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Throwing Out the Baby with the Bath Water: A Comment on Green, Kim, and Yoon

Published online by Cambridge University Press:  09 July 2003

Nathaniel Beck  and
Jonathan N. Katz
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Abstract

Donald P. Green, Soo Yeon Kim, and David H. Yoon argue that many findings in quantitative international relations that use the dyad-year design are flawed. In particular, they argue that the effect of democracy on both trade and conflict has been vastly overstated, that researchers have ignored unobserved heterogeneity between the various dyads, and that heterogeneity can be best modeled by “fixed effects,” that is, a model that includes a separate dummy for each dyad.

We argue that the use of fixed effects is almost always a bad idea for dyad-year data with a binary dependent variable like conflict. This is because conflict is a rare event, and the inclusion of fixed effects requires us to not analyze dyads that never conflict. Thus while the 90 percent of dyads that never conflict are more likely to be democratic, the use of fixed effects gives democracy no credit for the lack of conflict in these dyads. Green, Kim, and Yoon's fixed-effects logit can tell us little, if anything, about the pacific effects of democracy.

Their analysis of the impact of democracy on trade is also flawed. The inclusion of fixed effects almost always masks the impact of slowly changing independent variables; the democracy score is such a variable. Thus it is no surprise that the inclusion of dyadic dummy variables in their model completely masks the relationship between democracy and trade. We show that their preferred fixed-effects specification does not outperform a model with no effects (when that model is correctly specified in other ways). Thus there is no need to include the masking fixed effects, and so Green, Kim, and Yoon's findings do not overturn previous work that found that democracy enhanced trade.

We agree with Green, Kim, and Yoon that modeling heterogeneity in time-series cross-section data is important. We mention a number of alternatives to their fixed-effects approach, none of which would have the pernicious consequences of using dyadic dummies in their two reanalyses.

Type
Symposium
Information
International Organization , Volume 55 , Issue 2 , Spring 2001 , pp. 487 - 495
Copyright
Copyright © The IO Foundation 2001

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References

Alt, James E., King, Gary, and Signorino, Curtis. 2001. Estimating the Same Quantities from Different Levels of Data: Time Dependence and Aggregation in Event Process Models. Political Analysis 9 (1): 21–44.CrossRefGoogle Scholar
Beck, Nathaniel. 1998. Modeling Space and Time: The Event History Approach. In Research Strategies in the Social Sciences, edited by Scarbrough, Elinor and Tanenbaum, Eric, 191–213. Oxford: Oxford University Press.CrossRefGoogle Scholar
Beck, Nathaniel, Katz, Jonathan N., and Tucker, Richard. 1998. Taking Time Seriously: Time-Series Cross-Section Analysis with a Binary Dependent Variable. American Journal of Political Science 42 (4): 1260–88.CrossRefGoogle Scholar
Beck, Nathaniel, and Tucker, Richard. 1997. Conflict in Time and Space. Center for International Affairs Working Paper 97-8. Cambridge, Mass.: Harvard University. Available at <https://wwwc.cc.Columbia.edu/sec/dlc/ciao/wps/tur01/>.Google Scholar
Green, Donald P., Kim, Soo Yeon, and Yoon, David H.. 2001. Dirty Pool. International Organization 55 (2): 441–68.CrossRefGoogle Scholar
Greene, William H. 2000. Econometric Analysis. 4th ed. Upper Saddle River, N.J.: Prentice-Hall.Google Scholar
Mansfield, Edward D., and Bronson, Rachel. 1997. Alliances, Preferential Trading Arrangements, and International Trade. American Political Science Review 91 (1): 94–107.CrossRefGoogle Scholar
Sargent, Daniel J. 1998. A General Framework for Random Effects Survival Analysis in the Cox Proportional Hazards Setting. Biometrics 54 (4): 1486–97.CrossRefGoogle ScholarPubMed
Schmidt, Peter, and Witte, Ann Dryden. 1989. Predicting Criminal Recidivism Using “Split Population” Survival Time Models. Journal of Econometrics 40 (1): 141–59.CrossRefGoogle Scholar
Sueyoshi, Glenn T. 1995. A Class of Binary Response Models for Grouped Duration Data. Journal of Applied Econometrics 10 (4): 411–31.CrossRefGoogle Scholar
Tsodikov, A. 1998. A Proportional Hazards Model Taking Account of Long-Term Survivors. Biometrics 54 (4): 1508–15.CrossRefGoogle ScholarPubMed
Western, Bruce. 1998. Causal Heterogeneity in Comparative Research: A Bayesian Hierarchical Modeling Approach. American Journal of Political Science 42 (4): 1233–59.CrossRefGoogle Scholar