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

  • Alicia Dailey Cooperman
Abstract

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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Corresponding author
* Email: alicia.cooperman@columbia.edu
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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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References
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Aronow, P. M., Samii, C., and Assenova, V. A.. 2015. Cluster–robust variance estimation for dyadic data. Political Analysis 23(4):564577.
Barnhart, J. D. 1925. Rainfall and the populist party in nebraska. American Political Science Review 19(03):527540.
Barrios, T., Diamond, R., Imbens, G. W., and Kolesar, M.. 2012. Clustering, spatial correlations, and randomization inference. Journal of the American Statistical Association 107(498):578591.
Borden, K. A., and Cutter, S. L.. 2008. Spatial patterns of natural hazards mortality in the united states. International Journal of Health Geographics 7(1):1.
Brückner, M., and Ciccone, A.. 2011. Rain and the democratic window of opportunity. Econometrica 79(3):923947.
Cameron, A. C., and Miller, D. L.. 2015. A practitioner’s guide to cluster-robust inference. Journal of Human Resources 50(2):317372.
Chen, J. 2013. Voter partisanship and the effect of distributive spending on political participation. American Journal of Political Science 57(1):200217.
Conley, T. G. 1999. Gmm estimation with cross sectional dependence. Journal of Econometrics 92(1):145.
Conley, T. G., and Topa, G.. 2002. Socio-economic distance and spatial patterns in unemployment. Journal of Applied Econometrics 17(4):303327.
Cooperman, A. D.2017. Replication data for: Randomization inference with rainfall data: Using historical weather patterns for variance estimation. Harvard Dataverse, http://dx.doi.org/10.7910/DVN/RJF61A.
Dell, M., Jones, B. F., and Olken, B. A.. 2012. Temperature shocks and economic growth: Evidence from the last half century. American Economic Journal: Macroeconomics 66–95.
Dell, M., Jones, B. F., and Olken, B. A.. 2014. What do we learn from the weather? The new climate–economy literature. Journal of Economic Literature 52(3):740798.
Erikson, R. S., Pinto, P. M., and Rader, K. T.. 2010. Randomization tests and multi-level data in us state politics. State Politics and Policy Quarterly 10(2):180198.
Erikson, R. S., Pinto, P. M., and Rader, K. T.. 2014. Dyadic analysis in international relations: A cautionary tale. Political Analysis 22(4):457463.
Fisher, R. A. 1935. The Design of Experiments . Edinburgh: Oliver & Boyd.
Gerber, A. S., and Green, D. P.. 2012. Field experiments: Design, analysis, and interpretation . WW Norton.
Gomez, B. T., and Hansford, T. G.. 2010. Estimating the electoral effects of voter turnout. American Political Science Review 104(2):268288.
Gomez, B. T., Hansford, T. G., and Krause, G. A.. 2007. The republicans should pray for rain: Weather, turnout, and voting in us presidential elections. Journal of Politics 69(3):649663.
Gomez, B. T., Hansford, T. G., and Krause, G. A.. 2015. Replication data for: The republicans should pray for rain. http://myweb.fsu.edu/bgomez/research.html.
Hsiang, S. M., Burke, M., and Miguel, E.. 2013. Quantifying the influence of climate on human conflict. Science 341(6151): 1235367.
Hsiang, S. M., and Jina, A. S.. 2015. The causal effects of environmental catastrophe on economic growth: Evidence from 6,700 tropical cyclones. NBER Working Paper No. 20352.
Hsiang, S. M., Meng, K. C., and Cane, M. A.. 2011. Civil conflicts are associated with the global climate. Nature 476(7361):438441.
Husak, G. J., Michaelsen, J. C., and Funk, C. C.. 2007. Use of the gamma distribution to represent monthly rainfall in africa for drought monitoring applications. International Journal of Climatology 27(7):935944.
Koubi, V., Bernauer, T., Kalbhenn, A., and Spilker, G.. 2012. Climate variability, economic growth, and civil conflict. Journal of Peace Research 49(1):113127.
Lind, J.2015. Spurious weather effects. CESifo Working Paper Series 5365.
Lyall, J. 2009. Does indiscriminate violence incite insurgent attacks? evidence from chechnya. Journal of Conflict Resolution .
Maccini, S., and Yang, D.. 2009. Under the weather: Health, schooling, and economic consequences of early-life rainfall. American Economic Review 99(3):10061026.
Madestam, A., Shoag, D., Veuger, S., and Yanagizawa-Drott, D.. 2013. Do political protests matter? evidence from the tea party movement. The Quarterly Journal of Economics .
Malcolm, B.2013. The Spatial and Temporal Anatomy of Seasonal Influenza in the United States, 1968–2008. Ph. D. thesis, Columbia University.
McKee, T. B., Doesken, N. L., and Kliest, J.. 1993. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference of Applied Climatology Boston: American Meteorological Society, pp. 179184.
Miguel, E., Satyanath, S., and Sergenti, E.. 2004. Economic shocks and civil conflict: An instrumental variables approach. Journal of Political Economy 112(4):725753.
NWS. 2009. Public geographic areas of responsibility (NWSM 10-507 ed.). National Weather Service.
NWS. 2012. NWS Regions. National Weather Service, Office of Science and Technology.
NWS. 2016. National weather service county warning area boundaries. Silver Spring, MD: National Weather Service.
Rubin, D. B. 1990. Formal mode of statistical inference for causal effects. Journal of Statistical Planning and Inference 25(3):279292.
Schutte, S., and Weidmann, N. B.. 2011. Diffusion patterns of violence in civil wars. Political Geography 30(3):143152.
Taylor, R. B., and Covington, J.. 1988. Neighborhood changes in ecology and violence. Criminology 26(4):553590.
Yang, D. 2008. Coping with disaster: The impact of hurricanes on international financial flows, 1970–2002. The B.E. Journal of Economic Analysis and Policy 8(1):145.
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Political Analysis
  • ISSN: 1047-1987
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