Political Analysis, 11:4
© 2003 by the Society for Political Methodology
Valuing Policy Alternatives: A Generalization
American University and Corporation for National and Community Service Washington, DC 20525 e-mail: ndietz{at}cns.gov
Department of Management and Strategy, Kellogg School of Management, Northwestern University, Evanston, IL 60208 e-mail: lawrence-rothenberg{at}kellogg.northwestern.edu
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.