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

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

December 17, 2021

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Format: Chicago

Paul A Austin, Marco Marini, Alberto Sanchez, Chima Simpson-Bell, and James Tebrake. Using the Google Places API and Google Trends Data to Develop High Frequency Indicators of Economic Activity, (USA: International Monetary Fund, 2021) accessed December 26, 2024

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.

Summary

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.

Subject: APIs, Big data, COVID-19, Economic and financial statistics, Economic sectors, Health, Manufacturing, Technology

Keywords: Africa, APIs, Big data, Business Register., COVID-19, Global, Google data, Google Trends data, High frequency indicator, High-frequency data, High-Frequency Data, Manufacturing, Reopening, Reopening Indicator

Publication Details

Pages:

47
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2021/295
Stock No:

WPIEA2021295
ISBN:

9781616355432
ISSN:

1018-5941

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