CONTROL DE TRABAJOS

This study is linked to the expenditure approach and looks at social expenditures as a percentage of GDP. Besides social expenditures, I also examine income inequality and GDP. Income inequality based on market incomes highlights the need for redistribution and social expenditures. The EVS 2008/2009 includes some countries that are not covered by Eurostat statistics and various other sources of social expenditure have been used for these countries as a result. Reliable data on social expenditure were not available for Bosnia-Herzegovina and Kosovo and these countries were therefore excluded from the analysis. Data sources are listed in Study II, Appendix B.

Using the data of the EVS 2008/2009 and the self-compiled macro data, I examine the following research questions on the basis of a two-step hierarchical analysis:

Are higher levels of social expenditures related to lower health inequalities?

Does income inequality increase health inequalities?

With the two-step hierarchical analysis I examine the effect of income on health—income- related health inequality—separately for each country. Bryan and Jenkins (2016) describe several advantages of the two-step approach. First, as I look at each country individually in the first step, I can identify outliers and possible causes of variations in the data. The two-step approach offers a valuable descriptive overview of health inequalities in Europe, which serves as helpful background knowledge in Study I and Study III, where I apply simultaneous multilevel analyses. Second, the insights into the level of health inequality per country enriches the "graphical approach that provides a non-statistical view of country-level variation" (Bryan & Jenkins, 2016: supplemantary material: 8). Third, for large sample sizes given in the European Values Study, the estimates have the correct standard errors and the approach has no methodological disadvantages. In the second step of the hierarchical analysis, the countries represent the units (i.e. 42 observations); the income coefficient as an indicator of health inequality is the dependent variable which offers an easy interpretation.

The analyses in Study II have also provided a basis for decisions which I then applied to the other research papers, such as the modelling of subjective health as the dependent variable or how to deal with (many) missing values of the independent variable.

I tested three ways of using subjective health as a dependent variable: ordinal, linear, and dichotomous. The comparison of the different modelling of subjective health—as a dummy

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variable for poor health and as ordinal dependent variable—shows no substantial differences. A robustness check included a linear regression on the metric use of the 5-point scale of self- rated health. The comparison of results shows only small differences between the ordinal and the linear model. This is important for complex simultaneous multilevel models and subjective health is therefore used as a metric dependent variable in linear regression models in Study I and Study III (see also Olsen & Dahl, 2007). In order to incorporate all the information given by the respondent, including the fine gradations at the edges of the response scales, I use subjective health as a 5-point variable in the other studies. In this way, I avoid the dilemma of combining the response categories in a dummy and the question of where the cut- off points should be set. Recoding subjective health into "poor health" or "less than good health" as some researchers do (Eikemo et al., 2008a; Eikemo et al., 2008b) is a reduced reflection of the respondent's information. I follow the WHO's positive definition of health by using the full range of answers given (for a further discussion see Chapter 4.4).

Robustness checks and sensitivity analyses show that the way in which missing values of income are dealt with has only a marginal impact on the results. Multiple imputations produced more conservative estimates but did not change the results. The results remained the same, regardless of whether I imputed the missing values of income or applied listwise deletion, i.e., cases with at least one missing value are excluded from the analyses. The influence of the missing values is not as strong as one would expect given the high numbers.

It is generally assumed that there is a negative relationship between generous social policies and health inequalities (Bergqvist et al., 2013). However, I could not confirm this in Study II, using social spending as indicator for social policies. I did not find any significant relation between social spending as a % of GDP and income-related health inequalities. A recent study by Alvarez-Galvez and Jaime-Castillo (2018) confirmed my findings.

As expected, income inequality is positively associated with health inequalities. Income inequality leads to health inequalities independent of national wealth. The control variable GDP had a negative effect on income-related health inequalities. The analyses showed that the Gini index dominated in the context of the three macro determinants of health inequalities and had a stable positive effect across the model variations (see Study II, Table 1: standardized beta coefficients).

Although the Gini index (based on market income) in the models has a significant influence on health inequalities, while social expenditure does not, this is nevertheless an

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indication of the importance of social policy in its role as a redistributive mechanism. Future research could examine the impact of social redistribution on health inequalities by using the Gini index, which is based on disposable income compared to market income.

The most recent EVS 2017 contains fewer countries than the EVS 2008, but the possibility of a time comparison is given for all countries of the EVS 2017, which would be an interesting follow-up analysis. The financial crisis may have had an impact on the EVS 2008 data, so a comparison nine years later would provide insights into whether and to what extent the financial crisis has affected the 2008 results.