Journal of Agricultural & FoodIndustrial Organization

Volume Article

Risk and Transactions Cost in Contracting:

Results from a Choice-Based Experiment

Darren Hudson∗ Jayson Lusk†

∗Mississippi State University, hudson@agecon.msstate.edu†Purdue University, jlusk@purdue.edu

Copyright c©2004 by the authors. All rights reserved. No part of this publication may bereproduced, stored in a retrieval system, or transmitted, in any form or by any means, elec-tronic, mechanical, photocopying, recording, or otherwise, without the prior written per-mission of the publisher, bepress. Journal of Agricultural & Food Industrial Organizationis produced by The Berkeley Electronic Press (bepress). http://www.bepress.com/jafio

Risk and Transactions Cost in Contracting:

Results from a Choice-Based Experiment∗

Darren Hudson and Jayson Lusk

Abstract

Contracting is a contentious issue in agriculture. Competing theories assert that riskor transactions cost drive contracting decisions, with some argument that autonomy alsoplays a role. We examine preferences for different contract attributes using a choice-based conjoint experiment. Results of a study of agricultural producers show that bothrisk and transactions cost play a role in contracting decisions. Autonomy also plays arole, especially to the extent that producers wish to avoid total loss of autonomy. Theseresults suggest that the effects of risk and transactions cost are relative and should bothbe considered when analyzing contracting decisions.

KEYWORDS: risk, transactions cost, autonomy, contracting, choice-based conjoint,experiments, surveys

∗Authors are Associate Professor, Department of Agricultural Economics, MississippiState University, and Associate Professor, Department of Agricultural Economics, PurdueUniversity. Contact: Darren Hudson, Dept. Ag. Econ., Box 5187, Mississippi State, MS,39762, hudson@agecon.msstate.edu. The comments of Stan Spurlock, David Laughlin,seminar participants at the 2nd annual Conference on Contracts, and the referees aregreatly appreciated. The usual caveats apply. The authors also thank the experimentparticipants. This research was supported by a USDA NRI Grant ##00-35400-9253.

Introduction

Contracts have been an important theoretical topic in economics for some time. The view of contracts is typically filtered through two theoretical constructs: principal-agent theory (Cheung, 1969; Stiglitz, 1974) and transactions cost theory (Coase, 1937; Williamson, 1979; Hart, 1995). The typical principal-agent model begins with the assumption that the producer (agent) is risk averse and the buyer (principal) is risk neutral. Output depends on the producer's effort and exogenous factors (e.g., weather), and only the producer can shirk (moral hazard). These assumptions generate the well-established outcome of tension between properly aligning incentives and risk avoidance. Specifically, as a risk averse agent, the producer should be willing to forgo some level of income, a risk premium, to shift risk to the buyer of the product.

Empirical evidence in support of the risk-shifting hypothesis is thin with most empirical studies finding little support for risk as a determining factor in contract choice (Allen and Lueck, 1995, 1999; Leffler and Rucker, 1991; Hobbs, 1997; Prendergrast, 1999).1 As an alternative, these authors suggest that the data support a transactions cost view of the world. Standard principal-agent models assume that contracts are costless to write (and complete). By contrast, transactions cost theory assumes writing and fulfilling contracts is costly, thus focusing attention on monitoring and enforcement costs and post-contractual opportunism as a result of relationship-specific investment (Klein, Crawford, and Alchain, 1978).

Ackerberg and Botticini (2002) recently criticized this line of empirical literature for failing to account for endogenous matching that occurs between principal and agent. That is, principals and agents may be naturally matched for some reason, say geography or soil conditions, and that failure to account for this possibility may bias empirical estimates of the importance of risk. Ackerberg and Botticini (2002) find that after accounting for endogeneous matching, risk was an important determinant of contract choice.

The empirical literature on contracts has two interrelated elements. First, it has almost exclusively employed secondary contract data in their analyses. Ex post analysis of revealed preference data of this type is extremely useful in making inferences about observed relationships between contract choice and the underlying economic variables. However, this approach necessarily assumes that the participants were facing all relevant alternatives so that the choice made can safely be assumed to be optimal for those participants. That is, if an agent chose not to contract, is it because this choice is optimal among all feasible options, or simply optimal between the contract alternatives they faced when making that decision? Also, secondary data are often confounded by uncontrollable factors. This paper attempts to control for confounded factors by employing a choice-based experiment.

Second, current empirical literature sheds little light on the trade-offs producers would be willing to make to enter a contract. That is, what factors are more likely to induce a producer to choose a particular contract? Knowledge of these trade-offs could be potentially useful in contract design to evaluate the potential acceptance of a contract. In addition, knowledge of these trade-offs could aid in understanding the costs and

1 Knober and Thurman (1995) and Goodhue (2000) are notable exceptions focusing entirely on price risk shifting in broiler contracts.

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benefits of concentration that is occurring in agriculture in part because of the increases in production contracting (Sexton, 2000).

The objective of this paper is to examine contract choice from an ex ante perspective while accounting for theoretically important variables from both principal-agent and transactions cost models. We utilize a choice based conjoint (CBC) experiment to estimate the marginal values (marginal utilities) for different contract attributes. The analysis finds general support for both risk and transactions cost explanations of contracting, but finds that transactions cost factors may, on the margin, outweigh risk factors. This finding may explain the general support for the transactions cost hypothesis in previous literature, but also suggests that, like Ackerberg and Botticini (2002), risk cannot be dismissed as an explanatory factor.

Economic Framework and Methods

We begin by assuming that a producer derives utility from the attributes of a production/marketing arrangement,2 which is denoted from a certainty equivalent perspective by:

),,),(( IATCIVARuU = , (1)

where VAR(I) is income variability, TC is transactions cost, I is income, and A is autonomy in decision-making.3 We assume 0)(/ <∂∂ IVARU , meaning that increases in income risk decreases utility (the producer is risk averse). Thus, a reduction in risk (or risk shifting) is assumed to increase utility. Also, 0/ <∂∂ TCU , or increases in transactions cost decrease utility so that reductions in transactions cost (e.g., a decreased required level of relationship specific investment) will increase producer utility.Finally, 0/ >∂∂ AU , or increases in autonomy in decision-making increases producer utility. Although not commonly considered in theoretical literature, Gillespie and Eidman (1998) found that the level of autonomy significantly affects pork producers' contract choice.

To operationalize this model, choice-based conjoint (CBC) analysis was used to determine impacts of contract attributes on producer utility. One may assume that a contract is a bundle of attributes, each of which provide utility/disutility to the producer (Lancaster, 1966). The CBC method has been used to estimate the utility of product attributes in a variety of settings (Unterschultz et al., 1998; Lusk, Roosen and Fox, 2003; Beggs, Cardell and Hausman; Adamowicz et al., 1997, 1998; Jayne et al., 1996; Roe, Boyle and Tiesl, 1996). CBC analysis can effectively predict the success of new products (Jayne et al.) and has been shown to be consistent with consumers' revealed preferences (Adamowicz, Louviere, and Williams, 1994; Adamowicz et al., 1997) and has been shown to be robust to hypothetical bias (Carlsson and Martinsson, 2001). Finally, CBC

2 This arrangement could be any number of contract types including spot cash sales (no contract). Some might consider cash sales simply to be a spot contract, but we distinguish it here to explicitly recognize that a producer may prefer to operate without any type of formal contractual arrangement with a buyer.3 We note that income may be interrelated with other variables. This point is specifically addressed in the empirical model.

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analysis is also appealing because it is based on random utility theory (Louviere, Hensher, and Swait, 2000) and allows for multi-attribute valuation.

Choice-Based Experiment

A CBC experiment was conducted via personal interview using 19 agricultural producers in an agricultural extension program in Weslaco, TX and 30 agricultural producers at Tunica, MS.4 Two factors motivated sample selection: cost and diversity. First, the agricultural extension group in Texas was already assembled and represented a low cost, ready sample of willing respondents. The Mississippi sample was also relatively easy to establish through the assistance of the National Agricultural Statistics Service (NASS). Second, these two samples are geographically diverse, with different crop mixes, growing conditions, etc., allowing for a diversity of preferences should they exist.

In the experiment, we presented producers with several discrete choices between two different contracts as well as marketing in the cash market (three different choices), each of which was described by a set of attributes (Table 1). The producers were asked which contract was most preferred in several repeated scenarios, an example of which is shown in Figure 1. The contract attributes were chosen to directly determine the effects of risk, transactions cost and autonomy on contracting decisions. The ranges of the contract attributes shown in Table 1 were established through responses to a pre-experiment survey conducted in Mississippi. The pre-survey was designed to provide insight into the types of contracts being used, methods of risk shifting, degree of asset specificity, income level, etc., so that values presented to the producer were realistic.

Table 1. Attributes and Attribute Levels Used in Choice-Based Conjoint Experiment.Attribute Attribute Levels

Level 1 Level 2 Level 3Expected Income $135,000 $150,000 $165,000Price Risk Shifted None Semi-Fixed FixedAutonomy None Some SameAsset Specificity 10% 30% 50%Provision of Inputs 0% 50% 100%Length of Contract 1 3 5

4 The sample of producers in Mississippi was drawn by the National Agricultural Statistics Service (NASS), but was not a random sample. Respondents were selected on their willingness to cooperate withNASS and participate in a data collection exercise. The respondents were recruited by providing one night's stay at a conference hotel and dinner for two at a nearby restaurant. The interviewer was the same in both locations, and all questions were administered in the same order. The advantage of the personal interview format is that it allows the respondents to seek clarification from the monitor, which better insures reliability of answers from respondents (McFarlane and Garland, 1994; Ayidiya and McClendon, 1990). However, the personal interview format is expensive to administer, necessarily limiting sample size. It should be noted, however, that these 49 participants generated 708 observations on contract choice.

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Scenario 1

Options A and B represent two different descriptions for a contract/marketing arrangement. Please check (�) the option (A, B, or C) that you would be most likely to choose.Contract attribute Contract A Contract B Cash Sales

Expected Income $165,000 $135,000 $150,000

Price Risk Shifted to Buyer Fixed Semi-Fixed None

Level of Autonomy(Relative to Cash Sales)

Lower None Same

Required Investment in Specific Assets

10% 10% 10%

Provision of Inputs 50% 100% 0%

Length of Contract/Arrangement

1 1 0

I would choose . . .

Figure 1. Example Choice Set Used in Conjoint Experiment.

Because we wished to evaluate theoretical constructs and not specific individual contracts, the following variables are necessarily abstract. That is, this analysis does not attempt to value a specific type of contract. Rather, the focus of analysis is on theoretically important variables. This choice sacrifices the specificity of contracts within a given product, but allows a broader examination of theoretical predictions. In each CBC choice set (e.g., Figure 1), producers were asked to evaluate contracts described by six attributes: expected income, price risk shifted to buyer, autonomy, asset specificity, contractor provision of inputs, and contract length. These attributes were chosen to test relevant theoretical constructs identified by previous literature.

Respondents were told to assume that their average farm income (net variable production expenses) averaged $150,000 for the past five years. The variable "Expected Income" referred to the expected income for a particular contract. Income was chosen because it is not product specific, where price would be product specific. The base income was varied by ± 10% to arrive at the three levels of income in the experiment. The range of income levels should provide enough variation in income to examine the effects of expected income on contract choice and was consistent with the income variability reported in the pre-survey.

"Price risk shifted to the buyer" was the primary risk avoidance variable in the experiment. The degree of risk shifting is difficult to capture with a continuous variable in this survey format. We formulated it as a discrete variable for ease in respondent understanding. A value of "None" was described to mean that the producer had to bear all price risk (and hedging in the futures market was not possible). That is, the contract offered no price protection and was equivalent to selling in the cash market (in terms of

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risk shifting). The "Semi-Fixed" level was described as a contract with some, but not complete price protection.5 This level was introduced as the intermediate point between no risk shifting and complete risk shifting. The final value of "Fixed" was described as the buyer taking all price risk from the producer. The producer may face penalties if the incorrect quantity or quality is delivered, but otherwise the price was described as fixed. The risk-shifting attribute might interact with the expected income attribute. If price risk is either moderately or completely shifted to the buyer, the producer becomes more certain about the expected income.6 This potential interaction was accounted for in the experimental design as described below.

The "Provision of Inputs" refers to the percentage of total inputs provided by the contractor.7 Because the Income variable is net of production expenses, this variable can also be viewed as a risk avoidance variable. If more inputs are provided by the contractor, the producer's exposure to input price variability decreases and expected income becomes more certain.8 Thus, this variable should provide positive utility to the producer. The cash marketing option was assumed to have no provision of inputs.

The "Level of Autonomy" was included as a contract attribute to test the finding of Gillespie and Eidman (1998) that producers prefer autonomy, and should therefore be willing to forgo income to maintain autonomy. A value of "None" means that the contract does not allow the producer any autonomy; the contractor makes all production and marketing decisions. The producer only provides the labor input (and, perhaps, some infrastructure). A value of "Some" was described as the contract allowing the producer to make production management decisions, but the producer was bound by contract in marketing. Finally, a value of "Same" was described as a contract where the producer is free to make all production and marketing decisions. This is equivalent to marketing in the cash market.

"Asset Specificity" refers to the percentage of total assets that must be invested in assets that can only be used in one productive enterprise, and is the primary variable used to represent a transactions cost.9 This variable was varied at 10%, 30% and 50%, meaning that, for example, 50% of total assets must be invested in assets that can only be used in one productive enterprise to enter the contract. Respondents were told to assume

5 This, along with other variables, is subject to individual interpretation. We attempted to provide a consistent definition to the entire group and all producers were allowed to seek clarification in a group setting so all other participants could hear. Examples of these attributes relevant to the producers were provided so that all were generally considering the same levels of the attributes. However, the individual interpretation might lead to measurement error. We believe the definitions and discussion mitigated that error, but the degree of measurement error is not known.6 Note, however, that the producer is not completely certain about income. That is, the price in this case was fixed, but production is not.7 In many types of agricultural contracts, the contractor provides inputs. For the contractor, this provision is a means of quality control. For the producer, this provision is a means of shifting input price risk.8 There is always concern that inputs provided by contractors are of inferior quality to other inputs. To avoid confounding effects, respondents were told that the inputs were of the same quality they would purchase themselves.9 While producers were provided with examples of relationship specific investments such as poultry houses in broiler contracts, it is difficult to determine directly whether producers actually associated this investment with potential hold-up problems. As Allen and Lueck (1992) have pointed out, rent appropriation is often not a problem in agriculture so that producers might not be accustomed to viewing asset specificity in this manner. However, the results as presented later suggest that producers did, in fact, recognize the costs of potential hold-up problems.

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that any such investment made as a result of this contract had an amortization period of five years. Given that increases in the level of specific asset investment increase the possibility of opportunistic behavior, this variable is hypothesized to have an inverse relationship with producer utility (negative marginal utility). The cash marketing option was described as having a specific asset investment of 10%.

Finally, "Contract Length" refers to the length of contract in years. A value of 1 means that the contract is for one year. Respondents were told that the attributes of a contract would not change over the life of the contract, or, once signed, the provisions within the contract would not change. For example, if a contract had an expected income of $150,000 and the length was 5 years, the producer would have an expected income of $150,000 per year over the five-year period. Respondents were also told that there was no guarantee that the contract would be renewed at the end of the contract period. For cash marketing, the contract length was assumed to be zero.

Experimental Design

Proper experimental design is imperative for the success of any CBC application. The advantage of the CBC technique is that it allows the deliberate manipulation of contract attributes across choice sets to test specific hypotheses. However, administering an experiment with the full factorial design of all possible combinations of attribute levels would be cumbersome and expensive. In the current study, there are 6 attributes with three levels each, or 36=729 possible unique contracts. We generate a fractional factorial design with a smaller set of possible combinations while maintaining efficiency of our estimates (Louviere, Hensher, and Swait, 2000).10

In experimental design, researchers must be concerned with the ‘effects’ that they are attempting to estimate (Louviere, Hensher, and Swait, 2000). That is, a simple model attempts to estimate the main effects, while more complex models incorporate two-way and higher order interactions between the attributes. The more interaction effects that are incorporated, the more complex and sizable the experimental design. An experimental design that only incorporates main effects cannot be used to estimate two-way and higher order interaction effects. In the current analysis, we incorporated all main effects, two-way interaction effects between income and all other variables,11 and two-way interaction effects between contract length and asset specificity.

The set of potential contracts was created by first generating the full factorial design, and then extracting from that design an array that maximizes design efficiency12

10 A second area of concern is environmentally correlated attributes. That is, some variables tend to move together in reality. For example, perhaps the provision of inputs moves with price risk shifted to the buyer in actual contracts. An orthogonal design, despite having superior statistical properties, does not account for this environmental correlation, which has led to some consternation and concern on the part of researchers (Cattin and Wittink, 1982; Green and Srinivasan, 1978). However, Moore and Holbrook (1990) have demonstrated that, while somewhat important theoretically, the use of orthogonal designs in the face of environmentally correlated attributes is not a problem in practice.11 Allowing for interaction effects between income and all other variables allows the direct effect of income to be altered by the levels of other variables.12 Design efficiency here refers to the D-efficiency criterion used in the PROC FACTEX/OPTEX procedures in SAS. The objective of the D-efficiency is to maximize the information matrix of the design.

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(Kuhfeld, Tobias, and Garratt, 1994)).13 The resulting design contained 73 different choice sets (an example can be seen in Figure 1). Each choice set contained two possible contracts plus the option for cash marketing (or, equivalently, no contract). It would be unrealistic to expect each individual to examine all 73 different choice sets. Thus, each choice set was randomly selected without replacement and assigned to one of the five blocks (four blocks of sixteen and one block of nine). Blocks were then randomly assigned to an individual.14 Data were also collected on age, crops grown, farm income, and experience with contracting for each respondent.

Estimation

Following Louviere, Hensher and Swait (2000), a random utility model is defined as:

ijijij VU ε+= (2)

where Uij is the ith producer's utility of choosing marketing arrangement j, Vij is the deterministic portion of utility and εij is the stochastic component of utility. Figure 1 shows that there are three options--Contract A, Contract B, and Cash Sales (defined as A, B, and C)--that the producer has to choose from. The probability of choosing any of these j marketing arrangements is:

{ } { }iikikijij CkallforVVchosenisj ∈+≥+= ;PrPr εε (3)

where Ci is the choice set for producer i (Ci = {A,B,C}). Assuming that the random errors in equation 1 are independently and identically distributed across the j alternatives and N individuals with a Type I extreme value distribution and scale parameter equal to 1, the probability of producer i choosing contract j is given by:

{ } ∑∈

=

Ck

V

V

ik

ij

e

echosenisjPr (4)

Equation 4 was estimated as a conditional logit model composed of the attribute levels reported in Table 1. Numerical variables (i.e., Income, Asset Specificity, Provision of Inputs, and Contract Length) were entered as continuous variables in the regression. Qualitative variables can be entered as either dummy variables, or effects coded variables15 (Louviere, Hensher, and Swait, 2000).

13 Recent work by Lusk (2002) on CBC experimental design shows that the approach followed by Kuhfeld, Tobias, and Garratt (1994) performs as well at identifying the underlying utility function as any other experimental design.14 In the models that follow, block effects were examined, but no significant block effect was found. Thus, only models without block effects are presented for clarity.15 Effects coded variables are similar to dummy variables except that they take on a value of -1 when the attribute is at the "base" level, making the base equal to the negative sum of the other dummy variables. For example, Autonomy contains three levels. Assume that "None" is the base. If Autonomy was equal to "Some," the effects coded dummy variable for Some would equal 1 and Same would equal 0. If Autonomy

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There are several reasons why coding the effects is preferred to normal dummy variables in this application (Adamowicz, Louviere, and Williams, 1994). First, traditional dummy variable coding confounds the alternative specific constants, where effects coded variables are uncorrelated with the intercepts. Second, use of effects coding allows the recovery of the marginal utility for the base value of the attribute. Despite these advantages, effects coding and dummy variables are qualitatively equivalent. Effects coding was chosen as a matter of convenience.

One issue of importance in this analysis is the value producers place on the different attributes. The estimated coefficients from equation 4 represent the marginal utility of that attribute, which can be used to estimate the "implicit" marginal value of each attribute in monetary terms. These values represent the income increase (decrease) needed to offset the positive (negative) utility provided by a particular attribute. For example, assume that producers received positive marginal utility from both income and degree of risk shifted to the buyer. These assumptions suggest that the producer is willing to forgo some income to increase risk shifting. By examining the ratio of the parameter estimate for risk shifting relative to the parameter estimate of income (the ratio of marginal utilities), an estimate of the amount of money the producer is willing to forgo to shift risk is obtained.16

In many cases, one might expect heterogeneity in preferences within the population. A "mixed" or "random parameters" logit (RPL) model can be used to investigate heterogeneity of preferences (Revelt and Train, 1998). In the RPL model, the β's from equation 4 are allowed to vary across the population with an assumed distribution (in this case, normal). This specification relaxes the restriction that every respondent exhibits constant marginal utilities for contract attributes. The advantage of the RPL method is that it is not subject to the independence from irrelevant alternatives found in the conditional logit model and accounts for the repeated observations taken from each respondent (see Revelt and Train (1998) for computational details of the RPL). The results of this model provide an indication of the variability of contract preferences within the sample.

Results

The Sample

The characteristics of the producers in the Texas (TX) and Mississippi (MS) samples are shown in Table 2. Compared with the 1997 Census of Agriculture for those regions, these producers are larger and younger than the average. Thus, this sample is not particularly representative of the overall population. Younger and larger farmers are likely to attend these types of meetings more frequently than older and smaller farmers.

was equal to "Same," the effects coded dummy variable for Some would equal 0 and Same would equal 1. However, if Autonomy was equal to "None," the effects coded dummy variable for Some would equal -1 and Same would equal -1.16 In applications where this is an attribute of a product a consumer is purchasing, this ratio is called the marginal willingness to pay. In this case, the ratio is interpreted as a marginal willingness to accept. That is, it represents the marginal amount of income the producer is willing to forgo to obtain risk shifting. Alternatively, it is the amount of income the producer must receive to bear all risk in the contract.

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This fact limits the generalizability of the results, but does not negate the hypothesis tests. If the attributes considered are important for these producers, then future work can be used to determine if the results hold for a more representative sample. Sample representativeness is much more difficult to guarantee in a personal interview context and is often unnecessary in CBC analysis if one simply wishes to examine whether the attributes are important to some segment of the population (Beggs, Cardell and Hausman, 1981).

Table 2. Descriptive Statistics of Experiment Participants.Attribute Mississippi Texas

Mean Stand. Dev. Mean Stand. Dev.Gross Farm Income $600,000 $322,437 $518,421 $329,229Age (years) 46.57 10.29 46.53 9.73% Producing Corn 67% 0.48 57% 0.51% Producing Cotton 83% 0.38 74% 0.45% Producing Soybeans 70% 0.47 11% 0.32% Producing Wheat 0% 0.00 5% 0.23% Producing Fruits 0% 0.00 5% 0.23% Producing Vegetables 0% 0.00 16% 0.37% Producing Cattle 37% 0.49 53% 0.51Prior Contracting Experience (%) 73% 0.45 89% 0.32 Cash Forward 50% 0.51 84% 0.38 Marketing Pools 50% 0.51 26% 0.45 Resource Providing Contracts 0% 0.00 11% 0.32 Production Mgmt. Contracts 3% 0.18 26% 0.45

Regression Results

First, we estimated a main effects conditional logit model (Table 3). Models with interaction effects were estimated, but are not reported here to conserve space. No interaction effects were statistically significant, which is consistent with general findings that main effects make up the preponderance of the observed variability in choice (Louviere, Hensher, and Swait, 2000). The fact that the interaction effect between asset specificity and contract length was not statistically significant suggests that one of the primary methods of mitigating the transactions cost effect of asset specificity is not employed in this sample. Given the results of Allen and Lueck (1992), this result appears reasonable.

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Table 3. Conditional Logit Regression Results-Main Effects Model.Variable Estimated Coefficient t-valueASC1 -0.0103 0.469ASC2 -0.1139 0.505Expected Income 0.00004 7.832**

Asset Specificity -0.0158 3.798**

DFixed 0.3071 3.497**

DSemi-Fixed 0.2179 2.279*

DNone -0.2412 2.475*

DSome -0.0488 0.533Contract Length -0.2443 5.958**

Provision of Inputs 0.0045 2.716**

χ2 value 83.404**

Number of Observations 708* Statistically significant at the 0.05 level.** Statistically significant at the 0.01 level.

To account for potential contract preference heterogeneity, we estimated an RPL model, the results of which are shown in Table 4. In this analysis, all parameters were allowed to vary except for the expected income and the alternative specific constants (ASCs) (it is much simpler to calculate willingness to pay/accept when income is held fixed). Allowing parameter estimates to be random alters the magnitude of the mean parameters to some degree, but the major relationships (signs and statistical significance) remain unchanged. Because the statistical properties of the RPL are superior to the conditional logit when heterogeneity is present, the RPL model results are used for interpretation.

The ASCs are not statistically significant indicating that these producers viewed cash marketing (no contract) and the offered contracts equivalently, on average. Given that the two contracts presented were generic, this result confirms that the producers exhibited no systematic preference for one generic contract over another as would be expected if respondents were answering consistently. In addition, the lack of statistical significance of the ASCs suggests that the attributes presented adequately explained contract behavior.

Increases in contract expected income are significantly related to increases in probability of contract choice (or income has a positive marginal utility). Results indicate that producers significantly value risk shifting as denoted by the coefficients for DFixed

and DSemi-Fixed. As would be expected, complete risk shifting (DFixed) provides more utility than only partial risk shifting (DSemi-Fixed). In addition, a test of the joint hypothesis that the risk shifting dummy variables were zero was rejected at the α = 0.05 level (Wald statistic = 29.58). Because these dummy variables were effects coded, the utility for no risk shifting is simply the negative sum of the two risk shifting dummy variables. Thus, producers derive disutility from contracts with no risk shifting. These results are consistent with risk aversion, lending support to the risk-avoidance hypothesis as a motivation for contracting.

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Table 4. Random Parameters Logit Regression Results-Main Effects Model.Variable Estimated Coefficient t-value

Random Parameters in Utility FunctionAsset Specificity -0.0272 2.807***

Provision of Inputs 0.0063 2.055**

Contract Length -0.3517 4.045***

DFixed 0.3796 2.422**

DSemi-Fixed 0.3673 1.977**

DNone -0.6256 2.043**

DSome 0.0187 0.114Non-Random Parameters in Utility Function

ASC1 0.0702 0.230ASC2 0.0586 0.185Expected Income 0.00006 4.740***

Derived Standard Deviations of Parameter DistributionsS(Asset Specificity) 0.0401 1.942*

S(Provision of Inputs) 0.0141 1.874*

S(Contract Length) 0.0347 0.109S(DFixed) 0.7419 1.931*

S(DSemi-Fixed) 0.6045 1.451S(DNone) 1.0036 2.144**

S(DSome) 0.1753 0.361χ2 value 233.438***

Number of Observations 708* Statistically significant at the 0.10 level.** Statistically significant at the 0.05 level.*** Statistically significant at the 0.01 level.

Producers derived significant disutility from investment in relationship specific assets, suggesting that producers would prefer to invest in assets with multiple uses to avoid rent appropriation. More generally, this result confirms prior results in other contexts (Allen and Lueck, 1992, 1995, 1999; Leffler and Rucker, 1991; Hobbs, 1997) that transactions cost is a motivation for contracting. However, contrary to these findings, this analysis also supports risk shifting as a motivation as well, which is also consistent with recent work (Ackerberg and Botticini, 2002). A possibility is that when controlling for matching problems as this experimental approach does, the impact of risk becomes more apparent. However, if the effect of transactions cost were relatively larger, ex post analysis of contract choice would likely suggest that only transactions cost mattered. These results suggest that perhaps transactions cost are relatively more important, but risk cannot be dismissed as a determining factor in contract choice.

The role of risk is further reinforced by the statistically significant utility derived from input provision. That is, because the income variable was defined as net of variable production expenses, provision of inputs can be viewed as a mechanism to shift input price risk from the producer to the contractor. Interpreted in this way, the statistically significant utility on provision of inputs can be viewed as utility derived from shifting input price risk. Again, given this attribute's statistical significance, we can conclude that input price risk is a determining factor in contract choice.

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In terms of autonomy, complete autonomy loss (DNone) was statistically significant and inversely related to producer utility. This confirms the findings of Gillespie and Eidman (1998) of the importance producers place on autonomy in decision-making in contract choice. However, producers did not distinguish between some autonomy loss (DSome) and complete autonomy in decision-making. This result may be related to some degree of measurement error associated with producer interpretation of "some" autonomy loss. A joint hypothesis that the autonomy dummy variables were zero was rejected at the α = 0.05 level (Wald statistic = 10.77). The results, then, support the hypothesis of the importance of autonomy.

Finally, contract length is inversely related to producer utility, suggesting that producers prefer shorter contracts. Traditionally, one would expect longer contracts to be preferred to economize on renegotiation costs and avoid quasi-rent appropriation (Joskow, 1987; Crocker and Masten, 1988). However, as Allen and Lueck (1992) have pointed out, in agriculture where quasi-rents are sometimes less prominent and common-law enforcement of contracts is strong, the need for long-term contracts is diminished. Thus, this preference for shorter contracts is consistent with prior research in agricultural contracts.

A major focus is on the derived standard deviations of the parameter distributions. According to these results, significant preference heterogeneity is observed for asset specificity, provision of inputs, complete risk shifting, and complete loss of autonomy as indicated by the statistical significance of the derived standard deviations of parameter distributions (Table 4). To illustrate the impacts of preference heterogeneity, the mean and standard deviation of willingness to pay/accept (WTPA) on these attributes were calculated (Table 5). The mean WTPA is simply calculated as the ratio of the coefficient of interest (e.g., asset specificity) to the coefficient on expected income. The standard deviation of the WTPA is derived by taking the ratio of the derived standard deviation of the parameter distribution for the attribute of interest to the coefficient on expected income (Revalt and Train, 1998). Percentages of the population exhibiting a WTPA above (or below) a particular WTPA value is obtained by evaluating the normal cumulative distribution (or inverse cumulative distribution) at that value with the mean and standard deviation derived from above.

Table 5. Marginal Willingness to Pay/Accept and Derived Standard Deviations.Variable Mean WTPAa STD WTPAb Pr(WTPA < 0)c

Asset Specificity -$426.34 $628.58 0.75Provision of Inputs $98.82 $221.73 0.32DFixed $5,950.78 $11,630.03 0.30DSome -$9,807.18 $15,732.87 0.73a Willingness to Pay/Accept (WTPA).b Derived Standard Deviation of WTPA.c Based on a normal distribution with associated mean and standard deviation.

For asset specificity, producers exhibit a mean WTPA of -$426.34, meaning that for each additional percentage of total assets that must be invested in a relationship specific asset, producers must be compensated $426 to induce contract adoption, on average. Alternatively, producers would be willing to forgo $426 in expected annual

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income for reducing relationship specific asset investment by 1 percent.17 The derived standard deviation on WTPA for asset specificity is $628.58. Using these estimates, 75% of the population would be expected to have a WTPA of less than 0.18

In terms of risk shifting, producers were willing to forgo $5,950.78 in expected annual income to completely shift price risk to the contractor. Given that the expected annual income was, on average, $150,000, this represents a willingness to forgo 4% of annual income to completely shift price risk. At the same time, the derived standard deviation on price risk shifting was $11,630.03. Based on these data, we would expect about 70% of the population to derive positive utility from complete price risk shifting. In terms of risk aversion, these results suggest that 30% of the population is either risk-neutral or -seeking, which is consistent with other experimental work on risk aversion in agriculture (Pennings and Garcia, 2001).

A second risk shifting measure was provision of inputs. Here, producers exhibited a mean WTPA of $98.82 for each percentage of total inputs provided by the contractor. Again, the positive utility observed here is consistent with risk aversion in that producers were willing to forgo income to have inputs provided. The derived standard deviation on the WTPA is $221.73, suggesting that approximately 68% would derive positive marginal utility from input provision.

Finally, the mean WTPA for complete autonomy loss is -$9,807.18, suggesting that producers must be compensated about $9,800 in expected annual income to accept a contract with complete loss of autonomy. The standard deviation on the WTPA is $15,732.87, suggesting that 73% of the population would be expected to derive disutility from complete autonomy loss. These results generally reveal heterogeneity in contract attribute preferences, which is not surprising in itself, but reinforces the difficulty in contract design. Complexity in contract design has two important implications. First, sufficient heterogeneity significantly complicates the ability of the contractor to design a contract that will attract a sufficient number of producers to garner needed supplies of the product. Second, as is particularly important in agriculture, sufficient heterogeneity in contract preference complicates the policy formation process. As preference heterogeneity increases, the ability of the government to design concise, cohesive policies diminishes (Hueth, 2000; Bourgeon and Chambers, 2000).

Conclusions

This analysis utilized a choice-based conjoint experiment to examine preferences for contract attributes. Personal interviews of agricultural producers in Texas and Mississippi were conducted to gather choice information. Random utility models were estimated, and estimates of the monetary value of attributes were derived from these marginal utility estimates.

17 Given this estimate, producers must be compensated $21,300 for moving from no relationship specific investment to investing 50% of total assets in relationship specific assets.18 The fact that 25% appeared to derive positive utility may seem counterintuitive. This result may arise from the assumption of normality of the parameter distribution. Use of a log-normal distributional assumption resulted in qualitatively similar results, with about 30% of the population expressing a WTPA close to zero. The results using the normality assumption are presented for convenience and to be consistent with the other parameters presented below.

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There are two general conclusions that can be drawn from this research. First, in contrast to previous empirical studies, we find that risk avoidance is an important determinant of contract choice. While our findings concur with previous research on the importance of transactions cost, risk shifting also affects the choice of contract. Taken together, these findings suggest that the effects of risk and transactions cost are relative. If the marginal utility of transactions cost attributes outweighs the marginal utility of risk avoidance attributes, the observed relationship in an ex post setting should indicate that transactions cost drove the decision to contract. Our results are consistent with this hypothesis and provide a plausible explanation for the lack of importance of risk in previous studies.

Second, we find that preferences for contract attributes exhibit significant heterogeneity within the population. While not surprising, these results indicate that this heterogeneity could have profound impacts on the efficacy of contract and public policy design. Although principal-agent models depend on heterogeneity in agent type, preference heterogeneity complicates the ability of the contractor to write contracts that will successfully attract sufficient producer participation to garner necessary product supplies.

The sample used in this study was not representative of the overall population of agricultural producers in the sampled areas. However, these results mean that more research is needed with more representative samples to determine whether the conclusions drawn can be extended to the population. Finally, the choice-based experiment was used to test hypotheses from relevant theoretical literature. However, this technique could also be extended to test acceptance of particular contract forms for particular commodities.

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