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.
The sensitivity (i.e., elasticity and built-in flexibility) of the U. S. individual income tax to changes in national income is of great interest to researchers and policymakers. However, the direct measurement of this sensitivity—that is, the measurement obtained from time-series observations of the relevant variables—has always been difficult, and even at times impossible, because changes in the legal structure of the tax have been too frequent to provide enough observations that relate to the same legal structure to allow statistically significant coefficients to be determined. This was particularly true in the United States before 1954, when the rates were changed frequently; it has also been true since 1963, when important changes occurred in rates, personal exemptions, deductions, and other features. In contrast, during the period between 1954 and 1963, hardly any significant statutory changes occurred in the tax.