SCHOOL OF GRADUATE STUDIES

HARAMAYA UNIVERSITY

THE BROKERAGE INSTITUTIONS AND SMALLHOLDER MARKET LINKAGES IN THE MARKETING OF

HORTICULTURAL CROPS IN FOGERA WOREDA, SOUTH GONDAR, AMHARA NATIONAL REGIONAL

STATE

Simegnew Tamir (MSc)

Major Advisor: Kinde Getnet (PhD)

Co-Advisor: Jema Haji (PhD)

October, 2012

PRESENTATION OUTLINE

INTRODUCTION

RESEARCH METHDOLOGY

RESULTS AND DISCUSSION

CONCLUSION AND RECOMMANDATION

1.INTRODUCTION

1.1. Background

• Primary development goal of the Government is to achieve food security andsustain high economic and export growth levels

• Growth and Transformation Plan (GTP) investment areas:scaling up model farmers practicesimproving agricultural water use and irrigationincreasing the production of high value crops

Background Cont…

• Ethiopia has highly-diversified agro-ecological conditions which are suitable for production of horticultural crops

• Amhara Region is one of the potential area

• Fogera Woreda is an emerging commercial agriculture in which smallholders produce and market horticultural crops for the local and national markets using the brokerage institutions

1.2. Problem Statement

• Fogera Woreda is an emerging commercial area in the production of horticultural crops

• To sustain this well structured market networks and linkages are required (organization among farmers, institutions and infrastructure)

• The horticulture market is characterized by imperfect information

• Thus, the existence of strong brokerage institutions in Fogera is a characteristic feature

Problem Statement Cont…

• The Woreda experts and decision makers tried to stop the brokers by using cooperatives

• However, the cooperatives failed in linking farmers to wholesalers due to nature of the products, lack of organized market system and imperfect market information

• Issues of market coordination and the institutional environment attract a considerable attention among the development community

Problem Statement Cont…

• Of all the institutions, several studies have documented the crucial role played by brokers

• ARARI (PRA), illegal brokerage activity in horticultural marketing is one of the priority research problem.

• However, the institutions are not studied in the area, no attempt has been made to explain the interaction between the brokers and smallholders

• The study focused to fill such knowledge gaps

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 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 institutions on farmers market participation and income generation capacity

• To identify the determinants of wholesalers decisions on whether to use brokerage institutions or not as a means of market linkage to farmers; and

• To identify the determinants of wholesalers extent of brokerage intuitions usage under imperfect market condition of horticultural marketing

3. RESEARCH METHDOLOGY

3.1. Description of the Study Area

• Fogera Woreda, South Gondar Zone, Amhara Region• Woreta town, 625 km from Addis Ababa and 55 km from

Bahir Dar • 27 rural and 3 urban PAs Map of study area.docx

3.2. Methods of Data Collection

• Both primary and secondary data were used• Primary data: semi-structured questionnaire and check-list• Trained enumerators were used for data collection • Pre-testing and rapid market appraisal(RMA)

3.3. Sampling Procedures

Farmers sampling

• 5 kebeles selected randomly

• Farmers grouped in to participant and non participant in the brokerage institutions

• 143 farmers selected randomly from both groups

Brokers, rural assemblers , wholesalers and retailers

sampling

• Monitoring, 4 months• Friends: Brokers (Baye and Huno),

Wholesalers (Mengistu, Setegn and Gizat)

• Peaceful Café and Pension (agreement and payment place)

• 55 brokers (snowball sampling)• 52 wholesalers (randomly)• 20 rural assemblers and 45 retailers

from main retail markets

3.4. Methods of Data Analysis

3.4.1. Descriptive statistics• Percentages, standard deviation, t-test and chi squared

test were used

3.4.2. Econometric models

3.4.2.1. Propensity score matching model• To achieve 2nd and 3rd objective, participants and non

participants comparison were used, steps:1. Estimation of the propensity scores

2. Identify the common support region

3. Matching using matching algorithms and Balancing test

4. Estimation of average treatment effect and Bootstrapping

5. Sensitivity analysis

3.4.2.2. Heckman two stage model

• To achieve the 4th and 5th objective, Heckman two stage model with selection bias were used

• Assumption: traders follow a sequential decision process, with a discrete choice on ‘whether or not’ to use brokers and

• A subsequent continuous decision on ‘how much’ to use brokers

• Two equations

1. Selection equation

2. Outcome equation

4. RESULTS AND DISCUSSION

4.1. The Brokerage Institutions

4.1.1. Socioeconomic profile of brokersVariables Category Percent (%)

Sex Male 100

Female 0

Religion Orthodox 100

Others 0

Marital status Single 16.4

Married 83.6

Education level Illiterate 3.6

Adults education 16.4

Literate 80

Main occupation Farmer 58.2

Youth 21.8

Trader 20

Results….Cont

• Most of the brokers are youngsters (18-63)

• Strong brokerage activity in onion marketing

• Only 4.2% of the farmers use brokerage institutions for marketing of tomato -2 brokers

• Brokers act as rural assemblers in tomato marketing

4.1.2. Characteristics and economic roles of brokerage institutions

• Most of the brokers (98.2%) work the business informally without having license

• The study characterized brokers in to two ways:

Based on place:Rural brokersPeri-urban brokers (Gumara and Abewana Kokit)Urban brokers (Woreta)

Based on occupation:Farmer BrokersYouth Brokers (grade 10 and 12 complete and school dropouts)Cereal traders (rice)Brokers chain and transaction flow.docx

The brokerage institutions main characteristics and roles in the area include:

• Are better informed• Are skilled socially to bargain and facilitate linkage • Create economies of scale• They stabilize market conditions• They reduce transaction cost• Sources of secure market for smallholders• Provide credit for the wholesalers being as collateral

for the farmer (trust and credit based transaction)

4.1.3. Brokerage institutions and their activity in the context of Fogera Woreda

• Brokers act in different ways:

1. When the wholesaler comes to Fogera Woreda: here they act in two ways

A. When the wholesaler is regular customer or residence in the Woreda (10%):

They directly contact the wholesaler with the farmer , the payment of 0.10 ETB/Kg as a commission fee

B. When the wholesaler is not regular customer (20%): No contact between farmer and wholesalers (brokers, first

discuss with farmer then negotiate wholesaler) 0.10 ETB/Kg commission fee + FERQ (0.1 ETB- 1 ETB)

2. Thrust based transaction (70%):

• This case happens when the wholesaler do not come to Fogera Woreda

• Transaction will be made only by wholesaler telephone order

• No contact between farmers and wholesalers

• In addition to 0.10 ETB/Kg commission fee, there is FERQ (0.10 ETB - 1.00 ETB) depending on the volume of transaction and customer relationships.

4.1.4. Brokers attraction mechanism of wholesalers

• Brokers attract wholesalers by two ways:

1. Weight cheating from farmers and

2. Reducing FERQ

• Weight cheating has two advantages for the broker obtaining regular wholesaler customers for the future

and having his own share from it

• Weight cheating ranges from 6% to 20%

4.1.5. The rationale behind the emergence of farmer brokers

CommercializationAttract wholesalers from

different parts of Ethiopia

Creates demand for urban and peri-urban brokers

To cover wide areas (PAs), employ farmer and youth brokers

Experience creates wholesaler customers and direct linkage to stop the

exploitive act of urban and peri-urban brokers

This linkage creates new demand for employed

youth and farmer brokers

To satisfy the new demand and cover distant areas

they employ other farmer brokers

4.1.6. Market outlets or target markets of brokerage institutions

Brokerage institutions base almost all parts of Ethiopia as market outlets (50%,15%,13%,10%,5%,3%,2%,2%)

SNNPR

Harar

Somali

Benishangul-Gumz

Oromiya

Tigray

Amhara

Addis Ababa

0 10 20 30 40 50Percent

4.1.7. Producers perception of brokerage institutions

• Most (73.4%) farmers believe that brokers play significant and important role in linking farmers to traders

• Brokers provide price and quality information

• All of the farmers believe that brokers cheat weight, provide false price information and block direct contact of farmers to traders

4.1.8. Night transaction and loading

• Most (more than 92%) of the transaction is undertaken during the night time

• There are two ideas:

Brokers believe that night loading helps to reduce the perishablity of the horticulture during transportation to distant area

Producers believe that night loading is the system developed to easily cheat weight and block direct contact between producers and wholesalers

4.1.8. Constraints of brokerage institutions

• The brokerage institutions are constrained by :Working capitalContract failure Strong competition between brokers There are no any financial institutions There is no formal contractual agreement ,andTerritory conflict

4.1.9. Opportunities to the brokers

• There are opportunities for the business in the area:

– Increasing production of horticultural crop– Increasing demand from different parts of the country – Information and linkage gaps between farmers and

wholesalers– Brokers are residents in the area

4.2. Brokerage Institutions and Smallholder Market Linkages

• The result is based on 143 (76-participant and 67 non participant) sample farm households

4.2.1. Descriptive Statistics• Socioeconomic, demographic and social capital aspects

Variables Category Participant (76)

Non participant (67)

χ2

Percent (%) Percent (%)

Sex Female 13.16 4.48 2.88*

male 86.84 95.52

Cell phone No 81.58 64.18 31.56***

Yes 18.42 35.52

Variables Participant (76) Non participant (67)

T-value

Mean Mean

Age 42.54 37.01 -2.86***

Education level 1.52 3.42 3.42***

Family size 3.36 3.26 -0.47

TLU 5.97 5.40 -1.11

Total land (ha) 1.43 1.69 1.43

Irrigable land (ha) 0.77 1.16 2.2**

Exper. in Hort. Pro. 9.18 8.79 -0.61

Distance from DAs 4.41 2.69 -2.92***

Distance from Woreta 14.64 10.49 -3.4***

Distance from asphalt 3.76 1.37 -6.16***

No. regular customers 0.85 2.12 3.29***

No. of trading contact 7.95 8.17 0.18

4.2.2. Propensity score matching model

4.2.2.1. Estimation of propensity scores

• Binary variable which indicates whether the household is participated in the brokerage institutions or not (dependent variable)

• Explanatory variables (socioeconomic, social capital..)

• Variance inflation factor (VIF)

• Breusch-Pagan / Cook-Weisberg test for heteroscedasticity

• Logistic regression result

Variables Coefficients Z- value

Age 0.056** 2.03

Sex -0.157 -0.16

Marital status -0.308 -0.22

Education level -0.163* -1.90

Family size -0.052 -0.19

Livestock 0.109 1.11

Total land size 0.183 0.31

Irrigable land size -0.022 -0.04

Exp. Hort. production -0.021 -0.33

Distance from DAs 0.156* 1.81

Cell phone -1.710*** -3.09

Distance from Woreta 0.006 0.16

Distance from asphalt 0.631*** 3.67

No. of regular customer -0.331** -2.02

No. trading contacts -0.027 -0.65

constant -2.479 -1.01

4.2.2.2. Common support condition

• Only observations in the common support region are considered

• 0.06 - 0.9 (p-score for participants)

• 0.003- 0.89 (p-score for non participants)

• P-score (0.060 - 0.89) are in the common support region

• Kernel density of propensity scores before and after matching Kernel density estimate.docx

4.2.2.3. Matching of participant and non-participant households

Selection of matching algorithms:

• Best matching algorithm

Balances all the observable covariates Ends with low pseudo-R2 and Gives large number of observations in the common

support

Matching estimator Performance criteria

Balancing test Pseudo-R2 Matched sample

1. Radius caliper matching 0.01 15 1.0 32

0.05 15 0.24 51

0.1 15 0.098 57

0.25 15 0.062 65

0.5 15 0.086 74

2. Kernel matching no 15 0.065 100

0.08 15 0.052 100

0.1 15 0.046 100

0.25 15 0.045 100

0.3 15 0.046 100

0.5 15 0.06 100

3. Nearest neighbor matching

1 14 0.207 100

2 14 0.082 100

3 15 0.080 100

4 15 0.063 100

5 15 0.05 100

4.3. Impacts of the Brokerage Institutions

4.3.1. Average treatment effect (Impact)

4.3.4. Sensitivity Analysis

• using Rosenbaum bounding approach• Shows the effects of unobserved factors• Resistant up to 200%- pure effect of brokerage institutions

Outcomes ATT T

Net income (Profit) 4393.62 2.53**

% marketed surplus 13.55 2.86**

Amount of production -5.08 -0.25

Land allocation -0.05 -0.24

4.4. Brokerage Institutions and Wholesaler Market Linkages

4.4.1.Descriptive Statistics of wholesalers

Variables Participant Non participant T- value

Mean Mean

Distance from the Woreda 165.48 25 -1.96*

Age 31.5 28.17 0.26

Experience in horticulture trading 5.3 5.17 -0.10

Education level 8.69 11.33 1.69*

No. of persons working the business 1.76 1.33 -1.05

Capacity of storage facility 35.17 12.5 -1.77*

Current working capital 24086 22666 -0.21

No. regular farmer customer 2.91 17.83 5.72***

No. regular retailer customer 4.15 0 -2.77***

No. regular wholesaler customer 2.84 0.83 -2.53**

Number of trading contacts to Woreda 8.24 120 4.7**

Total marketing cost 547231 516225 -0.21

• Descriptive statistics (wholesalers)Variables Category Participant

(%)Non participant (%)

χ2

Type of road Gravel 6.5 0 0.41

Asphalt 93.5 100

Sex Female 2.2 0 0.13

Male 97.8 100

Marital status Single 30.43 50 0.92

Married 69.57 50

Religion Muslim 8.7 16.67 0.38

Orthodox 91.3 83.33

Storage facility No 17.39 66.67 7.26***

Yes 82.61 33.33

Credit access No 86.96 83.33 0.06

Yes 13.04 16.67

Regular buyer customer

No 17.39 83.33 12.31***

Yes 82.61 16.67

4.4.2.Determinants of wholesalers decisions

• 1st step of the Heckman two stage (probit estimation)Variables Coefficients Z-vale

Distance from the Woreda 0.011* 1.8

Type of road access -1.801 -0.25

Age 0.635 0.31

Experience in hort. trading -0.477 -1.01

Marital status -1.01 -0.32

Education level -0.117 -0.38

No. persons working the business -0.934 -0.44

Ownership of storage facility 5.908** 2.03

Current working capital 0.0001 0.23

Access to credit -4.981 -1.4

No. regular farmer customer -0.594** -1.96

Regular buyer customer in other area 1.871** 2.04

No. of trading contacts to the Woreda -0.055* -1.87

Total marketing cost 0.0001** 2.06

Constant -7.025 -0.51

4.4.3.Determinants of share of brokered transactions (2nd step of the Heckman two stage (OLS) result)

Variables Coefficients Z-value

Logarithm of distance from the Woreda 3.883 0.84

Type of road access 1.234 0.1

Age -0.571 -1.47

Experience in hort. trading -0.26 -0.08

Marital status 9.915 1.25

Education level 0.369 0.79

No. persons working the business -1.26 -0.37

Logarithm of current working capital 1.803 0.16

Access to credit 3.902 0.52

No. regular farmer customer -1.86* -1.85

No. regular retailer customer 0.908 1.47

No. regular wholesaler customer 3.573* 1.92

Sq. root of experience in using brokers -1.753 -0.12

Sq. root of capacity of storage facility -0.406 -0.38

Logarithm of total marketing cost 0.016 1.05

Constant 70.364 1.35

5. CONCLUSION AND RECOMMANDATIONS

Brokerage institutions:

• Are secure source of market for smallholder producers

• Play important role in trust and credit based transaction by creating market linkage and increasing profit of producers

• Create employment for youth groups

• However, they have problems by providing false market information

• Thus, formalization of the brokers by forming groups and providing licenses as well as training

• Standardization of weighing balance

• Training of farmers about weighing, marketing, and

• Providing market information for farmers using development agents is very crucial to solve the problems in the area.

THANK YOU!!!