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Estimating the impact of e-commerce on retail exit and entry using Google Trends

Author

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  • David C Vitt

    (Department of Economics, Farmingdale State College)

Abstract

I address the degree to which variation in exposure to e-commerce is associated with establishment entry and exit in the retail industry at the county level. To measure exposure to e-commerce, I rely on within-state variation in relative search frequency for the phrase “amazon prime†as reported by Google Trends. To generate exogenous variation in this e-commerce exposure measure, I use within state variation in the relative search frequency for “porn†and “cat videos†. Fixed effects instrumental variable estimates suggest at least 10 of the 27 retail industry groups experience net exit with increasing e-commerce exposure, while at least 6 experience net entry. To address endogeneity concerns about my instruments, particularly that they are driven by a notion of “hipster-ness†, I conduct a robustness check to show that my results fail to replicate in consideration of a strategy to tease out this identification threat.

Suggested Citation

  • David C Vitt, 2020. "Estimating the impact of e-commerce on retail exit and entry using Google Trends," Economics Bulletin, AccessEcon, vol. 40(1), pages 679-688.
  • Handle: RePEc:ebl:ecbull:eb-18-00816
    as

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    References listed on IDEAS

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    1. Karen Clay & Ramayya Krishnan & Eric Wolff, 2001. "Prices and Price Dispersion on the Web: Evidence from the Online Book Industry," Journal of Industrial Economics, Wiley Blackwell, vol. 49(4), pages 521-539, December.
    2. Castelnuovo, Efrem & Tran, Trung Duc, 2017. "Google It Up! A Google Trends-based Uncertainty index for the United States and Australia," Economics Letters, Elsevier, vol. 161(C), pages 149-153.
    3. Karen Clay & Ramayya Krishnan & Eric Wolff, 2001. "Prices and Price Dispersion on the Web: Evidence from the Online Book Industry," NBER Chapters, in: E-commerce, pages 521-539, National Bureau of Economic Research, Inc.
    4. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    5. David C. Vitt & Alexander F. McQuoid & Charles Moore & Stephen Sawyer, 2018. "Trigger warning: the causal impact of gun ownership on suicide," Applied Economics, Taylor & Francis Journals, vol. 50(53), pages 5747-5765, November.
    6. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
    7. Chris Hand & Guy Judge, 2012. "Searching for the picture: forecasting UK cinema admissions using Google Trends data," Applied Economics Letters, Taylor & Francis Journals, vol. 19(11), pages 1051-1055, July.
    8. Aaron Yelowitz & Matthew Wilson, 2015. "Characteristics of Bitcoin users: an analysis of Google search data," Applied Economics Letters, Taylor & Francis Journals, vol. 22(13), pages 1030-1036, September.
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    Cited by:

    1. Bauer, Anahid & Fernández Guerrico, Sofía, 2023. "Effects of E-commerce on Local Labor Markets," IZA Discussion Papers 16345, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    Google Trends; Retail; entry; exit; e-commerce; Internet;
    All these keywords.

    JEL classification:

    • L8 - Industrial Organization - - Industry Studies: Services
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance

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