Abstract

We develop a Bayesian procedure for estimation and inference for spatial models of roll call voting. Our approach is extremely flexible, applicable to any legislative setting, irrespective of size, the extremism of the legislative voting histories, or the number of roll calls available for analysis. Our model is easily extended to let other sources of information inform the analysis of roll call data, such as the number and nature of the underlying dimensions, the presence of party whipping, the determinants of legislator preferences, or the evolution of the legislative agenda; this is especially helpful since generally it is inappropriate to use estimates of extant methods (usually generated under assumptions of sincere voting) to test models embodying alternate assumptions (e.g., logrolling) . A Bayesian approach also provides a coherent framework for estimation and inference with roll call data that eludes extant methods; moreover, via Bayesian simulation methods, it is straightforward to generate uncertainty assessments or hypothesis tests concerning any auxiliary quantity of interest or to formally compare models. In a series of examples we show how our method is easily extended to accommodate theoretically interesting models of legislative behavior. Our goal is to move roll call analysis away from pure measurement or description towards a tool for testing substantive theories of legislative behavior. 1.

Citations