Using Weather Data and Climate Model Output in Economic Analyses of Climate Change

By M. Auffhammer, Solomon Hsiang, W. Schlenker, A. Sobel. Review of Environmental Economics and Policy, 2013

709 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Global and Planetary Change, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/fde0fc02922c79bf53c3560f3b6e1c0fe3280a47

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Citations in the corpus

Temperature Effects on Productivity and Factor Reallocation: Evidence from a Half Million Chinese Manufacturing Plants (2018, background): “8 Auffhammer et al. (2013) note the importance of keeping a continuous weather record when using daily weather data because missing values may contaminate annual estimates derived from daily data.”
Quantifying the Influence of Climate on Human Conflict (2013, background): “For instance, temperature and precipitation time series tend to be negatively correlated in much of the tropics, and drought indices tend to be spatially correlated (51, 126).”
Climate Change, Firm Performance, and Investor Surprises (2019, positive): “Compared to the location- and season-specific average temperature conditions, the exact realization in any given year is randomly distributed and exogenously determined (see Auffhammer et al. [2013] and Dell et al.”
The Use of Panel Models in Assessments of Climate Impacts on Agriculture (2017, background): “, humidity, wind speed, solar radiation) that have been shown to influence the water balance of a crop are infrequently measured and thus difficult to include in a regression framework (Auffhammer et al. 2013).”
Climate Change and Agriculture: Subsistence Farmers’ Response to Extreme Heat (2019, background): “…2 shows the distribution of temperatures observed during the last completed growing season for our whole sample.18 15Note that reanalysis datasets use ground-level readings as a main input and thus can be less precise in contexts with a low number of monitoring stations (Auffhammer et al., 2013).”
Feeling the Heat: Temperature, Physiology & the Wealth of Nations (2013, background): “[2013], Auffhammer et al. [2013]), such annual fluctuations in weather variables can be considered essentially random.”
The impact of climate conditions on economic production. Evidence from a global panel of regions (2020, methodology): “Hence, statistically interpolated data may be more accurate than re-analysis data which are more advantageous for data-poor regions (Auffhammer et al., 2013).”
Economic Impacts of Climate Change on Agriculture: The Importance of Additional Climatic Variables Other than Temperature and Precipitation (2017, background): “21 Auffhammer et al. (2013) suggest that it is important to have a relatively continuous weather record for weather stations when averaging daily station-level data across space.”
Temperature and temperament: Evidence from Twitter (2020, background): “For more complete discussions of the subject across a range of areas, see Auffhammer et al. (2013), Houser et al. (2014), Graff Zivin, Hsiang, and Neidell (2018), Auffhammer (2013), and Dell, Jones, and Olken (2014).”
Irrigation offsets wheat yield reductions from warming temperatures (2017, background): “Importantly, we observe weather at each field location and thus do not have to rely on interpolated weather that could induce attenuationbias due to measurement error [21].”
Measuring climatic impacts on energy consumption: A review of the empirical literature (2014, background): “Auffhammer et al. (2013) provide a detailed discussion of climate models and their use in the social sciences.”
Negative impacts of climate change on cereal yields: statistical evidence from France (2017, methodology): “Finally, following [22], the downscaled projections were added to the historical gridded climate data and department-level projected data were obtained using agricultural area weights as previously mentioned.”