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In this article, we present a new theory that, given the economic consequences of military spending, some governments may use military spending as a means of advancing their domestic non-military objectives. Based on evidence that governments can use military spending as welfare policy in disguise, we argue that the role of ideology in shaping military spending is more complicated than simple left-right politics. We also present a theory that strategic elites take advantage of opportunities presented by international events, leading us to expect governments that favor more hawkish foreign policy policies to use low-level international conflicts as opportunities for increasing military spending. Using pooled time-series data from 19 advanced democracies in the post-World War II period, we find that government ideology, measured as welfare and international positions, interacts with the international security environment to affect defense spending. [ABSTRACT FROM AUTHOR]

Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last half-decade become common in American politics and political behavior. Scholars in this subset of political science have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, researchers in much of comparative politics and international relations, and others with similar data, have been unable to do the same because the best available imputation methods work poorly with the time-series cross-section data structures common in these fields. We attempt to rectify this situation with three related developments. First, we build a multiple imputation model that allows smooth time trends, shifts across cross-sectional units, and correlations over time and space, resulting in far more accurate imputations. Second, we enable analysts to incorporate knowledge from area studies experts via priors on individual missing cell values, rather than on difficult-to-interpret model parameters. Third, because these tasks could not be accomplished within existing imputation algorithms, in that they cannot handle as many variables as needed even in the simpler cross-sectional data for which they were designed, we also develop a new algorithm that substantially expands the range of computationally feasible data types and sizes for which multiple imputation can be used. These developments also make it possible to implement the methods introduced here in freely available open source software that is considerably more reliable than existing algorithms. [ABSTRACT FROM AUTHOR]