The Annals of Applied Statistics

Assessing differences in legislators’ revealed preferences: A case study on the 107th U.S. Senate

Chelsea L. Lofland, Abel Rodríguez, and Scott Moser

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Roll call data are widely used to assess legislators’ preferences and ideology, as well as test theories of legislative behavior. In particular, roll call data is often used to determine whether the revealed preferences of legislators are affected by outside forces such as party pressure, minority status or procedural rules. This paper describes a Bayesian hierarchical model that extends existing spatial voting models to test sharp hypotheses about differences in preferences using posterior probabilities associated with such hypotheses. We use our model to investigate the effect of the change of party majority status during the 107th U.S. Senate on the revealed preferences of senators. This analysis provides evidence that change in party affiliation might affect the revealed preferences of legislators, but provides no evidence about the effect of majority status on the revealed preferences of legislators.

Article information

Ann. Appl. Stat. Volume 11, Number 1 (2017), 456-479.

Received: February 2015
Revised: May 2016
First available in Project Euclid: 8 April 2017

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Spatial voting model hypothesis testing spike-and-slab prior revealed preferences factor analysis


Lofland, Chelsea L.; Rodríguez, Abel; Moser, Scott. Assessing differences in legislators’ revealed preferences: A case study on the 107th U.S. Senate. Ann. Appl. Stat. 11 (2017), no. 1, 456--479. doi:10.1214/16-AOAS951.

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Supplemental materials

  • Supplement: Algorithm details and additional simulations. The supplementary materials present a detailed description of the full conditional distributions associated with the Markov chain Monte Carlo algorithms used to fit the models described in the paper, as well as a brief discussion of a set of simulations to investigate the performance of the model when legislators exhibit different preferences across both sets of bills.