Front Matter
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Copyright Page

© 2021 International Monetary Fund

WP/21/295

IMF Working Paper

Statistics Department

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

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

Authorized for distribution by J. R. Rosales

December 2021

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.

ABSTRACT: As the pandemic heightened 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.

article image

Contents

  • I. MOTIVATION

  • II. SOURCE DATA

    • A. Google Places and Google Trends

  • III. METHODS

    • A. Operating Status Indicators

    • B. Business Activity Indicators

  • IV. USING GOOGLE DATA FOR GDP NOWCASTING

  • V. CONCLUSIONS

  • REFERENCES

  • Box

  • 1. Textual Description of the Manufacture of Consumer Electronics Industry

  • Figures

  • 1. Google Trend “Flights” – Canada

  • 2. Google Trends: Demand for Ford Escape – Canada

  • 3. Operating Indicator (weighted by reviews) for Selected City Centers

  • 4. Business Re-opening Indicator for Selected City Centers

  • 5. Review Activity Indicator

  • 6. Change in Google Trends Compared to Change in Real Quarterly GDP

  • 7. Transportation and Storage: Comparison between Official Data (GDP-H), Google Trends (TRE-H), and Reopening Indicator (REOP) for Selected Countries

  • 8. Transportation and Storage: Nowcasts for 2020-Q2 and 2020-Q3

  • Tables

  • 1. Fields of Information that can be Extracted for Each Place using Google Places API.

  • 2. Statistical Concept: Units

  • 3. Statistical Concept: Operating Status

  • 4. Statistical Concept: Territory

  • 5. Statistical Concept: Size

  • 6. Construction of Google Trends Index: Example

  • 7. Search Topics Related to Consumer Electronics for Australia

  • 8. Construction of Operational Indicator-Example

  • 9. Reopening Indicator

  • 10. Indicator of Business Activity Using Reviews

  • 11. Stock / Change in Reviews – Paris City Center Beauty Salons

  • 12. Monthly Google Trends SVIs at ISIC 4-digit level for Accommodation and Food Service Activities (I) for Australia

  • 13. Monthly Google Trends SVIs at ISIC Section Level for Accommodation and Food Service Activities (I) for Australia

  • 14. Transportation and Storage: Regression Results

  • Annexes

  • I. Technical Aspects of Google Trends and Google Places API

  • II. Data and Methods with the IMF google R Package

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