Towards effective policy measures to reduce informal payments in healthcare: addressing sample selection bias and measurement error in surveys

Previous research has primarily utilized surveys to assess the extent of informal payments, identify key drivers, and recommend policy interventions. However, reliance on surveys presents challenges, including representativeness issues and social desirability bias, which may result in underestimated prevalence and misinformed policy measures. The aim of this paper is to evaluate the influence of these biases on estimating the prevalence of informal payments and on the development of effective policies to reduce informal payments. Reporting data from the third wave of Life in Transition Survey conducted in 2016 across 34 countries, a significant misalignment between reported and (estimated) actual behaviours regarding informal payments was found. The results of a Probit model adjusted for sample selection and measurement error revealed that, among those who made informal payments, approximately 20 % of respondents declared the opposite while the global prevalence of individuals making informal payments in the analysed countries is approximately 18 %. The implications for policy measures towards informal payments in public healthcare are then discussed.