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Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity

Author

Listed:
  • Edward L. Glaeser

    () (Harvard University)

  • Hyunjin Kim

    () (Harvard Business School)

  • Michael Luca

    () (Harvard Business School, Negotiation, Organizations & Markets Unit)

Abstract

Can new data sources from online platforms help to measure local economic activity? Government datasets from agencies such as the U.S. Census Bureau provide the standard measures of local economic activity at the local level. However, these statistics typically appear only after multi-year lags, and the public-facing versions are aggregated to the county or ZIP code level. In contrast, crowdsourced data from online platforms such as Yelp are often contemporaneous and geographically finer than official government statistics. In this paper, we present evidence that Yelp data can complement government surveys by measuring economic activity in close to real time, at a granular level, and at almost any geographic scale. Changes in the number of businesses and restaurants reviewed on Yelp can predict changes in the number of overall establishments and restaurants in County Business Patterns. An algorithm using contemporaneous and lagged Yelp data can explain 29.2 percent of the residual variance after accounting for lagged CBP data, in a testing sample not used to generate the algorithm. The algorithm is more accurate for denser, wealthier, and more educated ZIP codes.

Suggested Citation

  • Edward L. Glaeser & Hyunjin Kim & Michael Luca, 2017. "Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity," Harvard Business School Working Papers 18-022, Harvard Business School, revised Oct 2017.
  • Handle: RePEc:hbs:wpaper:18-022
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    File URL: http://www.hbs.edu/faculty/pages/download.aspx?name=18-022.pdf
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    References listed on IDEAS

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    1. Jorge Guzman & Scott Stern, 2016. "Nowcasting and Placecasting Entrepreneurial Quality and Performance," NBER Chapters, in: Measuring Entrepreneurial Businesses: Current Knowledge and Challenges, pages 63-109, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Susan Athey & Michael Luca, 2019. "Economists (and Economics) in Tech Companies," Journal of Economic Perspectives, American Economic Association, vol. 33(1), pages 209-230, Winter.
    2. Jonathan Hersh & Matthew Harding, 2018. "Big Data in economics," IZA World of Labor, Institute of Labor Economics (IZA), pages 451-451, September.
    3. Soraya SEDKAOUI & Rafika Benaichouba, 2019. "How data analytics drive sharing economy business models?," Proceedings of International Academic Conferences 9911754, International Institute of Social and Economic Sciences.
    4. Bailey, Michael & Farrell, Patrick & Kuchler, Theresa & Stroebel, Johannes, 2020. "Social connectedness in urban areas," Journal of Urban Economics, Elsevier, vol. 118(C).
    5. Bailey, Michael & Farrell, Patrick & Kuchler, Theresa & Ströbel, Johannes, 2019. "Social Connectedness in Urban Areas," CEPR Discussion Papers 13822, C.E.P.R. Discussion Papers.
    6. Michael Bailey & Patrick Farrell & Theresa Kuchler & Johannes Stroebel, 2019. "Social Connectedness in Urban Areas," NBER Working Papers 26029, National Bureau of Economic Research, Inc.
    7. Ron Tidhar & Kathleen M. Eisenhardt, 2020. "Get rich or die trying… finding revenue model fit using machine learning and multiple cases," Strategic Management Journal, Wiley Blackwell, vol. 41(7), pages 1245-1273, July.
    8. Schintler, Laurie A. & Fischer, Manfred M., 2018. "Big Data and Regional Science: Opportunities, Challenges, and Directions for Future Research," Working Papers in Regional Science 2018/02, WU Vienna University of Economics and Business.
    9. Indaco, Agustín, 2019. "From Twitter to GDP: Estimating Economic Activity From Social Media," MPRA Paper 95885, University Library of Munich, Germany.

    More about this item

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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