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Dyadic Analysis in International Relations: A Cautionary Tale

Published online by Cambridge University Press:  04 January 2017

Robert S. Erikson ,
Pablo M. Pinto  and
Kelly T. Rader
Robert S. Erikson*
Affiliation:
Department of Political Science, Columbia University, New York, NY, 10027
Pablo M. Pinto
Affiliation:
Department of Political Science, Columbia University, New York, NY, 10027. e-mail: pp2162@columbia.edu
Kelly T. Rader
Affiliation:
Department of Political Science, Yale University, New Haven, CT, 06520. e-mail: kelly.rader@yale.edu
*
e-mail: rse14@columbia.edu (corresponding author)
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Abstract

International relations scholars frequently rely on data sets with country pairs, or dyads, as the unit of analysis. Dyadic data, with its thousands and sometimes hundreds of thousands of observations, may seem ideal for hypothesis testing. However, dyadic observations are not independent events. Failure to account for this dependence in the data dramatically understates the size of standard errors and overstates the power of hypothesis tests. We illustrate this problem by analyzing a central proposition among IR scholars, the democratic trade hypothesis, which claims that democracies seek out other democracies as trading partners. We employ randomization tests to infer the correct p-values associated with the trade hypotheses. Our results show that typical statistical tests for significance are severely overconfident when applied to dyadic data.

Type
Research Article
Information
Political Analysis , Volume 22 , Issue 4 , Autumn 2014 , pp. 457 - 463
Copyright
Copyright © The Author 2014. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: For comments on earlier drafts we are thankful to Donald Green, Soo Yeon Kim, Jeff Lax, Mark Manger, Yotam Margalit, Ken Scheve, Vera Troeger, Robert Walker, and participants at various academic presentations. Boliang Zhu provided excellent research assistance. Replication materials for this article are available from the Political Analysis dataverse at http://dx.doi.org/10.7910/DVN/23510UNF:5:YY9Ujh8lYTjBOtSWvyRtmw== IQSS Dataverse Network [Distributor] V1 [Version]. Supplementary materials for this article are available on the Political Analysis Web site.

References

Angrist, Joshua D., and Pischke, Jorn-Steffen. 2009. Mostly harmless econometrics: An empiricist's companion. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Bliss, Harry, and Russett, Bruce. 1998. Democratic trading partners: The liberal connection, 1962–1989. Journal of Politics 60(4): 1126–47.Google Scholar
Dixon, William J., and Moon, Bruce E. 1993. Political similarity and American foreign trade patterns. Political Research Quarterly 46(1): 5–25.Google Scholar
Donahue, John, and Wolfers, Justin. 2005. Uses and abuses of empirical evidence in the death penalty debate. Stanford Law Review 58: 791–845.Google Scholar
Edgington, Eugene S., and Onghena, Patrick. 2007. Randomization tests. 4th ed. Boca Raton, FL: Taylor and Francis Group.Google Scholar
Erikson, Robert S., Pinto, Pablo M., and Rader, Kelly T. 2010. Randomization tests and multi-level data in U.S. state politics. State Politics and Policy Quarterly 10: 180–98.CrossRefGoogle Scholar
Fisher, R. A. 1935. The design of experiments. Edinburgh: Oliver and Boyd.Google Scholar
Gleditsch, Kristian S. 2002. Expanded trade and GDP data. Journal of Conflict Resolution 46: 712–24.CrossRefGoogle Scholar
Green, Donald P., Kim, Soo Yeon, and Yoon, David H. 2001. Dirty pool. International Organization 55(2): 441–68.CrossRefGoogle Scholar
Helland, Eric, and Tabarrok, Alexander. 2004. Using Placebo Laws to test “more guns, less crime.” Advances in Economic Analysis and Policy 4: 1–7.Google Scholar
Kennedy, Peter E., and Cade, Brian S. 1996. Randomization tests for multiple regression. Communications in Statistics-Simulation and Computation 25: 923–29.CrossRefGoogle Scholar
Mansfield, Edward D., Milner, Helen V., and Peter Rosendorff, B. 2000. Free to trade: Democracies, autocracies, and international trade. American Political Science Review 94(2): 305–21.Google Scholar
Moulton, Brent R. 1990. An illustration of a pitfall in estimating the effects of aggregate variables on micro units. Review of Economics and Statistics 72(2): 334–38.CrossRefGoogle Scholar
O'Gorman, Thomas W. 2005. The performance of randomization tests that use permutations of independent variables. Communications in Statistics Simulation and Computation 34: 895–908.Google Scholar
Tomz, Michael, Goldstein, Judith, and Rivers, Douglas. 2007. Do we really know that the WTO increases trade? Comment. American Economic Review 97: 2005–18.Google Scholar
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