Conference Presentation: Uncovering Vote Trading Through Networks and Computation

The empirical study of vote trading is very challenging due to the unobservable nature of trading agreements, which hinders the identification of vote trading in real-world data. 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 preserving 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 simulation study in which we have full control over the prevalence of vote trading in the data. Finally, we demonstrate our method in two applications based on roll calls in the US Congress and contrast our method and results with previous empirical work on vote trading. Our results provide first insights into the prevalence and variability of vote trading in the US House over the last four decades.