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Valuing Policy Alternatives: A Generalization

Published online by Cambridge University Press:  04 January 2017

Nathan Dietz
Affiliation:
American University and Corporation for National and Community Service Washington, DC 20525. e-mail: [email protected]
Lawrence S. Rothenberg
Affiliation:
Department of Management and Strategy, Kellogg School of Management, Northwestern University, Evanston, IL 60208. e-mail: [email protected]

Abstract

Those interested in political phenomena such as voting have found random utility models, originally developed for decisions such as transportation choice, especially attractive, as the underlying model can yield a statistical model with a few simple, realistic assumptions. Unfortunately, such models have proven difficult to apply to situations with more than two votes and three alternatives or an unknown cutpoint. Additionally, as we show, standard applications of such models to voting, while producing consistent parameter estimates, yield standard errors that are too small and, due to a failure to employ all relevant theoretical information, biased ideal point estimates. We specify a general model applicable to any number of votes and alternatives, with correct standard errors and unbiased ideal point estimates. We apply this model to a number of cases studied by previous scholars involving legislative voting over the minimum wage: (1) when there are two votes and two known cutpoints (K. Krehbiel and D. Rivers, American Journal of Political Science, 1988, 32, 1151–1174); (2) when there are three votes and three known cutpoints (J. Wilkerson, American Journal of Political Science, 1991, 35, 613–623); and (3) when there are three votes but where one cutpoint is unknown given a lack of knowledge about the impact of a policy (J. Wilkerson, American Journal of Political Science, 1991, 35, 613–623) or the possibility of sophisticated voting (C. Volden, Journal of Politics, 1998, 60, 149–173). We show that in various contexts our analysis improves on existing methods, yielding consistent and efficient ideal point estimates and a better-fitting model with improved predictive accuracy.

Type
Research Article
Copyright
Copyright © Political Methodology Section of the American Political Science Association 2003 

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References

Bailey, Michael. 2001. “Ideal-Point Estimation with a Small Number of Votes: A Random-Effects Approach.” Political Analysis 9:192210.CrossRefGoogle Scholar
Bhat, Chandra R. 1994. “Imputing a Continuous Income Variable from Grouped and Missing Income Observations.” Economics Letters 46:311319.CrossRefGoogle Scholar
Chesher, Andrew, and Irish, Margaret. 1987. “Residual Analysis in the Grouped Data and Censored Normal Linear Model.” Journal of Econometrics 34:3362.Google Scholar
Clinton, Joshua D., Jackman, Simon, and Rivers, Douglas. 2000. “The Statistical Analysis of Legislative Behavior: A Unified Approach.” Typescript. Stanford: Stanford University.Google Scholar
Cramer, J. S. 1986. Econometric Applications of Maximum Likelihood Methods. Cambridge: Cambridge University Press.Google Scholar
Dietz, Nathan, and Rothenberg, Lawrence S. 2000. “Foundations of Policy Stability: The Institutional Basis of Non-Market Pricing.” Paper presented at the 2000 Annual Meetings of the Midwest Political Science Association, Chicago, IL, April 27-30.Google Scholar
Greene, William. 1997. Econometric Analysis, 3rd ed. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Krehbiel, Keith, and Rivers, Douglas. 1988. “The Analysis of Committee Power: An Application to Senate Voting on the Minimum Wage.” American Journal of Political Science 32:11511174.Google Scholar
Lewis, Jeffrey B. 2001. “Estimating Voter Preference Distributions from Individual-Level Voting Data.” Political Analysis 9:275297.CrossRefGoogle Scholar
Londregan, John. 2000. “Estimating Legislator Ideal Points.” Political Analysis 8:3556.Google Scholar
Long, J. Scott. 1997. Regression Models for Categorial and Limited Dependent Variables. Advanced Quantitative Techniques in the Social Sciences Number 7. Thousand Oaks, CA: Sage.Google Scholar
Maddala, G. S. 1983. Limited-Dependent and Qualitative Variables in Econometrics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
McKelvey, Richard D., and Zavoina, William. 1975. “A Statistical Model for the Analysis of Ordinal Level Dependent Variables.” Journal of Mathematical Sociology 4:103120.CrossRefGoogle Scholar
Pagan, Adrian, and Vella, Frank. 1989. “Diagnostic Tests for Models Based on Individual Data: A Survey.” Journal of Applied Econometrics 4:S29S59.Google Scholar
Poole, Keith T., and Rosenthal, Howard W. 1997. Congress: A Political-Economic History of Roll Call Voting. New York: Oxford University Press.Google Scholar
Stern, Steven. 1991. “Imputing a Continuous Income Variable from a Bracketed Income Variable with Special Attention to Missing Observations.” Economics Letters 37:287291.Google Scholar
Stewart, Mark B. 1983. “On Least Squares Estimation when the Dependent Variable is Grouped.” Review of Economic Studies 50:737753.Google Scholar
Terza, Joseph V. 1985. “Ordinal Probit: A Generalization.” Communications in Statistics (Theory and Methods) 14:111.Google Scholar
Volden, Craig. 1998. “Sophisticated Voting in Supermajoritarian Settings.” Journal of Politics 60:149173.Google Scholar
Wilkerson, John. 1991. “Analyzing Committee Power: A Critique.” American Journal of Political Science 35:613623.Google Scholar