Daily Fluctuations in Weather and Economic Growth at the Subnational Level: Evidence from Thailand

TL;DR

This study investigates how daily temperature fluctuations affect Thailand's regional economic growth, revealing significant impacts especially in agriculture, and projects climate change effects on future economic output with considerations of model uncertainties.

Contribution

It provides the first detailed analysis of short-term weather impacts on subnational economic growth in Thailand, incorporating climate projections and lagged temperature effects.

Findings

Temperature fluctuations negatively affect agricultural growth.

Climate change could significantly reduce future regional income.

Lagged temperature effects amplify projected economic losses.

Abstract

This paper examines the effects of daily temperature fluctuations on subnational economic growth in Thailand. Using annual gross provincial product (GPP) per capita data from 1982 to 2022 and high-resolution reanalysis weather data, I estimate fixed-effects panel regressions that isolate plausibly exogenous within-province year-to-year variation in temperature. The results indicate a statistically significant inverted-U relationship between temperature and annual growth in GPP per capita, with adverse effects concentrated in the agricultural sector. Industrial and service outputs appear insensitive to short-term weather variation. Distributed lag models suggest that temperature shocks have persistent effects on growth trajectories, particularly in lower-income provinces with higher average temperatures. I combine these estimates with climate projections under RCP4.5 and RCP8.5 emission scenarios to evaluate province-level economic impacts through 2090. Without adjustments for biases in climate projections or lagged temperature effects, climate change is projected to reduce per capita output for 63-86% of Thai population, with median GDP per capita impacts ranging from -4% to +56% for RCP4.5 and from -52% to -15% for RCP8.5. When correcting for projected warming biases - but omitting lagged dynamics - median losses increase to 57-63% (RCP4.5) and 80-86% (RCP8.5). Accounting for delayed temperature effects further raises the upper-bound estimates to near-total loss. These results highlight the importance of accounting for model uncertainty and temperature dynamics in subnational climate impact assessments. All projections should be interpreted with appropriate caution.