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A Propensity Score Reweighting Approach to Estimating the Partisan Effects of Full Turnout in American Presidential Elections

Published online by Cambridge University Press:  04 January 2017

Thomas L. Brunell
Affiliation:
Department of Political Science, Northern Arizona University, Flagstaff, AZ 86011-5036. e-mail: tom.brunell@nau.edu
John DiNardo
Affiliation:
440 Lorch Hall, Ford School of Public Policy, University of Michigan, Ann Arbor, MI 48109-1220. e-mail: jdinardo@umich.edu

Abstract

Borrowing an approach from the literature on the economics of discrimination, we estimate the impact of nonvoters on the outcome of presidential elections from 1952–2000 using data from the National Election Study (NES). Our estimates indicate that nonvoters are, on average, slightly more likely to support the Democratic Party. Of the 13 presidential elections between 1952 and 2000 we find no change in the eventual outcome of the election with two possible exceptions: 1980 and 2000. Thus our results are not all that dissimilar from other research on participation. Higher turnout in the form of compulsory voting would not radically change the partisan distribution of the vote. When elections are sufficiently close, however, a two percentage point increase may suffice to affect the outcome. Limitations of the NES data we use suggest that our estimates underestimate the impact of nonparticipation. We also compare our method with other econometric techniques. Finally, using our findings we speculate as to why the Democratic Party fails to undertake widespread “get out the vote” or registration drives.

Type
Research Article
Copyright
Copyright © Society for Political Methodology 2004 

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References

Acemoglu, Daron, and Robinson, James A. 2003. “Democratization.” In The Political Origins of Dictatorship and Democracy. Unpublished.Google Scholar
Ashenfelter, Orley, and Oaxaca, Ronald L. 1987. “The Economics of Discrimination: Economists Enter the Courtroom.” American Economic Review 77: 321325.Google Scholar
Barsky, Robert, Bound, John, Charles, Kerwin K., and Lupton, Joseph. 2002. “Accounting for the Black-White Wealth Gap: A Nonparametric Approach.” Journal of the American Statistical Association 97: 663673.Google Scholar
Bennett, Stephen Earl, and Resnick, David. 1990. “The Implications of Nonvoting for Democracy in the United States.” American Journal of Political Science 34: 771802.CrossRefGoogle Scholar
Biewen, Martin. 1999. “Measuring the Effects of Socio-Economic Variables on the Income Distribution: An Application to the East German Transition Process.” Discussion Paper Series. Heidelberg, Germany: Ruprecht-Karls-University.Google Scholar
Blinder, Alan S. 1973. “Wage Discrimination: Reduced Form and Structural Equations.” Journal of Human Resources 8: 436455.Google Scholar
Cambell, Angus, Converse, Philip E., Miller, Warren E., Stokes, Donald E. 1960. The American Voter. New York: Wiley.Google Scholar
Card, David. 1999. “The Causal Effect of Education on Earnings.” In The Handbook of Labour Economics, eds. Ashenfelter, Orley and Card, David. Amsterdam: North-Holland, pp. 18011863.Google Scholar
Citrin, Jack, Schickler, Eric, Sides, John. 2003. “What if Everyone Voted? Simulating the Impact of Increased Turnout in Senate Elections.” American Journal of Political Science 47: 7590.CrossRefGoogle Scholar
DeNardo, James. 1980. “Turnout and the Vote: The Joke's on the Democrats.” American Political Science Review 74: 406420.Google Scholar
DiNardo, John. 2002. “Propensity Score Reweighting and Changes in Wage Distribution.” Unpublished.Google Scholar
DiNardo, John, Fortin, Nicole, Lemieux, Thomas. 1996. “Labor Market Institutions and the Distribution of Wages.” Econometrica 64: 10011045.Google Scholar
Heckman, James. 1990. “Varieties of Selection Bias.” American Economic Review 80: 313318.Google Scholar
Heckman, James, Hotz, Joseph. 1985. “Alternative Methods for Evaluating the Impact of Training Programs.” Journal of the American Statistical Association 84: 862880.Google Scholar
Heckman, James, Ichimura, Hidehiko, Smith, Jeffrey, Todd, Petra E. 1996. “Labor Market Institutions and the Distribution of Wages.” Econometrica 64: 10011045.Google Scholar
Heckman, James, Robb, Richard Jr. 1985. “Alternative Methods for Evaluating the Impact of Inventories.” In Longitudinal Analysis of Labor Market Data, eds. Heckman, James and Singer, Burton. Cambridge: Cambridge University Press, pp. 156245.CrossRefGoogle Scholar
Heckman, James, LaLonde, Robert J., Smith, James A. 1999. “The Economics and Econometrics of Active Labour Market Programmes.” In The Handbook of Labour Economics, eds. Ashenfelter, Orley and Card, David. Amsterdam: North-Holland, pp. 18652073.Google Scholar
Heckman, James, and Tobias, Justin, Vytlacil, Edward. 2001. “Simple Estimators for Treatment Parameters in a Latent Variable Framework.” Unpublished.Google Scholar
Heckman, James, Vytlacil, Edward. 2001a. “Causal Parameters, Structural Equations, Treatment Effects, and Randomized Evaluations of Social Programs.” Unpublished.Google Scholar
Heckman, James, Vytlacil, Edward. 2001b. “Structural Equations, Treatment Effects, and Econometric Policy Evaluation.” Unpublished.Google Scholar
Hirano, Keisuke, Imbens, Guido, Ridder, Geert. 2000. “Efficient Estimation of Average Treatment Effects using the Estimated Propensity Score.” NBER Technical Working Paper T0251 7: 201217.Google Scholar
Horvitz, D. G., and Thompson, D. J. 1952. “A Generalization of Sampling without Replacement from a Finite Population.” Journal of the American Statistical Association 47: 663685.Google Scholar
Lee, Lung-Fei, Porter, Robert H. 1984. “Switching Regression Models with Imperfect Sample Separation Information—An Application on Cartel Stability.” Econometrica 52: 391418.Google Scholar
Lemieux, Thomas. 2002. “Decomposing Changes in Wage Distributions: A Unified Approach.” Canadian Journal of Economics 35: 646688.CrossRefGoogle Scholar
Lijphart, Arend. 1997. “Unequal Participation: Democracy's Unresolved Dilemma.” American Political Science Review 91: 114.Google Scholar
Oaxaca, Ronald. 1973. “Male-Female Wage Differentials in Urban Labor Markets.” International Economic Review 14: 693709.Google Scholar
Olson, Craig A. 1998. “A Comparison of Parametric and Semiparametric Estimates of the Effect of Spousal Health Insurance on Weekly Hours Worked by Wives.” Journal of Applied Econometrics 13: 543565.3.0.CO;2-J>CrossRefGoogle Scholar
Ornstein, Norman J., and Mann, Thomas E., Malbin, Michael J. 1997. Vital Statistics on Congress 1995–1996. Washington, DC: CQ Press.Google Scholar
Piven, Frances Fox, Cloward, Richard. 1988. Why Americans Don't Vote. New York: Pantheon.Google Scholar
Rosenbaum, Paul, Rubin, Donald. 1983. “The Central Role of the Propensity Score in Observational Studies of Causal Effects.” Biometrika 70: 4155.Google Scholar
Teixeira, Ruy A. 1992. The Disappearing American Voter. Washington, DC: Brookings Institution.Google Scholar
Tucker, Harvey J., and Vedlitz, Arnold, DeNardo, James. 1986. “Does Heavy Turnout Help Democrats in Presidential Elections?American Political Science Review 84: 12911298.Google Scholar
Verba, Sidney, Schlozman, Lehman Kay, Brady, Henry E. 1995. Voice and Equality: Civic Volunteerism in American Politics. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
Winship, Christopher, Morgan, Stephen L. 1999. “The Estimation of Causal Effects from Observational Data.” Annual Review of Sociology 25: 659706.Google Scholar
Wolfinger, Raymond, Rosenstone, Steven J. 1980. Who Votes? New Haven, CT: Yale University Press.Google Scholar
Wooldridge, Jeffrey M. 1997. “On Two-Stage Least Squares Estimation of the Average Treatment Effect in a Random Coefficient Model.” Economics Letters 56: 129133.Google Scholar
Wooldridge, Jeffrey M. 2002. Econometric Analyses of Cross Section and Panel Data. Cambridge, MA: MIT Press.Google Scholar
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