Quantifying vote trading through network reciprocity

Our aim with this paper was to create the first method to estimate vote trading in congresses using roll call data. We develop a network approach and succeed at providing the first large-scale estimates of vote trading and validating our results with previous micro-level studies.

Building on the concept of reciprocity in directed weighted networks, we propose a framework to study legislative vote trading. We first discuss the conditions to quantify vote trading empirically. We then illustrate how a simple empirical framework–complementary to existing approaches–can facilitate the discovery and measurement of vote trading in roll-call data. The application of the suggested procedure preserves the micro-structure of trades between individual legislators, shedding light on, so far, unstudied aspects of vote trading. Validation is provided via Monte Carlo simulation of the legislative process (with and without vote trading). Applications to two major studies in the field provide richer, yet consistent evidence on vote trading in US politics.