THE IMPACTS OF BROKERAGE INSTITUTIONS IN THE MARKETING OF
HORTICULTURAL CROPS IN FOGERA DISTRICT
Simegnew Tamir, Kinde Get...
PRESENTATION OUTLINE
INTRODUCTION
RESEARCH METHDOLOGY
RESULTS AND DISCUSSION
CONCLUSION AND RECOMMANDATION
1.INTRODUCTION
1.1. Background
• Ethiopia has highly-diversified agro-ecological conditions
suitable for production of hor...
Background Cont…
• The horticulture market is characterized by imperfect
information
• No cooperatives to coordinate peris...
1.2. Objectives of the Study
The general objective of the study was:
• To assess the economic roles played by the brokerag...
2. RESEARCH METHDOLOGY
2.1. Description of the Study Area
• Fogera District, Amhara Region, South Gondar Zone
• 625 km fro...
2.3. Sampling and Data Analysis
Sampling
• 5 kebeles selected randomly
• 143 farmers selected randomly
from participant an...
3. RESULTS AND DISCUSSION
3.1. The Brokerage Institutions
Socioeconomic profile of brokers
Variables Category Percent (%)
...
• Most of the brokers are youngsters (18-63)
• Strong brokerage activity in onion marketing
• Only 4.2% of the farmers use...
3.2. Characteristics and economic roles of
brokerage institutions
• Most of the brokers (98.2%) work the business
informal...
The brokerage institutions main characteristics
and roles in the area include:
• Are better informed
• Are skilled sociall...
Brokerage institutions activity in Fogera District
Brokers act in different ways:
1. When the wholesaler comes to Fogera D...
2. Trust based transaction (70%):
• This case happens when the wholesaler did not
come to Fogera District
• Transaction wi...
Brokers attraction mechanism of wholesalers
• Brokers attract wholesalers by two ways
 Weight cheating from farmers and
...
3.3. Market outlets or target markets of
brokerage institutions
 Brokerage institutions base almost all parts of Ethiopia...
3.4. Brokerage Institutions and Smallholder
Market Linkages
• The result is based on 143 (76-participant and 67 non
partic...
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*...
3.5.Estimation of propensity scores (Logistic
Regression)
– Participation : dependant Variable
Variables Coefficients Z- v...
Common support and matching
• 0.06 - 0.9 (p-score for participants)
• 0.003- 0.89 (p-score for non participants)
• P-score...
3.6. Impacts of the Brokerage Institutions
Average treatment effect (Impact)
Sensitivity Analysis
• using Rosenbaum boundi...
4. CONCLUSION AND RECOMMANDATIONS
Brokerage institutions
• Are source of secure market for smallholder
producers
• Play i...
• However, they have problems by providing false
market information and weight cheating
• Thus, this study recommends
for...
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The impacts of brokerage institutions in the marketing of horticultural crops in Fogera District

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Presented Simegnew Tamir, Kinde Getnet and Jema Haji at the Nile Basin Development Challenge (NBDC) Science Workshop–2013, Addis Ababa, Ethiopia, 9 – 10 July 2013

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The impacts of brokerage institutions in the marketing of horticultural crops in Fogera District

  1. 1. THE IMPACTS OF BROKERAGE INSTITUTIONS IN THE MARKETING OF HORTICULTURAL CROPS IN FOGERA DISTRICT Simegnew Tamir, Kinde Getnet and Jema Haji Nile Basin Development Challenge (NBDC) Science Workshop Addis Ababa, Ethiopia, 9–10 July 2013
  2. 2. PRESENTATION OUTLINE INTRODUCTION RESEARCH METHDOLOGY RESULTS AND DISCUSSION CONCLUSION AND RECOMMANDATION
  3. 3. 1.INTRODUCTION 1.1. Background • Ethiopia has highly-diversified agro-ecological conditions suitable for production of horticultural crops • Amhara Region is one of the potential area • Fogera District is an emerging commercial agriculture • To sustain this well structured market networks and linkages are required (organization among farmers, institutions and infrastructure)
  4. 4. Background Cont… • The horticulture market is characterized by imperfect information • No cooperatives to coordinate perishable products • Creates fertile ground for the existence of brokerage institutions in Fogera District • ARARI (PRA), brokerage activity in the area is one of the priority research problem. • However, the institutions are not studied in the area in terms of their impact and economic role • The study focused to fill such knowledge gaps
  5. 5. 1.2. Objectives of the Study The general objective of the study was: • To assess the economic roles played by the brokerage institution and identify determinants of decisions on whether to use brokers or not in the study area The specific objectives of the study were: • 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
  6. 6. 2. RESEARCH METHDOLOGY 2.1. Description of the Study Area • Fogera District, Amhara Region, South Gondar Zone • 625 km from Addis Ababa and 55 km from Bahir Dar • 27 rural and 3 urban PAs 2.2. 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)
  7. 7. 2.3. Sampling and Data Analysis Sampling • 5 kebeles selected randomly • 143 farmers selected randomly from participant and non participant • Monitoring, 4 months • Friends with brokers • Peaceful Café and Pension (agreement and payment) • 55 brokers (snowball sampling) • 52 wholesalers • 20 rural assemblers and 45 retailers in the main market Data Analysis • Descriptive statistics (Percentages, standard deviation, t-test and chi squared test) • Econometric models (Propensity score matching model )
  8. 8. 3. RESULTS AND DISCUSSION 3.1. The Brokerage Institutions 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
  9. 9. • 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
  10. 10. 3.2. 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-58.2% Youth Brokers (grade 10 and 12 complete and school dropouts)-21.8% Cereal traders (rice)-20%
  11. 11. 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)
  12. 12. Brokerage institutions activity in Fogera District Brokers act in different ways: 1. When the wholesaler comes to Fogera District: A.When the wholesaler is regular customer or residence in the District • Contact the wholesaler with the farmer , 0.10 ETB/Kg as a commission fee (10%) B. When the wholesaler is not regular customer (20%): • No contact between farmer and wholesalers. • 0.10 ETB/Kg commission fee paid to the broker, there is a price gap of 0.10 ETB to 1.00 ETB between farm gate price and wholesale purchase price, (FERQ)
  13. 13. 2. Trust based transaction (70%): • This case happens when the wholesaler did not come to Fogera District • Transaction will be made only by wholesaler telephone order • No contact between farmers and wholesalers • in addition to 0.10 ETB commission fee, there is FERQ (0.10 ETB - 1.00 ETB) depending on the volume of transaction and customer relationships.
  14. 14. Brokers attraction mechanism of wholesalers • Brokers attract wholesalers by two ways  Weight cheating from farmers and  Reducing (FERQ) the price gap between farm gate price and wholesaler purchase price • 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%
  15. 15. 3.3. 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%)
  16. 16. 3.4. Brokerage Institutions and Smallholder Market Linkages • The result is based on 143 (76-participant and 67 non participant) sample farm households 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
  17. 17. 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
  18. 18. 3.5.Estimation of propensity scores (Logistic Regression) – Participation : dependant Variable 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
  19. 19. Common support and matching • 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 • Best matching algorithm  Balances all the observable covariates  Ends with low pseudo-R2 and Gives large number of observations in the common support • Kernel matching algorism with a band width of 0.25
  20. 20. 3.6. Impacts of the Brokerage Institutions Average treatment effect (Impact) 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
  21. 21. 4. CONCLUSION AND RECOMMANDATIONS Brokerage institutions • Are source of secure market for smallholder producers • Play important role in trust and credit based transaction by creating market linkage and increasing profit of producers • Create employment role for youth groups
  22. 22. • However, they have problems by providing false market information and weight cheating • Thus, this study recommends formalization of the brokers by forming groups, providing licenses and training standardization of weighing balance training of farmers and providing market information for farmers
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