Towards effective policy measures to reduce informal payments in healthcare: addressing sample selection bias and measurement error in surveys Maria Felice Arezzo a , Giuseppina Guagnano a , Colin C. Williams b , Adrian V. Horodnic c,* a Department of Methods and Models for Economics, Territory and Finance, Sapienza university of Rome, Rome, Italy b Management School, University of Sheffield, Sheffield, United Kingdom c Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, Iasi, Romania A R T I C L E INFO Keywords: Informal payments Institutional theory Policy measures Bias ABSTRACT 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 representa- tiveness 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. 1. Background Informal payments in healthcare represent a significant policy concern due to their inequitable consequences, often leading to restricted access to necessary care for vulnerable patients [1,2]. Far from being a marginal phenomenon, informal payments are widespread across both high-income and low- and middle-income countries [3]. Their prevalence is particularly high in post-socialist health systems, where between 35 % and 60 % of patients report making informal payments when accessing healthcare services, as observed in countries such as Poland, Hungary, Bulgaria, Lithuania, Romania and Ukraine [4]. In 2020, within the European Union, the healthcare sector reported the highest incidence of informal payments, affecting 6 % of all respondents [5]. Often perceived as a way “to solve a problem” [6], informal pay- ments are frequently justified by patients as expressions of gratitude, efforts to secure additional services or faster, higher-quality care [7,8]. They may also arise in response to explicit requests from providers or out of fear of being denied treatment [7,8]. From the provider perspective, the acceptance of informal payments is frequently attributed to inade- quate remuneration and systemic underfunding [9]. Given their impli- cations for equity, trust in the health system and overall efficiency, understanding and addressing informal payments is important for the development of effective health policy measures. To propose policy measures aimed at addressing informal payments in the public healthcare system, previous research has largely relied on surveys to assess the magnitude of the informal payments phenomenon [4,10–14], identify its key drivers [11,12,14–20], and recommend pol- icies and actions to tackle informal payments in health systems. Simi- larly, systematic review studies revealed that surveys are commonly used when investigating determinants [21] and policies [22] towards informal payments in healthcare. In the policy design process [23], surveys are therefore essential for identifying whether informal pay- ments represent a relevant problem and for selecting appropriate mea- sures to address the problem. Nevertheless, surveys encounter several challenges, such as repre- sentativeness issues (i.e., sample selection bias) and social desirability bias in the answers of the respondents (i.e., measurement error in the * Corresponding author at: Grigore T. Popa” University of Medicine and Pharmacy, Universit˘ații Street, No. 16, Iași 700115, Romania. E-mail address: adrian-vasile-horodnic@umfiasi.ro (A.V. Horodnic). Contents lists available at ScienceDirect Health policy journal homepage: www.elsevier.com/locate/healthpol https://doi.org/10.1016/j.healthpol.2025.105367 Received 25 November 2024; Received in revised form 16 April 2025; Accepted 1 June 2025 Health policy 159 (2025) 105367 Available online 1 June 2025 0168-8510/© 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.