Excess mortality in the United States (US) is significantly higher than in other countries at similar levels of socioeconomic development (National Research Council, 2013). The US is also disadvantaged in terms of morbidity and, more generally, the prevalence of risk factors for major diseases (Chowdhury, 2023). In a recent article, we measured the contribution of multi-morbidity at death to the mortality gap between the US and three other high-income countries with comparable data, namely France, Italy, and Spain, using multiple cause of death data for 2017 (Barbieri et al., 2025). 

An original approach to identify multi-morbidity contribution to death

Unlike most previous studies on multi-morbidity based on survey data, we used information from routinely collected vital statistics data on mortality. This approach has the advantage of providing widespread, systematic, cost-efficient, and highly comparable information on multi-morbidity at death, given that all high-income countries now use unified definitions and the same data collection instruments, i.e. the World Health Organization recommended death certificate with all causes of death coded to the International Classification of Diseases.

Causes of death are listed on two different sections of the certificate. In Part 1, certifiers report all the medical causes involved in the sequence of health conditions that led directly to death. In Part 2, they record all other causes that were not part of the sequence(s) listed in Part 1, but that nonetheless contributed to death by influencing the course of the morbid process, for instance by eroding an individual’s overall health status. These conditions are called associated causes of death. Our study analyzed these multiple causes of death, which include all the causes and conditions reported on both sections of the death certificate, to classify deaths according to the type of morbid process.

Using all death certificates in the year 2017 (the most recent for which we could access the data in all four study countries at the time of the analysis), we grouped deaths into three categories, depending on whether the morbid process could be considered as 1) simple, 2) multi-morbid, or 3) ill-defined. Following Grippo et al. (2024), we identified all the originating causes, i.e. the conditions that initiated a chain of morbid events. Our definitions are as follows:

• Simple: processes that characterize deaths with a single originating cause and no associated cause listed on Part 2 of the death certificate;

• Multi-morbid: processes that characterize deaths with more than one originating cause or with associated causes; and

• Ill-defined: processes that characterize deaths with no well-defined causes, meaning that the death certificate did not list any known cause of death or only symptoms, signs or poorly informative conditions (e.g. “respiratory arrest”) (Figure 1).

We calculated mortality rates by sex and age for each of the three types of morbid processes in the four study countries and assessed the contribution of multi-morbidity to excess mortality by sex and age group and to the gap in life expectancy at birth between the US and the three comparison countries.

The major contribution of multi-morbidity to US excess mortality

Our analysis demonstrates that multi-morbid processes contribute disproportionately to the large mortality and life expectancy gaps between the US on the one hand, and France, Italy and Spain on the other, for both men and women. In 2017, life expectancy at birth was 78.7 years for both sexes combined in the US, much lower than in the other three countries (82.4 years in France, 82.7 years in Italy, and 83.0 years in Spain). Multi-morbid processes contributed 51% of the gap in life expectancy at birth with Italy, 73% of the gap with Spain, and 75% of the gap with France (Figure 2). This is all the more striking because multi-morbid processes in the US contribute to a smaller share of the age-standardized death rate (46%) than simple processes (50%; the remaining 4% being attributed to ill-defined processes). This result suggests that the US disadvantage arises disproportionately from the excess mortality linked to complex, multi-condition health profiles.

As regards overall mortality, the US disadvantage in multi-morbidity is particularly concentrated among working-age adults (20 to 64-year-olds). In this age group, most of the loss is attributable to multi-morbid processes. Among men, this type of process accounted for 63% of the difference with Italy, 77% with Spain, and 88% with France; among women, the corresponding figures were 59%, 66%, and 71%.

Between one fifth and one quarter of all deaths would have been averted in the US if the country had experienced the mortality rates of Italy (20%), France (23%), or Spain (23%), with a very large proportion attributable to multi-morbid processes. Excess US deaths from multi-morbid processes represented 17–19% of the total number of deaths in the US, depending on the comparison country, versus 4–11% for those resulting from simple morbid processes. The average number of years of life lost by Americans whose deaths would have been averted if they had experienced the mortality risks of their more advantaged peers was found to be 23 years with respect to the mortality rates of France (27 years for men and 20 years for women), 27 years with respect to Spain (31 years for men and 24 years for women), and 30 years with respect to Italy (32 years for men and 28 years for women). These large values reflect the fact that excess US deaths tend to be concentrated at relatively young adult ages, when the potential for additional years of life is high.

Implications

Multi-morbidity creates multiple challenges for medical infrastructures which, in most countries, are still designed to manage a single disease with a highly segmented system of care. Multi-morbidity is associated with increased mortality risks, disability, reduced functional status, poor quality of life, complicated treatment options, and adverse drug events due to incompatibilities, calling for coordinated and comprehensive health care (Fortin et al., 2007). The disproportionate burden of multi-morbidity at death in the US certainly explains in part why the US spends, on average, 60% more per inhabitant on health care than other high-income countries. Because each disease comes with its own risk factors and determinants, the high prevalence of multi-morbidity in the US suggests that no single factor is likely to explain the gap with peers. It is thus unlikely that a unique intervention, program, or policy will be sufficient to close the life expectancy gap between the US and other countries. In the meantime, the disproportionate burden of multi-morbidity in the US will continue to complicate efforts to sustain progress in longevity and to improve health and quality of life of the general population. 

References

Barbieri, M., Désesquelles, A., Egidi, V., Frova, L., Grippo, F., Meslé, F., Pappagallo, M., & Trias-Llimós, S. (2025). Multi-Morbidity at Death and the US Disadvantage in Mortality: M. Barbieri et al. European Journal of Population, 41(1), 28.

Chowdhury, S. R., Das, D. C., Sunna, T. C., Beyene, J., & Hossain, A. (2023). Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis. EClinicalMedicine, 57.

Fortin, M., Soubhi, H., Hudon, C., Bayliss, E. A., & Van den Akker, M. (2007). Multimorbidity’s many challenges. Bmj, 334(7602), 1016–1017.

Grippo, F., Frova, L., Pappagallo, M., Barbieri, M., Trias-Llimós, S., Egidi, V., Meslé, F., & Désesquelles, A. (2024). Beyond the underlying cause of death: an algorithm to study multi-morbidity at death. Population Health Metrics, 22(1), 36.

National Research Council, & Committee on Population. (2013). US health in international perspective: Shorter lives, poorer health.

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Over the past several decades, the United States has witnessed an extraordinary widening of policy divides across its states (Grumbach, 2019). And because where you live is increasingly tied to how you live, as state policy contexts within the country pull father apart, so too do the patterns of American longevity (Montez et al, 2020). While the former geographic divides of the 20th century, especially those separating Black and White life chances, have not disappeared, they have begun to reorganize themselves along a new axis, that of states’ policy orientations.

Mapping 60 years of policy polarization

In a recent study (Luck, 2025) I show how polarized state policy regimes translate into longevity inequalities. By pairing 60 years of historical policy orientation data with three decades of vital statistics records, my research tracks longevity among non-Hispanic Black and White men and women across clusters of states that share similar long-term trajectories of policy liberalism. 

Figure 1 illustrates this changing policy landscape within the United States. Policy liberalism increases progressively from Group 6, representing the most conservative policy environments, to Group 1, the most liberal, but over the 60 years of study, these divides widen considerably. The spatial pattern of these shifts is also recognizable: the most conservative-shifting clusters lie mostly in the South, the most liberal in the West and Northeast, and more moderate contexts across parts of the Midwest. Importantly, because Black and White populations are unevenly distributed across these regions, they are also unevenly distributed across policy regimes, a key demographic underpinning of today’s policy-linked health divides.

Varied growth in the liberal longevity advantage

Figure 2 shows how Black and White life expectancy has changed between 1990 and 2019 across these state policy clusters. Across all race-sex groups, more liberal states achieved far larger gains in life expectancy between 1990 and 2019 than their conservative counterparts. In the most liberal states, life expectancy rose by 3.4 years for White women and an extraordinary 10.7 years for Black men. Meanwhile, the most conservative states registered modest improvements at best, and for White women, there was essentially no progress at all.

Yet the most striking finding is not just the widening gap, but the reordering of long-standing racial patterns when looking across policy environments. By 2019, Black residents in liberal states had life expectancies equal to or exceeding those of White residents in certain conservative states. Black women in these liberal states outlived White women in the most conservative states by more than a year, a dramatic break from patterns that defined the late twentieth century.

Among White men and women, the relationship between policy and longevity resembles a smooth gradient: each step toward more liberal policy regimes corresponds with higher life expectancy. But for Black populations, patterns are more complex. Life expectancy advantage for Black men, in particular, is concentrated in the most liberal policy cluster. Notably, the lowest life expectancies for Black residents are not in the most conservative Southern states but in certain more moderate policy Midwestern groups, states identified to have particularly troubling trends for Black mortality in recent decades (Elo et al, 2025). 

What’s driving the divides?

My study also takes a deeper look at which age groups and causes of death contributed to the growing divide (Figure 3). For Black men, the liberal advantage is rooted in improvements below age 50. Declines in HIV/AIDS and homicide mortality were especially powerful, adding about 2.5 extra years of life expectancy in liberal compared with conservative states. Black women also saw meaningful gains at younger ages, though less pronounced. For White residents, the story shifts to midlife and older adulthood. Improvements between ages 50 and 74, driven by reductions in circulatory disease, cancer, and respiratory illness, account for most of the growing liberal advantage among both White men and women. Among women of both races, gains at ages 75+ and reductions in deaths linked to mental and nervous system disorders, including dementias, also played a meaningful role.

One countervailing pattern cuts across every population group: drug-related deaths narrowed the liberal advantage. Across every race-sex group, drug mortality contributed negatively to the growth of the liberal advantage, meaning the gaps would have been even larger were it not for the opioid epidemic.

Looking forward

Taken together, these findings highlight a profound shift in the U.S. population health landscape. State policy environments have become a powerful upstream determinant of mortality and crucially, not all populations are affected equally (Luck, 2025a; Luck, 2025b). Findings show that the greatest gains in liberal states accrue to Black populations, who have historically faced the steepest mortality risks, but also reveal a troubling stagnation of White life expectancy, particularly among women, in conservative states.

These divides are unlikely to narrow. As states continue to drift apart along partisan and policy lines, the health consequences of these divergent contexts will grow even sharper, especially if conservative states continue to lag in areas such as chronic disease prevention, social welfare provision, health care access, and violence and injury prevention.

In a polarized era, the ideological path of a state’s policy context is increasingly intertwined with the length and quality of the lives of its residents, reshaping both the racial and geographic contours of longevity in America.

References 

Elo, I.T., Luck, A.N., & Tuder, S. (2025). Pre‐COVID‐19 Geographic Inequalities in Non‐Hispanic Black US Life Expectancy, 1990–2019. Population and Development Review, 51(3), 1075-1113.

Grumbach, J.M. (2018). From Backwaters to Major Policymakers: Policy Polarization in the States, 1970–2014. Perspectives on Politics, 16(2), 416–435. https://doi.org/10.1017/S153759271700425X

Luck, A.N. (2025a) Variation in Black and White Life Expectancy Across State Policy Groups, 1990–2019: A Research Note. Demography , 62(5):1483–1496. https://doi.org/10.1215/00703370-12260835

Luck, A.N. (2025b) The political landscape of death: state policy polarization and mortality among Black and White Americans, Social Forces, 2025; soaf177, https://doi.org/10.1093/sf/soaf177

Montez, J.K., Beckfield, J., Cooney, J.K., Grumbach, J.M., Hayward, M.D., Koytak, H.Z., Woolf, S.H., & Zajacova, A. (2020). US State Policies, Politics, and Life Expectancy. The Milbank Quarterly, 98(3), 668–699. https://doi.org/10.1111/1468-0009.12469

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Global trends toward population aging have placed demography at the center of policy discussions. Planners and policy makers need information about how to protect the health of older people to preserve productivity and limit costs. However, demography’s current toolkit is focused on completed lifespans or healthspans (Balachandran, 2020). These metrics, including estimates of life expectancy, healthy life expectancy, disability adjusted life years, and so on, are effective in summarizing differences between populations accumulated across the full life courses of the individuals who contribute data. However, they do not distinguish health deficits established early in life from the ongoing changes in people’s bodies that are the essence of aging. 

The Pace of Aging

For new policies and programs to mitigate societal burden related to population aging (Beard et al., 2016), they must slow or delay declines in health and function among older people. To provide optimally sensitive metrics for population surveillance and intervention evaluation, measurements are needed that can distinguish ongoing aging-related changes in organs, tissues, and capacities from differences in health that are legacies of early life. 

We developed an approach, called Pace of Aging, to measure the rate of aging-related decline in the biological integrity of adult humans using longitudinal data tracking the health and function of multiple organ systems in the body (Belsky et al., 2015). In a recent study, we adapted our method to fit the data and populations included in two national-level studies of aging: the US Health and Retirement Study (HRS) and the English Longitudinal Study of Aging (ELSA) (Balachandran et al 2025). The measurements we derived are now publicly available to users of these datasets and the method can be applied to studies around the world as they begin to accumulate sufficient longitudinal data.  

Measuring Pace of Aging for demographic analysis

Our original Pace of Aging method was developed within the New-Zealand-based Dunedin Longitudinal Study, a single-year birth cohort followed across the transition from young adulthood to midlife and including in-depth clinical exams at each wave of measurement (Belsky et al., 2015). We adapted this method to accommodate sparser data from a mixed-age sample of older people (Balachandran et al., 2025) with the goal of establishing new measurements and a methodological approach that can be useful for demographers. Our analysis included dried blood spot measurements of C-reactive protein, glycated hemoglobin, cystatin C (HRS only), and hemoglobin (ELSA only), physical exam measurements of blood pressure, waist circumference, and lung function, and performance test measurements of gait speed, grip strength, and balance. Changes in these biomarkers were modeled over an eight-year follow-up period to ascertain each individual’s rate of change on each biomarker. These changes were composited into a single index and normalized against the average rate for a reference group formed from participants below age 65. The resulting metric summarizes the average rate of change in a person’s physical state expressed as a ratio of “years” of biological decline experienced per 12 calendar months of follow-up. 

Differences in rates of aging across population subgroups

Pace of Aging was highly variable in older adults in the United States and the United Kingdom. People who were older at study baseline exhibited faster Pace of Aging over follow-up, consistent with prior observations that the speed of aging accelerates towards the end of life. Population groups with shorter healthy lifespans, such as those with lower levels of education, also exhibited faster Pace of Aging. 

We examined the significance of variation in Pace of Aging by testing differences in health risks between people who were aging slowly and those who were aging more rapidly. Follow-up for health outcomes extended beyond the Pace of Aging measurement interval by an additional 6-8 years. Across the US and UK samples, older people with faster Pace of Aging were more likely to accumulate new chronic diseases over follow-up, to become disabled, to become cognitively impaired and to develop dementia, and to die. Effect sizes for associations ranged from a ~10% increase in risk for incident chronic disease to a near doubling of mortality risk per standard deviation of Pace of Aging. 

Implications

The adapted Pace of Aging method, along with specific measurements from US and UK aging studies, can enhance demographic research on healthy longevity at the population level. It offers a framework for demographers worldwide to measure the Pace of Aging in their own population studies (Balachandran et al., 2025). Many studies, within the Gateway of Global Aging initiative, already have or will soon gather the required data, i.e., three repeated measurements of blood, organ function, and performance tests. The Pace of Aging measure potentially supports diverse research areas: sociology and economics can explore how social factors affect healthy aging; medicine and gerontology can assess the impact of health events on aging; life-course epidemiology can distinguish early life influences from existing health issues; and health equity studies can identify disparities and the effects of economic and health shocks. These insights from the Pace of Aging analysis can support population monitoring projects to observe trends in healthy longevity and assist in identifying policies and social programs that promote a healthy lifespan among living individuals who have not yet developed disabling chronic conditions, in line with the goals of public health bodies.

References

AAVV (2025). Pace of aging matters for healthspan and lifespan in older adults. Nature Aging, 5(6), 964–965. https://doi.org/10.1038/s43587-025-00903-4

Balachandran, A. (2020). Population ageing in Europe and Asia: Beyond traditional perspectives. University of Groningen, The Netherlands. October. https://research.rug.nl/files/135497886/Complete_thesis.pdf

Balachandran, A., Pei, H., Shi, Y., Beard, J. R., Caspi, A., Cohen, A. A., Domingue, B. W., Eckstein Indik, C., Ferrucci, L., Furuya, A., Kothari, M., Moffitt, T. E., Ryan, C. P., Skirbekk, V., Zhang, Y. S., & Belsky, D. W. (2025). Pace of Aging analysis of healthspan and lifespan in older adults in the US and UK. Nature Aging. https://doi.org/10.1038/s43587-025-00866-6

Beard, J. R., Officer, A., de Carvalho, I. A., Sadana, R., Pot, A. M., Michel, J.-P., Lloyd-Sherlock, P., Epping-Jordan, J. E., Peeters, G. G., & Mahanani, W. R. (2016). The World report on ageing and health: A policy framework for healthy ageing. The Lancet, 387(10033), 2145–2154.

Belsky, D. W., Caspi, A., Houts, R., Cohen, H. J., Corcoran, D. L., Danese, A., Harrington, H., Israel, S., Levine, M. E., & Schaefer, J. D. (2015). Quantification of biological aging in young adults. Proceedings of the National Academy of Sciences, 112(30), E4104–E4110.

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For more than 50 years, people in the U.S. have reported that their ideal family size is about 2.5 children (Behrman 2024; Hagewen and Morgan 2005). The large share of Americans reporting ideal family sizes of two children points to the persistence of a widespread “two-child norm” in American family life. Yet, since 2010, the average number of children born per woman in the U.S. has dropped below two and the decline is ongoing (Osterman et al. 2025). 

The persistence and puzzle of the two-child norm

This gap between the number of children Americans say is ideal and the number of children they have is puzzling. For decades, the U.S. showed close alignment between ideal family size and actual fertility, unlike many European countries where this gap has been long-standing (Hagewen and Morgan 2005). The growing disconnect between ideal family size and actual childbearing raises questions about the strength of the two-child norm in the contemporary U.S.
Standard surveys ask people about their ideal family size without considering the complex realities of life today. Factors like financial stress, work demands, partnership status, and caregiving responsibilities can influence whether having two children is truly a top priority. To understand how strong preferences for two children really are, we need to look at the choices people make when juggling multiple competing demands. 

Measuring the strength of preference for two children

We conducted a nationally representative online survey of 1,785 American women of childbearing ages (18-35) using a method called a “conjoint survey experiment” (Behrman, Marshall, and Keusch 2025). Instead of asking for a single ideal number of children, this method presents participants with different family scenarios that vary in terms of number of children and other life factors such as financial status, relationship status, work hours, family policies and so on. Respondents choose which scenario they prefer, revealing the importance of each factor when forced to make trade-offs.
This approach lets us answer two key questions:

1) Do young women prefer two children when considering other family, financial, and work demands?

2) How does preference for two children compare to preference for other factors of family life such as relationship status or financial stability?

Our study found that preference for two children was not significantly stronger than preference for zero, one, or three children when competing work, financial, and family demands were taken into account (Figure 1). However, women clearly preferred families with two children over those with four, thus suggesting preference away from very large families. 

We also found that factors such as relationship status, financial security, work hours, and the availability of family-friendly policies had a stronger influence on family preferences than the number of children. For example, being married (rather than single or living with someone) and having enough money to pay household bills (rather than financially struggling) mattered more for family preferences than whether the family had two children. These results indicate that preference for two children is weak compared to preferences about other dimensions of family life. 
Interestingly, younger women aged 18 to 25 showed a notably higher preference for having no children compared to older women in the sample (Figure 2). This could be because childbearing is less central to the life goals of young Americans. Other studies support this, showing that young adults often place more importance on career fulfillment and social relationships than on having children (Parker and Minkin 2023). Alternatively, this finding could be driven by the younger ages of these respondents who may change their minds about childbearing as they grow older and consider starting families more seriously. 

What does this mean for U.S. fertility? 

Our results help make sense of why U.S. fertility rates are falling even though the two-child ideal remains common in surveys. They indicate that having two children is not a strong preference for many young American women when faced with competing work, family, and financial pressures. Instead, preferences for family size appear less important than other life priorities such as achieving financial stability or finding a long-term partner. While the two-child norm may remain an important cultural reference point, achieving the two-child ideal does not appear to be one of the highest priorities for many American women of reproductive ages.

References 

Behrman, Julia A. 2024. “Ideal Family Size and Reproductive Orientations: An Exploration of Change Over Time in the United States.” Demography 61(5):1535–57. doi:10.1215/00703370-11577510.

Behrman, Julia, Emily A. Marshall, and Florian Keusch. 2025. “An Experimental Approach to Assessing Young Women’s Childbearing Preferences: A Research Note on the United States.” Demography 11962185. doi:10.1215/00703370-11962185.

Hagewen, Kellie J., and S. Philip Morgan. 2005. “Intended and Ideal Family Size in the United States, 1970-2002.” Population and Development Review 31(3):507–27. doi:10.1111/j.1728-4457.2005.00081.x.

Osterman, M. J. K., B. E. Hamilton, J. A. Martin, A. K. Driscoll, and C. P. Valenzuela. 2025. Births: Final Data for 2023. vol 74, no 1. Hyattsville, MD: National Center for Health Statistics.

Parker, Kim, and Rachel Minkin. 2023.“What Makes for a Fulfilling Life?” .

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Most advice about claiming a Social Security old-age pension in the U.S. says: “Wait to claim, and you’ll receive higher monthly payments” (Meyer and Reichenstein, 2010). But there’s one crucial factor often left out of the conversation: no one knows how long they will live (Edwards and Tuljapurkar, 2005). This uncertainty, which we call demographic risk, fundamentally changes how we should think about retirement decisions.

In our recent study (Jiang et al., 2025), we show that this risk doesn’t go away, even for people who are well-informed and make rational plans. And importantly, we find that many people who claim their Social Security pension early, often seen as making a poor choice, may actually be acting in a very reasonable way.

The hidden risk of delaying benefits

At least in theory, Social Security pensions are designed to be actuarially fair: people who claim later get more per year but collect for fewer years. If everyone lived exactly to their average life expectancy, then claiming early or late would make no difference in lifetime benefits (Shoven and Slavov, 2014).

But no one lives exactly to the average, even among people with the same income, education, or health; lifespans vary widely, and that variation matters. We call this irreducible demographic risk: the uncertainty in how long you will live, even if you know everything else about your situation. Think of pension claiming like investing. Delaying can boost your monthly benefit, but it also means you’re betting on living longer. If you die earlier than expected, you may get little, or nothing at all.

We built a simplified model that captures the essential actuarially fair features of the U.S. Social Security system. To isolate the effect of lifespan uncertainty, we focused on two risks: dying before claiming and living only a short time after. We measured this using the coefficient of variation, the standard deviation divided by the mean. It captures how much lifetime benefits vary relative to the average, and helps compare risk across groups with different life expectancies. The result is clear: delaying increases financial risk, especially for males (Figure 1, left panel) or people with lower incomes (Figure 1, right panel).

Why early claiming can be a rational choice

About half of Americans start claiming a Social Security pension at the earliest possible age, 62 years. Some experts call this irrational but our model suggests otherwise. For people with limited savings or uncertain health, claiming early can help reduce risk. It ensures they receive at least some benefits, even if they don’t live long. Others may choose a middle ground, claiming a few years later to balance risk and return. There’s no one-size-fits-all answer. What matters is how much uncertainty you can afford to carry.

Using U.S. mortality data, we compared outcomes for people in different income groups. Those in the bottom 20% of income not only live shorter lives on average, but also face greater uncertainty in how long they will live (see the right panel of Figure 1). That means delaying benefits is riskier for them, because there’s a higher chance they won’t live long enough to benefit from the delay. Policies that encourage everyone to delay claiming may unintentionally harm those with fewer resources (Isaacs and Choudhury, 2017), by pushing them toward risk they cannot bear.

To ensure our results weren’t just a fluke, we tested them under a range of scenarios: 

• People with different levels of risk aversion

• Couples with different age gaps and claiming strategies

• Analytical and simulation-based approach

No matter how we built the model, demographic uncertainty stayed front and center. Even rational, informed people face this unavoidable risk, and it gets larger the longer they wait to claim.

Policy implications: one size does not fit all

Many retirement planning tools and policies assume that people can plan based on average life expectancy. But our research shows that average predictions can be dangerously misleading, especially for those at the bottom of the income ladder. If policymakers want to support fair and effective retirement decisions, they must account for this irreducible risk. Encouraging delayed claiming across the board could widen existing inequalities in retirement outcomes.

You can save, plan, and optimize. But you can’t predict how long you’ll live, and that unpredictability has real financial consequences. For millions of Americans, especially those in poorer health or with lower incomes, claiming their Social Security pension early isn’t a mistake: it’s a rational response to risk. Understanding this can help us build more equitable and realistic policies for an aging society.

References

Jiang, S., Zuo, W., Guo, Z., & Tuljapurkar, S. (2025). When to claim a pension: the effect of uncertainty in ages at death. Genus, 81(5). https://doi.org/10.1186/s41118-025-00243-6

Edwards, R. D., & Tuljapurkar, S. (2005). Inequality in life spans and a new perspective on mortality convergence. Population and Development Review, 31(4), 645–674. 

Shoven, J. B., & Slavov, S. N. (2014). Recent changes in the gains from delaying Social Security. Journal of Financial Planning, 27(3), 32–41. 

Isaacs, K. P., & Choudhury, S. (2017). The growing gap in life expectancy by income: Recent evidence and implications for the social security retirement age. Congressional Research Service.

Meyer, W., & Reichenstein, W. (2010). Social security: When to start benefits and how to minimize longevity risk. Journal of Financial Planning, 23(3), 49–59.

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Between 2010 and 2020, large numbers of unauthorized immigrants crossed the border into the U.S. each year. This steady inflow appeared to challenge demographic estimates showing slow growth in the population of unauthorized immigrants, whose number hovered around 11 million throughout the decade. Why did these estimates remain almost the same year after year despite the nearly continuous influx of new arrivals?

The reason is that migration is a two-way process. Just as new undocumented immigrants enter the U.S. each year, others leave, die, or legalize their status. In a recent article, I show that outflows among unauthorized migrants have been substantial (Van Hook 2024).

Inflows are offset by outflows

Populations grow only when inflows outpace outflows. Figure 1 shows the number of unauthorized migrant inflows (blue) and outflows (orange) per 1,000 U.S. population. During the economic boom years of 2000 to 2006, inflows far exceeded outflows, and the unauthorized population grew by over half a million per year. This period was marked by rapid economic growth and high demand for foreign labor from Mexico and Central America in the construction and service sectors.

With the onset of the Great Recession, however, work opportunities evaporated. Many people lost their jobs, including unauthorized immigrants, and some went back to their home countries. At the same time, many would-be migrants opted to stay home. In-migration dropped, out-migration spiked, and the unauthorized population declined by 1.5 million in the space of just two years. 

Since about 2010, inflows have been more closely balanced by outflows. Between 2010 and 2020, 5.6 million unauthorized immigrants entered the country or overstayed a visa, while 5.1 million died, legalized or emigrated. The close correspondence between inflows and outflows helps explain why the overall number of unauthorized immigrants during this period grew by less than half a million during the decade, from 10.6 million in 2010 to 11.0 million in 2020.    

Duration of stay

To understand why outflows occur, it is helpful to consider the trajectory of the average migrant. Using a common demographic technique, the life table, I found that the average migrant is likely to remain in an unauthorized legal status for 16 years. This is a long time but not a lifetime. Fully one-third are no longer unauthorized migrants within ten years of entering the country. 

Most of these outflows take the form of out-migration. At least 23 percent of unauthorized migrants appear to leave the country within ten years. In contrast, only 11 percent find ways to legalize, and 1 percent die. The 11 percent who legalize is an upper-bound estimate. It is based on the information available to the public from the Department of Homeland Security on all immigrant adjustments and not just those who adjust from an unauthorized to a legal immigration status. 

The time migrants spend living with an unauthorized migration status varies across groups (Figure 2). Mexicans and Central Americans spend the longest time. They can expect to live undocumented in the United States for an average of 22 years. After ten years of residence, roughly 20 percent will have returned home and only 6 percent of Mexicans and 3 percent of Central Americans will have legalized. These low legalization rates reflect their limited options under U.S. immigration law. As poor labor migrants with low levels of education, their chances of legalizing under employment-based classes of admission are slim. Additionally, many crossed the U.S.-Mexico border without inspection (by evading immigration checkpoints or being smuggled into the country), so are subject to laws that forbid them from adjusting status without first leaving the U.S. for many years.

This contrasts with migrants from other parts of Latin America, Asia, Europe, and Africa. On average, they can expect to spend 11 years in unauthorized status before leaving, adjusting to legal permanent residence status, or dying, half the average duration of Mexicans and Central Americans. Europeans, Canadians and Oceanians stand out, with 25 percent leaving the country and up to 25 percent legalizing within 10 years. A large share of these migrants overstayed a student or work visa and are highly educated. They may not want to stay in the U.S. because they have opportunities elsewhere, and those who do stay may be able to legalize through employment or family visas. 

Declines in duration

The duration spent in unauthorized status has been declining over the last two decades. Between 2000 and 2006, the average was 22 years. With the onset of the Great Recession, however, return migration rates rose three-fold. The average duration declined to 16 years and remains at this level today. 

These changes happened because groups that spend a long time in unauthorized status (Mexicans and Central Americans) are gradually being replaced by groups with shorter durations. Between 2007 and 2022, the number of Mexican-born unauthorized immigrants dropped from 7.7 to 5.1 million. At the same time, the share from all other regions increased, and these newer groups tend to spend less time in an unauthorized status than Mexicans.

Looking ahead

The latest estimates of the unauthorized population do not extend beyond 2022 and do not fully capture recent increases in entries at the U.S.-Mexico border. Encounters at the U.S.-Mexico border surged more than four-fold from 401,000 in 2020 to 1.7 million in 2021, and then further increased to 2.4 million in 2022 and 2.5 million in 2023. As more data become available, we expect to see growth in the unauthorized foreign-born population, but it remains unclear how much growth. That will depend on how long new arrivals remain in the country in an unauthorized status. Despite these uncertainties, the overarching trend is clear. Large shares of unauthorized immigrants leave the country or legalize, and they are less “permanent” now than two decades ago.  

References

Van Hook Jennifer (2024) Beyond Stocks and Surges: The Demographic Impact of the Unauthorized Immigrant Population in the United States. Population and Development Review, online first, https://doi.org/10.1111/padr.12683

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In 2023, nearly one in four children in America aged 0‒17 years, totaling about 19 million, were living in a household without their biological, step, or adoptive father, mostly with their mother (85% of cases). In fact, living in a single-parent households with a father absent is the second most common U.S. living arrangement, and the proportion of these households has doubled since 1970 (US Census Bureau, 2023). 
Single-parent households in the US are the result of various factors, including declining marriage rates, divorces, widowhood, non-married parents who split up and parents who are single by choice.

Levels 

According to a global survey of 130 countries conducted in 2019 (Kramer 2019), the US had the highest rate of children living in single-parent households of any nation in the world, at about 23%, or more than three times the worldwide level of 7% (Figure 1).

The prevalence of these cases was markedly lower in neighboring Canada and Mexico (15% and 7%, respectively), and much lower in developing countries, all below 5%.

Trends

For decades, the share of U.S. children living with a single parent has been rising, accompanied by a decline in marriage rates and a rise in births outside marriage (Livingston 2018). The percentage of US children living in single-parent households nearly tripled between 1960 and 2023, increasing from 9% to 25% (US Census Bureau 2023). In the same period, the share of households with fathers missing more than doubled, increasing from 11% to 25% (Figure 2). 

The proportions of children living in a father-absent household varies considerably among America’s three major social groups: in 2023, the shares were 20%, 29% and 50% among Whites Hispanics and Blacks, respectively, and in all three groups they are substantially higher than 50 years before (Figure 3). 

The rise in births to unmarried mothers has contributed strongly to the increase in households with missing fathers. While many unmarried women cohabit with a partner at the time of giving birth, these relationships fail at twice the rate of marriages (Brewer 2023). In 2022, 40% of all births in the US were to unmarried women, a four-fold increase since 1970. Again, there is notable variability across the major US social groups: 16% for Asians, 22% for Whites, 42% for Hispanics and 69% for Blacks (Osterman 2024).

Consequences and perspectives

Children who grow up in a household with only one biological parent are worse off, on average, than children who grow up with both of their biological parents, regardless of the parents’ race or educational background (McLanahan and Sandefur 1996). For instance, they are nearly four times more likely to live in poverty (Brewer 2023), have more trouble academically, scoring poorly on tests of reading and mathematics, and are more likely to drop out of school. 

Boys living in these conditions are more likely to engage in negative social behaviors (Kearney 2023) and girls often experience lack of confidence and struggle with decision-making (Rothwell 2023). 

Nearly half of US adults in a 2021 national survey – men especially (59% of men versus 37% of women; Hurst 2022) – said that raising children in a single-mother household is generally a bad thing for society. However, despite these widespread negative opinions, current trends in marriage, cohabitation and divorce, and social norms concerning non-marital births and family type, suggest that the situation is unlikely to change any time soon. The same holds for the resulting inequalities that will affect the future lives of these children. 

References

Brewer, Jack (2023)  “Fatherlessness and its effects on American society”. May 15, America First Policy Institute, Washington, D.C.

Hurst, Kiley (2022) “Rising share of Americans see women raising children on their own, cohabitation as bad for society”. March 11, Pew Research Center, Washington D.C.

Kearney, Melissa (2023) The Two-Parent Privilege. University of Chicago Press, Chicago, Illinois. 

Kramer, Stephanie (2019) “U.S. has world’s highest rate of children living in single-parent households”. December 12, Pew Research Center, Washington D.C.

Livingston, Gretchen (2018) “About one-third of U.S. children are living with an unmarried parent”. April 27, Pew Research Center, Washington D.C.

McLanahan, Sara and Gary Sandefur (1997) Growing Up with A Single Parent: What Hurts and What Helps. Harvard University Press, Cambridge, Massachusetts.

Osterman, Michelle J. et al. (2024)  “Births: Final Data for 2022”.  April 4, National Vital Statistics Reports, CDC, Washington, D.C.

Rothwell, Jonathan (2023) “Parenting Is the Key to Adolescent Mental Health”. November 30, Gallup and The Institute for Family Studies, Charlottesville, Virginia.

US Census Bureau (2023) “Living Arrangements of Children Under 18 Years Old: 1960 to Present”. November, Washington, D.C.

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Child care in the the U.S. is both costly and scarce, and fails to meet the needs of children and parents. The country’s child care system is dominated by the private sector which receives very limited financial support: as a result, child care costs across America are untenable for most families (Landivar 2024). The U.S. Treasury Secretary has described child care in America as “a broken market”.

Costs

While the U.S. Department of Health considers that 7% of household income as an affordable benchmark for child care, in 2023, two-thirds of U.S. parents with young children spent 20% or more of their household income on child care, up from about half in 2022 (Solá 2023).  The average cost of child care for two children is higher than the average rent in all 50 states and the average mortgage payment in 45 states (Luhby 2024). Likewise, in about 40 states, the average annual price of infant care in a child care center exceeds annual, in-state university tuition costs. 

Child care expenses for parents are often more than $1,000 per month. In 2021, for example, preschool, infant care and day care cost between $14,000 and $24,000 annually for couples in the ten U.S. states with the highest annual child care expense (Figure 1). In addition to their high cost, child care services are often difficult to find. Approximately 75% of families report difficulties, and about 32% of children under the age of five cannot access a child care slot (Editorial Staff 2024). Moreover, since 2019, there has been a 12% decline in licensed family child care homes.

Child care providers are also struggling with limited space and staffing shortages, in part because wages in this sector are low, with a median hourly wage of $14.60 in May 2023 (US Bureau of Labor Statistics 2024). Due to the national shortage of workers and facilities, the costs of day care and preschool are rising at nearly twice the pace of inflation: the national average annual price of child care increased by 23% between 2017 and 2023 (Luhby 2024).

Consequences

The price tag for having two children in child care while working full-time is significantly higher in the U.S. than in most OECD countries (Figure 2). 

Many parents with young children in America are thus forced to choose between spending a significant portion of their income on child care, finding a cheaper, less desirable option, or leaving the workforce altogether to become full-time caregivers.  Child care issues are more likely to impact a mother’s than a father’s career. About half of mothers with young children are considering leaving the workforce due to inadequate childcare. The US has gone from leading its peers in women’s workforce participation rates in the 1990s to lagging dramatically behind them today. About one-fifth of mothers who do not currently work would look for a job if they had better access to quality child care, and about one-third of mothers unable to find affordable child care have already dropped out of the labor force. In 2023, 25% of  mothers were economically inactive, up from just 15% in 2022. Subsidized child care and preschool increase the likelihood that mothers will remain in work and pursue a career. Besides, child care helps children develop physically, mentally, socially and emotionally, thereby increasing their chances for lifelong success. Numerous studies about child care in the U.S. have indicated the benefits of early-childhood education (Wise 2024). 

Policies 

One of the major reasons why child care is more expensive in the US than in the rest of the developed world is that other countries devote a larger share of government spending to child care provision (Buchholz 2024).  America’s two major political parties have different views on the role of the federal government in this area. While Democratic elected officials have proposed more federal spending on child care, many Congressional Republicans argue against federal intervention and are opposed to federal tax increases for this purpose. The vast majority of U.S. voters feel a sense of urgency concerning the need for affordable, quality child care. Nearly 80% of voters support increased federal funding for states to expand their child care and early learning programs. That view among voters is evident across party lines and is felt strongly by parents and non-parents alike (Patrick 2024).

Conclusions 

Those opposed to subsidized child care tend to ignore the economic realities of modern America. In many households both parents want to work, and for a growing share of parents, being employed is simply not a choice. The U.S. Department of Labor has highlighted the need for greater federal investments in child care services. Businesses and policymakers are also paying close attention to America’s child care crisis because a good child care system that accommodates the needs of working parents contributes to a healthy workforce and economy.   In sum, child care in America is in crisis largely because it is costly and scarce. Increased federal support and funding would contribute significantly to improving child care affordability and availability .

References

Buchholz, Katharina (2024) “U.S. Childcare Cost Higher than In Other Developed Countries”. 10 September, Forbes.

Editorial Staff (2024) “This is how much child care costs in 2024” January 17, Care.Com. 

Grundy, Adam (2024) “Rising Cost of Child Care Services a Challenge for Working Parents”. January 9, U.S. Census Bureau, Washington, D.C.

Landivar, Christin (2023) “New Childcare Data Shows Prices Are Untenable for Families”. January 24, U.S. Department of Labor, Washington, D.C.

Luhby, Tami (2024) “Here’s where child care costs at least twice as much as rent”. May 15, CNN. 

Patrick, Stephen W. (2024) “Voter Support for Policies Associated With Child Health as National Campaign Priorities”. September 27,  JAMA Health Forum.

Shine, Ian (2023) “These countries have the highest childcare costs in the world”. July 19, World Economic Forum, Switzerland. 

Solá, Ana Teresa (2023) “Majority of parents spend 20% or more of household income on child care, report finds”. August 7, CNBC. 

U.S. Bureau of Labor Statistics (2024) “Occupational Outlook Handbook: Childcare Workers”. August 29, Washington, D.C. Wise, Jessica N. Wise,

Katarina Reyes, Sandra McKay (2024) “The Case for Early Childhood Education as Health and Economic Policy”. September 6, Baker Institute, Rice University, Houston, Texas, U.S.

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Since the early 1970s, educational homogamy, the tendency to assortatively match into marriage based on educational level, has increased significantly in the US. At the same time, hypogamy, the tendency for highly educated women to marry down in education, has also increased from very low levels. The share of homogamous and hypogamous marriages in the stock of prevailing marriages has increased at the expense of hypergamy (women marrying more educated partners). These trends in marriage stock must correspond to changes in the educational structure of marriage inflow, i.e., among newlyweds, and/or selective marriage outflow, i.e., divorce patterns, where homogamous and hypogamous marriages are more stable and accumulate in the marriage stock. It has been shown that homogamous marriages are indeed more stable, such that the educational structure of divorce has contributed to the rise in educational homogamy in the marriage stock (e.g., Schwartz, 2010).

Unilateral divorce legislation in the US

The period of rising homogamy and hypogamy has also been characterized by a dramatic increase in divorce rates, partly attributed to the adoption of unilateral divorce legislation (UDL). Between 1970 and 1988, 29 US states changed their marriage dissolution laws to a unilateral system, which allows a person to divorce without the agreement of his or her spouse, even in absence of a fault. In an influential analysis, Wolfers (2006) studied state divorce rates up to 1988 and concluded that the adoption of UDL sharply increased divorce rates, but that this effect dissipated within a decade of the legislative reform. Whether UDL affected marriage rates is less clear. 

To date, these two major marriage-market trends ‒ rising educational homogamy/hypogamy and rising divorce rates ‒ are studied in largely separate literatures. Despite the evidence on the importance of UDL for overall divorce rates, and on the importance of the educational structure of divorce for rising homogamy in the marriage stock, little is known about whether UDL affected the educational structure of divorce, or likewise, the educational structure of newlyweds. In a recent study (Geghetsik and Jurajda, 2024), we asked whether UDL-induced higher divorce risks affected the educational structure of marriage inflow and outflow. To estimate these UDL effects, we leveraged the different timing of UDL introduction across US states. 

Our analysis relies on precise measures of the educational structure of marriage inflows and outflows by state and year based on under-utilized US administrative data from registered certificates of marriages and divorces over the period 1970‒1988. Unlike survey data, the administrative records allow us to study the educational composition of marriage inflows and outflows within an analytical difference-in-differences framework. We study first and higher-order marriages separately, because it is well known that highly educated women entering higher-order marriages face a ‘marriage squeeze’ (Qian & Lichter, 2018), i.e., find it difficult to match to equally educated partners.

Over the period 1970‒1988, divorce was least likely for homogamous marriages, almost as unlikely for hypergamous marriages but high for hypogamous marriages. We find that much of the stability advantage of homogamy plays out within the first two years of marriage. This is relevant to the study of marriage inflows using survey data, in which newlyweds are identified as those recently married, because such samples are affected by survival bias due to differential divorce rates across marriage types, even in samples of marriages that occurred at most two years before the survey interview. 

Newlyweds

Turning to newlyweds, we find that homogamy among newlyweds did not increase relative to non-homogamy during our study period. However, there were significant changes in the educational structure of US marriage inflows: The odds of hypogamy increased relative to hypergamy, and so did the odds of homogamy relative to hypergamy (see Figure). 

Next, we assess the role of UDL for educational sorting in marriage inflows and outflows. We find no evidence that making divorce easier affected the educational structure of marriage inflow, but we uncover robust evidence based on first marriages that UDL lowers the stability of hypergamous marriages relative to homogamous ones, and that it also reduces the large stability disadvantage of hypogamous couples relative to hypergamy. We thus depict a marriage market where, among newlyweds, ‘traditional’ hypergamous marriages are losing ground to both homogamy and hypogamy, and where hypogamous marriages are more likely to end in divorce. These patterns hold for both first and higher-order marriages. With the introduction of unilateral divorce legislation (UDL), the picture changes substantially for first marriages, but not for higher-order ones. Among first marriages, UDL has improved the stability of hypogamous marriages and lowered that of hypergamous ones, while homogamy is starting to enjoy a stability advantage over hypergamy. In sum, we find that UDL contributed to the rise in homogamy.

Underlying mechanisms

We uncover two mechanisms behind these changes. First, we find that UDL disproportionately leads to less educated wives in hypergamous marriages initiating divorce, which underpins the reduction in the stability gap between hypergamous and hypogamous first marriages. Second, our findings suggest that highly educated women entering their first marriage are able to compensate for the higher (perceived) riskiness of marriage under UDL by forming more stable matches in terms of match quality dimensions other than education. This compensation mechanism appears particularly strong for hypogamous first marriages, consistent with the reduction in their stability disadvantage under UDL. We thus provide support for an under-researched response in marriage formation behavior, where the quality of matches in formed unions is influenced by norm- or regulation-induced changes in expected union stability. Such an adjustment mechanism has been suggested for couples entering cohabitation, because leaving cohabitation is easier than leaving marriage (Schoen and Weinick, 1993), and is also one of the consequences of the introduction of UDL in a marriage market model proposed by Reynoso (2024). 

References

Afunts, G. & Jurajda, S. (2024). Who Divorces Whom: Unilateral Divorce Legislation and the Educational Structure of Marriage. Demography, online first.

Qian, Z. & Lichter, D. T. (2018). Marriage markets and intermarriage: Exchange in first marriages and remarriages. Demography, 55(3), 849–875.

Reynoso, A. (2024). The impact of divorce laws on the equilibrium in the marriage market. Journal of Political Economy, in press.

Schoen, R. & Weinick, R. M. (1993). Partner choice in marriages and cohabitations. Journal of Marriage and Family, 55(2), 408.

Schwartz, C. R. (2010). Pathways to educational homogamy in marital and cohabiting unions. Demography, 47(3), 735–753.

Wolfers, J. (2006). Did unilateral divorce laws raise divorce rates? A reconciliation and new results. American Economic Review, 96(5), 1802–1820.

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While survival, or differential survival, is a traditional topic of interest in socio-demographic studies, the issue of life quality, and differential life quality, has gained momentum in recent years. Self-rated health (SRH) refers to an individual’s subjective assessment, experience, or perception of their own health status. An excellent proxy of overall health, morbidity (sickness) and mortality later in life, SRH represents a valuable tool in public health monitoring and demographic research. 

Quantifying well-being: metrics, meaning, and the human experience  

In the United States, African Americans generally report poorer health than their White American counterparts, with some reductions in the racial gap from the 1970s to the early 2000s (Cummings & Jackson, 2008; Hughes & Thomas, 1998; Yang & Lee, 2009). On the other hand, studies on the gender gap in health status reveal that women have made significant strides in well-being since the 1970s, with SRH disparities narrowing and reaching parity by the early 2000s (Cummings & Jackson, 2008; Schnittker, 2007). While these studies have advanced our knowledge regarding U.S. patterns and trends in SRH over time, they are in part limited, because they:

1) only examine SRH trends prior to the 2007-2009 U.S. Great recession, or

2) pay little attention to how racial disparities in health vary by gender (Bowleg 2012)

These limitations are important because in the years since the recession, well-being patterns in the United States have worsened, with general declines across a range of social indicators of quality of life (e.g., happiness, levels of worry, stress, and pain; Cummings 2020; Deaton 2012) coupled with well-publicized increases in so called “Deaths of Despair” or preventable health conditions linked to despair and hopelessness, such as suicide, and opioid-alcohol related-mortality, affecting the U.S. White population in particular (Case and Deaton 2020).

The racial and gender dynamics of how Americans assess their own health

In a recent paper (Cummings 2023), leveraging data from the General Social Survey (GSS) between 1972 to 2018, I examined trends in SRH at the intersection of race and gender. Two key questions guided my study:

1) What is the pattern of racial and gender disparities in SRH from 1972–2018? 

2) Did racial and gender disparities in SRH from 1972–2018 decrease, increase, or remain unchanged?

Results show that while African American women and men express feeling healthier today than they did in the 1970s, and White women report feeling about as healthy as back then, White men report feeling comparatively worse. The pattern of decline in SRH experienced by White men was most pronounced after the recession (continuing through the economic recovery). 

Figure 1 reports predicted probabilities across each outcome category (i.e., poor, fair, good, excellent) based on regression models adjusting for group differences across a range of sociodemographic factors (e.g., age, region, marital status, employment, education, household income, and occupational prestige). The lines illustrate a trend of virtual convergence in SRH disparities from the early 1970s to the late 2010s across demographic groups, with only one exception—“good health”—which remained largely unchanged over time and across groups. The most notable change in health status over time occurs at the upper bound of SRH (“excellent health”, panel d). For example, the likelihood of reporting “excellent health” declined significantly for White men and women from the early 2000s (–30% and –16%, respectively). In contrast, African American men and women showed a general pattern of improvement, with increases of 4% and 14%, respectively, in the likelihood of reporting “excellent” SRH.

Socioeconomic attainment (e.g., having a high income or education) and work (e.g., being employed or having a job with high occupational prestige) were primary factors that protected or improved the health status of all groups, but the strength of the positive impact of these factors on good or excellent health differed depending on gender and race (Cummings 2023). For example, the benefit of being a high-income earner, having a college degree or being employed full-time appeared to be more protective to the health status of White women and men, while for African American women and men, having a job with a high occupational prestige was more protective.

Conclusions

The findings of this paper coincide with an interdisciplinary body of research demonstrating declines in the quality of life of White Americans since the recession. This finding appears to exist across objective indicators of well-being like mortality and life expectancy (Case & Deaton, 2015). Similarly, I find that SRH disparities have followed a pattern of convergence, with African American women and men witnessing gains in their SRH status since the 1970s. In contrast, White women overall have experienced little to no change in their health status and White men have experienced absolute declines. Perhaps even more surprisingly, however, the unparalleled pattern of decline in SRH experienced by White men was most pronounced after the recession (continuing through the economic recovery). This finding may suggest that the health status of White men was not simply shaped by economic forces brought on by the recession in real time, but possibly by other forces most likely preceding the 2016 U.S. presidential election, i.e., the election of the first African American U.S. President. This explanation aligns with the work of Malat, Timberlake, and Williams (2011), demonstrating immediate improvement in self-rated health among Blacks preceding the election of President Obama, but no such change or improvement in the health status of Whites.

This work also coincides with my own previous work (see Cummings, 2020) which also found an unprecedented pattern of decline in happiness among the White male population after the Great Recession, which parallels their greater relative decline in financial happiness. Although White Americans did not suffer greater financial or job loss during the Great Recession than their African American peers (the opposite is the case), White Americans (but especially White men) may have “perceived” that their financial loss was greater, and perhaps that they were primary victims of the economic downturn, in part leading to declines in their well-being.    

References

Beck, A. N., Finch, B. K., Lin, S.-F., Hummer, R. A., & Masters, R. K. (2014). Racial disparities in self-rated health: Trends, explanatory factors, and the changing role of socio-demographics. Social Science & Medicine, 104, 163–177.

Bowleg, L. (2012). The problem with the phrase women and minorities: Intersectionality—An important theoretical framework for public health. American Journal of Public Health, 102(7), 1267–1273.

Case, A., & Deaton, A. (2015). Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century. Proceedings of the National Academy of Sciences, 112(49), 15078–15083

Cummings, J. L. (2020). Assessing U.S. racial and gender differences in happiness, 1972–2016: An intersectional approach. Journal of Happiness Studies, 21(2), 709–732.

Cummings, J. L. (2023). Entangled Inequalities: U.S. Trends in Self-Rated Health at the Intersection of Gender and Race, 1972–2018. Social Indicators Research, 169, 759–774 (2023)

Deaton, A. (2012). The financial crisis and the well-being of Americans 2011 OEP Hicks Lecture. Oxford Economic Papers, 64, 1–26.

Hughes, M., & Thomas, M. E. (1998). The continuing significance of race revisited: A study of race, class, and quality of life in America 1972–1996. American Sociological Review, 63, 785–795. 

Malat, J., Timberlake, J., & Williams, D.R. (2011). The Effects of Obama’s Political Success on Self-Rated Health of Blacks, Hispanics, and Whites. Ethnicity & Disease. 21 (3): 349-355.  

Schnittker, J. (2007). “Working more and feeling better: women’s health, employment, and family life, 1974-2004.” American Sociological Review 72 (2): 221-238.

Yang, Y., & Lee, L. C. (2009). Sex and race disparities in health: Cohort variations in life course patterns. Social Forces., 87(4), 2093–2124.

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