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The Structure of Utility in Spatial Models of Voting

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  • Royce Carroll
  • Jeffrey B. Lewis
  • James Lo
  • Keith T. Poole
  • Howard Rosenthal

Abstract

Empirical models of spatial voting allow legislators' locations in a policy or ideological space to be inferred from their roll‐call votes. These are typically random utility models where the features of the utility functions other than the ideal points are assumed rather than estimated. In this article, we first consider a model in which legislators' utility functions are allowed to be a mixture of the two most commonly assumed utility functions: the quadratic function and the Gaussian function assumed by NOMINATE. Across many roll‐call data sets, we find that legislators' utility functions are estimated to be very nearly Gaussian. We then relax the usual assumption that each legislator is equally sensitive to policy change and find that extreme legislators are generally more sensitive to policy change than their more centrally located counterparts. This result suggests that extremists are more ideologically rigid while moderates are more likely to consider influences that arise outside liberal‐conservative conflict.

Suggested Citation

  • Royce Carroll & Jeffrey B. Lewis & James Lo & Keith T. Poole & Howard Rosenthal, 2013. "The Structure of Utility in Spatial Models of Voting," American Journal of Political Science, John Wiley & Sons, vol. 57(4), pages 1008-1028, October.
  • Handle: RePEc:wly:amposc:v:57:y:2013:i:4:p:1008-1028
    DOI: 10.1111/ajps.12029
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    Cited by:

    1. Keith Krehbiel & Zachary Peskowitz, 2015. "Legislative organization and ideal-point bias," Journal of Theoretical Politics, , vol. 27(4), pages 673-703, October.
    2. Ryan J. Vander Wielen & Michael J. Vander Wielen, 2020. "Unpacking the unknown: a method for identifying status quo distributions," Public Choice, Springer, vol. 182(1), pages 49-72, January.
    3. Bolton, Patrick & Li, Tao & Ravina, Enrichetta & Rosenthal, Howard, 2020. "Investor ideology," Journal of Financial Economics, Elsevier, vol. 137(2), pages 320-352.
    4. James Lo, 2013. "Voting Present," SAGE Open, , vol. 3(4), pages 21582440135, December.
    5. Tanguiane, Andranick S., 2019. "Combining the third vote with traditional elections," Working Paper Series in Economics 132, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    6. Richard F. Potthoff, 2018. "Estimating Ideal Points from Roll-Call Data: Explore Principal Components Analysis, Especially for More Than One Dimension?," Social Sciences, MDPI, vol. 7(1), pages 1-27, January.
    7. Christopher Hare & Keith T. Poole, 2015. "Measuring ideology in Congress," Chapters, in: Jac C. Heckelman & Nicholas R. Miller (ed.), Handbook of Social Choice and Voting, chapter 18, pages 327-346, Edward Elgar Publishing.
    8. Tanguiane, Andranick S., 2022. "Analysis of the 2021 Bundestag elections. 2/4. Political spectrum," Working Paper Series in Economics 152, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    9. Kaviani, Mahsa S. & Kryzanowski, Lawrence & Maleki, Hosein & Savor, Pavel, 2020. "Policy uncertainty and corporate credit spreads," Journal of Financial Economics, Elsevier, vol. 138(3), pages 838-865.

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