We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend 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. Second, commonly used data collection strategies are grossly inefficient for rare events data. 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, such as in international conflict data with more than a quarter-million dyads, only a few of which are at war. 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 nonevents (peace). This enables scholars to save as much as 99 % of their (nonfixed) data collection costs or to collect much more meaningful explanatory

Some of the most important phenomena in international conflict are coded as "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 ...

While constructivist scholars have recognized an important role for norms in international relations, they have not considered the changing proportion of democratic states in the international system as a potential source of norm formation. I argue that democratic norms become international norms as the proportion of democratic states in the international system increases, focusing on the democratic norm of third party dispute resolution. I reach the novel conclusion that non-democratic states are more likely to behave like democratic states, adopting democratic norms, as the proportion of democracies increases. Empirical analysis of peaceful settlement attempts of territorial claims in the Americas supports this hypothesis. Third party settlement is sixteen times more likely for non-democratic dyads when the proportion of democracies in the system is 50 % than when the proportion is zero. My theory and empirical results offer new insight into the democratic peace literature and the constructivist literature on international norms. 1 Normative approaches to the democratic peace assert that states externalize the norms that characterize their domestic political processes. One important norm that stems from a democratic political system is the peaceful resolution of disputes, especially through the use of courts as arbitrators. It is only natural