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"Quantifying the potential effects of climate on aggregate economic outcomes like GDP is a key part of understanding the broader economic impacts of a changing climate.  Lots of studies now use modern panel econometrics to study how past fluctuations in temperature have affected GDP around the world, including the landmark 2012 paper by Dell, Jones, and Olken (henceforth DJO), our 2015 paper (henceforth BHM), and a number of more recent papers that either analyze new data on this topic (e.g. subnational GDP or income data, see here or here or here) or revisit earlier results."

"Newell, Prest, and Sexton 2018 (draft here, henceforth NPS) is one recent paper that revisits our earlier work in BHM. Because we've gotten a lot of questions about this paper, Sol and I wanted to share our views of what we think we can learn from NPS.  In short, our view is that their approach is not suitable to the question they are trying to ask, and that their conclusions as stated in their abstract (at least in their current draft) are directly contradicted by their results.  This is important because their conclusions appear to shed important light on the aggregate economic impacts of unmitigated climate change. Unfortunately we do not believe that this is the case."

"NPS seek to take a data-driven approach to resolving a number of empirical issues that come up in our earlier work.  These include: (1) Does temperature affect the level or growth rate of GDP? (2) What is the "right" set of fixed effects or time trends to include as controls in analyzing the effect of temperature on GDP? (3) And what is the "right" functional form to describe the relationship between temperature and GDP changes?  These choices -- particularly (1) and (3) -- are certainly influential in the findings of earlier studies.  If temperature affects growth rates rather than levels, this can imply huge effects of future temperature increases on economic output, as small impacts on growth aggregate over time.  If temperature has a globally nonlinear effect on output, as we argue in BHM, this suggests that both wealthy and poor countries can be affected by changes in temperature -- and not only poor countries, as suggested in earlier analyses.  Resolving these questions is key to understanding the impacts of a warming climate, and its great that papers like NPS are taking them on."

"However, we have some serious qualms with the approach that NPS take to answer these questions.  NPS wish to use cross-validation to select which set of the above choices performs "best" in describing historical data.  Cross-validation is a technique in which available data are split between disjoint training and testing datasets, and candidate models are trained on the training data and then evaluated on the held-out test data using a statistic of interest (typically RMSE or r-squared in these sorts of applications).  The model with the lowest RMSE or highest r-squared on test data is then chosen as the preferred model."

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Monday, October 19, 2020

Friday, September 11, 2020

Monday, August 31, 2020

Thursday, June 11, 2020

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