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Disaggregated household meat demand with censored data

Author

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  • Brian Coffey
  • Ted Schroeder
  • Thomas Marsh

Abstract

Previous research on meat demand has generally used highly aggregated data (across time, products and consumers). However, meat products across species are likely stronger substitutes than some products from the same species. Further, demand for specific meat products would be expected to respond differently to market information about food safety or other events. This study uses monthly consumer panel data collected between 1992 and 2000 to estimate a disaggregated meat product demand system. The use of the expectations maximization algorithm is introduced to estimate a demand system that adjusts for the econometric problem of censored data resulting from purchased shares of some products by individuals often being equal to zero. Results indicate that certain individual meat products have noticeably different own-price elasticities than existing aggregate meat product estimates of their respective species. Some individual meat products have stronger substitutes across species than within species (e.g. beef steak and pork chops are substitutes, but beef roast and ground beef or not substitutes for steak).

Suggested Citation

  • Brian Coffey & Ted Schroeder & Thomas Marsh, 2011. "Disaggregated household meat demand with censored data," Applied Economics, Taylor & Francis Journals, vol. 43(18), pages 2343-2363.
    • Handle: RePEc:taf:applec:v:43:y:2011:i:18:p:2343-2363
    DOI: 10.1080/00036840903194238
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    References listed on IDEAS

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    1. Purcell, Wayne D., 1998. "Measures of Changes in Demand for Beef, Pork, and Chicken, 1975-1998," Staff Papers 232527, Virginia Polytechnic Institute and State University, Department of Agricultural and Applied Economics.
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    Cited by:

    1. Diansheng Dong & Christopher G. Davis & Hayden Stewart, 2015. "The quantity and variety of households’ meat purchases: A censored demand system approach," Agricultural Economics, International Association of Agricultural Economists, vol. 46(1), pages 99-112, January.
    2. Tan, Andrew K. G. & Yen, Steven T. & Hasan, Abdul Rahman & Muhamed, Kamarudin, 2015. "Determinants of Purchase Likelihoods and Amounts Spent on Meat in Malaysia: A Sample Selection System Approach," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 44(01), pages 1-16, April.
    3. Jeon, Younghyeon & Hoang, Hoa K. & Thompson, Wyatt & Abler, David & Miller, J. Isaac, 2022. "Revealing the fundamental parameters of a food demand system using estimated elasticities," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322182, Agricultural and Applied Economics Association.
    4. Peltner, Jonas & Thiele , Silke, 2021. "Elasticities of Food Demand in Germany – A Demand System Analysis Using Disaggregated Household Scanner Data," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 70(01), January.
    5. Luke, Jaime R. & Tonsor, Glynn T. & Brown, D. Scott, 2024. "Wholesale pork demand: Understanding primal-level heterogeneity," Journal of Commodity Markets, Elsevier, vol. 34(C).
    6. Tselepidakis, Elina, 2012. "Food Safety And The Demand For Meat Products," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124968, Agricultural and Applied Economics Association.
    7. Beatty, Timothy & Katare, Bhagyashree, 2016. "What Drives Media Reporting of Food Safety Events? Evidence From U.S. Meat Recalls," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 239243, Agricultural and Applied Economics Association.
    8. Bilgic, Abdulbaki & Yen, Steven T., 2013. "Household food demand in Turkey: A two-step demand system approach," Food Policy, Elsevier, vol. 43(C), pages 267-277.
    9. Peltner, Jonas & Thiele, Silke, 2021. "Elasticities of Food Demand in Germany – A Demand System Analysis Using Disaggregated Household Scanner Data," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 70(01), January.
    10. Rentsch, Dennis & Damon, Amy, 2013. "Prices, poaching, and protein alternatives: An analysis of bushmeat consumption around Serengeti National Park, Tanzania," Ecological Economics, Elsevier, vol. 91(C), pages 1-9.
    11. Peter R. Tozer & Thomas. L. Marsh & Evgeniy V. Perevodchikov, 2015. "Economic Welfare Impacts of Foot-and-Mouth Disease in the Canadian Beef Cattle Sector," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 63(2), pages 163-184, June.
    12. Tselepidakis, Elina, 2015. "Food Safety and the Demand for Leafy Greens," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205583, Agricultural and Applied Economics Association.
    13. Lee L. Schulz & Ted C. Schroeder & Tian Xia, 2012. "Studying composite demand using scanner data: the case of ground beef in the US," Agricultural Economics, International Association of Agricultural Economists, vol. 43, pages 49-57, November.
    14. Auberth Henrik Venson & Larissa Barbosa Cardoso & Flaviane Souza Santiago & Kênia Barreiro de Souza & Renata Moraes Bielemann, 2023. "Price elasticity of demand for ready-to-drink sugar-sweetened beverages in Brazil," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-15, November.
    15. Martin Browning & Lars Gårn Hansen & Sinne Smed, 2013. "Rational inattention or rational overreaction? Consumer reactions to health news," IFRO Working Paper 2013/14, University of Copenhagen, Department of Food and Resource Economics.

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