Weather, climate, and economic growth: A dynamic panel analysis using global subnational data

Using a global subnational dataset covering over 2800 regions, this study analyzes the effects of weather and climate on economic output. We first show that it is crucial to account for serial correlation in output growth. While a static annual panel model suggests a significant aggregate growth effect of temperature (weather effects), this effect disappears and remains significant only in poor regions once we include the lags of output growth, implying that static models could overstate climate damages. Second, to capture the dynamics of climate adaptation, we further extend the long-difference model to allow output responses to evolve over time (climate effects). We find that higher temperatures have a persistent but not permanent negative effect in poor, hot regions, suggesting the development of adaptive capacity. In contrast, temperature effects are largely absent in rich regions. As a result, the income gap between rich and poor regions is projected to widen, exacerbating climate inequality. In addition, we find no robust effects of precipitation in either the annual panel or the long-difference models.