Evasion of health insurance contributions in Colombia : determinants, predicting evaders and the unintended effects of equalizing the packages of benefits - Peña Maldonado, Carlos Julián (2018)
Objectives: In this paper we analyze both static and dynamic factors of evasion in health insurance contributions. On static factors, we assess 1) the determinants of evasion of health insurance by informal workers (both enrollees in the subsidized regime and beneficiaries in the contributory regime); and 2) create and assess a tool to identify these individuals. On the dynamic factors of evasion, we assess the unintended effects of equalizing the package of benefits of the health system on evasion. Methods: We use a national representative household survey called “Gran Encuesta Integrada de Hogares” that provides a range of individual and household level annual data between 2008 and 2016. We first use linear probability and Probit models to estimate the determinants of evasion. Furthermore, by using the determinants of evasion, we create an instrument to identify evaders of the subsidized health insurance regime. Finally, a difference-in-differences model is used to assess the unintended effects of the equalization of the package of benefit on evasion. Results: Regarding static factors for evasion, we find that young men with higher education degrees are more likely to evade health insurance. Using this information, we developed a tool to identify evaders of health insurance and assess its internal validity. While assessing dynamic factors of evasion, we find that the equalization of the package of benefit is associated to increased evasion to the contributory regimen. Evasion of health insurance by informal workers after the equalization of the package of benefits may represent at least 5.7% of the total health expenditure. Discussion: The evasion of health insurance contributions to the healthcare system in Colombia is a substantial problem that threatens its long-term sustainability. It is necessary to implement strategies to better identify evaders of health insurance. A tool with this purpose is created and internally validated in this research work.