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

others. Jun, Yoo and Choi (2016) traces the ten years of research using Google Trends since the company made this source of data available in 2006. Noting that the availability of timely data is a long standing challenge for policy making and analysis for low-income developing countries, Narita and Yin (2018) explored the use of Google Trends data to narrow such information gaps. Many organizations have since developed timely leading indicators using Google data (Google Trends, Google Mobility data, Google APIs) that track well official measures of economic activity

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

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. JEL Classification Numbers: C81,E01. Keywords: Reopening, COVID-19, High-Frequency Data

Mr. Serhan Cevik

, 2016 , “ Flying to Paradise: The Role of Airlift in the Caribbean Tourism Industry ,” IMF Working Paper No. 16/33 ( Washington, DC : International Monetary Fund ). Askitas , N. , and K. Zimmermann , 2009 , “ Google Econometrics and Unemployment Forecasting ,” Applied Economics , Vol. 55 , pp. 107 – 120 . Bangwayo-Skeete , P. , and R. Skeete , 2015 , “ Can Google Data Improve the Forecasting Performance of Tourist Arrivals? A Mixed-Data Sampling Approach ,” Tourism Management , Vol. 46 , pp. 454 – 464 . Box , G. , and G

Mr. Serhan Cevik
The widespread availability of internet search data is a new source of high-frequency information that can potentially improve the precision of macroeconomic forecasting, especially in areas with data constraints. This paper investigates whether travel-related online search queries enhance accuracy in the forecasting of tourist arrivals to The Bahamas from the U.S. The results indicate that the forecast model incorporating internet search data provides additional information about tourist flows over a univariate approach using the traditional autoregressive integrated moving average (ARIMA) model and multivariate models with macroeconomic indicators. The Google Trends-augmented model improves predictability of tourist arrivals by about 30 percent compared to the benchmark ARIMA model and more than 20 percent compared to the model extended only with income and relative prices.
Mr. Futoshi Narita and Rujun Yin

de Pedraza García , 2015 , “ Can internet searches forecast tourism inflows? ” International Journal of Manpower , 36 : 1 , 103 – 116 . https://0-doi-org.library.svsu.edu/10.1108/IJM-12-2014-0259 Askitas , Nikolaos , and Klaus F. Zimmermann , K. F. , 2009 , “ Google Econometrics and Unemployment Forecasting ,” DIW Berlin Discussion Paper No. 899 . Bangwayo-Skeete , Prosper F. , and Ryan W. Skeete , 2015 , “ Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach ,” Tourism Management , 46 ( C

Mr. Futoshi Narita and Rujun Yin
Timely data availability is a long-standing challenge in policy-making and analysis for low-income developing countries. This paper explores the use of Google Trends’ data to narrow such information gaps and finds that online search frequencies about a country significantly correlate with macroeconomic variables (e.g., real GDP, inflation, capital flows), conditional on other covariates. The correlation with real GDP is stronger than that of nighttime lights, whereas the opposite is found for emerging market economies. The search frequencies also improve out-of-sample forecasting performance albeit slightly, demonstrating their potential to facilitate timely assessments of economic conditions in low-income developing countries.
Mr. Francesco Caselli, Mr. Francesco Grigoli, Mr. Damiano Sandri, and Mr. Antonio Spilimbergo

a cell different from the home cells. More details on the data construction are provided in Lourenco et al. (2020 ). The mobility patterns detected by Vodafone are broadly in line with those according to Apple and Google data. 3 Figure 1 shows that all indicators correlate fairly closely at the national level. Correlations between the Vodafone indicator and the Apple and Google indicators range between 93 and 99 percent for Italy and Portugal and are 72 and 88 percent for Spain. The geographical disaggregation of the Vodafone data allows to appreciate the

Mr. Francesco Caselli, Mr. Francesco Grigoli, Mr. Damiano Sandri, and Mr. Antonio Spilimbergo
Lockdowns and voluntary social distancing led to significant reduction in people’s mobility. Yet, there is scant evidence on the heterogeneous effects across segments of the population. Using unique mobility indicators based on anonymized and aggregate data provided by Vodafone for Italy, Portugal, and Spain, we find that lockdowns had a larger impact on the mobility of women and younger cohorts. Younger people also experienced a sharper drop in mobility in response to rising COVID-19 infections. Our findings, which are consistent across estimation methods and robust to a variety of tests, warn about a possible widening of gender and inter-generational inequality and provide important inputs for the formulation of targeted policies.