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International relations scholars make frequent use of pooled cross-sectional regression in which N dyads over T time points are combined to create NT observations. Unless special conditions are met, these regressions produce biased estimates of regression coefficients and their standard errors. A survey of recent publications in international relations shows little attention to this issue. Using data from the period 1951–92, we examine the consequences of pooling for models of militarized disputes and bilateral trade. When pooled models are reestimated to allow for stable but unobserved differences among dyads, the results are altered in fundamental ways.
Green et al. (2001) studied this question.