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A Spatial Model of Legislative Voting with Perceptual Error

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  • Ladha, Krishna K

Abstract

This paper presents a unidimensional spatial model of voting in which members of Congress vote based on an imprecise perception of roll-call alternatives. The model is applied to the Senate roll-call votes of 1977 to estimate the ideal points of the senators, and to examine the role of a liberal- conservative dimension in describing votes on economic issues relative to social issues. In light of the spatial model, the paper argues that "standard" models of roll-call voting are not based on a theory of choice, and hence, that it is difficult to interpret their results. Alternatives to standard models, based on the spatial model, are developed and are used to analyze the role of ideology in deciding a specific economic issue: coal strip-mining. Copyright 1991 by Kluwer Academic Publishers

Suggested Citation

  • Ladha, Krishna K, 1991. "A Spatial Model of Legislative Voting with Perceptual Error," Public Choice, Springer, vol. 68(1-3), pages 151-174, January.
    • Handle: RePEc:kap:pubcho:v:68:y:1991:i:1-3:p:151-74
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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. Kenneth Koford, 1994. "What Can We Learn About Congressional Politics From Dimensional Studies Of Roll‐Call Voting?," Economics and Politics, Wiley Blackwell, vol. 6(2), pages 173-186, July.
    3. Keith T. Poole & Howard Rosenthal, 1994. "Dimensional Simplification And Economic Theories Of Legislative Behavior," Economics and Politics, Wiley Blackwell, vol. 6(2), pages 163-172, July.
    4. Christopher Hare & Tzu-Ping Liu & Robert N. Lupton, 2018. "What Ordered Optimal Classification reveals about ideological structure, cleavages, and polarization in the American mass public," Public Choice, Springer, vol. 176(1), pages 57-78, July.
    5. 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.
    6. Mark M. Berger & Michael C. Munger & Richard F. Potthoff, 2000. "The Downsian Model Predicts Divergence," Journal of Theoretical Politics, , vol. 12(2), pages 228-240, April.

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