Analyses and hypotheses

4.1.1 Historical evolution of poverty and income inequality between 1979-2011 Poverty ratio, poverty-gap and income inequality

In a first step the analysis will focus on the evolution in terms of poverty and the inequality of the distribution of monthly household incomes over all three waves. To this end the yearly headcount indexes, poverty gap indexes and Gini-coefficients have been calculated and the corresponding Lorenz-curve graphs plotted. This gives a first insight into the general dynamics that are at work and which will be further explored in the following statistical models. This part of the analysis exclusively relies on the key target variable of monthly household income for each of the three waves and the constructed poverty variable. Both have been described in the previous chapter.

The hypothesis that are tested in this part concern the stagnation or an increase in

inequalities for incomes among the elderly population in Geneva and Valais over the last three decades. Here, I follow the postulates ofAlderson & Nielsen (2002) who claim that there is a reversal of the so-called Kuznetsian U-Turn in Western industrial societies with an increase in inequalities. This hypothesis also follows the main theoretical framing of this thesis which is a Marxist class-framing. According to such a stance, inequalities persist or even increase since they are an inherent characteristic of modern capitalist societies and a result of elite domination and exploitation (see for example Harvey, 2010, 2014; Keister & Southgate, 2012).

Control model / stratification-variable model for poverty

Following this first description of the data for monthly household income, the exploratory models prioritize the analysis of poverty instead of the whole distribution and its inequality. The rationale for this approach has been extensively discussed in the theory chapter. A first model is built with the variables of stratification. It models the effect of canton, age-group and sex on the odds of being poor in old-age and can also be regarded as a control model, since these variables have been used for the original sample-stratification.

Conceptually, I will look at the dynamics in poverty for each of the three waves and compare them. Accordingly, it is to be expected that in the observed period from 1979 to 2011 there should be a diminution of poverty in the higher age-groups. The reason for this hypothesis is that poverty among the old-old and the oldest-old should naturally decrease as a result of improved pension systems. The latter provide pensioners with adequate economic resources (principally through the so-called AVS and in cases where the latter is insufficient through complementary welfare programs, see Bertozzi, Bonoli,

& Gay-des-Combes, 2005) to be situated above the absolute poverty line (Paugam, 1991a; Wanner & Gabadinho, 2008).

Moreover, the same reduction in poverty should be observed for women. Again, in that period pension systems and social welfare systems in general have been adjusted to eliminate – or at least decrease - the gender inequality that they previously created.

Among others, these adjustments principally concern the treatment of widowers who previously did not have any right to their husbands AVS savings after his death as well as adjustments in cases of divorce (Wanner & Fall, 2012). Beyond that, there has been an increase of the share of women who are professionally active instead of focusing on family-work and domestic activities. This should further contribute to a significant decrease in gender differences in old-age poverty (Tillmann, 2010; Tillmann et al., 2014). Nevertheless, as a number of studies indicate, a valid counter-hypothesis is that despite all these efforts and improvements female poverty in old-age persists (Pilgram &

Seifert, 2009).

Finally, I also expect a decrease in regional differences. In 1979 the canton of Valais was still strongly lagging behind the canton of Geneva in terms of poverty. In fact, Valais still was a highly rural canton with a much higher share of poor elderly. Already in 1994

there were signs that these differences were decreasing (Lalive d’Épinay et al., 2000) and I expect this effect to have fully decreased in 2011. I hypothesize that there is a tendency of harmonization among the cantons in terms of social welfare, economic development and thus poverty-rates.

Social stratification model for poverty

In the social stratification model, the key hypothesis of this thesis is tested: According to the Marxist class-framework, inequalities are primarily a result of class-dynamics.

Again, it has to be emphasized that these class-dynamics are operationalized with the indicator of education which in turn permits to assess both, inter- and intra-class dynamics. Whether this holds true for economic inequality among the elderly population and across the last three decades will be the main focus of this model. As has been explained in the theory part, given that this thesis employs a three-level class-measure which distinguishes between two levels of working-class, the results will provide an insight into both, inter- and intra-class dynamics.

It has to be repeated that between 1979 and 2011, the elderly population has changed fundamentally. Table 26 shows the estimates for each of these years in the elderly population regarding educational status.

Low (%) Average(%) High (%)

1979 67 11.5 21.5

1994 46.5 15 38.5

2011 18.8 31.7 49.5

Table 26: Educational attainment people 1979-2011 Source: COMP Note: Estimates for the population aged 65 and older based on COMP data (adjusted with sampling-weights)

Clearly, the table 26 shows how there has been a complete reversal in elderly people's educational background. In 1979 there were 67% of people who only possessed the obligatory or even no formal education, the share of this population has decreased to 18.8% - more than three times less. Parallel to this, people with a form of higher education rose from 21.5% in 1979 to more than double this amount at 49.5. The same applies for people with an average educational background, meaning people who have an apprenticeship. That share increased from roughly 12% to almost a third of the population in 2011.

The baseline dynamics are given by the previous control or sampling-criteria model. To this model I will then add the variable education, creating a nested model. Here, we test the main hypothesis that in each wave the main poverty dynamics are strongly captured with the variable education (as a proxy for class-membership). I also suppose that a large part of cohort-related poverty can be explained with education and class. In fact, the reason behind this relation is found in shifts in the composition of the population: The different cohorts that are captured in the three waves of the survey feature different

structures in terms of education and class. Since the 1950s and especially over the last three decades there has been a strong tendency of improved education. In generations that have been educated since then, less people have no or only little formal education.

At the same time more and more people have secondary, professional and even higher educational levels. To a certain extent, this also reflects the thesis of a moyennisation (Tillmann, 2010) of the population. Hence, there might be less people in such lower classes. This can also be due to the fact that some cohorts managed to undergo trajectories of a certain upward social mobility.

Simply put, I suppose that the poverty in all three waves is primarily related to education and thus to class membership. Above all, there should be strong effects for the factor of high education, capturing inter-class effects. However, based on the outlined theory I also posit that there must be strong effects for the factor “low education” which assesses the strength of inter-class dynamics among the working class, between unskilled and skilled workers. Furthermore, while there might be a decreases the share of unskilled workers over time as cohorts are renewed – the reason being the improved educational system which leaves less and less people with little or no education - this does not imply any changes in the general negative dynamic that affects such unskilled workers. On the contrary, the negative effects of low education are supposed to persist until today; the

„scarring“ effect that low education creates should remain valid over time and across cohorts. A preliminary analysis using direct standardization (which can be found in the annex in part 9.4) fully confirms this hypothesis. It will be of cardinal interest to reassess and confirm these findings in the modeling part of this chapter.

Civil status model for poverty

In the second block of nested models, the aim is to test the interrelation of the social stratification model with civil status. In fact, civil status has been a factor that for a long time had considerable impact on poverty in old-age (Chauvel, 2001a). Above all it was widowers, who were particularly susceptible to poverty since the AVS system did in the beginning not allow them to access their husband's savings. This has been discussed in the previous chapter. Furthermore, divorce is another civil status which has more recently been shown to be associated with poverty in old-age (Lalive d’Épinay et al., 2000; Pilgram & Seifert, 2009; Vlachantoni, 2012).

Similar to the previously presented evolution in terms of educational levels among the elderly population, civil status has seen important transformations over the last three decades. Table 28 shows the distribution of this variable in the COMP dataset.

Married(%) Single (%) Separated / divorced (%)

Widow (%)

1979 52.2 9.7 4.9 33.2

1994 59.5 7.8 6.6 26.1

2011 59.5 6.1 13 21.4

Table 27: Distribution of civil status Source: COMP Note: Estimates for the population aged 65 and older based on COMP data (adjusted with sampling-weights)

While being married remains the predominant civil status, there have been significant changes in the other categories. The share of people who are divorced has almost tripled from 4.9% in 1979, to 6.6% in 1994 and finally to 13% in 2011. Widowers grow slightly more scarce with a relative share of 21.4% compared to 26.1 in 1994 and 33.2% in 1979.

Finally, singles grow even more rare at a level of 6.1%, representing roughly a 30%

decrease from its original level in 1979 (9.7%).

Technically, the impact of civil status will be tested by using the social stratification model as a basis to which I then add the civil status variable. In this model we test to what extent educational and class differences are mediated by the variable civil status.

The working hypotheses concern that the dynamic that has been observed by Lalive d'Epinay and colleagues (2000) has been sustained up until 2011. They found that already in 1994 civil status had lost its previously very strong determining force. Given the continuing efforts by the government to correct sources of discrimination in the Swiss pension system – this particularly with regards to women who obviously were implicitly more affected in situations of widowhood or divorce – civil status should not be an important factor in predicting old-age poverty anymore (Wanner & Fall, 2012).

4.1.2 Historical evolution of functional and mental health among the elderly 1979-2011 Evolution on an aggregate level

Parallel to the previous section on the evolution of poverty in old-age the part on the historical evolution of functional and mental health starts with an overall analysis of the evolution between 1979 and 2011 on an aggregate level. Since Lorenz curves and gini-indices are not meaningful for the data in this part, it will principally rely graphical representations highlighting the temporal trends.

Keeping in line with the narrative of inequalities within progress, the hypotheses in this part are centered around the idea that there might be significant improvements on an overall level but that inequality over the same period has increased (Mackenbach et al., 2008).Hence, there might be progress for functional health with less people suffering from minor and major health impairments and improvements for mental health with less

people suffering from depressive symptoms. Yet, there remains a non-negligible population that continues to be in poor health.

Control model for functional and mental health

The first step in the model-based data analysis consisted again in establishing a baseline model which captures the effects for age-, gender- and canton-related health inequalities.

The same variables as for poverty are used to do so for both functional as well as mental health. Also, as in the previous poverty model this is performed using both an approach that estimates models for each wave in each year as well as a merged approach that estimates overall effects as well as interaction effects with the yearly waves. The hypotheses for both of these approaches, once again, remain absolutely identical.

The main hypotheses are that health problems – being „in difficulty“, being „dependent“

or suffering from depression symptoms– increase with age as a result of a “natural”

progress of aging. However, comparing the three waves I expect a decrease of such age-effects on the grounds that socio-sanitary progress creates a situation where people remain independent and in good mental health for much longer and being in difficulty, dependent and suffering from depression increasingly only applies to the oldest-old or people of the „fourth“ age (see Bundesamt für Statistik, 2012; Lalive d’Epinay et al., 2000; Leu, Burri, & Priester, 1997).

Secondly, regarding sex-related health inequalities, I will test the hypothesis that both functional and mental health problems are more frequent among women (Dahl, 1993;

Stronks, Mheen, Bos, & Mackenbach, 1995). The key reason for important gender differences are related to the fact that women live longer than men. This over-mortality among men has various reasons. One factor is related to socio-professional differences whereas men more often engage in physically demanding manual work. Also,the literature suggests that in addition to such work-related differences certain health problems are more frequent among men due to differences in lifestyle and behavior in general. This refers to activities such as smoking, for example(Marmot, Ryff, Bumpass, Shipley, & Marks, 1997; Smith, Bartley, & Blane, 1990).

Finally, with regards to cantonal and regional differences, Lalive d'Epinay and colleagues have shown that in 1979 the two studied cantons of Valais and Geneva were significantly different with regards to functional health (Lalive d’Épinay et al., 2000).

However, they also showed that in 1994 this gap had diminished greatly. Thus it can be expected that they are even more similar in 2011. I also posit that this holds true for the mental health indicator.

Social stratification model for functional and mental health

The social stratification model for functional health then tests the key hypotheses of class-dynamics (inter- and intra-class) for the dimension of health inequalities. This is

done following the exact same procedure as for poverty.

The key hypothesis to test is that class has a cardinal impact on health inequalities (Bambra, 2011) according to a social stratification and class-theory framework.

Furthermore, I test whether these stratification dynamics are the same for both functional as well as mental health. As has been pointed out in the theory chapter, social stratification and class-theory are among the key explanatory frameworks for persisting health inequalities in Western European countries (Mackenbach, 2012).

4.1.3 Modeling approach

A final clarification that has to be made before focusing on the actual models regards the methods of analysis. A cardinal aspect of the COMP dataset is that it consists of three merged waves of a survey that have been carried out at different times. The chapter on the historical evolution of inequalities will use two distinct approaches to address this specific characteristic of the COMP dataset. The first approach consisted of isolated models for each wave of the survey. The second approach consisted estimating models on the complete merged COMP dataset and controlling for the temporal differences of the waves by adding the corresponding interaction effects. For all of analyses concerning the COMP dataset both approaches will be employed in parallel. The rationale behind this double-approach can be found in section 3.8 of the previous chapter.