Presented by Simegnew Tamir during his MSc thesis defense, Haramaya University, October 2012

1. 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

2. PRESENTATION OUTLINE
INTRODUCTION
RESEARCH METHDOLOGY
RESULTS AND DISCUSSION
CONCLUSION AND RECOMMANDATION

3. 1.INTRODUCTION
1.1. Background
• Primary development goal of the Government is
to achieve food security and
sustain high economic and export growth levels
• Growth and Transformation Plan (GTP) investment areas:
scaling up model farmers practices
improving agricultural water use and irrigation
increasing the production of high value crops

4. 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

5. 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

6. 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

7. 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

8. 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

9. • 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

10. 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)

11. 3.3. Sampling Procedures
Brokers, rural assemblers
, wholesalers and retailers
Farmers sampling sampling
• 5 kebeles selected • Monitoring, 4 months
randomly • Friends: Brokers (Baye and
• Farmers grouped in to Huno), Wholesalers
(Mengistu, Setegn and Gizat)
participant and non
participant in the • Peaceful Café and Pension
(agreement and payment place)
brokerage institutions
• 55 brokers (snowball sampling)
• 143 farmers selected
• 52 wholesalers (randomly)
randomly from both
• 20 rural assemblers and 45 retailers
groups
from main retail markets

12. 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

13. 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

14. 4. RESULTS AND DISCUSSION
4.1. The Brokerage Institutions
4.1.1. Socioeconomic profile of brokers
Variables 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

15. 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

16. 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 brokers
Peri-urban brokers (Gumara and Abewana Kokit)
Urban brokers (Woreta)
 Based on occupation:
Farmer Brokers
Youth Brokers (grade 10 and 12 complete and school
dropouts)
Cereal traders (rice)Brokers chain and transaction flow.docx

17. 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)

18. 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)

19. 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.

20. 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%

21. 4.1.5. The rationale behind the emergence of
farmer brokers
Attract wholesalers from
Commercialization different parts of
Ethiopia
To cover wide areas
(PAs), employ farmer Creates demand for urban
and youth brokers and peri-urban brokers
Experience creates This linkage creates new
wholesaler customers and demand for employed
direct linkage to stop the youth and farmer brokers
exploitive act of urban and
peri-urban brokers
To satisfy the new demand
and cover distant areas
they employ other farmer
brokers

22. 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%)
Addis Ababa
Amhara
Tigray
Oromiya
Benishangul-Gumz
Somali
Harar
SNNPR
0 10 20 30 40 50
Percent

23. 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

24. 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

25. 4.1.8. Constraints of brokerage institutions
• The brokerage institutions are constrained by :
Working capital
Contract failure
Strong competition between brokers
There are no any financial institutions
There is no formal contractual agreement ,and
Territory conflict

26. 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

27. 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 Non participant χ2
(76) (67)
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

28. Variables Participant (76) Non participant T-value
(67)
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

29. 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

30. 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

31. 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

32. 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

33. 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 1 14 0.207 100
matching 2 14 0.082 100
3 15 0.080 100
4 15 0.063 100
5 15 0.05 100

34. 4.3. Impacts of the Brokerage Institutions
4.3.1. Average treatment effect (Impact)
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
4.3.4. Sensitivity Analysis
• using Rosenbaum bounding approach
• Shows the effects of unobserved factors
• Resistant up to 200%- pure effect of brokerage institutions

35. 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

36. • 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 No 17.39 83.33 12.31***
customer Yes 82.61 16.67

37. 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

38. 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

39. 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

40. • 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.