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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

literature 4.4 The dynamic effects of climate shocks 4.5 Heterogeneity 5 Macro-Fiscal Outcomes 5.1 A systematic empirical approach to macro-fiscal impacts 5.2 Estimates of the impact of climate shocks on macro-fiscal outcomes 6 Conclusion A Appendix A.1 Source Data A.2 Definition of weather variables A.3 Data Analysis A.4 Additional Result Tables A.5 Additional Figures List of Figures 1 Illustrating the role of high spatial resolution when using absolute thresholds 2 Variable selection and OLS estimation outcomes as λ varies

Berkay Akyapi, Mr. Matthieu Bellon, and Emanuele Massetti

Drought Prevalence and Max T°C above 35 (0.183). Harsh Drought Prevalence is also positively correlated with Mean Temperature (0.159) because temperature plays a role in the definition of the PDSI drought indicator. Max T°C above 35 and Mean Temperature are modestly correlated (0.356) but Mean Temperature is not retained by the LASSO. Figure 2: Variable selection and OLS estimation outcomes as λ varies (country and year effects) Note: The figures show various outcomes from implementing the LASSO for different penalty parameters. The estimated model has GDP per