arXiv 2 Feb 2023 · Statistics — Applications
arXiv:2302.01236 · PDF · DOI · OpenAlex · Extracted main text
I measure adaptation to climate change by comparing elasticities from short-run and long-run changes in damaging weather. I propose a debiased machine learning approach to flexibly measure these elasticities in panel settings. In a simulation exercise, I show that debiased machine learning has considerable benefits relative to standard machine learning or ordinary least squares, particularly in high-dimensional settings. I then measure adaptation to damaging heat exposure in United States corn and soy production. Using rich sets of temperature and precipitation variation, I find evidence that short-run impacts from damaging heat are significantly offset in the long run. I show that this is because the impacts of long-run changes in heat exposure do not follow the same functional form as short-run shocks to heat exposure.
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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 | Burke, Marshall, Emerick, Kyle (2016) Adaptation to climate change: Evidence from US agriculture | 1.000 | 23 | 5 | 100% |
| 2 | Schlenker, Wolfram, Roberts, Michael J (2009) Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. | 1.000 | 11 | 5 | 100% |
| 3 | Klosin, Sylvia, Vilgalys, Max (2022) Estimating Continuous Treatment Effects in Panel Data using Machine Learning with an Agricultural Application self | 0.950 | 7 | 3 | 86% |
| 4 | Chernozhukov, Victor, Newey, Whitney K, Singh, Rahul (2022) Automatic debiased machine learning of causal and structural effects | 0.909 | 16 | 3 | 75% |
| 5 | Crane-Droesch, Andrew (2018) Machine learning methods for crop yield prediction and climate change impact assessment in agriculture | 0.874 | 7 | 2 | 100% |
| 6 | Barreca, Alan, Clay, Karen, Deschenes, Olivier, Greenstone, Michael,… (2016) Adapting to climate change: The remarkable decline in the US temperature-mortality relationship over the Twentieth Century | 0.737 | 3 | 2 | 100% |
| 7 | Chernozhukov, Victor, Chetverikov, Denis, Demirer, Mert, Duflo, Esth… (2018) Double/debiased machine learning for treatment and structural parameters | 0.737 | 3 | 2 | 100% |
| 8 | Schlenker, Wolfram, Roberts, Michael J (2006) Nonlinear Effects of Weather on Corn Yields* | 0.737 | 3 | 2 | 100% |
| 9 | Lemoine, Derek (2018) Estimating the Consequences of Climate Change from Variation in Weather | 0.585 | 3 | 1 | 100% |
| 10 | Hsiang, Solomon (2016) Climate econometrics | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 38 scored citations.