Estimating Program Participation with Partial Validation

TL;DR

This paper develops methods to accurately estimate program participation in surveys with misclassified responses by leveraging partial validation, improving bias correction without costly validation data.

Contribution

It introduces a novel approach transforming two-sided misclassification into one-sided, enabling consistent estimation using partially validated responses.

Findings

Proposed estimators are consistent and asymptotically normal.

Monte Carlo simulations show improved finite sample performance.

Empirical application on health insurance in Ghana illustrates practical utility.

Abstract

This paper considers the estimation of binary choice models when survey responses are possibly misclassified but one of the response category can be validated. Partial validation may occur when survey questions about participation include follow-up questions on that particular response category. In this case, we show that the initial two-sided misclassification problem can be transformed into a one-sided one, based on the partially validated responses. Using the updated responses naively for estimation does not solve or mitigate the misclassification bias, and we derive the ensuing asymptotic bias under general conditions. We then show how the partially validated responses can be used to construct a model for participation and propose consistent and asymptotically normal estimators that overcome misclassification error. Monte Carlo simulations are provided to demonstrate the finite sample performance of the proposed and selected existing methods. We provide an empirical illustration on the determinants of health insurance coverage in Ghana. We discuss implications for the design of survey questionnaires that allow researchers to overcome misclassification biases without recourse to relatively costly and often imperfect validation data.