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The Impact Of Health Information And Demographic Changes On Aggregate Meat Demand

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  • Schroeter, Christiane
  • Foster, Kenneth A.
Abstract
Over the past few decades, U.S. meat consumption patterns have changed. Food consumption patterns are influenced by changing demographic characteristics, changing lifestyles, increasing health, and nutrition concerns. Prior research suggests that these factors have significant influence on the demand for meat (Capps and Schmitz; Kinnucan, Hsia, and Jackson). By incorporating a demographic and a health information variable in the meat demand system, this study aims to quantify and interpret important non-price determinants of meat demand. Demographic and health information variables might act as demand shifters in the model. Evaluating the effects of changes on meat demand delivers information on the potential existence of structural change in the underlying consumer utility function. This research statistically estimates the impact of health and demographic information on the aggregate U.S. demand for beef, pork, poultry and fish over the period of 1970 to 1999. To estimate these demands, this study employed both the Almost Ideal Demand System (AIDS) by Deaton and Muellbauer and the Inverse Almost Ideal Demand System (IAIDS) by Eales and Unnevehr. The well-defined preference structure and consistent aggregation from the micro to the market level has made the AIDS especially popular when modeling the demand for meat. The IAIDS is used in this study, because of the focus on perishable agricultural products; thus, it might seem appropriate to have quantities as exogenous permitting prices to adjust in order to allow short-run market clearance. Comparing the results of each demand system provides insight into which demand system most appropriately represents the variables of interest. However, in significant contrast to past studies that have focused on the role of health information on meat demand, this study will additionally explore how structural change depends on the endogeneity of prices and quantities (Capps and Schmitz; Eales and Unnevehr; Kinnucan, Hsia, and Jackson). This study treats demographic and health information measures as concomitant variables in the standard AIDS and IAIDS share equations. This research also evaluates whether these variables support the type of structural change in meat demand that have been proposed in previous research, such as that by Moschini and Meilke who suggest a mid 1970s demand change. The health index in the model is represented by the cumulative sum of the net number of reviewed medical journal articles published, which support the linkage between cholesterol and heart disease. The study uses the original Brown and Schrader index as base data, subsequently weighted by a factor representing the relative proportion of all journal articles providing negative cholesterol information (Kinnucan, Hsia, and Jackson). Demographic information is represented by women's participation in the labor force. Results from this study indicate that the index of the percentage of women in the work force is a determining factor in estimating meat demand. This variable represents several demographic changes that have occurred over the past two decades. More women, particularly mothers, work, which leads to an increase in households with both parents in the work force. With more time spent outside the household, less time can be devoted to preparing meals for the family. The demand for easy-to-prepare meal solutions rises, and leads to a modification in consumption behavior. Results of this study indicate that the poultry and fish industries have responded to consumers' demand for easily prepared meat products. In contrast to previous research (Kinnucan, Hsia, and Jackson; Capps and Schmitz), this study shows that the health information index does not have a significant effect on aggregate meat demand. These results suggest that in aggregate, the concerns about cholesterol consumption have had little, if any, effect on meat demand. Disentangling the effects of health information and demographic changes on meat demand merits further investigation. The availability of this information will help producers develop products which better correspond to consumer tastes, preferences and demographics. Retailers will also benefit by developing more effective marketing strategies and an opportunity to expand market share. As a result, consumers could benefit from improved availability of products and information that meet their needs and circumstances.

Suggested Citation

  • Schroeter, Christiane & Foster, Kenneth A., 2004. "The Impact Of Health Information And Demographic Changes On Aggregate Meat Demand," 2004 Annual meeting, August 1-4, Denver, CO 20130, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    • Handle: RePEc:ags:aaea04:20130
    DOI: 10.22004/ag.econ.20130
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    References listed on IDEAS

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    1. Giancarlo Moschini & Karl D. Meilke, 1989. "Modeling the Pattern of Structural Change in U.S. Meat Demand," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(2), pages 253-261.
    2. James S. Eales & Laurian J. Unnevehr, 1993. "Simultaneity and Structural Change in U.S. Meat Demand," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 75(2), pages 259-268.
    3. Kinnucan, Henry W. & Chang, Hui-Shung (Christie) & Venkateswaran, Meenakshi, 1993. "Generic Advertising Wearout," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 61(03), pages 1-15, December.
    4. Poray, Michael C. & Foster, Kenneth A. & Dorfman, Jeffrey H., 2000. "Measuring An Almost Ideal Demand System With Generalized Flexible Least Squares," 2000 Annual meeting, July 30-August 2, Tampa, FL 21796, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    5. Moschini, GianCarlo & Vissa, Anuradha, 1992. "A Linear Inverse Demand System," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 17(2), pages 1-9, December.
    6. Moro, Daniele & Sckokai, Paolo, 1999. "A Quadratic Inverse Demand System," 1999 Annual meeting, August 8-11, Nashville, TN 21560, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    7. Chalfant, James A, 1987. "A Globally Flexible, Almost Ideal Demand System," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(2), pages 233-242, April.
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    13. John D. Jackson, 1997. "Effects of Health Information and Generic Advertising on U.S. Meat Demand," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(1), pages 13-23.
    14. Deborah J. Brown & Lee F. Schrader, 1990. "Cholesterol Information and Shell Egg Consumption," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 72(3), pages 548-555.
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    16. Jean-Paul Chavas, 1983. "Structural Change in the Demand for Meat," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 65(1), pages 148-153.
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    2. Grant, Jason H. & Foster, Kenneth A., 2005. "An Inverse Almost Ideal Demand System of Fresh Tomatoes in the U.S," 2005 Annual meeting, July 24-27, Providence, RI 19193, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    3. Evans, Jason R. & D'Souza, Gerard E. & Collins, Alan R. & Brown, Cheryl & Sperow, Mark, 2011. "Determining Consumer Perceptions of and Willingness to Pay for Appalachian Grass-Fed Beef: An Experimental Economics Approach," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 40(2), pages 1-18, August.
    4. Anders, Sven M. & Moeser, Anke, 2008. "Using Retail Scanner Data to Assess the Demand for Value-based Ground Meat Products in Canada," 2008 International Congress, August 26-29, 2008, Ghent, Belgium 44154, European Association of Agricultural Economists.

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