Temperature and temperament: Evidence from Twitter
By Patrick Baylis. 2020
137 citations recorded by the source corpus.
Abstract How do people value their climate? This paper demonstrates a new approach to estimating preferences for nonmarket goods using social media data. I combine more than a billion Twitter updates with natural language processing algorithms to construct a rich panel dataset of expressed sentiment for the United States and six other English-speaking countries around the world. In the U.S., I find consistent and statistically significant declines in expressed sentiment from both hot and cold temperatures. To better understand how preferences may adapt, I document heterogeneity in both regional and seasonal responses. I complete the U.S. analysis with a suite of validation exercises to understand the magnitude of these effects and two methods to estimate willingness-to-pay for climate amenities. Finally, I document similar relationships between temperature and expressed sentiment for four out of the six non-U.S. countries I examine.
Subfields: General Decision Sciences, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/c6f87f06155b563acd37413d0fd10954b89782e2
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In General Decision Sciences
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Citations in the corpus
- The Economic Geography of Global Warming (2021, methodology): “…has been employed to quantify the weather effects on mortality (Barreca et al. , 2016; Carleton et al. , 2022), amenities (Albouy et al. , 2016; Baylis, 2020), crime and conflict (Burke et al. , 2015a), migration (Missirian and Schlenker, 2017), crop yields (Schlenker and Roberts, 2009), and…”