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.
Guerrero, Omar A and Matter, Ulrich, Uncovering Vote Trading through Networks and Computation (November 4, 2016). Available at SSRN: https://ssrn.com/abstract=2864421 or http://dx.doi.org/10.2139/ssrn.2864421