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

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

ii  

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.

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Name of Thesis Major-Advisor Signature Date

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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).

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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.

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DEDICATION

I dedicated this thesis manuscript to my father TAMIR ENDALEW.

iv  

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: ----------------

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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 2006. 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.

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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.

vii  

LIST OF ABRIVATIONS AND ACRONYMS

ADLI Agriculture Development Led Industrialization

ANRS Amhara National Regional State

BoFED Bureau of Finance and Economic Development

CSA Central Statistics Authority

CSE Conservation Strategy of Ethiopia

ECX Ethiopian Commodity Exchange

FEDRE Federal Democratic Republic of Ethiopia

FIML Full Information Maximum Likelihood

GDP Gross Domestic Product

GTP Growth and Transformation Plan

ILRI International Livestock Research Center

IPMS Improving Productivity and Market Success of Ethiopian Farmers

Kms Kilometers

MoARD Ministry of Agriculture and Rural Development

MSF Ministry of State Farm

MSI Ministry of State Industry

NIE New Institutional Economics

NGOs Non Government Organizations

OLS Ordinary Least Squares

PADETS Participatory Agricultural Demonstration, Extension and Training System

PASDEP Plan for Accelerated and Sustainable Development to End Poverty

PRSP Poverty Reduction Strategy Paper

PSM Propensity Score Matching

RMA Rapid Market Appraisal

SDPRP Sustainable Development and Poverty Reduction Program

 

viii  

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 1

1.1. Background of the Study 1

1.2. Problem Statement 3

1.3. Objectives of the Study 6

1.4. Significance of the Study 7

1.5. Scope and Limitations of the Study 7

1.6. Organization of the Thesis 8

2. LITERATURE REVIEW 9

2.1. Definitions of Related Terms 9

2.2. Major Policy Reforms in Ethiopia Related to Market Institutions 11

2.3. Commodity Exchange 12

2.3.1. What is commodity exchange? 12

2.3.2. The rationale behind the establishment of Ethiopian commodity exchange 13

2.3.3. Ethiopian commodity exchange current status 14

2.4. The New Institutional Economics Approach 15

2.4.1. Transaction costs 15

2.4.2. Institutions to facilitate exchange 16

2.4.3. Social capital 17

2.5. Market Imperfection and the Brokerage Institutions in Ethiopia 17

2.6. Properties of Horticultural Production and Marketing 18

2.6.1. General properties of horticultural products 18

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2.6.2. Overview of Horticultural Crops production and Marketing in Ethiopia 20

2.6.3. Horticultural Crop Production and Marketing in Fogera Woreda 21

2.6.3.1. Production problems 22

2.6.3.2. Marketing problems 22

2.6.3.3. Production opportunities 23

2.7. Impact Evaluation Methods 23

2.7.1. Experimental methods 24

2.7.2. Quasi and non-experimental methods 24

2.8. Propensity Score Matching 27

2.9. Empirical Studies on Horticultural Marketing Systems and the Role of Brokerage

Institutions in Developing Countries and Ethiopia 29

3. RESEARCH METHDOLOGY 34

3.1. Description of the Study Area 34

3.1.1. Land use pattern and population of Fogera Woreda 34

3.1.2. Priority farming systems 36

3.2. Methods of Data Collection 38

3.3. Sampling Procedures 38

3.3.1. Farmers sampling 39

3.3.2. Brokers, rural assemblers and wholesalers sampling 39

3.3.3. Retailers sampling 40

3.4. Methods of Data Analysis 41

3.4.1. Descriptive statistics 41

3.4.2. Econometric models 41

3.4.2.1. Propensity score matching model 41

3.4.2.2. The Ordinary Least Square (OLS) regression 53

4. RESULTS AND DISCUSSION 60

4.1. The Brokerage Institutions 60

4.1.1. Socioeconomic profile of brokerage institutions 60

4.1.2. Which horticultural products have significant brokerage activity in the area? 62

4.1.3. Characteristics and economic role of brokerage institutions 62

4.1.5. The rationale behind the emergence of farmer brokers 70

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4.1.6. Market outlets or target markets of brokerage institutions 72

4.1.7. Producer’s perception of brokerage institutions 72

4.1.8. Night transaction and loading 72

4.1.9. Constraints of brokerage institutions 73

4.1.10. Opportunities to the brokers 74

4.2. Brokerage Institutions and Smallholder Market Linkages 74

4.2.1. Descriptive statistics 74

4.2.1.1. Demographic characteristics of sample households 74

4.2.1.2. Socio-economic characteristics of sample households 76

4.2.1.3. Institutional and organizational aspects 77

4.2.1.4. Social capital 78

4.2.2. Propensity score matching model 78

4.2.2.1. Estimation of propensity scores 79

4.2.2.2. Common support condition 83

4.2.2.3. Matching of participant and non-participant households 86

4.3. Impacts of the Brokerage Institutions 88

4.3. 1. Impact on net return from onion production 89

4.3. 2. Impact on percentage of marketed surplus 89

4.3. 3. Impact on Amount of Onion Produced and Land Allocated to Onion Production 90

4.3.4. Sensitivity Analysis 91

4.4. Brokerage Institutions and Wholesaler Market Linkages 92

4.4.1. Demographic profiles of the wholesalers 92

4.4.2. Socio-economic characteristics and assets of wholesalers 94

4.4.3. Institutional and organizational aspects 94

4.4.4. Wholesaler’s perceptions of brokerage institutions 95

4.4.5. Determinants of share (percentage) of brokered transactions 96

5. SUMMARY, CONCLUSIONS AND RECOMMANDATIONS 99

5.1. Summary 99

5.2. Conclusions and Recommendations 102

6. REFERENCES 105

7. APPENDICES 111

xi  

LIST OF TABLES

Table Page

1. Land use pattern of Fogera Woreda………………………………………………………..35

2. Farming system by ecological zone in Fogera Woreda………………………………..…..36

3. Sampling frame and the sample size………………………………………...……………..39

4. Variable definition and measurements for PSM…………………………...………………52

5. Variable definition and measurements for Heckman two stage model…………………….59

6. Frequency distributions of brokerage institutions………………………………….………60

7. Descriptive statistics for continuous variables………………………………………..……62

8. Descriptive statistics of some variables……………………………………………………67

9. Descriptive statistics of sample households on pre-intervention characteristics…………..75

10. Descriptive statistics of sample households (for dummy variables)…………….…..……78

11. Logit results of households’ brokerage institution participation…………………………80

12. Balancing test of matched sample…………………………………………………….…..87

13. Performance of matching estimators under the three criteria…………………………….88

14. Impact of brokerage institutions……………………………………………………...…..89

15. Result of sensitivity analysis using Rosenbaum bounding approach…………………….91

16. Descriptive statistics of sample wholesalers (for continuous variables)………...……..…93

17. Descriptive statistics of sample wholesalers (for dummy variables)……………….…….95

18. Results of Ordinary Least Square (OLS) estimation …………………………………….96

 

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LIST OF FIGURES

Figure Page

1. Map of the study area………………………………………………………………………37

2. Broker’s chain and flow of transactions using brokerage institutions……………………..68

3. Kernel density of propensity scores before matching……………………………………...84

4. Kernel density estimates of participants before and after common support…………….…85

5. Kernel density estimate of propensity scores of non-participants households before and

after common support…………………………………………………..…………………….86

xiii  

LIST OF APPENDIX TABLES

Appendix Table Page

1. Multicollinearity test for explanatory variables in PSM…………………………...……..112

2. Multicollinearity test for explanatory variables in OLS……………………...……….….113

3. Conversion factor used to calculate TLU…………………………………………………113

4. Labor supply conversion factor (person day equivalent)………………………………....114

 

 

 

 

 

 

 

 

 

 

 

xiv  

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 4393.62 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

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 4.287 million ha from which, horticulture covered about 88.98 thousands of

2  

ha and produced over 5.8 million quintal through employing over 3.5 million small-holders.

Onion covered 12,174.60 and tomato covered 892.72 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 2011. A considerable number of farmers in the district are involved in

commercial production of vegetables, mainly onion and tomato, using both irrigated and rain-

fed 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.

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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

4  

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

5  

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?

6  

• 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.

7  

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.

1.5. 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.

8  

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.

9  

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).

10  

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

11  

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.

2.2. 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

12  

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).

2.3. Commodity Exchange

2.3.1. 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 low-

income 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.

13  

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.

2.3.2. 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).

14  

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.

2.3.3. Ethiopian commodity exchange current status

The Ethiopia Commodity Exchange (ECX) is a commodities exchange established April 2008

in Ethiopia. In Proclamation 2007-551, 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.

15  

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.

2.4. The New Institutional Economics Approach

2.4.1. 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.

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2.4.2. 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 12th and 13th 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 long-

distance 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

17  

is established primarily by repeated interaction. The incidence of theft and breach of contract

is low, and recourse to the legal system is rare.

2.4.3. 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).

2.5. 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

18  

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.

2.6. Properties of Horticultural Production and Marketing

2.6.1. 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

19  

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.

20  

2.6.2. 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

21  

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).

2.6.3. 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).

22  

2.6.3.1. 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.

2.6.3.2. 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.

23  

2.6.3.3. 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.

2.7. 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,

24  

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.

2.7.1. 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).

2.7.2. 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

25  

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

26  

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 cross-

sectional 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

27  

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).

2.8. 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

28  

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).

29  

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.

2.9. 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 long-

distant trade (Scott, 1985). In India, in the traditional marketing system, small landholding

farmers depend on intermediaries for credit (Lokanathan and De Silva, 2010).

30  

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.

31  

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

32  

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

33  

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.

34  

3. RESEARCH METHDOLOGY

3.1. Description of the Study Area

Based on the BOFED (2011), Amhara Region has a population of 18.54 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 157,126.85 square kilometers, this region

has an estimated population density of 115.9 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 110 58 N latitude and 370 41 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.

3.1.1. 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

1216.3 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 9819.98 hectares; oil seeds 6137 hectares; root crops 1034.29

35  

hectares; and vegetables 882.08 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 51472 44%

Grazing Land 26999 24%

Area covered with water (wet land ) 23354 20%

Infrastructure including settlement 7075 6%

Un productive land (hills) 4375 3.70%

Forest land 2190 1.80%

Swamp land 1698 1.40%

Perennial crops 2190 0.20%

Total 117414 100%

Source: ILRI /IPMS, 2008

36  

3.1.2. 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

1900 – 2000 15 Cereals (maize, teff, finger millet), noug, vegetables,

apiculture, cattle, goats.

2000 – 2400 4 Barley, Horse beans, potato, apiculture, sheep, cattle

Total 27

Source: ILRI /IPMS, 2008

 

Sour

Figur

rce: IPMS, 2

re 1: Map o

2005

f the study aarea

37 

38  

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.

3.3. 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.

39  

3.3.1. 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, 2011.

Table 3. Sampling frame and the sample size

Kebeles Total households Sample households Total

Participant Non-

participant

Participants Non-

participant

Diba sifatera 77 67 6 5 11

Abewana

Kokit

126 111 10 8 18

Kuhar

michaiel

115 101 9 8 17

Shena 201 178 15 13 28

Bebeks

Mariyam

478 422 36 33 69

3.3.2. 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

40  

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.

3.3.3. 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.

41  

3.4. Methods of Data Analysis

Both the descriptive statistics and econometric methods were used for the analysis of data.

3.4.1. 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.

3.4.2. Econometric models

3.4.2.1. 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.

42  

Following Pindyck and Rubinfeld (1981), the cumulative logistic probability function is

specified as:

( ) [ ] ⎥⎦

⎤⎢⎣

⎡∑+

=⎥⎦

⎤⎢⎣

⎡+==

+−=∑ ii

iiiiX

m

i eXFZFP

βαβα

11

1 (1)

Where; e: represents the base of natural logarithms (2.718…)

Xi: represents the ith explanatory variable

Pi: the probability that a farmer participates in the brokerage institution services

α and βi: 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 (Pi) to the probability that he/she will not be a participant

(1-Pi). The probability that he/she will not be a participant is defined by:

[ ] ⎥⎦⎤

⎢⎣⎡+

=−i

i ZeP

111

(2)

Using equations (1) and (2), the odds ratio becomes

i

i

i

i

i ZZ

Z

eee

PP

=⎥⎦

⎤⎢⎣

⎡++

=⎥⎦⎤

⎢⎣⎡− −1

11 (3)

Alternatively,

⎥⎥⎦

⎤

⎢⎢⎣

⎡+

−

∑=⎥

⎦

⎤⎢⎣

⎡++

=⎥⎦⎤

⎢⎣⎡−

=

m

t

tit

i

i

i

iX

Z

Z

eee

PP 1

11

1

βα

(4)

43  

Taking the natural logarithms of equation (4) will give the logit model as indicated below.

mimii

i

i XXXP

PZi βββα ++++=⎥⎦⎤

⎢⎣⎡−

= ............1

ln 211 2

(5)

If we consider a disturbance term, ui, the Logit model becomes

ititi UXZm

t++= ∑

=1βα

(6)

So the binary Logit will become:

( ) ( )XfPBS =Pr (7)

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;

[ ]iXX = (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.

44  

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-R2 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 R2 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) (Yi) is specified as:

( ) ( )01 =−== iiiii DYDYτ (9)

45  

Where; τi: is treatment effect (effect due to participation in the service of brokers), Yi: is the

outcome on household i, Di: 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 Yi (Di = 1) and Yi (Di = 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 Yi (Di = 1) or Yi (Di = 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:

( ) [ ] [ ]1)0(1)1(1 =−==== DYEDYEDEATT ττ (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.

[ ] [ ] [ ] [ ]0)0(1)0(0)0(1)1( =−=+==−= DYEDYEDYEDYE ATTτ (11)

46  

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:

[ ]{ [ ])(,0)0()(,1)1()1/( XPDYEXPDYEE DXPPSMATT =−== =τ (12)

47  

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.

48  

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.

49  

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

50  

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

51  

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

52  

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

institutions

Where, 1= yes, 0= no

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

53  

3.4.2.2. 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,

[ ]0,1 >+= iii uZT α

[ ]0,0 <+= iii uZT α (13)

Ti, a brokerage-use indicator, is a dummy variable and the realization of a latent continuous

variable {Zi’α + ui}. When Ti =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 Ti equals unity is the ‘brokerage-use intensity’ Bi observed. Bi

explains how much whole seller i uses brokerage institutions and represents the share of

brokered transactions out of total transactions. Therefore,

Bi = Bi*, if Ti = 1

Bi= not observed, if Ti = 0 (14)

Where, Bi* (the potential share of brokered transaction, a latent variable corresponds to)

iii XB εβ +=* (15)

Therefore, once whole seller i decide to use brokers (Ti) = 1, the observed Bi is positive.

According to Wooldridge, 2002; equations 13, 14 and 15 are valid under the assumptions: (Xi,

Zi and Ti) are observed, there correlation between the two error terms (there is selection bias),

normal distribution of error terms and (Xi, Zi) are exogenous vector of the covariates for i=1,

…,N Heckman (1979) and Melino (1982).

54  

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.

iii vXB += β , with 0)1,( ==iii TXvE (17)

The OLS estimation would use all observations for which Ti = 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

55  

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.

56  

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.

57  

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

58  

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

59  

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

Variables

Type Definitions Measurements

PBT Continuous Percentage of brokered

transaction out of total

transaction

Percent

60  

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

4.1.1. Socioeconomic profile of brokerage institutions

Table 6. Frequency distributions of brokers

Variable Category Frequency Percent (%)

Sex Male 55 100

Female 0 0

Religion Orthodox Christian 55 100

Others 0 0

Marital status Single 9 16.40

Married 46 83.60

Education level

Illiterate 2 3.60

Adults education 9 16.40

Literate (formal) 44 80.00

Main occupation

Farmer 32 58.201

Youth 12 21.802

Trader 11 20.00

1. 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

61  

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 peri-

urban brokers for this service they have a commission of 0.05-0.1 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 18.00 to 63.00 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

62  

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 5.47. 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 55 18 63 32.65 10.59

Experience in brokerage

activity 55 3 15 6.5 3.02

Family size 46 2 14 5.6 2.93

Land size 32 0.4 7 6.19 6.66

Number of cattle 32 1 12 5.47 2.99

Number of water pump 31 0 7 1.52 1.15

4.1.2. 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.

63  

4.1.3. 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

64  

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 7.50

65  

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

66  

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 15,403.00 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

67  

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 55 50 16000 1721.10 3077.24

Income from

brokerage activity 55 500 160000 17210.10 30772.40

Loss due to contract

failure (ETB) 21 518 85000 15403.00 18944.41

Working capital

(ETB) 55 500 100000 3311.50 14801.14

Number of regular

customers

(wholesalers)

55 0 40 5.80

7.70

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.

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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

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

Wholesalers 

Lower-tier Farmer Brokers1 (13.60%)

Youth Brokers (school

drop outs, grade 10 and

12 complete) (21.80%)

Upper-tier Farmer Brokers2 (44.60%) 

Brokers at Woreta, Gumara and Abewana Kokit (20.00%) 

Producers

69  

4.1.4. 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

70  

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.

4.1.5. 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

71  

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.

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4.1.6. 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

4.1.7. 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.

4.1.8. 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

73  

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.

4.1.9. 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.

74  

4.1.10. 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.

4.2. 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.

4.2.1. Descriptive statistics

4.2.1.1. 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.

75  

Table 9. Descriptive statistics of sample households on pre-intervention characteristics

Pre-intervention Variables

Sample

Households

(N=143)

Participant

(N=76)

Non participant

(N=67)

Difference in

means

T-Value

Mean Std.Er Mean Std.Er Mean Std.Er Mean Std.Er

AgHH 39.60 0.99 42.54 1. 64 37.01 1.09 -5.52 1.93 -2.86***

SxHH 1.91 0.02 1.87 0.04 1.95 0.03 0.08 0.05 1.70**

MsHH 0.96 0.02 0.95 0.03 0.96 0.02 0.01 0.03 0.16

ELHH 2.53 0.29 1.52 0.36 3.42 0.41 1.9 0.56 3.42***

FSiHH1 3.31 0.11 3.36 0.15 3.26 0.15 -0.10 0.22 -0.47

FSiHH 6.13 0.21 5.94 0.29 6.29 0.30 0.35 0.42 0.83

TLU 5.67 0.25 5.97 0.38 5.40 0.34 -0.57 0.51 -1.11

TLSha 1.57 0.09 1.43 0.08 1.69 0.16 0.27 0.19 1.43

ILHa 0.97 0.09 0.77 0.06 1.16 0.15 0.39 0.18 2.2**

EHP 8.97 0.32 9.18 0.48 8.79 0.42 -0.39 0.64 -0.61

DRDA 3.49 0.30 4.41 0.55 2.69 0.26 -1.71 0.59 -2.92***

DHMP 0.43 0.04 0.18 0.05 0.64 0.06 0.47 0.07 6.32***

DRWM 12.43 0.63 14.64 1.1 10.49 0.63 -4.15 1.22 -3.40***

DRFMAR 2.49 0.22 3.76 0.36 1.37 0.18 -2.39 0.39 -6.16***

NRC 1.52 0.20 0.85 0.19 2.12 0.32 1.27 0.38 3.29***

NTRC 8.07 0.59 7.95 0.74 8.17 0.92 0.22 1.20 0.18

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

76  

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 42.54 years while it was 37.01 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.

4.2.1.2. 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 non-

participant and participant households. This might be due to the reason that households who

77  

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 17.68 TLU which indicates that there is a high degree of disparity

in the ownership of livestock between the sample households.

4.2.1.3. 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

78  

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-

intervention

Variables

Category Participant

(N=76)

Non participant

(N=67)

Total χ2

N % N % N %

SxHH Female 10 13.16 3 4.48 13 9.09 2.88*

Male 56 86.84 64 95.52 130 90.91

DHMP No 62 81.58 43 64.18 105 73.43 31.56***

Yes 14 18.42 24 35.52 38 26.57

Source: Author’s Survey, 2011

*** and *means significant at the 1 and 10% probability levels, respectively.

4.2.1.4. 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.

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4.2.2. Propensity score matching model

4.2.2.1. 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-R2 value is (0.4294) (Table 11). A

higher pseudo R2 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.

80  

Table 11. Logit results of households’ brokerage institution participation

Variables Coefficients Std.Er Z value

Age .056** .028 2.03

Sex -.157 .996 -0.16

MsHH -.308 1.410 -0.22

ELHH -.163* .086 -1.90

FSiHH1 -.052 .282 -0.19

Livestock .109 .098 1.11

TLSha .183 .586 0.31

ILHa -.022 .574 -0.04

EHP -.021 .065 -0.33

DRDA .156* .087 1.81

DHMP -1.710*** .554 -3.09

DRWM .006 .038 0.16

DRFMAR .631*** .172 3.67

NRC -.331** .164 -2.02

NTRC -.027 .042 -0.65

Constant -2.479 2.458 -1.01

Sample size (N) 143

LR chi2(15) 84.88

Prob > chi2 0.00

Pseudo R2 0.42

Log likelihood -56.394

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 (non-

participants) in brokerage institutions service with similar probability of participation or using

brokerage institutions given the explanatory variables. If the numbers of explanatory variables

81  

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 non-

participants 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.

82  

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

83  

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.

4.2.2.2. 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.

84  

Figure 3: Kernel density of propensity scores before matching

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

0.060 to 0.999 with the mean probability of participation being 0.725. On the other hand, the

probability of not participating of the non participant households in the brokerage institutions

service ranges from 0.003to 0.895 with mean of 0.242. From the result, observations with the

predicted probability between 0.060 and 0.895 are in the common support region with the

possibility of getting good match from the other group. Observations with predicted

0

.5

1

1.5

2 

Density

0  .5 1psmatch2: Propensity Score

Kernel density estimatekdensity _pscorekdensity _pscore

kernel = epanechnikov, bandwidth = 0.1169

Kernel density estimate

85  

probability less than 0.060 and greater than 0.895 have been disregarded out from further

analysis

Figure 4: Kernel density estimates of participants before and after common support

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 0.895. 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 0.060. 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).

0 

.5 

1 

1.5

2 

Density 

0  .5 1psmatch2: Propensity Score

Kernel density estimatekdensity _pscore

kernel = epanechnikov, bandwidth = 0.1070

Kernel density estimate

86  

Figure 5: Kernel density estimate of propensity scores of non-participants households before

and after common support

4.2.2.3. 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.

0

.5

1

1.5

2 

Density

0  .2 .4 .6 .8 1psmatch2: Propensity Score

Kernel density estimatekdensity _pscore

kernel = epanechnikov, bandwidth = 0.0888

Kernel density estimate

87  

Table 12. Balancing test of matched sample

Explanatory

variables

Before matching (143) Kernel matching (Band width 0.25)

(100)

Participant

HHs

Control

HHs

T Participant

HHs

Control

HHs

T

AgHH 42.54 37.01 -2.86*** 40.09 38.45 0.67

SxHH 1.87 1.95 1.70** 1.93 1.92 0.15

MsHH 0.95 0.96 0.16 .98 .97 0.12

ELHH 1.52 3.42 3.42*** 1.67 2.42 -1.15

FSiHH1 3.36 3.26 -0.47 3.28 3.15 0.49

TLU 5.97 5.40 -1.11 5.59 5.57 0.04

TLSha 1.43 1.69 1.43 1.39 1.41 -0.12

ILHa 0.77 1.16 2.2** .80 .81 -0.10

EHP 9.18 8.79 -0.61 7.44 7.55 -0.13

DRDA 4.41 2.69 -2.92*** 2.93 3.44 -0.66

DHMP 0.18 0.64 6.32*** .23 .32 -0.94

DRWM 14.64 10.49 -3.40*** 12.42 12.37 0.03

DRFMAR 3.76 1.37 -6.16*** 2.23 2.27 -0.13

NRC 0.85 2.12 3.29*** 1.00 .91 0.27

NTRC 7.95 8.17 0.18 7.70 8.64 -0.56

Source: Author’s Survey, 2011 *** and **means significant at the 1 and 5% probability levels, respectively. 1. Labor supply conversion factor (person day equivalent)

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-R2 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

88  

Table 13. Performance of matching estimators under the three criteria

Matching estimator Performance criteria

Balancing test* Pseudo-R2 Matched sample size

Radius Caliper matching

0.01 15 1.000 32

0.05 15 0.240 51

0.1 15 0.098 57

0.25 15 0.062 65

0.5 15 0.086 74

Kernel Matching

With no band width 15 0.065 100

With 0.08 band width 15 0.052 100

With 0.1 band width 15 0.046 100

With 0.25 band width 15 0.045 100

With 0.3 band width 15 0.046 100

With 0.5 band width 15 0.060 100

Neighbor matching

1 neighbor 14 0.207 100

2 neighbor 14 0.082 100

3 neighbor 15 0.080 100

4 neighbor 15 0.063 100

5 neighbor 15 0.050 100

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

89  

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.Err1 T

NIO 4393.62 1781.51 2.53**

PMSU 13.55 13.84 2.86**

AOP -5.084 36.72 -0.25

LAOP -0.053 0.22 -0.24

1. The bootstrapped SE is obtained after 100 replications

**, significant at 5% probability levels Source: Own estimation result

4.3. 1. 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 4393.62 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.

4.3. 2. 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

90  

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 2010. 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 4.00-7.00 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

4.3. 3. 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

91  

production. The reason is that higher land allocation and high production is affected by other

factors like previous year price.

4.3.4. 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-12 6.4e-09 6.9e-07 .000018 .000192 .001141 .004497 .013149 .030763

PMSU P<0.000 P<0.000 P<0.000 1.1e-16 8.0e-15 2.3e-13 3.3e-12 2.9e-11 1.8e-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

92  

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.

4.4. 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 sub-

topics so as to independently discuss each subtopic.

4.4.1. 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.

93  

Table 16. Descriptive statistics of sample wholesalers (for continuous variables)

Variables Sample wholesalers

(N=52)

Participant (N=46) Non participant

(N=6)

T-Value

Mean Std.Er Mean Std.Er Mean Std.Er

DRFWM 149.27 23.47 165.48 25. 47 25.00 21.00 -1.96*

AGWS 31.11 0.95 31.50 0.98 28.17 3.28 0.26

EXWSHT 5.28 0.44 5.30 0.47 5.17 1.40 -0.10

ELWS 9.00 0.51 8.69 0.55 11.33 0.71 1.69*

NPWB 1.71 0.13 1.76 0.14 1.33 0.33 -1.05

CASF 32.56 4.18 35.17 4.43 12.50 9.81 -1.77*

CWCWS 23923 2163 24086 1995 22666 11769 -0.21

NRFC 4.36 1.06 2.91 0.74 17.83 4.69 5.72***

NRRC 3.67 0.51 4.15 0.54 0.00 0.00 -2.77***

NRWCOA 2.61 0.27 2.84 0.27 0.83 0.83 -2.53**

NTCFWM 47.67 6.60 8.24 6.22 120.00 0.00 4.70**

TMCOST 543653 45843 547231 50234 516225 107204 -0.21

EUB 4.96 0.47 5.61 0.44 0.00 0.00 -4.51***

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.

94  

4.4.2. 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.

4.4.3. 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.

95  

Table 17: Descriptive statistics of sample wholesalers (for dummy variables)

Pre-

intervention

Variables

Category Participant

(N=46)

Nonparticipant

(N=6)

Total χ2

N % N % N %

TRDA Gravel 3.00 6.50 0.00 0.00 3.00 5.80 0.41

Asphalt 43.00 93.50 6.00 100 49.00 94.20

SXWS Female 1.00 2.20 0.00 0.00 1.00 1.90 0.13

Male 45.00 97.80 6.00 100.00 51.00 98.10

MSWS Single 14.00 30.43 3.00 50.00 17.00 32.69 0.92

Married 32.00 69.57 3.00 50.00 35.00 67.31

RLWS Muslim 4.00 8.70 1.00 16.67 5.00 9.61 0.38

Orthodox 42.00 91.30 5.00 83.33 47.00 90.39

HOSF No 8.00 17.39 4.00 66.67 12.00 23.08 7.26***

Yes 38.00 82.61 2.00 33.33 40.00 76.92

ACWS No 40.00 86.96 5.00 83.33 45.00 13.46 0.06

Yes 6.00 13.04 1.00 16.67 7.00 86.54

HRCAPO No 8.00 17.39 5.00 83.33 82 57.34 12.31***

Yes 38.00 82.61 1.00 16.67 61 42.66

Source: Author’s Survey, 2012 ***, means significant at 1 % probability levels

4.4.4. 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

96  

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.

4.4.5. Determinants of share (percentage) of brokered transactions

Table 18. Results of OLS estimation

Variables Coefficients Robust Std. Err. T-Value

DRFWM .025** .009 2.67

TRDA 5.753 10.864 0.53

EXWSHT -1.346* .746 -1.80

NRBC .561** .269 2.09

MSWS 7.493 5.495 1.36

ELWS .013 .447 0.03

NPWB -1.253 2.081 -0.60

HOSF 5.727 6.074 0.94

CWCWS .00007 .0001 0.56

ACWS -.873 3.804 -0.23

NRFC -2.872*** .743 -3.87

NRRC .284 .558 0.51

NRWCOA 4.226*** 1.342 3.15

NTWM -.102 .070 -1.45

TMCOST1 .00002*** 8.53e-06 2.74

_cons 59.406 15.398 1.26

F( 15, 30) = 14.23

Prob > F = 0.0000

R-squared = 0.835

Root MSE = 11.242

Source: Author’s Survey, 2012 *, ** and ***means significant at the 10%, 5% and 1% probability levels. 1. cost of not using brokers

97  

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 0.025. 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 1.346. 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 0.561. 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 2.872. 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,

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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 4.226. 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 0.00002. 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.

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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.

5.1. 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.

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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

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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

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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-R2 test and maximum

possible number of observations matched). The result of the study revealed that, smallholder

farmers using brokerage institutions have got 4393.62 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.

5.2. 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

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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

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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.

 

 

 

 

 

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7. APPENDICES 

 

 

 

 

 

 

 

 

 

 

 

 

 

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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 1.33

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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 0.013

Source: Storck et al., 1991 

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Appendix 4. Labor supply conversion factor (person day equivalent) Age group in years Male Female

<10 0.0 0.0

10-13 0.35 0.35

15-50 1.0 0.80

>50 0.55 0.5

Source: Storck et al., 1991

 

 

 

 

 

 

 

 

 

 

 

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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 10-15 years 16-65 years >65 years

Female <6 years 6-10 years 10-15 years 16-65 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

_______________________ _________________ _______________________ _________________

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III. Resource ownership in 2003 E.C 13. Fixed assets Resources 1) Yes

2) No Number

1. Houses Grass roofed Iron sheet 2. Water pump 3. Others 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 Forest Land

Irrigable land Non irrigable land

Own land Rented in land Obtained as Gift Total land Under operation

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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)

Amount soled

Income

Rice Chick Pea Guaya Teff Onion Tomato Potato Others 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)

Amount sold (quintal)

Income

Onion Tomato Garlic Cabbage and leafy vegetables

Potato

Green pepper Others 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

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19. What were the inputs used to produce horticultural crops/ onion in 2003 E.C production Year

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_________________________

Type of inputs 1 )Yes 2) No

Amount used per timad in kg/litter

Source* Price/litter/kg 1) Credit 2) Cash

Fertilizer DAP Urea Organic Seeds used

Insecticides Herbicides Others

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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

Water sources Distance of water source from farm land

Irrigation materials used

No of Watering per month

Onion Tomato Potato Others

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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___________________________

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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

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67. Months in which prices of horticulture become expensive?........................................... 68. Months in which prices of horticulture become minimum?..................................... 69. 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?.......................................... 77. 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-------------- 84. 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

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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

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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

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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_____________ 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

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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

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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? ___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

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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 2011/12 2010/11 2009/10 2008/09 2007/08 2. Inputs supplied

Production year

Type of inputs used for horticulture production Fertilizer(Q) Improved seed(Q) Chemicals(litter) Farm implements

2011/12 2010/11 2009/10 2008/09 2007/08 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

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5. Number of licensed traders Year Number of horticulture licensed traders in different markets

Woreta Bahir Dar Gondar Total 2011/12 2010/11 2009/10 2008/09 2007/08 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?

Area in ha.

No of

investors

Capital

registered

Status Pre-

implementation

implementation

operational <5 6-10 11-20 20-30 >30

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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

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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?.............................................................................................................. 20. 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?.......................................................... 27. 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……………………….. 30. Are you a licensed broker? 1) Yes 2) No 31. If no why?...................................................................................................................... 32. 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 2012 1) yes 2) no 37. If yes for Q35 how much ?.................................

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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

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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

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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___ 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_________

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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

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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…………….

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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_____________

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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

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