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Randomization Inference with Rainfall Data: Using Historical Weather Patterns for Variance Estimation

  • Alicia Dailey Cooperman

Many recent papers in political science and economics use rainfall as a strategy to facilitate causal inference. Rainfall shocks are as-if randomly assigned, but the assignment of rainfall by county is highly correlated across space. Since clustered assignment does not occur within well-defined boundaries, it is challenging to estimate the variance of the effect of rainfall on political outcomes. I propose using randomization inference with historical weather patterns from 73 years as potential randomizations. I replicate the influential work on rainfall and voter turnout in presidential elections in the United States by Gomez, Hansford, and Krause (2007) and compare the estimated average treatment effect (ATE) to a sampling distribution of estimates under the sharp null hypothesis of no effect. The alternate randomizations are random draws from national rainfall patterns on election and would-be election days, which preserve the clustering in treatment assignment and eliminate the need to simulate weather patterns or make assumptions about unit boundaries for clustering. I find that the effect of rainfall on turnout is subject to greater sampling variability than previously estimated using conventional standard errors.

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Author’s note: I am grateful to Donald Green, Gregory Wawro, Tara Slough, Alex Coppock, Lindsay Dolan, Robert Erikson, Patrick Healy, Ayal Margalith, and two anonymous referees for their helpful comments, suggestions, and encouragement. I thank the Columbia Digital Social Science Center for assistance with processing the precipitation data. I thank Brad Gomez, Thomas Hansford, and George Krause for making their replication data available. For replication materials, see Cooperman (2017). Supplementary materials for this article are available on the Political Analysis website.

Contributing Editor: Jonathan N. Katz

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Political Analysis
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