1

Motivation

• Broader Context:The limits of the market liberalisation agenda The relevance of the New Institutional Economics

• Specific Context:Brokers - Market institutions in Ethiopia

How does the brokerage institution contribute to the development of agricultural markets in Ethiopia?

2

Structure of the Presentation• Introduction

• Two Main Topics

• Trader Descriptive Statistics: A Comparative Perspective (1996-2002-

2007/8)

• Hypotheses & Premises for the Econometric Approach

• Econometric Model & Estimation Method

• Data & Methodology

• Regression Results

• Conclusions

• Policy Implications & Recommendations for Future Research

3

Introduction

March 1990: Grain market liberalization in order to get prices right

Agricultural markets remained Thin, Personalised, Seasonal, Cash-based and offering Limited Arbitrage Opportunities

Two main constraints facing traders: High transaction costs and Imperfect market information

Post-structural market reforms focused on getting markets right and getting institutions right

Brokers: informal market institution and informal commodity exchange

4

5

Two Main Topics

• Analysis of the main characteristics of Ethiopian agricultural traders Comparison of descriptive statistics from three different surveys

• Investigation of traders’ use of brokers in agricultural markets Decisions: Whether or not to use brokers

For how much (Share of brokered transactions)

6

Surveys consideredSurveys consideredTraders' Surveys

Survey

1996 E. Gabre-Madhin dissertation survey("Market Institutions, Transaction Costs, and Social Capital in the Ethiopian Grain Market")

Number of respondents 169

Number of markets13

Survey

2002 IFPRI and ILRI “Survey of grain and coffee traders”

Number of respondents 561

Number of markets45

Survey EDRI and IFPRI “ECX Trader survey 2007” Number of respondents 457Number of markets 21

Characteristics of Traders

•Grain trading is a male dominated business•Orthodox Christians and Muslims tend to engage more in the business

Years in Grain trade, Educational level and Age

• Similar years in grain trade and level of education over the three periods

• Average age is increasing

Traders’ parents occupation

• Grain trading business may not be an inherited business

• From time to time, people whose parents are farmers are engaging in the business

Assets

• Number of traders who own vehicles is increasing• During 2002 and 2007 almost all of the grain traders had

storage facilities

Assets continued…

• Huge increase in Landline telephone/radio ownership

• Drastic change in mobile phone ownership• During 2002 almost all the traders had warehouses but only 29% had

electricity at their warehouse.

• Generally, gross margins and net margins have declined noticeably between 1996 and 2007

• The decline is even more amplified by real prices

1996 2002 2007 1996 2002Sales price (ETB/ quintal) 75 170 313 238 143Purchase price (ETB/quintal) 51 160 300 249 140

Total transaction cost (Birr/ quintal) 19 9 10 -9.0 1.20Gross margin 24 10 13 -11.5 2.80Net margin (Birr/quintal) 5 3 3 -2.2 -0.20

1996 2002 2007 1996 2002Sales price (Birr / quintal) 128 299 285 122.7 -4.8Purchase Price 87 282 274 214.1 -3.1Total transaction cost (Birr /quintal) 32 16 9 -71.8 -41.3

Gross margin (Birr/quintal) 41 17 11 -72.2 -33.4Net margin (Birr/quintal) 9 5 3 -70.1 -51.7

Costs and Margins (Nominal) Absolute change since:

Costs and Margins (Real) Percentage change since:

Transaction cost componentsTable: Composition of Transaction Costs

1996 2002

Handling 18 10 9 -9.3 -0.89Sacking 8 22 18 9.8 -3.99Transport 31 63 54 23.2 -8.63Storage 0 0.2 1.3 1.3 1.10Road stops 5 0.3 0.8 -4.2 0.48Brokers 8 3 7 -1.5 3.10Travel 1 0.4 0.5 -0.5 0.12Others 29 1 10 -18.9 8.58Total 100 100 100 0.0 0.00

Transaction Cost Components (percentage)

Absolute change since

1996 2002 2007

• Transport costs constitutes the majority of transaction costs• Handling cost and brokers cost are also significant• Other costs , such as payment to daily laborers, tips during sales etc , are large

during 1996 and 2007

• Transaction costs in real terms were lower during 2007 as compared to the other two periods.

• Transport cost in real terms has declined by more than 50% between 1996 and 2007

Marketing margins and profit

1996 2002 2007 1996 2002 2007 1996 2002 2007 1996 2002 2007 1996 2002 2007Tigray 6 2 15 70 66 75 82 94 94 14 7 4 3 1 3Amhara 5 1 3 89 41 98 75 97 96 20 5 3 4 0 1Oromia 1 8 0 104 67 111 75 89 96 25 7 5 0 4 -1Addis Ababa 1 2 -1 83 49 98 94 96 98 5 3 2 1 1 0Dire dawa 12 -2 3 35 82 86 92 97 96 2 5 4 5 -2 1Harari 11 11 38 47 87 94 7 4 5 2Affar 1 96 96 3 1SNNP -1 104 103 3 -6Benishangul -4 130 91 13 -4Total 5 3 3 82 60 94 81 94 96 16 6 4 3 1 -1

Net profit as a %age of sale price

RegionProfit

Marketing cost as %age of marketing

marginPurchase price as %age of sale price

Marketing costs as a %age of sale price

• Profits vary significantly between regions• Marketing costs as %age of margins, and as

%age of sales price also has considerable variation between regions

Table: Trading Disputes with Suppliers and Customers

Causes of Disputes Yes No Yes No Causes of Disputes Yes No Yes NoBad quality of purchased product 55 45 55 45

Payment after the agreed upon date 71 29 32 68

Disagreement over measuring system 29 71 19 81 Partial payment 63 37 26 74Stolen property 3 97 3 97 No payment 60 40 27 73Delivery after agreed upon date 60 40 32 68

Attempted to renegotiate agreed upon price 37 63 13 87

Partial delivery 43 57 23 77bad quality of purchased product - - 25 75

No delivery 45 55 18 82Attempt to renegotiate agreed upon price 44 56 21 79

Trading Disputes with Suppliers Trading Disputes with Customers2002 2007 2002 2007

Table: Dispute Resolution Mechanisms

2002 2007 2002 2007Kebele /courts 1.0 1.2 1.8 0.0Woreda and above courts 0.2 0.0 0.9 1.2Association arbitration - 0.0 0.0 0.4 0.0Community mediation (Shimagile) 2.1 11.3 5.1 7.8Informal mediation by friends and peers 3.7 14.6 3.7 11.9Brokers 1.4 0.6 0.4 0.8Clan leaders 0.3 0.0 0.4 0.0Personal resolution (without other intervention) 45.5 56.0 56.0 59.7No resolution 45.7 16.4 30.9 18.5Other mechanisms 0.2 - 0.5 -

Resolution MechanismsWith Suppliers With Customers

20

Hypotheses & PremisesTraders in the Ethiopian agricultural markets behave in a rational way,

according to a transaction costs minimization rule

To this aim, tradersa)Implement trading practicesb)Use assets (physical, financial, human and social capital)c)Try to access financial institutions & in/formal credit

The use of brokers (trading practice) &/or of social capital (asset)allows the contextual minimisation of all transaction costs

The use of brokers depends on a), b) & c)

Discrete choice on ‘whether’ to use brokers Continuous decision on ‘how much’ to use brokers for Do not depend on the same set of variables May have marginal effects with different signs

𝑃𝑟(B > 0) 𝐸(B|B> 0)

21

Econometric Model & Estimation Method

and are modelled through the Heckman sample selection approach

First Stage: Selection Equation (Probit model):

Second Stage: Outcome Equation (Ordinary Least Sqares):

,

𝐸(B|B> 0) 𝑃𝑟(B > 0)

22

P𝑖 = 1 ሼZ𝑖′𝛼+ u𝑖 > 0ሽ P𝑖 = 0 ሼZ𝑖′𝛼+ u𝑖 ≤ 0ሽ

Bi∗ = Xi′β+ εi ൬

ε𝑖u𝑖൰|X𝑖,Z𝑖 ~ 𝑁൭൬00൰,൬𝜎𝜀2 𝜎𝜀𝑢𝜎𝜀𝑢 𝜎𝑢2൰൱

B𝑖 = B𝑖∗, 𝑖𝑓 P𝑖 = 1 B𝑖 not observed, 𝑖𝑓 P𝑖 = 0

Notes: - Estimation both by FIML (with robust Huber (1967)/White (1980)/Sandwich standard errors) & by LIML (Heckit model); Very similar results

- FIML estimation converges to a solution if X is a strict subset of Z

𝜎𝑢2 ≡ 1

E1ሺBሻ− E0ሺBሻ =

=𝑃𝑟1ሺB> 0ሻሾ𝐸1ሺB|B> 0ሻ− 𝐸0ሺB|B> 0ሻሿ+ 𝐸0ሺB|B> 0ሻሾ𝑃𝑟1ሺB> 0ሻ− 𝑃𝑟0ሺB> 0ሻሿ 23

Marginal Effects for the Heckman selection model

𝜕EሺB𝑖|P𝑖 = 1, X𝑖,Z𝑖ሻ𝜕𝑥𝑘𝑖 = 𝛽𝑘 − 𝛼𝜅𝜌𝜀𝑢𝜎𝜀𝜆𝑖ሺZ𝑖′𝛼ሻሾ𝜆𝑖ሺZ𝑖′𝛼ሻ+ Z𝑖′𝛼ሿ, 𝑖 = 1,…,N1

𝜕EሺB𝑖ሻ𝜕𝑥𝑘𝑖 = 𝛽𝑘ΦሺZ𝑖′𝛼ሻ+ 𝛼𝜅𝜙ሺZ𝑖′𝛼ሻሾX𝑖′𝛽+ 𝜌𝜀𝑢𝜎𝜀ሺZ𝑖′𝛼ሻሿ=

𝜕EሺB𝑖|P𝑖=1ሻ𝜕𝑥𝑘𝑖 ∗ Φ൫Z𝑖′𝛼൯ + 𝜕Φ൫Z𝑖′𝛼൯𝜕𝑥𝑘𝑖 ∗EሺB𝑖|P𝑖 = 1ሻ

Marginal effect on the probability to use brokers,

weighted by 𝐸ሺB𝑖|T𝑖 = 1ሻ, the

Expected shares of brokered transactions by traders using brokers

TOTAL EFFECTS DUE TO WHOLESALES THAT ARE NEW USERS OF THE BROKERAGE SERVICES

Conditional marginal effect, weighted by Φ൫Z𝑖′𝛼൯, the Probability for

traders to use brokers in their main markets

TOTAL EFFECTS DUE TO TRADERS CURRENTLY USING BROKERS

Conditional Marginal Effects (for traders already using brokers)

Unconditional Marginal Effects (for the overall sample of traders)Continuous variables: McDonald and Moffitt decomposition(1980)

Discrete variables: Cong (2000)

24

Data & Methodology“ECX Trader Survey 2007” (Gabre-Madhin, EDRI and IFPRI): 457 wholesalers in 21 markets recalled their activity from October/November 2006 to April/May 2007

25

Data & Methodology (cont.)

Buyers and sellers are distinguished according to:•Main transacted product:

Food Crops: Cereals, PulsesCash Crops: Coffee, Oilseeds

•Location – Base markets classification: following Chamberlin et al. (2006) classification of smallholder-relevant agricultural domains

Agricultural potential: Moisture-reliable, Drought-prone, Pastoralist Areas, A.A.Market access: Time to towns of 5,000 or more peoplePopulation density

Chamberlin et al. (2006): Food crop commercialization by smallholders Is generally higher in moisture-reliable domains than in the drought-prone domains

Considering specific agricultural domains, it is higherfor Moisture-reliable domains in Areas of lower market accessfor Drought-prone domains in Areas with favourable market access

Increases with population density

26

27

Heckman model for Buyers: Regression ResultsVariables

Outcome equation Selection equation

Conditional Marginal Effects

Marginal Effectfor

Coeff. Coeff.ASSETSSocial CapitalNo. of Trading Contacts in the Main Market -0.109

(0.066)-0.039*

(0.024)No. of Regular Suppliers -0.087***

(0.025)0.101(0.062)

-0.096***

(0.024)0.036(0.022)

Human CapitalNo. of Employees Engaged in Search 0.684***

(0.161)0.246***

(0.057)No. of Trader’s Substitutes 0.400**

(0.171)0.144**

(0.062)Financial Assets & Access to CreditWorking Capital 0.043**

(0.017)-0.060(0.065)

0.048***

(0.018)-0.021(0.023)

Credit Access -0.106**

(0.053)-0.270*

(0.151)-0.082(0.050)

-0.096*

(0.053)CONTRACTUAL PERFORMANCE: CostsAnnualised Physical Marketing Costs -0.018

(0.043)-0.006(0.016)

Fixed/Operational Costs 0.205***

(0.062)0.073***

(0.022)TRADING PRACTICESDistance from the Base to the Main Market 0.102**

(0.043)0.117(0.149)

0.092**

(0.040)0.042(0.054)

ACCESS TO PHYSICAL INFRASTRUCTUREStorage Capacity -0.018

(0.013)-0.135***

(0.049)-0.007(0.013)

-0.049***

(0.018)Asphalted Roads -0.687***

(0.254)-0.984(0.886)

-0.595**

(0.243)-0.275(0.170)

Dry-Weather Roads -0.606***

(0.207)-0.044(0.883)

-0.602***

(0.195)-0.016(0.311)

All-Weather Roads -0.654***

(0.240)0.144(0.866)

-0.667***

(0.220)0.053(0.326)

𝑷𝒓(𝐁> 0)

28

Heckman model for Buyers: Regression Results (cont.)

Buyers of cereals based in drought-prone areas:

As distance increases from 7 to 230 kilometres, they are 23.42 percentage points more likely to use brokers than the rest of traders

After the travelled distance reaches and overcomes 160km, all buyers with these characteristics are likely to ask brokers to manage some or all of their long-distant transactions

0

.2

.4

.6

.8

1

Prob

abili

ty o

f Usi

ng B

roke

rs a

nd 9

5%CI

0 2 4 6 8

Natural Logarithm of Distance

Subset of Buyers of Cereals based in Drought-Prone Areas Predicted Probability of Using Brokers as a function of Distance and 95%CI

29

Heckman model for Buyers: Regression Results (cont.)

As distance between base and main markets increases, buyers’ location matters less in determining the probability of brokerage use

0

.2

.4

.6

.8

1 Pr

obab

ility

diff

eren

ce a

nd 9

5%C

I

2 3 4 5 6 7

Variation of the Probability Difference after Changes in Distance and 95%CI

Natural Logarithm of Distance

Predicted Probability that Buyers Use Brokers; Gap between Drought-Prone and Other Areas

Heckman model for Buyers: Regression Results (cont.)

As distance increases, the predicted probability of using brokers is more than double when roads are all-weather or dry-weather roads than when they are asphalted roads.

The worst the condition of the road linking the base to the main markets of each buyer, the more it is likely that buyers turn to brokers.

30

0

.5

1

Pred

icte

d Pr

ob. o

f Usi

ng B

roke

rs a

nd 9

5%C

I

95%

CI

2 3 4 5 6 7 Natural Logarithm of Distance

Asphalted Roads All-Weather Roads

Asphalted versus All-Weather Roads and 95%CI

Predicted Probability of Using Brokers as a function of Distance

31

Heckman model for Buyers: Regression Results (cont.)

𝑷𝒓(𝐁> 0) Variables

Outcome equation Selection equation

Conditional Marginal Effects

Marginal Effect for

Coeff. Coeff.ACCESS TO FINANCIAL INSTITUTIONSBank in the Main MarketMAIN CROP TRADEDCereals

Coffee

Pulses

LOCATION: Agricultural Development Domains Moisture-Reliable Areas

Drought-Prone Areas

Pastoral Areas

Base Market – Low-Market Access & High-Population Density

Base Market – High-Market Access & High-Population Density

Constant

0.264***

(0.095)0.136(0.128)0.196*

(0.119) -0.150*

(0.077)0.074(0.080)-0.206(0.193)-0.177**

(0.078) -0.027(0.063)0.272(0.213)

1.208***

(0.400)-0.312(0.246)-0.673**

(0.297)-0.292(0.335)

0.832***

(0.260)1.665***

(0.274)-0.033(0.436)-0.105(0.219)-0.130(0.197)-2.759***

(0.786)

0.291***

(0.093)0.198(0.124)0.222*

(0.114)

-0.222***

(0.076)-0.055(0.067)-0.203(0.188)-0.168**

(0.077)-0.016(0.061)

0.310***

(0.061)-0.114(0.091)-0.207***

(0.074)-0.098(0.104)

0.291***

(0.086)0.593***

(0.079)-0.012(0.155)-0.037(0.076)-0.046(0.070)

Number of observationsOf which uncensoredLog pseudolikelihoodWald test of indep. eqns. (rho = 0)/χ^2 (1)p-value for the Wald test/athrho

/lnsigma

rhosigmaLambda

449162-241.6735.490.0190.450**

(0.192)-1.261***

(0.068) 0.4220.2830.120

Ycond=0.703 Psel=0.323

32

Heckman model for Sellers: Regression Results

𝑷𝒓(𝐁> 0)

Variables Outcome equation Selection equation

Conditional Marginal Effects

Marginal Effectfor

Coeff. Coeff.

ASSETSSocial Capital

No. of Regular CustomersHuman Capital

-0.089***

(0.027)0.049(0.059)

-0.079***

(0.026)0.010(0.011)

No. of Employees Engaged in Search -0.560***

(0.167)-0.109***

(0.033)

No. of Years of Operation -0.292(0.224)

1.156**

(0.483)-0.056(0.198)

0.225**

(0.091)

Square of the No. of Years of Operation 0.052 -0.237**

(0.049) (0.108)0.004(0.044)

-0.046**

(0.021)

Financial AssetsWorking Capital 0.173**

(0.080)0.034**

(0.016)

CONTRACTUAL PERFORMANCE: Costs & Trading Disputes

Annualised Physical Marketing Costs 0.098*

(0.056)0.019*

(0.011)

Fixed/Operational Costs -0.102(0.079)

-0.020(0.015)

Trading Disputes with Customers: No Payment 0.452**

(0.189)0.101**

(0.047)

TRADING PRACTICESDistance from the Base to the Main Market 0.043

(0.043)-0.031(0.062)

0.037(0.042)

-0.006(0.012)

33

Heckman model for Sellers: Regression Results (cont.)

𝑷𝒓(𝐁> 0)

Variables Outcome equation Selection equation

Conditional Marginal Effects

Marginal Effectfor

Coeff. Coeff. ACCESS TO PHYSICAL INFRASTRUCTURE

Storage Capacity 0.042**

(0.019)-0.070(0.052)

0.027(0.017)

-0.014(0.010)

Asphalted Roads -0.066

(0.238)0.128(0.371)

-0.040(0.229)

0.026(0.080)

Dry- & All-Weather Roads

MAIN CROP TRADEDCereals

Coffee

Pulses

LOCATION: Agricultural Development DomainsMoisture-Reliable Areas

Drought-Prone Areas

Pastoral Areas

Base Market – Low-Market Access & High-Population Density

Base Market – High-Market Access & High-Population Density

Constant

0.055(0.314)

0.222*

(0.133)0.336**

(0.169)0.206(0.196)

-0.454(0.296)-0.553*

(0.297)-0.171(0.359)-0.172(0.113)0.055(0.107)1.370***

(0.465)

-0.081(0.570)

-0.570*

(0.305)-0.678*

(0.383)-0.582(0.437)

0.960**

(0.472)1.234***

(0.472)0.722(0.643)-0.013(0.245)0.031(0.235)-3.981***

(1.071)

0.038(0.295)

0.108(0.109)0.194(0.153)0.084(0.173)

-0.258(0.262)-0.312(0.257)-0.031(0.320)-0.175*

(0.104)0.062(0.100)

-0.015(0.101)

-0.128*

(0.078)-0.094**

(0.037)-0.084*

(0.044)

0.183**

(0.083)0.321**

(0.138)0.196(0.216)-0.002(0.047)0.006(0.046)

Number of observations Of which uncensored Log pseudolikelihood LR test of indep. eqns. (rho = 0)/χ^2 (1) p-value for the Wald test /athrho /lnsigma rho sigma Lambda

41469-160.7665.530.019-0.943**

(0.400)-1.089***

(0.187)-0.7370.337-0.248

Ycond=0.561 Psel=0.115

34

Heckman model for Sellers: Regression Results (cont.)

In 2006/07 production year, the predicted shares of brokered sales were significantly greater by 0.571 units for

sellers of cereals and pulses

based in moisture-reliable domains and

in less accessible and less densely-populated markets

Thus, brokers enhance selling opportunities for sellers based in agricultural domains suitable forfood crop commercialization by farmers

35

Heckman model results: Decomposition of Unconditional Marginal Effects for Buyers

36

Heckman model results: Decomposition of Unconditional Marginal Effects for Sellers

37

Heckman model results: Decomposition of Unconditional Marginal Effects

Assuming that the decomposition of unconditional marginal effects on actual quantities transacted has only marginally changed from 2006/07

Policy interventions in agricultural markets are likely to have a greater influence on the trading decisions (about whether and for how much to use brokers) of

Those buyers who are currently using brokers

Those sellers that could potentially decide to use brokers after a change in the conditions of their exogenous environment.

38

Conclusions

The most relevant transaction costs that wholesalers tend to minimise are information and negotiation costs and this is predominantly the case for buyers.

Transaction costs in real terms are declyning from 1996 to 2007.

During the same period, trader assets’ ownership is increasing.

The services of brokers are particularly valuable for:Small-scale trading businesses endowed with limited storage capacity (generally, food

crop traders)Traders with only few years of trading experienceTraders having no access to credit Traders lacking social capital (No. of trading contacts and regular partners).

Brokers support buyers where agricultural potential and crop commercialisation by farmers are low and sellers where these two parameters are high.

Distance impacts on buyers’ decision to use brokers, especially For buyers of cereals based in drought-prone agricultural domainsWhen roads linking base to main markets are all-weather roads.

The availability of a bank in the trader’s main market raises the predicted probability that buyers hire brokerage services.

39

Policy Implications & Recommendation for Future Research

The findings of this study support the enhancement and formalization of the activities of brokers.

As brokers can facilitate commercialisation across different smallholder-relevant domains, the enhancement of their activities could improve the functioning of Ethiopian agricultural markets.

Further investigation of the role played by brokers in promoting localised market opportunities (for ex. within drought-prone areas), for which the role of smaller urban centres as potential hubs for market development is as relevant as that of major centres a greater distance.

40

Thank You!

41

42

43

Heckman model for Buyers: Regression Results

Information & Negotiation Costs: The predicted probability that buyers use brokers increases with search labour (human capital) and decreases with the number of trading contacts (social capital); the greater the number of regular suppliers, the smaller the shares of brokered purchases

Brokerage services mainly benefit small-scale trading businesses with limited working capital and storage capacity and with no or limited access to credit

Chamberlin et al. (2006) found that farmers’ commercialization of cereals and pulses is higher in moisture-reliable domains with lower market access and drought-prone domains with good market access. We found that the predicted probability that buyers use brokers is the highest for buyers of cereals based in drought-prone areas and in markets with limited access and low population density

In general, the share of brokered purchases decreases as population density increases, is bigger for traders based in highly-accessible markets and is greater for buyers of cereals and pulses

The presence of a bank (financial institution) in the traders’ main markets increases the predicted probability that they use brokers

44

Heckman model for Sellers: Regression Results

Sellers seem to be less concerned about the minimization of information costs: the more they can afford to engage manpower in gathering price information (human capital), the less they are likely to use brokers

Shares of brokered sales decrease after an increase in the number of regular customers (social capital)

The relationship between the probability to use brokers and the number of years of operation (human capital) is inverted U-shaped: new trading businesses are more likely to use brokers and this probability decreases as experience is cumulated

Contractual risk: sellers who experience, since the start of the production year, lack of payment from their customers are more likely to use brokers

At the 5 percent significance level, coffee sellers are less likely to use brokers, but their potential/desired shares of brokered sales (for the overall sample of sellers) are positive and exceed in absolute value the shares of other crop traders

45

Summary Statistics:

The overwhelming majority of traders are male, sole-owners and with an average of 12 years in the trading business

Wholesalers are mostly located in moisture-reliable domains (more than 50 percent), in areas characterised by high market access and high population density (47 percent) and are mainly trading cereals (69.5 percent)

The sample is highly heterogeneous; wholesalers of cereals and pulses (i.e. food crops) have an average working capital four times smaller than that of coffee and oil seed (i.e. cash crops) traders. Moreover, storage facilities exclusively controlled by local cash-crop traders can store, on average, 9 percent more produce than the facilities controlled by food-crop traders

Contractual risk: 30 percent of sellers choose not to sell on credit to avoid the risk of no-payment, while 65 percent of buyers rely on cash-and-carry transactions to reduce monitoring and enforcement costs

93 percent of total purchases and sales are conducted in the traders’ main markets, where the number of trading contacts is the greatest

46

Summary Statistics (cont.):

Traders’ main markets are well-endowed with electricity, landline and mobile networks.

43 percent of all traders has access to formal and informal credit and, on average, 35 percent of wholesalers are members of trade associations

While main and base markets are distant markets for a larger proportion of buyers than sellers (25 versus 20 percent) , sellers tend to travel on average for longer distances (364km versus 289km for buyers)

Buyers travel a longer distance if roads are asphalted (409km) than if roads are dry- or all-weather roads (172km)

Buyers in drought-prone and pastoralist areas travel on average 157km more than buyers in moisture-reliable areas; sellers in moisture-reliable domains travel on average 97km more than sellers in drought-prone domains

The mean distance covered by cash crop traders (buyers and sellers alike) is 185km greater than the distance covered by food crop wholesalers

47

Summary Statistics (cont.):

From 1996 to 2006/07, we observe a drop in the number of traders regularly using brokers in their transactions from 85 percent reported by Gabre-Madhin (2001a) down to 68 percent found here

From 1996 to 2006/07, we observe a rise in the predicted shares of total (distant and local) brokered transactions for those traders who are using brokers; these shares have doubled for buyers (from approximately 35.5 to 69.5 percent) and have increased by two-thirds for sellers (from about 31 to 51.1 percent)

48

Variables used in the Analysis – Exclusion Restrictions/Selection Instruments VARIABLE DESCRIPTION UNIT

Model for

Buyers

Model for

Sellers

ASSETS

No. of Trading Contacts in the

Trader’s Main Market

(SOCIAL CAPITAL)

The trader engages with trading contacts in conversations regarding market conditions; it is not necessary for trading contacts to carry out a trading business (i.e. trading contacts could be local officials, the Ethiopia Grain Trade Enterprise, EGTE, etc.)

ln(x+1) -

No. of Employees

engaged in search

(HUMAN CAPITAL)

Number of people in the enterprise that participate in collecting price information

ln(x+1)

No. of Trader’s Substitutes

(HUMAN CAPITAL)

Number of people (among family helpers, permanent workers and manager, apart from the owner) who are authorised to buy and/or sell in the name of the enterprise

ln(x+1) -

CONTRACTUAL

PERFORMANCE (Costs &

Trading Disputes)

Annualised Physical

Marketing Costs

Annualised sum of the costs from purchase to sale for all transactions realised in a twelve-month period (Costs for: empty sacks, bagging and sewing, loading and off-loading, market levies, transport, bribes and tips at road stops, payment to intermediary agents, storage, radio and telephone messages for purchases/sales etc.)

ln

Fixed/ Operational

Costs

Operating costs borne in a twelve-month period (Costs for: rental of shops and/or storage facility/ies and related electricity, pest control, radio and telephone messages for purchases/sales, maintenance and insurance of vehicles, municipality taxes, inland revenue tax for trading business)

ln

TRADING DISPUTES:

Non-Payment

Since the start of the production year 2006/07, the seller has faced this problem at least once with his/her customers

yes=1 -

ACCESS TO

FINANCIAL

INSTITUTIONS Bank Availability of a financial institution

in the wholesaler’s main market yes=1 -

49

Variables used in the Analysis – Variables in both the Outcome & the Selection Equation

VARIABLE DESCRIPTION UNIT Model

for Buyer

s

Model for

Sellers

ASSETS

Regular suppliers/ customers

(SOCIAL CAPITAL)

Number of people from whom the trader purchases/sells regularly in his/her main market (regularity entails a number of interactions greater than three over a production year)

ln(x+1)

Human Capital

Number of years of operation of the trading business ln(x+1) - Square of the number of years of operation of the trading business

ln(x+1)^2 -

Working Capital &

Access to Credit

Amount of funds at the trader’s disposal for the purpose of buying and marketing grain

ln(x) Selection instrument

Since the start of 2006/7 production year, the trader got access to any form of credit (including informal sources) AND/OR if s/he needed additional funds for the trading business, s/he knew whom to ask for a loan AND/OR s/he belongs to an ekub (rotating savings and credit association)

yes=1 -

TRADING

PRACTICES Distance

Euclidean distance (in km) between the trader’s main market (for purchases/sales) and the market where s/he is based

ln(x+1)

ACCESS TO

PHYSICAL

INFRASTRUCTURE

Storage Capacity Maximum quantity storable (quintals) in one or more storage facilities under the trader’s exclusive control

ln(x+1)

Road Type: Asphalted

Dry-Weather All-Weather

Non-Relevant

Type of road linking the trader’s main market with the market where s/he is based (non-relevant=omitted category, in case the two markets coincide)

yes=1

(categorial variable)

(All-

Weather & Dry-

Weather represent

one category)

MAIN CROP TRADED

Cereals Beverages (coffee)

Pulses Oilseeds

The produce for which the trader purchased/sold the greatest quantities during 2006/7 production year is identified and classified according to crop-specific dummies (oilseeds=omitted category)

yes=1

(categorial variable)

LOCATION (BASE MARKET) AGRICULTURAL DEVELOPMENT

DOMAINS (Smallholder-Relevant

Domains)

AGRICULTURAL POTENTIAL: Drought-Prone

Pastoralist Moisture-Reliable

Central Market

Binary variables indicating where the trader’s base market is located (central market=omitted category)

yes=1 (categorial variable)

ACCESS TO MARKET & POPULATION DENSITY: Low Market Access & Low Population Density High Market Access & High Population Density Low Market Access & High Population Density (=omitted category)

yes=1 (categorial variable)

50

Trader Descriptive Statistics: A Comparative Perspective

1996 Data from: ‘Market Institutions, Transaction Costs and Social Capital in the Ethiopian Grain Market’, Gabre-Madhin (IFPRI, 2001)

2002 Data from: ‘The impact of markets on sustainable land management in the Ethiopian highlands; Survey of grain and coffee traders’, ILRI & IFPRI (2002)

2007/8 Data from: ‘ECX Trader Survey 2007’, Gabre-Madhin, EDRI & IFPRI (2007)

51

Literature ReviewTO POTENTIALLY DELETE

• The crucial role played by brokers in facilitating crop commercialisation in agricultural markets has been widely documented for traders in Sub-Saharan Africa, Latin America, India and China

• Very few studies thoroughly investigated the variables influencing economic agents’ decision to use brokers (Dessalegn et al., 1998; Fafchamps and Gabre-Madhin, 2001; Gabre-Madhin, 2001a; Jabbar et al., 2008)

• Only one study tried to explain and econometrically represent the actual decision process followed by traders and leading to their choice to use brokers (Gabre-Madhin, 2001a)

• Aim: Shine new light on wholesalers’ use of brokers in the Ethiopian grain markets