Improving studies of war outcomes and termination : introducing the battles of modern warfare data set
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[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] This project asks the question of how the battle-level analysis of war improves our understanding of interstate conflict outcomes and war termination. The HERO dataset, commonly used in the literatures, is often criticized in terms of selection bias, historical accuracy of data, and ambiguous coding rules. For this reason, I compile a new improved battle dataset, BMW (the Battles of Modern Warfare). I made several efforts to reduce selection error and measurement error based on clear and objective coding rules for choosing cases by taking diverse data source references as well as putting notes on each data point; as a result, BMW covers more than 78 percent of all interstate wars during the twentieth century, and its total number of battles is 568. The BMW data are intended to encourage quantitative studies on the battle level of war particularly related to military effectiveness, democratic peace theory, war outcomes, the art of war, battlefield outcomes, and war termination. I argue that the BMW data allow researchers to rigorously test hypotheses and new casual arguments that can not be studied with war-level data or material variable focused analysis. Through two empirical chapters using the BMW data, I show several interesting findings. The overall findings on beneficial impact of democracy on military effectiveness and beneficial impact of the modern system on battlefield outcomes are robust even when controlling for alternative explanations.
Access is limited to the campus of the University of Missouri-Columbia.