While many scholars have posited a simultaneous relationship between trade and conflict, very few empirical studies have specified the relationship as such. Those that did employed samples that were relatively limited in spatial-temporal coverage. None have employed conflict indicators based on Militarized Interstate Dispute (MID) data due to the difficulty of including discrete dependent variables in a simultaneous equation framework. We overcome both limitations in this study and offer results with important theoretical implications. In line with the most recent published studies, we design our model to "condition" the estimates on recent histories of dyadic trade and conflict. We apply Maddala's estimator, which is designed for a two-equation system in which one endogenous variable is continuous and the other is dichotomous. While the signs of all control variable coefficients match those reported in mainstream trade-conflict literature, our main result does not. We find what we call the "primacy of politics"; i.e., that conflict indeed inhibits trade while the effect of dyadic interdependence on the likelihood of conflict is statistically insignificant. Extensive sensitivity analyses show the results to be consistent across estimators as well as alternative model specifications and trade data sets. We conclude that liberal claims about interdependence and conflict may be problematic.The relationship between economic interdependence and interstate armed conflict continues to fuel lively debates among scholars. The majority of published empirical studies support the liberal position that trade reduces conflict, though some scholars challenge this claim directly and others argue that contingencies and boundary conditions still need to be specified (Mansfield and Pollins 2001). Importantly, there is agreement that the relationship may well be reciprocal (that is, trade affects conflict and conflict moves trade), yet there is very little work to date which takes this possibility into account. Almost all published studies to date estimate the relationship in single-equation form. That is, the study looks only at how trade influences conflict, or it examines only the effect of conflict on trade-

The cdsimeq command implements the two-stage probit least squares estimation method described in Maddala (1983) for simultaneous equations models in which one of the endogenous variables is continuous and the other endogenous variable is dichotomous.1 The cdsimeq command implements all the necessary procedures for obtaining consistent estimates for the coefficients, as well as their corrected standard errors.

The effect of trade on military conflict is one of the most important questions in international relations. liberals argue that trade brings peace, neo-realists and neo-Marxists reason that trade brings conflict, while classical realists contend that trade has no impact on conflict. This article investigates theoretically and empirically some of the most important issues that remain in this literature: the roles of geographical proximity, country size, the handling of the trade data, and the conceptualization of conflict. employing a simultaneous equations model, we find that the claim that trade brings peace is not robust, but rather it is conflict that reduces trade.

Some scholars have rushed to judgment about the nature of the relationship between trade and conflict, making strong assumptions about the data upon which their conclusions rest. In this paper, we test these assumptions, showing that they are often not warranted and, thus, pose threats to many of our conclusions about trade's impact on conflict. We discuss official trade statistics; the treatment of missing trade data; and problems with some decision rules being adopted within our research community. We introduce the new Correlates of War (COW) Trade Data Set; discuss the rationale behind our coding decisions; and compare this data set with other sets. The end result is a series of findings that should help our field advance its understanding of the often difficult issue of trade's relationship with international conflict.

Several leading international relations theories argue that the degree of interest similarity is an important determinant of dyadic conflict and cooperation. Empirical scholarshave long wrestled with operationalizing and measuring this central, yet elusive, concept. Signorino and Ritter's (1999) S algorithm, combined with multiple data sources, provides an attractive solution to this problem. To date, however, many scholars have failed to take full advantage of this solution. In this research note we examine the properties of S via simulation and with real data sources, highlighting its virtues and potential limitations. In particular, we stress the need to include multiple data sources in the computation and provide scholars with an easy-to-use tool to greatly simplify this task.Keywords interstate dyads, interest similarity, preference similarity, Scompute Signorino and Ritter (1999) propose a methodology for calculating the similarity of two column vectors, S, which has the potential to fundamentally change the way scholars of international relations (IR) measure the key concept of dyadic interest similarity. 1 Not only is S intuitively appealing, but it allows for various weighting schemes and the easy combination of multiple variables. These features should make S attractive to IR scholars on a theoretical level. To date, however, examination and use of the new method has been limited. Many studies continue to employ other algorithms, and others, while using S, fail to take full advantage of the method. In this research note we examine the properties of S,