00074918.2015.1107676

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Bulletin of Indonesian Economic Studies

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Allocative Efficiency of Agrifood Traders: Shrimp

Traders in Indonesia

Dale Yi & Thomas Reardon

To cite this article: Dale Yi & Thomas Reardon (2015) Allocative Efficiency of Agrifood Traders: Shrimp Traders in Indonesia, Bulletin of Indonesian Economic Studies, 51:3, 405-423, DOI: 10.1080/00074918.2015.1107676

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ALLOCATIVE EFFICIENCY OF AGRIFOOD

TRADERS: SHRIMP TRADERS IN INDONESIA

Dale Yi Thomas Reardon

University of Adelaide Michigan State University; University of Adelaide

Shrimp is one of Indonesia’s most important agricultural export commodities, with an annual export value exceeding $1 billion. If this high-value sector is to remain competitive and continue to grow, rural traders must be able to eficiently allocate scarce labour and factor inputs to trade shrimp. This study tests for factors leading to allocative ineficiency in the shrimp trade, by estimating a stochastic cost frontier on the basis of data from a survey of 200 shrimp traders in Central Java and South Sulawesi. Our results show that larger irms have a distinct cost advantage in trade and, owing in part to greater access to factor markets, are more eficient in allocat -ing factors. Small irms can improve their allocative eficiency and become more competitive by specialising in trading one shrimp variety and by using output con-tracts to mitigate risk in the output market.

Keywords: traders, allocative eficiency, shrimp JEL classiication: D61, Q02, Q13

INTRODUCTION

Yotopoulos and Lau (1973) deined economic eficiency as a combination of technical or productive eficiency (basically, an engineering concept) and price eficiency or allocative eficiency (AE), (a concept of economic behaviour, expressing irm decisions on factor allocation as a function of output and input prices). According to Yotopoulos and Lau, it is possible for a irm to be techni-cally eficient but not allocatively eficient, and vice versa. ‘A irm is said to be price-eficient’, they note, ‘if it maximises proits . . . . Proit maximisation implies equalisation of the value of the marginal product of each variable input to its price’ (214).

The technical eficiency of farms has been explored extensively in a well-known literature on cropping farms (for example, Bravo-Ureta et al. 2006; Battese 1992) and aquaculture farms (Iliyasu et al. 2014; Sharma and Leung 2003; Begum et al. 2013; Irz and McKenzie 2003). The AE of cropping farms has also been the topic of a number of studies (including Barrett 1997), as has the AE of aquacultural farms (for example, Alam and Murshed-e-Jahan 2008). These studies suggest that there is large variation among farm households in their AE, measured by what Barrett, Sherlund, and Adesina (2008) called the ‘textbook deinition’ of AE, which we refer to here as ‘the AE condition’, as implied above in the quote from Yotopoulos

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and Lau. Barrett, Sherlund, and Adesina noted that deviation from the AE condi-tion can be due to simple allocative ineficiency (an irracondi-tional behaviour); racondi-tional behaviour by farmers because of various market failures, such as in land, credit, and insurance; or price risk (Barrett 1996), yield risk (Srinivasan 1972), and search or transactions costs (Binswanger and Rosenzweig 1986).

Traders, however, have been largely left out of the economic literature on either aspect of economic eficiency. The agrifood economic literature on AE focuses nearly exclusively on farmers and, as far as we are aware, neglects agrifood trad-ers (whether wholesaltrad-ers or broktrad-ers). There have been very few studies of agri-food traders from the viewpoint of technical eficiency or productivity, and we could ind no published study on AE among agrifood traders.1

In general, few econometrics-based articles (and very few compared with the farm sector) in the international economic literature on developing countries consider any aspect of agrifood traders. The main exceptions are some studies on transaction and inancial costs and irm and transaction scale on trader mar-keting margins (for example, Dawe et al. 2008, for rice, for the Philippines and Thailand; Minten and Kyle 1999 for Zaire; Fafchamps and Vargas Hill 2008 for Uganda; and Fafchamps, Gabre-Madhin, and Minten 2005 for Benin, Madagascar, and Malawi); on trader entry (Barrett 1997 for Madagascar); and on the spatial integration of crop markets (for example, Moser, Barrett, and Minten 2009 for Madagascar, and Badiane and Shively 1998 for Ghana).

The absence of AE analysis in the literature on traders is important for sev-eral reasons. Traders generate a substantial share of costs and value added in food value chains, and are inluential in determining the competitiveness and resource-allocation eficiency of food systems. For shrimp in Indonesia, the focus of this article, rural traders provide essential services in the value chain—from assembling, grading, sorting, and marketing, to transporting shrimp from rural production areas to processing facilities (Van Duijn, Beukers, and Van der Pijl 2012)—all while having to meet strict export standards.

We assume that there will be great interest in analyses of AE among hetero-geneous traders in developing countries, such as our case of shrimp traders in Indonesia. The technical eficiency and AE of the trader segment are important for competitiveness in a global export market where competition is keen and mar-gins are thin. Moreover, this topic has been largely absent from the international academic literature on food markets. We expect to ind that heterogeneous traders face the kinds of challenges that have been found, in the farm-household litera-ture, to be associated with deviations from the AE condition—idiosyncratic mar-ket failure of information, credit, and transport marmar-kets; price risk; and various transaction costs (Williamson 1989).

We also expect to ind that heterogeneous traders use a variety of strategies, given predetermined assets, to deal with these challenges. The interplay of chal-lenge and strategy determines performance. We posit that the AE outcomes in the face of these challenges will be a function of (a) the scale of the trader’s irm

1. A rare exception of interest to our paper is Fafchamps and Minten’s (2002) analysis of grain traders’ productivity as a function of social networks to access credit and market information in Madagascar.

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(with an initial hypothesis of scale neutrality for AE, inferred from Fafchamps, Gabre-Madhin, and Minten’s [2005] indings for scale on margins); (b) the market mechanism or institution chosen by the trader (with an initial hypothesis that the trader’s choosing a contract mechanism, rather than the spot market, to buy and sell intermediate inputs will lead to greater AE); and (c) the endowment of the trader in the organisational ‘capital’ of ‘social networks’ (with an initial hypoth-esis that this endowment will increase the traders’ AE, owing to its potential for conveying market information and reducing the costs of enforcing contracts and inter-irm coordination [Robison, Schmid, and Barry 2002; Jabbar et al. 2008]).

In this article, we examine the determinants of trader AE and test the above hypotheses among shrimp traders in Indonesia. Shrimp is of interest because it is one of the country’s most important agricultural export commodities. By the mid-2000s, Indonesia became the second-largest global shrimp producer and the fourth-largest shrimp exporter (FIAS 2006). With an annual export value exceed-ing $1 billion, the shrimp sector provides incomes and livelihoods to more than 120,000 rural households (MMAF 2011).

DATA AND SAMPLING

We use data from our own survey of shrimp traders in Indonesia. Conducted during February and March 2012, the survey collected detailed information on traders’ demographic characteristics, capital endowments, costs of input procure-ment and output marketing, prices of factor and intermediate inputs, quantities of shrimp traded, and transaction characteristics. Our sample is drawn from the population of shrimp traders in four major shrimp-producing or shrimp-trading districts in Central Java and South Sulawesi, and ranges from large multi-truck warehouse traders in main ports to small, home-based traders in remote areas. The variations among these four districts capture many incentives and capacities that exist in shrimp trade.

Central Java is on the most heavily populated and developed island of Indonesia, where infrastructure is dense and the rural economy is diversiied. However, its aquaculture performance is somewhat below that of South Sulawesi because of declining water quality and prevalence of disease. South Sulawesi is in a less-developed region with poorer infrastructure; its rural economy is less diverse and more focused on agriculture. However, because it became an aqua-culture zone later than Central Java, and because it has less urban environmen-tal interaction than that province, its water resources are better for aquaculture. Sampling across these two provinces allowed us to capture variations in prices, infrastructure, asset endowments, and marketing patterns.

To capture variations in the proitability of shrimp trade as it relates to distance from the provincial port, within each province we chose two districts: one near the main port and one far from that port. We asked each district isheries ofice for a list of all villages, markets, and warehouse areas that housed shrimp traders, and veriied this list by consulting local extension agents, leaders of shrimp-pro-ducing organisations, and industry actors. From the census results, we randomly drew a sample of 65 traders in South Sulawesi and 135 traders in Central Java (table 1). Our level of randomisation is at the district level, so generalisations about our indings should be restricted by district boundaries.

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THEORETICAL MODEL

The production function of a shrimp trader is conceptually different from a con-ventional production function that represents the physical transformation of a set of inputs into an output. In the case of shrimp exports, the trade irm allo-cates inputs, such as hired labour, own labour, transport, and communication, to provide a set of standardised services that are embedded in each unit of trade,

q. Thus, q represents a quantity of trade tasks that must be performed to trade shrimp: purchasing from farmers, assembling the product, sorting and grading, packaging in boxes with ice, logging shipments to ensure traceability, loading, transporting, unloading, and marketing the output to the processor or exporter.

While the trade activities for a typical commodity are heterogeneous (owing to varying levels of value-adding, grading, and storage, for example), the set of activities involved in shrimp trade is highly standardised because shrimp is an export commodity that must comply with international regulations and stand-ards. Thus, there are few opportunities for traders to deviate from this standard set of activities and still sell into the export supply chain. For example, traders can-not value-add by processing shrimp (that is, by peeling, deveining, and behead-ing), because international food-safety standards restrict processing to certiied shrimp-processing plants.

To estimate the cost-minimising behaviour of the trade irm in providing a quantity of trade tasks, q, we begin with a short-run model of a trader whose objective is to minimise cost by choosing a vector of factor inputs, x, given a vector of market prices, P, and a vector of ixed inputs, K:

min

x P'x, subject to q= f x

(

)

The trader is bound to trade a quantity, q, by using production technology, f(•), that is a function of the variable factor inputs (such as labour, transport, and com-munication), x, and inputs that are ixed in the short-run (such as scales, vehicles, and warehouse space), K.

We can then represent the trader’s optimisation problem by using the follow-ing Lagrangian expression:

L₌_P_'_x₊λ⎡⎣_q− _f _x,K

)

⎤⎦ (1)

After differentiating the Lagrangian with respect to the control variables, x, and manipulating the irst-order conditions, we can derive the minimum-cost function, which represents the lowest possible cost that a irm can incur to trade quantity q. The function depends on factor prices, P; output quantity, q; and ixed

inputs, K:

TABLE 1 Sample Totals

Central Java South Sulawesi Total

Zone 1: Near port 80 40 120

Zone 2: Far from port 55 25 80

Total 135 65 200

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P,q,K

(

)

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To model deviations from the cost-minimising point, we add two terms to the cost function: vi , an exogenous, unmanageable variation to the cost; and u, a pos-itive deviation from the cost-minimising point owing to suboptimal allocation decisions. The magnitude of u represents the additional cost that was incurred by the trader owing to the misallocation of factors or idiosyncratic transaction costs:

P,q,K

(

)

+v+u

( )

z (3)

where u is a function of a vector of variables, z, that measures variation in trader characteristics, vertical coordination, and social networks (the three variables about which we have presented hypotheses as conditioners of AE). These vari-ables do not enter directly into the cost function but may be correlated with cost-ineficiencies or transaction costs speciic to the trader.

MODEL SPECIFICATION

Dependent Variable (Trading Cost)

In this article, we represent cost in two ways: the total weekly cost of oper-ating the trade irm, and the unit cost of weekly trade. The total weekly cost includes the labour used to sort, grade, package, load, transport, unload, and market shrimp; the cost of petrol, tolls, and services used to transport shrimp; and the cost of mobile-phone calls made to coordinate transactions. It does not include payments for factors that cannot be adjusted within a week, such as warehouses, vehicles, and equipment. The unit cost of weekly trade is the aver-age per-kilogram cost incurred for a week of trade activity, from procurement to inal sale. We use the unit-cost form of the cost variable to test the scale effect on AE.

Explanatory Variables

Factor prices, output quantity, and ixed capital inputs are represented in the esti-mation model in the following ways.

Factor Prices (P)

Wage of hired labour: The wage is measured in rupiahs per worker per day. Casual hired labour is one of the most important inputs in the shrimp trade enterprise, and the ability of the irm to hire the cost-minimising number of casual labourers for a given day of shrimp trading may be one of the key factors in the irm’s per-formance in minimising costs.

Distance travelled: Variation in transport cost is represented by the total distance by road from input procurement to output sale, as reported by the trader. Because the government controls the prices of fuel and tolls, there is no variation in trans-port prices in our sample and we must draw on variation in the distance travelled in order to control for heterogeneity in the transport costs incurred by the trader. As a result, the cost function can be interpreted as the minimum cost of trading a quantity of shrimp, q, over a given distance.

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Output Quantity (q)

Output quantity is measured by the total throughput of the irm in a week of trade. It is measured in kilograms of shrimp traded from the farmer to the pro-cessor. The quantity of throughput is proportional to the total quantity of trade services provided by the trader.

Fixed Capital Inputs (K)

Area of the trade depot or warehouse: Depots and warehouses are used to assem-ble shipments from multiple sources. They also serve as facilities for grading and sorting shrimp, and as storefronts. This variable measures the total area, in square metres, of the trade depot or warehouse that the trader owns or rents.

Standardised capital indices: We control for heterogeneity in capital endowments by including standardised capital indices that capture variation in productive assets. We conducted a principal component analysis of 12 capital items, and the ensuing analysis yielded two principal components: trade equipment and trans-port assets. We use the indices to reduce the number of dimensions in the capi-tal vector from 12 to 2. This allows conservation of degrees of freedom, reduces collinearity, and helps stochastic frontier results to converge. The two resultant indices are as follows:

• Index of trade equipment: Capital items primarily used to construct this index are scales, storage containers, cold-storage equipment, and mobile phones. This index measures variation in capital items owned by the trader and used to store or weigh shrimp.

• Index of transport assets: Capital items primarily used to construct this index are trucks, utilities, cars, and other small vehicles. This index measures variation in capital items owned by the trader and used to transport shrimp. Deviations from the Cost Frontier (Z)

Variables used to model deviations from the cost function fall into three catego-ries: trader characteristics, social networks, and vertical coordination.

Trader Characteristics

We include several variables to represent heterogeneity in trader characteristics in determining AE:

Gender: We use a dummy variable to indicate whether the trade operator is female. Female traders may have more dificulty in accessing shrimp suppliers, accessing local labour markets to hire casual workers, and accessing output mar-kets. Evidence has shown that factors are often underused in female-operated farm plots owing to intra-household competition for scarce factors (Quisumbing 1996; Udry et al. 1995).

Education: We measure education by the years of school attended by the trader. Research has shown that education leads to higher levels of technical eficiency and AE in agriculture (Ali and Byerlee 1991; Huffman 2001). We hypothesise that education will improve AE in agricultural trade as well.

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Social Networks

Social-network endowment is measured by a dummy variable that indicates whether the trader is related to, or is neighbours with, someone participating in downstream segments of the shrimp industry (such as processing, exporting, or retailing). Traders with social ties downstream may have better access to timely and relevant market information, which would allow them to respond to prices and allocate resources more quickly than those without social ties; they may also have lower costs of bargaining in the output market, because of the repu-tation invested in the social relationship. Research on social capital in isheries management emphasises the importance of community-based co-management and the dificulty of quantifying dimensions of social capital, such as leadership (Gutiérrez, Hilborn, and Defeo 2011). While there are many dimensions of social capital, we include our social-network variable as our best available proxy for the capacity of individual traders to lead and co-manage a complex supply chain with downstream agents.

Specialisation in trade: We use a dummy variable to indicate whether the irm trades only one variety of shrimp. The net effect of specialisation on costs depends on the trade-off between having a larger opportunity set (as trading multiple varieties means that traders can choose from a larger set of suppliers and buy-ers to transact with) and gaining from specialising in a single variety (as invest-ing greater effort into reducinvest-ing the costs of procurinvest-ing and marketinvest-ing one variety may make traders more eficient). Specialising in a variety can also reduce the transaction costs associated with accessing information on how much grading and sorting is required for that variety, when speciic farmers of that variety in their catchment zone will be harvesting, and which output markets are associated with that variety.

Firm scale: We measure the scale of the irm by the weekly throughput of shrimp, in kilograms. The scale of the trade irm may affect its AE: larger traders may have greater AE because of better access to credit, labour, and output markets—the ease of access to which can affect the allocation of factors—or their ability to spread ixed transaction costs over a larger quantity. However, the scale effect may not play out in reality; Fafchamps, Gabre-Madhin, and Minten (2005), for example, found no scale effect on trader margins in Benin, Madagascar, or Malawi.

Vertical Coordination

Vertical coordination is the institutional and organisational arrangements of trans-actions, involving the methods and mechanisms of search, measurement, bar-gaining, and enforcement that were used to buy or sell shrimp. In shrimp trade, input procurement and output marketing transactions are arranged using one of two methods of vertical coordination: spot-market arrangements, where transac-tions are lexible and negotiated at the time of sale, or contracted arrangements, where transactions are negotiated in advance. We include two dummy variables to capture heterogeneity in vertical coordination:

• Upstream contract: A contract is used to secure shrimp from a rural trader or farmer.

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• Downstream contract: A contract is used to secure an output market to urban wholesalers, processors, exporters, or retailers.

The net effect of contract use on AE will depend on the relative transaction costs of using a contract, which may reduce search and bargaining costs and improve access to credit but may also increase enforcement costs compared with spot arrangements; and the relative risk of using a contract, which may reduce the risk of insuficient supply and output sales but also may force the trader to forego opportunities to transact lexibly with agents.

EMPIRICAL METHOD

We empirically model the cost function in two forms. First is the conventional form, which maps output quantity, prices, and ixed inputs to the minimum cost of operation:

Total weekly cost:

(

Pi,qi,Ki,β

)

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where C is the function representing the lowest cost possible given observation i’s

prices (P), quantity traded weekly (q), ixed inputs (K), and a vector of cost func-tion parameters (β). Second is the unit-cost form of the cost function that maps prices, quantity traded, and ixed inputs to the per-unit cost of trade:

Average cost per kilogram:

(

Pi,qi,Ki,γ

)

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where g is the function representing the lowest unit cost possible, given observa-tion i’s prices (P), quantity traded weekly (q), ixed inputs (K), and vector of unit-cost function parameters (γ).

Stochastic Cost Frontier

Stochastic frontier analysis (SFA) is a parametric statistical approach to eficiency analysis (Aigner, Knox Lovell, and Schmidt 1977; Kumbhakar and Lovell 2003). It decomposes the realised cost of a irm into three components: frontier cost func-tion, allocative ineficiency, and exogenous variation. In implementafunc-tion, SFA requires that we make three speciications to identify model parameters: the func-tional form of the cost frontier; the distribution of the error term, v; and the distri-bution of the allocative ineficiency term, u. We use the common speciication of

the Cobb–Douglas cost frontier; a normal distribution of the disturbance term, v; and a half-normal heteroskedastic distribution of the one-sided ineficiency dis-turbance term, u.

Total cost: C P

(

,q,K,β

)

= _j=1βjln

( )

Pj k

∑

+βk+1K+βk+2ln

( )

Unit cost: g P

(

,q,K,γ

)

= _j=1γjln

( )

Pj k

∑

+γk+1K+γk+2ln

( )

vi∼ ,σv 2

(

)

ui=σu i, i∼

(

)

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uiuj

(

)

(

vivj

)

= ,f i≠ j

We model the causes of ineficiency as heteroskedasticity in u that depends on a set of variables, z, that determine ineficiency. We use the exponential

speciica-tion of the heteroskedasticity, as follows: σui

(

ziθ

)

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where θ is a vector of parameters that models the allocative ineficiency in trade. The log-likelihood function for observation i is as follows (Coelli 1995):

−1

2ln 2

(

)

−ln

( )

σ −ln Φ

ziθ

σ2λ

⎛ ⎝⎜ ⎞ ⎠⎟ ⎛ ⎝⎜ ⎞ ⎠⎟+

ln Φ

(

1−λ

)

ziθ −εi

σ2_λ 1−λ

(

)

⎛ ⎝ ⎜ ⎞ ⎠ ⎟ ⎛ ⎝

⎜⎜ ⎞_⎠⎟⎟ −12

εi+ziθ σ ⎛ ⎝⎜ ⎞⎠⎟ 2 ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ i=1 N

∑

where σ= σu

+σv 2

λ= σu

σu 2

+σv 2

εi=Ci−g P

(

i,ki γ

)

and Φ

( )

i _{is the standard normal cumulative distribution function.} Robustness Check with Data Envelope Analysis

The main drawback of SFA is that identiication can hinge on a functional form assumption for the cost function. To test the robustness of the results of our SFA, we compare them with the results from a non-parametric data envelope analysis (DEA), a programming method that does not impose an assumption about func-tional form (Ramanathan 2003). DEA does, however, assume a deterministic cost frontier where there is no exogenous variation in cost. Comparing results from DEA and SFA, we can see how much our results change under two different sets of assumptions:

• DEA: No functional form assumption, no exogenous variation. • SFA: Functional form assumption, allows exogenous variation.

DEA proceeds in two steps. First, we calculate AE by using linear program-ming to form a cost-frontier surface, which is a piecewise hull constructed from linear combinations of benchmark irms, and determine the distance between the frontier’s surface and a irm’s realised cost. Second, we regress this distance (the ineficiency measure) on variables hypothesised to determine ineficiency. We use a truncated regression, because all benchmark irms are expressed as being fully eficient, and we specify that the distribution of ineficiency follow a half-normal distribution, just as in SFA.

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Variables Male Female Overall

Upstream contract (binary) 0.45 0.33 0.40

(0.05) (0.05) (0.04)

Downstream contract (binary) 0.35 0.07 0.23

(0.05) (0.03) (0.03)

Specialised in one variety only (binary) 0.37 0.25 0.32

(0.05) (0.05) (0.03)

Social capital downstream (binary) 0.60 0.09 0.38

(0.05) (0.03) (0.04)

Education of the trader (years) 7.38 6.03 6.79

(0.29) (0.30) (0.21)

Observations 104 80 184

Note: Standard errors are in parentheses

TABLE 3 Descriptive Statistics by Specialisation and Social Capital

Variables Specialised

Not specialised

Social capital

No social

capital Overall Cost per kg (Rp) 1,595.49 1,436.15 1,767.76 1,319.47 1,487.28 (202.80) (135.65) (199.30) (133.10) (112.61) Total quantity traded (kg) 260.83 848.57 1,052.27 425.30 659.99

(77.92) (264.20) (469.35) (72.51) (182.03) Price of traditional shrimp

(30/kg) (Rp 1,000/kg)

69.43 73.27 60.79 78.78 72.04

(1.97) (1.10) (1.19) (0.97) (0.99) Price of HYV shrimp

(60/kg) (Rp 1,000/kg)

40.86 41.99 37.70 43.98 41.63

(1.43) (0.83) (1.05) (0.91) (0.73) Price of casual labour

(Rp 1,000/day)

47.09 41.99 53.09 37.96 43.63

(3.40) (4.54) (8.35) (1.36) (3.27) Transportation capital of irm

(index)

–0.03 –0.00 0.51 –0.32 0.00

(0.06) (0.10) (0.17) (0.03) (0.07) Productive capital

endowment (index)

–0.09 0.00 0.30 –0.22 0.00

(0.04) (0.09) (0.12) (0.06) (0.07) Area of trading depot (m2₎ _7.38 _18.02 _19.81 _11.50 _14.61

(2.76) (6.05) (4.39) (6.20) (4.21)

Observations 60 127 70 117 187

Note: Standard errors are in parentheses. HYV = high-yield variety.

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TABLE 4 Descriptive Statistics by Contract Use

Variables

No contract

Upstream contract

Down-stream contract

Both

contracts Overall Cost per kg (Rp) 1,127.50 1,622.70 1,405.08 2,923.64 1,487.28 (137.64) (205.32) (287.11) (465.47) (112.61) Total quantity traded (kg) 155.03 198.55 1,627.00 3,139.44 659.99

(15.85) (24.58) (463.25) (1,657.07) (182.03) Price of traditional shrimp

(30/kg) (Rp 1,000/kg)

72.60 69.94 76.76 69.06 72.04

(1.60) (1.43) (3.02) (2.19) (0.99) Price of HYV shrimp

(60/kg) (Rp 1,000/kg)

41.32 39.37 46.91 42.49 41.63

(1.08) (0.72) (3.07) (1.79) (0.73) Price of casual labour

(Rp 1,000/day)

35.52 50.00 52.63 49.97 43.63

(1.32) (10.07) (6.02) (6.29) (3.27) Transportation capital of irm

(index)

–0.31 –0.11 0.76 0.66 –0.01

(0.03) (0.05) (0.33) (0.44) (0.07) Productive capital

endowment (index)

–0.32 –0.07 0.54 0.71 –0.03

(0.02) (0.04) (0.28) (0.39) (0.06) Area of trading depot (m2₎ _2.02 _6.34 _49.00 _51.50 _14.61

(0.55) (1.24) (26.61) (15.86) (4.21)

Observations 87 56 26 18 187

Note: Standard errors are in parentheses. HYV = high-yield variety.

DESCRIPTIVE STATISTICS

Transaction Quantities and Costs

The quantity of shrimp traded by each trader varies greatly in our sample (tables 2–4). The mean of the distribution is 659 kilograms per week. The distribution of weekly throughput (igure 1) shows that the scale of the enterprise is a skewed distribution with a mode at around one tonne but with a long tail that extends to six tonnes.

Our sample also has a large gender differential; most trade enterprises oper-ated by women tend to be clustered in the low range, while those operoper-ated by men tend to be larger. If there are eficiencies of scale, the discrepancy in irm scale between men and women may be leading to poorer performance by women; we must therefore control for scale in order to identify the effect of gender on AE.

In trade costs, there is little variation among traders in our sample. The aver-age cost of trading one kilogram of shrimp is Rp 1,487 ($0.15), which is only 2% of the farm-gate price of shrimp. In addition, there is a large spread in the costs per kilogram in trade. Figure 2 shows that the mode of unit costs is just under Rp 1,000 and that a tail extends to Rp 7,500.

Trader Characteristics and Behaviour

Contract Use

Around 40% of traders used upstream contracts and 23% used downstream con-tracts. There appears to be a trade-off between reducing the unit cost of trade and

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(tonnes of shrimp traded per week)

Female

Male Kernel

density

0 2 4 6

0.0005 0.0010 0.0015 0.0020

0.0000

FIGURE 2 Distribution of Trading Costs (Rp million per tonne) Kernel

density

0 2 4 8

0.0002 0.0004 0.0006

0.0000

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securing a market for inputs and outputs with contracts. Traders using contracts for both inputs and outputs were trading more than three megatonnes per week but incurred more costs than traders not using contracts, while the latter were, on average, trading only 155 kilograms per week. It appears that contract users are trading on smaller margins but making up for it by increasing throughput.

Gender

Women play a large role in shrimp trade in Indonesia, operating 43% of irms. Fafchamps, Gabre-Madhin, and Minten (2005) and Fafchamps and Minten (1999) also found a high incidence of female-operated trade enterprises in other coun-tries. The prevalence of women traders provides an opportunity to compare the relative performance by gender in trading shrimp.

Education

On average, shrimp traders have 6.8 years of formal education, which is lower than the national average of 7.5 years (UNDP 2014). In our sample, males have more education (7.4 years), on average, than females (6.1 years). This difference may affect the latter’s performance.

Social Networks

Many traders (38%) had a relative or neighbour in a downstream segment of the shrimp value chain. Those with this social capital traded twice the volume of those without it. In addition, traders with better social connections downstream tended to have more equipment and vehicles for transportation.

Specialisation

A large proportion of traders (32%) specialised in one variety of shrimp. Those who did so traded only 260 kilograms per week, which is much less than the 848 kilograms per week of diversiied traders. The costs of trade per kilogram were very similar, however, even though specialised traders had less capital and trans-port capital, operated in smaller trade depots, and faced higher wages for labour.

REGRESSION RESULTS

Our estimation of the cost frontier yielded normal results, and satisies the con-ditions of non-decreasing cost with respect to wages. In addition, the distance travelled has a positive and statistically signiicant effect on the cost of trade. We found that irms with a higher trade volume had lower costs per kilogram traded. Overall, there is a signiicant amount of allocative ineficiency. On average, trad-ers incur costs 15% higher than what is allocatively eficient. The level of inefi-ciency in the sample ranges between 2% and 65%.

Table 5 presents the regression results from maximum-likelihood estimation. The salient results from our estimation of parameters modelling allocative inef-iciency are as follows. First, traders with higher levels of education have sig-niicantly greater AE, incurring costs closer to the frontier than those with less education. This inding is similar to results reported in the literature on farm pro-duction eficiency (Huffman 2001; Reimers and Klasen 2013). Increasing educa-tional attainment will be important for improving eficiency, especially for female traders, who, in general, lag behind their male counterparts in education.

(15)

(1) (2) (3) (4)

Equation Variables Cost Cost/kg Cost Cost/kg

Cost Total quantity traded (kg) 0.78*** –0.22*** 0.79*** –0.21** (0.07) (0.07) (0.09) (0.09) Price of casual labour (Rp 1,000/day) 0.28 0.28 0.24 0.24

(0.18) (0.18) (0.17) (0.17) Distance to shrimp production area (km) 0.34*** 0.34*** 0.35*** 0.35***

(0.06) (0.06) (0.06) (0.06) Transportation capital of irm (index) –0.15 –0.15 –0.00 –0.00

(0.15) (0.15) (0.15) (0.15) Area of trading depot (ha) 0.17* 0.17* 0.13 0.13

(0.10) (0.10) (0.09) (0.09) Productive capital endowment (index) 0.11 0.11 –0.06 –0.06

(0.16) (0.16) (0.17) (0.17)

District = Rembang 0.14 0.14 0.21 0.21

(0.16) (0.16) (0.16) (0.16) District = Bulukumba 0.55** 0.55** 0.82*** 0.82***

(0.26) (0.26) (0.23) (0.23)

District = Barru 0.52** 0.52** 0.80*** 0.80***

(0.21) (0.21) (0.18) (0.18)

Constant 0.02 0.02 –0.04 –0.04

(0.68) (0.68) (0.70) (0.70)

ln(σ2_V₎ _{–1.52*** –1.52*** –1.63*** –1.63***}

(0.35) (0.35) (0.34) (0.34)

ln(σ2_U₎ _{Total quantity traded (kg)} _–0.31* _–0.31*

(0.18) (0.18)

Upstream contract (binary) 1.03*** 1.03***

(0.30) (0.30)

Downstream contract (binary) –1.05** –1.05**

(0.50) (0.50) Specialises in one variety (binary) –0.61* –0.61* (0.36) (0.36) Social capital downstream (binary) –0.80** –0.80**

(0.35) (0.35)

Trader gender is female (binary) 0.18 0.18

(0.32) (0.32)

Education of the trader (years) –0.10** –0.10**

(0.04) (0.04)

Constant 0.55*** 0.55*** 3.45*** 3.45***

(0.20) (0.20) (1.26) (1.26)

Observations 188 188 187 187

Note: Standard errors in parentheses. * p < 0.1; ** p < 0.05; *** p < 0.01.

(16)

Traders with extensive social networks in downstream segments of the value chain are more likely to operate near the cost frontier. This result corroborates evidence from previous studies that linked participation in social networks to higher margins (Fafchamps and Minten 2001; Fafchamps and Minten 2002); our result posits that this increase in margins stems, in part, from greater AE. Traders who are connected with downstream agents may be more aware of when proces-sors receive large orders and ramp up demand, and may have more nuanced information on how much labour is required to meet the minimum requirements for grades. In addition, these relationships may help traders reduce search costs in shrimp output markets. With improved access to this type of information and easier access to output markets, shrimp traders appear to be better at coordinating labour, reducing communication costs, and consolidating shipments to minimise the cost of trade.

Third, we found that traders specialising in one variety of shrimp have greater AE than those procuring multiple varieties. This result suggests that specialising in one variety and acquiring variety-speciic knowledge of production conditions and output markets will help to reduce operation costs. In this regard, the gains of spe-cialisation outweigh the beneits of a diversiied business with a larger choice set.

Fourth, large irms have greater AE than small irms. This may be because there are ixed costs associated with accessing markets, such as those for casual hired labour. Larger irms can disperse these ixed costs over a larger quantity of traded shrimp and can access labour easily, whereas small irms may be restricted to the availability of labour in their village or social circle. It may also be because the substitution surface for higher isoquants is smoother than those for lower iso-quants, owing to the discrete nature of labour and capital inputs. The discreteness may explain poorer allocative performance among small operators and may be driving concentration in the segment.

Vertical coordination (the use of contracts) has mixed effects on AE. While trad-ers who have upstream contracts are not allocatively eficient, those who have downstream contracts with processors are more allocatively eficient than their counterparts. For downstream contracts, the trader faces signiicantly more risk in the output market than in the input market. After purchase, the trader must ind a suitable market quickly for the highly perishable shrimp, and must incur high search costs if there are any bottlenecks in marketing output. Downstream con-tracts can mitigate risk and prevent the under-allocation of factors due to trader risk-aversion. Once an output market is secure, the trader may be more willing to hire labour, operate at an eficient scale, and reduce the costs of searching for an output market.

Being tied down by an upstream contract, on the other hand, may force traders to purchase shrimp at times and places that are less than optimal. Having a con-tract with a supplier may tie the hands of traders so that they cannot lexibly allo-cate resources—speciically, it may prevent them from pooling shipments from different locations and times in order to achieve economies of scale in transport. This may be only a temporal result, however, as our survey took place during the high-season for aqua culture shrimp (the late rainy season). While our results show that a contract with a supplier is ineficient during a period of abundant supply, having a contracted supplier could be advantageous during the low sea-son, when input is scarce. We were not able to analyse seasonal variation in AE.

(17)

To test the robustness of the results from our SFA, we compared them with results from the linear programming DEA method, which does not assume a func-tional form of the cost frontier. The results from our DEA are similar to those from our SFA, demonstrating that the results are not conditioned by the functional form assumption (table 6).

CONCLUSIONS

This is the irst article in the agrifood economics literature to analyse the AE of traders. This is important because the trade segment is a substantial contributor to both costs and value added in export markets, and a key determinant of the competitiveness of high-value product exports. Eficiency of the commerce sector in general, and of AE in particular, has been severely neglected in the economics literature, which has used these concepts to analyse the farm segment only.

Shrimp traders in Indonesia are, on average, operating 15% above the cost fron-tier, but with substantial heterogeneity across traders. We found that there are economies of scale improving shrimp traders’ AE (in contrast to the little evidence from the crop-trade literature that shows neutrality of scale on margins, based on Fafchamps, Gabre-Madhin, and Minten 2005). We also found, however, that there are no economies of scope spurring AE; rather, we found the opposite (that it is advantageous for the trader to specialise in a type of shrimp). These results might explain the gradual concentration in the trading segment that we were informed

TABLE 6 Truncated Regression Results (DEA ineficiency)

Variables

(1) (2)

Cost Cost/kg

Total quantity traded (kg) –0.09** –0.01

(0.19) (0.03)

Upstream contract (binary) 0.09** 0.04**

(0.05) (0.02)

Downstream contract (binary) –0.24*** –0.07**

(0.07) (0.03)

Specialises in one variety (binary) –0.10* –0.04*

(0.02) (0.02)

Trader is related to a downstream agent (binary) 0.07 0.01

(0.05) (0.02)

Trader gender is female (binary) 0.03 0.02

(0.05) (0.02)

Education of the trader (years) –0.02*** –0.01**

(0.01) (0.00)

Constant 1.33*** 0.912***

(0.19) (0.08)

Note: Standard errors are in parentheses * p < 0.1; ** p < 0.05; *** p < 0.01.

(18)

of in our rapid reconnaissance before conducting our survey—that this concen-tration is linked to a general phenomenon in Asia of the long-term reduction of the role of the ield brokers in food value chains as roads and communications improve (Reardon 2015).

We found that contracts with buyers downstream (rather than farmers upstream) was beneicial for AE. This appears linked to the signiicant risk faced by traders in markets for highly perishable shrimp, and to the much lower risk and search costs of sourcing shrimp from farmers and ield brokers. Flexibility in sourcing (that is, the ability to buy shrimp without a contract) probably explains increased AE in the absence of contracts with upstream suppliers.

We also found that the endowment of the trader in the organisational capital of social networks fosters AE, mirroring the relation to higher margins found in Fafchamps and Minten’s (2001, 2002) work. However, we found that this was signiicant and important in the case of traders’ links with downstream buyers speciically (whether processors or exporters), apparently as a means to reduce uncertainty about the market and to lower search costs.

REFERENCES

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Ali, Mubarak, and Derek Byerlee. 1991. ‘Economic Eficiency of Small Farmers in a Chang -ing World: A Survey of Recent Evidence’. Journal of International Development 3 (1): 1–27.

Badiane, Ousmane, and Gerald E. Shively. 1998. ‘Spatial Integration, Transport Costs, and the Response of Local Prices to Policy Changes in Ghana’. Journal of Development Eco-nomics 56 (2): 411–31.

Barrett, Christopher B. 1997. ‘Food Marketing Liberalization and Trader Entry: Evidence from Madagascar’. World Development 25 (5): 763–77.

———. 1996. ‘On Price Risk and the Inverse Farm Size-Productivity Relationship’. Journal of Development Economics 51 (2): 193–215.

Barrett, Christopher B., Shane M. Sherlund, and Akinwumi A. Adesina. 2008. ‘Shadow Wages, Allocative Eficiency, and Labor Supply in Smallholder Agriculture’. Agricul -tural Economics 38 (1): 21–34.

Battese, George E. 1992. ‘Frontier Production Functions and Technical Eficiency: A Survey of Empirical Applications in Agricultural Economics’. Agricultural Economics 7 (3–4): 185–208.

Begum, Mst. Esmat A., Mohammad I. Hossain, and Evangelos Papanagiotou. 2013. ‘Tech -nical Eficiency of Shrimp Farming in Bangladesh: An Application of the Stochastic Production Frontier Approach’. Journal of the World Aquaculture Society 44 (5): 641–54. Binswanger, Hans P., and Mark Rosenzweig. 1986. Contractual Arrangements, Employment

and Wages in Rural Labor Markets in Asia. New Haven, CT: Yale University Press. Bravo-Ureta, Boris E., Daniel Solís, Víctor H. Moreira López, José F. Maripani, Abdour

-ahmane Thiam, and Teodoro Rivas. 2006. ‘Technical Eficiency in Farming: A Meta-Regression Analysis’. Journal of Productivity Analysis 27 (1): 57–72.

Coelli, Tim. 1995. ‘Estimators and Hypothesis Tests for a Stochastic Frontier Function: A Monte Carlo Analysis’. Journal of Productivity Analysis 6 (3): 247–68.

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Dawe, David C., Piedad F. Moya, Cheryll B. Casiwan, and Jesusa M. Cabling. 2008. ‘Rice Marketing Systems in the Philippines and Thailand: Do Large Numbers of Competitive Traders Ensure Good Performance?’. Food Policy 33 (5): 455–63.

Fafchamps, Marcel, Eleni Gabre-Madhin, and Bart Minten. 2005. ‘Increasing Returns and Market Eficiency in Agricultural Trade’. Journal of Development Economics 78 (2): 406–42. Fafchamps, Marcel, and Ruth Vargas Hill. 2008. ‘Price Transmission and Trader Entry in Domestic Commodity Markets’. Economic Development and Cultural Change 56 (4): 729–66.

Fafchamps, Marcel, and Bart Minten. 1999. ‘Relationships and Traders in Madagascar’.

Journal of Development Studies 35 (6): 1–35.

———. 2001. ‘Social Capital and Agricultural Trade’. American Journal of Agricultural Eco -nomics 83 (3): 680–85.

———. 2002. ‘Returns to Social Network Capital among Traders’. Oxford Economic Papers 54 (2): 173–206.

FIAS (Foreign Investment Advisory Service). 2006. Improving Indonesia’s Competitiveness: Case Study of Textile and Farmed-Shrimp Industries, vol. 1. Washington, DC: FIAS. Gutiérrez, Nicolás L., Ray Hilborn, and Omar Defeo. 2011. ‘Leadership, Social Capital and

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(1) (2) (3) (4)

Equation Variables Cost Cost/kg Cost Cost/kg

Cost Total quantity traded (kg) 0.78*** –0.22*** 0.79*** –0.21** (0.07) (0.07) (0.09) (0.09) Price of casual labour (Rp 1,000/day) 0.28 0.28 0.24 0.24

(0.18) (0.18) (0.17) (0.17) Distance to shrimp production area (km) 0.34*** 0.34*** 0.35*** 0.35***

(0.06) (0.06) (0.06) (0.06) Transportation capital of irm (index) –0.15 –0.15 –0.00 –0.00

(0.15) (0.15) (0.15) (0.15) Area of trading depot (ha) 0.17* 0.17* 0.13 0.13

(0.10) (0.10) (0.09) (0.09) Productive capital endowment (index) 0.11 0.11 –0.06 –0.06

(0.16) (0.16) (0.17) (0.17)

District = Rembang 0.14 0.14 0.21 0.21

(0.16) (0.16) (0.16) (0.16) District = Bulukumba 0.55** 0.55** 0.82*** 0.82***

(0.26) (0.26) (0.23) (0.23) District = Barru 0.52** 0.52** 0.80*** 0.80***

(0.21) (0.21) (0.18) (0.18)

Constant 0.02 0.02 –0.04 –0.04

(0.68) (0.68) (0.70) (0.70)

ln(σ2_V₎ _{–1.52*** –1.52*** –1.63*** –1.63***}

(0.35) (0.35) (0.34) (0.34)

ln(σ2_U₎ _{Total quantity traded (kg)} _–0.31* _–0.31* (0.18) (0.18)

Upstream contract (binary) 1.03*** 1.03***

(0.30) (0.30) Downstream contract (binary) –1.05** –1.05**

(0.50) (0.50) Specialises in one variety (binary) –0.61* –0.61* (0.36) (0.36) Social capital downstream (binary) –0.80** –0.80**

(0.35) (0.35)

Trader gender is female (binary) 0.18 0.18

(0.32) (0.32) Education of the trader (years) –0.10** –0.10**

(0.04) (0.04)

Constant 0.55*** 0.55*** 3.45*** 3.45***

(0.20) (0.20) (1.26) (1.26)

Observations 188 188 187 187

Note: Standard errors in parentheses. * p < 0.1; ** p < 0.05; *** p < 0.01.

(2)

Traders with extensive social networks in downstream segments of the value

chain are more likely to operate near the cost frontier. This result corroborates

evidence from previous studies that linked participation in social networks to

higher margins (Fafchamps and Minten 2001; Fafchamps and Minten 2002); our

result posits that this increase in margins stems, in part, from greater AE. Traders

who are connected with downstream agents may be more aware of when

proces-sors receive large orders and ramp up demand, and may have more nuanced

information on how much labour is required to meet the minimum requirements

for grades. In addition, these relationships may help traders reduce search costs

in shrimp output markets. With improved access to this type of information and

easier access to output markets, shrimp traders appear to be better at coordinating

labour, reducing communication costs, and consolidating shipments to minimise

the cost of trade.

Third, we found that traders specialising in one variety of shrimp have greater

AE than those procuring multiple varieties. This result suggests that specialising in

one variety and acquiring variety-speciic knowledge of production conditions and

output markets will help to reduce operation costs. In this regard, the gains of

spe-cialisation outweigh the beneits of a diversiied business with a larger choice set.

Fourth, large irms have greater AE than small irms. This may be because there

are ixed costs associated with accessing markets, such as those for casual hired

labour. Larger irms can disperse these ixed costs over a larger quantity of traded

shrimp and can access labour easily, whereas small irms may be restricted to the

availability of labour in their village or social circle. It may also be because the

substitution surface for higher isoquants is smoother than those for lower

iso-quants, owing to the discrete nature of labour and capital inputs. The discreteness

may explain poorer allocative performance among small operators and may be

driving concentration in the segment.

Vertical coordination (the use of contracts) has mixed effects on AE. While

trad-ers who have upstream contracts are not allocatively eficient, those who have

downstream contracts with processors are more allocatively eficient than their

counterparts. For downstream contracts, the trader faces signiicantly more risk in

the output market than in the input market. After purchase, the trader must ind

a suitable market quickly for the highly perishable shrimp, and must incur high

search costs if there are any bottlenecks in marketing output. Downstream

con-tracts can mitigate risk and prevent the under-allocation of factors due to trader

risk-aversion. Once an output market is secure, the trader may be more willing to

hire labour, operate at an eficient scale, and reduce the costs of searching for an

output market.

Being tied down by an upstream contract, on the other hand, may force traders

to purchase shrimp at times and places that are less than optimal. Having a

con-tract with a supplier may tie the hands of traders so that they cannot lexibly

allo-cate resources—speciically, it may prevent them from pooling shipments from

different locations and times in order to achieve economies of scale in transport.

This may be only a temporal result, however, as our survey took place during

the high-season for aqua culture shrimp (the late rainy season). While our results

show that a contract with a supplier is ineficient during a period of abundant

supply, having a contracted supplier could be advantageous during the low

sea-son, when input is scarce. We were not able to analyse seasonal variation in AE.

(3)

To test the robustness of the results from our SFA, we compared them with

results from the linear programming DEA method, which does not assume a

func-tional form of the cost frontier. The results from our DEA are similar to those from

our SFA, demonstrating that the results are not conditioned by the functional

form assumption (table 6).

CONCLUSIONS

This is the irst article in the agrifood economics literature to analyse the AE of

traders. This is important because the trade segment is a substantial contributor

to both costs and value added in export markets, and a key determinant of the

competitiveness of high-value product exports. Eficiency of the commerce sector

in general, and of AE in particular, has been severely neglected in the economics

literature, which has used these concepts to analyse the farm segment only.

Shrimp traders in Indonesia are, on average, operating 15% above the cost

fron-tier, but with substantial heterogeneity across traders. We found that there are

economies of scale improving shrimp traders’ AE (in contrast to the little evidence

from the crop-trade literature that shows neutrality of scale on margins, based on

Fafchamps, Gabre-Madhin, and Minten 2005). We also found, however, that there

are no economies of scope spurring AE; rather, we found the opposite (that it is

advantageous for the trader to specialise in a type of shrimp). These results might

explain the gradual concentration in the trading segment that we were informed

TABLE 6

Truncated Regression Results (DEA ineficiency)

Variables

(1) (2)

Cost Cost/kg

Total quantity traded (kg) –0.09** –0.01

(0.19) (0.03)

Upstream contract (binary) 0.09** 0.04**

(0.05) (0.02)

Downstream contract (binary) –0.24*** –0.07**

(0.07) (0.03)

Specialises in one variety (binary) –0.10* –0.04*

(0.02) (0.02)

Trader is related to a downstream agent (binary) 0.07 0.01

(0.05) (0.02)

Trader gender is female (binary) 0.03 0.02

(0.05) (0.02)

Education of the trader (years) –0.02*** –0.01**

(0.01) (0.00)

Constant 1.33*** 0.912***

(0.19) (0.08)

Note: Standard errors are in parentheses * p < 0.1; ** p < 0.05; *** p < 0.01.

(4)

of in our rapid reconnaissance before conducting our survey—that this

concen-tration is linked to a general phenomenon in Asia of the long-term reduction of

the role of the ield brokers in food value chains as roads and communications

improve (Reardon 2015).

We found that contracts with buyers downstream (rather than farmers

upstream) was beneicial for AE. This appears linked to the signiicant risk faced

by traders in markets for highly perishable shrimp, and to the much lower risk

and search costs of sourcing shrimp from farmers and ield brokers. Flexibility in

sourcing (that is, the ability to buy shrimp without a contract) probably explains

increased AE in the absence of contracts with upstream suppliers.

We also found that the endowment of the trader in the organisational capital

of social networks fosters AE, mirroring the relation to higher margins found in

Fafchamps and Minten’s (2001, 2002) work. However, we found that this was

signiicant and important in the case of traders’ links with downstream buyers

speciically (whether processors or exporters), apparently as a means to reduce

uncertainty about the market and to lower search costs.

REFERENCES

Ali, Mubarak, and Derek Byerlee. 1991. ‘Economic Eficiency of Small Farmers in a Chang-ing World: A Survey of Recent Evidence’. Journal of International Development 3 (1): 1–27.

Barrett, Christopher B. 1997. ‘Food Marketing Liberalization and Trader Entry: Evidence from Madagascar’. World Development 25 (5): 763–77.

———. 1996. ‘On Price Risk and the Inverse Farm Size-Productivity Relationship’. Journal of Development Economics 51 (2): 193–215.

Barrett, Christopher B., Shane M. Sherlund, and Akinwumi A. Adesina. 2008. ‘Shadow Wages, Allocative Eficiency, and Labor Supply in Smallholder Agriculture’. Agricul-tural Economics 38 (1): 21–34.

Battese, George E. 1992. ‘Frontier Production Functions and Technical Eficiency: A Survey of Empirical Applications in Agricultural Economics’. Agricultural Economics 7 (3–4): 185–208.

Begum, Mst. Esmat A., Mohammad I. Hossain, and Evangelos Papanagiotou. 2013. ‘Tech-nical Eficiency of Shrimp Farming in Bangladesh: An Application of the Stochastic Production Frontier Approach’. Journal of the World Aquaculture Society 44 (5): 641–54. Binswanger, Hans P., and Mark Rosenzweig. 1986. Contractual Arrangements, Employment

and Wages in Rural Labor Markets in Asia. New Haven, CT: Yale University Press. Bravo-Ureta, Boris E., Daniel Solís, Víctor H. Moreira López, José F. Maripani,

Abdour-ahmane Thiam, and Teodoro Rivas. 2006. ‘Technical Eficiency in Farming: A Meta-Regression Analysis’. Journal of Productivity Analysis 27 (1): 57–72.

Coelli, Tim. 1995. ‘Estimators and Hypothesis Tests for a Stochastic Frontier Function: A Monte Carlo Analysis’. Journal of Productivity Analysis 6 (3): 247–68.

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