Explaining Rare Events in International Relations

International Organization, Vol. 55, No. 3, pp. 693-715, Summer 2001

23 Pages Posted: 17 Jan 2008

See all articles by Gary King

Gary King

Harvard University

Langche Zeng

University of California, San Diego

Abstract

Some of the most important phenomena in international conflict are coded s "rare events data," binary dependent variables with dozens to thousands of times fewer events, such as wars, coups, etc., than "nonevents". Unfortunately, rare events data are difficult to explain and predict, a problem that seems to have at least two sources. First, and most importantly, the data collection strategies used in international conflict 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 (e.g., wars) and a tiny fraction of non-events (peace). This enables scholars to save as much as 99% of their (non-fixed) 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.

Suggested Citation

King, Gary and Zeng, Langche, Explaining Rare Events in International Relations. Available at SSRN: https://ssrn.com/abstract=1083729

Gary King (Contact Author)

Harvard University ( email )

1737 Cambridge St.
Institute for Quantitative Social Science
Cambridge, MA 02138
United States
617-500-7570 (Phone)

HOME PAGE: http://gking.harvard.edu

Langche Zeng

University of California, San Diego ( email )

9500 Gilman Drive
Code 0521
La Jolla, CA 92093-0521
United States

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