In Study III, I applied the institutional approach and studied the characteristics of one social policy scheme in particular. I chose minimum income protection as a last safety net and looked at the statutory national benefit rates from the SPIN/SaMip database. This might differ from the actual payment, which can be lower (due to sanctions) or higher (due to claims for additional benefits, such as housing supplements) than the statutory rate. The use of the statutory rates ensures comparability between countries and social systems. Furthermore, I assume that the levels of the statutory rates are relevant and known in society, and influence the whole society reaching beyond the target group.
In this paper, health inequalities were modelled based on the effect of income on health. I assume that minimum income protection influences the link between income and health, but not between education or occupational status and health; income is therefore the decisive social determinant of health in Study III. The research question specifies the focus on income:
How is minimum income protection associated with income-related health inequalities?
Do higher benefit levels improve the health of the lowest income groups and the middle income groups?
Education and occupational status are included as control variables in the analyses. The analyses were carried out using a simultaneous multilevel model with two variables at the macro level: GDP per capita and benefit levels of minimum income protection. I tested the moderating effect of benefit levels on the relationship between income and health with a cross-level interaction.
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I have operationalized household income for each country in quintiles and calculated cross-level interaction terms for five different income groups accordingly. In this way, I was able to answer the second research question as to whether minimum income protection also has an effect on middle and higher income groups. Even though the benefits of the minimum income protection as the last safety net are means-tested (Bahle, 2019), I assumed that knowledge of generous benefit levels also has effects on the middle class: it provides a degree of security, reduces stress, and therefore has a positive effect on health.
Study III differs from the other studies in the use of the data. As mentioned above, the institutional approach requires specific information on institutional measures. Publicly available statistics are less common in countries with low GDP because of the administrative effort and bureaucracy involved in obtaining correct institutional and/or statistical information on social policy programs. Many post-communist countries are not included in the usual databases and, had I used EVS data, I would have been forced to omit many countries from my analysis. In contrast, the European Social Survey (ESS) covers many EU member states plus Iceland, Israel, Norway, Russia, Switzerland, Turkey, and Ukraine and has been running every two years since 2002. It is therefore well-suited to combine microdata with macro-level data, e.g., from Eurostat, and I was able to analyze six rounds from 2002 to 2012.
Benefit levels of minimum income protection have a significant positive effect on individual subjective health. Contrary to my assumptions, however, the influence of the benefits on health inequalities could not be confirmed. The income gradient in health at the micro level is also reflected in the cross-level interactions terms; the positive effect of minimum income protection decreases with each quintile down the income distribution. The lower income groups thus had no health advantage over the other income groups in countries with higher benefit levels. In countries with higher generosity of benefits, such as Luxembourg, it appears that income-related health inequalities are even greater than in countries with lower benefits, such as Poland or Lithuania. Overall, I did not find any confirmation that minimum income benefits reduced income-related health.
Recipients or beneficiaries of minimum income protection and social assistance were not identifiable in the ESS data and the number of recipients is consequently subsumed in the lowest 20% of the income distribution. While the ESS do not provide the data to test whether minimum income protection has a positive effect on the health of this group in particular, compared to other income groups, it is this kind of analysis that may contribute to comparative research on social policy and health inequalities. The question could be
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addressed through a panel analysis of the health of recipients before social assistance benefits are claimed, during receipt, and after termination. Comparisons of the degree of stigmatization of the different minimum income protection schemes in Europe could also be considered.
Additional research questions could relate to the coverage of minimum income protection in the population and the non-take-up of benefits despite entitlement. To what extent does the health of recipients differ from the health of people who are entitled but do not claim benefits? Here, too, stigmatization of recipients might play a role.
I look at benefit levels of minimum income protection because I want to understand the impact of a social policy which is considered the last safety net on health inequalities. Would health inequalities look different if there was an unconditional basic income instead of means- tested, targeted minimum income protection? According to the fundamental cause theory (Link & Phelan, 1995), health inequalities persist because socioeconomic status determines not only money but also power, knowledge, and prestige. However, beyond the resource aspect of an unconditional basic income, other aspects of the unconditional basic income, such as trust and solidarity, could have a reducing effect on health inequalities. First results of the basic income experiment3 of Kela, the social insurance institution in Finland, show that the treatment group has better health than the control group (Kangas et al., 2019).