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

Listed author(s):
  • Edward L. Glaeser

    ()

    (Harvard University)

  • Hyunjin Kim

    ()

    (Harvard Business School)

  • Michael Luca

    ()

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

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.

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File URL: http://www.hbs.edu/faculty/pages/download.aspx?name=18-022.pdf
File Function: Revised version, 2017
Download Restriction: no

Paper provided by Harvard Business School in its series Harvard Business School Working Papers with number 18-022.

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Length: 56 pages
Date of creation: Sep 2017
Date of revision: Oct 2017
Handle: RePEc:hbs:wpaper:18-022
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