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Explaining Rare Events in International Relations

Published online by Cambridge University Press:  09 July 2003

Gary King  and
Langche Zeng
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Abstract

Some of the most important phenomena in international conflict are coded as “rare events”: binary dependent variables with dozens to thousands of times fewer events, such as wars and coups, than “nonevents.” Unfortunately, rare events data are difficult to explain and predict, a problem stemming from at least two sources. First, and most important, the data-collection strategies used in international conflict studies are grossly inefficient. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (wars, for example) and a tiny fraction of nonevents (peace). This enables scholars to save as much as 99 percent of their (nonfixed) data-collection costs or to collect much more meaningful explanatory variables. Second, logistic regression, and other commonly used statistical procedures, can underestimate the probability of rare events. We introduce some corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. We also provide easy-to-use methods and software that link these two results, enabling both types of corrections to work simultaneously.

Type
Articles
Information
International Organization , Volume 55 , Issue 3 , Summer 2001 , pp. 693 - 715
Copyright
Copyright © The IO Foundation 2001

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References

Achen, Christopher A. 1999. Retrospective Sampling in International Relations. Paper presented at the 57th Annual Meeting of the Midwest Political Science Association, Chicago.Google Scholar
Beck, Nathaniel, King, Gary, and Zeng, Langche. 2001. Improving Quantitative Studies of International Conflict: A Conjecture. American Political Science Review 94 (1):21–35.CrossRefGoogle Scholar
Bennett, D. Scott, and Stam, Allan C. III . 1998a. EUGene: Expected Utility Generation and Data Management Program. Version 1.12. Available at ⟨http://wizard.ucr.edu/cps/eugene/eugene.html?⟩.Google Scholar
Bennett, D. Scott, and Stam, Allan C. III. 1998b. Theories of Conflict Initiation and Escalation: Comparative Testing, 1816–1980. Paper prepared for the annual meeting of the International Studies Association, Minneapolis.Google Scholar
Breslow, Norman E. 1996. Statistics in Epidemiology: The Case-control Study. Journal of the American Statistical Association 91 (433): 14–28.Google Scholar
Breslow, Norman E., and Day, N. E.. 1980. Statistical Methods in Cancer Research. Lyon: International Agency for Research on Cancer.Google Scholar
Bueno de Mesquita, Bruce. 1981. The War Trap. New Haven, Conn.: Yale University Press.Google Scholar
Bueno de Mesquita, Bruce, and Lalman, David. 1992. War and Reason: Domestic and International Imperatives. New Haven, Conn.: Yale University Press.CrossRefGoogle Scholar
Cosslett, Stephen R. 1981. Efficient Estimation of Discrete-Choice Models. In Structural Analysis of Discrete Data with Econometric Applications, edited by Manski, Charles F. and McFadden, Daniel, 51–111. Cambridge, Mass.: MIT Press.Google Scholar
Esty, Daniel C., Goldstone, Jack, Gurr, Ted Robert, Harff, Barbara, Levy, Marc, Dabelko, Geoffrey D., Surko, Pamela T., and Unger, Alan N.. 1998. The State Failure Task Force Report: Phase II Findings. McLean, Va.: Science Applications International Corporation.Google Scholar
Esty, Daniel C., Goldstone, Jack, Gurr, Ted Robert, Harff, Barbara, Surko, Pamela T., Unger, Alan N., and Chen, Robert S.. 1998. The State Failure Project: Early Warning Research for U.S. Foreign Policy Planning. In Preventive Measures: Building Risk Assessment and Crisis Early Warning Systems, edited by Davies, John L. and Gurr, Ted Robert, 27–38. Lanham, Md.: Rowman and Littlefield.Google Scholar
Geller, Daniel S., and Singer, J. David. 1998. Nations at War: A Scientific Study of International Conflict. New York: Cambridge University Press.Google Scholar
Holland, Paul W., and Rubin, Donald B.. 1988. Causal Inference in Retrospective Studies. Evaluation Review 12 (3):203–31.Google Scholar
Huth, Paul K. 1988. Extended Deterrence and the Outbreak of War. American Political Science Review 82 (2):423–43.CrossRefGoogle Scholar
Hsieh, David A., Manski, Charles F., and McFadden, Daniel. 1985. Estimation of Response Probabilities from Augmented Retrospective Observations. Journal of the American Statistical Association 80 (391):651–62.Google Scholar
Imbens, Guido. 1992. An Efficient Method of Moments Estimator for Discrete Choice Models with Choice-Based Sampling. Econometrica 60 (5):1187–1214.CrossRefGoogle Scholar
King, Gary, and Zeng, Langche. 2000. Estimating Risk and Rate Levels, Ratios, and Differences in Case-Control Data. Available at ⟨http://gking.harvard.edu⟩.Google Scholar
King, Gary, and Zeng, Langche. Forthcoming. Improving Forecasts of State Failure. World Politics.Google Scholar
King, Gary, Keohane, Robert O., and Verba, Sidney. 1994. Designing Social Inquiry: Scientific Inference in Qualitative Research. Princeton, N.J.: Princeton University Press.CrossRefGoogle Scholar
King, Gary, Tomz, Michael, and Wittenberg, Jason. Forthcoming. Making the Most of Statistical Analyses: Improving Interpretation and Presentation. American Journal of Political Science.Google Scholar
Lancaster, Tony, and Imbens, Guido. 1996a. Case-Control with Contaminated Controls. Journal of Econometrics 71 (1–2):145–60.Google Scholar
Levy, Jack S. 1989. The Causes of War: A Review of Theories and Evidence. In Behavior, Society, and Nuclear War, vol. 1, edited by Tetlock, Phillip E., Husbands, Jo L., Jervis, Robert, Stern, Paul C., and Tilly, Charles, 2120–333. New York: Oxford University Press.Google Scholar
Manski, Charles F. 1999. Nonparametric Identification Under Response-Based Sampling. In Nonlinear Statistical Inference: Essays in Honor of Takeshi Amemiya, edited by Hsiao, C., Morimune, K., and Powell, J.. New York: Cambridge University Press.Google Scholar
Manski, Charles F., and Lerman, Steven R.. 1977. The Estimation of Choice Probabilities from Choice-based Samples. Econometrica 45 (8):1977–88.Google Scholar
Maoz, Zeev, and Russett, Bruce. 1993. Normative and Structural Causes of Democratic Peace, 1946–86. American Political Science Review 87 (3):624–38.CrossRefGoogle Scholar
Nagelkerke, Nico J. D., Moses, Stephen, Plummer, Francis A., Brunham, Robert C., and Fish, David. 1995. Logistic Regression in Case-control Studies: The Effect of Using Independent as Dependent Variables. Statistics in Medicine 14 (8):769–75.Google Scholar
Prentice, R. L., and Pyke, R.. 1979. Logistic Disease Incidence Models and Case-control Studies. Biometrika 66 (3):403–11.CrossRefGoogle Scholar
Ripley, Brian D. 1996. Pattern Recognition and Neural Networks. New York: Cambridge University Press.CrossRefGoogle Scholar
Rosenau, James N., ed. 1976. In Search of Global Patterns. New York: Free Press.Google Scholar
Schaefer, Robert L. 1983. Bias Correction in Maximum Likelihood Logistic Regression. Statistics in Medicine 2:71–78.CrossRefGoogle ScholarPubMed
Signorino, Curtis S. 1999. Strategic Interaction and the Statistical Analysis of International Conflict. American Political Science Review 93 (2):279–87.Google Scholar
Signorino, Curtis S., and Ritter, Jeffrey M.. 1999. Tau-b or Not Tau-b: Measuring the Similarity of Foreign Policy Positions. International Studies Quarterly 43 (1):115–44.CrossRefGoogle Scholar
Tucker, Richard. 1998. The Interstate Dyad-Year Dataset, 1816–1997. Version 3.0. Available at ⟨http://www.fas.harvard.edu/~rtucker/data/dyadyear/⟩.Google Scholar
Tucker, Richard. 1999. BTSCS: A Binary Time-Series—Cross-Section Data Analysis Utility. Version 3.0.4. Available at ⟨http://www.fas.harvard.edu/~rtucker/programs/btscs/btscs.html⟩.Google Scholar
Vasquez, John A. 1993. The War Puzzle. New York: Cambridge University Press.CrossRefGoogle Scholar