External validation of voter turnout models by concealed parameter recovery
We conduct a model validation analysis of several behavioral models of voter turnout, using laboratory data. We call our method of model validation concealed parameter recovery, where estimation of a model is done under a veil of ignorance about some of the experimentally controlled parameters—in this case voting costs. We use quantal response equilibrium as the underlying, common structure for estimation, and estimate models of instrumental voting, altruistic voting, expressive voting, and ethical voting. All the models except the ethical voting model recover the concealed parameters reasonably well. We also report the results of a counterfactual analysis based on the recovered parameters, to compare the policy implications of the different models about the cost of a subsidy to increase turnout.
KeywordsVoter turnout Model validation Concealed parameter recovery Laboratory experiments
JEL ClassificationD72 C52 C92
We gratefully acknowledge the financial support of the National Science Foundation (SES-0962802, SES-1426560). We thank the participants at several seminar and conference presentations for their useful comments and suggestions.
- Downs, A. (1957). An economic theory of democracy. New York: Harper and Row.Google Scholar
- Feddersen, T., & Pesendorfer, W. (1996). The swing voter’s curse. American Economic Review, 86, 408–24.Google Scholar
- McFadden, D. (1981). Econometric models of probabilistic choice. In C. Manski & D. McFadden (Eds.), Structural analysis of discrete data with econometric applications. Cambridge, MA: MIT Press.Google Scholar
- Merlo, A. (2006). Whither political economy? Theories, facts and issues. In Richard Blundell, Whitney Newey, & Torsten Persson (Eds.), Advances in economics and econometrics, theory and applications: Ninth world congress of the econometric society. Cambridge: Cambridge University Press.Google Scholar
- Palfrey, T., & Prisbrey, J. (1997). Anomalous behavior in linear public goods experiments: How much and why? American Economic Review, 87, 829–46.Google Scholar
- Pezanis-Christou, P., & Romeu, A. (2016). Structural analysis of first-price auction data: Insights from the laboratory. Working paper #2016–17, School of Economics, University of Adelaide.Google Scholar
- Poole, K. (1999). Nonparametric unfolding of binary choice data. Political Analysis, 8(2), 211–237.Google Scholar
- Tullock, G. (1967). Toward a mathematics of politics. Ann Arbor, MI: University of Michigan Press.Google Scholar