2022-07-28-IMF-Estimating_Macro-Fiscal_Effects_of_Climate_Shocks_From_Billions_of_Geospatial_Weather_Observations_73页_3mb

Summary

• Problem Addressed: Traditional studies often use average annual temperature and precipitation to estimate the macroeconomic effects of climate shocks, potentially missing impacts from extreme weather events due to low resolution and frequency.

• Methodology:

  • Utilizes high-resolution, daily geospatial weather data (30 km grid) from 1979-2019 through ERA5 dataset.
  • Employs LASSO regression to select relevant climate variables from 164 weather indicators, enhancing explanatory power.
  • Uses local projection method for dynamic effects and panel data analysis with controls for country and year fixed effects.

• Key Findings:

  • GDP reductions occur with high temperatures and droughts, while mild temperatures boost growth. Effects persistently impact GDP per capita by ~0.2 percentage points per standard deviation.
  • Fiscal responses show counter-cyclicality in expenditure and debt, mitigating shock impacts, though revenue reacts pro-cyclically to high temperatures.
  • Heterogeneity: Poorer and agricultural countries are more vulnerable to climate shocks.

• Policy Implications: Focus on fiscal space and capacity-building to address shocks, as traditional averages understate risks. High-resolution data enables better prediction and adaptation strategies.

• Limitations: Method not suitable for long-term climate change impacts due to focus on short-term weather fluctuations.