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

Paul Austin, Marco Marini, Alberto Sanchez, Chima Simpson-Bell and James Tebrake

No 2021/295, IMF Working Papers from International Monetary Fund

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.

Keywords: Reopening; COVID-19; High-Frequency Data; Business Register.; Google Trends data; Google data; high-frequency data; high frequency indicator; reopening Indicator; APIs; Big data; Manufacturing; Africa; Global (search for similar items in EconPapers)
Pages: 47
Date: 2021-12-17
New Economics Papers: this item is included in nep-big
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