Supply Response of Farm Program in Rice-Growing States

By Chowdhury, A. A. Farhad; Herndon Jr., Cary W. | International Advances in Economic Research, November 2000 | Go to article overview

Supply Response of Farm Program in Rice-Growing States


Chowdhury, A. A. Farhad, Herndon Jr., Cary W., International Advances in Economic Research


CARY W. HERNDON JR. [*]

This study aims to develop an acreage response model for rice-growing states using a policy-inducing variable. The single equation regression model for each rice-producing state is estimated by using the ordinary least square multiple regression procedure after log transformation. Data for the period 1959 to 1988 for acreage plantation, market price, target price, loan rate, and acreage reduction rate are used in designing the policy-inducing price. The estimated parameter shows a significant inverse relationship between the rice acreage planted and policy-inducing prices in all of the rice-growing states, with the exception of Louisiana. The estimated short-run elasticities for Arkansas, California, Louisiana, Mississippi, and Texas are -0.36, -0.68, 0.30, -0.37, and -0.59, respectively. The heterogeneity in the magnitude of the elasticities suggests the need for redesigning the rice programs to generate desirable acreage responses from all of the rice-growing states. (JEL Q10)

Introduction

In the U.S., rice is grown mainly in five states: Arkansas, California, Louisiana, Mississippi, and Texas. These states differ in physical restraints, average yields, acreage planted, types of rice planted, market price, and returns above cost. The farmers' response to acreage planted in these states is influenced by expected crop prices, input costs (such as machinery, fertilizer, and seed), climactic expectations, presence of plant pests (such as insects and diseases), the nature and extent of fixed-scale factors, the current state of technology, and, also importantly, government farm policy provisions. As a result, while designing the acreage response model, this research gave considerable attention to the method of incorporating a government farm policy variable in the acreage response equation.

There have generally been three rice policy periods:

1) 1955-73, during which acreage allotments [1] and marketing quotas [2] were in effect, following a dramatic increase in rice production in 1954;

2) 1974-81, when acreage allotments and marketing quotas were abolished for the 1974 and 1975 crop years. Target prices [3] and loan rates [4] were made effective with the beginning of the 1976 crop year; and

3) 1982-90, which was based on provisions of the 1981 and 1985 farm bills.

The 1981 program eliminated the rice acreage allotment system and introduced the base acreage [5] concept. The payment-in-kind (PIK) provision went into effect in 1983 when there was a surplus rice production. This reduced Commodity Credit Corporation [6] inventories and raised market prices within reason [Green, 1990].

Although the objective of government farm policies has been to achieve increases in farm income, reduce surpluses, and stabilize prices, rice stocks and rice market prices during the different policy periods increased and varied widely [U.S. Department of Agriculture (USDA), 1992, p. 21]. As a result, forecasting producer planting expectations has been a challenging task for policy makers and national agricultural planners [Garst and Miller, 1975, pp. 30-7]. However, this could be overcome by formulating a supply-inducing variable, in conjunction with market information which adequately reflects the essence of government farm programs, and then to incorporate it with the acreage response equation [Bailey and Adams, 1989, p. 3].

Another concern regarding the farm program is that it contains no regional considerations. All rice-producing states have a uniform loan rate and target price, in spite of their differences in physical restraints, availability of irrigation water, average yields, and returns above cost. Therefore, designing a supply-inducing variable incorporating farm programs with regional disparities may be more conducive to predicting producer planting expectations in different rice-growing states. This, in turn, would help policy planners to undertake policy measures with the goal of providing uniform benefits to rice growers consistent with regional needs. …

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