Hostname: page-component-8448b6f56d-c4f8m Total loading time: 0 Render date: 2024-04-25T02:19:13.824Z Has data issue: false hasContentIssue false
  • English
  • Français

Using Auxiliary Data to Estimate Selection Bias Models, with an Application to Interest Group Use of the Direct Initiative Process

Published online by Cambridge University Press:  04 January 2017

Frederick J. Boehmke
Frederick J. Boehmke*
Affiliation:
Department of Political Science, University of Iowa, 341 Schaeffer Hall, Iowa City, IA 52242. e-mail: frederick-boehmke@uiowa.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Recent work in survey research has made progress in estimating models involving selection bias in a particularly difficult circumstance—all nonrespondents are unit nonresponders, meaning that no data are available for them. These models are reasonably successful in circumstances where the dependent variable of interest is continuous, but they are less practical empirically when it is latent and only discrete outcomes or choices are observed. I develop a method in this article to estimate these models that is much more practical in terms of estimation. The model uses a small amount of auxiliary information to estimate the selection equation parameters, which are then held fixed while estimating the equation of interest parameters in a maximum-likelihood setting. After presenting Monte Carlo analyses to support the model, I apply the technique to a substantive problem: Which interest groups are likely to to be involved in support of potential initiatives to achieve their policy goals?

Type
Research Article
Information
Political Analysis , Volume 11 , Issue 3 , Summer 2003 , pp. 234 - 254
Copyright
Copyright © Political Methodology Section of the American Political Science Association 2003 

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.)

References

Achen, Christopher H. 1986. The Statistical Analysis of Quasi-Experiments. Berkeley, The University of California Press.Google Scholar
Baumgartner, Frank R., and Leech, Beth. 1998. Basic Interests: The Importance of Groups in Politics and Political Science. Princeton, NJ: Princeton University Press.Google Scholar
Bishop, Yvonne M.M., Fienberg, Stephen E., and Holland, Paul W. 1975. Discrete Multivariate Analysis: Theory and Practice. Cambridge, MA: MIT Press.Google Scholar
Bloom, David E., and Killingsworth, Mark R. 1985. “Correcting for Truncation Bias Caused by a Latent Truncation Variable.” Journal of Econometrics 27:131–135.Google Scholar
Boehmke, Frederick J. 2000. Beyond the Ballot: The Effect of Direct Democracy on Interest Group Behavior. Ph.D. dissertation. California Institute of Technology.Google Scholar
Boehmke, Frederick J., and Patty, John W. 2002. “Voter Cues and Information in Direct Legislation Settings.” University of Iowa. Working Paper.Google Scholar
Brehm, John. 1993. The Phantom Respondents: Opinion Surveys and Political Representation. Ann Arbor: University of Michigan Press.Google Scholar
Brehm, John. 1999. “Alternative Corrections for Sample Truncation: Applications to the 1988, 1990, and 1992 Senate Election Studies.” Political Analysis 8:147–165.Google Scholar
Broder, David S. 2000. Democracy Derailed. Orlando, FL: Harcourt Brace.Google Scholar
Donovan, Todd, Bowler, Shaun, McCuan, David, and Fernandez, Ken. 1998. “Contending Players and Strategies: Opposition Advantages in Initiative Campaigns.” In Citizens as Legislators: Direct Democracy in the United States, eds. Bowler, Shaun, Donovan, Todd, and Tolbert, Caroline J. Columbus: Ohio State University Press.Google Scholar
Dubin, Jeffrey A., and Rivers, Douglas. 1989. “Selection Bias in Linear Regression, Logit and Probit Models.” Sociological Methods and Research 18:360–390.Google Scholar
Ernst, Howard R. 2001. “The Historical Role of Narrow-Minded Interests.” In Dangerous Democracy: The Battle Over Ballot Initiative in America, eds. Sabato, Larry J., Ernst, Howard R., and Larson, Bruce A. New York: Rowman and Littlefield.Google Scholar
Franklin, Charles H. 1989. “Estimation Across Data Sets: Two-Stage Auxiliary Instrumental Variables Estimation (2SAIV).” Political Analysis 1:1–23.Google Scholar
Gerber, Elizabeth R. 1999. The Populist Paradox: Interest Group Influence and the Promise of Direct Legislation. Princeton, NJ: Princeton University Press.Google Scholar
Greene, William H. 1993. Econometric Analysis. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Heckman, James J. 1976. “The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models.” Annals of Economic and Social Measurement 5:475–492.Google Scholar
Heckman, James J. 1979. “Sample Selection Bias as a Specification Error.” Econometrica 47:153–161.Google Scholar
Honaker, James, Joseph, Anne, King, Gary, and Scheve, Kenneth. 1999. Amelia: A Program for Missing Data (Windows Version). Cambridge, MA: Harvard University. (Available from http://www.GKing.harvard.edu.Google Scholar
King, Gary. 1989. Unifying Political Methodology: The Likelihood Theory of Statistical Inference. Ann Arbor, The University of Michigan Press.Google Scholar
King, Gary, Honaker, Anne, Joseph, James, and Scheve, Kenneth. 2001. “Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation.” American Political Science Review 95:49–69.Google Scholar
Kmenta, Jan. 2000. Elements of Econometrics. Ann Arbor: University of Michigan Press.Google Scholar
Maddala, G. G. 1983. Limited Dependent and Qualitative Variables in Econometrics. Cambridge: Cambridge University Press.Google Scholar
Meng, Xiao-Li, and Rubin, Donald B. 1992. “Performing Likelihood Ratio Tests with Multiply-Imputed Data.” Biometrika 79:103–111.Google Scholar
Murphy, Kevin M., and Topel, Robert H. 1985. “Estimation and Inference in Two-Step Econometric Models.” Journal of Business and Economic Statistics 3:370–379.Google Scholar
Sartori, Anne E. 2003. “An Estimator for Some Binary-Outcome Selection Models Without Exclusion Restrictions.” Political Analysis 11:111–138.Google Scholar
Sherman, Robert. 2000. “Tests of Certain Types of Nonresponse in Surveys Subject to Item Nonresponse or Attrition.” American Journal of Political Science 44:362–374.Google Scholar
Smith, Daniel. 1998. Tax Crusaders. New York: Routledge.Google Scholar
Winship, Christopher, and Mare, Robert D. 1992. “Models for Sample Selection Bias.” Annual Review of Sociology 18:327–350.Google Scholar
Supplementary material: File

Boehmke supplementary material

Appendix

Download Boehmke supplementary material(File)
File 527.6 KB