Hostname: page-component-8448b6f56d-m8qmq Total loading time: 0 Render date: 2024-04-17T17:15:48.392Z Has data issue: false hasContentIssue false

The Structure of Political Choices: Distinguishing Between Constraint and Multidimensionality

Published online by Cambridge University Press:  13 April 2021

William Marble
Affiliation:
Ph.D. Candidate, Department of Political Science, Stanford University, Stanford, CA, USA. E-mail: wpmarble@stanford.edu
Matthew Tyler*
Affiliation:
Ph.D. Candidate, Department of Political Science, Stanford University, Stanford, CA, USA. E-mail: mdtyler@stanford.edu
*
Corresponding author Matthew Tyler

Abstract

In the literatures on public opinion and legislative behavior, there are debates over (1) how constrained preferences are and (2) whether they are captured by a single left–right spectrum or require multiple dimensions. But insufficient formalization has led scholars to equate a lack of constraint with multidimensional preferences. In this paper, we refine the concepts of constraint and dimensionality in a formal framework and describe how they translate into separate observable implications for political preferences. We use this discussion to motivate a cross-validation estimator that measures constraint and dimensionality in the context of canonical ideal point models. Using data from the public and politicians, we find that American political preferences are one-dimensional, but there is more constraint among politicians than among the mass public. Furthermore, we show that differences between politicians and the public are not explained by differences in agendas or the incentives faced by the actors.

Type
Article
Copyright
© The Author(s) 2021. Published by Cambridge University Press on behalf of the Society for Political Methodology

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Edited by Jeff Gill

References

Ahler, D. J., and Broockman, D. E.. 2018. “The Delegate Paradox: Why Polarized Politicians Can Represent Citizens Best.” Journal of Politics 80(4):11171133.CrossRefGoogle Scholar
Akaike, H. 1973. “Information Theory and an Extension of the Maximum Likelihood Principle.” In Proceedings of the Second International Symposium on Information Theory, 267281. Budapest: Akademiai Kiado.Google Scholar
Ansolabehere, S., Rodden, J., and Snyder, J. M. 2006. “Purple America.” Journal of Economic Perspectives 20(2):97118. https://doi.org/10.1257/jep.20.2.97.CrossRefGoogle Scholar
Bafumi, J., and Herron, M. C.. 2010. “Leapfrog Representation and Extremism: A Study of American Voters and Their Members in Congress.” American Political Science Review 104(3):519542. https://doi.org/10.1017/S0003055410000316.CrossRefGoogle Scholar
Bond, R., and Messing, S.. 2015. “Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook.” American Political Science Review 109(1):6278. https://doi.org/10.1017/S0003055414000525.CrossRefGoogle Scholar
Bonica, A. 2013. “Ideology and Interests in the Political Marketplace.” American Journal of Political Science 57(2):294311. https://doi.org/10.1111/ajps.12014.CrossRefGoogle Scholar
Broockman, D. E. 2016. “Approaches to Studying Policy Representation.” Legislative Studies Quarterly 41(1):181215. https://doi.org/10.1111/lsq.12110.CrossRefGoogle Scholar
Clinton, J. D., Jackman, S., and Rivers, D.. 2004. “The Statistical Analysis of Roll Call Data.” American Political Science Review 98(2):355370.CrossRefGoogle Scholar
Converse, P. E. 1964. “The Nature of Belief Systems in Mass Publics.” In Ideology and Discontent, edited by Apter, D., 206261. New York: The Free Press.Google Scholar
Crespin, M. H., and Rohde, D. W.. 2010. “Dimensions, Issues, and Bills: Appropriations Voting on the House Floor.” Journal of Politics 72(4):976989. https://doi.org/10.1017/S0022381610000472.CrossRefGoogle Scholar
Freeder, S., Lenz, G. S., and Turney, S.. 2019. “The Importance of Knowing ‘What Goes With What’: Reinterpreting the Evidence on Policy Attitude Stability.” Journal of Politics 81(1):274290.CrossRefGoogle Scholar
Hastie, T., Tibshirani, R., and Friedman, J.. 2009. Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edn. New York: Springer.CrossRefGoogle Scholar
Heckman, J. J., and Snyder, J. M.. 1997. “Linear Probability Models of the Demand for Attributes with an Empirical Application to Estimating the Preferences of Legislators.” The RAND Journal of Economics 28:S142S189. https://doi.org/10.2307/3087459.CrossRefGoogle Scholar
Hill, S. J., and Tausanovitch, C.. 2015. “A Disconnect in Representation? Comparison of Trends in Congressional and Public Polarization.” Journal of Politics 77(4):10581075.CrossRefGoogle Scholar
Imai, K., Lo, J., and Olmsted, J.. 2016. “Fast Estimation of Ideal Points with Massive Data.” American Political Science Review 110(4):120. https://doi.org/10.1017/S000305541600037X.CrossRefGoogle Scholar
Jeong, G.-H., Lowry, W. R., Miller, G. J., and Sened, I.. 2014. “How Preferences Change Institutions: The 1978 Energy Act.” Journal of Politics 76(2):430445. https://doi.org/10.1017/S0022381613001370.CrossRefGoogle Scholar
Jessee, S. A. 2009. “Spatial Voting in the 2004 Presidential Election.” American Political Science Review 103(1):5981. https://doi.org/10.1017/S000305540909008X.CrossRefGoogle Scholar
Kinder, D. R. 2003. “Belief Systems After Converse.” In Electoral Democracy, edited by MacKuen, M., and Rabinowitz, G.. Ann Arbor: University of Michigan Press.Google Scholar
Lauderdale, B. E., Hanretty, C., and Vivyan, N.. 2018. “Decomposing Public Opinion Variation into Ideology, Idiosyncrasy and Instability.” Journal of Politics 80(2):707712.CrossRefGoogle Scholar
Maraun, M. D., and Rossi, N. T.. 2001. “The Extra-Factor Phenomenon Revisited: Unidimensional Unfolding as Quadratic Factor Analysis.” Applied Psychological Measurement 25(1):7787. https://doi.org/10.1177/01466216010251006.CrossRefGoogle Scholar
Paisley, J., and Carin, L.. 2009. “Nonparametric Factor Analysis with Beta Process Priors.” In Proceedings of the 26th International Conference on Machine Learning, 777784. New York: ACM.Google Scholar
Poole, K. T., and Rosenthal, H.. 1997. Congress: A Political-Economic History of Roll Call Voting, vol. 314. Oxford: Oxford University Press.Google Scholar
Salakhutdinov, R., and Mnih, A.. 2008. “Bayesian Probabilistic Matrix Factorization Using Markov Chain Monte Carlo.” In Proceedings of the 25th International Conference on Machine Learning, 880887. New York: ACM.CrossRefGoogle Scholar
Schwarz, G. 1978. “Estimating the Dimension of a Model.” The Annals of Statistics 6(2):461464.CrossRefGoogle Scholar
Shor, B., and McCarty, N. M.. 2011. “The Ideological Mapping of American Legislatures.” American Political Science Review 105(3):530551. https://doi.org/10.1017/S0003055411000153.CrossRefGoogle Scholar
Treier, S., and Hillygus, D. S.. 2009. “The Nature of Political Ideology in the Contemporary Electorate.” Public Opinion Quarterly 73(4):679703. https://doi.org/10.1093/poq/nfp067.CrossRefGoogle Scholar
Tyler, M., and Marble, W.. 2020a. “Replication Data for: The Structure of Political Choices: Distinguishing Between Constraint and Multidimensionality.” Code Ocean. https://doi.org/10.24433/CO.3851328.v1.CrossRefGoogle Scholar
Tyler, M., and Marble, W.. 2020b. “Replication Data for: The Structure of Political Choices: Distinguishing Between Constraint and Multidimensionality.” Harvard Dataverse, Version V1. https://doi.org/10.7910/DVN/SWZUUE.CrossRefGoogle Scholar
Supplementary material: PDF

Marble and Tyler supplementary material

Online Appendix

Download Marble and Tyler supplementary material(PDF)
PDF 704.4 KB
Supplementary material: Link

Marble and Tyler Dataset

Link