Academic journal article Journal of Agricultural and Resource Economics

Cross-Sectional Estimation of U.S. Demand for Beef Products: A Censored System Approach

Academic journal article Journal of Agricultural and Resource Economics

Cross-Sectional Estimation of U.S. Demand for Beef Products: A Censored System Approach

Article excerpt

Demands for beef products are investigated using the U.S. Department of Agriculture's 1987-88 Nationwide Food Consumption Survey data. The censored translog demand system is estimated with full-information and simulated maximum-likelihood procedures. These procedures represent different approaches to evaluation of multiple probability integrals in the likelihood function, but produce very similar parameter and elasticity estimates. Findings suggest sociodemographic variables play important roles in the demand for beef, and that demand for different cuts of beef should be treated differently.

Key words: demand elasticities, GHK simulator, limited dependent variables, translog demand system

Introduction

Red meat consumption in the United States has decreased significantly in the past few decades following a steady downward trend started in the late 1970s. Per capita consumption of beef, accounting for approximately 60% of total red meat, reached an all-time high of 88.8 pounds in 1976. It dropped about 19% to 72.1 pounds in 1980, remained relatively flat in the early 1980s, and then steadily declined from 74.6 pounds in 1985 to 56.1 pounds in 1998.

Considerable interest and concern have focused on the trend of declining red meat consumption, with special attention given to beef consumption. Smallwood, Haidacher, and Blaylock provide a review of the literature on meat demand with a broad perspective on significant economic and demographic factors affecting the demand for meat. Chavas (1989) suggested that changes in meat consumption could be explained mainly by changing meat prices and lifestyles of American consumers.

Numerous time-series studies have focused on price effects in the demand for meat (e.g., Chavas 1983; Dahlgran; Moschini and Meilke; Wohlgenant). Few analyses, however, have incorporated the use of cross-sectional microdata to examine the effects of changing lifestyles, tastes, and preferences on meat demand. Manchester suggests demand analyses based on aggregate time-series data are unsatisfactory because aggregate data usually mask many changes in the groups that comprise the whole. Furthermore, analyses based on aggregate economic measures provide price and income elasticities but not shifters for the demand function related to changes in socioeconomic and demographic characteristics of the population.

Another source of data available for food demand analysis is the consumer panel data collected by private firms such as ACNielsen. However, these panel data are often more expensive to obtain and not as readily available as public data sources such as the U.S. Department of Agriculture's (USDA's) Nationwide Food Consumption Survey (NFCS) used in the present study.

With the increasing availability of microdata, more recent studies have focused on the effects of demographic characteristics (Capps and Havlicek; Gao and Spreen; Heien and Pompelli; Nayga; Park et al.) and taste change (Gao, Waffles, and Cramer) on demand for disaggregated meat products. Demand studies based on microdata provide better insights on how different groups within the population behave. Taking individual households at the micro level, microeconomic models enable better estimation of demand parameters and improvement of forecasts over those assuming average effects for all members of the population based on aggregate data (Manchester). Accurate forecast of future demands is particularly important to decision makers in the beef industry, as well as government officials, in formulating sound marketing strategies and public policies.

The analysis of microdata, however, is often hindered by the problem of zeros in the dependent variables. Earlier meat demand studies did not address such issues of censored dependent variables (Capps and Havlicek; Heien and Pompelli).1 It is well known that estimation procedures not accounting for the censored dependent variables produce biased and inconsistent parameter estimates. …

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