Understanding Off-the-Books Politics: Conducting Inference on the Determinants of Sensitive Behavior with Randomized Response Surveys

This study presents a survey-based method for conducting inference into the determinants of sensitive political behavior. The approach combines two well-established literatures in statistical methods in the social sciences: the randomized response (RR) methodology utilized to reduce evasive answer bias and the generalized propensity score methodology utilized to draw inferences about causal effects in observational studies. The approach permits one to estimate the causal impact of a multivalued predictor variable of interest on a given sensitive behavior in the face of unknown interaction effects between the predictor and the confounders as well as nonlinearities in the relationship between the confounders and the sensitive behavior. Simulation results point to the superior performance of the RR relative to direct survey questioning using this method for samples of moderate to large size. The utility of the approach is illustrated through an application to corruption in the public bureaucracy in three countries in South America.

[1]  C. Drake.  Effects of misspecification of the propensity score on estimators of treatment effect , 1993 .

[2]  Mitchell Young,et al.  Votes and Violence: Electoral Competition and Ethnic Riots in India. , 2006 .

[3]  Peter G. M. van der Heijden,et al.  Meta-Analysis of Randomized Response Research , 2005 .

[4]  John C. Duffyx,et al.  Randomized Response Models for Estimating the Distribution Function of a Quantitative Character , 1984 .

[5]  Javier Auyero,et al.  The Dynamics of Collective Violence: Dissecting Food Riots in Contemporary Argentina , 2007 .

[6]  J. Copas.  Binary Regression Models for Contaminated Data , 1988 .

[7]  P. Rosenbaum.  The Role of Known Effects in Observational Studies , 1989 .

[8]  M. Pinto-Duschinsky.  Financing Politics: A Global View , 2002 .

[9]  Joe Kerkvliet.  ESTIMATING A LOGIT MODEL WITH RANDOMIZED DATA: THE CASE OF COCAINE USE , 1994 .

[10]  D. Horvitz,et al.  Application of the Randomized Response Technique in Obtaining Quantitative Data , 1971 .

[11]  P. Schmidt,et al.  Limited-Dependent and Qualitative Variables in Econometrics. , 1984 .

[12]  Daniel Treisman,et al.  What Have We Learned About the Causes of Corruption from Ten Years of Cross-National Empirical Research? , 2007 .

[13]  Esmeralda A. Ramalho.  Regression models for choice-based samples with misclassification in the response variable , 2002 .

[14]  Daniel Corstange,et al.  Sensitive Questions, Truthful Answers? Modeling the List Experiment with LISTIT , 2009, Political Analysis.

[15]  Paul E. Tracy,et al.  The Validity of Randomized Response for Sensitive Measurements , 1981 .

[16]  Anthony Y. C. Kuk,et al.  Asking sensitive questions indirectly , 1990 .

[17]  G. Imbens.  The Role of the Propensity Score in Estimating Dose-Response Functions , 1999 .

[18]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[19]  Ernesto Calvo,et al.  Who Delivers? Partisan Clients in the Argentine Electoral Market , 2004 .

[20]  Kosuke Imai,et al.  Causal Inference With General Treatment Regimes , 2004 .

[21]  J. Hox,et al.  A Comparison of Randomized Response, Computer-Assisted Self-Interview, and Face-to-Face Direct Questioning , 2000 .

[22]  S. Stokes.  Perverse Accountability: A Formal Model of Machine Politics with Evidence from Argentina , 2005, American Political Science Review.

[23]  W. R. Simmons,et al.  The Unrelated Question Randomized Response Model: Theoretical Framework , 1969 .

[24]  M. Seligson.  The Impact of Corruption on Regime Legitimacy: A Comparative Study of Four Latin American Countries , 2002, The Journal of Politics.

[25]  C. Mitchell Dayton,et al.  Improved estimation of academic cheating behavior using the randomized response technique , 1987 .

[26]  G. Imbens,et al.  Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score , 2000 .

[27]  P. Holland.  Statistics and Causal Inference , 1985 .

[28]  C. Mitchell Dayton,et al.  Covariate Randomized Response Models , 1988 .

[29]  G. Maddala.  Limited-dependent and qualitative variables in econometrics: Introduction , 1983 .

[30]  R. Munck.  Politician's dilemma: Building state capacity in Latin America , 1996 .

[31]  D. Rubin.  Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[32]  Peter G. M. van der Heijden,et al.  A validation of a computer‐assisted randomized response survey to estimate the prevalence of fraud in social security , 2006 .

[33]  Matias D. Cattaneo,et al.  Efficient semiparametric estimation of multi-valued treatment effects under ignorability , 2010 .

[34]  S L Warner,et al.  Randomized response: a survey technique for eliminating evasive answer bias. , 1965, Journal of the American Statistical Association.

[35]  Eric C. C. Chang,et al.  Competitive Corruption: Factional Conflict and Political Malfeasance in Postwar Italian Christian Democracy , 2001 .

[36]  Guido W. Imbens,et al.  EFFICIENT ESTIMATION OF AVERAGE TREATMENT EFFECTS , 2003 .

[37]  Petra E. Todd,et al.  Matching As An Econometric Evaluation Estimator , 1998 .

[38]  Rahul Mukerjee,et al.  Optionally Randomized Response Techniques , 1985 .

[39]  A. van Soest,et al.  Parametric and Semiparametric Estimation in Models with Misclassified Categorical Dependent Variables , 2000, SSRN Electronic Journal.

[40]  Johannes A. Landsheer,et al.  Trust and Understanding, Two Psychological Aspects of Randomized Response , 1999 .

[41]  P.G.M. Van der Heijden,et al.  Some logistic regression models for randomized response data , 1996 .

[42]  C. Ellertson,et al.  The Measure of Induced Abortion Levels in Mexico Using Random Response Technique , 2006 .

[43]  D. Ruppert,et al.  Measurement Error in Nonlinear Models , 1995 .

[44]  M. Chavance.  [Jackknife and bootstrap]. , 1992, Revue d'epidemiologie et de sante publique.

[45]  Tore Dalenius,et al.  A New Randomized Response Design for Estimating the Mean of a Distribution , 1979 .

[46]  James H. Kuklinski,et al.  Racial prejudice and attitudes toward affirmative action , 1997 .

[47]  Theory and Methods: Likelihood‐based Estimation of the Regression Model with Scrambled Responses , 1998 .

[48]  Petra E. Todd,et al.  Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme , 1997 .

[49]  Juan-José Díaz,et al.  An Assessment of Propensity Score Matching as a Nonexperimental Impact Estimator , 2005, The Journal of Human Resources.

[50]  Arijit Chaudhuri,et al.  Optional versus compulsory randomized response techniques in complex surveys , 2005 .

[51]  A. Pérez-Liñán.  Informal Institutions and Democracy: Lessons from Latin America , 2007, Perspectives on Politics.

[52]  Daniel M. Brinks.  Informal Institutions and the Rule of Law The Judicial Response to State Killings in Buenos Aires and Sao Paulo in the 1990s , 2003 .

[53]  Lakhbir S. Hayre,et al.  Scrambled randomized response methods for obtaining sensitive quantitative data , 1983 .

[54]  Léonard Wantchekon,et al.  Clientelism and Voting Behavior: Evidence from a Field Experiment in Benin , 2003 .

[55]  J. Hausman,et al.  Misclassification of the dependent variable in a discrete-response setting , 1998 .

[56]  B. Olken,et al.  Corruption Perceptions vs. Corruption Reality , 2006 .

[57]  S. Levitsky,et al.  Informal Institutions and Democracy: Lessons from Latin America , 2006 .

[58]  Peter G. M. van der Heijden,et al.  The Analysis of Multivariate Misclassified Data With Special Attention to Randomized Response Data , 2004 .