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Berkay Akyapi, Mr. Matthieu Bellon, and Emanuele Massetti
A growing literature estimates the macroeconomic effect of weather using variations in annual country-level averages of temperature and precipitation. However, averages may not reveal the effects of extreme events that occur at a higher time frequency or higher spatial resolution. To address this issue, we rely on global daily weather measurements with a 30-km spatial resolution from 1979 to 2019 and construct 164 weather variables and their lags. We select a parsimonious subset of relevant weather variables using an algorithm based on the Least Absolute Shrinkage and Selection Operator. We also expand the literature by analyzing weather impacts on government revenue, expenditure, and debt, in addition to GDP per capita. We find that an increase in the occurrence of high temperatures and droughts reduce GDP, whereas more frequent mild temperatures have a positive impact. The share of GDP variations that is explained by weather as captured by the handful of our selected variables is much higher than what was previously implied by using annual temperature and precipitation averages. We also find evidence of counter-cyclical fiscal policies that mitigate adverse weather shocks, especially excessive or unusually low precipitation episodes.
Berkay Akyapi, Mr. Matthieu Bellon, and Emanuele Massetti

Shocks From Billions of Geospatial Weather Observations Prepared by Berkay Akyapi, Matthieu Bellon, and Emanuele Massetti * Contents 1 Introduction 2 Methods 2.1 Empirical model specification 2.2 Local projection method 2.3 Selecting relevant weather variables and estimating their effects 3 Data 3.1 Weather data sources and aggregation over time and space 3.2 Variable definitions 3.3 Summary statistics 4 GDP Results 4.1 Climate variable selection 4.2 The effect of weather variables on GDP growth 4.3 Comparisons with the

Berkay Akyapi, Mr. Matthieu Bellon, and Emanuele Massetti

Bayesian information criterion (BIC) to assess the relative quality of the selected models. The LASSO produces “biased” coefficient estimates because the penalty term shrinks them. 8 To estimate the “unbiased” effect of weather shocks, we finally re-estimate the model with the climate variables selected by the LASSO using standard OLS methods. 9 3. Data 3.1. Weather data sources and aggregation over time and space We use temperature and precipitation data from the ERA5 dataset compiled by the European Centre for Medium-Range Weather Forecasts (ECMWF