arXiv 25 Jun 2025 · Econometrics
arXiv:2506.20105 · PDF · DOI · OpenAlex · Extracted main text
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.
appendix boundary found by appendix_command · 59% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Olivier Deschênes and Michael Greenstone (2011) Climate change, mortality, and adaptation: Evidence from annual fluctuations in weather in the us | 1.000 | 8 | 3 | 100% |
| 2 | Melissa Dell, Benjamin F Jones, and Benjamin A Olken (2012) Temperature shocks and economic growth: Evidence from the last half century | 0.944 | 19 | 4 | 84% |
| 3 | Tamma Carleton, Amir Jina, Michael Delgado, Michael Greenstone, Trev… (2022) Valuing the global mortality consequences of climate change accounting for adaptation costs and benefits | 0.941 | 6 | 4 | 83% |
| 4 | Marshall Burke, Solomon M Hsiang, and Edward Miguel (2015) Global non-linear effect of temperature on economic production | 0.879 | 25 | 5 | 68% |
| 5 | Wolfram Schlenker and Michael J Roberts (2009) Nonlinear temperature effects indicate severe damages to us crop yields under climate change | 0.874 | 5 | 2 | 100% |
| 6 | Melissa Dell, Benjamin F Jones, and Benjamin A Olken (2014) What do we learn from the weather? the new climate-economy literature | 0.737 | 3 | 2 | 100% |
| 7 | Tatyana Deryugina, Garth Heutel, Nolan H Miller, David Molitor, and… (2019) The mortality and medical costs of air pollution: Evidence from changes in wind direction | 0.737 | 3 | 2 | 100% |
| 8 | Tatyana Deryugina and Solomon M Hsiang (2014) Does the environment still matter? daily temperature and income in the united states | 0.693 | 6 | 1 | 100% |
| 9 | Solomon Hsiang (2013) Visually-weighted regression | 0.644 | 4 | 2 | 50% |
| 10 | Maximilian Auffhammer, Solomon M Hsiang, Wolfram Schlenker, and Adam… (2013) Using weather data and climate model output in economic analyses of climate change | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 32 scored citations.