IMF Working Papers describe research in progress by the author(s) and are published to elicit
comments and to encourage debate. The views expressed in IMF Working Papers are those of the
author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
IMF Working Papers describe research in progress by the author(s) and are published to elicit
comments and to encourage debate. The views expressed in IMF Working Papers are those of the
author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
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.