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Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data

Author

Listed:
  • Susan Athey
  • David Blei
  • Robert Donnelly
  • Francisco Ruiz
  • Tobias Schmidt

Abstract

We estimate a model of consumer choices over restaurants using data from several thousand anonymous mobile phone users. Restaurants have latent characteristics (whose distribution may depend on restaurant observables) that affect consumers' mean utility as well as willingness to travel to the restaurant, while each user has distinct preferences for these latent characteristics. We analyze how consumers reallocate their demand after a restaurant closes to nearby restaurants versus more distant restaurants, comparing our predictions to actual outcomes. We also address counterfactual questions such as what type of restaurant would attract the most consumers in a given location.

Suggested Citation

  • Susan Athey & David Blei & Robert Donnelly & Francisco Ruiz & Tobias Schmidt, 2018. "Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 64-67, May.
    • Handle: RePEc:aea:apandp:v:108:y:2018:p:64-67
    Note: DOI: 10.1257/pandp.20181031
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    References listed on IDEAS

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    1. Francisco J. R. Ruiz & Susan Athey & David M. Blei, 2017. "SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements," Papers 1711.03560, arXiv.org, revised Jun 2019.
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    Cited by:

    1. J. Daniel Aromí & M. Paula Bonel & Julián Cristiá & Martín Llada, 2020. "Socio-economic status and mobility during the COVID-19 pandemic: An analysis of large Latin American urban areas," Asociación Argentina de Economía Política: Working Papers 4307, Asociación Argentina de Economía Política.
    2. Victor Couture & Cecile Gaubert & Jessie Handbury & Erik Hurst, 2019. "Income Growth and the Distributional Effects of Urban Spatial Sorting," NBER Working Papers 26142, National Bureau of Economic Research, Inc.
    3. Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Jan 2023.
    4. Krueger, Rico & Bierlaire, Michel & Daziano, Ricardo A. & Rashidi, Taha H. & Bansal, Prateek, 2021. "Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity," Journal of choice modelling, Elsevier, vol. 41(C).
    5. Robert Donnelly & Francisco J.R. Ruiz & David Blei & Susan Athey, 2021. "Counterfactual inference for consumer choice across many product categories," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 369-407, December.
    6. Panle Jia Barwick & Yanyan Liu & Eleonora Patacchini & Qi Wu, 2019. "Information, Mobile Communication, and Referral Effects," NBER Working Papers 25873, National Bureau of Economic Research, Inc.
    7. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences with Staggered Adoptions," Papers 2312.05985, arXiv.org.
    8. Badruddoza, Syed & Amin, Modhurima & McCluskey, Jill, 2019. "Assessing the Importance of an Attribute in a Demand SystemStructural Model versus Machine Learning," Working Papers 2019-5, School of Economic Sciences, Washington State University.
    9. Du, Tianyu & Kanodia, Ayush & Athey, Susan, 2023. "Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python," Research Papers 4106, Stanford University, Graduate School of Business.
    10. Tatiana de Macedo Nogueira Lima, 2022. "Documento de Trabalho 03/2022 - Aprendizado de máquina e antitruste," Documentos de Trabalho 2022030, Conselho Administrativo de Defesa Econômica (Cade), Departamento de Estudos Econômicos.
    11. Xie, Lusi & Adamowicz, Wiktor & Lloyd-Smith, Patrick, 2023. "Spatial and temporal responses to incentives: An application to wildlife disease management," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
    12. Evan Munro & Serena Ng, 2022. "Latent Dirichlet Analysis of Categorical Survey Responses," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 256-271, January.
    13. Gabriel E. Kreindler & Yuhei Miyauchi, 2019. "Measuring Commuting and Economic Activity inside Cities with Cell Phone Records," Boston University - Department of Economics - Working Papers Series WP2020-006, Boston University - Department of Economics, revised Apr 2020.
    14. Federica Daniele & Mariona Segu & David Bounie & Youssouf Camara, 2022. "Bike-friendly cities: an opportunity for local businesses? Evidence from the city of Paris," THEMA Working Papers 2022-09, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.

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    More about this item

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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