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

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  • Boonsaeng, Tullaya
  • Carpio, Carlos E.

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.

Suggested Citation

  • Boonsaeng, Tullaya & Carpio, Carlos E., 2019. "A Comparison of Food Demand Estimation from Homescan and Consumer Expenditure Survey Data," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 44(1), January.
    • Handle: RePEc:ags:jlaare:281316
    DOI: 10.22004/ag.econ.281316
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    2. 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.
    3. 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.
    4. Younghyeon Jeon & Hoa Hoang & Wyatt Thompson & David Abler, 2024. "A meta‐analysis of U.S. food demand elasticities to detect the impacts of scanner data," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 46(2), pages 760-780, 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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