The U.S. is experiencing considerable population aging. While older adults constituted 17% of the total U.S. population in 2020, the share is predicted to reach 22% by 2040 (Vespa et al. 2018). Population aging poses serious challenges as health declines with increasing age, sometimes to the point where independent living becomes impossible.

Co-impairment

Extensive research has been conducted to understand co-impairment, the joint burden of cognitive impairment and limitations in basic activities of daily living such as walking, dressing, and eating. Co-impairment is considerably more detrimental to the affected individuals as well as to the caregivers’ health and well-being than either burden experienced separately (Riffin et al. 2017; Yu et al. 2017). However, no study has investigated the risk of experiencing co-impairment in one’s lifetime: at what age and for how long co-impairment is experienced (expectancy). 

Over one million older adults experience co-impairment in the U.S. (Riffin et al. 2017) and, with rapid population aging, this number is likely to increase. In recent research (Sharma et al. 2023), we examined the burden of co-impairment among older individuals in the U.S. using data from the Health and Retirement Study, 1998-2016, and multistate models. In addition to estimating the widely used metric of expectancy, we also estimated the lifetime risk and mean age at onset of co-impairment by gender, race/ethnicity/nativity, and educational level, as health disparities are high in the U.S. (Hayward et al. 2014).

Table 1 presents the main results. Overall, 41% of men and 56% of women aged 50 are expected to experience co-impairment in their remaining life expectancy. Blacks, foreign- and U.S.-born people of Latin American origin or descent (Latinx), and the lowest educated experience substantially greater lifetime risk of co-impairment than Whites and the highest educated, regardless of gender.

Table 2 presents the average age at onset of co-impairment for racial/ethnic/nativity subpopulations by educational attainment. Black women and men pay the highest penalty for having the lowest level of education. They have the youngest co-impairment onset (at age 64 years), resulting in dramatic disparities of about two decades compared with the highest educated Whites.

Table 3 shows the co-impairment expectancies for all the racial/ethnic/nativity subgroups by educational attainment. In each racial/ethnic subpopulation, those with the lowest education have the longest co-impairment expectancy. In particular, the lowest educated foreign-born Latinx live the longest in co-impairment (men: 5.0 years, women: 9.7 years).

Educational intervention: a potential way to narrow the disparities

Figure 1 shows the racial/ethnic/nativity disparities in co-impairment expectancy (e.g., between Blacks and Whites) in the counterfactual and empirical scenarios. In the counterfactual scenario, educational inequalities across race/ethnicity/nativity are eliminated. We assign the educational distribution of Whites to Blacks and Latinx and recalculate their expectancies, with differences from the Whites that, not surprisingly, shrink. 

The U.S. has a long road ahead to attain health equity

Being co-impaired poses serious disadvantages for the affected individuals, family members, caregivers, and society. Our study provides novel insights into its burden and offers evidence of dramatic disparities in the older U.S. population. We find that older women, Blacks, U.S.- and foreign-born Latinx, and less than high school educated adults experience a considerably greater burden of co-impairment. For instance, we find a difference of almost nine years in co-impairment expectancy between the highest educated White men and lowest educated foreign-born Latinas aged 50.

These findings may help health planners to identify the vulnerable subgroups of caregivers who may need additional resources or support. Particularly, Blacks and Latinx rely more heavily on family members for care and support than Whites (Rote and Moon 2018). Since Blacks, Latinx, and the lowest educated have an earlier onset of co-impairment and live considerably longer in co-impairment than others, their caregivers are subject to greater strain and are themselves at greater risk of poor health in the future.

Maintaining the health and well-being of the growing older population has become a public health priority in the U.S., a serious challenge for health planners and policymakers, also considering that the shares of the most vulnerable groups, Latinx and Blacks, are increasing (Vespa et al. 2018). The considerable disparities across subpopulations in co-impairment indicate that the U.S. has a long road ahead to attain health equity. Nevertheless, at least 31% of the racial/ethnic/nativity disparities in co-impairment expectancy are attributable to educational inequalities. This promising finding emphasizes the importance of an educational intervention in narrowing the future racial/ethnic/nativity disparities in health.

References

Hayward, M. D., Hummer, R. A., Chiu, C. T., González-González, C., & Wong, R. (2014). Does the Hispanic Paradox in U.S. Adult Mortality Extend to Disability?. Population Research and Policy Review, 33(1), 81–96. https://doi.org/10.1007/s11113-013-9312-7

Riffin, C., Van Ness, P. H., Wolff, J. L., & Fried, T. (2017). Family and Other Unpaid Caregivers and Older Adults with and without Dementia and Disability. Journal of the American Geriatrics Society, 65(8), 1821–1828. https://doi.org/10.1111/jgs.14910

Rote, S. M., & Moon, H. (2018). Racial/Ethnic Differences in Caregiving Frequency: Does Immigrant Status Matter?. The Journals of Gerontology. Series B, Psychological sciences and social sciences, 73(6), 1088–1098. https://doi.org/10.1093/geronb/gbw106

Sharma, S., Hale, J. M., Myrskylä, M., & Kulu, H. (2023). Racial, Ethnic, Nativity, and Educational Disparities in Cognitive Impairment and Activity Limitations in the United States, 1998–2016. Demography, 60(5), 1441-1468.

Vespa, J., Armstrong, D. M., and Medina, L. (2018). Demographic turning points for the United States: Population projections for 2020 to 2060. Washington, DC: US Department of Commerce, Economics and Statistics Administration, US Census Bureau. Retrieved from https://www.census.gov/content/dam/Census/library/publications/2020/demo/p25-1144.pdf

Yu, W. C., Chou, M. Y., Peng, L. N., Lin, Y. T., Liang, C. K., & Chen, L. K. (2017). Synergistic effects of cognitive impairment on ADL disability in all-cause mortality among men aged 80 years and over: results from longitudinal older veterans study. PLoS One, 12(7), e0181741. https://doi.org/10.1371/journal.pone.0181741

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Germany is aging

The German population is among the oldest in the world and is aging rapidly. Projections of the German Federal Statistical Office (Statistisches Bundesamt, 2022) indicate that in 2050 around 26% of the population will be over age 67, which will be the statutory retirement age by 2031. As the number of retirees increases and the workforce shrinks (Fuchs et al. 2018), this rapid population aging will be a challenge for the German social security system, and for the German pension system in particular. 

Extending the length of working life has been proposed as a remedy, as it increases the size of the workforce while reducing the number of retirees. Several institutions, such as the European Commission and the OECD, have been advocating for this shift (e.g., European Commission, 2010; OECD 2015). Meanwhile, many countries, including Germany, have implemented reforms with the major goal of prolonging working life. For instance, the statutory retirement age in Germany is currently being increased from 65 to 67.

Inequalities in the length of working life

Policies aimed at extending working life often follow a one-size-fits-all approach. As these policies are frequently applied with few or no exceptions, or are aimed at workers in stable employment (e.g., Phillipson 2019), they might have negative effects on more economically insecure and vulnerable groups. For instance, increasing the retirement age for almost everyone, as is currently the case in Germany, may increase the duration of working life for highly educated men with high employment rates, while low educated individuals face a high risk of unemployment during the additional years they are forced to stay in the labor market.

A series of studies in several countries have revealed substantial socioeconomic inequalities in the length of working life (e.g., Dudel & Myrskylä 2017). In a recent paper (Dudel et al. 2023), we provide new evidence of such inequalities for older workers in Germany, and focus on several dimensions of inequality: gender, occupation, education, and region (distinguishing between the former socialist eastern Germany and western Germany). Below we provide an overview of some of the key findings, focusing on general trends and inequalities by education.

General trends in Germany 

Our key indicator is working life expectancy (WLE₅₅), defined as the average remaining lifetime spent in work. It is measured in years and, for our study, we computed it between ages 55 and 64, adjusting it for working hours. This means that our results are based not only on whether individuals are or are not gainfully employed, but also on whether they are working full- or part-time. We used data from the German Microcensus for the years 1996 to 2019, and calculated WLE₅₅ for the cohorts born between 1941 and 1955 (Fig. 1). 

For instance, the number of additional years western German men could expect to remain employed at age 55 rose from 5.5 years for those born in 1941 to 7.4 years for those born in 1955. For all groups, WLE₅₅ increased by around 2.0 to 2.5 years over the observed birth cohorts. This shift is likely attributable to several factors, including pension reforms aimed at extending working life and better health among younger cohorts. 

In both western and eastern Germany, men worked for longer than women. However, the gender gap was always smaller in the east than in the west, and it decreased somewhat in eastern Germany, while remaining roughly constant in western Germany. Similarly, eastern German women worked longer than their western German counterparts. Among men, by contrast, the opposite pattern is observed, with western German men working longer. These regional and gender differences are legacies of eastern Germany’s socialist past and the unification process. When the east was socialist, policies aimed at integrating women into the labor market led to higher female employment rates in eastern Germany than in the west. This pattern continued after German unification, even during periods when unemployment rates in eastern Germany were high.

Educational inequalities in Germany

Employment in late adult life depends also on education, for which, in our paper, we considered three levels:
• low (equivalent to ISCED levels 0 to 2), 
• medium (ISCED levels 3 to 4), and 
• high (ISCED levels 5 to 6). 

Results are displayed in Figure 2. When interpreting them, it is important to keep in mind that the lowest educational group is smaller and more selective in eastern than in western Germany, as less than 10% of the population aged 55 to 64 in eastern Germany have low educational attainment.

On average, WLE₅₅ increased for all educational groups, but with considerable heterogeneity in both level and pace. For instance, beyond age 55, low-educated eastern German women worked, on average, only three additional years, while highly educated western German men worked almost nine. Moreover, in eastern Germany, the increase in WLE₅₅ was notably smaller for low-educated individuals than for the other educational groups. For example, the gap between highly educated and low-educated eastern German men increased from roughly three to four years.

Conclusions

In summary, the length of late working life has been steadily increasing in Germany for all the socioeconomic groups we considered. This trend is in line with the aim of many recent policy reforms to increase retirement age. However, our findings show that there are substantial and increasing socioeconomic inequalities in the length of working life after age 55 among older workers. Researchers and policymakers should focus more on these inequalities, as they might be outcomes of systematic social disadvantages among specific groups of older workers. To help us better understand inequalities in WLE₅₅, we are currently extending our calculations of WLE to cover all working ages. If, for instance, low-educated workers start working at earlier ages than highly educated workers, inequalities in total WLE might be smaller than inequalities in just WLE₅₅, i.e. among older workers only.

References

Dudel, C., Myrskylä, M. (2017): Working Life Expectancy at Age 50 in the US and the Impact of the Great Recession. Demography 54: 2101-2123.

Dudel, C., Loichinger, E., Klüsener, S., Sulak, H., Myrskylä, M. (2023): The extension of late working life in Germany: trends, inequalities, and the East-West divide. Demography. https://doi.org/10.1215/00703370-10850040 

European Commission (2010): Annual growth survey: Advancing the EU’s comprehensive response to the crisis.

Fuchs J., Söhnlein D., Weber B., Weber E. (2018). Stochastic forecasting of labor supply and population: an integrated model. Population Research and Policy Review, 37, 33-58.

OECD. (2015): Pensions at a glance 2015. Available at OCED.

Phillipson C. (2019). “Fuller” or “extended” working lives? Critical perspectives on changing transitions from work to retirement. Ageing & Society, 39, 629-650.

Statistisches Bundesamt, 2022: 15. koordinierte Bevölkerungsvorausberechnung – Deutschland. Available online.

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Health conditions tend to deteriorate across immigrant generations, so that first-generation immigrants frequently have better health than second and higher order generations: a process known as “negative health assimilation” (Rumbaut 1994, Hamilton et al. 2011, Mossakowski 2015). Little is known, however, about the health of children in exogamous families, where one parent is native and the other immigrant. 

Data, variables, and method

In a recent paper, we provide evidence on the role of mixed families in shaping the health gaps between different generations of immigrants in Finland (Loi et al. 2021).

The study population includes children aged 0–14 at the end of 2000, covering the 1986–2000 birth cohorts. The data derive from the Finnish population register, the Finnish hospital discharge register, and the Finnish medication purchases register. The outcomes of interest are health care records on somatic conditions, mental disorders and injuries.

• Somatic conditions include all types of conditions, such as, for instance, cancer, severe infections, asthma, epilepsy, and type 1 diabetes.

• Mental disorders include any psychopathology, such as mental and behavioral disorders due to psychoactive substance use, mood/affective disorders, neurotic syndromes, behavioral and emotional disorders.

• Injuries include all types of injuries, such as burns, poisoning, and toxic effects of substances.

The study group includes the following categories of children:

• native (the reference category): born in Finland or abroad to native parents;

• first-generation immigrant: born abroad to parents who were also born abroad;

• second-generation: born in Finland to parents who were born abroad and later migrated to Finland; and

• 2.5-generation: born to one native and one immigrant parent.

We further divide children of the 2.5 generation into those who have an immigrant mother and those who have an immigrant father. 

We pool all the person-years and apply logistic regressions with standard errors clustered by individual. We estimate three models for each of the outcomes. These are:

• model 1, where we study the relationship between immigrant generation and health,

• model 2, where we include family socioeconomic resources, and

• model 3, where we include stressors experienced in early childhood.

The generational health gradient

Adolescents with one immigrant and one native parent have a higher level of health care needs than adolescents whose parents are either both immigrants or both native. This holds especially for mental health problems.

Figure 1 shows the health gaps across immigrant generations on any cause of health care need, compared to natives. In Model 1 we observe a general pattern of increasing risk of receiving care over the immigrant generations. Compared with natives, first-generation immigrants have a lower risk. This result reflects a selection mechanism: adolescents who were born abroad and immigrated with their parents benefit from a positive health selection, which is typical of first-generation immigrants (Abraído-Lanza et al. 1999; Kennedy et al. 2006). For the second generation, the risk of receiving care is similar to that of natives, while 2.5-generation children face a higher risk, especially if the foreign-born parent is the father.

In Model 2, we refine the analysis and test whether family demographic and socioeconomic characteristics contribute to explaining the health differences across immigrant generations. We include mother’s age at childbirth and education, whether information on the father is missing, and household income. Although associated with the outcome, these characteristics do not help to significantly reduce the health gaps across generations. In Model 3, we test whether stressors that occurred earlier in childhood can help explain the pattern. These stressors include whether the adolescent’s parents divorced before the his or her 10th birthday, whether the child lives in a two-parent family, and whether the mother has mental health problems. Living in a two-parent family is associated with a lower risk of receiving care, whereas parental divorce before the child’s 10th birthday is associated with poorer health. Having a mother with a mental health condition is associated with a higher risk of poor health in adolescence. Once again, all these mechanisms help explain part of the difference across immigrant generations, but not all of it. 

Differences by cause of health care needs

Figure 2 shows the association between immigrant generation and health care needs by cause: mental disorders, somatic conditions, and injuries. Health differences compared with natives across generations of adolescents are particularly strong in the case of mental health: children of the first and second generation have a lower risk of receiving treatment for mental disorders, suggesting a persistence of the health advantage.  

Children with one native and one immigrant parent face a higher risk of mental disorders, and this relationship is stronger when the immigrant parent is the father.

Conclusions

Adolescents with one immigrant and one native parent constitute a specific health risk group. As other “explanatory” variables fail to reduce the gap with respect to the reference group, this may be a consequence either of other unobserved familial characteristics (e.g. tensions between partners) or of the broader social context in the receiving country. Adolescents with a native-born and an immigrant parent may face obstacles to integration that cause them to develop mental disorders more easily than others. These adolescents are becoming more and more numerous in immigration societies, and their health is likely to influence their later outcomes (in school, in the labor market and in social life); they probably need higher levels of care and support than they have received until now. 

Funding

This research was financially supported by the Max Planck Society within the framework of the research initiative “The Challenges of Migration, Integration and Exclusion” (WiMi).

References 

Abraído-Lanza, A. F., Dohrenwend, B. P., Ng-Mak, S. S., & Turner, J. B. (1999). The Latino mortality paradox: A test of the “salmon bias” and healthy migrant hypotheses. American Journal of Public Health, 89, 1543–1548.

Hamilton, E. R., Cardoso, J. B., Hummer, R. A., & Padilla, Y. C. (2011). Assimilation and emerging health disparities among new generations of U.S. children. Demographic Research, 25, 783–818. 

Kennedy, S., McDonald, J. T., & Biddle, N. (2006). The healthy immigrant effect and immigrant selection: Evidence from four countries (SEDAP Research Paper No. 164). Hamilton, Ontario, Canada: Social and Economic Dimensions of an Aging Population Research Program, McMaster University.

Loi S., Pitkänen J., Moustgaard H., Myrskylä M., Martikainen P.; Health of Immigrant Children: The Role of Immigrant Generation, Exogamous Family Setting, and Family Material and Social Resources. Demography 2021; 9411326.

Mossakowski, K. N. (2015). Disadvantaged family background and depression among young adults in the United States: The roles of chronic stress and self-esteem. Stress and Health, 31, 52–62.

Rumbaut, R. G. (1994). The crucible within: Ethnic identity, self-esteem, and segmented among children of immigrants. International Migration Review, 28, 748–794.

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Life expectancy has been steadily increasing in high-income countries. At the same time, fertility is around or below the replacement level. Consequently, populations are rapidly aging. In the U.S., the share of the population aged 65 and older is projected to increase from 15% in 2014 to 24% in 2060 (Colby and Ortman 2015).

Population aging casts doubt on the long-term sustainability of social security systems, because as the population grows older, the share of economically active population shrinks. To encourage individuals to stay in the labor market longer and to increase the average length of working life, policies such as increasing retirement age are being implemented. In the U.S., for instance, the Social Security retirement age has been raised from age 65 to 66 for individuals born in 1943 to 1954, and it will increase further for individuals born in 1955 and in later years.

Despite its crucial importance for the sustainability of social security systems, little is known about the length of working life. In a recent paper (Dudel and Myrskylä 2017), we use longitudinal data of the U.S. Health and Retirement Study and multistate models to find out more about the length of working life – which we call working life expectancy – and the factors that shape it, like gender and education. We focus on the effects of the recent financial crisis, as well as on gender and racial differences.

Older Americans and the financial crisis

In Figure 1, trends in the length of working life by gender are shown covering four periods: from 1993 to 1997; from 1998 to 2002; from 2003 to 2007; and from 2008 to 2011. Our results, which are based on a period perspective, show how long people would work if the economic conditions of a certain period, such as 1993-1997, prevailed over their entire working lives.

Based on the economic conditions of the 2008-2011 period, the working life expectancy at age 50 would be around 13 years for American men and 11 years for American women. In relative terms, men would spend more than two-fifths of their remaining lifetime working, and women one-third.

When we compare the results for 2008-2011 with those of earlier periods, we find that working life expectancy during this period is close to that of the 2001 recession (1998-2002); the financial crisis lowered working life expectancy by 1-2 years. It appears that these adverse labor market conditions had a greater effect than incentives to stay in the labor market. This effect was smaller for women than for men, in part because more men than women were working in the industries most affected by the crisis.

While working life expectancy saw ups and down during the period under study, the remaining life expectancy at age 50 steadily increased (Figure 1). This means that the proportion of life expectancy spent working past age 50 declined over time, albeit irregularly.

Strong differentials by race/ethnicity

Our findings also show that there is considerable heterogeneity across sub-populations. These differences can be seen in Figure 2, which shows the length of working life by gender and race/ethnicity.

The working life expectancy of black males at age 50 is consistently between four to five years lower than that of non-Hispanic white males, and this gap has been only slightly affected by the recent crisis. These results, of course, are also influenced by differences between the groups in education, occupation, etc. However, even accounting for these differences – between blacks and whites, for instance – the gap in the length of the working life persists, though at a lower level, suggesting that other variables are operating, e.g., the relative disadvantages of, and discrimination against, blacks in the labor market (Altonji and Blank 1999; Pager 2009).

The 2007-2008 crisis had the strongest negative impact on male Hispanics, who lost more than two years of working life expectancy between 2003-2007 and 2008-2011. In contrast, Hispanic females actually experienced a notable increase in the 2008-2011 period, of around one year. One explanation for this finding is that labor force participation of female Hispanics was relatively low before the crisis, so there was considerable potential for the so-called “added worker” effect: i.e., for inactive individuals who were not in the labor force, such as homemakers, to enter the labor market when their partner became unemployed.

How do Americans compare to the rest of the world?

While there is considerable variation in the length of working life in the U.S. by race/ethnicity, even the subpopulations with low working life expectancy tend to have a longer average working life than people in other high-income countries for which data are available, including in the UK (Butt et al. 2008), Spain (Dudel et al. 2018), and Finland (Leinonen et al. 2016).

For instance, in 2012 in Finland, men aged 50 could expect to work for another nine years (Leinonen et al. 2016), or roughly one-third of their remaining life expectancy. Non-Hispanic white, black, and Hispanic males in the U.S. in the 2008-2011 period all had a remaining working life expectancy of between nine and 13 years, i.e. at least two-fifths of remaining life expectancy. The results for U.S. women by race/ethnicity are all close to the Finnish average of 10 years.

Conclusions

Our results show that there has been no overall expansion of the length of working life, partly due to the recent crisis, which has led to a decline in working life expectancy. The differences in working life expectancy across subpopulations are substantial, with gender, race/ethnicity, and the intersection of these factors playing important roles. This heterogeneity is a cause for concern, as the working life expectancy of some groups, and particularly of blacks, is markedly lower than it is for others. Policies aimed at increasing the length of working life should better address this heterogeneity.

References

Altonji, J. G. and Blank, R. M. (1999). Race and gender in the labor market. In Ashenfelter, O. C. and Card, D., editors, Handbook of Labor Economics, pages 3143–3259. Elsevier.

Butt, Z., Haberman, S., and Verral, R. (2008). Calculating compensation for loss of future earnings: Estimating and using working life expectancy. Journal of the Royal Statistical Society, Series A 171: 763–805.

Colby, S. L. and Ortman, J. M. (2015). Projections of the size and composition of the U.S. population: 2014 to 2060. Washington, D.C.: U.S. Census Bureau.

Dudel, C., Myrskylä, M. (2017): Working Life Expectancy at Age 50 in the US and the Impact of the Great Recession. Demography 54: 2101-2123.

Dudel, C., López Gómez, M. A., Benavides, F. G., Myrskylä, M. (2018): The Length of Working Life in Spain Levels, Recent Trends, and the Impact of the Financial Crisis. European Journal of Population: forthcoming (available online).

Engemann, K. M. and Wall, H. J. (2009). The effects of recessions across demographic groups. Federal Reserve Bank of St. Louis Working Paper 2009-052A.

Leinonen, T., Martikainen, P., and Myrskylä, M. (2016). Life expectancy by labor force status and social class: Recent period and cohort trends and projections for Finland. Journal of Gerontology, Series B: Psychological Sciences and Social Sciences: Advance access.

Pager, D. (2009). Discrimination in a low-wage labor market. A field experiment. American Sociological Review 74:777–799.

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