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Using the Google Places API and Google Trends Data to Develop High Frequency Indicators of Economic Activity
Author:
Mr. Paul A Austin
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,
Mr. Marco Marini
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,
Alberto Sanchez
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,
Chima Simpson-Bell
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, and
James Tebrakenull

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Series:
IMF Working Papers
Volume/Issue:
Volume 2021: Issue 295
Language:
English
Publication Date:
17 Dec 2021
DOI:
https://doi.org/10.5089/9781616355432.001
ISBN:
9781616355432
ISSN:
1018-5941
Pages:
47
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
Legal-disclaimer:
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
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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.
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Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund
Volume 2021: Issue 295