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A Comparison of Food Demand Estimation from Homescan and Consumer Expenditure Survey Data

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  • Tullaya, Boonsaeng
  • Carlos, Carpio

Abstract

This study evaluates the differences between the Exact Affine Stone Index (EASI) demand model estimates obtained using Consumer Expenditure Survey (CEX) data and Nielsen Homescan data. Results indicated that elasticities obtained from CEX and Homescan data–based demand models differ not only statistically but also economically. Own-price elasticities obtained from the CEX data–based demand model were more inelastic than those obtained from the demand model estimated using Homescan data. Further, differences between expenditure elasticities did not follow a specific pattern. We found evidence suggesting that the main source of differences is the price index used for the estimation.
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Suggested Citation

  • Tullaya, Boonsaeng & Carlos, Carpio, 2014. "A Comparison of Food Demand Estimation from Homescan and Consumer Expenditure Survey Data," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170543, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea14:170543
    DOI: 10.22004/ag.econ.170543
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    2. Irz, Xavier & Mazzocchi, Mario & Réquillart, Vincent & Soler, Louis-Georges, 2015. "Research in Food Economics: past trends and new challenges," Revue d'Etudes en Agriculture et Environnement, Editions NecPlus, vol. 96(01), pages 187-237, March.
    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. Chen Zhen & Mary Muth & Abigail Okrent & Shawn Karns & Derick Brown & Peter Siegel, 2019. "Do differences in reported expenditures between household scanner data and expenditure surveys matter in health policy research?," Health Economics, John Wiley & Sons, Ltd., vol. 28(6), pages 782-800, June.
    5. Joey Blumberg & Gary Thompson, 2022. "Nonparametric segmentation methods: Applications of unsupervised machine learning and revealed preference," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(3), pages 976-998, May.

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