Evolution of poverty in old-age 1979-2011

4.2.1Poverty and income inequality in old-age 1979-2011

The first analysis focuses on the evolution of household incomes among elderly people in the cantons Valais and Geneva between 1979 and 2011. This is done with a focus on income inequality as well as poverty. It provides a first broad insight into the evolution of the economic situation of retired people over the observed period.

Figure 16: Lorenz curve economic resources Geneva & Valais 1979-2011 Source: Own calculations, COMP dataset

The graph for the Lorenz curves between 1979 and 2011 (shown in figure 16) clearly suggests that inequalities have decreased in this period. In 1979 (depicted with the curve in red), inequality was clearly the highest. In 1994 (green curve) there was a marginal but still somewhat visible decrease and shift of the curve towards the left, indicating a decrease of inequalities. Finally, in 2011 (blue curve) the Lorenz curve seems set apart and clearly towards the left. This is an indication for a decrease in economic inequalities until 2011. However, the first two curves are that close together, that additional information is needed to draw any meaningful conclusion. To this end, we can look at different indicators of inequality as well as prevalence and depth of poverty.

The Gini-coefficient gives insights into the inequality in incomes. Here, the values confirm the previous observations. In 1979 the Gini-coefficient was situated at 0.38.

Between 1979 and 1994 it then decreased roughly 5% to reach 0.36. Finally, in 2011 for the VLV dataset it was as low as 0.32 suggesting a further drop of around 11% compared

to its precedent level in 1994. Between 1979 and 2011 the overall drop in income inequalities among elderly people in Geneva and Valais was of approximately 16%.

Again, this indicator suggests an improvement in terms of economic resources.

Inequality, it seems, has decreased between 1979 and 2011.

The Lorenz curves and Gini-coefficients thus suggest that incomes have become distributed more equally over the last three decades. But how has the situation changed for those at the bottom of the income distribution, people living in poverty? Here, a first insight can be gained by looking at the share of people living below the line of absolute poverty. This indicator, the poverty headcount index, lies at 0.48 in 1979. In other words, about half of the population over 65 in 1979 was living in poverty. 15 years later, in 1994, the situation seems to have improved somewhat as roughly four people in ten was living under the same economically difficult circumstances. In 2011, only about one fifth of the population is still living in poverty. Based on these indicators it can be said that poverty among the elderly has been more than halved over the last three decades. While this certainly signifies a vast improvement compared since 1979, poverty has by no means been eradicated. The value of 0.22 in 2011 indicates that a substantial number of people live in a situation that is marked by severe financial hardship. These conditions and the social consequences thereof should not be underestimated. Yet, the significant progress that is illustrated by these numbers should not be neglected either.

Finally, while the poverty headcount ratio is easy to interpret, it does have considerable shortcomings. Notably, it does not give any information on the population that is living in poverty. If the income of those living below the poverty line would be halved, this would not be reflected in this first measure (Vecchi, 2007, pp. 71–72). Hence, it is necessary to investigate this aspect as well. This can be done using the poverty gap ratio.

As has been described in the section on methods, it is a measure of the depth or the intensity of poverty. To be precise, it is the “extent to which individuals on average fall below the poverty line, and expresses it as a percentage of the poverty line” (Haughton

& Khandker, 2009, pp. 71–72). Based on the COMP data, the poverty gap ratio in 1979 was at 0.21. Since the absolute poverty line in 1979 was situated at 1000 Fr., this means that poor people in that year on average experienced a 20% shortfall in terms of incomes compared with the line of absolute poverty. In 1994 this index was, based on the poverty-line of 2000 Fr., practically identical at 0.19. In 2011, however, a significant shift can be observed as the value drops almost three-fold to 0.083. This additional evidence on the intensity of poverty supports the previous findings on inequality and the absolute share of people living in poverty.

The evidence from this first block of analyses strongly points towards a narrative of progress and improvements in terms of economic resources for the elderly in Switzerland. Incomes are distributed more equally, less people are poor and among those who are the gap towards the poverty-line seems to decrease. The previous results are summarized in table 28.

Measure Gini-coefficent Poverty headcount Poverty gap

1979 0.38 0.48 0.21

1994 0.36 0.39 0.19

2011 0.32 0.22 0.083

Table 28: Control model poverty in old-age 1979-2011 Source: Own calculations based on COMP dataset Note: Binomial logit model displaying odds-ratios

These findings are contrary to the outlined working hypotheses which posited an increase or at the very least a stability in inequalities. Firstly, based on Alderson and Nielsen (2002), I expected a so-called „reversal of the Kuznetisan U-turn“ and thus an increase in inequalities. The second support for the working hypotheses was given by the Marxist framework according to which inequalities should equally grow as a result of continuing class-discrimination with the elite-classes continuously amassing wealth and the working-classes being discriminated. However, both of these hypotheses have to be rejected. Instead, there seems to be considerable evidence for progress and decrease of income inequalities in old-age.

Can this result be considered a testament of the success of modern capitalism? After all, estimates based on the COMP data show that poverty rates in the elderly population between 1979 and 2011have indeed decreased from 51% in 1979, to 38.1 % in 1994 and finally, to 21.2% in 2011. A sort of trickle-down phenomenon of economic growth? Or is it the result of improving old-age security systems that ensure a basic income for all elderly citizens in Switzerland? The following sections will shed light on these questions, starting with a clearer view of the composition of poverty in old-age in Valais and Geneva between 1979 and 2011.

4.2.2 The evolution of age-, sex- and cantonal patterns in old-age poverty 1979-2011 This first three analytical models were built around the binary variable poverty and included the explanatory variables sex, age-group and canton. These were also the variables of stratification for each of these three waves. As such, they will be included in all of the following models as control variables.

1979 1994 2011

AIC 1864.7 1815.6 1136.9

BIC 1902 1852.5 1171.9

Intercept 0.24*** 0.29*** 0.1***

Women 1.62*** 1.12 1.79***

Canton Valais (Ref. Geneva) 4.99*** 3.45*** 2.1***

Age group 70-74 (Ref. 65-69) 1.41* 0.81 1.48

75-79 1.65** 1.31 1.52

80-84 2.29*** 1.61** 1.59

85-94 3.59*** 1.22 1.55

Note: ^*p<0.1; ^**p<0.05; ^***p<0.01

Table 29: Control model poverty in old-age 1979-2011 Source: Own calculations based on COMP dataset Note: Binomial logit model displaying odds-ratios

The results of this first model for each of the three waves are shown in table 29. Three main observations can be made: Age-effects which are strong in 1979 disappear over the observed period. Cantonal differences decrease but persists between 1979 and 2011 and finally, gender-differences in 2011 are more or less at the same level as in 1979 or are even marginally increased. Poverty in old-age transitioned from a phenomenon that affected all age-groups, and particularly the oldest generations, towards a phenomenon that is less age-dependent and rather follows gender- and regional-specific logics instead.

More in detail, the most tremendous shifts can be seen in the age-variable. In 1979, there are very strong age- or cohort related effects. People aged 85-94 were over 3.5 times as likely to be in a situation of poverty as their peers aged 65-69. This effect became weaker with each lower age-group: For people aged 80-84 the odds-ratio was roughly one-third lower than among those aged 85-94. Individuals that were aged between 75-79 when they were interviewed in 1979 then showed only a weakly increased odds-ratio of 1.41 compared with 65-69 olds. In 1994 this age-related pattern was absent for almost all age-groups except for people aged 80-84. In 2011, these age-effects have completely disappeared and none of the odds-ratios in the third model in 2011 remains statistically significant.

This disappearance of age-related effects confirms the working hypotheses that were set in the previous chapter. The underlying reasons are manifold but above all, they are related to the installment of old-age pension systems that ensure – at least theoretically – every elderly citizen in Switzerland enough financial resources to be above the poverty line. The results found in this section confirm that these measures have been highly successful in preventing poverty. With this finding, the positive trends that have been observed between the first two waves of the survey between 1979 and 1994 (Lalive d’Épinay et al., 2000) are not only confirmed, but their continuing impact until 2011 can be proven. Continuously, poverty among the oldest generations decreased. The observed

dynamics with regards to age are also a result of people living and working in a time that was largely characterized by economic growth and material prosperity which has provided them with ideal employment opportunities and ideal career paths (Wanner &

Gabadinho, 2008).

The second important change can be observed for cantonal differences whereas the gap between Geneva and Valais clearly decreases between 1979 and 2011 but still persists. In 1979 the odds-ratio for Valais was of 4.99, signifying that elderly individuals in Valais had roughly five times the risk of being poor compared to those in Geneva. A mere 15 years later in 1994 this gap had decreased to 3.45. It further lowered to reach the level of 2.1 in 2011. In other words, there has been a substantial shift in terms of cantonal differences from an initial situation where people in Valais were roughly five times as susceptible to be living in poverty compared to Geneva to being around twice as likely.

The gap has become smaller but still remains significant. This confirms the observations that had been made in 1994 by Lalive d'Epinay and colleagues (2000) who found a decrease in poverty in both cantons and thus a homogenization between the two.

Concerning gender effects, the results based on the COMP dataset do not reflect these previous findings of progress. The situation in 2011 (an odds-ratio of 1.79) resembles that of the situation of departure in 1979 (with an odds-ratio of 1.62). In fact, the results might even indicate that gender-inequalities have increased. Also, there is a somewhat strange anomaly in these results due to the fact that in 1994 the non-significant odds-ratio of 1.12 suggests that gender-specific poverty-patterns were absent in that particular wave of the survey. The interpretation of the stagnation between 1979 and 2011 can be related to findings in the literature that show persisting gender-inequalities in numerous other areas of health such as life-expectancy. Health, as it appears, is a dimension that has been and continues to be strongly gendered. So far, this confirms the working hypotheses that have been set in the previous chapter.

The insights from this first approach using separate statistical models for each of the three waves is compared to the second approach for modeling these effects which consisted of estimating a single model for the whole merged COMP dataset. In this merged approach, the temporal component of this dataset is reflected with the use of interaction terms.

AIC 4839.3

BIC 4971.8

Intercept 1979 0.26***

Intercept 1994 0.28***

Intercept 2011 0.1***

Interact. Women/1979 1.61***

Interact. Women/1994 1.16

Interact. Women/2011 1.74***

Interact. Valais (Ref. Geneva)/1979 4.75***

Interact. Valais (Ref. Geneva)/1994 3.47***

Interact. Valais (Ref. Geneva)/2011 2.23***

Interact. Age-group 70-74 (Ref. 65-69)/1979 1.37*

Interact. Age-group 70-74 (Ref. 65-69)/1994 0.84 Interact. Age-group 70-74 (Ref. 65-69)/2011 1.45 Interact. Age-group 75-79 (Ref. 65-69)/1979 1.6**

Interact. Age-group 75-79 (Ref. 65-69)/1994 1.32 Interact. Age-group 75-79 (Ref. 65-69)/2011 1.5 Interact. Age-group 80-84 (Ref. 65-69)/1979 2.19***

Interact. Age-group 80-84 (Ref. 65-69)/1994 1.64**

Interact. Age-group 80-84 (Ref. 65-69)/2011 1.63 Interact. Age-group 85-94 (Ref. 65-69)/1979 3.3***

Interact. Age-group 85-94 (Ref. 65-69)/1994 1.29 Interact. Age-group 85-94 (Ref. 65-69)/2011 1.62

Note: ^*p<0.1; ^**p<0.05; ^***p<0.01

Table 30: Control model with interactions for poverty in old-age Source: Own calculations based on COMP dataset Note: Binomial logit model displaying odds-ratios

As has been discussed in the first section of this chapter, the approach using the merged dataset and interaction effects in one single model is above all a measure in order to confirm the previous results based on the three separate models. The results for the basic model featuring the variables of sample-stratification are shown in table 30. They confirm all of the previous findings for the isolated models: Gender-effects which are strong in 1979 (rows 6-8 showing the effects for gender in 1979, 1994 and 2011), disappear in 1994 and reappear at about the same level as in 1979. Strong age-effects can be observed in 1979. They then become relatively marginal in 1994 and are completely absent in 2011. This can be seen in the 12 rows from the bottom, whereas each age-group-effect is shown for each of the three waves. Finally, cantonal effects between Geneva and Valais that become weaker after 1979 but still persist up until 2011, which is visible in rows 9-11.

Differences can only be seen in the strength of the described effects. They are continuously weaker in the merged model compared to the isolated models. However,

the variation generally remains in the area of 10% at most. Therefore, it appears that the unified model captures the same effects as the separated ones with only marginal differences of up to 10%. Hence, the biases and potential errors which are due to

„unobserved heterogeneity“ in the varying populations (see section 3.8.5) can be considered minimal.

4.2.3 The evolution of social stratification in old-age poverty 1979-2011

This second block of models assesses the relevance of the social stratification framework for the explanation of old-age poverty over the last three decades between 1979 and 2011. Technically, each of the the previous control models have been extended with the variable education. Education, as has been argued at length in the theoretical chapter, is used in this thesis as a proxy for class-membership.

1979 1994 2011

Basic Educ. Basic Educ. Basic Educ.

AIC 1864.7 1783.4 1815.6 1796.7 1136.9 1134.9

BIC 1902 1831.3 1852.5 1844.2 1171.9 1179.9

Intercept 0.24*** 0.26*** 0.29*** 0.34*** 0.1*** 0.09***

Women 1.62*** 1.4** 1.12 1.03 1.79*** 1.7**

Canton Valais (Ref. Geneva) 4.99*** 4.17*** 3.45*** 3.08*** 2.1*** 2.06***

Age group 70-74 (Ref. 65-69) 1.41* 1.45* 0.81 0.78 1.48 1.42

75-79 1.65** 1.6** 1.31 1.24 1.52 1.4

80-84 2.29*** 2.54*** 1.61** 1.48* 1.59 1.52

85-94 3.59*** 3.37*** 1.22 1.09 1.55 1.42

Low education (Ref. apprenticeship) 1.55* 1.27 1.64*

Higher education 0.4*** 0.68* 1.28

Note: ^*p<0.1; ^**p<0.05; ^***p<0.01

Table 31: Educational model poverty in old-age Source: Own calculations based on COMP dataset Note: Binomial logit model displaying odds-ratios

For each year, the table 31 shows first the control model (which is nested in the following model) in the first column, followed by the social stratification model based on education in the second.

Generally speaking, with the exception of 1994 which shows somewhat different dynamics, it is above all lower education that continues to have an important effect between 1979 and 2011. Accordingly, people with little or no formal education have more or less the same odds-ratio to be poor in 1979 as in 2011. Given that I use education as an indicator for class, this result confirms the social stratification framework: Belonging to the lower classes largely determines a person's financial situation in old-age. This dynamic has remained the same over the last three decades.

More in detail, the results for education itself differ for each wave. In 1979 both levels have an impact documenting inter- as well as intra-class dynamics: Low education does – the odds-ratio of 1.55 suggests that people with little or no education have a roughly 55% increase in the likelihood to be poor – as well as having a high education for which case the odds-ratio of 0.4 signifies that these upper classes are 60% less likely to be poor compared to those with an apprenticeship. In 1994 the impact of education changes slightly and only the protective effect of higher education can be made out. The odds-ratio of 0.68 for the latter suggests a 42% decreased odds for poverty. The effect of education in 2011 is different once again. There, only the negative effect of lower education is visible as a statistically significant factor. The order of this factor is only marginally different from its level in 1979 with an odds-ratio of 1.64 compared to 1.55 in 1979. As was already observed for gender-effects in the control model, the results for the educational model in 1994 seem to be running against the patterns that are found in 1979 and 2011. Again, the reasons for this effect are difficult to determine without further in-depth analyses. On the other hand, between 1979 and 2011 there is one similarity which is that people with „low“ education are significantly more likely to be poor. This is a highly interesting result, especially when put into the context of the findings from the previous section. While inequality may have decreased on an overall level, between 1979 and 2011, in over thirty years of development the dynamics for uneducated people have remained the same.

Furthermore, what is also common to the dynamics in all three waves is that the addition of education does not discard the previously observed effects for sex, age or canton. In 1979 gender-effects are marginally captured as the odds-ratio passes from 1.62 to 1.4.

Cantonal differences are more substantially affected but remains important, as is documented in the decrease of the odds-ratio for Valais (compared to Geneva) from 4.99 to 4.17. Age-effects only change marginally with the most substantial shift taking place for people aged 85-94 with a decrease of the odds-ratio from 3.59 to 3.37. Obviously, in 1979 all of the aforementioned effects are particularly strong, especially those related to age and canton. This might explain why these effects remain existent despite the addition of education. In 1994 the situation of departure (the control model) is quite different to that in 1979, as has been described in the previous section. Still, the effect that the additional variable education has is comparable to the results for the model in 1979.

None of the existing effects in the baseline model are captured but they are slightly less pronounced. Cantonal differences between Valais and Geneva (which serves as reference category) drops from 3.45 to 3.08 while remaining statistically significant. The only existing age-effect for people aged 80-84 passes from 1.61 to 1.48. Finally, in 2011, The same logic can be observed once again. Education decreases the baseline effects but fails to capture them entirely rendering any of them statistically insignificant. This applies to gender (odds-ratio only marginally decreases from 1.79 to 1.7) as well as canton (odds-ratio for Valais remaining practically identical at 2.06 in the educational model compared to 2.1 in the previous one). Age-effects is statistically insignificant in both models.

How can varying effect of education in these three waves be explained? I claim that the main key to understanding these results lies in the fact that each of these waves features

a population of elderly people that are fundamentally different from each other. They differ in age-composition but most importantly educational achievements have completely shifted. This has already been described previously. Basically, the trend showed significant progresses in this area. Obviously, the effect that education therefore has differs quite dramatically from wave to wave. The absence of any positive effect for higher education in 2011 could therefore be explained with this structural shift that lead to a situation where half of the population is situated in this category. It can almost be said that having a form of higher education has become the „standard“ profile. Similarly, the structural shifts that characterized the population in 1994 might be responsible for

a population of elderly people that are fundamentally different from each other. They differ in age-composition but most importantly educational achievements have completely shifted. This has already been described previously. Basically, the trend showed significant progresses in this area. Obviously, the effect that education therefore has differs quite dramatically from wave to wave. The absence of any positive effect for higher education in 2011 could therefore be explained with this structural shift that lead to a situation where half of the population is situated in this category. It can almost be said that having a form of higher education has become the „standard“ profile. Similarly, the structural shifts that characterized the population in 1994 might be responsible for