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Frontiers: Estimating the Long-Term Impact of Major Events on Consumption Patterns: Evidence from COVID-19

Author

Listed:
  • Shin Oblander

    (Marketing, Columbia University, New York, New York 10027)

  • Daniel Minh McCarthy

    (Marketing, Emory University, Atlanta, Georgia 30322)

Abstract
We propose a general and flexible methodology for inferring the time-varying effects of a discrete event on consumer behavior. Our method enables analysis of events that span the target population being analyzed, where there is no contemporaneous “control group” and/or it is not possible to measure treatment status, by comparing the purchasing behavior of cohorts acquired at different times. Our method applies nonparametric age-period-cohort models, commonly used in sociology but with limited adoption in marketing, in conjunction with a predictive model of the counterfactual no-event baseline (i.e., an event study model). We use this method to infer how the COVID-19 pandemic affected 12 online and offline consumption categories. Our results suggest that the pandemic initially drove significant spending lifts at e-commerce businesses at the expense of brick-and-mortar alternatives. After two years, however, these changes have largely reverted. We observe significant heterogeneity across categories, with more persistent changes in subscription-based categories and more transient changes in categories based on discretionary purchases, especially those of durable goods.

Suggested Citation

  • Shin Oblander & Daniel Minh McCarthy, 2023. "Frontiers: Estimating the Long-Term Impact of Major Events on Consumption Patterns: Evidence from COVID-19," Marketing Science, INFORMS, vol. 42(5), pages 839-852, September.
    • Handle: RePEc:inm:ormksc:v:42:y:2023:i:5:p:839-852
    DOI: 10.1287/mksc.2023.1443
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    References listed on IDEAS

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    1. Nan Yang & Yong Long Lim, 2018. "Temporary Incentives Change Daily Routines: Evidence from a Field Experiment on Singapore’s Subways," Management Science, INFORMS, vol. 64(7), pages 3365-3379, July.
    2. Jean‐Pierre Dubé & Günter J. Hitsch & Peter E. Rossi, 2010. "State dependence and alternative explanations for consumer inertia," RAND Journal of Economics, RAND Corporation, vol. 41(3), pages 417-445, September.
    3. Charles J. Corrado, 2011. "Event studies: A methodology review," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 51(1), pages 207-234, March.
    4. Gary Charness & Uri Gneezy, 2009. "Incentives to Exercise," Econometrica, Econometric Society, vol. 77(3), pages 909-931, May.
    5. Kosei Fukuda, 2010. "A cohort analysis of household vehicle expenditure in the U.S. and Japan: A possibility of generational marketing," Marketing Letters, Springer, vol. 21(1), pages 53-64, March.
    6. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
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    Cited by:

    1. Ryan Dew & Nicolas Padilla & Anya Shchetkina, 2024. "Your MMM is Broken: Identification of Nonlinear and Time-varying Effects in Marketing Mix Models," Papers 2408.07678, arXiv.org.

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