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Using the Google Places API and Google Trends Data to Develop High Frequency Indicators of Economic Activity

Author

Listed:
  • Mr. Paul A Austin
  • Mr. Marco Marini
  • Alberto Sanchez
  • Chima Simpson-Bell
  • James Tebrake

Abstract

As the pandemic heigthened policymakers’ demand for more frequent and timely indicators to assess economic activities, traditional data collection and compilation methods to produce official indicators are falling short—triggering stronger interest in real time data to provide early signals of turning points in economic activity. In this paper, we examine how data extracted from the Google Places API and Google Trends can be used to develop high frequency indicators aligned to the statistical concepts, classifications, and definitions used in producing official measures. The approach is illustrated by use of Google data-derived indicators that predict well the GDP trajectories of selected countries during the early stage of COVID-19. To this end, we developed a methodological toolkit for national compilers interested in using Google data to enhance the timeliness and frequency of economic indicators.

Suggested Citation

  • Mr. Paul A Austin & Mr. Marco Marini & Alberto Sanchez & Chima Simpson-Bell & James Tebrake, 2021. "Using the Google Places API and Google Trends Data to Develop High Frequency Indicators of Economic Activity," IMF Working Papers 2021/295, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2021/295
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    Citations

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    Cited by:

    1. Alessio Ciarlone, 2023. "Remittances in times of crisis: evidence from Italian corridors," Temi di discussione (Economic working papers) 1402, Bank of Italy, Economic Research and International Relations Area.
    2. Lolić, Ivana & Matošec, Marina & Sorić, Petar, 2024. "DIY google trends indicators in social sciences: A methodological note," Technology in Society, Elsevier, vol. 77(C).

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