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Uncovering Vote Trading Through Networks and Computation

We develop a new methodological framework for the empirical study of legislative vote trading. Building on the concept of reciprocity in directed weighted networks, our method facilitates the measurement of vote trading on a large scale, while estimating the micro-structure of trades between individual legislators. In principle, it can be applied to a broad variety of voting data and refined for various specific contexts. It allows, for example, to study how vote trading in a specific legislative assembly varies over time. We validate our method with a computational model in which we control the level of vote trading. Finally, we demonstrate our framework in an analysis of four decades of roll call voting in the U.S. Congress.