Tag: Mortality

Gender Gap in Life Expectancy and Its Socio-Economic Implications

Posted on October 1, 2024 | FROGEE Brief, Policy Brief

Silhouetted crowd of people walking in a public square, symbolizing societal impacts of the gender gap in life expectancy.

Today women live longer than men virtually in every country of the world. Although scientists still struggle to fully explain this disparity, the most prominent sources of this gender inequality are biological and behavioral. From an evolutionary point of view, female longevity was more advantageous for offspring survival. This resulted in a higher frequency of non-fatal diseases among women and in a later onset of fatal conditions. The observed high variation in the longevity gap across countries, however, points towards an important role of social and behavioral arguments. These include higher consumption of alcohol, tobacco, and fats among men as well as a generally riskier behavior. The gender gap in life expectancy often reaches 6-12 percent of the average human lifespan and has remained stubbornly stable in many countries. Lower life expectancy among men is an important social concern on its own and has significant consequences for the well-being of their surviving partners and the economy as a whole. It is an important, yet under-discussed type of gender inequality.

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Gender Gap in Life Expectancy and Its Socio-Economic Implications

Today, women on average live longer than men across the globe. Despite the universality of this basic qualitative fact, the gender gap in life expectancy (GGLE) varies a lot across countries (as well as over time) and scientists have only a limited understanding of the causes of this variation (Rochelle et al., 2015). Regardless of the reasons for this discrepancy, it has sizable economic and financial implications. Abnormal male mortality makes a dent in the labour force in nations where GGLE happens to be the highest, while at the same time, large GGLE might contribute to a divergence in male and female discount factors with implications for employment and pension savings. Large discrepancies in life expectancy translate into a higher incidence of widowhood and a longer time in which women live as widows. The gender gap in life expectancy is one of the less frequently discussed dimensions of gender inequality, and while it clearly has negative implications for men, lower male longevity has also substantial negative consequences for women and society as a whole.

Figure A. Gender gap in life expectancy across selected countries

Source: World Bank.

The earliest available reliable data on the relative longevity of men and women shows that the gender gap in life expectancy is not a new phenomenon. In the middle of the 19th century, women in Scandinavian countries outlived men by 3-5 years (Rochelle et al., 2015), and Bavarian nuns enjoyed an additional 1.1 years of life, relative to the monks (Luy, 2003). At the beginning of the 20th century, relative higher female longevity became universal as women started to live longer than men in almost every country (Barford et al., 2006). GGLE appears to be a complex phenomenon with no single factor able to fully explain it. Scientists from various fields such as anthropology, evolutionary biology, genetics, medical science, and economics have made numerous attempts to study the mechanisms behind this gender disparity. Their discoveries typically fall into one of two groups: biological and behavioural. Noteworthy, GGLE seems to be fairly unrelated to the basic economic fundamentals such as GDP per capita which in turn has a strong association with the level of healthcare, overall life expectancy, and human development index (Rochelle et al., 2015). Figure B presents the (lack of) association between GDP per capita and GGLE in a cross-section of countries. The data shows large heterogeneity, especially at low-income levels, and virtually no association from middle-level GDP per capita onwards.

Figure B. Association between gender gap in life expectancy and GDP per capita

Source: World Bank.

Biological Factors

The main intuition behind female superior longevity provided by evolutionary biologists is based on the idea that the offspring’s survival rates disproportionally benefited from the presence of their mothers and grandmothers. The female hormone estrogen is known to lower the risks of cardiovascular disease. Women also have a better immune system which helps them avoid a number of life-threatening diseases, while also making them more likely to suffer from (non-fatal) autoimmune diseases (Schünemann et al., 2017). The basic genetic advantage of females comes from the mere fact of them having two X chromosomes and thus avoiding a number of diseases stemming from Y chromosome defects (Holden, 1987; Austad, 2006; Oksuzyan et al., 2008).

Despite a number of biological factors contributing to female longevity, it is well known that, on average, women have poorer health than men at the same age. This counterintuitive phenomenon is called the morbidity-mortality paradox (Kulminski et al., 2008). Figure C shows the estimated cumulative health deficits for both genders and their average life expectancies in the Canadian population, based on a study by Schünemann et al. (2017). It shows that at any age, women tend to have poorer health yet lower mortality rates than men. This paradox can be explained by two factors: women tend to suffer more from non-fatal diseases, and the onset of fatal diseases occurs later in life for women compared to men.

Figure C. Health deficits and life expectancy for Canadian men and women

Source: Schünemann et al. (2017). Note: Men: solid line; Women: dashed line; Circles: life expectancy at age 20.

Behavioural Factors

Given the large variation in GGLE, biological factors clearly cannot be the only driving force. Worldwide, men are three times more likely to die from road traffic injuries and two times more likely to drown than women (WHO, 2002). According to the World Health Organization (WHO), the average ratio of male-to-female completed suicides among the 183 surveyed countries is 3.78 (WHO, 2024). Schünemann et al. (2017) find that differences in behaviour can explain 3.2 out of 4.6 years of GGLE observed on average in developed countries. Statistics clearly show that men engage in unhealthy behaviours such as smoking and alcohol consumption much more often than women (Rochelle et al., 2015). Men are also more likely to be obese. Alcohol consumption plays a special role among behavioural contributors to the GGLE. A study based on data from 30 European countries found that alcohol consumption accounted for 10 to 20 percent of GGLE in Western Europe and for 20 to 30 percent in Eastern Europe (McCartney et al., 2011). Another group of authors has focused their research on Central and Eastern European countries between 1965 and 2012. They have estimated that throughout that time period between 15 and 19 percent of the GGLE can be attributed to alcohol (Trias-Llimós & Janssen, 2018). On the other hand, tobacco is estimated to be responsible for up to 30 percent and 20 percent of the gender gap in mortality in Eastern Europe and the rest of Europe, respectively (McCartney et al., 2011).

Another factor potentially decreasing male longevity is participation in risk-taking activities stemming from extreme events such as wars and military activities, high-risk jobs, and seemingly unnecessary health-hazardous actions. However, to the best of our knowledge, there is no rigorous research quantifying the contribution of these factors to the reduced male longevity. It is also plausible that the relative importance of these factors varies substantially by country and historical period.

Gender inequality and social gender norms also negatively affect men. Although women suffer from depression more frequently than men (Albert, 2015; Kuehner, 2017), it is men who commit most suicides. One study finds that men with lower masculinity (measured with a range of questions on social norms and gender role orientation) are less likely to suffer from coronary heart disease (Hunt et al., 2007). Finally, evidence shows that men are less likely to utilize medical care when facing the same health conditions as women and that they are also less likely to conduct regular medical check-ups (Trias-Llimós & Janssen, 2018).

It is possible to hypothesize that behavioural factors of premature male deaths may also be seen as biological ones with, for example, risky behaviour being somehow coded in male DNA. But this hypothesis may have only very limited truth to it as we observe how male longevity and GGLE vary between countries and even within countries over relatively short periods of time.

Economic Implications

Premature male mortality decreases the total labour force of one of the world leaders in GGLE, Belarus, by at least 4 percent (author’s own calculation, based on WHO data). Similar numbers for other developed nations range from 1 to 3 percent. Premature mortality, on average, costs European countries 1.2 percent of GDP, with 70 percent of these losses attributable to male excess mortality. If male premature mortality could be avoided, Sweden would gain 0.3 percent of GDP, Poland would gain 1.7 percent of GDP, while Latvia and Lithuania – countries with the highest GGLE in the EU – would each gain around 2.3 percent of GDP (Łyszczarz, 2019). Large disparities in the expected longevity also mean that women should anticipate longer post-retirement lives. Combined with the gender employment and pay gap, this implies that either women need to devote a larger percentage of their earnings to retirement savings or retirement systems need to include provisions to secure material support for surviving spouses. Since in most of the retirement systems the value of pensions is calculated using average, not gender-specific, life expectancy, the ensuing differences may result in a perception that men are not getting their fair share from accumulated contributions.

Policy Recommendations

To successfully limit the extent of the GGLE and to effectively address its consequences, more research is needed in the area of differential gender mortality. In the medical research dimension, it is noteworthy that, historically, women have been under-represented in recruitment into clinical trials, reporting of gender-disaggregated data in research has been low, and a larger amount of research funding has been allocated to “male diseases” (Holdcroft, 2007; Mirin, 2021). At the same time, the missing link research-wise is the peculiar discrepancy between a likely better understanding of male body and health and the poorer utilization of this knowledge.

The existing literature suggests several possible interventions that may substantially reduce premature male mortality. Among the top preventable behavioural factors are smoking and excessive alcohol consumption. Many studies point out substantial country differences in the contribution of these two factors to GGLE (McCartney, 2011), which might indicate that gender differences in alcohol and nicotine abuse may be amplified by the prevailing gender roles in a given society (Wilsnack et al., 2000). Since the other key factors impairing male longevity are stress and risky behaviour, it seems that a broader societal change away from the traditional gender norms is needed. As country differences in GGLE suggest, higher male mortality is mainly driven by behaviours often influenced by societies and policies. This gives hope that higher male mortality could be reduced as we move towards greater gender equality, and give more support to risk-reducing policies.

While the fundamental biological differences contributing to the GGLE cannot be changed, special attention should be devoted to improving healthcare utilization among men and to increasingly including the effects of sex and gender in medical research on health and disease (Holdcoft, 2007; Mirin, 2021; McGregor et al., 2016, Regitz-Zagrosek & Seeland, 2012).

References

About FROGEE Policy Briefs

FROGEE Policy Briefs is a special series aimed at providing overviews and the popularization of economic research related to gender equality issues. Debates around policies related to gender equality are often highly politicized. We believe that using arguments derived from the most up to date research-based knowledge would help us build a more fruitful discussion of policy proposals and in the end achieve better outcomes.

The aim of the briefs is to improve the understanding of research-based arguments and their implications, by covering the key theories and the most important findings in areas of special interest to the current debate. The briefs start with short general overviews of a given theme, which are followed by a presentation of country-specific contexts, specific policy challenges, implemented reforms and a discussion of other policy options.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

What Can We Learn from Regional Patterns of Mortality During the Covid-19 Pandemic?

Posted on February 20, 2023 | Policy Brief

Doctor outside COVID-19 isolation center representing covid-19 pandemic mortality

Given the nature of the spread of the virus, strong regional patterns in fatal consequences of the Covid-19 pandemic are to be expected. This brief summarizes a detailed examination of the spatial correlation of deaths in the first year of the pandemic in two neighboring countries – Germany and Poland. Among high income European countries, these two seem particularly different in terms of the death toll associated with the pandemic, with many more excess deaths recorded in Poland as compared to Germany. Detailed spatial analysis of deaths at the regional level shows a consistent spatial pattern in deaths officially registered as related to Covid-19 in both countries. For excess deaths, however, we find a strong spatial correlation in Germany but little such evidence in Poland. These findings point towards important failures or neglect in the areas of healthcare and public health in Poland, which resulted in a massive loss of life.

Introduction

While almost all European countries currently refrain from imposing any Covid-19 related restrictions, the pandemic still takes a huge economic, health and social toll across societies worldwide. The regional variation of incidence and different consequences of the pandemic, observed over time, should be examined to draw lessons for ongoing challenges and future pandemics. This brief outlines a recently published paper by Myck et al. (2023) in which we take a closer look at two neighboring countries, Germany and Poland.  Within the pool of high-income countries, these are particularly different in terms of the death toll associated with the Covid-19 pandemic. In 2020 in Poland, the excess deaths rate (with reference to the 2016-2019 average) was as high as 194 per 100,000 inhabitants, over 3 times higher than the 62 deaths per 100,000 inhabitants in Germany (EUROSTAT, 2022a, 2022b). While, in relative terms, the death toll officially registered as resulting from Covid-19 infections in 2020 was also higher in Poland than in Germany, the difference was considerably lower (about 75 vs 61 deaths per 100,000 inhabitants, respectively) (Ministry of Health, 2022; RKI, 2021). Population-wise Germany is 2.2 times larger than Poland and, before the pandemic struck, the countries differed also in other relevant dimensions related to the socio-demographic structure of the population, healthcare and public health. The nature of Covid-19 and the high degree of regional variation between and within the two countries along some crucial dimensions thus make Germany and Poland an interesting international case for comparison of the pandemic’s consequences. We show that the differences in the spatial pattern of deaths between Germany and Poland may provide valuable insight to the reasons behind the dramatic differences in the aggregate numbers of fatalities (Myck et al., 2023).

Regional Variation in Pandemic-Related Mortality and Pre-Pandemic Characteristics

We examine three measures of mortality in the first year of the Covid-19 pandemic in 401 German and 380 Polish counties (Kreise and powiats, respectively): the officially recorded Covid-19 deaths, the total numbers of excessive deaths (measured as the difference in the number of total deaths in year 2020 and the 2015-2019 average) and the difference between the two measures. Figure 1 shows the regional distribution of these three measures calculated per 1000 county inhabitants. All examined indicators were generally much higher in Poland as compared to Germany. In Poland, deaths officially registered as caused by Covid-19 were concentrated in the central and south-eastern regions (łódzkie and lubelskie voivodeships), while in Germany they were concentrated in the east and the south (Sachsen and Bayern). Excess mortality was predominantly high in German regions with high numbers of Covid-19 deaths, but also in nearby regions. As a result, these same regions also show greater differences between excessive deaths and Covid-19 deaths. On the contrary, high excessive deaths can be noted throughout Poland, including the regions where the number of Covid-19 deaths were lower. In the case of Poland, spatial clusters are much less obvious for both excess deaths and the difference between excess and Covid-19 deaths. To further explore the degree of regional variation between and within countries with respect to the mortality outcomes, we link them to regional characteristics such as population, healthcare and economic conditions, which might be relevant for both the spread of the virus and the risk of death from Covid-19. In Figure 2 we illustrate the scope of regional disparities with examples of (a) age structure of the population, (b) the pattern of economic activity and (c) distribution of healthcare facilities in years prior to the pandemic.

Figure 1. Regional variation of death incidence in 2020: Germany and Poland.

Note: The panels share a common legend based on the quintile distribution of Covid-19 deaths, with two additional categories added at the top and bottom of the scale. County borders in white, regional borders in yellow and country border in grey. Source: Myck et al. (2023).

Figure 2. Pre-pandemic regional variation of socio-economic indicators: Germany and Poland.

Note: Two top and bottom categories in the legend cover 10% of observations each, the rest of categories cover 20% of observations each. County borders in white, regional borders in yellow and country border in grey. Source: Myck et al. (2023).

Shares of older population groups (aged 85+ years) are clearly substantially higher in Germany compared to Poland, and within both countries these shares are higher in the eastern regions. On the other hand, the proportion of labor force employed in agriculture is significantly higher in Poland and heavily concentrated in the eastern parts of the country. In Germany, this share is much lower and more evenly spread. This indicator illustrates that socio-economic conditions in 2020 were still substantially different between the two countries. The share of employed in agriculture is also important from the point of view of pandemic risks – it reflects lower levels of education, and specific working conditions that make it challenging to work remotely yet entail less personal contact and more outdoor labor. The distribution of hospital beds reflects the urban/rural divide in both countries. It is also a good proxy for detailing the differences in the overall quality of healthcare between the two countries, i.e. displaying significantly better healthcare infrastructure in German counties.

Uncovering the Spatial Nature of Excess Deaths in Germany and Poland

While spatial similarities among regions are present along many dimensions, they are particularly important when discussing such phenomena as pandemics, when infection spread affects nearby regions more than distant ones. With regard to the spatial nature of excess deaths in the first year of the pandemic, a natural hypothesis is thus that the pattern of these deaths should reflect the nature of contagion. This applies primarily to excess deaths directly caused by the pandemic (deaths resulting from infection with the virus). At the same time, some indirect consequences of Covid-19 such as limitations on the availability of hospital places and medical procedures, or lack of medical personnel to treat patients not affected by Covid-19, are also expected to be greater in regions with a higher incidence of Covid-19. On the other hand, spatial patterns are much less obvious in cases where excess deaths would result, for example, from externally or self-imposed restrictions such as access to primary health care, reduced contact with other people, diminished family support, or mental health problems due to isolation. While these should also be regarded as indirect consequences of the pandemic, as they would arguably not have realized in its absence, these consequences do not necessarily relate to the actual spread of the virus. Our in-depth analysis of the spatial distribution of the three examined mortality-related measures, therefore, allows us to make a crucial distinction in possible explanations for the dramatic differences in the observed death toll in the first year of the pandemic in Germany and Poland. We explore the degree of spatial correlation in the three mortality outcomes using multivariate spatial autoregressive models, controlling for a number of local characteristics (for more details see Myck et al., 2023).

We find that in Germany, all mortality measures show very strong spatial correlation. In Poland, we also confirm statistically significant spatial correlation of Covid-19 deaths. However, we find no evidence for such spatial pattern either in the total excess deaths or in the difference between excess deaths and Covid-19 deaths. In other words, in Poland, the deaths over and above the official Covid-19 deaths do not reflect the features to be expected during a pandemic. As the results of the spatial analysis show, these findings cannot be explained by the regional pre-pandemic characteristics but require alternative explanations. This suggests that a high proportion of deaths results from a combination of policy deficits and individual reactions to the pandemic in Poland. Firstly, during the pandemic, individuals in Poland may have principally withdrawn from various healthcare interventions as a result of fear of infection. Secondly, those with serious health conditions unrelated to the pandemic may have received insufficient care during the Covid-19 crisis in Poland, and, as a consequence, died prematurely. This may have been a result of lower effectiveness of online medical consultations, excessive limitations to hospital admissions – unjustified from the point of view of the spread of the virus, and/or worsened access to healthcare services as a result of country-wide lockdowns and mobility limitations. The deaths could also have resulted from reduced direct contact with other people (including family and friends as well as care personnel) and mental health deterioration as a consequence of (self)isolation. Our analysis does not allow us to differentiate between these hypotheses, but the aggregate excess deaths data suggests that a combination of the above reasons came at a massive cost in terms of loss of lives. The consequences reflect a very particular type of healthcare policy failure or policy neglect in the first year of the pandemic in Poland.

Our study also shows that a detailed analysis of country differences concerning the consequences of the ongoing pandemic can serve as a platform to set and test hypotheses about the effectiveness of policy responses to better tackle future global health crises.

Acknowledgement

The authors wish to acknowledge the support of the German Research Foundation (DFG, project no: BR 38.6816-1) and the Polish National Science Centre (NCN, project no: 2018/31/G/HS4/01511) in the joint international Beethoven Classic 3 funding scheme – project AGE-WELL. For the full list of acknowledgements see Myck et al. (2023).

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Old-Age Poverty and Health – How Much Does Income Matter?

Posted on September 30, 2013 | Policy Brief

20130930 Old-Age Poverty and Health Image 01

The question concerning the material situation of older people and its consequences for their wellbeing seems to be more important than ever. This is especially true given rapid demographic changes in the Western World and economic pressures on governments to reduce public spending.  We use data from the Survey of Health, Ageing and Retirement in Europe (SHARE) to examine different aspects of old-age poverty and its possible effects on deterioration in health. The data contains information on representative samples from 12 European countries including the Czech Republic and Poland. We use the longitudinal dimension of the data to go beyond cross sectional associations and analyze transitions in health status controlling for health in the initial period and material conditions. We find that poverty matters for health outcomes in later life. Wealth-defined and subjective poverty correlates much more strongly with health outcomes than income-defined measure. Importantly subjective poverty significantly increases mortality by 58.3% for those aged 50–64 (for details see Adena and Myck, 2013a and 2013b). 

Measuring Poverty

When measuring poverty, the standard approach is to define the poverty threshold at 60% of median equalized income. This standardized measure offers some advantages, such as simplicity and comparability with already existing studies. However, there are valid arguments against its use when analyzing old-age poverty. The permanent-income theory provides arguments against current income as a major determinant of quality of life of older people. Moreover, poverty defined with respect to current income while taking account of household size through equalization, ignores other important aspects of living costs such as disability or health expenditures. Additionally, most analysis using income-poverty measures ignore such aspects as housing ownership and housing costs.

Our analysis examines different aspects of poor material conditions of the elderly. The first poverty definition refers to respondents’ wealth as an alternative to income-defined poverty. Poor households, defined with reference to wealth (“wealth poverty” – WEALTH), are those that belong to the bottom third of the wealth distribution of the sample in each country. For this purpose, household wealth is the sum of household real assets (net of any debts) and household gross financial assets. Secondly, we compare the above poverty measures to a subjective measure of material well-being. This measure is based on subjective declarations by respondents, in which case (“subjective poverty” – SUB) individuals are identified as poor on the basis of a question of how easily they can make ends meet. If the answer is “with some” or “with great” difficulty, individuals in the household are classified as “poor”.

One reflection of potential problems with the standard income poverty measure becomes visible when it is compared with the subjective measure. The graph below shows the differences in country rankings when using one or the other poverty measure.  The country with the greatest disproportion is Czech Republic. While being ranked as second according to the income measure, it is ninth according to the subjective measure.

Figure 1. Country Ranks in Old-Age Poverty According to an Income versus a Subjective Measure

Slide1

Source: Authors’ calculations using SHARE data (Wave 2, release 2.5.0).

Even more striking is the fact that the differences between ranks are not because of over or under classification of individuals as poor, but rather because of misclassification. Figure 2 shows that there is little overlap between different poverty measures. The share of individuals classified as poor according to all three measures is only 7.95%, whereas it is 60% according to at least one of the measures.

Figure 2. Poverty Measure Overlap

Slide1

Notes: Data weighted using Wave 2 sample weights. Source: Authors’ calculations using SHARE data (Wave 2, release 2.5.0).
 

Measuring Well-Being

We examine three binary outcomes measuring the well-being of the respondents – two reflecting physical health, and one measuring individuals’ subjective health. The two measures of physical health are generated with reference to the list of twelve symptoms of bad health and the list of twenty-three limitations in activities of daily living (ADLs). In both cases, we define someone to be in a bad state if they have three or more symptoms or limitations. The two definitions are labelled as: “3+SMT” (three or more symptoms) and “3+ADL” (three or more limitations in ADLs). Subjective health “SUBJ” is defined to be bad if the subjective health assessment is “fair” or “poor”. Finally, we also analyze mortality as an “objective” health outcome.

Poverty and Transitions in Well-Being and Health

There is some established evidence in the literature that poverty negatively affects health and other outcomes at different stages of life.[1] At the same time, there is little evidence on how the choice of the poverty measure might result in under- or over-estimation of the effects of poverty. We address this question by examining different poverty measures as potential determinants of transitions from good to bad states of health.

The results confirm that living in poverty increases an individual’s probability of deterioration of health. In a compact form, Figure 3 presents our results from 12 separate regressions (4 outcomes, three poverty measures). Here we report the odds ratios related to the respective estimated poverty dummies. Individuals classified as poor according to the income measure are 37.7% more likely to report bad subjective health in a later wave of the survey than their richer counterparts; they are 4.5% more likely to suffer from 3 or more symptoms; 18.7% more likely to suffer from 3 or more limitations; and 5% more likely to die. The last three effects, however, are not statistically significant.

In contrast, the effects of wealth-defined poverty and subjectively assessed poverty are 2-8 times stronger than those of income poverty, and they are also significant for all outcomes but death. Overall, wealth-defined poverty and subjective assessment of material well-being strongly correlate with deterioration in physical health (exactly the same goes for improvements in health, see Adena and Myck 2013b).

Figure 3. Poverty and Transitions from Good to Bad States Overlap

Slide2

Notes: Data weighted using Wave 2 sample weights. Source: Authors’ calculations using SHARE data (Wave 2, release 2.5.0, Wave 3, release 1, Wave 4, release 1).
 

Poverty and Mortality in the Age Group 50-64

Our analysis reveals differences between age groups and confirms the decreasing importance of income (and thus income defined poverty) with age. As compared to the average effects presented in Figure 3, for the younger age group 50–64 income poverty proves more important as a determinant of bad outcomes, with transition probabilities between 20 and 40% for all outcomes (see Figure 4). The magnitudes are closer to those of other poverty measures, but still lower in all cases. Importantly, we find that wealth-defined and subjective poverty is an important determinant of death in the age group 50–64.

Figure 4. Poverty and Transitions from Good to Bad States 50-64 Slide3
Notes: Data weighted using Wave 2 sample weights. Source: Authors’ calculations using SHARE data (Wave 2, release 2.5.0, Wave 3, release 1, Wave 4, release 1).
 

Conclusions

The role of financial conditions for the development of health of older people significantly depends on the measure of material well-being used. In this policy brief, we defined poverty with respect to income, subjective assessment, and relative wealth. Of these three, wealth-defined poverty and subjective assessment of material well-being strongly and consistently correlate with deterioration and improvements in physical and subjective health. We found little evidence that relative income poverty plays a role in changes in physical health of older people. This suggests that the traditional income measure of household material situation may not be appropriate as a proxy for the welfare of older populations, and may perform badly as a measure of improvements in their quality of life or as a target for old-age policies. To be valid, such measures should cover broader aspects of financial well-being than income poverty. They could incorporate aspects of wealth and the subjective assessment of material situations as well as indicators more specifically focused on the consumption baskets of the older population.

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References

  • Adena, Maja and Michal Myck (2013a): “Poverty and transitions in key areas of quality of life”, in: Börsch-Supan, Axel,  Brandt, Martina , Litwin, Howard and Guglielmo Weber (eds.) “Active Ageing and Solidarity between Generations in Europe – First Results from SHARE after the Economic Crisis.”
  • Adena, Maja and Michal Myck (2013b) Poverty and Transitions in Health, IZA Discussion Paper 7532, IZA-Bonn.

 

[1] For a literature review, see our publications.