Variable selection is a crucial aspect of formulating a model to empirically examine data, as omitted variables can create spurious association, while inclusion of irrelevant variables can bias the results of one’s estimates. To mitigate such problems, researchers rely on theory to guide their selection of variables to include in their models. Unfortunately, in social science, there often exist several plausible theories to explain actions, and hence several models that researchers can use in their empirical work. This lack of unique theory is evident in examining trade’s effect on conflict, as there are three main theories on this and each suggests a different effect for trade interdependence on conflict. Empirically, the effect of trade on conflict remains uncertain, as researchers Barbieri and Oneal & Russett have come to disparate conclusions using different measures of trade interdependence (models). Each of their inferences is based on the belief that the variables they select form the ‘true’ model that generates the data. The problem is that theory is unable to indicate whether one model is more appropriate than another, which creates uncertainty over the empirical effects of trade on conflict. To account for uncertainty in model selection, the author allows for several models by applying Bayesian model averaging (BMA) to the study of conflict. Accounting for this uncertainty, he finds that trade interdependence does not have a significant effect on the prediction of militarized conflict, whereas joint democracy continues to reduce conflict.
The classical liberal belief is trade, which economically benefits countries, creates ties binding the interests of countries and reduces conflict. While the vast majority of the empirical literature supports this view, recent research questions these findings by also considering the reciprocal relationship between trade and conflict. If conflict also influences trade, then trade is an endogenous right hand side regressor and previous estimates which ignore this are inconsistent. This article determines when one uses appropriate instruments for the endogenous regressors that trade reduces conflict and conflict reduces trade. Failure to use such instruments results in inconsistent estimates and can lead to the spurious conclusion that trade increases conflict. The lesson is the use of inappropriate instruments can be worse than not using them at all. 4 Use of a probit or logit model in the first stage should not be used. See Kelejian (1971) and Angrist & Krueger (2001) for further discussion. 5 The standard errors of this second stage will be incorrect as they are based on the fitted values of conflict rather than the actual values. See Wooldridge (2002: 95) for the correction. Stata's ivreg2 command automatically corrects the standard errors and is used below to produce the estimates of the linear trade model.
The United States recently proposed to sell Saudi Arabia advanced weaponry worth 20 billion dollars over the next 10 years. The volume of trade, while significant, is second in the news headline that the United States would provide Saudi Arabia with precision-guided bombs, upgrades to its fighters, and new naval vessels. Trade of strategic commodities, such as armaments, suggests a strong interdependence between countries, which may influence international relations differently than the same volume of toys traded between nations. The author posits the volume and pattern of commodities that countries trade with each other are both relevant to interstate conflict. Commodities are heterogeneous and thus vary in terms of their strategic importance, substitutability, and ease of expropriation. This heterogeneity, along with the volume of trade, influences the opportunity cost of lost trade caused by conflict. This article empirically examines whether the pattern of trade is relevant to conflict for the period 1962—2000. The results from both single and simultaneous equations models indicate that increasing the share of bilateral trade in energy, non-ferrous metals, and electronics increases conflict, whereas for chemicals and arms it reduces conflict. Differences in these strategic commodities’ elasticity of import demand and export supply, along with their ease of expropriation, contribute to the heterogeneous effects.
The importance that is placed on graduation rates as a measure of the success of institutions of higher education warrant the ongoing research into understanding the determinants of these educational outcomes. This study examines the role of institutional factors in determining graduation rates at doctoral universities. While controlling for student characteristics, we find that institutional characteristics are an important determinant of four-, five-, and six-year graduation rates. Student-faculty ratios, percentage of faculty that are full time, total expenditures and tuition and fees all play a significant role in explaining graduation rates at the universities in our sample. / GOENNER AND SNAITH
The purpose of this paper is to build a predictive model of enrollment that provides data driven analysis to improve undergraduate recruitment efforts. We utilize an inquiry model, which examines the enrollment decisions of students that have made contact with our institution, a medium sized, public, Doctoral I university. A student, who makes an inquiry to our university such as by returning a request for information form, often provides far less information than is available from applicants. Despite this fact we find that characteristics of the student, as well as geographic and demographic data based on the student's zip code are significant predictors of enrollment. Accounting for uncertainty in our model's specification, we find that we are able to predict out of sample the enrollment decision of 89% of student inquiries. We also demonstrate how these findings can be used to improve marketing efforts.
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