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How do people update? The effects of local weather fluctuations on beliefs about global warming

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Abstract

Global warming has become a controversial public policy issue in spite of broad scientific consensus that it is real and that human activity is a contributing factor. It is likely that public consensus is also needed to support policies that might counteract it. It is therefore important to understand how people form and update their beliefs about climate change. Using unique survey data on beliefs about the occurrence of the effects of global warming, I estimate how local temperature fluctuations influence what individuals believe about these effects. I find that some features of the updating process are consistent with rational updating. I also test explicitly for the presence of several heuristics known to affect belief formation and find strong evidence for representativeness, some evidence for availability, and no evidence for spreading activation. I find that very short-run temperature fluctuations (1 day–2 weeks) have no effect on beliefs about the occurrence of global warming, but that longer-run fluctuations (1 month–1 year) are significant predictors of beliefs. Only respondents with a conservative political ideology are affected by temperature abnormalities.

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Notes

  1. The empirical evidence on updating is mixed. Evidence for various forms of irrational updating includes DeBondt and Thaler (1984) in finance, Terrell (1994) and Clotfelter and Cook (1993) in lottery play, and Egan and Mullin (2012), Risen and Critcher (2011), and Cameron (2005) in climate change beliefs.

  2. In a controlled experiment, Risen and Critcher (2011) find that indoor temperatures also affect beliefs.

  3. This is true in a diverse array of subject areas, from economics (Charness and Levin 2005) and finance (Chiang et al. 2011) to criminology (Anwar and Loughran 2011), psychology (Le Mens and Denrell 2011), and biology (Valone 2006).

  4. In this case, Bayes’s formula is given by \( Pr\left( {G\left| E \right.} \right)=\left( {Pr\left( {E\left| G \right.} \right)Pr(G)} \right)/\left( {Pr\left( {E\left| G \right.} \right)Pr(G)+Pr\left( {E\left| {NG} \right.} \right)\left( {1-Pr(G)} \right)} \right) \) where G and NG are states of the world with and without global warming, respectively, and E is the observed evidence.

  5. Local temperatures might also be significant predictors of beliefs if respondents observe them with less noise than accompanies temperatures in other locations, which leads them to give those temperatures greater weight when using Bayes’s Rule. In this case, even if on average the observed weather is the same, Bayesian updaters will give weather that is measured with less noise greater weight in the updating process, which will create de facto spatial variation.

  6. Belief formation when spreading activation is relevant has also been mathematically modeled by Mullainathan (2002) in a process that he dubs “associativeness.”

  7. Copyright © 2012, Gallup, Inc., All Rights Reserved. Reprinted with permission.

  8. The sample is representative of the US. Respondents are surveyed by phone. Global warming is not the sole focus of the survey: Topics include energy, the economy, US environmental policies, Arctic drilling, and environmental behaviors.

  9. “Refused” and “Don’t know” are treated as missing in the regression analysis. These options never include more than 5 % of the sample; for most of the questions, less than 3 % of respondents chose these options.

  10. Li et al (2011) also elicit the degree to which respondents worry about global warming.

  11. If there are multiple weather stations in a county, I average their daily measurements.

  12. The respondents are called between 5 p.m. and 9 p.m. local time, making the inclusion of that day’s temperatures reasonable.

  13. A full set of results is available upon request.

  14. It also does not matter whether these categories are coded in increasing or decreasing order.

  15. Technical details about ordered probit can be found in the Electronic Supplementary Material. Also see Wooldridge (2002) for more information about this estimation procedure.

  16. I describe the construction of this variable in Section 3.2.

  17. Mathematical details involved in the regression analysis can be found in the Electronic Supplemental Materials.

  18. Observing the effect of the average number of standard deviations over the week before the survey (shown in the Electronic Supplemental Materials) produces similar results. Similarly, using raw deviations rather than standard deviations does not change the conclusion.

  19. See Schwarz (1999) and Bertrand and Mullainathan (2001) for a discussion and examples.

  20. In particular, the mean (median) temperature on the day of the survey is about 54 (55.5) degrees while the 95th and 99th percentiles are 81.5 and 86.3°, respectively, with most of the higher temperatures occurring in states in which such temperatures are common: California, Florida, Texas, and Louisiana. The distributions of temperatures for the other days in the week before the survey are similar.

  21. Presenting this effect simply requires transforming the average estimated coefficient into a marginal one for this particular category. It does not change the estimating equation or the coding of the question. See Electronic Supplemental Materials for more details and for the point estimates.

  22. Because the fraction of abnormal days theoretically varies from 0 to 1 (over longer periods, the fraction never reaches 1 in practice), this coefficient can also be interpreted as the effect of going from zero days having temperature deviations outside the defined thresholds to all days having temperature deviations outside the thresholds.

  23. See Electronic Supplementary Materials for a specific example.

  24. For space reasons, I omit the thresholds of 10 % and 90 %. The results for those thresholds are small and insignificant.

  25. For example, Shanahan and Good (2000) find that climate issues were more likely to be covered in the New York Times during periods of unusually high temperatures.

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Acknowledgments

I am very grateful to Amy Finkelstein and Michael Greenstone for invaluable discussions and extensive feedback. I thank Stefano DellaVigna for insightful suggestions, and Jason Abaluck, Jerry Hausman, Dan Keniston, Randall Lewis, Anna Mikusheva, Mar Reguant-Rido, Joseph Shapiro, and three anonymous referees for helpful comments. I acknowledge the financial support of the MIT Energy Initiative, the MIT Shultz Fund, and the National Science Foundation.

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  1. University of Illinois at Urbana-Champaign, Champaign, IL, USA

    Tatyana Deryugina

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Correspondence to Tatyana Deryugina.

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Deryugina, T. How do people update? The effects of local weather fluctuations on beliefs about global warming. Climatic Change 118, 397–416 (2013). https://doi.org/10.1007/s10584-012-0615-1

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