Elsevier

Agricultural Economics

Volume 31, Issues 2–3, December 2004, Pages 277-284
Agricultural Economics

Do counter-cyclical payments in the 2002 US Farm Act create incentives to produce?☆

https://doi.org/10.1016/j.agecon.2004.09.013Get rights and content

Abstract

Analytical results in the literature suggest that counter-cyclical payments create risk-related incentives to produce even if they are ‘decoupled’ under certainty [Hennessy, D.A., 1998. The production effects of agricultural income support polices under uncertainty. Am. J. Agric. Econ. 80, 46–57]. This paper develops a framework to assess the risk-related incentives to produce created by commodity programmes like the loan deficiency payments (LDPs) and the counter-cyclical payments (CCPs) in the 2002 US Farm Act. Because CCPs are paid based on fixed production quantities they have a weaker risk-reducing impact than LDPs. The latter have a direct impact through the variance of the producer price distributions, while the impact of CCPs is due only to the covariance between the CCP and the producer price distributions. The methodology developed by [Chavas, J.-P., Holt, M.T., 1990. Acreage decisions under risk: the case of corn and soybeans. Am. J. Agric. Econ. 72 (3), 529–538] is applied to calculate the appropriate variance–covariance matrix of the truncated producer price distributions under the 2002 Farm Act. Risk premia are computed showing that the risk-related incentives created by CCPs are significant and do not disappear for levels of production above the base production on which CCPs are paid.

Introduction

Between 1998 and 2001, market loss assistance (MLA) payments were paid to United States crop producers on top of the fixed amount provided by production flexibility contracts (PFC) established in the 1996 FAIR Act. These MLA payments were provided to offset low market prices. The 2002 Farm Act (Farm Security and Rural Investment Act or FSRI Act) has institutionalised this type of support measure in the form of the counter-cyclical payments (CCPs) programme, which will make payments according to fixed area and yields. However, the payment amount depends counter-cyclically on current market prices.
This paper deals with the risk-related effects of the CCPs.1 The starting point is the derivation by Hennessy (1998) of general conditions under which optimal production decisions will be affected by support measures that are ‘decoupled’ under certainty. Under quite general conditions, Hennessy finds that if farmers are risk averse, counter-cyclical payments will increase production and therefore are not decoupled. There is econometric evidence of risk averse behaviour by US farmers as shown, for instance, in Love and Buccola (1991), Saha et al. (1994), Chavas and Holt (1996) and Lence (2000). Studies by Saha, et al. and Lence show consistency with decreasing absolute risk aversion (DARA) behaviour. Applications of these results to policy analysis can be found in OECD, 2003, OECD, 2004.
The design of the CCPs, the analytical work by Hennessy and the empirical evidence concerning farmers’ risk aversion imply that the CCPs programme creates incentives to produce. However, the magnitude of these incentives remains an empirical question. This paper uses a mean-variance approach (see, e.g., Newbery and Stiglitz, 1981, or Coyle, 1992, Coyle, 1999, in the context of duality models) to determine the magnitude of the CCPs risk-related incentives.
The paper is organised as follows: In Section 2 an analytical expression for the risk premium is derived from first order condition for a maximum certainty equivalent profit. This expression is used to compute risk premia under CCPs in Section 3. The methodology requires using the developments in Chavas and Holt (1990) to calculate means and the variance–covariance matrix of truncated distributions of prices. Some insights on the sensitivity of the results to parameter values are provided in Section 4. Finally, concluding remarks are presented in Section 5.

Section snippets

Modelling counter-cyclical payments

Let us consider a representative farmer producing one output. It is assumed that the output price is stochastic and the farmer tries to maximise expected utility from profit π(Q,P˜). We assume that the derivatives of the profit with respect to the output price P˜ and the quantity produced Q are positive (i.e., πP˜>0andπQ,P˜>0), as can be generally accepted. Let us also assume a payment m=β×g(P˜). Proposition 1 in Hennessy (1998) implies that under decreasing absolute risk aversion (DARA) the

Computing production incentives

Computing the risk premium in (10) requires calculating the variance–covariance matrix of the truncated price distributions Max(PT,P˜) and Max(PL,P˜). These distributions determine the new ‘stochastic’ environment faced by each representative producer of each programme commodity. The first column in Table 1 shows the average producer price in 2001 for each programme commodity, extracted from OECD databases.3

Main determinants of risk premia associated with CCPs

From Eqs. (8) and (10) it can be proved that the effective incentive price (including the risk premium) is a decreasing function of risk aversion R and the level of current production Q, and an increasing function of the coverage of CCPs α. In this section, we analyse the sensitivity of the results in Fig. 1 that estimate the changes in the effective incentive price due to lower risk premia created by the new CCPs. This is illustrated for corn in Fig. 2 that shows the sensitivity of the

Conclusions

Previous analytical work by Hennessy provided a general proof that counter-cyclical payments create incentives to produce. This paper has used specific functional forms to model the impacts of payments under the LDPs and CCPs programmes as they were decided in the FSRI Act, in the context of a risk averse farmer maximising expected utility. The methodology proves to be useful to assess risk-related impacts of crop programmes. Both CCPs and loan deficiency payments are found to create

Acknowledgments

The authors wish to thank many colleagues in the Directorate for Food, Agriculture and Fisheries of the OECD, where the underlying analysis was undertaken. The daily interaction with them has significantly contributed to this paper. We would particularly like to thank Stefan Tangermann for his input to the early development of the analysis and to Darryl Jones for useful drafting improvements. The paper has also benefited from comments from those attending a seminar at the Economic Research

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There are more references available in the full text version of this article.
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The views expressed are our own and not those of the OECD Secretariat or its member countries, nor those of INRA.
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