Accounting for self-protective responses in randomized response data from a social security survey using the zero-inflated Poisson model

TL;DR

This paper introduces a zero-inflated Poisson model to account for self-protective responses in randomized response surveys, improving estimates of sensitive behavior like social security noncompliance.

Contribution

It develops a novel statistical model that explicitly accounts for self-protective responses, enhancing analysis of sensitive survey data.

Findings

Model effectively captures excess zeros due to self-protection

Incorporates predictors for true violations and response behavior

Improves accuracy of noncompliance estimates

Abstract

In 2004 the Dutch Department of Social Affairs conducted a survey to assess the extent of noncompliance with social security regulations. The survey was conducted among 870 recipients of social security benefits and included a series of sensitive questions about regulatory noncompliance. Due to the sensitive nature of the questions the randomized response design was used. Although randomized response protects the privacy of the respondent, it is unlikely that all respondents followed the design. In this paper we introduce a model that allows for respondents displaying self-protective response behavior by consistently giving the nonincriminating response, irrespective of the outcome of the randomizing device. The dependent variable denoting the total number of incriminating responses is assumed to be generated by the application of randomized response to a latent Poisson variable denoting the true number of rule violations. Since self-protective responses result in an excess of observed zeros in relation to the Poisson randomized response distribution, these are modeled as observed zero-inflation. The model includes predictors of the Poisson parameters, as well as predictors of the probability of self-protective response behavior.