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Matthew E. Kahn, Mr. Kamiar Mohaddes, Ryan N. C. Ng, M. Hashem Pesaran, Mr. Mehdi Raissi, and Jui-Chung Yang
We study the long-term impact of climate change on economic activity across countries, using a stochastic growth model where labor productivity is affected by country-specific climate variables—defined as deviations of temperature and precipitation from their historical norms. Using a panel data set of 174 countries over the years 1960 to 2014, we find that per-capita real output growth is adversely affected by persistent changes in the temperature above or below its historical norm, but we do not obtain any statistically significant effects for changes in precipitation. Our counterfactual analysis suggests that a persistent increase in average global temperature by 0.04°C per year, in the absence of mitigation policies, reduces world real GDP per capita by more than 7 percent by 2100. On the other hand, abiding by the Paris Agreement, thereby limiting the temperature increase to 0.01°C per annum, reduces the loss substantially to about 1 percent. These effects vary significantly across countries depending on the pace of temperature increases and variability of climate conditions. We also provide supplementary evidence using data on a sample of 48 U.S. states between 1963 and 2016, and show that climate change has a long-lasting adverse impact on real output in various states and economic sectors, and on labor productivity and employment.
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

-collinearity issues, especially when adding multiple lags. The algorithm we use balances under and over-fitting issues. It relies on splitting our sample into training and test sets to select the variables that maximize the R-squared out of sample on the testing sets. Further, we follow the machine learning literature with an additional grid search to refine the selection and to obtain a robust and parsimonious set of relevant climate variables. We find that a handful of weather variables have a significant impact on GDP per capita. Some of these variables capture droughts and

Matthew E. Kahn, Mr. Kamiar Mohaddes, Ryan N. C. Ng, M. Hashem Pesaran, Mr. Mehdi Raissi, and Jui-Chung Yang

Growth, 1960–2014 (Using Absolute Value of Deviations of Climate Variables from their Historical Norm) 4. Long-Run Effects of Climate Change on per Capita Real GDP Growth, 1960–2014 (Historical Norms as the Moving Averages of Past 20 Years) 5. Long-Run Effects of Climate Change on per Capita Real GDP Growth, 1960–2014 (Historical Norms as the Moving Averages of Past 40 Years) 6. Effects of Climate Change on per Capita Real GDP Growth, 1960–2014 7. Percent Loss in GDP per capita by 2030, 2050, and 2100 under the RCP 2.6 and RCP 8.5 Scenarios 8. Percent Loss in

Monetary Fund WP/19/215 IMF Working Paper Fiscal Affairs Department Long-Term Macroeconomic Effects of Climate Change: A Cross-Country Analysis* Prepared by** Matthew E. Kahn, Kamiar Mohaddes, Ryan N. C. Ng, M. Hashem Pesaran, Mehdi Raissi and Jui-Chung Yang Authorized for distribution by Catherine Pattillo October 2019 Abstract We study the long-term impact of climate change on economic activity across countries, using a stochastic growth model where labor productivity is affected by country-specific climate variables—defined as deviations of temperature and

International Monetary Fund. Monetary and Capital Markets Department

Model Intercomparison Project (CMIP5)—of four climate variables, defined as anomalies relative to historical simulations over the period 1986–2005. The extremities of the vertical bars show the equal-weighted average of the 90th and the 10th percentiles of the projections. Projections are based on the high-emissions scenario Representative Concentration Pathway (RCP) 8.5. See Online Annex 5.1 for the list of sample economies, as well as a definition of the RCP scenarios and the future climate variables. Extreme weather events—or climatic hazards—can turn into

Alassane Drabo

, 2012 ; Burke et al. 2015 ; Harari and Ferrara, 2018 ; Helderop and Grubesic, 2019 ). Helderop and Grubesic (2019) state that extreme weather events significantly degrade human capital and infrastructures. This destruction of the absorptive capacity may reduce the economic returns of existing and additional capital, especially, financing coming from abroad. In the literature, some authors investigate the association between climate variables, foreign financing and economic growth ( Guillaumont and Chauvet, 2001 ; Dalgaard, 2004 ). Dalgaard (2004) finds that