Working Paper: NBER ID: w19087
Authors: Maximilian Auffhammer; Solomon M. Hsiang; Wolfram Schlenker; Adam Sobel
Abstract: Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of datasets available, and then discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overview of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.
Keywords: climate change; weather data; economic analysis; climate models; empirical research
JEL Codes: Q0; Q54
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| weather data (Y10) | economic outcomes (F61) |
| extremely warm temperatures (Q54) | economic outcomes (F61) |
| weather and climate measures (Q54) | interpretation of estimated coefficients (C51) |
| averaging nonmissing weather station data (C80) | measurement error (C20) |
| correlation between weather variables across space (C49) | omitted variable bias (C20) |
| multiple weather indicators (C39) | unbiased estimates of climate change impacts (Q54) |
| spatial correlation among weather variables (C49) | biased standard errors (C51) |