Abstract
Medical insurance fraud (MIF) poses a substantial global financial challenge, necessitating effective regulatory strategies, especially in China, where such measures are in a critical developmental phase. This study investigates the effectiveness of various regulatory components in deterring MIF among enrollees and explores preference heterogeneity among individuals with different characteristics, utilizing a discrete choice experiment survey. Grounded in deterrence theory, our conceptual framework incorporates five attributes: intensity of economic penalties, restrictions on medical insurance benefits, deterioration of social reputation, and certainty and celerity of penalties. Employing a D-efficiency design, 24 choice sets were generated and blocked into three versions. A multistage stratified sampling method was adopted to collect data from the basic medical insurance enrollees in Shanghai. The survey was conducted from September to October 2022. The sample representativeness was further improved via the entropy balancing approach. Data from the final sample of 1034 respondents were analyzed using mixed logit models (MIXLs), incorporating interactions with individual characteristics to assess preference heterogeneity. Results reveal that escalating economic penalties, suspending insurance benefits, listing individuals as unfaithful parties, ensuring penalty certainty, and expediting enforcement significantly enhance the deterrent effect. We observed preference heterogeneity across different demographics, including age, gender, education, health status, and employment status. The study underscores the pivotal role of economic penalties in deterring MIF, while also acknowledging the significance of non-economic measures such as enforcement efficiency and social sanctions. These findings offer valuable insights for policymakers to tailor and strengthen regulatory schemes against MIF, contributing to the advancement of more effective and precise healthcare policies.
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