.12

3,278.70

3,238.57

40.13

2,391.77

2,594.76

-202.99

Water and Land

829.55

487.84

341.72

Livestock

911.31

1,072.13

-160.83

Buildings

360.83

811.08

-450.25**

Equipment

290.08

223.71

66.37

Farm input use

886.93

643.81

243.12

Fertilizer

285.27

190.58

94.68***

Improved seeds

165.56

94.78

70.78***

Local seeds

423.34

345.33

Other inputs

8.07

11.03

Labor

4.70

2.09

Income Total household

animalasset fixedasset tabiadismak

Farm expenditures

Mean difference

1,309.57*

0.04

Investments Total

78.01** -2.96 2.67***

Notes: *,**,*** respectively significant at 10%, 5% and 1%

Households with access to non-farm income tend to have a family head that is almost 10 years younger than those in households not having non-farm earnings. This difference is significant at the level of one percent. This indicates that households with younger heads are more likely to participate in non-farm activities. The contrary is true for education, indicating that non-farm activities require more education or skills. Remarkable is that the gender of the household head, the households‟ edir membership and the number of working forces do not vary much between the two groups and these differences are insignificant. Households with access to non-farm income live significant further away from the regional capital city, which is however doubtful as integration in

45


markets is lower. Households without non-farm income are more likely to have access to irrigation. More interesting is, as shown in Table 4.6, that farm expenditures differ insignificantly between the two groups. Households that have access to non-farm income spend on average more on total investments. However, this difference is also insignificant. The picture is somewhat different if we divide the investments according to their nature. On the one hand, households with non-farm income invest more in livestock and buildings, of which only the latter is significant. On the other hand, households without access to non-farm income invest more in water and land and equipment of which only the former is significant. Farm input use is higher for households that do not have access to non-farm income, then dose with access. The same can be said for the value of fertilizer, improved seeds, local seeds and labor use. All these difference are significant. Only the value of other inputs is higher for households who participate in RNFE, but this difference is insignificant. So based on the descriptive statistics, some indications about the impact of non-farm income on farm investments can be made. We will however analyze this impact more detailed through empirical analysis.

4.1.4

Control and instrumental variables

In addition to income and investments, several individual and household characteristics are used in the empirical estimation. These variables include a set of individual, household and regional characteristics, as summarized in Table 4.7. The individual variables refer to the characteristics of the household head (fm1) and include gender (fm1sex), age (fm1age), schooling (fm1schooled) and educational level (fm1leveled). Table 4.7 shows that on average 73% of the household heads is male and they have an average age of almost 44 years. Only one on three household heads has had some form of education. In case of some education, they finished on average at least primary school. So, most households in the Geba catchment are headed by a male of middle age who acquired limited education. Among the household characteristics we distinguish households‟ wealth, membership in social capital, access to irrigation and the number of adult labor forces. The households‟ wealth is measured by the households‟ resources of land (hhlandholdsize), livestock (animalasset) and fixed assets (fixedasset). The households‟ resource base influences the ability of the households to finance investments and to obtain credit. The land

46


accessible to households in the Geba catchment is on average 4.4 Tsimidi4 (1.1 ha) and is measured as the total plot size of the plots owned by the households. This illustrates that most farmers in the Geba catchment are smallholders. Table 4.7: Summary statistics of individual and household characteristics

Variable

Obs

Mean

Std. Dev.

Min

Max

fm1sex

733

0.73

0.44

0

1

fm1age

733

43.93

14.61

16

87

fm1schooled

733

0.37

0.48

0

1

fm1leveled

268

2.42

1.37

1

12

hhlandholdsize

733

4.38

3.37

0

23

animalasset

733

6,359.25

8,131.34

0

111,900

fixed

733

13,257.14

50,790.70

22

1,201,296

adults

733

2.93

1.55

0

10

tabiadismak

733

70.20

29.68

42

120

edirm

733

0.23

0.42

0

1

haves

733

0.11

0.32

0

1

takenloan1000

733

0.50

0.50

0

1

havei

733

0.22

0.41

0

1

The average value of the livestock owned by the households is almost 6,359 ETB and is measured as the sum of the number of each animal multiplied by the value of that type of animal. The animal types taken into consideration are oxen, cows, heifers, calves, sheep, goats, horses, camels, mules, donkeys, beehives and poultry. The households‟ fixed asset is on average nearly 11,612 ETB and includes furniture and households‟ durable, agricultural equipment, electronics, valuables and housing equipment. To avoid simultaneity bias between households‟ wealth indicators and farm investments in the empirical estimation, we define new variables for animal („livestock‟) and fixed assets („fixed‟) in which all types of respectively animals and fixed assets on which investment information is available are excluded. Households have on average almost three adult labor forces available and it can take up to maximum 10 persons. Almost one out of four households is member of edir, resembling the social capital of the household. Edir, in the context of rural and specifically the research site, is defined by Kidane (undated) as a community–based

4

1 Tsimid = 1/4 of an hectare

47


institution established on mutual interest of members. Its primary objective is to support members during the time of crises, such as death of family members. Social capital is an indicator for farmer networks and access to (better) information. One out of five households in our sample has access to irrigation, which is highly important in smallscale agriculture. This number is higher than the 5.7% reported by Woldenhanna (2002). The distance to the regional capital city Mekelle (tabiadismak) is included as regional variable, and is on average 70 km. Distance is an important factor determining the access to the RNFE sector and markets. This variable captures many area specific characteristics like population density, land size and various agro-climatic features. Next to this, households were asked if they had access to savings and if they have taken up loan in the last five years. On average only 11% of the households had savings during the last production season, while 50% has ever taken out a loan of at least 100 ETB. These rather low responses could indicate the existence and importance of liquidity constraints. Information about credit and savings is endogenous to both farm investments and non-farm income and these variables are therefore excluded from the empirical analysis. Finally, Table 4.8 reports the descriptive statistics of the two variables used to instrument non-farm income. It can be seen that households on average have more than two dependent members in the household, and this number can range from zero to seven people. The non-farm share in the tabia is on average 26%, which indicates that on average and on the tabia level, the non-farm employment rate is slightly higher than 25%. Table 4.8: Summery statistics of the instruments used in the 2SLS

Instruments dependent non-farmshare_tabia

Mean

Std. Dev.

Min

Max

2.35

1.49

0

7

26.40

10.21

15.04

42.03

Note: observations=733

48


4.2

Multivariate analysis

The empirical analysis was conducted using the statistical software package STATA11. We ran a two-stage least squares regresses on equation eq. 1 using an instrumental variable estimator, as outlined above. In stata11, the IVREG2 command will be used to obtain an 2SLS estimation. We will first discuss the first stage and afterwards the second stage. The former is necessary to check the relevance, significance and strength of our instruments. Our main interest lied in the second stage, and more specifically in the sign and significance of the impact of non-farm income on farm investments. We compared the 2SLS estimation with the OLS estimation to check the direction of the omitted variable bias. After running the regression in STATA11, the homoskedasticity5 assumption of our regression was checked. Homoskedasticity means that the variance of the error term is constant, conditional on the explanatory variables. It assumes that the error terms are independently and identically distributed. If the homoskedasticity assumption is not valid (this is non-constant variance), we speak of heteroskedasticity (Wooldridge 2002b). It is important to assume homoskedasticity, as it is needed to justify the usual statistical test (Baum et al., 2003). Moreover, Wooldridge (2000b) states that if heteroskedasticity is suspected, the reported standard errors in the estimation are invalid and corrective action should be taken. We tested for homoskedasticity using the Pagan-Hall general test statistic. This statistic involves regression of the squared OLS residuals on the independent variables (Wooldridge, 2002b). The null hypothesis, that the disturbance is homoskedastic, was strongly rejected, revealing the presence of (arbitrary) heteroskedasticity. The problem of heteroskedasticity can partially be addressed through the use of heteroskedasticity-consistent conventional

IV

estimator

(or is

robust)

consistent

standard but

not

errors efficient

and in

statistics.

the

presence

The of

heteroskedasticity (Baum et al., 2003). As a consequence, we used the two-step feasible generalized method of moments (2-Step GMM) estimator to get efficient estimates of the coefficients and consistent estimates of the standard errors (Baum, 2006; Baum et al., 2003). The generalized method of moments (GMM) estimator of the coefficients in equation eq. 1 is based on the moment or orthogonality conditions. The

5

Homoskedasticity assumes that the error terms are independently and identically distributed

49


advantage of the GMM is that it does not require the distribution of the data to be fully known, unlike the maximum likelihood estimation. The disadvantage of the use of GMM is the poor small sample properties (Baum et al., 2003).

4.2.1

First Stage Results

The first stage of the 2SLS model is a simple OLS regression of equation (2), whereby non-farm income is regressed on the control variables and the IV. The OLS estimates with robust standard errors are reported in Table 4.9. The interpretation of these OLS results must be done with much caution, because most literature analyzing the determinants of non-farm income use more specific models like Heckman or Cragg double hurdle model. These models try to deal with incidental truncation of potential outcomes: non-farm income might not be observed because of the outcome of another latent variable. Households only have non-farm income if they participate in the RNFE sector. These models therefore consist of two parts: first the participation in non-farm activities is analyzed, and second for those who participate in non-farm activities, the level of non-farm income is analyzed. However, these models have been criticized because non-farm income is an observed outcome: it is zero for households that do not have access to non-farm activities (corner solution). Moreover, it is very difficult to meet the exclusion restriction (Hertz, 2009). Our main interest lies in the second stage of the 2SLS, and the first stage will only be used to indicate which factors may influence non-farm income (and hence determine the access to non-farm activities) and, more importantly, to evaluate the relevance of the instrumental variables. As can be seen from Table 4.9, most control variables do not seem to significantly influence non-farm income. However, the age of the household head seems to have a significant and negative impact on non-farm income. This seems to be odd, as it is expected

that

older people earn

more income, but

this might illustrate the

shortcomings of the OLS regression to determine non-farm income determinants. Because we were not able to separate the effect of the control variables on the participation and level of non-farm income, it is impossible to distinguish the effect of the age of the household head on either participation in or the level of non-farm income. It can however be assumed that younger household heads are more likely to participate in non-farm activities, and therefore age has a negative impact on non-farm income. Younger household heads might be less bounded to traditional agricultural activities. This is in agreement with the results found by Woldenhanna and Oskam (2001). The authors suggest population pressure to hinder younger farm households to

50


get enough land to support their livelihood and they are therefore obliged to participate in non-farm activities. Non-farm activities also require more physical strength and effort, which is more likely to be done by younger individuals. Besides this, older household heads were historically prohibited to participate in non-farm activities, making them inexperienced with non-farm income and more productive in farm activities. Table 4.9: First stage OLS regression results

OLS fm1sex fm1age fm1schooled

Coef. 0.3283 - 0.0673***

Std. Err. 0.2759 0.0088

0.0645

0.2328

hhlandholdsize

- 0.0667

0.0487

livestock

- 0.0667*

0.0339

fixed

- 0.0094

0.0844

tabiadismak

0.0073

0.0051

edirm

0.2662

0.2709

havei

- 0.2565

0.2845

adults

0.3218***

0.0815

dependents

0.2201***

0.0751

non-farmshare_tabia

0.0674***

0.0121

constant

5.2844

0.8467

Joint significance of IV F test of excluded instruments

20.11***

Angrist-Pischke multivariate F test of IV

20.11***

Underidentification test Kleibergen-Paap rk LM statistic

35.41***

Weak identification test Cragg-Donald Wald F statistic

19.39

Kleibergen-Paap Wald rk F statistic

20.11

Weak-instrument-robust inference Anderson-Rubin Wald test

16.70***

Stock-Wright LM S statistic

15.70***

Notes: *,**,*** respectively significant at 10%, 5% and 1%; robust standard errors

The effect of both gender and education is positive, but not significant however. It is often hypothesized that an increase in educated level lowers the incentive for farm activities and encourages participation in (more remunerative) non-farm activities

51


(Corral and Reardon, 2001; De Janvry and Sadoulet, 2003). Van den Berg and Kumbi (2006) find that in Ethiopia, only formal and primary education significantly increase non-farm income, while higher education seems to be irrelevant. We have tried to incorporate different dummies for education levels, but none of them where statistically significant. This could be explained by the fact that non-farm activities are dominated by food for work and unskilled wage employment, which does not require much education or skills. Our results do not strongly suggest a gender bias, which is in line with the observations of van den Berg and Kumbi (2006). It seems as if the non-farm sector does not significantly consist of jobs that have higher returns to education nor does men have better access to non-farm activities. Surprisingly, regional effects do not have a significant negative effect. We expected households that live further from the capital city to be less engaged in non-farm activities, because of fewer opportunities and more costs related with transport. Our results indicate a positive effect, but the coefficient is statistically not different from zero. Membership in social capital does increase non-farm income, but its impact is not significant. Finally, the number of adult labor forces has a positive and very significant impact on the level of non-farm income. As we expected, non-farm income is positively related with the number of adults that are potential working forces in household. The latter could indicate the availability of excess family labor that cannot be allocated on the farm and households are therefore obligated to look for non-farm activities. Moreover, Woldenhanna (2000) notes that larger family size decreases the marginal value of households‟ consumption of leisure. Our finding is in accordance with the results of Ruben and van den Berg (2001), Woldenhanna and Oskam (2001), Matsche and Young (2004) and van den Berg and Kumbi (2006). The animal assets of the households also have a negative and statistically significant impact on non-farm income. Again, it looks like households with a higher value of their livestock participate less in non-farm activities and hence have lower non-farm income. Having more animals will probably indicate that households are highly engaged in farm activities and livestock require intensive care (Ruben and van den Berg, 2001). The impact of both the total plot size (as indicator of farm size) and the total value of the fixed assets on the level of non-farm income is negative, but statistically not significantly different from zero. Overall, shortage of households‟ wealth indicators can push farm households into non-farm activities: farm households without land or with small farms; and households with less fixed or animal assets seem to seek for additional income by participating in non-farm income. Having more assets could

52


increase the returns to labor on the farm and work less off the farm. However, only the number of livestock has a significant impact. Another push factor could be the lack of access to irrigation, although not statistically significant. Non-farm income might be important to set up an irrigation system. Another explanation could be that, because access to irrigation decreases non-farm income, households with irrigation systems spend more time on the farm. In conclusion, our results do not provide an indication of the existence of entry barriers: better educated or endowed households do not earn more non-farm income. This is consistent with the findings of Ruben and van den Berg (2006) in Oromia, but in contradiction with Block and Webb (2001) in Ethiopia and Woldenhanna and Oskam (2002) in Tigray. The requirement that instruments are correlated with the endogenous non-farm income can easily be tested by examining the fit of the first stage regression. Both instruments are positive and highly significant related with non-farm income, which suggests the relevance of the instruments. The positive impact of non-farm work opportunities is straightforward: having better access to non-farm activities makes it more likely to participate in non-farm activities and hence increases non-farm income. The more dependents in a household (hence a larger household) increases the non-farm income. This confirms our hypothesis that households with more members that need to be supported are more likely to participate in non-farm activities. As they are not potential working forces and therefore cannot significantly increase farm productivity, the household will be „pushed‟ to look for additional income sources. This finding is in line with Block and Webb (2001) who find a positive relation between participation in nonfarm activities and the dependency ratio. Woldenhanna (2000) finds that the number of dependents affects non-farm work decisions because larger family size increases the availability of labor, reduces the marginal utility of consumption and leisure. The instruments passed the test for joint significance of the instruments, as both the Ftest for excluded instruments and the Angrist-Pischke multivariate F-test of excluded instruments were highly significant. They were both higher than the critical values6 developed by Stock-Yogo (2005) for single endogenous regressor. This indicates that these test values are high enough to reject the hypothesis that the weak instrument bias is greater than 10% of the magnitude of the endogeneity bias under OLS, at the 5

6

Stock-Yogo weak ID test critical values for single endogenous regressor at 10% maximal IV size is 19.93

53


%

level

of

significance.

The

Kleibergen-Paap

rk

LM

statistic

was

used

as

underidentification test and is highly significant, indicating the relevance of our instruments. Also the weak identification tests (Cragg-Donald Wald F-statistic and Kleibergen-Paap Wald rk F-statistic) were conducted to test whether the estimator is weakly identified: non-zero but small correlation between non-farm income and the two instruments. These statistics were higher than the critical values given by the StockYogo weak ID test, revealing the strength of our instruments. Finally, the Weakinstrument-robust inference test was used to test for the significance of the endogenous regressors in the structural equation being estimated. The test was also highly significant, rejecting the null hypothesis that the coefficients of the regressors in the structural equation are jointly equal zero.

4.2.2

Second Stage Results

We began our empirical analysis with the impact of non-farm income on total farm expenditures. Farm expenditures included all expenses on farm related activities. In the last two sections, we expanded our analysis to the impact of non-farm income on two types of investments, and the results will be discussed separately. For each regression estimation, both OLS and 2SLS estimates are reported. Before we interpret the regression estimates, an endogeneity test is performed to indicate whether it is necessary to use an IV method or not. Using IV methods always comes with the price of efficiency loss. We are however willing to pay this price if the OLS estimates are inconsistent and biased. It is therefore useful to test the necessary of IV method and the appropriateness of the OLS estimation (Baum et al., 2003). As the conditional homoskedasticity is violated, we use the heteroskedasticity robust GMM distance endogeneity test7. This test is a variant of the Durbin-Wu-Hausman test of the endogeneity of regressors. Under the null hypothesis, non-farm income can actually be treated as exogenous.

7

The C statistic is computed as the difference between two J statistics: one using the unrestricted regression

with the regressor being tested as endogenous (less efficient but consistent estimator) and one using the restricted regression with the regressor treated as exogenous (more efficient estimator) (Baum et al 2003)

54


4.2.2.1

The impact of non-farm income on farm expenditures

Both OLS and 2SLS estimations of farm expenditures are reported in Table 4.10. Our results show that we fail to reject the null hypothesis that non-farm income is exogenous in the farm expenditure regression. As a consequence, it is not proven that the OLS estimates will yield inconsistent estimates. Moreover, it implies that we do not have a statistical-based argument to prefer the IV method over the OLS regression. However, such endogeneity tests are restricted as they depend on the instruments used for non-farm income. It is therefore difficult to conclude that non-farm income is actually endogenous based on these kinds of endogeneity tests, and the test might only serve as an indication of the presence of endogenous effects of non-farm income. We prefer to work with the IV estimation as the endogenous effects of non-farm activities have a strong theoretical background. With the gmm option, the overidentifying condition can be tested by the Hansen J statistic. The null hypothesis is that the instruments are jointly valid, conditional on at least one being valid and that the excluded instruments are correctly excluded from the estimated equation. Rejection implies that the instruments do not satisfy the orthogonality assumption. This is plausible because either they are incorrectly excluded from the regression or because they are not truly exogenous. We fail to reject the null hypothesis (p=0.15) which implies that the validity of the used instrument is not disproven. Interpretation of this statistic is however not straightforward. As Hertz (2009) has pointed out, rejection of the null hypothesis could indicate that none, some or either all of the instruments are valid. Table 4.10 shows that non-farm activities have a positive and significant impact on farm expenditures. Non-farm income increases the total expenditures households made on farm related activities in both the OLS and IV estimation. After comparison of the OLS and 2SLS models, we see that the coefficient of non-farm income in the OLS estimation is five times lower than the coefficient of non-farm income in the IV estimation (respectively 0.04 and 0.18). the IV method hence predicts that a percentage increase in non-farm income increases farm expenditures with 0.18% and this effect is significant at the one percent level. In the OLS estimation, the effect is only 0.04%. We see that the OLS underestimates the effect of non-farm income on farm expenditures. When potential factors that make non-farm income endogenous are removed, the effect of non-farm income on farm expenditure is even greater. As a result, failing to correct for the endogeneity of non-farm income underestimates the impact of non-farm income on total farm investments indicating that the coefficient of

55


non-farm income in the OLS regression is downward biased. We assume that the downward bias is caused by omitted variables. Table 4.10: OLS and 2SLS estimations results of total farm expenditures

non-farmincome

OLS Coef. 0.0365 *

fm1sex

1.1756 ***

Std. Err. 0.0206

IVREG2 Coef. Std. Err. 0.1792* 0.0924

0.1817

1.1586***

0.1831

- 0.0043

0.0055

0.0049

0.0082

fm1schooled

0.1685

0.1212

0.1639

0.1260

hhlandholdsize

0.0546 ***

0.0197

0.0678***

0.0223

livestock

0.0541 ***

0.0161

0.0655***

0.0170

fixed

0.2103 ***

0.0552

0.1881***

0.0598

fm1age

tabiadismak

- 0.0046 *

0.0025

-0.0055**

0.0025

edirm

0.5918 ***

0.1227

0.5678***

0.1269

havei

0.2737 **

0.1350

0.3082**

0.1478

adults

0.2621 ***

0.0437

0.2180***

0.0546

constant

3.8269 ***

0.5361

2.8614***

0.8395

Endogeneity test Endogeneity test of endogenous regressors

2.53

Overidentification test Hansen J statistic

2.09


We describe the effect of individual and household characteristics on the amount spend on farm expenditures more detailed for investments in durables and farm input use separately. Some first indications are given here. Of the household head characteristics it is surprising that only gender has a significant impact on farm expenditures. Male headed households are more likely to farm on a bigger scale and thus spend more money on farm expenditures. Both the age and schooling of household does not seem to have a significant impact on farm expenditures. All types of household wealth indicators are positive and significant (at the one percent level) related with farm expenditures. Having more agricultural and fixed assets increases the expenses on total farm investments. The distance to Mekelle is negative related with farm expenditures, indicating the importance of being close located to markets and the capital city. However, this effect

56


is not so pronounced: every addition km that the household is located further from Mekelle decreases farm expenditure with 0.6%. As we expected, households with access to irrigation significant spend more money on farm expenditures than households that do not have access to irrigation. This result shows that more inputs are used and investments are required if households have an irrigation system on their farm. Also membership in edir enhances the farm expenditures, which underlines the importance of social capital. This effect is the strongest (except for gender): being member in edir increases farm investments by 57% and this effect is highly significant at the one percent level. Finally, the number of adult labor forces in the household has a positive impact on farm expenditures and this effect is highly significant. When households have more members that could participate in labor markets, they are able to spend more on farm expenditures.

4.2.2.2

The impact of non-farm income on durables investments

We turn now to the effect of non-farm income on the money households spend on durable investments. Table 4.11 reports the coefficients of the OLS and IV estimation of non-farm income, individual and household characteristics. Our results show that the null hypothesis of the endogeneity test is rejected at one percent significance level. As a consequence, the OLS estimates will be incoherent. The endogenous effect of nonfarm income on farm investment is hence meaningful and an IV method is required. The overidentifying restriction is tested with the Hansen J statistic. We significantly fail to reject the null hypothesis, and this effect is much stronger as before (p=0.991), which proves the validity of the instruments. We thus have strong statistical evidence that our instruments are meaningful and the 2SLS method will give the most appropriate results. The results indicate that non-farm income has a statistically significant, positive and strong impact on farm investments in both regressions. The elasticity of non-farm income in the OLS regression is 0.087 and 0.58 in the IV regression. The results of the IV regression indicate that an increase in non-farm income by one percent increases farm investments with 0.58%. Comparing both estimations, the statistical significance remains the same but the coefficient of the IV estimation is more than six times the OLS coefficient. When potential factors that make non-farm income endogenous are removed, the effect of non-farm income on farm investments is even greater

57


Table 4.11: OLS and 2SLS estimations results of farm investments

non-farmincome

OLS Coef. 0.0869 ***

fm1sex

1.0199 ***

0.2651

0.9109***

0.3069

fm1age

0.0068

0.0083

0.0372***

0.0141

fm1schooled

0.3621 *

0.2115

0.2833

0.2417

hhlandholdsize

0.0160

0.0355

0.0876*

0.0469

livestock

0.0484

0.0296

0.0805**

0.0358

fixed

0.3146 ***

0.0860

0.2368**

0.1040

- 0.0160 ***

0.0047

- 0.0199***

0.0052

edirm

0.7910 ***

0.1904

0.7700***

0.2350

havei

0.2866

0.2089

0.4680*

0.2701

adults

0.3330 ***

0.0704

0.1717*

0.0990

constant

2.0796 ***

0.7573

tabiadismak

Std. Err. 0.0320

IVREG2 Coef. 0.5819***

- 1.2001

Std. Err. 0.1686

1.3541


11.52***


0.00


As expected, the age and gender of the household head have a positive impact on farm investments and are significant at the one percent level. The gender effect is the strongest of all effects: male headed households have larger, almost double, farm investment than female headed households. It indicates that there is still a clear gender bias. The effect of age is less strong: every additional year of life experience increases farm investments with almost four percent. Hence, male and older household heads positively affect the money spend on durable investments. More surprising is that schooling does not have a significant impact on farm investments. We expected more educated household heads to have higher expenditures on farm investments because they are more literate. The coefficient of education in the OLS estimation might suggest this, however the coefficient in the IV method is not statistically significant. Households‟ wealth on the contrary has a clear positive impact on the total investments households made on water and land, livestock, equipment and buildings. Every additional Tsimidi of land a household owns, increases farm investments with 9 %. Almost the same effect is reported for livestock and fixed assets, but their coefficients are lower. A percentage increase in the total value of heifer, calves or camels owned

58


increases farm investments with 0.08%. For fixed assets, the effect is slightly stronger: a percentage increase of the value of fixed assets increases farm investments with 0.24%. The household wealth indicators seem to have a positive effect on farm investments and the effect of landholdings is the most pronounced. Also access of the household to social capital has a positive impact on durable investments: being a member of edir increases the expenditure on durables with 77%. Similarly, the number of adult labor forces in the household increases farm investments, suggesting that households with more members of working forces are able to invest more in durables. Durable investments are increased by 17% for every additional adult labor force. Access to irrigation has a comparable effect: having an irrigation system increases the expenditure on durable investments with 47%. Finally, the only variable that seems to have a negative impact on farm investments is the distance to Mekelle. Every additional km increase in the distance from the households‟ residence to the capital city, decreases farm investments with two percent. Although this effect is small, it is statistically highly significant. We expected that households that live further away from Mekelle will have fewer opportunities to invest in durables.

4.2.2.3

The effect on different types of farm investments

Next to this, we broke farm investments up into its different parts: water and land, livestock, equipment and buildings. This is done to see whether non-farm income has more influence on a particular type of farm investment. We regressed non-farm income and the control variables on each of the types of investments. The results of the different IV estimations are reported in Table 4.12 and the results of the OLS estimation are given in appendix 2. For both investments in livestock and equipment, the results are similar with the results on total investments: non-farm income is positively related with investments and this relation is significant at the one percent level. The coefficient of non-farm income in the livestock and equipment regression is however higher than the coefficient of non-farm income in the total farm expenditures regression: 0.63 and 0.89 respectively. The effect of non-farm income on farm investments is thus most pronounced in investments made in equipment and livestock. Again, the OLS regression underestimates the effect of non-farm income on the two types of investments, and this downward bias in OLS estimation is most probably caused by the omitted variables bias.

59


Table 4.12: IV estimation results of investments in water&land, livestock, equipment and building

non-farmincome

2SLS estimation coefficients Water&Land Livestock Equipment 0.1557 0.6326 *** 0.8921***

Buildings 0.3539**

fm1sex

0.5844*

1.1762***

0.4714*

fm1age

0.0098

0.0604 ***

0.0667***

0.0110

fm1schooled

0.1395

0.9964***

hhlandholdsize

0.1630***

Livestock

0.0086

Fixed

- 0.1556

tabiadismak

- 0.0241***

- 0.0031

- 0.0409

0.1217

0.0002

0.0870

0.0330

0.1340 ***

0.0757*

0.0201

0.1911

0.1730

0.4631***

- 0.0003

- 0.0197***

- 0.0114**

Edirm

0.3675

0.8191 **

Havei

0.2727

0.7220 *

Adults

0.3467***

- 0.0089

- 0.0564

- 0.1774*

constant

2.1850

- 4.9654***

- 6.4511***

- 3.4033**

Endogeneity test Overidentification

1.34 10.82***

6.51** 0.21

- 0.1134

0.4794

0.5108

0.1489

41.02*** 0.41

0.12 2.17


Non-farm income has also a positive and significant impact (at the five percent level) on investments in buildings. The endogeneity test is however unable to reject the null hypothesis that non-farm income is exogenous and the IV method is hence not preferred or even necessary. We discussed the weakness of such endogeneity tests before, and assume that the model is valid. Finally, non-farm income has a positive, yet insignificant effect on investments made in water and land. The model validity is questioned as the endogeneity test cast doubts on non-farm income‟s endogeneity and the overidentification test fails to prove the instruments validity. It seems as if the number of dependents and the share of non-farm income are related with the investments in water and land. Indeed, when both of them are separately included as control variables, they significantly influence water and land investments.

4.2.2.4

The impact of non-farm income on farm inputs

Finally we estimated the impact of non-farm income on farm input use. We restricted the farm input use to the value of fertilizer and improved seeds. This means that we excluded local seeds (which however make up the biggest part) and labor use (which is the smallest part). This distinction was made to estimate the impact of non-farm income on modern farm inputs. The results of the estimation are reported in Table

60


4.13. The null hypothesis of the endogeneity test is strongly rejected, indicating the necessity of use of the IV estimation. The test of the validity of the instruments however performs dramatically. The overidentification test is strongly rejected which cast doubts on the legitimacy of the instruments used. Indeed, when we regress both instruments as part of the control variables they are highly significant in explaining the value of farm inputs. As a result, we will focus on the OLS estimation. Table 4.13: OLS and 2SLS estimations results of farm input use

non-farmincome fm1sex

OLS Coef. - 0.0535 **

Std. Err. 0.0269 0.2424

1.4288***

0.3257

- 0.0092

0.0069

-0.0615***

0.0151

fm1schooled

0.0602

0.1912

0.2297

0.2641

hhlandholdsize

0.2570 ***

0.0349

0.1375**

0.0597

livestock

0.1107 ***

0.0273

0.0514

0.0406

fm1age

1.3215 ***

IVREG2 Coef. Std. Err. -0.8939*** 0.1905

fixed

- 0.0630

0.0657

0.0385

0.0999

tabiadismak

- 0.0161 ***

0.0038

-0.0096*

0.0057

edirm

0.7076 ***

0.1946

0.7339**

0.2921

havei

0.8808 ***

0.1943

0.5569*

0.3087

adults

0.2136 ***

0.0605

0.4915***

0.1091

constant

2.8783 ***

0.6175

8.7494

1.5262


34.06***


13.05***


Participation in non-farm activities seems to have a negative impact on the expenditures made on fertilizer and improved seeds. This inverse effect is significant at the five percentage level, but the estimated coefficient is only small. A percentage increase in non-farm income decreases households‟ expenditures on farm inputs by 0.05%. Most of the control variables have the same effect on modern inputs expenditure as they had on total expenditure and durable investments. The estimation of effect of the sex and educational level of the household head, the landholding size, the value of the livestock assets, the distance to Mekelle, the membership in edir, the access to irrigation and the number of adult working forces in the household is similar in comparison with the estimations in the farm expenditure and durable investment

61


regressions. Male headed households with bigger landholdings seem to spend more money on farm inputs. The effect of the gender of the household is again the strongest: being a male household head increases farm investments with 132%. Moreover, if the household has access to irrigation or more labor forces, the level of expenditures on fertilizer and improved seeds is increased. This effect is the strongest for farm modern inputs: having an irrigation installation increases the expenditure on farm input with 88%. Next to this, if the household is member of edir they will spend more money on modern inputs. The latter effect has the same magnitude as before: being a member of edir increases the purchase of modern inputs with 71%. Households that live further away from Mekelle spend significantly less on modern farm inputs. Finally, both the education of the household head and the value of fixed assets do not have a significant impact on the expenditure on fertilizers and improved seeds. Their estimated coefficient is not statistically significant different from zero.

4.2.2.5

The effect of different types of non-farm income

Finally, we broke non-farm income up into its components and study their effect separately on farm investments. The rationale behind this distinction was that the two types of non-farm activities differ in nature. Hence, we regressed equation eq. 1 but with respectively wage and self-employment (business) income instead of total nonfarm income. We used durable investments rather than total farm expenditure because the model specification of the former are superior. Both OLS and IV estimation results of these regressions with wage and self-employment are respectively given in Table 4.14 and Table 4.15. As we were unable to construct separate instruments for both wage and self-employment income, we run the IV regression with the same instruments used for non-farm income. Appendix 1 reports the first stage results of the regression of the control variables and instruments on both wage and self-employment income. It seems as if the number of dependents and the share of non-farm income are good instruments for wage income, as all the previous discussed tests perform even better. Table 4.14 shows that the endogeneity test is rejected, indicating the requirement of an IV method. Wage income positively influences farm investments and has an elasticity of 0.42. Hence, a percentage increase in wage income increases farm investments with 0.42%. this effect is significant at the one percent level. Similarly, the OLS regression underestimates the

62


impact of the wage income on farm investments: the coefficient of wage income is only 0.07. Table 4.14: OLS and 2SLS estimations results of farm investments with wage income

wageincome

OLS Coef. Std. Err. 0.0748 ** 0.0283

IVREG2 Coef. Std. Err. 0.4233*** 0.1167

fm1sex

1.0132 ***

0.2646

0.8958***

0.2906

fm1age

0.0066

0.0083

0.0304**

0.0121

fm1schooled

0.3720 *

0.2116

0.3557

0.2265

hhlandholdsize

0.0158

0.0356

0.0731*

0.0413

livestock

0.0485

0.0296

0.0755**

0.0336

fixed

0.3230 ***

0.0857

0.2977***

0.0943

- 0.0161 ***

0.0048

- 0.0198***

0.0050

edirm

0.8214 ***

0.1928

0.9445***

0.2257

havei

0.2863

0.2097

0.4321*

0.2501

adults

0.3332 ***

0.0707

0.2027**

0.0880

constant

2.1436 ***

0.7527

tabiadismak

- 0.2387

1.0650


10.92***


0.03


Appendix 1 shows that for self-employment, the two instruments perform problematic, as they fail for the overidentification, underidentification and weak instrument test. The instruments

used

are

not

strong

related

with

self-employment

income.

Also

Woldenhanna and Oskam (2001) find that the number of dependents affects wage activities but not self employment activities. Moreover, according to Table 4.15, the endogenous nature of self-employment income is questioned. Although the model is not valid, it suggest that self-employment is negatively, but insignificant, related to farm investments.

63


Table 4.15: OLS and 2SLS estimates results of farm investments with business income

OLS

IVREG2 Coef. Std. Err. - 1.2242 1.1183

businessincome

Coef. 0.0188

Std. Err. 0.0329

fm1sex

1.0495***

0.2657

0.2941

0.7315

fm1age

0.0018

0.0082

- 0.0157

0.0221

fm1schooled

0.3790*

0.2129

0.2262

0.4099

hhlandholdsize

0.0036

0.0353

- 0.0001

0.0624

livestock

0.0429

0.0298

0.0423

0.0534

fixed

0.3241***

0.0852

0.6044** 0.2791

- 0.0151***

0.0048

- 0.0232** 0.0109

edirm

0.7873***

0.1920

1.2957** 0.5994

havei

0.2580

0.2081

0.0365

adults

0.3615***

0.0692

0.3088** 0.1220

constant

2.6227***

0.7389

4.7342

tabiadismak

0.4567 2.2847


2.19


4.42**


4.2.3

Discussion

4.2.3.1

The impact of non-farm income

Our results indicate a positive relation between non-farm activities and both farm expenditures and investments. This means that non-farm income increases the amount of money spent on farm expenditures and in particular on investments. Hence, our results provide evidence that participating in non-farm activities has a positive impact on agricultural activities: households are able to increase their expenditures and investments in farm activities. Our results are in line with the empirical evidence of the literature on investment linkages (Ruben and van den Berg, 2001; Anriquez and Daidone, 2009; Maertens, 2009; Stampini and Davis, 2009). The effect of non-farm income is the strongest on durable investments. We have distinguished the impact of non-farm income on the different types of investments and our results indicated that the impact of RNFE is particularly important for livestock and equipment. The former result might be explained by the findings of Pender and

64


Gebremedhin (2004) in Tigray. The authors find that investments in livestock increase crop productivity and households‟ income. Hence, it can be assumed that non-farm income would be invested in farm activities with the highest payoff such as livestock. The strong impact of non-farm income on equipment indicates the RNFE can support the purchase of new or improved tools, replacement of old materials and the adoption of modern techniques. This makes sense, as Woldenhanna (2002) finds that the dominant type of farm input in Tigray is traditional farming technology: simple hand tools and oxen-driven implements. Finally, the RNFE also has a strong impact on investments made in buildings. This result might suggest that non-farm income is seen as an income surplus which can be invested in important, though not vital, investments in the farm. Other sources of cash might be used to finance daily consumption needs. Non-farm income did not have a significant effect on investments made in water and land. This is a somewhat surprising result. Pender et al. (2006) note that the most common investments in Tigray are stone terraces and soil bunds. These investments have been widely promoted through food for work programs and community labor or private incentives for soil and water conservation. Pender et al. (2006) and Pender (2004) find that investments in stone terraces increases crop production, because they help households to overcome soil moisture deficiency. The average rate of return to stone terraces ranges between 34% (Pender and Gebremedhin, 2004) and 46% (Pender et al., 2006). Hence, it is not clear why non-farm income does not increase these investments, as it is found that these investments have a high payoff. Although the RNFE had a stimulating effect on investments in durables, the impact of non-farm income on farm input use was inverse. Households with more non-farm income spend significantly less money on modern farm input use. Our results are in line with other literature in Ethiopia that suggest or find the same relation. Both Pender (2004) and Holden et al. (2004) find a negative relation between non-farm activities and fertilizer use in Ethiopia. This outcome is however in contradiction with the results found by Woldenhanna (2000). He finds the use of variable farm inputs to be highly and statistically significant influenced by non-farm income. His results show that increase in non-farm income by 1% increases expenditure on input use by 0.43%. How can it be explained that non-farm income decreases expenditures on farm input? The profitability of modern inputs in low agricultural areas is limited (Pender, 2004; Ehui and Pender, 2005). Pender et al. (2006) find that on average, fertilizer use is unprofitable in Tigray because the use of fertilizer has only a marginal impact on crop production. This yield increase is insufficient to cover the average costs of fertilizer. 65


This explains the abhorrent attitude of farmers towards the adoption of modern inputs despite the governments‟ efforts to promote its use. Our descriptive statistics show that the use of fertilizer and improved seeds was restricted, and its value was minor in comparison with investments in durables (Table 4.1). Moreover, the purchase of modern inputs is closely bound with credit obtained from extension programs (Woldenhanna, 2002). Fertilizer is hence supplied on credit basis, and households participating in extension programs are less likely to spend their non-farm income on modern farm inputs (Kidane, undated). Not only are modern inputs unprofitable, Kidane (undated) suggests the net benefit cost ratio for farm input use to be low in comparison to alternative investment lines. It is hence more profitable to invest in livestock for example. Also Dercon and Christiaensen (2010) find that the main raison for not using modern inputs is the high price. Input use is expensive and real fertilizer prices have increased. Moreover, the availability of credit for inputs is widespread because of parastatal structures of fertilizer provision, but the high interest rates are problematic. This has decreased the relative output-fertilizer price ratio. This trend is especially problematic when harvests are poor, for example because of poor weather conditions. The sunk cost of fertilizer will hence decrease the returns, which will be lower than the returns if fertilizer was not used. The use of fertilizer is thus a risky activity with moderately higher returns compared to not using fertilizer (Dercon and Christiaensen, 2010). In the majority of regressions, we were unable to reject the endogenous nature of nonfarm income. Hence, the use of an IV method was necessary and superior to the OLS estimation. Overall, the two variables used to instrument non-farm income have proved to be appropriate instruments. In most regressions, the under-, over- and weakidentification tests have proven the validity and relevance of the instruments. However, both instruments were positively related with farm input use and water and land investments. We were aware of the possible relation between the number of dependents and farm decisions. However, the instrument pass all the tests in other models, so we can assume that our instruments are not too weak in general. In the expenditure and investment regressions, the OLS estimates underestimate the coefficient of non-farm income in comparison with the IV estimation. Hence, the impact of non-farm income is stronger if we eliminate the endogenous effects of non-farm income, causing the OLS estimation to be biased downward. Such a bias was expected and can be explained by several possible omitted variables. Various factors might be at stake, and because of the latter, the direction of the bias was not clear a priori. 66


However, as our hypothesis that households use income from RNFE to invest in farm activities is likely to be confirmed, this could indicate that the omitted variable bias is highly related with credit constraints. As we were not able to gather information and hence control for the latter, it is possible that they have a strong correlation with the omitted variables. Our results hence suggest that households participate in non-farm activities in order to earn additional cash to overcome credit or liquidity constraints. This additional income gives farm households the opportunity to invest in their farm activities. Similarly, Stampini and Davies (2009) and Lee and Sadawa (2007) find that in the context of liquidity constraints, omitting variables associated with these credit constraints causes a downward bias in the regression result. The direction of the bias is the result of the product of the signs of, on the one hand the covariance between non-farm income and the omitted variable and on the other hand the covariance between the omitted variable and farm investments. As Stampini and Davies (2009) point out, the former is positive (households react to credit constraints by engaging more in non-farm activities) and the latter is negative (credit constraints hinder households to spend money on farm investments). Lee and Sadawa (2007) find that the estimation of precautionary savings suffers from omitted variable bias when a large proportion of the households is credit constrained. This results in a downward bias: correcting for the omitted variable bias increases the regression coefficient of the estimated prudence. Finally, we have also distinguished the impact of the two types of non-farm activities on farm investment and found a distinct effect. The difference in impact of wage and selfemployment income is however not surprising. Wage employment is the dominant type of non-farm activity and therefore has the biggest contribution to the non-farm income and hence total household income. Hence, it dominates the impact of non-farm income on farm investments. Wage jobs are easily accessible and important for households during and outside farm-intense periods. Self-employment income is only a minor component of the households‟ total income, as can be seen in Table 4.2. This explains why the impact of non-farm income and wage income on farm investments is highly similar, whereas self-employment has a small (and even insignificant) impact. The different effect of wage and self-employment income on investments can be explained by the inherent differences between the two types of non-farm activities. Woldenhanna (2000) observes that households in Tigray participate in off-farm wage employment because of push factors and self-employment because of pull factors. The latter implies that non-farm wage activities can be considered as residual employment 67


that absorbs family labor which cannot be fully employed on the farm. Self employment activities are undertaken by households to reap the attractive returns. Wage and selfemployment activities also require different input requirements. Self employment generally requires more capital and managerial skills than wage employment so undertaking self-employment might be more difficult than wage employment. If the access to credit is constrained and own capital is restricted, farmers will prefer nonfarm activities that require less initial capital (Woldenhanna, 2000). Wage employment might also be a safer and more direct source of liquidity and plays a stronger role in inducing spendings (Oseni and Winters, 2009). Moreover, Hertz (2009) notes that wage income is an important cash-on-hand, especially if there are no alternative opportunities for saving, or if there is a big difference between the interest rates on savings versus loans. It will therefore have a stronger role in determining households‟ investment expenditures. It is more likely that households will participate in wage activities to overcome liquidity constraints. According to Hertz (2009), it makes sense that cheaper forms of finance would be used for farming if the coverage of current bills is mandatory and agricultural expenditure are optional and risky. Selfemployment jobs require investments, which compete with farm investments. Liquidity constraints hamper the entry to these jobs, hence these activities suffer from liquidity constraints rather than solving it. Only households with a good asset position may face relatively less credit constraints and hence prefer to work in (more remunerative) selfemployment activities (Woldenhanna, 2000). We therefore observe a negative, although not significant impact of self-employment income on farm investments.

4.2.3.2

The effect of control variables

The impact of the control variables on the amount of money spent by the households on expenditures, seems to be similar for total expenditures, durable investments and farm input use. Table 4.16 gives an overview of the effect of the different individual and household characteristics that have a statistically significant effect on total farm expenditures, durable investments and input use. The sex of the household head is an important factor that determines all tree expenditures. Male household heads invest substantially and significantly more than their female counterparts. Oseni and Winter (2009) find the same relationship and suggest that females are more likely to farm on a smaller scale and hence spend less on agricultural inputs. However, as the regression did not incorporate the amount of male and female household members, our results does not say anything about the gender bias within households. Other studies, like

68


Maertens (2009) find evidence of a clear gender bias within the household: female household members work on family plots while male household members control the land and make farm decisions. Table 4.16: Summary of the impact of non-farm income and control variables

Total expenditures

Durable investments

Input use

non-farmincome

+

+

-

fm1sex

+

+

+

fm1age

0

+

0

fm1schooled

0

0

0

hhlandholdsize

+

+

+

livestock

+

+

+

fixed

+

+

0

tabiadismak

-

-

-

edirm

+

+

+

havei

+

+

+

adults

+

+

+

Notes: +: positive impact, -: negative impact and 0 : no statistical significant impact

The age of the household head increases the expenditures on durable investments. Older individuals tend to have more agricultural experience and means to make investments than then their younger counterparts. It is however more likely that there is a convex relationship at work: investments increase with age until a certain threshold is reached and decrease afterwards (the life cycle effect, as noted in Woldenhanna, 2000). Kidane (undated) suggests that older people spend less of the non-farm income. Due to the possible risk of high multicollinearity we have chosen not to incorporate the squared term of the age of the household head in our regression. The insignificant relationship between input use and the age of the household head is rather surprising. We expected more educated household heads to have higher expenditures on farm investments because they are more literate. Our results suggest that education has a positive effect on all types of investments, and this is in line with most empirical evidence. These studies usually find a positive effect on investments but at a decreasing rate (Kilic et al., 2009). However, the effect of education is not statistical significant for any type of investment. This is consistent with the effect of education on fertilizer expenditure found by Kidane (undated). He suggests that education does not have the power to expand fertilizer use because the average education attainment in the Geba catchment is low (below first grade level).

69


The total size of landholdings and the value of the livestock owned by the household both increase farm investments of any kind. The effect of landholdings is much stronger than that of livestock. Our results suggest that landless farmers spend less on agricultural inputs than farmers who own their cultivated land. Oseni and Winters (2009) note that this is not surprising because landless households usually rent land or sharecrop, while landlords are responsible for the expenditures of investments. Larger farms may have the advantage of economies of scale and their use of modern inputs is more likely to be profitable. Households‟ value of fixed assets on the one hand increases investments in durables, while on the other hand it decreases input use. The latter is however statistically not significant. This confirms that households with more land and assets spend more money on farm related activities. This could indicate that more endowed farmers are more active in farm activities and therefore make more investments in their farm. Also Kidane (Undatedb) suggests that farmers with more assets have more incentives to use modern inputs or adopt new technologies. Households‟ wealth can help farmers to afford, bear or spread risks associated with adoption and larger farms have more opportunities to adopt new farming practices. Moreover, the larger the land size, the more beneficial fertilizer and input use becomes. Besides these household wealth indicators, social capital has a strong and highly significant impact on all types of farm investments. Social networks such as edir often offer relevant and qualitative information to its members, making them more likely to invest in agricultural activities. Kilic et al. (2009) suggest social capital enhances farm investments because it reduces risk and increases access to capital when reciprocal relations are important. The distance to Mekelle has the most pronounced negative impact on the amount of money spent on all types of investments. It is believed that rural areas close to urban centres have greater farm/non-farm linkages (Davis et al., 2002). Our results confirm that farmers that live near Mekelle have superior access to markets and are therefore in a better position to invest in their farm. Farmers they live further away from the regional capital are discouraged to use modern agricultural inputs because of the high transport cost (Kidane, undated). Having access to an irrigation system also enhances the expenditures on investments of any kind, indicating that irrigated land requires more inputs and is cultivated more intensively. This result shows that irrigation systems promote investments and the use of farm inputs. Finally, the number of adult labor forces has a positive impact on farm expenditure. This suggests that households with more contributing members of working forces are able to invest more in their farm activities.

70

Chapter 5: Conclusions and Recommendations

5

CONCLUSIONS AND RECOMMENDATIONS

5.1

Conclusion

In Ethiopia, rural development drives the agricultural sector in stimulating the rural economy. However, environmental problems, low agricultural productivity, inadequacy of previous policies and the emergence of the livelihood concept necessitate a wider and more synergistic development path. In this context, the development of the RNFE sector was inevitable. Nowadays the non-farm sector is considered to be a significant component of the rural economy. As the RNFE and agricultural sector become equally important in a synergistic development approach, it is essential to study how these two sectors are inter-linked. The aim of this study was therefore to explore the impact of RNFE on agricultural production, focusing on a possible complementary relation between non-farm income and farm investments. Recent development literature has hypothesized the existence of investment linkages. Investment linkages imply that farmers use non-farm income to finance farm activities which will result in an increase of the expenditures on farm investments and input use and enable farm modernization. These investment linkages are especially important for credit constrained households, since these are restricted from spend credit on farm investments. Non-farm income might be used to overcome these credit constraints. However, empirical evidence does not unambiguously confirm that non-farm income complements farm activities. It is studied that non-farm activities sometimes withdraw resources from the farm and hence compete with agricultural production. The primary objective of this study was therefore to determine whether non-farm income enhances farm expenditures through investment linkages. In addition, the importance of the RNFE sector for rural households in a developing country such as Ethiopia was of interest. Simple descriptive statistics confirm that nonfarm income constitutes a substantial part of the total household income and access seems not to be restricted. 80% of the households had at least some non-farm income and non-farm income on average accounts for 27% of the total household income. We found no indication for the existence of entry barriers which hamper the entrance into non-farm activities. Participation in non-farm activities is mainly determined by push factors: dependent household members that need to be supported, excess labor forces and low involvement in livestock activities. However, credit constraints are assumed to

71


be one of the most (or even the most) important push factor. We were unfortunately not able to incorporate this in our regression model. The lack of information about credit constraints is suggested to be the major source of unobserved heterogeneity (omitted variable bias), although several other factors might be at stake. Omitted variable bias causes non-farm income to be endogenously related with farm investments. As a consequence a bias in the OLS regression occurs. Moreover, decisions about RNFE and farm activities might be taken jointly, creating a simultaneity bias between farm investments and non-farm activities. Using IV methods, we were able to deal with both biases. Comparison of the IV and OLS estimations indicated that the OLS estimates of the coefficient of non-farm income are biased downwards, and this due to omission of liquidity constraints variables We suggested that unobserved credit constraints hamper households to invest on their farm, but push households to seek for additional, non-farm income sources. Using household survey data from Tigray province, we provided evidence for the investment linkage hypothesis. The regression results proved that RNFE has a substantial and positive impact on farm expenditures and investments. A percentage increase in non-farm income increases farm expenditures and investments with respectively 0.18% and 0.58%. Moreover, these investments seem to concentrate mainly on livestock and equipment. These results indicate that non-farm income is an important source to finance investments that have the highest payoff (and hence impact) on farm activities. It is assumed that this relationship is mainly driven by farm liquidity

constraints,

forcing

households

to

look

for

additional

credit

sources.

Households participate in the RNFE to gain a surplus income that can be used to invest in farm activities, in which they would be unable to engage otherwise. One seemingly surprising result was that non-farm income does not increase farm input use and hence does not fit in with the evidence of investment linkages. We suggested that this apparent contradiction is the result of local conditions. While extension and credit programs have increased the access of farmers to inputs (especially fertilizers), the efficiency (and profitability) of modern inputs is low because of a decreased output/fertilizer price ratio, higher rate of return to other investments or vulnerability to inadequate soil moisture. Hence, households prefer to spend the credit that is obtained by participating in non-farm activities on more profitable investments such as livestock or equipment. The latter are the two most important types of traditional technology used by farmers in Tigray.

72


The regression analysis also indicated the importance of dividing non-farm income into its distinct sources. In our analysis, non-farm activities include both wage and selfemployment activities. Wage income is the most important non-farm activity in Tigray. Wage jobs are mainly temporary and do not require specific education or training. They are therefore accessible to many rural farm households and form a direct source of cash. On the contrary, self-employment activities only have a minor share in the total household income and it is assumed that some initial investments are required. As a result, we noticed that wage income increases farm investments and expenditures, while self-employment did not have a significant impact on farm activities. Overall, the credit constraints farmers face can be superseded by participation in the RNFE sector. This implies that farm/non-farm linkages are present in the development of the RNFE, and should therefore play an important role in the rural development approach. The suggested virtuous circle is therefore possible: promoting access to RNFE increases farm modernization through investment linkages. Better access and availability of farm households to non-farm activities increases farm investments and expenditures. This in turn increases demand for non-farm activities, especially nonfarm wage labor. The next section summarizes policy recommendations, suggested from our analysis, that should contribute to a more synergistic rural development path.

5.2 

Policy Recommendations The analysis emphasizes the importance of non-farm income in rural areas of Ethiopia. Not only is it a substantial share of the total household income, it is used to overcome liquidity constraints. It seems therefore highly recommended to incorporate RNFE in rural development, as it can be assumed as an alternative path to overcome poverty.



Non-farm activities should be wider available and wage rates should improve in order to increase the incentive and capacity of households to participate in nonfarm activities. Especially wage employment has a strong impact on farm investments. Policies should encourage the growth of the RNFE and strengthen the link between the different activities and sectors.



As no indication of entry barriers to participate in the RNFE exist, it is not necessary to promote policies to overcome entry restrictions. Rather, the profitability of RNFE activities should be improved. Providing better training and 73


education, encouraging small-scale enterprises, improving access to markets and improving public infrastructure will stimulate growth in the RNFE sector and enable households to take advantage of the opportunities that already exist. 

In addition, our results show that farm and non-farm activities are inter-linked: non-farm income can be used to support agricultural production via investment linkages. Non-farm and farm activities cannot be seen in isolation. This implies that both the RNFE and farm sector should be simultaneously supported in rural policies in order to improve durable rural development and stimulate farm modernization. The link between both sectors should be reinforced by complementary programs and policies. Hence, a synergistic development path seems

to

be

appropriate

in

Ethiopia,

where

the

agricultural

sector

is

omnipresent. 

Policies should incorporate evidence of investment linkages in order to target credit constrained households. Liquidity restrictions in the agricultural sector hamper farm productivity but can be surmounted by additional sources of credit and liquidity. Systems should be set up to relax the credit constraints of poor households, besides promotion of non-farm activities. Development of rural credit markets or microfinance will help households to improve their farm activities.



RNFE has a positive impact mainly on farm investments, yet the inverse relation is observed for modern input use. While previous extension systems focused primarily on the distribution of fertilizers and modern inputs, it did not seem successful. Therefore, the Ethiopian government should embrace both farm and non-farm activities and emphasize on the development of non-farm activities because these foster investments in livestock, buildings and equipment.



Overall, a virtuous circle is possible and should therefore be promoted. However, more empirical evidence of such self-enforcing effects should be provided by means of further studies. It is thereby suggested to conduct further research.

74

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Stampini, M. & Davis, B. (2009). Does nonagricultural labor relax farmers‟ credit constraints? Evidence from longitudinal data for Vietnam. Agricultural Economics, 40: 177–188. Sumberg, J., Gilbert, E. & Blackie, M. (2004). Income diversity, technology choice and agricultural research policy in Sub-Saharan Africa. Development Policy Review, 22: 131-146. Tesfay, G.B. (2009). Econometric analyses of microfinance credit group formation, contractual risks and welfare impacts in Northern Ethiopia. Ph.D. thesis. Wageningen University, Holland. 146p. van den Berg, M., & Kumbi, G. E. (2006). Poverty and the rural nonfarm economy in Oromia, Ethiopia. Agricultural Economics, 35: 469-475. Woldenhanna, T. (2000). Economic analysis and policy implications of farm and offfarm employment: a case study in the Tigray Region of Northern Ethiopia. Ph.D. thesis, Agricultural Economics and Rural Policy Group, Wageningen University, Holland. 277p. Woldenhanna, T. (2002). Rural farm/non-farm income linkages in northern Ethiopia. In: Promoting farm/nonfarm linkages for rural development: case studies from Africa and Latin America. Davis, B., Reardon, T., Stamoulis, K., Winters, P. (eds.). FAO, Rome, 121-144. Woldenhanna, T., & Oskam, A. (2001). Income diversification and entry barriers: evidence of the Tigray region in northern Ethiopia. Food Policy, 26: 351-365. Wooldbridge, J.M. (2002). Econometric Analysis of Cross Section and Panel Data. MIT Press. Cambridge, MA. 805p. Wooldbridge, J. M. (2002b). Introductory Econometrics: A Modern Approach, 2nd edition. Thomson South-Western. Cincinnati, O.H. 735p.

80

Appendix 1

APPENDIX 1 Table A1.1: First stage OLS regression with wage income

OLS fm1sex

Coef. 0.5192*

Std. Err. 0.2795

fm1age

-0.0765***

0.0089

fm1schooled

-0.0843

0.2477

hhlandholdsize

-0.0553

0.0504

livestock

-0.0771**

0.0361

fixed

-0.1605*

0.0903

0.0095*

0.0053

tabiadismak edirm

-0.0410

0.2846

havei

-0.2657

0.3012

adults

0.3690***

0.0828

dependents

0.2796***

0.0772

non-farmshare_tabia

0.0945***

0.0127

constant

4.9861

0.8612

Joint significance of IV F test of excluded instruments

35.52***


35.52***


56.93***


33.86


35.52


16.70***


15.70***


81

Appendix 1

Table A1.2: First stage OLS regression with business income

fm1sex

OLS Coef. -0.4740

Std. Err. 0.3118

fm1age

-0.0162**

0.0079

fm1schooled

-0.1764

0.2406

hhlandholdsize

-0.0057

0.0412

livestock

0.0001

0.0350

fixed

0.2283**

0.0926

tabiadismak

-0.0066

0.0049

edirm

0.4053

0.2891

havei

-0.1640

0.2793

adults

-0.0138

0.0801

dependents

-0.0989

0.0734

0.0009

0.0130

-0.4740

0.3118

non-farmshare_tabia constant Joint significance of IV F test of excluded instruments

0.91


0.91


1.85


0.84


0.91


16.70***


15.70***


82

Appendix 2

APPENDIX 2 Table A2.1:OLS estimations of investments in water & land, livestock, equipment and building

non-farmincome fm1sex

OLS estimation coefficients Water&Land Livestock Equipment -0.0623 0.1360** 0.0492 0.1146 *

1.3598***

-0.0055

0.0301**

0.0154**

fm1schooled

0.1673

1.0729 * **

0.0936

0.1724

hhlandholdsize

0.1394***

-0.0334

-0.0008

fm1age

0.6252**

Buildings 0.1160***

-0.0723 *

0.5260** -0.0035

Livestock

-0.0090

0.1009**

0.0196

0.0033

Fixed

-0.1128

0.2686**

0.3080***

0.5012***

tabiadismak

-0.0229***

0.0037 *

-0.0129*** -0.0667

Edirm

0.3488

0.8483**

Havei

0.1728

0.5356 *

0.1953

Adults

0.3969***

0.1517

0.2137***

constant

3.7491***

-1.6775*

-0.8813

-0.0095** 0.4894* 0.0638 -0.0987 -1.8389**


83