SIMEGNEW TAMIR ENDALEW

Transcription

1 THE BROKERAGE INSTITUTIONS AND SMALLHOLDER MARKET LINKAGES IN MARKETING OF HORTICULTURAL CROPS IN FOGERA WOREDA, SOUTH GONDAR, AMHARA NATIONAL REGIONAL STATE M.Sc. Thesis SIMEGNEW TAMIR ENDALEW OCTOBER 2012 HARAMAYA UNIVERSITY

2 THE BROKERAGE INSTITUTIONS AND SMALLHOLDER MARKET LINKAGES IN MARKETING OF HORTICULTURAL CROPS IN FOGERA WOREDA, SOUTH GONDAR, AMHARA NATIONAL REGIONAL STATE A Thesis Submitted to the Department of Agricultural Economics, School of Graduate Studies HARAMAYA UNIVERSITY In Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE IN AGRICULTURE (AGRICULTURAL ECONOMICS) By SIMEGNEW TAMIR ENDALEW OCTOBER 2012 HARAMAYA UNIVERSITY

3 APROVAL SHEET SCHOOL OF GRADUATE STUDIES HARAMAYA UNIVERSITY As thesis research advisors, we hereby certify that we have read this thesis prepared under our direction, by Simegnew Tamir, entitled The Brokerage Institutions and Smallholder Market Linkages in the Marketing of Horticultural Crops in Fogera Woreda, South Gondar, Amhara National Regional State and recommend that it be accepted as fulfilling the thesis requirement Name of Thesis Major-Advisor Signature Date Name of Thesis Co-Advisor Signature Date As members of examining Board of the Final M.Sc. Open Defense, we certify that we have read and evaluated the thesis prepared by Simegnew Tamir and recommended that it be accepted as fulfilling the thesis requirement for the degree of Master of Science in Agriculture (Agricultural Economics) Name of Chairman Signature Date Name of Internal Examiner Signature Date Name of External Examiner Signature Date Final approval and acceptance of the thesis is contingent upon the submission of the final copy of the thesis to the Council of Graduate Studies (CGS) through the Department of Graduate Committee (DGC) of the candidate s major department. ii

4 DEDICATION I dedicated this thesis manuscript to my father TAMIR ENDALEW. iii

5 STATEMENT OF THE AUTHOR I hereby declare that this thesis is my work and that all sources of materials used for this thesis have been duly acknowledged. This thesis has been submitted in partial fulfillment of the requirements for M.Sc. degree at Haramaya University and is deposited at the University Library to be made available to borrowers under the rules of the library. I solemnly declare that this thesis is not submitted to any other institution anywhere for the award of any academic degree, diploma, or certificate. Brief quotations from this thesis are allowable without special permission provided that accurate acknowledgement of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the Department of Agricultural Economics or the Dean of the School of Graduate Studies, Haramaya University, when in his judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author. Name: Signature: Place: Haramaya University, Haramaya Date of Submission: iv

6 BIOGRAPHICAL SKETCH The author was born on August 21, 1984 in Motta, East Gojjam Zone of Amhara Region. He attended his Elementary School at Aba Motta Elementary School and his Junior Secondary School at Agew Gemja Bet Junior Secondary School. He completed his high school education at Ankasha Guagussa Senior Secondary School at Agew Gemja Bet. The author joined Debub/ Hawassa University, College of Agriculture in 2002 and graduated with B.Sc. degree in Agricultural Resource Economics and Management on July Right after graduation, he was employed in Amhara Regional Agricultural Research Institute as a Socio-Economic Researcher and Program Coordinator at Andassa Livestock Research Center. After four years of service in the Research Center he becomes Assistant Researcher I and worked as Researcher until he joined Haramaya University, School of Graduate Studies in October 2010 for his M.Sc. degree in Agricultural Economics. v

7 ACKNOWLEDGEMENTS First and foremost let me praise and honor my GOD for giving me the opportunity and capacity to accomplish my thesis and for his unreserved gift. I would like to express my deep gratitude to my major research advisor, Dr. Kinde Getenet, IWMI and co-advisor, Dr. Jema Haji, Haramaya University, for giving me time from their tight schedule for their continuous advice, intellectual stimulation, professional guidance and encouragement in undertaking this study, as well as for their friendly supervision. IWMI has to be appreciated for giving me financial support for the study. My particular appreciation and deepest gratitude goes to my mother Ayehu Tegegne who has devoted her life in nursing me with affection and love which plays great role in the success of my life. My brothers, Adugna, Manaye, Yebeltal, Zemenu, Abrham and Adisu and my only sister Tinebeb deserve appreciation for their love in the family and motivation in undertaking the entire work. My heartfelt appreciation and great thanks goes to Ato Keralem Ejigu, Center Director of Andassa Livestock Research Center, for providing me the necessary materials such as field car and technical assistances to undertake my field works in the Fogera Woreda. Moreover, I would also like to offer my sincere appreciation to all the Researchers, Technical Assistants (Demelash Dagnaw,Yohanes Menberu, Worku Sendek, Eyasu Lakew and Kegne Yismaw), Driver (Dereje) and administrative staff of Andassa Livestock Research Center who supported me in the course of the study. I feel deep sense of gratitude for my friend Leoulsegged Kassa, Researcher, for helping me in briefing the propensity score matching model and providing the commands. I would also like to extend my appreciation to Fogera Woreda office of agriculture and rural development workers, trade and transport staffs and development agents of study areas for their support in data collection. Finally, I would like to thank the people of the study villages, brokers and wholesalers (Baye, Mengstu, Sete, Gizat and Huno) who extended their warm hospitality and generously shared their views and made this work possible. vi

8 LIST OF ABRIVATIONS AND ACRONYMS ADLI ANRS BoFED CSA CSE ECX FEDRE FIML GDP GTP ILRI IPMS Kms MoARD MSF MSI NIE NGOs OLS PADETS PASDEP PRSP PSM RMA SDPRP Agriculture Development Led Industrialization Amhara National Regional State Bureau of Finance and Economic Development Central Statistics Authority Conservation Strategy of Ethiopia Ethiopian Commodity Exchange Federal Democratic Republic of Ethiopia Full Information Maximum Likelihood Gross Domestic Product Growth and Transformation Plan International Livestock Research Center Improving Productivity and Market Success of Ethiopian Farmers Kilometers Ministry of Agriculture and Rural Development Ministry of State Farm Ministry of State Industry New Institutional Economics Non Government Organizations Ordinary Least Squares Participatory Agricultural Demonstration, Extension and Training System Plan for Accelerated and Sustainable Development to End Poverty Poverty Reduction Strategy Paper Propensity Score Matching Rapid Market Appraisal Sustainable Development and Poverty Reduction Program vii

9 TABLE OF CONTENTS STATEMENT OF THE AUTHOR iv BIOGRAPHICAL SKETCH v ACKNOWLEDGEMENTS vi LIST OF ABRIVATIONS AND ACRONYMS vii TABLE OF CONTENTS viii LIST OF TABLES xi LIST OF FIGURES xii LIST OF APPENDIX TABLES xiii ABSTRACT xiv 1. INTRODUCTION Background of the Study Problem Statement Objectives of the Study Significance of the Study Scope and Limitations of the Study Organization of the Thesis 8 2. LITERATURE REVIEW Definitions of Related Terms Major Policy Reforms in Ethiopia Related to Market Institutions Commodity Exchange What is commodity exchange? The rationale behind the establishment of Ethiopian commodity exchange Ethiopian commodity exchange current status The New Institutional Economics Approach Transaction costs Institutions to facilitate exchange Social capital Market Imperfection and the Brokerage Institutions in Ethiopia Properties of Horticultural Production and Marketing General properties of horticultural products 18 viii

10 Overview of Horticultural Crops production and Marketing in Ethiopia Horticultural Crop Production and Marketing in Fogera Woreda Production problems Marketing problems Production opportunities Impact Evaluation Methods Experimental methods Quasi and non-experimental methods Propensity Score Matching Empirical Studies on Horticultural Marketing Systems and the Role of Brokerage Institutions in Developing Countries and Ethiopia RESEARCH METHDOLOGY Description of the Study Area Land use pattern and population of Fogera Woreda Priority farming systems Methods of Data Collection Sampling Procedures Farmers sampling Brokers, rural assemblers and wholesalers sampling Retailers sampling Methods of Data Analysis Descriptive statistics Econometric models Propensity score matching model The Ordinary Least Square (OLS) regression RESULTS AND DISCUSSION The Brokerage Institutions Socioeconomic profile of brokerage institutions Which horticultural products have significant brokerage activity in the area? Characteristics and economic role of brokerage institutions The rationale behind the emergence of farmer brokers 70 ix

11 Market outlets or target markets of brokerage institutions Producer s perception of brokerage institutions Night transaction and loading Constraints of brokerage institutions Opportunities to the brokers Brokerage Institutions and Smallholder Market Linkages Descriptive statistics Demographic characteristics of sample households Socio-economic characteristics of sample households Institutional and organizational aspects Social capital Propensity score matching model Estimation of propensity scores Common support condition Matching of participant and non-participant households Impacts of the Brokerage Institutions Impact on net return from onion production Impact on percentage of marketed surplus Impact on Amount of Onion Produced and Land Allocated to Onion Production Sensitivity Analysis Brokerage Institutions and Wholesaler Market Linkages Demographic profiles of the wholesalers Socio-economic characteristics and assets of wholesalers Institutional and organizational aspects Wholesaler s perceptions of brokerage institutions Determinants of share (percentage) of brokered transactions SUMMARY, CONCLUSIONS AND RECOMMANDATIONS Summary Conclusions and Recommendations REFERENCES APPENDICES 111 x

12 LIST OF TABLES Table Page 1. Land use pattern of Fogera Woreda Farming system by ecological zone in Fogera Woreda Sampling frame and the sample size Variable definition and measurements for PSM Variable definition and measurements for Heckman two stage model Frequency distributions of brokerage institutions Descriptive statistics for continuous variables Descriptive statistics of some variables Descriptive statistics of sample households on pre-intervention characteristics Descriptive statistics of sample households (for dummy variables) Logit results of households brokerage institution participation Balancing test of matched sample Performance of matching estimators under the three criteria Impact of brokerage institutions Result of sensitivity analysis using Rosenbaum bounding approach Descriptive statistics of sample wholesalers (for continuous variables) Descriptive statistics of sample wholesalers (for dummy variables) Results of Ordinary Least Square (OLS) estimation.96 xi

13 LIST OF FIGURES Figure Page 1. Map of the study area Broker s chain and flow of transactions using brokerage institutions Kernel density of propensity scores before matching Kernel density estimates of participants before and after common support Kernel density estimate of propensity scores of non-participants households before and after common support...86 xii

14 LIST OF APPENDIX TABLES Appendix Table Page 1. Multicollinearity test for explanatory variables in PSM Multicollinearity test for explanatory variables in OLS Conversion factor used to calculate TLU Labor supply conversion factor (person day equivalent) xiii

15 THE BROKERAGE INSTITUTIONS AND SMALLHOLDER MARKET LINKAGES IN MARKETING OF HORTICULTURAL CROPS IN FOGERA WOREDA, SOUTH GONDAR, AMHARA NATIONAL REGIONAL STATE SIMEGNEW TAMIR Major Advisor: Kinde Getnet (PhD) Co-Advisor: Jema Haji (PhD) ABSTRACT The main objective of this study was to analyze the economic roles played by the brokerage institutions in smallholder market linkages to the wholesalers in vegetable marketing and determinants of decisions on whether to use brokerage institutions or not under imperfect market condition in Fogera Woreda, North Western Amhara Region particularly focusing on onion and tomato. Both secondary and primary data were collected for the study. Primary data were collected from a very wide number of respondents at all stages of the market channel where brokers are expected to play role. Two stage sampling techniques were used to select the sample farmers. Descriptive and econometric statistical models were employed for data analysis using STATA software. The study implemented the propensity score matching and Ordinary Least Square (OLS) estimation. The result of the study showed that the brokerage institutions are characterized as urban, peri-urban and farmer brokers. There is significant brokerage activity only for onion marketing and in the case of tomato marketing the brokers act as rural assemblers. Most of the horticultural trading in the area is undertaken by credit and thrust based. Logistic regression estimation of Propensity Score Matching revealed that Age, education level, distance of residence from development agent office, distance of residence from Woreta market, distance of residence from main asphalt road, access to cell phone (mobile phone) and number of regular wholesaler customers significantly affected the participation decisions of the smallholders in the brokerage institutions services. Kernel Matching with band width of 0.25 was found to be the best matching algorithm. The result of the study also revealed that, smallholder farmers using brokerage institutions have got ETB higher net income and 13.55% of greater marketed surplus than those smallholders who do not use. The OLS regression estimation showed that distance of residence of wholesaler, experience in trading, number of regular broker customers, number of regular farmer customers and number of regular wholesaler buyer customers found in other areas and cost of not using brokers significantly affected the intensity of use of brokerage institutions. Generally, the brokerage institutions are playing significant and important role in forming market linkages between smallholders and wholesalers under imperfect market conditions with their limitations. Therefore, the study highly recommends the formalization of the brokerage institutions through licensing, training and continuous follow up in the Woreda considering the experience of ECX. Key words: Fogera, Brokerage institutions, PSM, OLS, ECX xiv

16 1. INTRODUCTION 1.1. Background of the Study The primary development goal of the Ethiopian government is to achieve food security and sustain high economic and export growth levels with the aim to eradicate poverty. The current Growth and Transformation Plan (GTP) agricultural investment areas are divided into implementation directions: scaling up model farmers practices to all farmers, improving agricultural water use and expanding irrigation development, proper utilization of agricultural land, extensive use of labor, linking specialization with diversification, efficient agricultural marketing and increasing the production of high value agricultural commodities using medium and small scale irrigation systems to enable at least double production. Thus, the commercialization aspect is to be assisted through well organized market linkages so that what is produced can be marketed and this needs organizational set up among farmers and development of infrastructure, market information and market institutions (MoARD, 2010). Ethiopia has highly-diversified agro-ecological conditions which are suitable for the production of various types of fruit and vegetables. However, the contribution of horticultural crops both to the diet and income of Ethiopians is insignificant. With the aim of enhancing agricultural development, the Government considers various projects, including small-scale irrigation mainly through rainfall harvesting and home gardening, to be of crucial importance. As a result, vegetable and fruit production is being more widely adopted, primarily to ensure food security and promote production of high-value crops for the market and improving the living conditions of smallholders (Abebe, 2008). In Amhara Region, agriculture contributed to about 55.8 % of the total regional GDP and accounted for an employment of 87.4 percent of the total population (BOFED, 2011). Crop production in the region is rain fed, supported by very little irrigation mainly for vegetables. According to CSA (2012) the total cultivated land size of ANRS by the year 2011/12 was estimated to be million ha from which, horticulture covered about thousands of

17 ha and produced over 5.8 million quintal through employing over 3.5 million small-holders. Onion covered 12, and tomato covered ha of land. Fogera Woreda, where the study focused, is endowed with diverse natural resource, with the capacity to grow different annual and perennial crops. Two major rivers are of great importance to the Woreda, Gumara and Rib. They are used for irrigation during the dry season for the production of horticultural crops mainly vegetables. Major types of vegetable crops grown in the area include potato, onion, tomato, garlic, green peppers and some leafy vegetables. Owing to its production potential (seasonal irrigation and rainfed-based, low cost, and organic agriculture) and easily accessible road transport to reach local markets (Abay, 2007), the area is experiencing an emerging commercial horticulture production by smallholders in recent years. According to the Fogera district Bureau of Agriculture and Rural Development, there was an estimated 19,774ha of land cultivated under horticulture crops in 2010, from which a total of 203,063tons of vegetables is produced. The respective figures increased to 20,635ha and 270,484tons in A considerable number of farmers in the district are involved in commercial production of vegetables, mainly onion and tomato, using both irrigated and rainfed agriculture. Such growing participation of farmers in commercial vegetable production is contributing to a changing farming system (especially in the livestock farming system) and to new livelihood strategies in the vegetable producing areas of the district. Smallholders in the Woreda participate in commercial agriculture by producing and marketing horticulture crops for local and national markets using the services of the brokerage institution. The marketing channel of tomato and onion crops is through the interconnection of different actors namely producing farmers, rural assemblers, wholesalers, retailers, consumers, transporters and brokers. Wholesalers and brokers control the whole channel (because of asymmetric market information) resulted in an exploitative market behavior in onion market. 2

18 1.2. Problem Statement Strong assumptions like large number of buyers and sellers, complete information, perfect mobility of resources, free entry and exit and price taken by all economic agents (price is determined by the market) are the characteristics of perfect competition (MasCollel et al., 1995). This ideal situation, however, does not exist in the real rural agricultural market like Fogera Woreda. When market participants do not have equal information on prices, quality and quantities of the product under transaction and on the number of trading agents in the market, there is an incentive for better informed agents to uphold information and maximize their private benefits (Cramton, 1984). Incomplete information increases transaction costs and leads to bargaining inefficiency. The dynamics of horticultural marketing has a great influence on farmer s response in terms of production and market participation which in turn influences the level of income and poverty situation among smallholder farmers. Four ingredients that determine the acceptance of vegetables through a marketing system are quality of the product, volume of high quality produce, continuity of both volume and quality, and price the grower expect to receive (Nonneck, 1989). Moreover, the marketing system is influenced by a number of production, product and market characteristics like perishabity, price and quantity risks, seasonality, product bulkiness, and geographic specialization (Kohls and Uhl, 1985). Despite policy support as one of the mechanisms for creating investment opportunities in the horticulture sector for production, transportation, grading, exporting and financing the venture there is great problem of horticultural marketing in Ethiopia. Moti (2007) investigated the role of markets in the smallholder farmers resource allocation for subsistence food crops and commercial cash crop production. The results revealed that limited marketing outlets and lack of price information were the major factors that hindered the move from subsistence farming to cash crop production. Furthermore, Bezabih and Hadera (2007) described lack of local markets to absorb supply, low produce prices, plethora of intermediaries, and lack of marketing institutions and coordination among farmers as the major constraints on the marketing of horticultural crops in Ethiopia. They argue that poor product handling and 3

19 packing, imperfect pricing systems, and lack of transparency in market information are also among the impediments in the marketing of horticultural crops in Ethiopia. Efficient coordination in traditional markets is a prerequisite for a successful smallholder commercialization towards rural transformation, poverty reduction and agrarian change in the developing countries. However, it is often staggered by the problem of market imperfection and institutional underdevelopment that increase transaction cost and risk faced by smallholders. In addition, well organized market linkage needs organizational set up among farmers and development of infrastructure, market information and market institutions. Of all the institutions that might contribute to enhance the operation of markets, several studies (eg. Jema, 2008; Shiferaw et al., 2009; Lokanathan and De Silva, 2010; Quattri et al., 2011) have documented the crucial role played by brokers. These studies outline the benefits farmers and wholesalers derive from engaging in the services of brokers such as technical support, finance, risk sharing and information. However, very few contributions have investigated the variables influencing the decision of economic agents to use brokers ( Eleni, 2001; Jabbar et al., 2008; Quattri et al., 2011) and only Eleni (2001) and Quattri et al. (2011) has attempted to explain the actual decision processes followed by traders in the use of brokers. When it comes to farmers, to our best knowledge, no attempt has been made to explain the determinants of farmers decisions to use brokers and their impacts on smallholder farmers. Yet, it is increasingly recognized that the formulation of market-enhancing policy and intervention programs require a clearer understanding of transaction costs, institutional marketing arrangements, and microeconomic trader behavior (Dercon 1996). There are no producer organizations, such as cooperatives to coordinate horticultural marketing purpose in Fogera on behalf of farmers, against a growing demand for the products in different parts of the country. Although multipurpose cooperatives had been established in the district a few years back, they remain inefficient to effectively coordinate the marketing activities and to successfully link farmers to markets. Because of this, success in horticulture crop production as high value crops is not necessarily translated into a market success in the area. Such institutional bottlenecks against an emerging horticultural market have created a 4

20 fertile ground for a strong presence of brokers in the horticultural market of Fogera. Though road infrastructure and use of mobile telephones among farmers for market access and information exchange is reasonable, direct linkage of farmers to the wholesale market (the major market for the horticulture crops produced) is very limited. As a result, the majority of smallholders opt to use brokers to sell their products to wholesalers, who distribute products to different consumer and seasonally deficit producer markets in the country. Given the large volume of horticulture products in the area, combined with seasonal glut and high perishability, efficient market coordination and logistics are necessary to link Fogera horticulture farmers with the wholesale markets and to enable them generate sufficient economic incentives. In rural areas where producer organizations are absent and market institutions are underdeveloped, posing a challenge for smallholder market linkage, brokers could fill the coordination gaps and logistical constraints to facilitate exchange. Fogera provides a useful case in this regard where the brokerage institution, which dominantly exists informally, plays an important role in coordinating the horticultural marketing activities, starting from the farm. According to Amhara Regional Agricultural Research Institute and Amhara Regional Bereau of Agriculture (2008) participatory rural appraisal report, one of the priority research problems in horticultural marketing in the Woreda was the role and functions of informal brokerage activity in the area. However, the brokers at Fogera horticulture market (who play a market coordination role by constituting an important element of the invisible hand ), are not closely studied, known, and described in terms of their profile, functions and roles, organizational setup, impacts, and limitations and constraints to improve the performance, efficiency, and impact of the brokerage institution as an important intermediary in the horticultural supply chain of the area. Perhaps, this is a result of the less recognition the brokerage institution receives. This paper is intended to contribute to filling this knowledge gap in the area by addressing research questions like: What are the socioeconomic profiles and economic roles of brokerage institutions? What are the major constraints and opportunities of the brokerage institution in the marketing of horticultural crops? 5

21 How do brokerage institutions act in the market linkages between farmers and wholesalers? Are brokers trusted institutions fulfilling desirable economic roles? Which of the variables significantly impact on farmers decisions on whether to use brokers or not and determinants of intensity brokerage use by wholesalers? What are the impacts of brokerage institutions for smallholder farmers 1.3. Objectives of the Study The general objective of the study was: To assess the economic roles played by the brokerage institution in smallholder market linkages and identify determinants of decisions on whether to use brokers or not under imperfect market condition in the study area. The specific objectives of the study were: To assess the socioeconomic profile, economic roles, constraints and opportunities of the brokerage institutions To identify the determinants of farmers decision whether to use brokerage institutions or not as a means of market linkage to wholesalers To measure the impact of brokerage activities on percentage of marketed surplus and income generation capacity; and To identify the determinants of wholesalers decisions on the extent of brokerage intuitions usage under imperfect market condition of horticultural marketing. 6

22 1.4. Significance of the Study Horticultural marketing in Ethiopia and Fogera Woreda in particular is constrained by number of factors such as seasonality of production, perishable nature of the product, bulkiness, imperfect market information and market power by traders. Many studies indicated that the dynamics of horticultural marketing have great interaction with farmer s participation and production response in developing countries and Ethiopia. Recent studies in developing countries indicated that the brokerage institutions play great role by solving market imperfection by providing market information, finance, technical support and risk sharing. Therefore, study of the brokerage institutions and smallholder market linkages in the horticultural marketing of Fogera district is very crucial to identify and inform Government and other development partners with possible strategies that would support horticulture marketing to improve the economy of the Region and more specifically the income of poor farmers which in turn helps farmers coming out of poverty Scope and Limitations of the Study The research concentrated on the Fogera District, South Gondar horticultural production area to major market centers (Gondar and Bahir Dar cities). The type of crop was limited to onion for its proportion in production and marketing in the area. Other vegetable crop types are not considered because their production is limited and have little proportion in marketing activities and no report of strong brokerage activities. Along the marketing chain the consumers were not considered because of the expectation of no brokerage activity between the retailers and consumers. The study has also considered only samples of the market actors along the horticultural market chains and detail investigations in relation to production and consumption studies were not undertaken. The other one is the limitations associated with the Propensity Score Matching Model. It needs large sample size, group overlap and hidden bias because matching only controls for observed variables. The research used different techniques such as increasing sample size, common support conditions and sensitivity analysis in order to reduce these limitations. 7

23 1.6. Organization of the Thesis Excluding the introduction, the next part of this thesis is organized in to four parts. The literature review includes the concepts of market, polices related to market, brokerage institutions, the new institutional economic theory, horticultural marketing, methodologies used in impact evaluation and empirical studies on the roles of brokerage institutions. The methodology part includes description of the study area, methods of data collection and data analysis. The result and discussion section presents the descriptive and econometric results and discusses the research outcomes. The final section of the Thesis presents summary of the findings of the study, conclusion and implications of the research. 8

24 2. LITERATURE REVIEW 2.1. Definitions of Related Terms Market: Kohls and Uhl (1985) define market as an an area for organizing and facilitating business activities and for answering the basic economic questions: what to produce, how much to produce, how to produce, and how to distribute production. Marketing: It is about flow of goods and services from their point of production to consumption (Abbott and Makeham, 1981; Kohls and Uhl, 1985). For Mendoza (1995), marketing is a system, which comprises several and usually stable and interrelated structures that along with production, distribution and consumption, strengthen the economic process. Usually, the marketing of agricultural products begins at the farm when the farmer plans his production to meet specific demand and market prospects (Abbott and Makeham, 1981; Kohls and Uhl, 1985). Market chain: It is the term used to describe the various links that connect all the actors and transactions involved in the movement of agricultural goods from the producer to the consumer. Commodity chain is the chain that connects smallholder farmers to technologies that they need on one side of the chain and to the product markets of the commodity on the other side. Marketable and marketed surplus: Marketable surplus is the quantity of the produce left out after meeting the farmers consumption and utilization requirements for kind payments and other obligations such as gifts, donation, charity, etc. Thus, marketable surplus shows the quantity left out for sale in the market. The marketed surplus shows the quantity actually sold after accounting for losses and retention by the farmers, if any and adding the previous stock left out for sale. Thus, marketed surplus may be equal to marketable surplus, it may be less if the entire marketable surplus is not sold out and the farmers retain some stock and if losses are incurred at the farm or during transit (Thakur et al., 1997). 9

25 The importance of marketed and marketable surplus has greatly increased owing to the recent changes in agricultural technology as well as social pattern. In order to maintain the balance between demand for and supply of agricultural commodities with rapid increase in demand due to higher growth in population, urbanization, industrialization and overall economic development, accurate knowledge on marketed/marketable surplus is essential in the process of proper planning for the procurement, distribution, export and import of agricultural products (Malik et al., 1993). Competitive market: In a competitive market, each agent makes inter temporal choices in a stochastic environment. Their attitudes toward risk, the production possibility set, and the set of available trades determine the equilibrium quantities and prices of assets that are traded. In an "idealized" representation agents are assumed to have costless contractual enforcement and perfect knowledge of future states and their likelihood. With a complete set of state contingent claims (also known as Arrow Debreu securities) agents can trade these securities to hedge against undesirable or bad outcomes. When a market is incomplete, it typically fails to make the optimal allocation of assets. Information Asymmetry: In economics and contract theory, information asymmetry deals with the study of decisions in transactions where one party has more or better information than the other. This creates an imbalance of power in transactions which can sometimes cause the transactions to go awry, a kind of market failure in the worst case. Market linkages: It is a process where an organized community validates and consolidates its production in new markets in a sustainable way. Broker: A broker is an individual or party (brokerage firm) that arranges transactions between a buyer and a seller, and gets a commission when the deal is executed. brokers are referred to as individuals (or organizations) who facilitate product distribution by bringing buyers and sellers together but do not take title to goods (Crawford, 1997).Brokers earn income from the commission paid to them by their clients (buyers and sellers) for the services they offered. It is also possible that a broker acts as a seller or as a buyer (becoming a 10

26 principal party in the business transaction) or, in some cases, acts on behalf of a principal (in both cases by taking title to goods). When they act as agents, they represent either the seller or the buyer, but not both at the same time. Opportunity cost: It is the cost of any activity measured in terms of the value of the next best alternative foregone (that is not chosen). It is the sacrifice related to the second best choice available to someone, or group, who has picked among several mutually exclusive choices. The opportunity cost is also the cost of the foregone products after making a choice. Opportunity cost is a key concept in economics, and has been described as expressing "the basic relationship between scarcity and choice". The notion of opportunity cost plays a crucial part in ensuring that scarce resources are used efficiently. Thus, opportunity costs are not restricted to monetary or financial costs: the real cost of output foregone, lost time, pleasure or any other benefit that provides utility should also be considered opportunity costs. Contract theory: In economics, contract theory studies how economic actors can and do construct contractual arrangements, generally in the presence of asymmetric information. Because of its connections with both agency and incentives, contract theory is often categorized within a field known as Law and economics. One prominent application of it is the design of optimal schemes of managerial compensation. In the field of economics, the first formal treatment of this topic was given by Kenneth Arrow in the 1960s Major Policy Reforms in Ethiopia Related to Market Institutions Major policy reforms were undertaken in Ethiopia in the early Nineties in order to substitute the centrally-planned and controlled socialist economy, in place since 1974, with a free market system. These reforms were based on the idea that eliminating distortionary economic interventions by the state was a precondition for getting prices right, which was itself necessary for spurring private investment and economic growth (Timmer, 1986). However studies indicated that liberalization succeeded in enhancing price transmission between the main regional markets (Jayne et al., 1998). According to the study made by 11

27 Barrett and Mutambatsere (2005) the withdrawal of parastatals from core input marketing activities created a void that the private sector often failed to fill due to underdeveloped physical communications, power and transport infrastructure, credit constraints and continued bureaucratic impediments that increased transaction costs for input suppliers. To address the challenges posed by failing and incomplete markets, the Ethiopian Government has implemented a number of post-structural market reforms focused instead on getting institutions right (Barrett and Mutambatsere, 2005) and getting markets right (World Bank, 2004) Commodity Exchange What is commodity exchange? To many, a commodity exchange connotes a highly sophisticated market system, with an electronic-based, highly evolved system of trading in future commodity positions, exemplified by markets such as the Chicago Board of Trade, the Tokyo Grain Exchange, or the London Metal Exchange, among others. To many, a commodity exchange is an advanced market mechanism for use in industrialized countries, out of the reach or inappropriate to lowincome countries. However, at its heart, a commodity exchange is simply a central place where sellers and buyers meet to transact in an organized fashion, with certain clearly specified and transparent rules of the game. In its wider sense, a commodity exchange is any organized market place where trade, with or without the physical commodities, is funneled through a single mechanism, allowing for maximum effective competition among buyers and among sellers. The fact of having a single market mechanism to bring together the myriad buyers and sellers at any point in time effectively results in the greatest concentration of trading for a given good. This market mechanism, such as a price bidding system or an auction system, results in what is known as price discovery, that is, the emergence of the true market-clearing price for a good at a particular point in time due to the highest possible concentration and competition among buyers and among sellers. 12

28 The difference between a commodity exchange and a typical wholesale or terminal market is that an exchange creates a mechanism for price discovery to occur in an organized manner, through a system of price bidding and through a set of rules governing the products brought to the market, the market actors, and the contracts between buyers and sellers The rationale behind the establishment of Ethiopian commodity exchange Prices of food staples in Ethiopia are highly volatile, due to erratic supplies and weakly integrated markets, reflected in high transport and transaction costs, which limit opportunities for smoothing prices through arbitrage across space (transport) and time (storage). Price volatility undermines both food security for consumers and incentives for food producers. Under the Derg regime, food trading was tightly controlled through the Agriculture Marketing Corporation (AMC); however, like many other African countries, Ethiopia underwent rapid market liberalisation in the 1990s, where prices controls were eliminated and the AMC was downsized. These reforms did not reduce food price volatility and have arguably exacerbated it (Eleni, 2001). Market actors react sluggishly to signals of changes in food supply or demand, leaving producers highly vulnerable to food price collapses and consumers equally vulnerable to food price inflation. Following bumper harvests in 2001 and 2002, for instance, grain prices collapsed by 80%, which undermined smallholder incomes and left 300,000 tonnes of grain rotting in the fields because it was not profitable to harvest (Eleni and Goggin, 2005; Jopson, 2007). In an innovative attempt to address these high transaction costs, the Ethiopian government is work with the International Food Policy Research Institute (IFPRI) and established Ethiopian Commodity Exchange (ECEX) covering six crops: coffee, sesame, haricot beans, maize, teff and wheat and livestock products. A commodity exchange performs three basic functions: (1) price transparency: enabling access for everyone to a neutral reference price; (2) price discovery: ensuring that demand and supply developments are easily reflected in price levels; (3) reduced transaction costs: making it easier to find buyers or suppliers through a centralised market-place. Commodity exchanges can also reduce price risk by trading in futures contracts, and the ECEX will aim to do this in the near future (Gabre-Madhin, 2006). 13

29 The Ethiopian Commodity Exchange is expected to reduce transaction costs by: (1) facilitating contact between buyers and sellers, (2) enabling centralised grading of products, (3) ensuring that contracts are enforceable, (4) providing a mechanism for price discovery, (5) simplifying transactions with standard contracts, and (6) transmitting information about prices and volumes which will be enabled through the installation of price tickers at 200 rural sites, giving farmers independent access to price information from the exchange in Addis Ababa. The reduction of transaction costs will enable various market actors, including smallholders, to benefit from a higher share of the final price. Increased information about market prices will also increase the bargaining power of smallholder farmers and enable them to make better investment decisions. This in turn, would generate incentives for increased production. Moreover, if the exchange is linked to a negotiable warehouse receipts system, this can also increase liquidity for farmers by facilitating access to credit borrowed against the receipt. At least on paper, the ECEX appears to be an excellent example of an intervention that has the potential to achieve both social protection and agricultural growth (i.e. livelihood protection plus livelihood promotion) in a single instrument Ethiopian commodity exchange current status The Ethiopia Commodity Exchange (ECX) is a commodities exchange established April 2008 in Ethiopia. In Proclamation , which created the ECX, its stated objective was "to ensure the development of an efficient modern trading system" that would "protect the rights and benefits of sellers, buyers, intermediaries, and the general public". The ECX is set up as a private company owned by a partnership of the market actors, members of the exchange, and the Ethiopian government, led by Dr. Eleni Gebre Medhin a former economist for the World Bank. As of July 2011, the physical presence of the ECX consists of 55 warehouses in 17 regional locations. It has grown from trading 138,000 ton in its first year to 508,000 tons in its third year, with nearly equal shares of coffee and oilseeds and pulses. The value of the ECX rose 368 percent between 2010 and 2011 to reach US$1.1 billion. 14

30 As of November 2010, the trading floor in Addis Ababa, handled 200 spot contracts in such commodities as Coffee, sesame, haricot beans, maize and wheat. It was assessed in July 2011 that total membership equaled 243 with total clients, who trade through members, numbered about 7,800. Farmer Cooperatives represented 2.4 millions smallholder farmers, which make up 12 percent of the membership. Currently, the ECX is the only stock or commodity exchange in Africa to have streamlined payment transfers down to "T+1" (Next day payment after a trade) from its clearinghouse to its partner commercial banks. Market data reach is expansive. "Push" price date is transmitted in real time to outdoor electronic ticker boards in 32 rural sites, to the ECX website, 256,000 mobile subscribers via instant messaging, the radio, TV and print media. "Pull" market data is available through a toll-free phone-in service. The service received more than 1 million calls in September 2011, 70 percent coming from rural callers The New Institutional Economics Approach Transaction costs According to the New Institutional Economics (NIE) approach, the unit of analysis is the transaction rather than the price. Exchange itself is costly. Transaction costs, which are distinct from physical marketing costs such as those for transport and storage, arise from the coordination of exchange among market actors. They include the costs of obtaining and processing market information (Hoff and Stiglitz, 1990), negotiating contracts (Williamson, 1985), monitoring agents (Bardhan, 1989), and enforcing contracts (Fafchamps, 1996). Transaction costs are unique to each market participant, implying that economic actors are not interchangeable. The presence of transaction costs, which are specific to each market actor, implies that there is no single effective market price at which exchange occurs (Sadoulet and de Janvry, 1995). Each agent in the market conducts transactions on the basis of his or her specific transaction costs. The implications of transaction costs are that markets are thin or fail if prohibitively high costs prevent exchange. 15

31 Institutions to facilitate exchange Institutions are defined as the rules of the game, both formal rules and informal constraints such as norms, conventions, and codes of conduct that provide the structure for human interaction (North, 1990). Institutions emerge to minimize these transaction costs and to facilitate market exchange. The evolution from personalized exchange to impersonal or anonymous exchange, supported by legal systems that enforce contracts, is central to the process of growth and development (North and Thomas, 1973). However, following Polanyi (1957), it is widely recognized that market transactions, particularly in developing countries, are often embedded in long-term, personalized relationships (Geertz, 1968). Personalized exchange emerges in response to commitment failure, in which the risk of breach of contract or opportunism is high, resulting from the lack of market information, inadequate regulation, and the absence of legal enforcement mechanisms. Institutions build trust and promote reputation and social capital, such as trade associations, solidarity networks, and groups that enhance ethnic or religious ties, emerge to circumvent commitment failure (Greif, 1993; Fafchamps, 1996). Historically, institutions have emerged in various contexts to facilitate anonymous trade. Historical institutional analysis of pre modern trade in medieval Europe by Milgrom et al. (1990) showed that an institution known as the Law Merchant enabled impersonal exchange to occur in 12 th and 13 th century Champagne fairs. The Law Merchant enabled trade through a reputation mechanism that stored information about traders past behavior and sanctioned violators of the commercial code. Greif (1993) views the Maghribi traders coalition formed in the 11th century as a means of overcoming the commitment problem intrinsic to longdistance trade. The coalitions of long-distance traders in 19th-century Mexican California promoted honest exchange through information sharing and punishing of cheaters. In contrast, Platteau (1994) argues that decentralized arrangements based on reputation are not sufficient to ensure honest behavior and that private and public-order institutions are necessary to create the social conditions necessary for markets to operate. The dominant contract enforcement mechanism in liberalized grain markets in Madagascar is trust-based relationships, where trust 16

32 is established primarily by repeated interaction. The incidence of theft and breach of contract is low, and recourse to the legal system is rare Social capital Although social scientists have long recognized the role of interpersonal relationships in human interaction (Coleman, 1988), the concept of social capital has been little used in economics (Barr, 1997). There are two possible meanings of social capital. The first definition sees social capital as a stock of trust and an emotional attachment to a group or society that facilitates the provision of public goods (Fukuyama, 1995). The second views social capital as an individual asset that provides private benefits to a single individual or firm (Aoki, 1984) Market Imperfection and the Brokerage Institutions in Ethiopia In perfect market situation, it is believed that there is perfect information, knowledge, no barrier to entry and exit, price determined by supply and demand and perfect mobility of resources. However this is ideal and the Ethiopian agricultural marketing is characterized by imperfect market conditions which is a deviation from perfect market condition. Market imperfection leads to high transaction costs and poor allocation of resources. Ethiopian agricultural traders face three major constraints that increase their transaction costs of participating in the grain market. First, traders do not benefit from a system of agricultural product standardization and inspection that would enable them to place orders with long distance partners for guaranteed qualities and quantities of grain. Instead they must be physically present at the time of transaction in order to visually inspect the grain that is being exchanged. Second, agricultural traders have very limited recourse to legal means for enforcing contracts. Thus, they trade only with partners whom they know well and trust in order to avoid the high costs of payment delinquency or reneging on the terms of the contract. Third, traders do not have access to a public market information system that enables them to know prices and flows in markets outside of their own. This limits traders ability to deliver agricultural product to 17

33 unknown markets or to set contracts to go into effect at a future point in time, thus limiting their scope of spatial or temporal arbitrage. These market constraints result in high transaction costs for partner search, information, and enforcement for Ethiopian traders. In order to reduce these costs, traders engage the services of brokers, known as delala, who act as intermediaries on their behalf (Eleni, 2001). The study also indicated that the majority of Ethiopia s grain traders, 85 percent, regularly use these intermediaries to conduct their long-distance transactions. Brokers, operating as commission agents, provide the service of matching regional buyers and sellers, as well as handling and inspecting shipments of grain and providing market information to their clients. Brokers have a distinct identity in the market because they do not take market positions themselves, but only act on behalf of traders. There are approximately 40 established brokers in the central market of Addis Ababa, compared to a total of 2,500 wholesalers in the country. These brokers handle roughly 16 percent of the total marketed surplus. Due to their central position, brokers are keenly aware of prices and flows in the market, and their presence enables the Addis Ababa market to function as a clearinghouse for grain in Ethiopia Properties of Horticultural Production and Marketing General properties of horticultural products Horticultural marketing is influenced by a number of factors that can be attributed to production, product, and market characteristics. Kohl and Uhl (1985) identified these attributes as: Perishability: horticultural crops are highly perishable; they start to lose their quality right after harvest and continued throughout the process until it is consumed. For this purpose elaborated and extensive marketing channels, facilities and equipments are vital. This behavior of horticultural crops exposed the commodity not to be held for long periods and fresh produce from one area is often sent to distant markets without a firm buyer or price. Prices may be negotiated while the commodities are en route, and they are frequently diverted 18

34 from their original destination of a better price can be found. Sellers might have little market power in determining a price. As a result, a great deal of trust and informal agreements are involved in marketing fresh vegetables. There could not always be time to write everything down and negotiate the fine details of a trade. The urgent, informal marketing processes often leads to disputes between buyers and sellers of fresh horticultural crops. Producers are normally price takers and are frequently exposed for cheating by any intermediary. Price /Quantity risks: Due to perishable nature and biological nature of production process there is a difficulty of scheduling the supply of horticultural crops to market demand. The crops are subjected to high price and quantity risks with changing consumer demands and production conditions. Unusual production or harvesting weather or a major crop disease can influence badly the marketing system. While food-marketing system demands stable price and supply, a number of marketing arrangements like contract farming provide stability. Seasonality: horticultures have seasonal production directly influencing their marketing. Normally they have limited period of harvest and more or less a year round demand. In fact, in some cases the cultural and religious set up of the society also matter demand to be seasonal. This seasonality also worsened by lack of facilities to store. Alternative product forms and markets: While different varieties and qualities could be grown for the fresh and processed markets, there could also be often alternative markets. These include form markets (fresh, frozen, dried, and canned), time markets (winter, summer) and place markets (different towns, foreign market). Product bulkiness: Since water is the major components of the product, it makes them bulky and low value per unit that is expensive to transport in fresh form every time. This, therefore, exposed farmers to lose large amount of product in the farm unsold. 19

35 Overview of Horticultural Crops production and Marketing in Ethiopia The potential for irrigation in Ethiopia is estimated to be about two million hectares. Due to limited experience in water management and control, limited capital available for investment and the diverse climate and disease vectors characteristics of the lowland areas (where most irrigation potential is located), irrigated agriculture is far below its potential. Thus production is heavily dependent on rainfall and uses little capital and technology. Consequently, the average productivity of both land and labor is extremely low and variable from season to season. Despite these favorable resource endowments, agricultural production has remained mostly close to subsistence level. Horticultural crops are rich in vitamins, carbohydrates and other nutrients that contribute to a major portion to an Ethiopian daily dish mix. Some nutritional deficiencies like vitamin A and C, and iron can be corrected by use of selected vegetable and root crops as well as fruits. In some areas of the country, root crops particularly potatoes and sweet potatoes are used as staple food for considerable portion of the population. Root crops in general and sweet potato in particular are drought resistant and serve as security food crops in drought prone areas. Furthermore, vegetables and root crops generate foreign currency earnings in the country. Horticultural crops play a significant role in developing country like Ethiopia, both in income and social spheres for improving income and nutrition status. In addition, it helps in maintaining ecological balance since horticultural crops species are so diverse. Further, it provides employment opportunities as their management being labor intensive, production of these commodities should be encouraged in labor abundant and capital scarce countries like Ethiopia. Ethiopia is a country with great variety of climate and soil types that can grow diversity of horticultural crops for home consumption and foreign markets. Currently, the majority of the horticultural crops product comes from the peasant smallholder farms. However, their areas of production and their contribution to the country s total agricultural output were not known much. Based on the survey per capital consumption of the annual fresh production assorted vegetables is about 2.86 million tons. From the total volume of horticultural products 95% is fresh vegetable production. There is no processing of vegetables 20

36 in the peasant smallholder farm. Production of canned and bottled vegetables is mainly in the Ministry of State Industry (MSI) and Ministry of State Farm (MSF). The success of the horticultural sector is largely based on the efficiency and flexibility of the marketing system. Though grown widely for subsistence purpose, most horticultural products contribute to the generation of income at household and country level. A bulk share of the potential demand of horticultural products is in urban areas and in foreign markets. This underscores the importance of efficient marketing strategies for various commodities. According to Ethiopian Export Promotion Agency, the current distribution chain of horticultural commodities in Ethiopia varies depending on the commodity and its level of commercialization (Sisay, 2004). Most of the fruits and vegetables produced in Ethiopia are consumed locally and are produced by smallholder farmers. After harvest, they are transported to rural market centers for local consumers or are bought at the farm by neighbors. Others are transported to bigger market centers where many producers utilize the open-air markets that are patronized occasionally, once or twice a week. Limited post harvest improvement is done for locally consumed fruits and vegetables. However, fruits like banana, orange, lemon, pineapples and avocadoes exported to Europe and Middle East are graded and packaged appropriately. Recently vegetables are also exported to Djibouti. Fruits, vegetables and flowers export consists of 1.27 % of the total export in Ethiopia in 2002 (Moti, 2007) Horticultural Crop Production and Marketing in Fogera Woreda Vegetables are produced in both rice based and cereal based farming systems. The major vegetables in the rice system are onion, pepper and tomatoes are important. In the Cereal/livestock system, pepper tomatoes and onions are important crops. Production problems related to vegetables are lack of knowledge, marketing problem and high risk due to poor shelf life. In addition, there are a number of diseases and pests that are affecting the productivity of these vegetables. Water management issue due to silting up of shallow wells is also a problem because it requires annual digging of these shallow wells (IPMS, 2005). 21

37 Production problems According to Abay (2007) the horticultural crop production in Fogera Woreda is constrained by number of problems like absence of appropriate post harvest handling practice such as onion farm field watering one or two days prior uprooting/harvesting in order to increase weight during selling was the usual practice that resulted in poor quality, easily damageable onion and eventually low price. The other problems are problem of pest and disease like root rot in the case of onion/ shallot and problem of African ball warm, cutworm, and fruit disease in the case of tomato and surface water shortage. In addition there is a problem related to poor production and marketing extension support and unorganized input delivery. Farmers used to get seeds from open market. There were no certification, quality test, and failure guarantees. As a result, in 2005 about 7.6 quintals of onion seed after distributed to farmers and sown, failed to grow and a large number of farmers lost. There is also a problem related to poor agronomic practices such as tillage, application of chemical fertilizer, watering and weeding in the production of horticultural crops in the area Marketing problems The Fogera Woreda horticultural crop is characterized by imperfect information which gives the opportunity for the presence of brokerage institutions. The imperfect information creates problems in the bargaining inefficiency in which informed market actors increase their own benefit while those actors who do not have information are marginalized. Since most of the farmers produce the same type of horticultural products at the same time, the supply of the product is in glut during the season compared to the demand leading to lower producer price associated with product bulkiness, perishablity and seasonality in the production. Moreover, there is no grading and standardization of the product, weight cheating is a common practice and market power is taken by the brokers and traders. The fail of cooperatives to coordinate farmers in marketing of horticultural crops such as onion leads to farmers price takers than makers. Lack of adequate marketing research information in the area is also another problem which hinders the government to take decisions in improving the market channel and the hole system. 22

38 Production opportunities The major opportunities for Fogera are the emergence of commercial agriculture with respect to horticultural crop production due the presence of high irrigation potentials in the area by the rivers Rib and Gumara. There are also natural spring water sources which are used for irrigation. The Fogera farmers have a comparative advantage of producing horticultural crops due to the cheap labor and no application of chemical fertilizer as the plain is filled with soil of the highland areas. Experience (learning effect) and neighborhood effect are much more important in technology adoption and production. The start of on farm onion seed production is also one of the opportunities for production increment as there is no problem of supply improved horticulture seed. The infrastructural facility such as road and telecommunication also plays vital role in marketing by attracting wholesalers from different parts of Ethiopia. The presence of farmers training centers and development agents in each kebeles also play great role in the production and improving farmer s management practices of horticultural crops Impact Evaluation Methods To know the effect of a program on a participating individual, we must compare the observed outcome with the outcome that would have resulted had that individual not been participating in the program. The "with" data can be collected without great difficulty. But, the "without" data s are fundamentally unobserved since an individual cannot be both a participant and a non participant of the program. Thus, the fundamental problem in any social program evaluation is the missing data problem (Ravallion, 2005). Estimating impact of a program requires separating its effect from intervening factors which may be correlated with the outcomes, but not caused by the program. This task of netting out the effect of the program from other factors is facilitated if control groups are introduced. Control groups consist of a comparator group of individuals or households who did not receive the intervention, but have similar characteristics as those receiving the intervention, called the treatment groups. In social sciences, choice of a particular approach depends, 23

39 among other things, on data availability, cost, and ethics to experiment (Ezemenari et al., 1999). In what follows, brief descriptions of the main impact evaluation methods are presented Experimental methods In the experimental methods, the design involves gathering a set of individuals (or other unit of analysis) equally eligible and willing to participate in the program and randomly dividing them into two groups: those who receive the intervention (treatment group) and those from whom the intervention is withheld (control group). This allows the researcher to determine program impact by comparing means of outcome variable for the two groups (Regalia, 1999). A random assignment of individuals to treatment and non-treatment groups ensures that on average any difference in outcomes of the two groups after the intervention can be attributed to the intervention. The main advantage of a randomized experiment is its ability to avoid problem of selection bias, which arises when participation in the program by individuals is related to their unobservable or unmeasured characteristics (like motivation and confidence), which in turn determine the program outcome. Obviously, randomization must take place before the program begins. Experimental or randomized designs are generally considered as the most robust of the evaluation methodologies. The other benefit of this technique is the simplicity in interpreting results-the program impact on the outcome is the difference between the means of the samples of the treatment group and the control group. The random assignment does not remove the selection bias but instead balances the bias between the participant (treatment) and non-participant (control) groups, so that it cancels out when calculating the mean impact estimate (Ezemenari et al., 1999; Jalan and Ravallion, 1999) Quasi and non-experimental methods Quasi-experimental design involves matching program participants with a comparable group of individuals who did not participate in the program. This simulates randomization but need not take place prior to the intervention. A quasi-experimental method is the only alternative 24

40 when neither a baseline survey nor randomizations with other methods are feasible options (Jalan and Ravallion, 2003). This evaluation design can be used when it is not possible to randomly select a control group, identify a suitable comparison group through matching methods or use reflexive comparisons. In such situations, program participants can be compared to non-participants using statistical methods to account for differences between the two groups (Ezemenari et al., 1999). A non-experimental approach is used in cases where program placement is intentionally located. There are two broad categories of non-experimental approach; before and after estimator and cross-sectional estimator. The essential idea of the before and after estimator is to compare the outcome variable for a group of individuals after participating in a program with the same group or a broadly equivalent group before participation and to view the difference as the estimate of average treatment effect on the treated. Cross-section estimators use non-participants to derive the counterfactual for participants in which case it becomes quasi-experimental method (Jalan and Ravallion, 2003). The most widely used type of quasi-experimental method is propensity score matching, in which the comparison group is matched to the treatment group by using the propensity score (predicted probability of participation given observed characteristics). A good comparison group comes from the same economic environment and is administered the same questionnaire as the treatment group. It is challenged since the unobservable characteristics may influence the outcome and it needs expertise knowledge (Jalan and Ravallion, 1999). Considering the advantages and drawbacks of each of the impact assessment methodologies, propensity score matching is selected for this study. Reflexive comparison is a quasi-experimental design, which is particularly useful in evaluations of full-coverage interventions such as nationwide policies and programs in which the entire population participate and there is no scope for a control group. This methodology is used, whereby the direct beneficiaries of the project were asked to assess its impact on their performance. The subjective nature of "self-evaluations" is of the shortfalls of the approach. In addition, the situation of program participants before and after the intervention may change 25

41 owing to myriad reasons independent of the program (Regalia, 1999). In the case where there can be a possibility for a control group, this method will not be applied. Instrumental Variable is a technique that identifies a factor that determines receipt of a project, but which does not influence outcomes of interest. This factor is then used to simulate who would have been in the treatment group, and who would have been in the control group if receipt of the project was based on that factor. The difference in outcomes between these simulated treatment and control groups is then the impact of the project. The instrumental variables are first used to predict program participation; then one sees how the outcome indicator varies with the predicted values (Alberto et al., 2002). Unlike the PSM, strong underlying assumption, the exclusion restriction that the instrumental variable is independent of outcomes given participation, has to be assumed here. The validity of the exclusion restriction required by the method is particularly questionable with only a single crosssectional data set; while one can imagine many variables that are correlated with participation, such as geographic characteristics of an area, it is questionable on a priori grounds that those variables are uncorrelated with outcomes given placement (Ravallion, 2005). Multivariate regression analysis is a non-experimental technique used to control for possible observable characteristics that distinguish participants and non-participants. Thus, if it is possible to control for all possible reasons why outcomes might differ, then this method is valid to estimate the treatment effect (Regalia, 1999). The widely used multivariate regression method requires the same conditional independence assumption as PSM, but also imposes strong arbitrary functional form assumptions concerning the treatment effects and the control variables. By contrast, PSM does not require a parametric model linking program participation to outcomes. Thus PSM allows estimation of mean impacts without arbitrary assumptions about functional forms and error distributions (Ravallion, 2005). Since PSM optimally balances observed covariates between the treatment and comparison groups, the difference-in-difference is a proposed method for solving this problem. In a difference-in-difference method of non-experimental impact evaluation, the difference in a given outcome between recipients of the project (the treatment group) and a comparison or 26

42 control group is computed before the project is implemented. The difference in outcomes between treatment and control groups is again computed some time after the project is implemented. Under the difference-in-difference technique, the impact of the project is the second difference less the first difference (Maffioli et al., 2008). Nonetheless, the methodology has its own limitations. There is a potential bias in difference-in-difference estimators when the changes over time are a function of initial conditions that also influence program placement. There is also the well-known bias for inferring long-term impacts that can arise when there is a pre-program difference of the participating and non-participating households (Ravallion, 2005). Propensity score matching and multivariate regression methods control for selection on observables whereas instrumental variable methods control for selection on unobservable explanatory variables. The validity of quasi and non-experimental evaluation depends on how well the model is specified (Jalan and Ravallion, 2003) Propensity Score Matching Among quasi-experimental design techniques, matched comparison techniques are generally considered a second-best alternative to experimental design (Baker, 2000). Intuitively, PSM tries to create the observational analogue of an experiment in which everyone has the same probability of participation. The difference is that in PSM it is the conditional probability (P(X)) that is intended to be uniform between participants and matched comparators, while randomization assures that the participant and comparison groups are identical in terms of the distribution of all characteristics whether observed or not. Hence, there are always concerns about remaining selection bias in PSM estimates (Ravallion, 2005). Unlike econometric regression methods, PSM compares only comparable observations and does not rely on parametric assumptions to identify the impacts of projects and it does not impose a functional form of the outcome, thereby avoiding assumptions on functional form and error term distributions, e.g., linearity imposition, multicollinearity and heteroscedasticity issues. In addition, the matching method emphasizes the problem of common support, thereby 27

43 avoiding the bias due to extrapolation to non-data region. Results from the matching method are easy to explain to policy makers, since the idea of comparison of similar group is quite intuitive. Matching the treated and the control subjects becomes difficult when there is a multidimensional vector of characteristics (Rosenbaum and Rubin, 1983). The PSM solves this type of problem by summarizing the pre-treatment characteristics of each subject into a single index variable, and then using the propensity score (PS) to match similar individuals. This constitutes the probability of assignment to treatment conditional on pre-treatment variables (Rosenbaum and Rubin, 1983). Matching estimates is more reliable if: (i) participants and controls have the same distribution of unobserved characteristics; (ii) they have the same distribution of observed characteristics; (iii) the same questionnaire is administered to both groups; and (iv) treated and control households are from the same economic environment. In the absence of these features, the difference between the mean impact of the participants and the matched non-participants is biased estimate of the mean impact of the project (Jalan and Ravallion, 1999). PSM is not without its potentially problematic assumptions and implementation challenges. First, PSM requires large amounts of data both on the universe of variables that could potentially confound the relationship between outcome and intervention, and on large numbers of observations to maximize efficiency (Bernard et al., 2010).Second, related to the previous point we can never be entirely sure that we have actually included all relevant covariates in the first stage of the matching model and effectively satisfied the conditional independence assumption (CIA). Furthermore, PSM is non-parametric: we do not make any functional form assumptions regarding the average differences in the outcome. Although the first stage involves specification choices - e.g., functional form like logit and probit, empirical analyses tend to find impact estimates that are reasonably robust to different functional forms. Moreover, if unobservable characteristics also affect the outcomes, PSM approach is unable to address this bias (Ravallion, 2005). 28

44 Irrespective of its shortcomings, PSM is extensively used in the recent literature on economic impact evaluation (Jalan and Ravallion 2003). It is very appealing to evaluators with time constraints and working without the benefit of baseline data given that it can be used with a single cross-section of data, where this study envisaged to employ Empirical Studies on Horticultural Marketing Systems and the Role of Brokerage Institutions in Developing Countries and Ethiopia Different scholars have undertaken different studies in horticultural crops marketing and the roles played by brokerage institutions. Different findings are assessed as follows: The essential role of intermediaries in agricultural markets has been documented for a number of Sub-Saharan Africa countries. For example, it has been found that brokers compensate for the lack of networks of business partners at traders disposal in Benin and Malawi (Fafchamps and Gabre-Madhin, 2001); they encourage impersonal exchange by acting as guarantors for the parties involved in trade in Tanzania (Eskola, 2005); they provide information, funding and technical assistance to wholesalers of fresh fruits and vegetables in Uganda (Bear and Goldman, 2005); and they represent the first alternative for farmers to other forms of collective action such as producer marketing groups in Kenya (Shiferaw et al., 2009). Also, in the livestock sector brokers facilitate pig marketing in the Northern part of Nigeria (Ajala and Adesehinwa, 2007) and livestock trade in Nairobi, which is a leading terminal market for livestock from throughout the Greater Horn of Africa. Given the cross border nature of these trading networks, trust between brokers and traders is essential (Bailey et al., 1999). The important role played by brokers has also been reported outside Africa. In Brazil, for example, they support farmers by helping to minimize price risk in futures and derivatives agricultural markets (Pessoa and Jank, 2002), while in Peru commission agents promote longdistant trade (Scott, 1985). In India, in the traditional marketing system, small landholding farmers depend on intermediaries for credit (Lokanathan and De Silva, 2010). 29

45 Eleni (2001) depicted the benefits that the use of brokers could bring to wholesalers while explaining why traders use brokers in the first instance. Using primary data collected in Ethiopia in 1996, the study demonstrated how the use of brokers by traders is positively related to transaction costs of search, defined as the shadow opportunity costs of search labor and of working capital kept in the form of grain stocks, and inversely related to social capital availability. The study also suggested that traders use of brokers is closely related to traders attempt to minimize prohibitively high transaction costs and depending on whether they are located in a surplus or deficit production region. Transaction cost economics essentially asserts that market institutions minimize transaction costs associated with market exchange and that markets evolve over time following changes in the nature and sources of transaction costs (Kherallah and Kirsten, 2001). Jabbar et al. (2008) further argue that traders own different assets (such as physical, financial, human and social capital) and adopt various trading practices, including the use of brokers, in order to reduce transaction costs. Among trading assets, the existing literature has given particular relevance to social capital. A geographic disaggregation of Ethiopia is therefore specified in this paper following Chamberlin et al., (2006) which allows the heterogeneity of production and marketing contexts prevailing in the country to be taken into account. Staal et al., (1997) and Eleni (2006) found that, apart from location, travelled distance and physical infrastructure availability also have an impact on traders ability to minimize transaction costs. The inadequacy of physical infrastructure (such as road networks, telecommunications and storage facilities) pushes searching, screening and bargaining costs up. Moreover, the farther wholesalers are from their main markets the more these costs rise. Schmidt and Shiferaw (2009) add that The shortest route in kilometers may not always be the fastest route. Hence, in order to investigate wholesalers use of brokers aimed at minimizing transaction costs, Euclidean distance between traders base and main market centers is considered in connection with dummy variables assessing the quality of roads linking these markets. 30

46 Bezabih and Hadera (2007) identified disease and pests, drought, shortage of fertilizer, low level of improved agricultural technologies and price of fuel for pumping water as the major constraints of horticulture production in Eastern Ethiopia. Other problems which they reported also include poor know how in product sorting, grading, packing, and traditional transporting affecting quality. Moreover, due to the increasing population pressure the land holding per household is declining leading to low level of production to meet the consumption requirement of the household. As a result, intensive production is becoming a means of promoting agro-enterprise development in order to increase the land productivity. Horticultural production gives an opportunity for intensive production and increases small holders farmers participation in the market. The study also confirmed that the flow of products is dictated by seasonal deficit where at times surplus producing site might also be receiver from the earlier receiving area at times of deficit. The absence of direct transaction or linkage between the producer and the large buyer was very common. Buyers follow contact persons who identify vegetables to be purchased, negotiate the price, and purchase and deliver the products. It categorized actors in the marketing channel as producers, intermediaries/ brokers, traders and consumers. Another interesting property that they found out is that brokers play a decisive role in the marketing system and determine the benefit reaching the producer. Onion and tomato are quite often purchased in the field with brokers. According to the study, three types of brokers: the farm level broker, local broker and urban broker exist. Each has their one separate task where the farmer level broker identifies plots with good produces and links the producer with a local broker. The local broker in turn communicates with the farmer and conveys the decisions made to the urban broker or collector. In this process the producer have contact with local agents and do not have direct contact with the other intermediaries. The third broker, urban broker, gets the information from ultimate buyers and sets the price. Here neither the farmer nor the traders set actual prices for the products. If the farmer insists on negotiating the price, the brokers gang up and boycott purchasing of the product leaving the product to rot. The farm level and local brokers get Birr 5 while the urban broker gets 10 Birr per quintal. If there are several brokers in an area, they negotiate not to compete on the 31

47 price offered by the broker. The changes in the value of products as they move away from production along the marketing channel to the consumer is the increased utility by making the goods available rather than adding value in terms of increased shelf life or increased safety. Moti (2007) analyzed horticulture marketing in central and eastern Ethiopia. The study assessed the role of horticulture for export earnings stability, farm resource allocation between food crops and cash crops, household decision making in crop choice-land allocation and market out let choice, and the influence of asymmetric price information on bargaining power of horticulture farmers. According to the study horticulture could be one of the way for agricultural commercialization of small-scale farmers with relatively better agricultural resource potential. It reported that diversifying the export base towards non-traditional agricultural commodities, as horticulture is important. The study added linking small-scale farm household horticultural production with export could help both in reducing export earning instability and enhancing farm household s income. In addition, it pointed out that the production of high value and labor-intensive horticulture products contributes to poverty reduction and rural development through generating higher income and better employment opportunities for landless households. The study also added the role of well functioning markets for Ethiopia where cooling and storage facilities are none for perishable crops. It advised improvement in market information and availability of alternative market outlets for subsistence farming to commercialize. Abay (2007) used Heckman two stage selection model and the result for market participation determinants showed that distance from main road, frequency of extension contact and number of oxen were found significant for onion while only experience of the farmers and distance from road for tomato. Similarly among the different variables that were hypothesized as determining factors for volume of market supply only sex of the respondent, active labor power, total size of owned land and quantity produced for onion and total size of farmland and quantity produced for tomato were significant.. This all show how much farmers did not consider price offer but clearing off. The recursive model result showed that volume handled by rural assembler, volume handled by other competing actors, and allocated land size that were significant for a choice of rural assembler for tomato. Selling price, volume handled by 32

48 rural assembler, volume handled by other competing actors and allocated land size came up with significant coefficients for onion. For decision choice of wholesaler volume handled by a wholesaler, volume handled by other competing actors and allocated land size were significant for both tomato and onion crops. Jema (2008) assessed the marketing performance of vegetables in eastern and central highlands of Ethiopia. Results showed that despite its poor performance, contract enforcement is mainly due to mutual trust and brokers mediation. Information access, trader-specific investments, farmer s age, whether the buyer is a trader, dependency on the trader, relationship duration, transaction frequency, and distance to the trader were found to be the significant factors affecting contract enforceability through brokers. Risks related to perishability and seasonality of supply, illiteracy, and client-buyer s type were found to be the significant factors causing contract breaches by the traders. In addition, traders produce pricing behavior in the procurement of vegetables from growers is analyzed. Results showed that traders capture a significant proportion of the marketing surplus due to market power and audacity to absorb risk with this share varying along the degree of Perishability and across cities. Quattri et al. (2011) used Heckman two stage models and examined that the brokerage services are particularly valuable for wholesalers lacking social capital and storage capacity, who are based in areas with low population density, and who trade at a distance especially when roads are not asphalted. Buyers in drought-prone domains rely on brokers more for their long-distance purchases, while sellers in moisture-reliable domains employ brokers more for their long-distance sales. These results provide useful indications regarding where and how the recent formalization of brokerage functions through the Ethiopian Commodity Exchange (ECX) could be most beneficial for the functioning of Ethiopian agricultural markets. 33

49 3. RESEARCH METHDOLOGY 3.1. Description of the Study Area Based on the BOFED (2011), Amhara Region has a population of million of which 9.13 million were men and 9.07 million were women. Urban inhabitants were 2.4 million or 12.6% of the total population. With an estimated area of 157, square kilometers, this region has an estimated population density of people per square kilometer. For the entire region 3,953,115 households were counted. This results to an average of 4.3 persons per household. The average family size in urban and rural area is 3.3 and 4.5 persons respectively. Vegetable producing Woreda s located in the north western part of the region include, Bahir Dar Zuria, Achefer, Mecha, Adet, Libo Kemkem, Fogera, Dera, Gondar Zuria and Chiliga. Fogera Woreda is one of the 106 Woreda s of the Amhara Regional State and found in South Gondar Zone. It is situated at N latitude and E longitude. Woreta is the capital of the Woreda and is found 625 km from Addis Ababa and 55 km from the Regional capital, Bahir Dar. The woreda is bordered by Libo Kemkem Woreda in the North, Dera Woreda in the South, Lake Tana in the West and Farta Woreda in the East. The Woreda is divided into 27 rural Peasant Associations and 3 urban kebeles Land use pattern and population of Fogera Woreda The total land area of the Woreda is 117,414 ha. The current land use pattern includes 44 percent cultivated land, 24 percent pasture land, 20 percent water bodies and the rest for others. The total population of the Woreda is 251,714. The rural population is estimated at 220,421. The proportion of male and female population is almost similar in both rural and urban areas. The number of agricultural households is 44,168. The mean annual rainfall is mm, with Belg and Meher cropping seasons. Its altitude ranges from 1774 up to 2410 masl allowing a favorable opportunity for wider crop production and better livestock rearing (IPMS, 2008). Most of the farm land was allocated for annual crops where cereals covered 51,472 hectares; pulses cover hectares; oil seeds 6137 hectares; root crops

50 hectares; and vegetables hectares. The major crops include teff, maize, finger millet and rice, in order of area coverage. According to IPMS (2005), average land holding was about 1.4 ha with minimum and maximum of 0.5 and 3.0 ha, respectively. Agricultural production in the Woreda is mainly rain fed far from its wide irrigation potential. Being one of the eight Woreda s bordering Lake Tana; Fogera shared a water body of 23,354 hectares from the total lake size. It s plain topography created the opportunity for a good size of irrigation potential. Actually, farm field water lodging in the rainy months (July up to half of September) is the common phenomena in the plain areas. Horticultural crops such as onion, garlic, tomato, potato, leafy vegetables and green paper are widely grown in the area. Bahir Dar and Gondar are the two big vegetable receivers in the area. These two towns are at 55 and 130 K.ms from Woreta. Gondar is found to the north of Woreta while Bahir Dar is to the south. The study area is one of the surplus crop producing areas and has a good potential for horticultural crop production which are produced mainly using irrigation. The area gets much of the flood water that accumulates around Lake Tana and the two big rivers, i.e., Rib and Gumara. The rivers bring eroded soil from uphill and deposit on the low land plain. Table 1. Land use pattern of Fogera Woreda Land Use Area Coverage per Ha % of Coverage Land planted with annual crops % Grazing Land % Area covered with water (wet land ) % Infrastructure including settlement % Un productive land (hills) % Forest land % Swamp land % Perennial crops % Total % Source: ILRI /IPMS,

51 Priority farming systems According to the Woreda Office of Agriculture, there are three agro-ecological Zones in the Woreda which grow different types of crops and are suitable for different species of livestock. Table 2. Farming System by Ecological Zone in Fogera Woreda Altitude range (masl) No of PAs Dominant crop and livestock 8 Rice, Finger millet, horticultural crops, noug, fish, cattle, sheep Cereals (maize, teff, finger millet), noug, vegetables, apiculture, cattle, goats Barley, Horse beans, potato, apiculture, sheep, cattle Total 27 Source: ILRI /IPMS,

52 Source: IPMS, 2005 Figure 1: Map of the study area 37

53 3.2. Methods of Data Collection Both primary and secondary data were used for this study. Secondary data were collected from office of Agriculture and Rural Development, Research Institutes, NGOs and Universities etc. The primary data for the study were collected from market actors starting from production to the end retailers which were conducted through interview and discussion. A semi-structured questionnaire and check-list were used for data collection. The information gathered was both quantitative and qualitative data. The enumerators recruited for the study districts were senior technical assistants in Amhara Regional Agricultural Research Institute, who are trained and experienced on methods of data collection and interviewing techniques. Moreover, the technical assistants were enumerators of the research center in the area. The researcher has trained and explained the contents of the questionnaire to the enumerators. Field trips were made before the actual survey to observe the overall features of the selected villages, smallholder horticultural producers and to undertake Rapid Market Appraisal (RMA). The questionnaire were pre-tested for key informants and checked by development agents, Woreda experts and enumerators. Its contents were refined on the basis of the results obtained during the pre-test. The researcher has made personal observations and informal discussions with farmers, development agents, district agricultural experts of Ministry of Agriculture and Rural Development using checklists. Continuous supervision monitoring of the area were also made to reduce error during data collection and to correct possible errors Sampling Procedures Multi-stage random sampling techniques were employed. The sample has covered farmers, brokers, rural assemblers; wholesalers and retailers on proportionate to size basis and research objectives. 38

54 Farmers sampling Two-stage random sampling strategies were adopted. First five kebeles of the Woreda were selected randomly. Second, the farmers were grouped as participants and non participants in brokerage institutions service for linkage to wholesalers then 143 farmers were selected by using random selection from both participants and non participants (Table 3). Participants who have more than five years of experience in using brokerage institutions were considered to easily understand the impact. The samples were selected based on representativeness of the population using sample size determination formula and then the selected farmers were interviewed. The survey was made from 8-28 on December, Table 3. Sampling frame and the sample size Kebeles Total households Sample households Total Participant Nonparticipant Participants Nonparticipant Diba sifatera Abewana Kokit Kuhar michaiel Shena Bebeks Mariyam Brokers, rural assemblers and wholesalers sampling Monitoring of the area for four months (January, February, March and April) were undertaken during time of marketing in order to understand the system, identify brokers working in the area and wholesalers coming from different parts of Ethiopia. Five days per week, the 39

55 researcher has monitored and identifies the brokers, wholesalers and the transaction process. For this study, 55 brokers were selected and interviewed, snow ball sampling technique was used in order to find and interview the brokers in the area. Since there were only two licensed and registered brokers in the Woreda, the researcher asks the two brokers first and then the two brokers show other brokers working in the area, In addition, observing the actual transaction process the researcher identify brokers undertaking the brokerage activity and interview using semi-structured questionnaires. During monitoring the researcher identify a place known as Peaceful Café and Pension, which is found in front of the Commercial Bank of Ethiopia in Woreta town. It is the place where agreement between brokers and wholesalers undertaken. People engaged in horticultural trading always sit discussing the price, transaction and payment is also undertaken in this place. The researcher becomes friends with brokers (Baye and Huno) and wholesalers (Mengistu, Setegn and Gizat) of the area in order to easily access the wholesalers coming from different parts of Ethiopia. Within four months during season of horticultural trading, among one hundred four (104) wholesalers fifty two (52) wholesalers were interviewed using random sampling methods from both participants and non participants of the brokerage institutions. Twenty (20) rural assemblers were randomly selected and interviewed from the seventy (70) rural assemblers in the Woreda. Frequent rapid informal and observational surveys were also done covering Fogera Woreda, Gondar, Addis Zemen, Debre Tabour and Bahir Dar Retailers sampling Based on sample size determination formula, forty five (45) vegetable retailers in the four main retail markets; Gondar, Bahir Dar, Gumara and Woreta were selected randomly from two hundred (200) retailers and interviewed using semi-structured questionnaire. 40

56 3.4. Methods of Data Analysis Both the descriptive statistics and econometric methods were used for the analysis of data Descriptive statistics In order to achieve the first objective descriptive statistics such as percentages, frequencies, tables, standard deviation, independent sample t-test and chi squared test were done Econometric models Propensity score matching model In order to achieve the second and third objectives, this study used with and without approach which best suits the purpose of this particular study i.e. brokerage institution participants and non participants comparison using propensity score matching model. The steps are: 1. Estimation of the propensity scores The first step in estimating the treatment effect is to estimate the propensity score. To get this propensity scores any standard probability model can be used (for example, logit, probit or multi-nominal logit) (Rajeev et al., 2007). Since the propensity to participate in use of brokerage institution is unknown, the first task in matching is to estimate this propensity. Any resulting estimates of brokerage institution effect rest on the quality of the participation estimate. This can be routinely carried out using a choice model. Which choice model is appropriate depends on the nature of the brokerage institution being evaluated. If it offers a single treatment, the propensity score can be estimated in a standard way using, for example, a probit or logit model, where the dependent variable is participation whether to use brokers or not and the independent variables are the factors thought to influence participation and outcome. 41

57 42 Following Pindyck and Rubinfeld (1981), the cumulative logistic probability function is specified as: ( ) [ ] + = + = = + = i i i i i i X m i e X F Z F P β α β α (1) Where; e: represents the base of natural logarithms (2.718 ) X i : represents the i th explanatory variable P i: the probability that a farmer participates in the brokerage institution services α and β i : are parameters to be estimated. Interpretation of coefficients will be easier if the logistic model can be written in terms of the odds and log of odds (Gujarati, 2004). The odds ratio implies the ratio of the probability that an individual will be a participant (P i ) to the probability that he/she will not be a participant (1-P i ). The probability that he/she will not be a participant is defined by: [ ] + = i i Z e P (2) Using equations (1) and (2), the odds ratio becomes i i i i i Z Z Z e e e P P = + + = (3) Alternatively, + = + + = = m t ti t i i i i X Z Z e e e P P β α (4)

58 Taking the natural logarithms of equation (4) will give the logit model as indicated below. Zi P P 1 i = i ln = α + β X i + β X i βmx If we consider a disturbance term, u i, the Logit model becomes m Zi = α + t=1 βt Xti + Ui mi (5) (6) So the binary Logit will become: Pr ( PBS ) = f ( X ) Where PBS is participation in brokerage institution service, f(x) is the dependent variable brokerage institution participation and X is a vector of observable covariates of the households; (7) X = [ ] X i (8) 2. Identify the common support region As suggested by Bernard et al. (2007) in order to ensure maximum comparability of the participants and nonparticipants of smallholders in the brokerage institution, the sample used for matching is restricted on those households who are located in the common support region. The common support region is where the values of propensity scores of both participant and non participant smallholders can be found. The basic criterion of this approach is to delete all observations whose propensity score is smaller than the minimum and larger than the maximum in the opposite group (Caliendo and Kopeinig, 2008). The ATT are only determined in the region of common support. Hence, an important step is to check the overlap and the region of common support between displaced and comparison group. To do so several ways are suggested in the literature, where the most straightforward one is a visual analysis of the density distribution of the propensity score in both groups. 43

59 3. Matching using matching algorithms After obtaining the predicted probability values conditional on the observable covariates (the propensity scores) from the binary estimation, matching will be done using a matching algorithm that is selected based on the data at hand. Alternative matching estimators can be employed in matching the participant and non participant households in the common support region. The final choice of a matching estimator can be done taking selecting criterion like balancing test, pseudo-r 2 and matched sample size. A matching estimator which balances all explanatory variables (i.e., results in insignificant mean differences between the two groups), a model which bears a low pseudo R 2 value and results in large matched sample size is a preferable matching algorism (Dehejia and Wahba, 2002; Habtamu, 2011). The different matching techniques are Kernel Matching (KM), Nearest Neighbor Matching (NNM) and Radius Caliper Matching (RCM). 4. Balancing test Balancing test in this context is a test conducted to know whether there is a statistical significant difference in the mean values of covariates for participant and non participant smallholders in the brokerage institution. The higher the number of covariates with equal mean after matching, the more balanced the covariates are. Keeping other selection criterion, the balancing test indicates the quality of the matching algorithm implemented. 5. Estimation of average treatment effect Then the impact of farmer s participation in the services provided by brokerage institution on a given outcome (outcome in this study is percentage of marketed surplus, household s net income from onion production, amount of onion produced and land allocated to onion production) (Y i ) is specified as: i( Di = 1 ) Yi( D = 0) τ i = Y i (9) 44

60 Where; τ i : is treatment effect (effect due to participation in the service of brokers), Y i : is the outcome on household i, D i : is whether household i has got the treatment or not (i.e., whether a household participated in the brokers service or not). However, one should note that Y i (D i = 1) and Y i (D i = 0) cannot be observed for the same household at the same time. Depending on the position of the household in the treatment (brokerage institution participation), either Y i (D i = 1) or Y i (D i = 0) is unobserved outcome (called counterfactual outcome). Due to this fact, estimating individual treatment effect τ i is not possible and one has to shift to estimating the average treatment effects of the population than the individual one. Most commonly used average treatment effect estimation is the average treatment effect on the treated (τ ATT ), and specified as: ( τ D = 1 ) = E[ Y(1) D = 1] E[ Y(0) = 1] τ ATT = E D (10) As the counterfactual mean for those being treated, E[Y(0) D = 1] is not observed, one has to choose a proper substitute for it in order to estimate the average treatment effect (ATT). One may think to use the mean outcome of the untreated individuals, E[Y(0) D = 0] as a substitute to the counterfactual mean for those being treated, E[Y(0) D = 1]. However, this is not a good idea especially in non-experimental studies. Because, it is most likely that components which determine the treatment decision also determine the outcome variable of interest. In this particular case, variables that determine household s decision to participate in the services developed by the brokers could also affect household s gross income from onion production, percentage of marketed surplus, quantity of production and land allocation etc. Therefore, the outcomes of individuals from treatment and comparison group would differ even in the absence of treatment leading to a self-selection bias. By rearranging, and subtracting E[Y(0) D = 0] from both sides, one can get the following specification for ATT. E [ Y( 1) D = 1] E[ Y(0) D = 0] = ATT + E[ Y(0) D = 1] E[ Y(0) D = 0] τ (11) 45

61 Both terms in the left hand side are observables and ATT can be identified, if and only if E[Y(0) D = 1] E[Y(0) D = 0] = 0. i.e., when there is no self-selection bias. This condition can be ensured only in social experiments where treatments are assigned to units randomly (i.e., when there is no self-selection bias). In non-experimental studies one has to introduce some identifying assumptions to solve the selection problem. The following are two strong assumptions to solve the selection problem. Conditional independence assumption Given a set of observable covariates (X) which are not affected by treatment (in our case, participation in brokerage institutions service), potential outcomes (household s gross income from onion production, percentage of marketed surplus, quantity of production and land allocation) are independent of treatment assignment (independent of how the brokerage service participation decision is made by the household). This assumption implies that the selection is solely based on observable characteristics, and variables that influence treatment assignment (participation in broker service decision is made by the household) and potential outcomes are simultaneously observed. Common support This assumption rules out perfect predictability of D given X. That is: 0 < P (D = 1 X) < 1 This assumption ensures that persons with the same X values have a positive probability of being both participants and non-participants in broker s service. Given the above two assumptions, the PSM estimator of ATT can be written as: { EY [ (1) D= 1, P( X) ] EY [ (0) D 0, P( )] τ (12) PSM ATT = EP ( X / D= 1) = X 46

62 Where; P(X) is the propensity score computed on the covariates X. Equation (12) is explained as; the PSM estimator is the mean difference in outcomes over the common support, appropriately weighted by the propensity score distribution of participants. 6. Bootstrapping Because analytical standard errors are not computable for the Kernel-density matching methods, Bernard et al (2007) have used 100 bootstrap replications to compute robust estimates for standard errors of the outcome indicator. Thus, the bootstrapped standard error must be reported on the ATT. 7. Sensitivity analysis Since it is not possible to estimate the magnitude of selection bias with non-experimental data, the problem can be addressed by sensitivity analysis. Rosenbaum (2005) proposes using Rosenbaum bounding approach in order to check the sensitivity of the estimated ATT with respect to deviation from the CIA. The basic question to be answered here is whether inference about treatment effects may be altered by unobserved factors or not. Specification tests In regression analysis, multicollinearity and heteroscedasticity are the issues which should be considered before making any inference based on the estimation results. The explanatory variables used in the logit model should be checked for the absence of strong multicollinearity. Variance Inflation Factor (VIF) technique were employed for checking the occurrence of multicollinearity in the model for the independent variables (Gujarati, 2004). A large VIF (VIF approaching 10) could dictate for a strong linear relationship between the explanatory variables while smaller value (VIF approaches to 1) indicate the model is free of multicollinearity. 47

63 Another problem in regression analysis is the problem of heteroscedasticity in the data. The traditional standard error estimate for logistic regression model based on maximum likelihood from independent observations is no longer proper for data sets with cluster structure since observations in the same clusters tend to have similar characteristics and are more likely correlated each other. Robust standard error estimates are needed to take into account of the intra-cluster correlation. In the present study there was no heteroscedasticity problem. Data were analyzed using STATA version 11 with propensity scores matching algorithm developed by Leuven and Sianesi (2003). Variable definition and measurement The quality of the matching basically depends on the selection of observable variables which determine the probability of participation of the household. Rubin and Thomas (1996) recommended that unless a variable can be excluded because there is a consensus that it is unrelated to outcome or is not a proper covariate, it is advisable to include it in the propensity score model even if it is not statistically significant. Demographic variables (like age, sex, family size), resources for production (like livestock ownership, either rain fed or irrigable land holding) and institutional factors (like distance from the market, Woreda town and extension office) are considered in the research to determine the decision of the household whether to participate or not. Some of the covariates selected for the purpose are time in variant (like sex and in most cases, distance parameters) while some of the variables may vary with time. Indicators for the presence of a change in the response of the household for intervention are defined based on literatures in similar works and on the nature of impact that should result after the intervention. The models we formulated and the way we interpret the results are guided by a comprehensive conceptual framework to avoid potential biases. Here are some of the theoretical relationships between dependent and independent variables used in the research and models to answer the research problems. 48

64 AgHH (Age of the household head): is a continuous variable in the study measured in year of experiences. IFPRI (2007) reported that the age of the head of the household negatively affected the participation decision in cooperatives. Dehejia and Wahba (2002) also found the age of the household head negatively affects the decision to participate in the program. Since communication and negotiation ability reduces with age, the study hypothesized age has positive effect on participation decision. EHP (Experience of the household in horticultural production): It is a continuous variable in the study measured in year of experiences. Experience in horticultural production increases understanding of the overall marketing system and market chain, hence the study hypothesized that it has negative effect on participation decision. SxHH (Sex of the HH head): is a dummy variable which takes, 0=female and 1= male. Female headed households are less likely to participate in labor intensive and risky market development projects. Sometimes, female households are more likely to participate if those projects need less amount of initial investment. A study by IFPRI (2007) found that male headed households are more likely to participate in cooperatives while Yibeltal (2008) found this variable insignificantly affected the participation decision of the household. So in this case, sex is expected to have negative effect on participation since females in the area are busy in house work such as child care and cooking they are less likely to participate in labour intensive tasks. MsHH (Marital status of the HH head): It is a dummy variable which takes the value 0= not married and 1= for married household heads. It is expected to have a positive effect because direct linkage to the wholesaler needs more time in searching market information. Thus married farmers are busy in family responsibility and more likely to participate in the broker s service. ELHH (Education level of the HH head): It is a continuous variable which takes the value of formal education level completed by the household and zero for adult s education and illiterate. As to the report by Yibeltal (2008) and IFPRI (2007), literacy level of the household 49

65 is insignificant to the participation decision of the household. Dehejia and Wahba (2002) found the year of schooling of the household head positively affects the decision to participate in a training program for the work force. Here it is expected to have negative effect on the participation decision because education is the most determining factor in any decision by improving the communication and negotiation capacity of households. Educated people also have no problem of weighing and numerical calculation. TLU (Tropical Livestock Unit of the HH): It is a continuous variable which is converted to TLU. A study by IFPRI (2007) reported that the ownership of livestock has no significant effect on the participation decision of cooperatives. This may be due to the fact that the study was carried out in multipurpose cooperatives and it may result insignificant influence of the variable. It is expected to have a positive effect on participation because it is one source of asset and households tend to manage their livestock s than wasting time in search of market information. FSiHH (Family size of the household): It is a continuous variable converted to conversion factors. Yibeltal (2008) found that this variable is insignificant in affecting the participation decision of food security program while IFPRI (2007) reported that farmers with larger family size were more likely to participate in cooperatives. It is hypothesized to have negative effect on participation as more labor means more time to search market information for direct market linkage. TLSha (Total land holding): It is a continuous variable and measured in hectares. The land holding of the farm household positively influences the participation decision of the household. Maffioli et al. (2008) reported that land holding of the household had positively affected the participation of households in agricultural extension delivery system. It is expected to have positive effect on participation as it is one source of asset and households tend to participate in order to save time to manage the land. ILHa (Irrigable land holding): It is a continuous variable and measured in hectares. Irrigated land is of the influencing factors of the household to participate in vegetable and 50

66 fruit development projects. A study in Argentina by Maffioli et al. (2008) reported that as the irrigated farm size of household s increases, they are likely to participate in agricultural development projects. It is hypothesized to affect participation negatively because more irrigable land means more production as horticultural products are produced using irrigation in the area. More production has in turn a comparative advantage of attracting wholesalers for direct linkage as it reduces transaction cost of having full of a car at a time. DRDA (Distance of residence from development agents office): It is a continuous variable measured in Kilo meters. The access for extension service has a positive effect on the participation decision of the household for market development projects. Yibeltal (2008) also found in his study that households nearer to office of the development (agricultural extension) agents office are more likely to be participated in the program. It is expected to affect participation positively because households far from development agents have less probability of obtaining extension service related to product marketing and market information DRFMAR (Distance of residence from main asphalt road): It is a continuous variable measured in Kilo meters. Yibeltal (2008), distance from the market (the time it takes to the nearest market) had negatively affected the participation affected the participation decision in a development project. It is expected to affect participation positively because households far from main asphalt road have less comparative advantage in attracting wholesalers for direct linkage due to high transaction cost. DRWM (Distance to the Woreda town/woreta market): It is a continuous variable measured in Kilo meters. In addition to the extension service, most of the organizational support services and technologies, market facilities and information centers are found in towns. As the distance from the Woreda town increases, the access for these facilities decreases and this in turn will limit the knowledge of the household about marketing. It is expected to affect participation positively because households far from Woreta town have less probability of obtaining wholesalers and information related to product marketing 51

67 HAMP (Household access for mobile or cell phone): It is a dummy variable which takes the value 0= for the households who do not have cell phone and 1= for those who do have cell phone. It is expected to affect participation negatively because households who own cell phone have high probability obtaining market information and wholesalers. NRC (Number of regular customers (wholesalers)): It is a continuous variable measured by considering regular wholesaler customers. It is expected to affect participation negatively because a household with more regular wholesalers means more demand for the product produced and no problem of market and information. NTRC (Number of trading contacts to the main (Woreta) market): It is a continuous variable measured by considering the frequency of contact to Woreta market related to horticultural marketing. It is expected to affect participation negatively because households with more contact have high market information and understanding of market actors. To analyze the data different variables (dependant and outcome) were used. Table 4 represents the measurement of the variables that were considered. Table 4. Variable Definition and Measurements for Propensity Score Matching model Variables Type Definitions Measurements Dependant variable UBRFM Dummy Farmers participation in brokerage Where, 1= yes, 0= no institutions Outcome variables NIO Continuous Net income from onion production ETB PMSU Continuous Percentage of marketed surplus Percent AOP Continuous Amount of onion produced Quintals LAOP Continuous Amount of land allocated to onion production Hectare 52

68 The Ordinary Least Square (OLS) regression In order to achieve the fourth objective, Ordinary Least Square (OLS) regression estimates were used. The equations below were developed under the assumption: traders follow a sequential decision process, with a discrete choice on whether or not to use brokerage institutions and a subsequent continuous decision on how much or intensity to use brokerage institutions. The selection equation describes whether a wholesaler is using brokerage institutions or not, [ Z + u 0] T 1, α > i = i i [ Z + u 0] T 0, α < (13) i = i i T i, a brokerage-use indicator, is a dummy variable and the realization of a latent continuous variable {Z i α + u i }. When T i =1, the marginal benefits of using brokers exceed the marginal costs (or lost profit due to brokerage fees and increase in purchase price). Only when the binary participation decision T i equals unity is the brokerage-use intensity B i observed. B i explains how much whole seller i uses brokerage institutions and represents the share of brokered transactions out of total transactions. Therefore, B i = B i *, if T i = 1 B i = not observed, if T i = 0 (14) Where, Bi* (the potential share of brokered transaction, a latent variable corresponds to) B * = β + ε (15) i X i i Therefore, once whole seller i decide to use brokers (T i ) = 1, the observed B i is positive. According to Wooldridge, 2002; equations 13, 14 and 15 are valid under the assumptions: (X i, Zi and Ti) are observed, there correlation between the two error terms (there is selection bias), normal distribution of error terms and (X i, Z i ) are exogenous vector of the covariates for i=1,,n Heckman (1979) and Melino (1982). 53

69 Testing for independences Testing for multicollinearity using Variance Inflation Factor (VIF) and heteroscedasticity using Breusch-Pagan / Cook-Weisberg test were done. Estimation methods The estimation method has two ways: when there is sample selection problem, the use of Heckman two stage selection models is recommended which handles the selection bias by incorporating the inverse mills ratio in the second equation. However, if there is no selection bias the use of Double Hurdle model is highly recommended. In this study, since there was a non comparable sample size between the participant and non participants in the brokers service which is 46 (forty six) and 6 (six) respectively, the study used the OLS estimation method for the outcome equation by rejecting the selection equation. The procedure is; without estimating the selection equation (13) for the entire sample of N observations, the estimates of β would be derived by running an Ordinary Least Squares (OLS) regression on the model. B = X β + v, with E ( v X, T = 1) = 0 (17) i i i i i i The OLS estimation would use all observations for which T i = 1, which means the subsample of whole sellers using brokers. Variable definition and measurement The quality of the result is highly affected by the selection of appropriate independent and dependant variables with their units of measurements. Demographic variables (like age, marital status), human capital (like number of persons working on the business, education level), social capital (number of trading contacts to Woreta, wholesalers having regular customers purchasing from them or not, Number of 54

70 regular farmer customers, number of regular retailer customer and number of regular wholesaler customer), Trading experience (years of horticultural trading experience to the area and years of broker using), physical infrastructure (like distance from the Woreda main market, access to asphalt road, access to storage facility and capacity used as selling place), financial assets (working capital and credit access) and marketing costs (including transaction cost, transportation cost, brokerage fee etc.) are considered in the research to determine the decision of the wholesalers whether to participate in the brokerage institutions or not and share of brokered transaction. Here the theoretical dependent and independent variables and their measurements are indicated for the research with respect two Heckman two stage models to answer the research problems. DRFWM (Distance of residence of wholesaler from Woreta market): It is a continuous variable measured in Kilo meters. A study in Ethiopia by Quattri et al. (2011) indicated that the conditional marginal effect of distance on brokered transaction is positive and significant (0.092) value, meaning that an increase in distance raises the share of brokered purchases for those buyers already using brokers. It is expected to have a positive effect on participation to the broker s service and intensity of brokerage use because when distance increases it will become very difficult for wholesalers to get market information about producers. TRDA (Type of road accessed by the wholesaler): It is a dummy variable which takes the value 0= for the wholesaler who do not have access to asphalt road and 1= for those who do have access for the asphalt road. It is hypothesized to have negative effect on participation and intensity of brokerage use because gravel roads took more time for transportation thus use of brokerage institutions is important in order to supply frequently on time and reduce time of searching market information. AGWS (Age of the wholesaler): It is a continuous variable measured in years. Since communication and negotiation ability reduces with age, the study hypothesized age has positive effect on participation decision. 55

71 EXWSHT (Experience of the wholesaler in horticultural trading): It is a continuous variable measured in years of experiences. Experience in horticultural trading increases understanding of the overall marketing system, market chain and creation of social relationship, hence the study hypothesized that it has negative effect on participation decision and intensity of brokerage activity. MSWS (Marital status of the wholesaler): It is a dummy variable which takes the value 0= not married and 1= for married wholesalers. It is expected to have a positive effect because direct linkage to the producers needs more time in searching market information. Thus married wholesalers are busy in family responsibility and more likely to participate in the broker s service. Thus, it is expected to have positive effect on participation and intensity of brokerage. ELWS (Education level of the wholesaler): It is a continuous variable which takes the value of formal education level completed by the household and zero for adult s education and illiterate. Here it is expected to have negative effect on the participation decision and intensity of brokerage use because education is the most determining factor in any decision by improving the communication and negotiation capacity of wholesalers. Educated wholesalers also have no problem of calculation of profit. NPWB (Number of persons working on business): It is a continuous variable which takes the value the number of persons converted to conversion factors. It is expected to have negative effect on the participation decision and intensity of brokerage use because more persons in the business means more time and labor in searching market information about producers for direct linkage. CWCWS (Current working capital of the wholesaler): It is a continuous variable which has the value in birr (ETB). It is expected to have positive effect on the participation decision and intensity of brokerage use because wholesalers having less capital tend to do not participate in the brokers service so as to reduce the commission payment. 56

72 ACWS (Access to credit for the wholesaler): It is a dummy variable which takes the value 0= if the wholesaler do not accessed credit for the business and 1= if accessed credit for the business. It is expected to have a negative effect on participation and intensity of brokerage use because direct linkage to the producers avoids commission payment. Thus, wholesalers who have accessed credit tend to do not participate in the broker s service so as to pay the credit by saving the benefits that will go to brokers. HOSF (Have own storage facility): It is a dummy variable which takes the value 0= if the wholesaler do not have own storage facility for the business and 1= if have own storage facility for the business, which can function as a selling place for the horticultural product. It is expected to have a positive effect on participation and intensity of brokerage use because direct linkage to the producers needs more time in searching market information. Thus, wholesalers who do have their own storage facility need to participate in the broker s service so as to frequently supply in order to satisfy the demand. CASF (Capacity of the storage facility): It is a continuous variable and measured in the number of quintals of the storage facility that it can carry at a time. It is expected to have positive effect on participation because if the wholesaler has large storage facility more supply is must to full the storage. Thus, the wholesalers tend to participate to satisfy the supply. NRBC (Number of regular Broker customers): It is a continuous variable measured by considering the number of regular broker customers to the wholesaler. It is expected to affect intensity of brokerage use positively because a wholesaler with more regular broker customers means more supply of the product using brokers with reduced FERQ because of the already established relationship NRFC (Number of regular farmer customers): It is a continuous variable measured by considering the number of regular farmer customers who always sole their product to the wholesaler. It is expected to affect participation and intensity of brokerage use negatively 57

73 because a wholesaler with more regular farmer customers means more supply for the product he want to purchase and no problem of market and information. HRCAPO (Have regular customers always purchasing onion from you): It is a dummy variable which takes the value 0= if the wholesaler do not have regular customer who always purchase onion from him and 1= if he has. It is hypothesized to affect participation positively. Wholesalers having regular buyers need to frequently supply to their customers and in order to satisfy the demand there will be higher probability of use of broker s service. NRRC (Number of regular retailer customers): It is a continuous variable measured by considering the number of regular retailer customers who always purchase from the wholesaler. It is hypothesized to affect participation and intensity of brokerage use positively. Wholesalers having regular retailer customers need to frequently supply to their customers and in order to satisfy the demand there will be higher probability of use of broker s service. NRWCOA (Number of regular wholesaler customers in other areas): It is a continuous variable measured by considering the number of regular wholesaler customers in other areas who always purchase from the wholesaler. It is hypothesized to affect participation and intensity of brokerage use positively. Wholesalers having regular wholesaler customers found in other areas need to frequently supply to their customers and in order to satisfy the demand there will be higher probability of use of broker s service. NTCFWM (Number of trading contacts to the Fogera Woreda market): It is a continuous variable measured by considering the frequency of contact to Fogera Woreda market (Gumara and Abewana Kokit) related to horticultural marketing. It is expected to affect participation and intensity of brokerage use negatively because wholesalers with more contact have high market information and understanding of market actors. EUB (Experience in using brokers) It is a continuous variable measured in years of experiences of wholesaler using brokers for market linkages. It is expected to affect intensity 58

74 of brokerage use positively because more experience with brokers means more trust based relationship with brokers. TMCOST (Total marketing cost): It is the amount of total marketing cost (transaction, transportation, brokerage fee and other costs) measured in birr (ETB) for descriptive statistics, while it is a cost of not using brokers in the OLS estimation. It is expected to affect the intensity of brokerage use positively. When the cost of not using brokers is very high due to increased transaction cost, wholesalers tend to use more of the brokers service so as to reduce the transaction cost. To analyze the data one dependant variables were used. Table 5 represents the measurement of the dependant variables that were considered in the OLS method. Table 5. Variable Definition and Measurements for OLS estimation Dependant Type Definitions Measurements Variables PBT Continuous Percentage of brokered transaction out of total transaction Percent 59

75 4. RESULTS AND DISCUSSION 4.1. The Brokerage Institutions In this section, the research describes the socioeconomic profile, characteristics, economic role, constraints and opportunities of brokerage institutions in linking smallholder horticultural crop (onion) producers with market outlets (wholesalers). This section has ten different sub-topics so as to independently discuss the above issues Socioeconomic profile of brokerage institutions Table 6. Frequency distributions of brokers Variable Category Frequency Percent (%) Sex Religion Marital status Education level Main occupation Male Female 0 0 Orthodox Christian Others 0 0 Single Married Illiterate Adults education Literate (formal) Farmer Youth Trader The 30.20% are upper-tier farmer brokers working for urban and peri-urban brokers, 14.4% are upper-tier farmer brokers who have direct linkage to wholesalers by their own and the rest 13.6% are lower-tier farmer brokers are employed by upper-tier farmer brokers and youth brokers (see figure 2) 2. The 7.20 % of the youth brokers are working for urban and peri-urban brokers and 14.60% work by their own by directly contacting to the wholesalers 60

76 The descriptive statistics analysis (Table 6) above showed that only males are engaged in the brokerage activity. The reason is that the task needs movement from place to place, better communication and forming friendship which needs more time and labor intensive. Females in the area are very busy on the works undertaken in the house such as child care and cooking. Since the proportion of Orthodox Christians is much higher than other religions all of the brokers in the study are Orthodox Christians. Most of the brokers are married while there are few numbers of brokers who are single. Education level plays very important role in any decisions. Brokerage activity by itself is highly related to education in the area of communication, negotiating, weighing and numerical calculations of transaction. Most (80.00%) of the brokers are literate from formal education, 16.4% are with adult education and 3.6 % are illiterate. The illiterate brokers are community elders within the service of brokers at urban (Woreta), Peri-urban (Gumara and Abewana Kokit) and their role is negotiating farmers to sell their product to urban and periurban brokers for this service they have a commission of ETB per kilogram. The highest percentages of brokers are farmers (58.20%). The reason is that the urban and peri-urban brokers are unable to cover all the kebeles of the producers due to high transaction cost of negotiating all of the farmers. Thus, the urban and peri-urban brokers form farmer brokers working for them with commission to reduce the transaction cost of covering all the areas of the Woreda. Since farmers are found in the residence and know the producers very well their transaction cost of finding and negotiating producers is very easy and low which gives them the opportunity to act as a broker. The youth (school dropout, grade 10 and 12 complete) brokers have also a remarkable share (21.8%). They are either employed by urban and peri-urban brokers or work independently by themselves by directly linking to wholesalers. The urban and peri-urban brokers (traders of cereals) account only 20.00%. The age structure of broker s ranges from to with mean 33.00, which implies that most of the brokers are youngsters while urban and peri-urban brokers employ community elders which are aged farmer brokers to easily negotiate farmers in the area. The average brokerage experience is greater than 6.00 years. Married brokers have mean of 5.60 family 61

77 sizes. Farmer brokers have a mean of 6.19 ha (which includes rented in land) of land size and the average number of cattle ownership for them is All of the farmer brokers have motor pump and known for horticultural production in the area. Table 7. Descriptive statistics for continuous variables of brokers Variables Sample (N) Minimum Maximum Mean Std.Dev. Age Experience in brokerage activity Family size Land size Number of cattle Number of water pump Which horticultural products have significant brokerage activity in the area? The study has found a significant and strong brokerage activity only on onion marketing. However, in other horticultural crops marketing such as tomato, potato, leafy vegetables, carrot and garlic there is no significant brokerage activity. The result of the study showed that only 4.2% of the farmers use brokerage institutions for marketing of tomato specifically in Abewana Kokit peasant association. There are only two brokers doing this brokerage activity in addition to onion. Tomato marketing is undertaken at urban (Woreta), peri-urban (Gumara and Abewana kokit) and on the main asphalt road (at four places). Farmers take their product to the asphalt road side, urban and peri-urban market places and they directly sell to retailers, wholesellers and brokers who act as rural assemblers in this case. Thus, the study has only considered brokerage activity in onion production for further analysis. 62

78 Characteristics and economic role of brokerage institutions Most of the brokers (98.2%) and wholesalers (32.7%) work the business informally without having license. The distinctions between the two are the brokerage institutions do not own the product while wholesalers have ownership of the product and brokerage institutions undertake linkage between the farmer and wholesaler with commission. Thus, brokerage institutions are commission agents while wholesalers took the onion to the central markets or sell for other trader in the Woreda by purchasing the onion from the producer. The main brokerage institutions characteristics and roles in the area include: They are better informed by buyers and or sellers: The brokerage institutions mostly include youngsters, educated, resident and very active persons. This makes them advantageous in high information searching, communication and negotiation ability which in turn helps them to have better information about the wholesaler and the smallholder producers in the area. They are skilled socially to bargain and forge links between buyers and sellers: As brokers are residents of the area and their high communication capacity helps them to have more number of regular farmer and wholesaler customers. In addition there contact and social interaction is very high which helps them in negotiating and bargaining the wholesalers and farmers. They bring the "linkage" to wholesalers and farmers who may not communicate with each other: A wholesaler coming from other area (Oromiya, Amhara, Tigray, SNNP, Somale and Benshangul gumuz Regions) of the country to the Fogera Woreda is not familiar with the Woreda and do not know the producers. Thus, it will be difficult for him to find the producers and undertake the transaction. On the other hand, smallholder producers especially those who are very far from the main asphalt road have no any information about the wholesaler coming to the Woreda and as the horticultural products are perishable and bulky the farmer has to be sure about the market before harvesting. Therefore, both of the farmers and wholesalers problems are easily solved by the brokerage institutions as they have detailed of information 63

79 on both of the actors. In this case these institutions bring linkages between farmers and wholesalers who may not communicate each other and undertake transaction. They bring economies of scale by accumulating small suppliers and selling to many wholesalers: There is a very significant difference in the mean of amount of onion produced in quintal/kg between participant and non participant households in the brokerage institutions. Non participant households produce more onion than the participants this gives them the advantage of directly linking to wholesalers as they produce full of one car and sell it at a time from the farm which creates the incentive for wholesaler by reducing the transaction and transportation cost in such a way that a wholesaler reduce the transportation cost by not moving from farm to farm to full his car and do not negotiate many farmers as he has got from one farmer. However, the smallholder producers which cannot produce full of a car at the time have no the incentive of attracting wholesaler for direct linkage. Following this gap the brokerage institutions contact all the smallholder producers and form groups considering their onion farm in such a way that each group accumulate the product at one place which can amount at least one full of car at a time. For example during monitoring the researcher has observed more than hundreds of groups formed by the brokerage institutions from two to ten smallholder producers at different places which can be accessed by the car in such a way that the farmer broker records each smallholder supply and the urban and peri-urban brokers arrange wholesalers. Each group can load more than one car at a time in one place. This process is creating economics of scale by accumulating small supplies from many smallholder producers which creates incentive for the wholesaler by reducing transaction and transportation cost. They stabilize market conditions for a supplier or buyer faced with many outlets and supply sources: One of the justifications for this is that, In February, 2012, the wholesale selling price for Kg of onion has increased and reached to 7.50 ETB in Addis Abeba. Following this many demands has come to brokerage institutions from wholesalers of Addis Abeba using telephone and based on this demand the brokerage institutions respond for this by sending 18 FSR car (18000 Kg) of onion in one day for three continuous days. This resulted higher supply in Addis Abeba causing a wholesale selling price reduction from

80 ETB to 6.00 ETB leading to loss for the wholesalers. At this time the brokerage institutions has stopped sending the product to Addis Abeba and shift other market centers such as Oromiya and Tigray Region in order to stabilize Addis Abeba market until the wholesalers are profitable. They reduce transaction cost of searching information and marketing cost for both farmers and wholesalers: As the brokerage institutions are well informed by wholesalers and producers, residents and have strong social capital they have more information, communication and negotiation capacity which helps to reduce the transaction cost. Smallholder producer groups also help wholesalers by reducing the transportation cost by accumulating the product from different farm lands at one suitable place for car access Act as a means of trust and facilitate trading during transaction between farmers and traders: Before harvesting the smallholder producer has to be sure about the market since the horticultural product is perishable and bulky in nature. Harvesting is undertaken in the area before one day or more to make the product ready for selling/loading keeping the quality (removing the mud and cover). Thus, there should be agreement between the farmer and the wholesaler before harvesting. This agreement is highly susceptible to contract failure in the area. There are many cases in the area like farmer Workneh Wude has faced in 2011, The farmer has directly contacted to the wholesaler and agreed on the price to harvest his product and make ready for loading before three days, following this agreement the farmer has harvested the onion and prepared for loading waiting for the coming of the wholesaler. However, the wholesaler was not come on that day to the farmer farm to load the onion because he has got and loaded other farmer product that is very near to the main asphalt road than Workneh with the same price. Because of this contract failure he has lost his benefit in such a way he has incurred costs in transporting some of his product from the farm to the main asphalt road and he has sold the remaining onion at lower price in the other day due to quality problem. However, this contract failure does not occur if the farmer uses the brokerage institutions for linkage to wholesalers. The reason is that brokerage institutions have many regular wholesaler customers requesting onion using phone call and also coming to the area if one fail they will sell for the other. Thus, the farmers using brokerage institutions 65

81 are full of trust, have secure market for their product and have no problem of harvesting before loading time. Facilitate credit based transaction for the wholesalers being as collateral for the farmer: Most of the transaction (more than 70%) in the horticultural marketing is under taken based on thrust. Since the wholesalers have working capital problem the transaction is undertaken by the arrangement between producer and wholesaler in such a way that first taking the onion from the farmer as a credit and then selling the product at different central markets after this the farmer took his payment based on agreed price. This arrangement is undertaken because of the existence of the brokerage institutions which are residents and well accepted in the community. The brokerage institutions act as a collateral for the farmer in undertaking the credit based transaction with the wholesaler. If there are no brokerage institutions surly such arrangements will not occur because of contract failure. Brokers who are educated have the ability to enforce the contracts. The researcher have seen two brokers receiving the credit from the wholesaler by directly going to Gondar wholesale market place in 2012 which amounts 65,000 ETB. In different studies agricultural brokers provide credit, market information and share risk for both the wholesaler and smallholder producer. However in Fogera Woreda, farmers provide their product to wholesalers as credit taking the brokers as collateral. Then a wholesaler sale a product and provide the credit (value of the product during transaction) to a broker after that the broker give to the farmers taking his own share. This contractual bases transaction is subjected to contract failure as there are no formal contract enforcement mechanisms. The mean loss due to contract failure in 2011 was 15, ETB between the wholesaler and brokers; this has effect on the farmers who provide their product on credit bases to the broker leading to delay in payment and sometimes farmer may not get whole payment. Of course, brokers provide market information to farmers related to quality and prices. But, the reality of price information is questioned by farmers. Brokers provide market information (quality and price) using telephone, direct discussion and providing sample for the wholesalers. The study characterized brokers in to two ways, first based on place of work as rural brokers (found in rural areas), peri-urban brokers (found in peri-urban areas) and urban brokers (found in urban 66

82 areas). The second characterization is based on their main occupation as farmer brokers (whose livelihood is dependent on farming), youth brokers (grade 10, 12 and college complete and school dropout youngsters) and cereal traders (formal traders of cereals such as rice). Urban brokers are either traders of cereals such as rice or youngsters found in the town working only the brokerage activity in the case of onion trading. Table 8. Descriptive statistics of some variables Variables Sample (N) Minimum Maximum Mean Std.Dev. Amount of onion transacted in quintal Income from brokerage activity Loss due to contract failure (ETB) Working capital (ETB) Number of regular customers (wholesalers) All urban brokers have employs (farmers and youth brokers) in the rural area working for them. The peri-urban brokers which are found in small towns of the Woreda such as Gumara and Abewana Kokit employ farmer brokers with commission in each kilogram of onion. They are traders of cereals and engaged in agricultural production specially in horticulture production by arranging contractual agreements with farmers in such a way that brokers provide seed, motor pump, fuel and chemicals while the farmer provide labor and land. Thus, half of the production provided for the broker and half for farmer. 67

83 The farmer brokers, there main occupation and livelihood is dependent on farming and are found in the villages of rural areas. The farmer brokers are further divided in to two as the lower-tier groups and upper-tier groups of farmer brokers. The upper-tier groups of farmer brokers and youth brokers employ the lower-tier groups of farmer brokers with commission without the knowledge of urban and peri-urban brokers. Thus, the lower-tier groups of farmer brokers work for the upper-tier groups of farmer brokers and youth brokers. The upper-tier groups of farmer brokers are either employed by urban and peri-urban brokers or work for themselves by directly creating linkage to wholesalers. Figure 2: Broker s chain and flow of transactions using brokerage institutions Wholesalers Brokers at Woreta, Gumara and Abewana Kokit (20.00%) Upper-tier Farmer Brokers 2 (44.60%) Youth Brokers (school drop outs, grade 10 and 12 complete) (21.80%) Lower-tier Farmer Brokers 1 (13.60%) Producers 1. These are lower-tier groups of farmer brokers who are employed by and work for the upper-tier farmer brokers and youth brokers without the knowledge of urban and peri-urban brokers 2. These are the upper-tier groups of farmer brokers employed by and work for the urban and peri-urban brokers 68

84 Brokerage institutions and their activity in the context of Fogera Woreda From different literature, brokers do not take title to the goods traded but link suppliers and customers. However the case in Fogera Woreda is slightly different even if they have no the title of ownership of the onion they act as their product once they have agreed with producers in terms of price and selling the product using them. Brokerage institutions in Fogera Woreda function as follows: When the wholesaler comes to Fogera Woreda: when the trader comes to the Fogera Woreda or the trader is from the Woreda itself, they act in two ways. One is they will contact the wholesaler with the farmer and the price will be determined by the trader and farmer with high bargaining power taken by traders. This service or practice works for all the wholesalers who are found in the Woreda. However, the trader coming from other area must be regular customer or known trader in the Woreda to get the above service. The broker will have the payment of only 0.10 ETB for each Kg of onion transacted as a commission. This case accounts less than 10% of the whole transaction in the Woreda. The second is, when the trader coming from other area is not well familiar to the area and not regular customer of the broker, brokers act as a trader in such a way that first they discuss with farmers and fix the price then they will contact to the trader and negotiate the price after that transaction will be made with no contact between farmer and wholesalers. Here, in addition to 0.10 ETB commission fee paid for the broker, there is a price gap of 0.10 ETB to 1.00 ETB between farm gate price and wholesale purchase price which will be in the pocket of the broker. This is known as FERQ. This one accounts about 20% of the total transaction. Trust based transaction: When the wholesaler do not come to Fogera Woreda, the brokers act as trader even if they have no the title of ownership in such a way that first they discuss with farmers and fix the price then they will contact to the trader using telephone and negotiate the price after that transaction will be made with no contact between farmer and wholesalers. The transaction is undertaken only by telephone orders by wholesalers to the brokers. Here, in addition to 0.10 ETB commission fee provided to the broker by the whole seller, there is a price gap of 0.10 ETB to 1.00 ETB between farm gate price and wholesale 69

85 purchase price depending on the volume of transaction and customer relationships. This gap is also known as FERQ. This gap matters the relationship for the coming years, if the wholesaler contact another broker and understood the price difference between wholesale payment and farm get price in addition to brokerage fee the probability of continuing their customer relationship become very low. Brokers in tomato trading: here there is no significant brokerage activity and 46.4% of the brokers act as rural assemblers and purchase tomato on the five market places of main asphalt road (road side) and either directly sell to wholesalers, retailers and consumers in the area or take the tomato to Gondar and Bahir Dar market centers to sell to retailers by renting a warehouse which can be used as a selling place in the area. Broker s attraction mechanism of wholesalers: brokers attract wholesalers by cheating weight from farmers and reducing the price gap between farm gate price and wholesaler purchase price. Weight cheating has two advantages for the broker one is that obtaining regular wholesaler customers for the future and having his own share from it. Weight cheating ranges from 6% to 20%. The acceptable weight reduction from normal weight during transaction by the farmers in the area is that a maximum of 6% for the horticultural products only because there will be weight loss during transportation and delay in selling for the wholesaler. Weight cheating by brokers and wholesalers is common in the area in both of participant and non participant households of brokerage institutions during transactions The rationale behind the emergence of farmer brokers Why it is common to obtain farmer brokers in two stages? When the production of horticultural crops production especially onion scaled up to many of the farmers due to farmers to farmers extension, NGOs and extension workers, the production has increased from time to time attracting wholesalers from different parts of Ethiopia. The high demand of onion by wholesalers creates the opportunity for urban and peri-urban brokers to cover all the peasant associations of the Woreda. However, it was very difficult for them to cover all the area due to high transaction cost and labor force. Thus, urban and peri-urban brokers employ 70

86 upper-tier farmer and youth brokers who are residence of the area and well accepted by the community with commission from each Kg of onion in order to easily bargain farmers and compute other brokers in the transaction. Since upper-tier farmer and youth brokers are residents in the rural area they have relative s producing the onion which gives them to easily have suppliers or regular customers. Thus, this in turn gives them the opportunity to easily join to the brokerage institutions. The study identified that most (more than 90%) of the farmer brokers started the service by using their relative farmers product. There are also the upper-tier farmer brokers who do have direct contact to the wholesalers. These types of brokers are emerged in order to withstand the exploitative act of urban and peri-urban brokers. First they were employed by urban and peri-urban brokers. In the mean time through experience in the brokerage activity, these brokers develop regular customers of wholesalers and using this opportunity they directly create linkage to wholesalers by passing the urban and peri-urban brokers (previous employers of them). This new linkage creates other demand opportunity for upper-tier farmer brokers from wholesalers. Experience in the brokerage activity also helps them to have large number of regular wholesalers. These in turn create high demand of onion from wholesalers for them. However, the upper-tier farmer brokers were not able to satisfy the high demand of regular wholesaler customers because they were unable to cover wide areas especially distant farmers (peasant associations) due to high transaction cost. As a result the experienced upper-tier farmer brokers employ lower-tier farmer brokers working for them with commission. The lower-tier groups of farmer broker s work for the upper-tier experienced farmer brokers and youth brokers. Generally, these lower-tier groups of farmer brokers are formed by the upper-tier farmer brokers and youth brokers in order to cover very far areas from the main asphalt road for reduction of high transaction costs (labor and negotiation cost). Therefore, due to the above facts farmer brokers dominate the brokerage activity in the area. 71

87 Market outlets or target markets of brokerage institutions Brokerage institutions base almost all parts of Ethiopia as their own market outlets. They have regular wholesalers and sell the horticulture product in Amhara Region (almost in all zones) (15%), Tigray Region (Mekele, Humera, Shere and Adwa) 13%, Oromiya Region (Wolega, Nazreth, Doni, Asebe teferi, Metu etc) (10%), Harari Region (Harar) 2%, Somali Region (Jijiga) 3%, Benshangul Gumz (Assosa, Gilgel Beles and Pawi) 5%, Southern Region (Welayta Sodo) 2% and Addis Abeba (50%) of the total production of horticulture in the Woreda. Because of the existence of brokerage institutions the Fogera market is linked to different parts of Ethiopia. The main reason of domination of Fogera tomato and onion to all of the above places is that low production costs in which smallholders at Fogera Woreda do not uses fertilizer and use mainly the cheap labor in the area Producer s perception of brokerage institutions Most (73.4%) farmers (both participant and non participant) believe that brokers play significant and important role in linking farmers to traders while only 26.6% of the farmers (all non participants of the brokerage institutions) believe that they have no important role in onion marketing. According to farmers, there importance is in linkage (35.7%), price information (0.6%), linkage and price information (0.7%) and linkage, quality information and price information (36.4%). All of the farmers (100%) believe that brokers cheat in weight, provide false price information and block direct contact of farmers to traders. Generally, most farmers believe that brokers are important in the area with formalization of the brokerage activity to avoid their exploitive act Night transaction and loading Most (more than 92%) of the transaction is undertaken during the night time. This has two implications. The logic rose by brokerage institutions, they believe that night loading helps to reduce the perishablity of the horticulture during transportation to distant area. For example if the onion is loaded at the night time to Addis Abeba, the onion reach for the morning market 72

88 being fresh and keeping the quality which gives high price incentive for the wholesaler by attracting retailers and consumers. Thus, brokerage institutions and wholesalers prefer to load from the farm and transport at the night time in order to keep the quality of the horticulture as standard. However, this logic is not accepted by the producers. Producers believe that night loading is the system developed to easily cheat weight and block direct contact of producers to wholesalers discussing the price of the onion. At the night time both producers and wholesalers have only one chance which is transacting and loading the product with the agreed broker price. That means there is no chance of avoiding the FERQ by discussing the price once the onion is harvested by the producer and the car is ready by the wholesaler. Thus, any default from the agreement leads to high transaction cost for both the producer and wholesaler. The researcher has proved from four brokers as night loading increases the percentage of weight cheating and reduces contract (price agreement) failure by the discussion between producer and wholesaler Constraints of brokerage institutions The brokerage institutions are constrained by the factors related to working capital, contract failure and strong competition between brokers. There are no any financial institutions which provide credit for the brokerage activity and there is no formal contractual agreement during transaction which makes contract enforcement very difficult in the area during contract failure. In addition, due to the absence of formal brokerage activity everybody can be a broker which leads to competition between brokers which in turn causes to conflict. During monitoring of the brokerage activity, the researcher has observed the conflict between two brokers for the area of specialization in which one broker enter the territory of the other broker to undertake transaction between producer and trader/wholesaler leading to another conflict between the broker family (the one who believes the area is my territory) and the producer family causing at least three people heavily injured. 73

89 Opportunities to the brokers The brokerage activity is not only constrained by problems but there are opportunities for the business in the area associated with high production of onion from time to time, increasing demand for the product from different parts of the country, farmers do not know wholesalers, wholesalers do not know the producers, information and linkage gaps between farmers and wholesalers which provides the opportunity for the broker to easily enter to the business in order to form linkage between the two market actors. In addition, since brokers are residents in the rural area, educated, young and have relatives engaged in the production in the rural area they have the ability to negotiate and easily communicate with farmers which gives them the opportunity to simply join the brokerage institutions Brokerage Institutions and Smallholder Market Linkages This section presents the difference between farmers using brokerage institutions (treated) and farmers which do not use brokerage institutions (untreated) with respect to demographic factors, socioeconomic characteristics, social capital, production systems, institutional and organizational aspects. It also presents the determinants of farmer s decisions on whether to use brokerage institutions or not for market linkage to the market outlets/wholesalers and the impacts of brokerage institutions on smallholder onion producers. The findings are discussed accordingly Descriptive statistics Demographic characteristics of sample households This study is based on the information collected from 143 sample farm households in Fogera Woreda, of which 76 were participants in the brokerage institutions while the rest were not. 74

90 Table 9. Descriptive statistics of sample households on pre-intervention characteristics Preintervention Variables Sample Households (N=143) Participant (N=76) Non participant (N=67) Difference in means Mean Std.Er Mean Std.Er Mean Std.Er Mean Std.Er T-Value AgHH *** SxHH ** MsHH ELHH *** FSiHH FSiHH TLU TLSha ILHa ** EHP DRDA *** DHMP *** DRWM *** DRFMAR *** NRC *** NTRC Source: Author s Survey, 2011 *** and **means significant at the 1 and 5% probability levels, respectively. 1. Labor supply conversion factor (person day equivalent) The descriptive results showed that (Table 9) the participants and non-participants of the brokerage institutions were not significantly different in family size, with the average family size of 5.94 and 6.29 respectively ranging from 2 to 14. The sample is composed of 63 male headed and 4 female headed non participant households and 67 male headed and 9 female headed participant households. There is significant difference between participant and non 75

91 participant farmers with respect to sex. This indicates female headed households tend to participate in the brokerage institutions to sell the horticulture product. Because, direct linkage to the wholesalers needs high communication ability, networked interaction, labor intensive and mobility from place to place but females in the area cannot undertake this because they are very busy undertaking house works and also social taboos hinder them. The age structure of the sample households showed that the average age of participants of the institutions was years while it was for those who didn t participate. There is a significant difference between the two this is because aged people are weak in communication and interaction which needs moving from place to place and labor intensive. Thus, they tend to participate in the brokerage institutions. The average years that the family had spend in horticultural production was 9.18 for those who participate in the brokerage institutions and it was 8.79 for those who didn t participate. The average experience in horticultural imply the farmers from both groups have had more than five year farming experience with no significant difference between the two groups. The level of education of the household heads is statistically different for the two groups and non participants were better-off in their level of education with mean 3.42 while 1.52 for the participants. Education plays the most important role in any decision. Educated people have greater communication and negotiation ability in addition they have no problem of calculating the transaction and profit. Thus, educated households tend to do not participate in the brokerage institutions in order to remove the FERQ and maximize their profit Socio-economic characteristics of sample households Land can be considered as time invariant since land redistribution hasn t been in practice for the last ten years. The average size of land holding of the non-participant and participant households is about 1.43 and 1.69 hectares, respectively and no significant difference between the two groups. Land is the basic asset of farmers as most investments in the agricultural sector require land. There is significant difference on irrigable land holding between the nonparticipant and participant households. This might be due to the reason that households who 76

92 have higher irrigable land size have the opportunity to produce more which in turn gives an incentive for them to attract wholesalers because wholesalers think of the reduced transaction cost in which they can have full of the car at a time from one producer. Livestock are mainly kept to be the main source of draft power for agricultural practices in a mixed farming system. Small ruminants in the area are kept mainly for income supplementation of households. The livestock ownership is not different between participant and non-participant households with the mean ownership of about 6 and 5 TLU for nonparticipants and participants respectively. The minimum amount owned by a household is 0 while the maximum was TLU which indicates that there is a high degree of disparity in the ownership of livestock between the sample households Institutional and organizational aspects All of the households have access to formal credit sources such as Amhara Credit and Saving Institutions (ACSI) as a result a variable access to credit were not considered in the model. Only 13.2% of households are member of cooperatives while 86.8 % of the households are not. In all of the kebeles of the Woreda there are development agents. However, there is significant difference in distance from residence to development agents between participant and non participant households. Telecommunication facility is the most important service in marketing of horticultural products by providing recent information and reducing the transaction cost of trading. The result showed that 81.58% the participant household s do not have cell phone (mobile) while only 18.42% have cell phone. However, 35.2% of the non participant households have cell phone while others do not have. There is significant difference between the participant and non participant households with respect to cell phone ownership. Higher percentage of mobile phone ownership helps the non participant households to easily call and find the wholesalers for selling their horticulture product. There are two main asphalt roads from Bahir Dar to Gondar and from Woreta to Debre Tabour. There is significant difference between participant and nonparticipant households with respect to distance of residence to Woreta (Woreda) market and main asphalt road. The reason is that when the households are far away from the main asphalt road and Woreta town, the 77

93 transaction cost of finding market information and wholesalers is very high. Thus, the households tend to use brokerage institutions in order to reduce the transaction cost. Table 10. Descriptive statistics of sample households (for dummy variables) Pre- Category Participant Non participant Total χ2 intervention (N=76) (N=67) Variables N % N % N % SxHH Female * Male DHMP No *** Yes Source: Author s Survey, 2011 *** and *means significant at the 1 and 10% probability levels, respectively Social capital Social capital plays very significant role in transaction. There is significant difference between the participant and non participant households with respect to the number of regular wholesaler customers and number of trading contacts to main (Woreta) market in marketing of horticultural products. Social capital reduces the transaction cost by reducing the negotiation and information searching costs. High social capital means less probability of participation in the brokerage institutions. Since, non participant households have higher social capital which reduces the transaction cost they tend to directly contact to wholesalers to sell their product than using brokerage institutions. 78

94 Propensity score matching model Estimation of propensity scores As indicated earlier, a binary variable which indicates whether the household is participated in the brokerage institutions or not was considered as dependent variable. In the estimation process, households were pooled in such a way that the dependent variable takes a value 1 if the household uses the brokerage institutions for linkage to wholesalers (participate in brokerage institutions) and 0 if the household do not use brokerage institutions for linkage. The variables (demographic, socio-economic, social capital and institutional aspects) included in the model are assumed to affect household s participation decision whether to use brokers or not as a linkage to the market outlet and have influences on the overall outcome of interest. The model was estimated with STATA 11 computing software using the propensity scores matching method called psmatch2 developed by Leuven and Sianesi (2003). Variance inflation factor (VIF) was applied to test for the presence of strong multicollinearity among the explanatory variables (see appendix 1). There was no explanatory variable dropped from the estimation model since no serious problem of multicollinearity was detected from the variance inflation factor (VIF). Breusch-Pagan / Cook-Weisberg test for heteroscedasticity were used to check the existence of heteroscedasticity of variance and there was no heteroscedasticity problem in the model. The pseudo-r 2 value is (0.4294) (Table 11). A higher pseudo R 2 value in this case means that households with in and out of the brokerage institutions do have much distinct characteristics. As indicated below (Table 11) only six of the fifteen explanatory variables which are theoretically supported to influence the decision to participate in the brokerage institutions for linkage and considered in the logit model have significant effect on the participation decision of the household. 79

95 Table 11. Logit results of households brokerage institution participation Variables Coefficients Std.Er Z value Age.056** Sex MsHH ELHH -.163* FSiHH Livestock TLSha ILHa EHP DRDA.156* DHMP *** DRWM DRFMAR.631*** NRC -.331** NTRC Constant Sample size (N) 143 LR chi2(15) Prob > chi Pseudo R Log likelihood Source: Own estimation result ***, ** and *means significant at the 1%, 5% and 10% probability levels, respectively. 1. Labor supply conversion factor (person day equivalent) The interest of the matching procedure is to get a household from broker non-users (nonparticipants) in brokerage institutions service with similar probability of participation or using brokerage institutions given the explanatory variables. If the numbers of explanatory variables 80

96 affecting the participation decision are limited, it created a good opportunity for matching and it makes the matching procedure less difficult since matching algorism is implemented to eliminate significant differences of explanatory variables between participant and nonparticipants groups. Age of the household head significantly and positively affected the probability of participation in using brokerage institutions service of the household. It coincides with the hypothesis that as the age of the household head increases, the household decides better to participate in brokerage institutions. This is due to the fact that aged people have weak communication and information searching ability in order to directly contact to traders/wholesalers to sale the onion. In other words, the younger the household head is, and the more likely will be the probability of not participating in the brokerage institutions for linkage in the marketing of onion. Education level of the household has a negative significant effect on the participation decision of the household in brokerage institutions. People with higher education level are good at communication, information searching, negotiation and undertaking transaction which leads to direct contact to traders to sell their product. This indicates that educated people have less probability of using brokerage institutions for linkage to wholesalers than uneducated people (illiterate and adult education). In Fogera Woreda the most determining factor for direct linkage of farmers to wholesalers is the thrust between them during transaction. The transaction can be undertaken if there is strong thrust between them in weighing and payment. If the household head is uneducated he has no knowledge about weighing and preferred to use brokerage institutions for market linkage than direct linkage to wholesalers as he is more familiar with the broker who lives in the residence and trustful on the broker. Payment place is also the most important issue for farmers and wholesalers. Farmers prefer to receive their payment at the farm while wholesalers prefer to pay at Woreta town this disagreement made uneducated farmers to sale their product using brokerage institutions while educated farmers have no problem of payment place rather the price itself. Thus, there will be easy agreement between farmers and wholesalers and they tend to directly contact to wholesalers to sell their product without using brokerage institutions. 81

97 Distance of residence of the household to development agent s office has a positive significant effect on the participation decision of the household in the brokerage institutions. Households which are far from the development agent office have higher probability to use brokerage institutions for linkage to the market outlet than households which are near to development agents. The reason for this fact is that when distance of the household s residence to the development agents increase, the household cannot have easy access for extension services related with product marketing techniques, market information and market linkages which lead the household to participate in brokerage institutions service for linkage than direct contact to the wholesalers. The two most important factors which affects households decisions whether to use brokerage institutions or not in Fogera Woreda are transaction costs and the issue of obtaining secure market outlet for the product. Having Cell phone (Mobile phone) or not has a negative significant effect on the participation decision of the households whether to use brokerage institutions or not for linkage to the traders/wholesalers. Households who have mobile phone have a higher probability of not using brokerage institutions for market linkage than those who do not have. Mobile phone makes communication and information searching very easy as a result it reduces the transaction cost of finding wholesalers. Therefore, it facilitates the direct contact of households to the traders. Distance of residence of the household to the main asphalt road has a positive and significant effect on the participation decision of the households in the brokerage institutions. Households which are far from the main asphalt road have higher probability to use brokerage institutions for linkage to the market outlet than households which are near to the main asphalt road. The reason for this fact is that when distance of the household s residence to the main asphalt road increases, the household cannot access information about the wholesalers and there will not be thrust between the wholesalers and the farmers in the transaction processes (payment become very difficult for the wholesalers at the farm which is distant from the asphalt road, the wholesaler do not thrust the farmer whether he has quality onion or not in the area and if there is no quality onion there will be high transaction cost for wholesaler to come out of the farm to the main road. On the other side, the farmer also do not have thrust on the 82

98 wholesaler in order to receive the payment for his product from the wholesaler in the Woreta town) which leads to higher probability of using brokerage institutions for market linkages in which the brokerage institutions are known and the transaction is safe from any default. Number of regular customers (wholesalers) of the households has a negative and significant effect on the participation decision of the household in brokerage institutions service. Households having large number of regular wholesalers have lower probability of participating in the brokerage institutions for market linkage than those who have lesser number of regular customers this is due to the fact that households prefer direct market contact to the wholesalers as they have larger number of regular customers who can purchase the product. Thus, there is no information problem and higher transaction cost to access them. In addition direct contact removes the FERQ which is advantageous for both producers and wholesalers. However, if the household have less number of regular wholesaler customers, this wholesalers cannot purchase all of his product because they are few which needs searching another market outlet or wholesaler this in turn leads to higher transaction cost of searching information and wholesalers. As a result the household prefer to use brokerage institutions for market linkage under this condition Common support condition The next step in propensity score matching technique is the common support condition. Only observations in the common support region matched with the other group considered and others should be out of further consideration. Once the region of common support is defined, households that fall outside this region have to be disregarded and the treatment effect cannot be estimated for this households. 83

99 Figure 3: Kernel density of propensity scores before matching 2 Kernel density estimate 1.5 Density psmatch2: Propensity Score kernel = epanechnikov, bandwidth = Kernel density estimate kdensity _pscore kdensity _pscore Figure 3 showed the distribution of the total households, participant and non-participant households, with respect to estimated propensity scores. As it is described in the figure, most of the participant and non-participant households are densely located in the right and left of the distribution. Since most of the participant and non-participants households are located in the right and left side of the distribution respectively, it makes the matching procedure complex. The predicted probability for those who are participating in the brokerage service ranges from to with the mean probability of participation being On the other hand, the probability of not participating of the non participant households in the brokerage institutions service ranges from 0.003to with mean of From the result, observations with the predicted probability between and are in the common support region with the possibility of getting good match from the other group. Observations with predicted 84

100 probability less than and greater than have been disregarded out from further analysis Figure 4: Kernel density estimates of participants before and after common support 2 Kernel density estimate 1.5 Density psmatch2: Propensity Score kernel = epanechnikov, bandwidth = Kernel density estimate kdensity _pscore As we can see from (Figure 4) most of the participant observations lie towards the right part of the graph. The common support condition obliges to drop down observations with probability of participation greater than Accordingly, fifty seven of the observations from the participants satisfy the common support condition while nineteen observations are ignored from further consideration. On the other hand most of the non participant observations lie towards the left part of the graph. The common support condition obliges to drop down observations with probability of participation less than Accordingly, twenty four observations from the non participant household s fall out of the common support region and forty three observations saved for the matching (Figure 5). 85

101 Figure 5: Kernel density estimate of propensity scores of non-participants households before and after common support 2 Kernel density estimate 1.5 Density psmatch2: Propensity Score kernel = epanechnikov, bandwidth = Kernel density estimate kdensity _pscore Matching of participant and non-participant households In an impact assessment study, households should have their good match from the control group. This will be maintained through balancing the covariates of the participant group to the covariates of the non-participant group. (Table 12) elaborates how this indicator is maintained in the research. The unmatched sample fails to satisfy the property in that participant households are on average significantly different in several aspects from the control households. Against the unmatched sample, matched samples using kernel with band width of 0.25 satisfy the property of balanced matching for all of the covariates. 86

102 Table 12. Balancing test of matched sample Explanatory variables Participant HHs Before matching (143) Kernel matching (Band width 0.25) (100) Control HHs T Participant HHs Control HHs AgHH *** SxHH ** MsHH ELHH *** FSiHH TLU TLSha ILHa ** EHP DRDA *** DHMP *** DRWM *** DRFMAR *** NRC *** NTRC Source: Author s Survey, 2011 *** and **means significant at the 1 and 5% probability levels, respectively. 1. Labor supply conversion factor (person day equivalent) T As it is clearly indicated in (Table 13) below, the three criteria were implemented to each matching algorism to identify the best matching technique. Kernel matching algorism with a band width of 0.25 was found to be the best estimator by balancing all the observable covariates, ends with low pseudo-r 2 and large number of observations in the common support. Accordingly, the research used the kernel matching algorism with band width of 0.25 for comparing the participants and non-participants of the brokerage institutions service with respect to the impact 87

103 Table 13. Performance of matching estimators under the three criteria Matching estimator Performance criteria Balancing test* Pseudo-R 2 Matched sample size Radius Caliper matching Kernel Matching With no band width With 0.08 band width With 0.1 band width With 0.25 band width With 0.3 band width With 0.5 band width Neighbor matching 1 neighbor neighbor neighbor neighbor neighbor Source: Own Estimation Result * means the number of explanatory variables which have no significant difference between the participant and non-participant households after matching 4.3. Impacts of the Brokerage Institutions In this section, the research describes the impacts of brokerage institutions in linking smallholder horticultural crop (onion) producers with market outlets (wholesalers) in terms of net return, percentage of marketed surplus, land allocated to onion production, amount of 88

104 onion produced and sensitivity of the impacts. This section has four different sub-topics so as to independently discuss the impacts. Table 14. Impact of Brokerage institutions Outcomes ATT Std.Err 1 T NIO ** PMSU ** AOP LAOP The bootstrapped SE is obtained after 100 replications **, significant at 5% probability levels Source: Own estimation result Impact on net return from onion production Brokerage institutions in Fogera Woreda create linkage between farmers and the market outlet (wholesalers). Thus, farmers using brokerage institutions have easy access to wholesalers which reduces the transaction cost of searching traders, market information, loss due to perishablity and transportation cost which in turn reduces the overall marketing cost. As net return is revenue reduced the total cost, a reduction in marketing cost means a reduction in total cost which leads to high net return. As indicated in the (Table 14) above, smallholder farmers using brokerage institutions have got ETB higher net incomes from onion production than those farmers who do not use brokerage institutions for linkage to the market outlet. This indicates that brokerage institutions are playing a significant and positive role in linking smallholder farmers to the market outlets Impact on percentage of marketed surplus Smallholder s use of brokerage institutions is highly associated with the issue of obtaining secure market for their product in all the production years. According to Woreda Experts and Development Agents there is significant fluctuation either increasing or decreasing in 89

105 horticultural production every year following the increase or decrease in price of the previous year respectively. In 2011 production year, It was very good year for horticultural production and onion production was high in the area following the high price incentive in Thus, in 2011 the price of onion has reached to 0.25 ETB for Kg of onion because the supply was much more than the demand and even most of the farmers specially farmers who do not use brokerage institutions do not sell much of their product, following this the farmers reduced allocation of more land to onion production and the supply in 2012 become very low relative to demand. Based on the monitoring of the study area for about four months (January, February, March and April) the price score for a Kg of onion was between ETB. In 2011, due to the high supply of onion, lower demand compared to production and perishable nature of the product brokerage institutions played great role in linking their smallholder customer farmers (broker users) to the market outlets and the percentage of non marketed onion from total production was lower than 27.27% while farmers who do not have the experience of using brokerage institutions specially those their residence is far from the main asphalt road were unable to sell their product and the non marketed onion from total production has reached to up to 79%. This is due to the fact that brokers have much higher regular wholesaler customers than farmers who do not use brokers, more information and very high communication capacity which leads them to control most of the wholesalers coming to the area. In addition, in the time of much supply brokerage institutions provide service first for their very experienced farmer customers that is based on experience in transaction. As shown in the (Table 14) above, the result of the study revealed that smallholder farmers who participated in brokerage institutions for linkage have 13.55% of greater marketed surplus than those smallholders who do not participate. This implies that brokerage institutions have significant and positive impact on marketed surplus in Fogera Woreda Impact on Amount of Onion Produced and Land Allocated to Onion Production As shown in the (Table 14) above, the result of the study indicated that brokers have no significant and positive impact on the amount of onion produced and land allocated to onion 90

106 production. The reason is that higher land allocation and high production is affected by other factors like previous year price Sensitivity Analysis It should be clear that matching estimators are not robust against hidden biases. Different researchers become increasingly aware that it is important to test the robustness of results to departures from the identifying assumption. Since it is not possible to estimate the magnitude of selection bias with non-experimental data, the problem can be addressed by sensitivity analysis. Rosenbaum (2002) proposes using Rosenbaum bounding approach in order to check the sensitivity of the estimated ATT with respect to deviation from the CIA (Conditional Independence Assumption). The basic question to be answered here is whether inference about treatment effects may be altered by unobserved factors or not. Table 15. Result of sensitivity analysis using Rosenbaum bounding approach Outcomes eᵞ=1 eᵞ=1.25 eᵞ=1.5 eᵞ=1.75 eᵞ=2 eᵞ=2.25 eᵞ=2.5 eᵞ=2.75 eᵞ=3 NIO 5.0e e e PMSU P<0.000 P<0.000 P< e e e e e e-10 AOP P<0.000 P<0.000 P<0.000 P<0.000 P<0.000 P<0.000 P<0.000 P<0.000 P<0.000 LAOP P<0.000 P<0.000 P<0.000 P<0.000 P<0.000 P<0.000 P<0.000 P<0.000 P<0.000 Source: Own estimation eᵞ(gamma)=log odds of differential due to unobserved factors where Wilcoxon significance level for each significant outcome variable is calculated The first column of the (Table 15) shows those outcome variables which bears statistical difference between treated and control households in our impact estimate above. The rest of the values which corresponds to each row of the significant outcome variables are p-critical at different critical value of eᵞ. Result showed that the inference for the effect of the brokerage institutions is not changing though the participants and non participant households in the brokerage institutions has been 91

107 allowed to differ in their odds of being treated up to 200% (eᵞ=3) in terms of unobserved covariates. That means for all outcome variables estimated, at various level of critical value of eᵞ, the p- critical values are significant which further indicate that the study have considered important covariates that affected both participation and outcome variables. The study couldn t get the critical value eᵞ where the estimated ATT is questioned even if the research have set largely up to 3, which is larger value compared to the value set in different literatures which is usually eᵞ=2 (100%).Thus, it is possible to conclude that the research impact estimates (ATT) are insensitive to unobserved selection bias and are a pure effect of brokerage institutions in the area Brokerage Institutions and Wholesaler Market Linkages In this section, the research describes the interaction between the brokerage institutions and wholesaler market linkages, the socioeconomic profile of wholesalers and the determinants of decisions of wholesalers on share of brokered transaction. This section has five different subtopics so as to independently discuss each subtopic Demographic profiles of the wholesalers The descriptive and inferential statistics analysis (Table 16) showed that most of the wholesalers are males. This is due to the fact that the business needs financial capital, high communication ability, moving from place to place and labor intensive. Since, females are busy in house in caring the family and cooking, they are not engaged in this business activity. There is a significant difference in distance of residence of wholesaler from the Fogera Woreda market place between participant and non participant wholesalers in the brokerage institutions. This indicates wholesalers far from the Fogera Woreda are more likely to participate in the brokerage institutions as they do not know the producers in order to reduce the transaction cost of searching information. 92

108 Table 16. Descriptive statistics of sample wholesalers (for continuous variables) Variables Sample wholesalers Participant (N=46) Non participant T-Value (N=52) (N=6) Mean Std.Er Mean Std.Er Mean Std.Er DRFWM * AGWS EXWSHT ELWS * NPWB CASF * CWCWS NRFC *** NRRC *** NRWCOA ** NTCFWM ** TMCOST EUB *** Source: Author s Survey, 2012 ***, ** and* means significant at the 15% and 10% probability levels, respectively Wholesalers who are non participant in the brokerage institutions are more educated than participants. Education increases the ability to communicate and negotiate easily. This indicates that educated wholesalers tend to directly link to smallholder producers rather than using brokerage institutions as means of linkage. There was no significant difference between participant and non participant wholesalers in terms of years of age and experience in the horticultural trade. There was also no difference in sex, religion and marital status between the two groups. 93

109 Socio-economic characteristics and assets of wholesalers There was no significant difference in number of persons working on the business, total marketing cost and current working capital between participants and nonparticipants of wholesalers in the brokerage institutions. There is a significant difference in social capital between the two groups (Table 16) which indicates that non participants have higher number of regular farmer customers, lower number of trader customers purchasing from them (lower number of regular retailer customers and lower number regular wholesaler customers in other areas) and higher number of trading contacts to Fogera Woreda market place than participants in the brokerage institution. The reason is that social capital is one of the most important factors in determining the decision of wholesaler whether to use brokerage institutions or not by affecting the transaction cost of searching information and negotiation. The study indicated significant difference between participant and non participant wholesalers in ownership of horticultural storage facility which can function as a selling place. The reason is that wholesalers who do have their own storage facility tend to use brokerage institutions for linkage to smallholder producers as they have a permanent selling place in order to frequently supply to the market. However, the capacity of storage facility was insignificant between the two groups because horticultural products are perishable and cannot be stored Institutional and organizational aspects There is a significant difference between the participant and non participant wholesalers in terms of distance from residence to the Fogera Woreda market place. Participants mean distance is higher than the non participants which have negative effect on market information and communications which increases transaction cost and increases participation. However, there was no significant difference between the two groups in credit access and the type of road (gravel or asphalt) accessed by the wholesaler. 94

110 Table 17: Descriptive statistics of sample wholesalers (for dummy variables) Preintervention Variables Category Participant (N=46) Nonparticipant (N=6) Total N % N % N % TRDA Gravel Asphalt SXWS Female Male MSWS Single Married RLWS Muslim Orthodox HOSF No *** Yes ACWS No Yes HRCAPO No *** Yes Source: Author s Survey, 2012 ***, means significant at 1 % probability levels χ Wholesaler s perceptions of brokerage institutions Most (96.2%) of the wholesalers (both participant and non participant) believe that brokerage institutions play significant and important role in linking wholesalers to producers while only 3.8% of the wholesalers (all non participants of the brokerage institutions) believe that they have no important role in onion marketing. According to wholesalers, their importance s are in linkage and providing market (price and quality) information using telephone, sample and taking the wholesaler to the farm which helps to reduce the transaction cost. All of the wholesalers (100%) believe that brokers provide false market information and block direct 95

111 contact of wholesalers to producers. Generally, most wholesalers believe that brokers are important in the area with formalization of the brokerage activity to avoid their exploitive act which is FERQ in addition to brokerage fee Determinants of share (percentage) of brokered transactions Table 18. Results of OLS estimation Variables Coefficients Robust Std. Err. T-Value DRFWM.025** TRDA EXWSHT * NRBC.561** MSWS ELWS NPWB HOSF CWCWS ACWS NRFC *** NRRC NRWCOA 4.226*** NTWM TMCOST *** 8.53e _cons F( 15, 30) = Prob > F = R-squared = Root MSE = Source: Author s Survey, 2012 *, ** and ***means significant at the 10%, 5% and 1% probability levels. 1. cost of not using brokers 96

112 The result of OLS regression showed that (Table 18), only six of the explanatory variables affected the intensity of brokerage use by wholesalers out of fifteen explanatory variables expected to affect percentage of brokered transaction. The significant variables are discussed below: DRFWM (Distance of residence of wholesaler from Woreta market): It has a significant and positive effect on the intensity of brokerage use. A unit increases in distance increases the percentage of brokered transaction by This is because when distance increases the transaction cost of finding market information and producers is very high. Thus, wholesalers tend to use brokers intensively for reducing transaction cost. EXWSHT (Experience of the wholesaler in horticultural trading): It has a significant and negative effect on the intensity of brokerage use. A unit increases in years of experience reduces the percentage of brokered transaction by This is due to the fact that, years of experience in horticultural trading helps to understand the marketing system and actors which helps to reduce the transaction cost of searching market information and producers. Thus, wholesalers tend to reduce the intensity of brokerage use by directly contacting to the producers. NRBC (Number of regular broker customer): It has a significant and positive effect on the intensity of brokerage use. A unit increases in number of regular broker customer increases the percentage of brokered transaction by This is because when a wholesaler develops regular broker customers, the wholesaler has the advantage of reduced FERQ due to the already established trust based relationship. Thus, wholesalers tend to use brokers intensively for reducing transaction cost of directly contacting producers. NRFC (Number of regular farmer customer): It has a significant and negative effect on the intensity of brokerage use. A unit increases in number of regular farmer customer reduces the percentage of brokered transaction by This is because when a wholesaler develops regular farmer customers, the wholesaler has the advantage of directly forming linkage to the producers with reduced transaction cost due to the already established relationship. Moreover, 97

113 more number of regular farmer customers means more supply of onion to the wholesaler without brokers. Thus, wholesalers tend to reduce the intensity of brokerage use by directly contacting to the producers. NRWCOA (Number of wholesaler customers in other areas): It has a significant and positive effect on the intensity of brokerage use. A unit increases in number of regular wholesaler customer found in other areas always purchasing onion from the wholesaler increases the percentage of brokered transaction by This is because when a wholesaler develops regular buyer wholesaler customers found in other areas, the wholesaler has more demand for his product this in turn needs frequent supply of onion for him. Thus, wholesalers tend to increase the intensity of brokerage use to frequently supply for his buyer wholesale customers and reduce transaction cost. TMCOST (Total marketing cost): This cost is calculated based on opportunity cost, what will be the cost if the wholesalers do not use brokers. It has a significant and positive effect on the intensity of brokerage use. A unit increases in the total marketing cost of not using broker s increases the percentage of brokered transaction by This is because when the cost of not using brokers increases due to high transaction cost, the wholesalers tend to increase the intensity of brokerage use in order to reduce transaction cost. 98

114 5. SUMMARY, CONCLUSIONS AND RECOMMANDATIONS In this section, summary of the whole findings of the study, conclusions based on the findings and their implications are presented in three sub sections Summary The main concern of this thesis was to analyze the brokerage institutions and smallholder market linkages to the wholesalers in vegetable marketing in Fogera Woreda, North Western Amhara Region particularly focusing on onion and tomato. The choice of the crops intentionally was based on their relative importance, marketability and existence of significant brokerage activities. The specific objectives included are assessing the characteristics, economic roles, constraints and opportunities of the brokerage institution, measuring the impacts of brokerage institutions on farmers market participation and income generation capacity under imperfect market condition and identifying the determinants of wholesalers decisions on whether and for how much to use brokers under imperfect market condition of vegetable marketing. Both secondary and primary data were collected for the study. Primary data were collected from a very wide range of respondents at all stages of the market channel where brokers are expected to act. Two stage sampling techniques were used to select the sample. A total of 143 smallholders producing vegetable crops (67 from participants of brokerage institutions and 76 from non participants) drawn from 5 Kebeles in Fogera Woreda, 55 brokers in the Woreda, 52 wholesalers (including wholesalers coming from different areas of the country) in the Woreda. For the study 45 retailers from four towns (Gondar, Bahir Dar, Gumara and Woreta) and 20 rural assemblers at Fogera Woreda were interviewed using structured questionnaires. Informal survey such as observation and rapid market appraisal with the help of focused group discussion and key informant discussion using checklists were the other primary data collection tool employed in the process. The data were collected in two phases, in phase one cross-section data were collected for twenty days and in the second phase data were collected by monitoring the area for about four months during the season of marketing. 99

115 Descriptive and econometric statistical models were employed for data analysis using STATA software. The study implemented the propensity score matching technique using explanatory variables which were theoretically supported to influence the decision of the smallholder farmers whether to use brokerage institutions or not and the outcome variables of interest. The study has also used OLS to identify the determinants of wholesaler s share of brokered transaction. The result of the study showed that all of the brokers are males. It also indicated that most of the brokers are youngsters and literate. The brokerage institutions are characterized by place of work as urban brokers (found at Woreta town), peri-urban brokers (found at villages of the main asphalt road such as Gumara and Abewana Kokit) and farmer brokers who are found in the rural villages. They are also characterized by their main occupation as farmer brokers (their livelihood is dependent on farming), youth brokers (grade 10, 12, college and school dropout jobless youngsters). The highest percentage of brokerage institutions are farmer brokers. Most farmer brokers are either employed by youth brokers or trader (cereal) brokers. The youth and trader brokers are characterized as urban and peri-urban brokers. The study also revealed that there is significant brokerage activity only for onion marketing and in the case of tomato marketing most of the brokers act as rural assemblers. The main brokerage institutions characteristics and roles in the area are brokers bring economics of scale, create linkage, reduce transaction cost of searching information and marketing cost for both farmers and wholesalers, act as a means of trust and facilitate trading during transaction between farmers and wholesalers and facilitate credit based transaction between the farmer and the wholesalers being as a collateral for the farmer or taking the product as credit from the farmer. In different studies agricultural brokerage institutions provide credit, market information and share risk for both the wholesaler. However in Fogera Woreda, farmers provide their product to a wholesaler as credit and a broker act as a collateral in this trust based transaction. After transaction, the wholesalers send the money to the broker and then the broker give money to the farmers taking his own share. Of course, brokerage institutions provide market 100

116 information to farmers related to quality and prices. But, the reality of price information is questioned by farmers. Brokerage institutions provide market information (quality and price) using telephone, direct discussion and providing sample for the wholesalers. Most of the horticultural trading in the area is undertaken by credit and trust based. This contractual bases transaction is subjected to contract failure as there are no formal contract enforcement mechanisms. Since the brokerage activity in the area is trust based there is high price gap between farm get price wholesale purchase prices which ranges from 0.10 ETB to 1.00 ETB in addition to brokerage fee of 0.10 ETB for each Kg of transacted onion. This gap is known as FERQ. It is highly dependent on customer relationship, amount of onion transacted and transaction experience. Brokerage institutions attract wholesalers by cheating weight from farmers and reducing the price gap between farm gate price and wholesaler purchase price. Weight cheating has two advantages for the broker one is that obtaining regular wholesaler customer for the future and having his own share from it. All of the farmers believe that brokers cheat in weight, provide false price information and block direct contact of farmers to traders. Generally, most (73.5%) farmers believe that brokers are important in the area with their problems but formalization of the brokerage activity is very crucial to make them more beneficial to the farmers. The brokerage institutions are constrained by the factors related to working capital, contract failure, strong competition and conflict between brokers. Increase in production, information and linkage gaps between farmers and wholesalers are the opportunities for the broker to easily enter to the business in order to form linkage between the two market actors. Logistic regression estimation of the Propensity Score Matching (PSM) algorithm revealed that six of the fifteen variables hypothesized to affect participation were found to be statistically significant. Age, education level, distance of residence from development agent office, distance of residence from Woreta market, distance of residence from main asphalt road, access to cell phone (mobile phone) and number of regular wholesaler customers significantly affected the participation decision of the smallholders in the brokerage 101

117 institutions or not. Kernel Matching with band width of 0.25 was found to be the best matching algorithm after a series of tests (the balancing test, pseudo-r 2 test and maximum possible number of observations matched). The result of the study revealed that, smallholder farmers using brokerage institutions have got ETB higher net income from onion production than those farmers who do not use brokerage institutions for linkage to the market outlet. It also showed that smallholder farmers who participated in brokerage institutions for linkage have 13.55% of greater marketed surplus than those smallholders who do not participate. However, the result of the study indicated that brokers have no significant and positive impact on the amount of onion produced and land allocated to onion production. Ordinary Least Square (OLS) regression estimation revealed that six of the fifteen variables hypothesized to affect intensity of brokerage use of wholesalers were found to be statistically significant. Distance, years of experience in trading, number of regular broker customer, number of regular farmer customers, number of regular buyer wholesaler customers, and total marketing cost (calculated in terms of cost of not using brokers) significantly affected the participation decision of the wholesalers Conclusions and Recommendations The overall analysis of the study can be concluded that brokerage institutions are characterized as farmer, peri-urban and urban brokers including farmers, youth brokers (school dropout and high school complete youngsters) and traders of cereals like rice. The brokerage institutions have strong chain in the Woreda and most of the transactions are undertaken by them and are playing role by searching different market outlets to almost all parts of Ethiopia. Since brokerage institutions are well informed by buyers and producers, are residents of the Woreda, educated and youngsters, they have easy information access and play significant role by providing market information, linking smallholders to wholesalers, creating economies of scale from many smallholders, easily bargain both smallholders and wholesalers and act as a collateral for both of actors which helps the smallholders and wholesalers to reduce 102

118 transaction cost under market imperfections. If brokerage institutions were not there, it was very difficult for wholesalers coming from the area to find smallholder producers. Therefore, empirically the idea that brokerage institutions are not important along the value chain is highly challenged here and brokerage institutions are the most important actors in the marketing of perishable products like onion which implies that greater attention should be given for them in order to sustain production and market linkages. Brokerage institutions are source of secure market for smallholder producers because they have many regular wholesaler customers coming from the different areas of the country. Thus, if a farmer have regular customer of broker and plan to produce onion he is secured for the market because of brokers. This in turn implies that brokerage institutions form market outlets for the smallholders. Most of the transaction in vegetable market such as onion is undertaken in trust and credit based and a wholesaler at Jijiga and Assosa can take onion from Fogera Woreda without coming to the Fogera with help of brokers simply by communicating using mobile phone. This in turn has great effect by reducing marketing and transaction cost for the wholesaler. Thus, avoiding brokerage institutions in the market chain means totally distorting the already established market linkages, as a result the study recommends the decision makers in the Woreda to consider the brokerage institutions as one of the important actors along the market chain. The impact evaluation of brokerage institutions indicated that brokerage institutions play significant and positive role by increasing smallholder s net income from onion production and percentage of marketed surplus. Brokerage institutions also play great role in creating employment role for youth groups which includes school dropouts, high school, preparatory school and college complete students. However, the brokerage institution at Fogera horticulture market, while fulfilling a market coordination role useful in linking farmers to markets, has limitations that arise mainly from its informality. The uncontrolled and unregulated business practices of brokers are subject to manipulation to cheat farmers (and wholesalers) in terms of product prices and weight. The institution lacks informal rules and 103

119 norms either to govern the business practices of the brokers. Moreover, the informality of the institution and the associated difficulty to have legally enforceable contracts make farmers highly exposed to the problem of default risk. The brokers themselves are also victims of their informality in that they lack access to credit services from financial institutions and to other support services. Generally, the informality of the brokerage institution is considered a source of incompetence, inefficiency, risk, and conflict. In view of this, we share the ideas of farmers and brokers extended during the discussions in recommending the formalization of the brokerage institution to improve its efficiency, performance, and impact. However, the trade-off of formalizing the institution and its net impacts on market coordination need to be well understood beforehand. Finally, the study recommends that a formalized and upgraded brokerage institution is commendable only as a third pillar for a better market coordination in the area. That is to say, in the best circumstances, even a formalized and upgraded brokerage institution should be considered only as a complement to, rather than as a substitute for, improved market institutions and effective producer organizations. The formalization activity can be adopted from the Ethiopian Commodity Exchange (ECX) experience. The study also recommends the ECX to include the horticultural crops such as onion in its commodity crop services. In addition, the study recommends training to farmers on marketing and weighing, standardization of weighing and provision of market information for the farmers in order to increase the benefit and income of farmers which helps them to come out of poverty. 104

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124 Mendoza, G., A primer on marketing channels and margins, PP In: Scott Gregory. J (eds.). Price Products and People. International Potato Center. Lima, Peru. Nonnecke, I.l., Vegetables production, Van Nostrand Reinhold Library of Congress. New York, USA. North, Douglass C., and R. Thomas The rise of the Western world: A new economic history. Cambridge: Cambridge University Press. North, Douglass C., Institutions, institutional change and economic performance. Cambridge: Cambridge University Press. Pindyck, R.S, and Rubinfeld, D.C., Econometric models and econometric factors. 2 nd ed. McGraw/Hill book Co.New York. Platteau, J.P., Behind the market stage where real societies exist, Part 1: The role of public and private order institutions. Journal of Development Studies, 30 (3): Polanyi, K., Trade and market in the early empires. New York: Free Press. Rajeev H. Dehejia and S. wahba, Propensity score-matching methods for non experimental causal studies. Ravallion, M., Evaluating anti-poverty programs: Policy research working paper 3625, World Bank, Washington D.C. Rosenbaum, P., Sensitivity Analysis in Observational Studies. In: S.B. Everitt and D. C. Howell (eds.). Encyclopedia of Statistics in Behavioral Science, 4: , John Wiley & Sons, Ltd, Chichester. Rosenbaum, P.R. and D.B. Rubin, The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1): Rubin, D. B., and N. Thomas, Matching Using Propensity Scores: Relating Theory to Practice. Biometrics Journal 52: Sadoulet, E., and A. de Janvry, Quantitative development policy analysis. Baltimore, Md.: Johns Hopkins University Press. Scott, G. J., Markets, myths and middlemen: A study of potato marketing in Central Peru. Lima, Peru: International Potato Center. Shiferaw, B., G. Obaran, G. Muricho and S. silim, Leveraging institutions for collective action to improve markets for smallholder producers in less-favored areas. 109

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126 7. APPENDICES 111

127 Appendix 1. Multicollinearity test for explanatory variables in PSM Variables VIF Age 1.65 Sex 1.14 MsHH 1.27 ELHH 1.26 FSiHH 2.08 Livestock 1.40 TLSha 4.90 ILHa 4.53 EHP 1.21 DRDA 1.22 DHMP 1.48 DRWM 1.50 DRFMAR 1.75 NRC 1.72 NTRC

128 Appendix 2. Multicollinearity test for explanatory variables in OLS regression Variables VIF DRFWM 1.64 TRDA 1.75 EXWSHT 1.65 NRBC 1.74 MSWS 1.49 ELWS 1.35 NPWB 1.38 HOSF 1.45 CWCWS 1.45 ACWS 1.25 NRFC 3.25 NRRC 2.26 NRWCOA 2.60 NTWM 3.05 TMCOST 2.93 Appendix 3. Conversion factor used to calculate TLU Livestock Category TLU Calf 0.34 Heifer 0.75 Cow and Ox 1.0 Horse 1.1 Donkey 0.7 Sheep and goat(adult) 0.13 Chicken Source: Storck et al.,

129 Appendix 4. Labor supply conversion factor (person day equivalent) Age group in years Male Female < > Source: Storck et al.,

130 Survey Questionnaire for Farmers First of all, I would like to thank in advance for your willingness to answer the questionnaire designed to collect data to study The Brokerage Institutions and Smallholder Market Linkages in Fogera Woreda. The information collected in this questionnaire is confidential and will be used for academic purposes only. I. General Information 1. Peasant Association 2. Village 3. Questioner No. 4. Name of enumerator 5. Date of data collection 6. Signature II. Households characteristics 1. House Holds Head Name 2.Age years 3. Sex 1) Male 2) Female 4. Experience in agriculture years 5. Religion 1) Orthodox Christian 2) Muslim 3) Catholic 4) Protestant 5) Others 6. Marital Status 1) Single 2) Married 3) Divorced 4) Widow 7. Educational level 1) Illiterate 2) Adults education 3) year of formal education 8. Family member Sex Age category Number Male <6 years 6-10 years years years >65 years Female <6 years 6-10 years years years >65 years 9. Number of family members working on the farm Male Female 10. Number of permanently hired laborer in 2003 E.C? 11. Did you or your family member earn income from off-farm activities? 1) Yes 2) No 12. If yes, what are the activities and income earned per year (in 2003 E.C)? Activities Income in birr/ year 115

131 III. Resource ownership in 2003 E.C 13. Fixed assets Resources 1) Yes 2) No 1. Houses Grass roofed Iron sheet 2. Water pump 3. Others Number 14. Livestock ownership Type of livestock No. owned in 2003 E.C Sold in number Income from sale(birr) Oxen Cows Heifers Yearling Calves Donkeys Horses Mules Sheep Goats Bee Colony Poultry Others 15. Land use 1) Land Holding Land Holding(Timad Cultivated land Grazing Land Own land Rented in land Obtained as Gift Total land Under operation Forest Land Irrigable land Non irrigable land 116

132 2) Type of crops grown from the cultivated land in 2003 E.C Type of crops Total land used (In Timad) 1) Rain fed 2) Irrigation Amount Produced (Qt) Rice Chick Pea Guaya Teff Onion Tomato Potato Others Amount soled Income 16. Do you use intercropping with horticultural crops? 1) Yes 2) No 17. If yes, with what crop you intercropped horticultural crops? IV. Production 18. A) Types of horticultural crops produced in 2003 E.C Types of horticulture Land allocated (ha) Amount produced (quintal) Onion Tomato Garlic Cabbage and leafy vegetables Potato Green pepper Others Amount sold (quintal) Income B) Horticultural production schedule (months) Types of horticulture Land preparation time Sowing time Harvesting time Onion Tomato Garlic Cabbage and leafy vegetables Potato Green pepper Others 117

133 19. What were the inputs used to produce horticultural crops/ onion in 2003 E.C production Year Type of inputs 1 )Yes Amount used Source* Price/litter/kg 1) Credit 2) No per timad in 2) Cash kg/litter Fertilizer DAP Urea Organic Seeds used Insecticides Herbicides Others Source* (Multiple answers are possible) 1) Market 2) Agricultural office 3) Ethiopian Improved Seed Agency 4) Ethiopian Spice Factories 5) Own production 20. What were the fixed costs used for horticultural production? Type of Capital goods Price/unit 21. What was the Labor used in Man day in 2003 E.C Production year for onion? Activities Man days/timad Wage/day Land Preparation Sawing Weeding Chemical application Harvesting 22. If producers do not use improved input in question 19, what was the main reason? 22.1 Not to use fertilizers 1) Lack of information 2) It is expensive 3) Not available 4) Shortage of money 5) Others 22.2 Not to use improved seed 1) Lack of information 2) It is expensive 3) Not available 4) Shortage of money 5) Others 22.3 Not to use chemicals 1) Lack of information 2) It is expensive 3) Not available 4) Shortage of money 5) Others 118

134 23. Do you use hired labor for horticultural production? 1) Yes 2) No 24. If yes, at what time do you hire labor? (Multiple answers are possible) 1) Land Preparation 2) Sawing 3) Weeding 4) Harvesting 5) Others (specify). 26. Did you store horticultural crop after harvest? 1) Yes 2) No 27. If you store, how do you store horticultural crop? (Multiple answers are possible) 1) using gotera 2) filling in sack and putting in kot 3) Others 28. What is the reason for storage? (Multiple answers are possible) 1) In expectation of future higher price 2) For own consumption 3) Low demands during harvest 4) Others 29. For how long do you store horticultural crop? Months 30. Are you benefited from storage/ from the expected increase price? 1) Yes 2) No 31. Have you lost from storage? 1) Yes 2) No 32. If yes for Q31 what was the loss in terms of Birr 33. What are the packaging materials used for sale? (Multiple answers are possible) 1) Plastic sacks/madabera 2) Sisal sacks/jonia 3) Akmada 4) Baskets 5) Others 34. How do you measure quality of horticultural crop? (Multiple answers are possible) 1) Color 2) Sizes 3) Tastes 4) Absence of foreign materials 5) Others 35. When did you start production of horticulture (experience)? years 36. How is the trend of horticultural production since 2003 E.C? 1) Increasing 2) Decreasing 3) No change 4) some years increase and the other years decrease 37. How much was the average productivity of onion per hectare in 2003 E.C? quintal 38. How much was the average productivity of tomato per hectare in 2003? quintal 39) Irrigation Practices A) Do you use irrigation for horticultural production? 1) Yes 2) No B) If yes for QA what are the main horticultural crops produced using irrigation C) Irrigation schedule Horticultural crops Onion Tomato Potato Others Water sources Distance of water source from farm land Irrigation materials used No of Watering per month 119

135 D) Irrigation costs for onion production Items No of units Total cost Labor Fuel Irrigation materials (pump ) Others V) Support Services 40. Is there a Development Agent in your Kebele? 1) Yes 2) No 41. Have you got extension services in 2003 E.C Production year to produce horticulture? 1) Yes 2) No 42. If yes, how often the extension agent contacted you? 1) Once a week 2) Once a month 3) Once per two week 4) Once per 3 months 5) Twice a year 6) Others 43. How far is your residence from the Development Agent? Km 44. What are the extension services you got from the Development agent on horticultural production? (Multiple answers are possible) 1) Crop production 2) Input utilization 3) Seedling raising 4) Post harvest handling 5) How to market the product 6 Others 45. From whom did you get extension services for horticultural production and marketing in addition to the Development Agent? (Multiple answers are possible) 1) Woreda office of Agriculture 2) Woreda Cooperatives expertise 3) The nearby multipurpose cooperative 4) Innovative farmers 5 Others 46. How far is your homestead from the input supplier? km 47. Do you have access to credit to produce horticulture? 1) Yes 2) No 48. Did you take credit in 2003 E.C production year? 1) Yes 2) No 49. If yes, how much money did you take? Birr 50. Is the credit sufficient for production? 1) Yes 2) No 51. How much was the interest rate? birr/100 birr/year 52. How is the interest rate? 1) High 2) Fair 3) Low 53. What was the main purpose of taking a credit for horticulture production? 1) To purchase fertilizer 2) To purchase improved seed 3) To purchase pesticides and fungicide 4) To buy oxen 5) To purchase farm equipment 6) Others 54. From whom did you get the credit? 1) Amhara Credit and Saving Institute (ACSI ) 2) Through Cooperatives 3) From Development Bank of Ethiopia 4) Commercial Bank of Ethiopia 5) Individual lenders 6) NGOS 7) Others 120

136 55. Do you have access to market information for horticultural production and marketing? Such as price of inputs and output? 1) Yes 2) No 56. What is the market information you got for horticulture production? (Multiple answers are possible) 1) About the price of input 2) About the price of out puts 3) When and where to purchase inputs 4) Post harvest handling and quality 5) Storage and packaging 6) Others 57. Where do you get market information? 1) Radio 2) friends and neighbor 3) Development Agent 4) Brokers 5) Telephone 6) Others VI. Marketing 58. Do you participate in horticultural marketing? 1/ yes 2/ no 59. If the answer is no for Q 58 why? 60. Market places Market places Distance from the residence (Km) Market days Woreta Road side Others 61. When did you supply horticulture to the market? 1) Right after harvest 2) after a month 3) After 3 month 4) after 6 months 5) After a year 6) others 62. For whom you sold the product? 1) Directly to consumers 2) Retailers 3) Wholesalers 4) Cooperatives 5) Rural assemblers 6) Others 63. Do you have regular customers? 1) yes 2) no 64. If yes what is the number of regular customers? 65. What are the numbers of trading contacts related to horticulture that you have made to the main markets 2003 E.C? 66. Prices of horticultural crops Type of horticulture Prices per Kg Minimum price Maximum price Onion Tomato Garlic Cabbage and leafy vegetables Potato Green pepper 121

137 67. Months in which prices of horticulture become expensive? Months in which prices of horticulture become minimum? How was the term of trade? 1) Cash 2) Credit 3) Both 70. Where did you sale your product? 1) In the village market 2) Woreta Marke 3) Bahir Dar 4) Gondar 5) Others 71. Who set the selling price in 2003 E.C? 1) Producers 2) Broker 3) Buyers 4) By negotiation 5) Others 72) Do you have used brokers for the last five years for marketing of horticultural/onion products 2003? 1) Yes 2) No 73. If the answer is yes for Q72 what was your reason for using brokers? 74. If the answer is yes for Q72 what is your experience in using brokers?...years 75. If yes for Q72 what is the brokered transaction for onion (qt) and for tomato (qt) 76. If yes for Q72 what is the brokerage fee? If your answer for Q72 is No why? 78. If your answer for Q72 is No what was the loss because of not using brokers?...birr 79. Do you believe that brokers have benefit in linking to the market? 1) Yes 2) No 80. If yes how? 81. If no why? 82. Do you have information about the market price before you sell? 1) Yes 2) No 83. How did you transport horticulture to the market? (multiple answers are possible) 1) Pack animals 2) Animal court 3) Cars 4) Back loading 5) Other Are you a member of cooperatives? 1/ yes 2/ no 85. If you are member of cooperatives by how much do you sale one kg of horticulture-----birr 88. How much was the marketing costs in 2003 E.C for onion No. Expenses type Total cost (birr) 1 Packing material/jonya 2 Loading 3 Unloading 4 Transport 5 Salary 6 Store rent 7 Storage losses 8 Telephone 9 Guard 10 Personal expenses 11 Brokers fee 12 Loss due to un soled onion 13 Others 122

138 86. What are the problems encountered in horticulture production and marketing? No. Type of the problem Cause of the problem Suggested solution Survey Questionnaire for wholesalers First of all, I would like to thank in advance for your willingness to answer the questionnaire designed to collect data to study Market imperfections, the Brokerage Institutions and Smallholder Market Linkages in Fogera Woreda. I. General Information 1. Zone 2.Woreda 3. Questioner No. 4. Name of enumerator 5. Date of data collection 6. Signature II. Traders characteristics 7. Age years 8. Sex 1) Male 2) Female 9. Experience in horticultural trade years 10. Religion 1) Orthodox Christian 2) Muslim 3) Catholic 4) Protestant 5) Others 11. Marital Status 1) Single 2) married 3) Divorced 4) Windows 12. Educational level 1) Illiterate 1) Adults education 3) year of formal education III. Assets owned 13. Fixed Assets owned Assets No. average Capacity Total Value(in birr) Shop Store With residence Separately Weighting balance - Telephone fixed - Telephone mobile - Vehicle(truck) Motor vehicle - Bicycle - Animal court Hand pool cart Pack animals - Grinding machine - others 123

139 IV. Financial resources own 14. How much is your working capital currently (2012)? birr 15. What is the source of your working capital? (Multiple answers are possible) 1) Loan 2) Own capital 3) Share 4) Gift 16. If the source is from loan what are the lending institutions? (Multiple answers are possible) 1) Small and Micro Enterprise 2) Amhara Credit and Saving Institute (ACSI) 3) Commercial bank of Ethiopia 4) Development Bank of Ethiopia 5) Private Banks 6) Others 17. How much was the interest rate? birr for formal lendersr birr for informal lenders VI. Buying and selling practices 18. How did you attract your suppliers? 1) By giving fair price 2) By using proper weighting 3) Promotion methods 4) Others 19. From whom you purchase the product? 1) Directly from producers 2) From retailers 3) From rural assemblers 5) Others 20. From which market you prefer to buy horticulture/onion? 1) Road side 2) farmers residence (at farm) 3) Woreta 4) Bahir Dar 5) Gondar 5) Others 21. Did you have regular suppliers of the product? 1) yes 2) no 22. If yes for Q 21 number the regular suppliers? Farmers brokers. 23. Where do you get market information? 1) Radio 2) friends and neighbors 3) Television 4) Brokers 5) from Government sources and NGOs 6) Telephone 7) Others 24. Do you use brokers for your horticultural/onion marketing systems? 1) yes 2) no 25. If yes for Q 24 what was the brokered transaction in quintal? and brokerage fee birr 26. If yes for Q what is your reason for using brokers? 27. If No for Q why? 28. Do you believe that brokers have benefit in linking wholesalers to the market or farmers? 1) Yes 2) No 29. What is the cost of not using brokers?...birr 30. What is your experience in using brokers? years 124

140 31. Horticultural transaction in 2003 E.C Types of horticulture Onion Tomato Garlic Cabbage and leafy vegetables Potato Green pepper Others Amount purchased (quintal) Amount sold (quintal) Purchase price/kg Selling price/kg Market places For whom do you sell 32. How did you measure the product during purchase? 1) Using weighting balance 2) Using sack 3) Using basket 4) Others 33. Who set purchase price of horticulture? 1) Myself 2) Sellers 3) By negotiation 4) based on central market price/addis Ababa price 5) By the forces of demand and supply 6) Others 34. Who sets the Selling price of horticulture? 1) Myself 2) Buyers 3) By negotiation 4) Based on central market price/addis Ababa price 5) By the forces of demand and supply 6) Others 35. If you set the purchase and selling price yourself how did you decide? 1) Based on the last year price 2) In consultations with other traders 3) Considering demand and supply 4) Others 36. Was the purchase and selling price the same for all competitors? 1) Yes 2) No 37. When did you purchase the product? 1) After harvest 2) Throughout the year 3) Others 38. How did you sale the product? 1) Directly to consumers 2) For retailer 3) Using brokers 4) Others 39. How did you differentiate quality during buying? 1) Based on its origin 2) Taste 3) Color 4) Degree of adulteration 5) Others 40. Is there price difference during buying and selling based on quality? 1) Yes 2) No 41. Did you store horticulture before you sale? 1) Yes 2) No 42. If yes, for how long you store the maximum before you sold? months 43. Do you have regular customers? 1) yes 2) no 44. If yes what is the number of regular customers? 45. What are the numbers of trading contacts that you have made to Fogera 2003 E.C? 46. What is the transaction cost? birr 47. Are you a licensed trader? 1) Yes 2) No 48. Are there unlicensed horticulture traders in the area? 1) Yes 2) No 49. What is the basis of taxation? 1) Volume of transaction handled 2) Fixed payments 3) Subjective counting 4) Others 50. Did you have records or balance sheet for your transaction? 1) Yes 2) No 125

141 51. What is the source of your information on demand, supply and price of the markets? 1) Radio 2) Television 3) Other traders 4) Telephone/brokers 5) Others 52. What modes of transportation do you used from point of purchase to store? 1) Pack animals 2) Human portages 3) Vehicles 4) Animal cart 5) Others 53. Do you have trade associations? 1) Yes 2) No 54. Did you undertake any processing activities? 1) Yes 2) No 55. How much was the marketing costs in 2012 for onion No. Expenses type Total cost What if u do not use brokers for users (total cost) 1 Packing material/jonya 2 Loading 3 Unloading 4 Transport 5 Salary 6 Store rent 7 Storage losses 8 Telephone 9 Guard 10 Personal expenses 11 Brokers fee 12 Searching and negotiation costs 13 Others 56. What are the major problems encountered causes and suggested solutions in your business? No. Type of the problem Causes of the problem Suggested solution 126

142 Checklist for focus group discussion for producers, development agents and Woreda experts I. General Information 1. Zone 2. District 3. Name of the PA 4. Name of the facilitator 5. Date of data collection 6. Number of participants Male Female Total 7. Signature II. Production 1. What are the inputs used for horticulture production in 2011? 2. What are the problems you faced to produce horticulture? 3. What are the factors that affect your decision to produce horticulture? III. Finance 4. What are the sources of your finance for horticulture production? 5 What are the problems you faced related to credit availability, amount and interest rate? IV. Marketing 6. From whom you got market information? What types of market information? 7. Who are your main input suppliers? 8. What are the problems you encountered with the time of input supply, amount and price? 9. What are the problems you encountered without put supply and price? 10. Who are the main customers for your output? 11. How do you set price of output? 12. How do you transport, store and process the product? 13. Are there brokers in your area? Who are they? 14. Are they important? 15. What is their role and function? 16. What will occur if the Government stops the brokers? 17. What is the problem related to brokers? 18. What is your comment and suggestion for horticulture production and Marketing? 127

143 Secondary data collection formats from different organizations I. Production 1. Cultivated land and total production for horticulture Production year Cultivated land Total production Productivity/ha 2011/ / / / /08 2. Inputs supplied Production Type of inputs used for horticulture production year Fertilizer(Q) Improved seed(q) Chemicals(litter) Farm implements 2011/ / / / /08 II. Marketing 3. Retail Price of onion and tomato in different markets year Markets Monthly retail price of horticulture (onion, tomato) (birr/kg ) in different markets J F M Ap May J Ju Au S O N De AV.pri 2011/12 woreta 2010/11 woreta 2009/10 woreta 2008/09 woreta 2007/08 woreta 2011/12 Bahir Dar 2010/11 Bahir Dar 2009/10 Bahir Dar 2008/09 Bahir Dar 2007/08 Bahir Dar 2011/12 Gondar 2010/11 Gondar 2009/10 Gondar 2008/09 Gondar 2007/08 Gondar 128

144 5. Number of licensed traders Year Number of horticulture licensed traders in different markets Woreta Bahir Dar Gondar Total 2011/ / / / /08 Total 6. Number of horticulture processors Markets Number of processors Male Female Total Woreta Bahir Dar Gondar Total 7. Number of buyers and sellers and volume of transaction in 2011/12 in Woreta market Wholesalers retailers Buyers Name Volume of transaction handled in Quintal Name Volume of transaction handled in Quintal Number Volume of transaction purchased in Quintal 8. Number of investors involved in horticulture production? Status Area in ha. No of investors Capital registered Preimplementation implementation operational < >30 129

145 Survey Questionnaire for Brokers First of all, I would like to thank in advance for your willingness to answer the questionnaire designed to collect data to study The Brokerage Institutions and Smallholder Market Linkages in Fogera Woreda. I. General Information 1. Zone 2.Woreda 3. Questioner No. 4. Name of enumerator 5. Date of data collection 6. Signature II. Socioeconomic characteristics 7. Age years 8. Sex 1) Male 2) Female 9). Experience in horticultural trade years 10. Religion 1) Orthodox Christian 2) Muslim 3) Catholic 4) Protestant 5) Others 11. Marital Status 1) Single 2) married 3) Divorced 4) Windows 12. Educational level 1) Illiterate 1) Adults education 3) year of formal education 13. Main occupation 1) farmer 2) student 3) unemployed 4) others III. Assets owned 14. Fixed Assets owned Assets No. Total Value(in birr) Shop Iron sheet house Grass roofed house Weighting balance Telephone fixed Telephone mobile Vehicle(truck) Motor vehicle Bicycle Animal court Hand pool cart Pack animals Grinding machine Others IV. Financial resources own 15. How much is your working capital currently (2012)? birr 16. What is the source of your working capital? (Multiple answers are possible) 1) Loan 2) Own capital 3) Share 4) Gift 17. If the source is from loan what are the lending institutions? (Multiple answers are possible) 1) Small and Micro Enterprise 2) Amhara Credit and Saving Institute (ACSI) 3) Commercial bank of Ethiopia 4) Development Bank of Ethiopia 5) Private Banks 6) Others 18. How much was the interest rate? birr for formal lenders birr for informal lenders 130

146 V. Brokers role and practices 19. What is your role in the horticulture/onion marketing system? Your mechanism How do you act?... How do you start this business? Service provision for farmers and wholesalers Type of service To When where how Price information Farmers wholesalers Quality and quantity Farmers wholesalers Credit provision Farmers wholesalers Sharing risks Farmers wholesalers Transportation /others Farmers wholesalers 20. Did you have regular customers? 1) yes 2) no 21. If yes for Q20 number of the regular customers? Farmers Wholesalers Retailers 22. Where do you get market information? 1) Radio 2) friends and neighbors 3) Television 4) from Government 5) Telephone/ central market 6) Others 23. Who set purchase price of horticulture? 1) Myself 2) Sellers 3) By negotiation 4) based on central market price/addis Ababa price 5) By the forces of demand and supply 6) Others 24. If you set the purchase price yourself how did you decide? 1) Based on the last year price 2) In consultations with other traders 3) Considering demand and supply 4) Others 25. Was the purchase price the same for all actors? 1) Yes 2) No 26 If no for Q 25 why? How did you differentiate quality during buying? 1) Based on its origin 2) Taste 3) Color 4) Degree of adulteration 5) Others 28. Was there a price difference during buying and selling based on quality? 1) Yes 2) No 29. how much difference in birr Are you a licensed broker? 1) Yes 2) No 31. If no why? Are there unlicensed horticulture brokers in the area? 1) Yes 2) No 33. Do you pay tax for the government? 1) Yes 2) No 34. What is the basis of taxation? 1) Volume of transaction 2) Fixed 3) Subjective 4) Others 35. Did you have records or balance sheet for your transaction? 1) Yes 2) No 36. Have you faced contract failure in ) yes 2) no 37. If yes for Q35 how much?

147 38. Destinations or market outlets of brokers in 2012 No Destination markets Means of transaction Transacted amount/qt 35. What are the major problems encountered causes and suggested solutions in your business? No. Type of the problem Causes of the problem Suggested solution 41. What are the opportunities of the business?... Survey Questionnaire for Rural Assemblers First of all, I would like to thank in advance for your willingness to answer the questionnaire designed to collect data to study Market imperfections, the Brokerage Institutions and Smallholder Market Linkages in Fogera Woreda. I. General Information 1. Zone 2.Woreda 3. Questioner No. 4. Name of enumerator 5. Date of data collection 6. Signature II. Traders characteristics 7. Age years 8. Sex 1) Male 2) Female 9. Experience in horticultural trade years 10. Religion 1) Orthodox Christian 2) Muslim 3) Catholic 4) Protestant 5) Others 11. Marital Status 1) Single 2) married 3) Divorced 4) Windows 12. Educational level 1) Illiterate 1) Adults education 3) year of formal education III. Assets owned 13. Fixed Assets owned Assets No. Total Value(in birr) Shop Iron sheet house Grass roofed house Weighting balance Telephone fixed Telephone mobile Vehicle(truck) Motor vehicle Bicycle Animal court Hand pool cart Pack animals Grinding machine Others 132

148 IV. Financial resources own 14. How much is your working capital currently (2012)? birr 15. What is the source of your working capital? (Multiple answers are possible) 1) Loan 2) Own capital 3) Share 4) Gift 16. If the source is from loan what are the lending institutions? (Multiple answers are possible) 1) Small and Micro Enterprise 2) Amhara Credit and Saving Institute (ACSI) 3) Commercial bank of Ethiopia 4) Development Bank of Ethiopia 5) Private Banks 6) Others 17. How much was the interest rate? birr for formal lendersr birr for informal lenders VI. Buying and selling practices 18. How did you attract your suppliers? 1) By giving fair price 2) By using proper weighting 3) Promotion methods 4) Others 19. From whom you purchase the product? 1) Directly from produc 2) Others 20. From which market you prefer to buy horticulture/onion? 1) Road side 2) farmers residence (at farm) 3) Woreta 4) Bahir Dar 5) Gondar 5) Others 21. Did you have regular suppliers of the product? 1) yes 2) no 22. If yes for Q 21 number the regular suppliers? Farmers brokers. 23. Where do you get market information? 1) Radio 2) friends and neighbors 3) Television 4) Brokers 5) from Government sources and NGOs 6) Telephone 7) Others 24. Do you use brokers for your horticultural/onion marketing systems? 1) yes 2) no 25. If yes for Q 24 what was the brokered transaction in quintal? and brokerage fee birr 26. If yes for Q what is your reason for using brokers? 27. If No for Q why? 28. Do you believe that brokers have benefit in linking wholesalers to the market or farmers? 1) Yes 2) No 29. What is the cost of not using brokers?...birr 30. What is your experience in using brokers? years 133

149 31. Horticultural transaction in 2003 E.C Types of horticulture Onion Tomato Garlic Cabbage and leafy vegetables Potato Green pepper Others Amount purchased (quintal) Amount sold (quintal) Purchase price/kg Selling price/kg Market places For whom do you sell 32. How did you measure the product during purchase? 1) Using weighting balance 2) Using sack 3) Using basket 4) Others 33. Who set purchase price of horticulture? 1) Myself 2) Sellers 3) By negotiation 4) based on central market price/addis Ababa price 5) By the forces of demand and supply 6) Others 34. Who sets the Selling price of horticulture? 1) Myself 2) Buyers 3) By negotiation 4) Based on central market price/addis Ababa price 5) By the forces of demand and supply 6) Others 35. If you set the purchase and selling price yourself how did you decide? 1) Based on the last year price 2) In consultations with other traders 3) Considering demand and supply 4) Others 36. Was the purchase and selling price the same for all competitors? 1) Yes 2) No 37. When did you purchase the product? 1) After harvest 2) Throughout the year 3) Others 38. How did you sale the product? 1) Directly to consumers 2) For retailer 3) Using brokers 4) Others 39. How did you differentiate quality during buying? 1) Based on its origin 2) Taste 3) Color 4) Degree of adulteration 5) Others 40. Is there price difference during buying and selling based on quality? 1) Yes 2) No 41. Did you store horticulture before you sale? 1) Yes 2) No 42. If yes, for how long you store the maximum before you sold? months 43. Do you have regular customers? 1) yes 2) no 44. If yes what is the number of regular customers? 45. What are the numbers of trading contacts that you have made to Fogera 2003 E.C? 46. What is the transaction cost? birr 47. Are you a licensed trader? 1) Yes 2) No 48. Are there unlicensed horticulture traders in the area? 1) Yes 2) No 49. What is the basis of taxation? 1) Volume of transaction handled 2) Fixed payments 3) Subjective counting 4) Others 50. Did you have records or balance sheet for your transaction? 1) Yes 2) No 51. What is the source of your information on demand, supply and price of the markets? 1) Radio 2) Television 3) Other traders 4) Telephone/brokers 5) Others 134

150 52. What modes of transportation do you used from point of purchase to store? 1) Pack animals 2) Human portages 3) Vehicles 4) Animal cart 5) Others 53. Do you have trade associations? 1) Yes 2) No 54. Did you undertake any processing activities? 1) Yes 2) No 55. How much was the marketing costs in 2012 for onion No. Expenses type Total cost What if u do not use brokers for users (total cost) 1 Packing material/jonya 2 Loading 3 Unloading 4 Transport 5 Salary 6 Store rent 7 Storage losses 8 Telephone 9 Guard 10 Personal expenses 11 Brokers fee 12 Searching and negotiation costs 13 Others 56. What are the major problems encountered causes and suggested solutions in your business? No. Type of the problem Causes of the problem Suggested solution Survey Questionnaire for Retailers First of all, I would like to thank in advance for your willingness to answer the questionnaire designed to collect data to study The Brokerage Institutions and Smallholder Market Linkages in Fogera Woreda. I. General Information 1. Zone 2.Woreda 3. Questioner No. 4. Name of enumerator 5. Date of data collection 6. Signature II. Traders characteristics 7. Age years 8. Sex 1) Male 2) Female 9. Experience in horticultural trade years 10. Religion 1) Orthodox Christian 2) Muslim 3) Catholic 4) Protestant 5) Others 11. Marital Status 1) Single 2) married 3) Divorced 4) Windows 135

151 12. Educational level 1) Illiterate 1) Adults education 3) year of formal education III. Assets owned 13. Fixed Assets owned Assets No. Total Value(in birr) Shop Iron sheet house Grass roofed house Weighting balance Telephone fixed Telephone mobile Vehicle(truck) Motor vehicle Bicycle Animal court Hand pool cart Pack animals Grinding machine Others IV. Financial resources own 14. How much is your working capital currently (2012)? birr 15. What is the source of your working capital? (Multiple answers are possible) 1) Loan 2) Own capital 3) Share 4) Gift 16. If the source is from loan what are the lending institutions? (Multiple answers are possible) 1) Small and Micro Enterprise 2) Amhara Credit and Saving Institute (ACSI) 3) Commercial bank of Ethiopia 4) Development Bank of Ethiopia 5) Private Banks 6) Others 17. How much was the interest rate? birr for formal lendersr birr for informal lenders VI. Buying and selling practices 18. How did you attract your suppliers? 1) By giving fair price 2) By using proper weighting 3) Promotion methods 4) Others 19. From whom you purchase the product? 1) Directly from producers 2) From wholesalers 3) From rural assemblers 5) Others 20. From which market you prefer to buy horticulture/onion? 1) Road side 2) farmers residence (at farm) 3) Woreta 4) Bahir Dar 5) Gondar 5) Others 21. Did you have regular suppliers of the product? 1) yes 2) no 22. If yes for Q 21 number the regular suppliers? Farmers brokers. 136

152 23. Where do you get market information? 1) Radio 2) friends and neighbors 3) Television 4) Brokers 5) from Government sources and NGOs 6) Telephone 7) Others 24. Do you use brokers for your horticultural/onion marketing systems? 1) yes 2) no 25. If yes for Q 24 what was the brokered transaction in quintal? and brokerage fee birr 26. If yes for Q what is your reason for using brokers? 27. If No for Q why? 28. Do you believe that brokers have benefit in linking wholesalers to the market or farmers? 1) Yes 2) No 29. What is the cost of not using brokers?...birr 30. What is your experience in using brokers? years 31. Horticultural transaction in 2003 E.C Types of horticulture Amount purchased (quintal) Amount sold (quintal) Purchase price/kg Selling price/kg Market places For whom do you sell Onion Tomato Garlic Cabbage and leafy vegetables Potato Green pepper Others 32. How did you measure the product during purchase? 1) Using weighting balance 2) Using sack 3) Using basket 4) Others 33. Who set purchase price of horticulture? 1) Myself 2) Sellers 3) By negotiation 4) based on central market price/addis Ababa price 5) By the forces of demand and supply 6) Others 34. Who sets the Selling price of horticulture? 1) Myself 2) Buyers 3) By negotiation 4) Based on central market price/addis Ababa price 5) By the forces of demand and supply 6) Others 35. If you set the purchase and selling price yourself how did you decide? 1) Based on the last year price 2) In consultations with other traders 3) Considering demand and supply 4) Others 36. Was the purchase and selling price the same for all competitors? 1) Yes 2) No 37. When did you purchase the product? 1) After harvest 2) Throughout the year 3) Others 38. How did you sale the product? 1) Directly to consumers 2) For retailer 3) Using brokers 4) Others 137

153 39. How did you differentiate quality during buying? 1) Based on its origin 2) Taste 3) Color 4) Degree of adulteration 5) Others 40. Is there price difference during buying and selling based on quality? 1) Yes 2) No 41. Did you store horticulture before you sale? 1) Yes 2) No 42. If yes, for how long you store the maximum before you sold? months 43. Do you have regular customers? 1) yes 2) no 44. If yes what is the number of regular customers? 45. What are the numbers of trading contacts that you have made to Fogera 2003 E.C? 46. What is the transaction cost? birr 47. Are you a licensed trader? 1) Yes 2) No 48. Are there unlicensed horticulture traders in the area? 1) Yes 2) No 49. What is the basis of taxation? 1) Volume of transaction handled 2) Fixed payments 3) Subjective counting 4) Others 50. Did you have records or balance sheet for your transaction? 1) Yes 2) No 51. What is the source of your information on demand, supply and price of the markets? 1) Radio 2) Television 3) Other traders 4) Telephone/brokers 5) Others 52. What modes of transportation do you used from point of purchase to store? 1) Pack animals 2) Human portages 3) Vehicles 4) Animal cart 5) Others 53. Do you have trade associations? 1) Yes 2) No 54. Did you undertake any processing activities? 1) Yes 2) No 55. How much was the marketing costs in 2012 for onion No. Expenses type Total cost What if u do not use brokers for users (total cost) 1 Packing material/jonya 2 Loading 3 Unloading 4 Transport 5 Salary 6 Store rent 7 Storage losses 8 Telephone 9 Guard 10 Personal expenses 11 Brokers fee 12 Searching and negotiation costs 13 Others 56. What are the major problems encountered causes and suggested solutions in your business? No. Type of the problem Causes of the problem Suggested solution 138

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