*4.2. Decomposition of Income-Related Inequality in OOP Burden*

The regression-based decomposition analysis we introduced above can be applied to income-related inequality of both health care utilization and OOP burden, allowing us to identify which covariates have a significant effect on each form of inequality. Here, we apply it to the burden of OOP only. Note that since OOP burden is a bounded ill-health variable, we have to define the dependent variable of our regression as explained in Appendix A. Income is excluded as an explanatory variable since it would distort the explanation of the correlation between income and health (Erreygers and Kessels 2013). For each wave of the survey we estimate two regressions, one for the rank-dependent index and the other for the level-dependent index, based on the same set of independent variables. We present both the marginal effects and the logworth values for key covariates to assess their importance as explanatory variables in Table 4 (rank-dependent indices) and Table 5 (level-dependent indices). The full results of all covariates can be found in Appendix B. A logworth value larger than 1.3 indicates that a variable (or group of variables) is significant at the 5% level and a logworth value above 2 indicates significance at the 1% level. For variables with more than two categories, the logworth values are combined over the categories.



below 5% level.



below 5% level.

Demographic and socio-economic factors such as education levels, employment status, occupation, residential regions, are among the most important determinants of incomerelated inequality in OOP burden across all years. Positive coefficients indicate that variables have a positive marginal effect on the observed inequality, i.e., tend to make income-related inequality of OOP burden less pro-poor, and the opposite holds for negative coefficients. For example, suffering from more major diseases tends to make income-related inequality of OOP burden more pro-poor, although the effect is often insignificant. The magnitudes of the coefficients in the decomposition of the level-dependent index in 2015 appear to be larger than the ones in other years. This might be due to the relatively high values of the OOP payments and household incomes in 2015. For both indices, factors that contribute to more pronounced pro-poor inequality in OOP burden seem to be higher education levels, being employed, having white-collar jobs and living in cities, given that the marginal effects of the associated variables are all negative. Social health insurance coverage appears to reduce the pro-poor inequality in OOP burden in some of the years, but in others the effects remain rather limited.
