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Consumer Research with Big Data: Applications from the Food Demand Survey (FooDS)

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  • Jayson L. Lusk

Abstract

In three separate studies based on data from the Food Demand Survey (FooDS), which has been conducted monthly for over three years, this paper explores heterogeneity in preference across consumers in traditional demand systems, heterogeneity in preferences over time in choice experiments, and the tail of the distribution for a particular food consumption pattern—vegetarianism. Results show that elasticities of demand for food at home and food away from home vary widely across different groups of consumers defined by a priori cluster analysis based on demographic and attitudinal variables. Results from a choice experiment are found to depend on when the experiment was conducted and on the market prices prevailing at the time of the survey. Given the large sample of consumers observed over time, there is sufficient data to demographically characterize a small portion of the population—vegetarians—using traditional logit models and a machine learning method - a classifications tree.

Suggested Citation

  • Jayson L. Lusk, 2017. "Consumer Research with Big Data: Applications from the Food Demand Survey (FooDS)," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 99(2), pages 303-320.
    • Handle: RePEc:oup:ajagec:v:99:y:2017:i:2:p:303-320.
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    File URL: http://hdl.handle.net/10.1093/ajae/aaw110
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    Citations

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    Cited by:

    1. Trey Malone & F. Bailey Norwood, 2020. "Gluten aversion is not limited to the political left," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 37(1), pages 1-15, March.
    2. Lusk, Jayson L. & Tonsor, Glynn T. & Schroeder, Ted C. & Hayes, Dermot J., 2018. "Effect of government quality grade labels on consumer demand for pork chops in the short and long run," Food Policy, Elsevier, vol. 77(C), pages 91-102.
    3. Caputo, Vincenzina & Lusk, Jayson L. & Nayga, Rodolfo M., 2018. "Choice experiments are not conducted in a vacuum: The effects of external price information on choice behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 145(C), pages 335-351.
    4. Trey Malone & Kevin Gomez, 2019. "Hemp in the United States: A Case Study of Regulatory Path Dependence," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 41(2), pages 199-214, June.
    5. Maik Kecinski & Kent D. Messer & Brandon R. McFadden & Trey Malone, 2020. "Environmental and Regulatory Concerns During the COVID-19 Pandemic: Results from the Pandemic Food and Stigma Survey," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 1139-1148, August.
    6. Qin, Fei & Wu, Steven Y., 2022. "Estimating Consumer Segments and Choices from Limited Information: The Application of Machine Learning Methods," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322473, Agricultural and Applied Economics Association.
    7. Biondi, Beatrice & Cornelsen, Laura & Mazzocchi, Mario & Smith, Richard, 2020. "Between preferences and references: Asymmetric price elasticities and the simulation of fiscal policies," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 108-128.
    8. McFadden, Brandon R. & Malone, Trey, 2018. "How will mandatory labeling of genetically modified food nudge consumer decision-making?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 77(C), pages 186-194.
    9. Caputo, Vincenzina & Scarpa, Riccardo & Nayga, Rodolfo M. & Ortega, David L., 2018. "Are preferences for food quality attributes really normally distributed? An analysis using flexible mixing distributions," Journal of choice modelling, Elsevier, vol. 28(C), pages 10-27.
    10. Elliott J. Dennis & Glynn T. Tonsor & Jayson L. Lusk, 2021. "Choosing quantities impacts individuals choice, rationality, and willingness to pay estimates," Agricultural Economics, International Association of Agricultural Economists, vol. 52(6), pages 945-962, November.
    11. Lauren Chenarides & Carola Grebitus & Jayson L Lusk & Iryna Printezis, 2022. "A calibrated choice experiment method [Combining revealed and stated preference methods for valuing environmental amenities]," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(5), pages 971-1004.
    12. AMADOU Zakou, 2020. "Which Sustainable Development Goals and Eco-challenges Matter Most to Niger s Farmers and Herdsmen? A Best Worst Scaling Approach," Agricultural Research & Technology: Open Access Journal, Juniper Publishers Inc., vol. 24(5), pages 168-174, July.
    13. Trey Malone & Jayson L. Lusk, 2018. "If you brew it, who will come? Market segments in the U.S. beer market," Agribusiness, John Wiley & Sons, Ltd., vol. 34(2), pages 204-221, March.
    14. Dawn Thilmany, 2021. "Rebalancing Our Portfolio: Envisioning a More Inclusive, Altruistic, and Engaged AAEA," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(2), pages 408-421, March.
    15. Lusk, Jayson L., 2019. "Income and (Ir) rational food choice," Journal of Economic Behavior & Organization, Elsevier, vol. 166(C), pages 630-645.
    16. Aye Chan Myae & Ellen Goddard, 2020. "Household behavior with respect to meat consumption in the presence of BSE and CWD," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 68(3), pages 315-341, September.
    17. Rebecca L. C. Taylor, 2022. "It's in the bag? The effect of plastic carryout bag bans on where and what people purchase to eat," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(5), pages 1563-1584, October.
    18. Maurizio Canavari & Andreas C. Drichoutis & Jayson L. Lusk & Rodolfo M. Nayga, Jr., 2018. "How to run an experimental auction: A review of recent advances," Working Papers 2018-5, Agricultural University of Athens, Department Of Agricultural Economics.
    19. Neill, Clinton L. & Holcomb, Rodney B., 2019. "Does a food safety label matter? Consumer heterogeneity and fresh produce risk perceptions under the Food Safety Modernization Act," Food Policy, Elsevier, vol. 85(C), pages 7-14.
    20. Daniel Francisco Pais & António Cardoso Marques & José Alberto Fuinhas, 2023. "How to Promote Healthier and More Sustainable Food Choices: The Case of Portugal," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    21. Lusk, Jayson L. & McFadden, Brandon R. & Wilson, Norbert, 2018. "Do consumers care how a genetically engineered food was created or who created it?," Food Policy, Elsevier, vol. 78(C), pages 81-90.
    22. 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.
    23. Caputo, Vincenzina & Lusk, Jayson L., 2022. "The Basket-Based Choice Experiment: A Method for Food Demand Policy Analysis," Food Policy, Elsevier, vol. 109(C).

    More about this item

    Keywords

    Big data; CART; choice experiment; cluster analysis; demand system; food at home; food away from home; machine learning; vegetarianism;
    All these keywords.

    JEL classification:

    • Q10 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - General
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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