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

following five time periods. The indicator is calculated as the number of open firms divided by the total number of firms that were temporarily closed in the baseline period. Table 9. Reopening Indicator Place Period B Period 1 Period 2 Period 3 Period 4 Period 5 A 2 2 2 2 2 2 B 2 2 2 2 2 1 C 2 2 1 2 1 1 D 2 2 1 1 1 1 E 2 1 1 1 1 1 Indicator 100 20 60 40 60 80 The above methodology was used to construct a business re