In countries where informal labour markets are widespread and public pensions fall short, stopping work at retirement age may not be an option. South Korea has among the highest life expectancies (83.5 years) and the highest rate of old-age employment among OECD countries; 57 per cent of 65–69-year-olds were employed in 2024 (OECD, 2025). But living and working longer doesn’t mean these extra years are spent in good health. This raises an important question: are people healthy enough to extend their working lives, or are they working longer despite poor health?

Pensions in South Korea

In South Korea the pension age is currently 63 years, expected to rise to 65 by 2033, but pension system covers fewer than half of older adults, which contributes to old-age poverty being among the highest in the OECD (OECD, 2018). As a result, many people need to work well into old age, whether to bridge the gap between retirement and pension, or to supplement the basic pension they do receive.

In a recent study (Lam et al., 2026), we estimated not just how long older South Koreans work, but how much of that time is spent with chronic disease, and looked for differences by gender, education, or urban-rural residence. We defined health using a count of nine chronic conditions, grouped into no disease, one disease, and two or more diseases (multimorbidity). By treating health as a gradient, rather than simply healthy and unhealthy, we get a fuller picture of the ageing process. 

Who works longer, and in what state of health?

Three findings stand out. First, men work far longer than women. From age 50 they can expect to work for a further 16.9 years, compared to 9.9 for women (Figure 1). For both men and women, nearly half of those working years are lived with at least one chronic disease. This gender difference in working life expectancy reflects longstanding inequalities in the labour market. Women’s working lives have long been defined by highly gendered norms around labour force participation and caregiving roles, leading women to have more accumulated disadvantages across their life course (Brinton & Oh, 2019). Conversely, men are more likely to face social pressure to continue as the family’s main provider and often stay more strongly attached to work (Lee & Yeung, 2021).

Figure 1. Weighted life expectancy from age 50 split by time spent in each state: Men and women, South Korea, 2006–22. 

Note: Coloured bars display the percentages of remaining life expectancy in each state; total weighted life expectancy in years is displayed at the top of each bar.
Source: Lam et al. (2026)

Second, educational differences were minimal. However, people with less education may work longer than those with more education, in line with another study from South Korea (Yang et al., 2025), but contrary to what is usually seen in the United States and Europe (Feraldi & Dudel, 2025; Sperlich et al., 2023).

Third, where people live seems to matter more than their education. Men living in rural areas work longer than any other group, more than four years longer than urban men, and keep working until around age 70 on average (Figure 2). They also spend about half their working lives with at least one chronic disease, and have more multimorbidity than men from urban areas. The same pattern of urban-rural inequality can be seen for women, with those living in rural areas working about six years beyond retirement age compared to urban dwelling women who withdraw from the labour market even before retirement age. 

Figure 2. Weighted life expectancy from age 50 split by time spent in each state, men and women in South Korea, 2006–22, by place of residence.

Note: Coloured bars display the percentages of remaining life expectancy in each state; total weighted life expectancy in years is displayed at the top of each bar.
Source: Lam et al. (2026)

While our study did not directly examine what might be driving the longer, unhealthier working life expectancy among people in rural areas, we expect it to be related to the structure of employment in rural areas and the history of rural-urban migration, compounded by unequal access to healthcare. Older adults living in rural areas are more often self-employed or working in agriculture, jobs less subject to mandatory retirement and rarely tied to an adequate pension. As younger generations migrated to cities for more education and work opportunities, the older generation who remained kept working, but whether through choice or necessity is unclear. 

Beyond “longer is healthier”

Our findings caution against assuming that longer working lives are inherently healthier, and that the findings from one context can be generalized to another. In South Korea, the people working longest are not always the most advantaged or the healthiest: those living in rural areas, and to a lesser extent those with low education, spend more time working despite poorer health compared to their urban and highly educated counterparts. 

This matters for policies that encourage extended working lives. The capacity to work longer, and to do so in good health, is not shared equally: it depends on gender, place of residence, and the institutional and social context people live in.

For South Korea, that points to a need for policies that ease the pressure to work despite poor health, including better pension coverage for self-employed and informal workers and improved preventive care and healthcare access in rural areas. Behind South Korea’s high rates of old-age employment lies a gendered and geographic division of labour, one that decides not just who works longest, but who does so in poor health.

References

Brinton, M. C., & Oh, E. (2019). Babies, Work, or Both? Highly Educated Women’s Employment and Fertility in East Asia. American Journal of Sociology, 125(1), 105–140. https://doi.org/10.1086/704369

Feraldi, A., & Dudel, C. (2025). Healthy Working Life Expectancy: Measurements and Socio-Economic Inequalities. In E. di Bella, V. Gioia, C. Lagazio, & S. Zaccarin (Eds.), Statistics for Innovation III (pp. 213–218). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-95995-0_36

Lam, A., Keenan, K., Kulu, H., & Myrskylä, M. (2026). Working longer despite poorer health? Inequalities in healthy and unhealthy working life expectancies in South Korea. Population Studies, 1–18. https://doi.org/10.1080/00324728.2026.2625712

Lee, Y., & Yeung, W.-J. J. (2021). The Country That Never Retires: The Gendered Pathways to Retirement in South Korea. The Journals of Gerontology: Series B, 76(3), 642–655. https://doi.org/10.1093/geronb/gbaa016

OECD. (2018). Working Better with Age: Korea. https://doi.org/10.1787/9789264208261-en

OECD. (2025). Pensions at a Glance 2025: OECD and G20 Indicators. OECD Publishing. https://doi.org/10.1787/e40274c1-en

Sperlich, S., Beller, J., Epping, J., Geyer, S., & Tetzlaff, J. (2023). Trends of healthy and unhealthy working life expectancy in Germany between 2001 and 2020 at ages 50 and 60: A question of educational level? J Epidemiol Community Health, 77(7), 430–439. https://doi.org/10.1136/jech-2023-220345

Yang, M., Lee, J., Lynch, M., Wilkie, R., Tongeren, M. van, & Kang, M.-Y. (2025). Educational and gender disparities in healthy working life expectancy among middle-aged and older adults in South Korea. Occupational and Environmental Medicine, 82(6), 293–299. https://doi.org/10.1136/oemed-2025-110239

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Doctoral training now coincides more than ever with women’s prime reproductive years.  In the United States in 1992, the median age at PhD completion was 36 for women and 33 for men. By 2022, both had converged to around 32. This convergence is a sign of progress, but it comes with an underappreciated cost: doctoral training now falls squarely within women’s prime reproductive years. Across many countries, the same window in which researchers are expected to publish, network, and build their reputations is also the window in which family formation typically occurs. For decades, the conventional wisdom told women to delay childbearing until after tenure (Mason, Wolfinger, and Goulden, 2013). 

In a recent study (Zhao et al., 2026), we used a global survey of 8,097 academic parents, linked to their actual publication records over time, to reconstruct how researchers juggle doctoral training and parenthood. Our results reveal striking gender asymmetries that begin during doctoral training and continue throughout academic careers.

Five paths, unequally distributed

In the study, we identified five distinct life-course trajectories based on the timing and sequencing of PhD completion and first parenthood. The most common one, followed by 28% of the sample, was completing the PhD early (before 30) but delaying parenthood until after 35. This pattern is particularly common among women born after 1970, consistent with a broader trend of later family formation among highly educated women.

Men, by contrast, were significantly more likely to fall into the trajectory involving early parenthood alongside doctoral training, becoming fathers before age 30 while also completing a PhD, and tending to have larger families. This trajectory was the smallest in the sample (under 10%) but showed the sharpest gender imbalance, and that imbalance has widened over successive birth cohorts.

The share of publishing researchers without a doctorate by age 40, a trajectory strongly associated with women, has declined dramatically over time, reflecting real progress in closing the doctoral gender gap. But progress at the entry level has not translated into equality in outcomes.

Parenthood penalises mothers, not fathers: what can institutions do?

The consequences of these different life-course trajectories become clear when we examine doctoral completion. Figure 1 shows the probability of completing a PhD by age according to gender and the number of children before PhD completion. Among researchers without children, women and men completed PhDs at almost identical rates. Researchers who had children before completing their PhD were substantially less likely to earn a doctorate than those without children, with women experiencing consistently larger disadvantages than men with the same number of children. The divergence between women’s and men’s PhD completion trajectories grew larger as the number of children increased. 

The timing of parenthood also shapes long-term scientific impact. Women’s citation impact declined progressively as parenthood occurred earlier in their careers. Those who had their first child before age 30 experienced the largest citation disadvantage relative to men, whereas women who delayed parenthood until after age 35 achieved citation impact comparable to their male peers.  Men showed no comparable pattern regardless of when they became fathers; their scientific impact remained largely unchanged. This mirrors what Morgan et al. (2021) described as a “fatherhood premium” in academia: becoming a parent tends to support men’s upward career trajectories while constraining women’s.

The same pattern is evident in career retention. Men were consistently more likely than women to remain in academic positions across all five identified trajectories, with the largest gender differences (7–9 percentage points) among those who completed their PhD early and became parents thereafter.

These findings challenge explanations of women’s underrepresentation in science that focus on ability or preferences. Among researchers without children, there is little evidence of a gender gap. The gap emerges and widens when caregiving responsibilities collide with the demands of doctoral training and early academic careers.

The implications are structural rather than prescriptive. Parental leave for PhD students remains inconsistently available and poorly formalised at many universities. Tenure and fellowship clocks rarely account for caregiving-related disruptions. Evaluation criteria implicitly reward uninterrupted productivity, disadvantaging those who take career breaks. Nordic countries offer a useful benchmark: their more generous leave provisions are associated with less severe career interruptions, though generous leave alone does not neutralise the pressure to maintain continuous output. The deeper issue is not that women make the wrong choice about when to have children, but that the academic timeline itself forces a choice in the first place.

Changing that requires more than isolated accommodations. Childcare provision for doctoral students, structured mentoring for parenting researchers, re-entry funding after career breaks, and reforms to how research performance is evaluated are not simply matters of fairness; they are also matters of scientific efficiency: the persistent loss of talented researchers at the doctoral stage represents a cost to the research enterprise as a whole.

References 

Mason, M. A., Wolfinger, N. H., and Goulden, M. (2013). Do Babies Matter? Gender and Family in the Ivory Tower. Rutgers University Press.

Morgan, A. C., Way, S. F., Hoefer, M. J. D., Larremore, D. B., Galesic, M., and Clauset, A. (2021). The unequal impact of parenthood in academia. Science Advances, 7(9). https://doi.org/10.1126/sciadv.abd1996

Zhao, X., Derrick, G. E., Sugimoto, C. R., & Larivière, V. (2026). Starting a PhD and becoming a parent: Gendered life-course trajectories in academic careers. Higher Education. https://doi.org/10.1007/s10734-026-01668-4

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Every time a patient goes into surgery, gives birth, or battles cancer, there is a good chance they will need someone else’s blood. Blood donors in South Korea are overwhelmingly in their teens, twenties, and thirties; blood recipients, instead, are typically in their seventies and eighties – and increasingly so, given population aging. The arithmetic, replicated country by country across East Asia, is unforgiving. Admittedly, this is just another example of the possible imbalances caused by population aging; however, its nature and the emotional impact of the topic seem to make it worth special consideration.

The arithmetic of aging and blood

Yu et al. (2020) were among the first to model what may happen to blood supply and demand in China between 2017 and 2036, as the country ages. Supply is projected to fall by 16 per cent while demand will rise by 33 per cent, leaving a potential shortfall of 21.2 million units by 2036, equivalent to more than a third of projected annual demand (a unit is approximately 450 ml, or a pint). The driving mechanism is straightforward: fewer people in the peak donating ages of 18–34, and far more in the heavy-transfusion-using ages of 70 and above, where blood use per person runs more than 25 times higher than among teenagers.

Not surprisingly, the same trend can be observed also in other countries. In Taiwan, for instance, the pool of eligible blood donors, people aged 17 to 65, is expected to shrink from 16 million in 2025 to just 8.7 million by 2060. Blood demand is projected to exceed supply by around 2027, with the annual shortfall surpassing one million units by 2060 (Lin et al. 2026). 

The Korean case

A similar problem will emerge in South Korea, one of the world’s fastest-aging societies (Kim and Kim 2020). In 2024, Korea’s blood donation rate (number of donations per 100 people) stood at 5.6% (Korean Redcross Blood Services 2025): higher than Japan’s 4.0% or France’s 3.9%, and among the highest globally. But that headline figure conceals a structural fragility: more than half of all donations come from people aged 16 to 29, a cohort shrinking rapidly as the country’s birth rate, already the world’s lowest, continues to fall (Figure 1). 

At the same time, elderly patients, who consume the most blood per person, are growing in number at a pace few health systems have had to manage before (Kim et al. 2024). 

From research to legislation

On 13 May 2026, South Korea’s Ministry of Health and Welfare announced the country’s Second Blood Management Basic Plan, covering 2026 to 2030. It is the most significant revision of blood donation rules in a decade.

The centerpiece is an increase in the upper age limit for blood donation. Currently, whole-blood donation is permitted up to the age of 69, and, for people aged 65 to 69, only if they donated between the ages of 60 and 64. Under the new plan, that ceiling may rise by even more than five years, as officials have not ruled out scrapping the age limit altogether, replacing it with an individual health assessment.

A second change abolishes a long-running chemical test on donated blood: the alanine aminotransferase test (ALT), which measures a liver enzyme. Introduced in the 1990s to guard against hepatitis, it was overtaken by more precise direct virus-detection methods but survived as a bureaucratic habit. Between 2020 and 2025, roughly 33% of donated blood (170 thousand units) was discarded because of ALT results alone, enough to fill hundreds of thousands of standard transfusion bags. That waste will now stop.

The plan also proposes practical incentives: streaming-service subscriptions as gifts for young donors, and regular blood donation buses in areas with no permanent blood bank.

The safety case for expanding the donor age is well supported. Older donors in good health produce blood that is as safe as that of younger donors, and several European countries, such as the UK and Italy, already permit donations beyond 65 under specific conditions (National Health Service (NHS) Blood and Transplant 2026; Ministero della Salute e Centro Nazionale Sangue 2026). The practical question is whether enough healthy older adults will come forward to make a meaningful difference. Korea’s experiment is likely to be watched closely.

From science to policy

Blood supply is also a demographic problem. Typically, donor eligibility rules were written for a young population, who also kept transfusion demand relatively low, and the system was in balance. But the world is rapidly changing: the sooner countries acknowledge the mismatch, and change their rules accordingly, the better placed they will be to keep their hospitals running.

The change in Korean law is a rare, clear example of research shaping policy, and quickly. Other aging societies would do well to read the evidence before the blood runs short.

References

Yu X et al. Population-based projections of blood supply and demand, China, 2017–2036. Bulletin of the World Health Organization, 2020;98(1):10–18. doi:10.2471/BLT.19.233361

Kim KW and Kim OS. Super aging in South Korea unstoppable but mitigatable: A sub-national scale population projection for best policy planning. Spatial Demography, 2020; 8:155–173. doi: 10.1007/s40980-020-00061-8

Kim OS et al. Future blood debt: projecting blood supply and demand of Korea based on subnational population projections (2021–2050). Journal of Korean Medical Science, 2024;39:e168. doi:10.3346/jkms.2024.39.e168

Lin Y-M et al. Forecasting blood supply and demand under population aging: implications and challenges for healthcare resource allocation. BMC Health Services Research, 2026;26:369. doi:10.1186/s12913-026-14150-9

Ministero della Salute e Centro Nazionale Sangue.  Accessed 15 June 2026. (In Italian) https://www.donailsangue.salute.gov.it/it/sangue-pagina/chi-puo-donare/

National Health Service (NHS) Blood and Transplant. Accessed 15 June 2026. https://www.blood.co.uk/who-can-give-blood/

Korean Ministry of Health and Welfare. Second Blood Management Basic Plan 2026–2030. Announced 13 May 2026. (In Korean) https://www.mohw.go.kr/board.es?mid=a10503010100&bid=0027&act=view&list_no=1490506&tag=&nPage=1

Korean Redcross Blood Services. Blood Services Statistical Yearbook 2025. (In Korean) https://www.bloodinfo.net/knrcbs/na/ntt/selectNttList.do?mi=1158&bbsId=1061&nttSn=548750

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It is a common observation that developed nations have sub-replacement fertility, meaning that the total fertility rate (TFR) – the total number of children a woman is expected to have – is below 2.1. Both scientific and public discourse often highlight this as a potential long-term issue for nations. Extreme political voices, particularly in low fertility contexts such as Europe, even frame it as a threat to national survival (Goetz et al. 2022).

While most developed nations experience low TFR, Israel is an outlier, as it has a very high TFR and overall low levels of childlessness. Previous studies note that this may be due to the unique combination of traditionalism, family-centred values, and nationalist sentiment originating from the presence of constant threat (Anson and Meir 1996). This suggests a possible association between national identity and fertility.

National identity and fertility

But what exactly is the concept of national identity? Social psychology points to two important components: an awareness of belonging to the nation, and the emotional importance attached to this belonging (Tajfel 1981). A stronger national identity makes the nation more important in people’s lives, making them more likely to follow its norms, pay attention to its needs, and prioritize its survival. The latter is especially important, as threats to the nation – whether perceived or real – bring national identity to the forefront and modify individual behaviour (Stephan et al. 2009).

At individual level, a strong feeling of national identity may stimulate fertility, because children may be seen as a personal contribution to the continuation of the nation, especially when there is the general perception that children are needed. Besides, childbearing is a long-term commitment and individuals with a stronger national identity may place greater trust in the prospects of the nation, increasing their sense of control over the future and making them more likely to invest in it.

Results

In a recent study (Erát, 2026), using data from childless 15–29-year-olds from the Hungarian Youth 2020 survey (6,312 respondents in total), I created a measure of national identity based on responses to eight questions such as “I feel that I am Hungarian” or “I am proud to be Hungarian”, where respondents rated their answers on a 1 to 5 scale (5 = total agreement). After controlling for common socioeconomic characteristics (age, gender, education, parental education, financial situation, living arrangement, partnership status, region, residence type, ethnicity) and attitude-related factors (religiosity, political position), I estimated the probability of specific childbearing intentions (Figure 2).

Compared to individuals with average national identity strength, those with a much weaker attachment to the nation were more likely to want no children. While intentions to have one child were not substantially affected, intentions to have a relatively large family (two or more children) were also associated with national identity. However, stronger national identity was also associated with a higher likelihood of non-response or uncertain answers.

How can we interpret these results? First, the broader context must be considered. While Hungary has long been a family-centric country, the political and social climate changed after 2010. During its self-described “demographic governance”, the government introduced selective childbearing support targeting middle- and upper-class families, promoted rhetoric that framed “others” (migrants and minorities) as threats to the nation, and began an “anti-gender” campaign aimed at returning to more traditional gender roles, particularly emphasizing childbearing as a woman’s duty (Waterbury 2020; Barát 2022; Melegh, 2016). While this did not achieve above-replacement fertility, it created an environment in which national threats, childbearing and traditional expectations were constantly overemphasized. This may have particularly affected the attitudes and intentions of the studied 15-29 age group. 

Considering this context, the results point towards a relatively clear association between national identity and fertility intentions, particularly in the case of childlessness and intentions to have two or more children. A stronger attachment to the nation increases the probability of responding to the nation’s needs and conforming to its expectations, which in the Hungarian case means having more children. But what about the non-response or uncertain answers, which became visibly more likely with stronger national identity? While no definitive explanation can be provided, perhaps these individuals feel a strong attachment to the nation without intending to have children, or at least not as many as the nation “prescribes”. Refraining from answering the question or expressing uncertainty may be a way to avoid directly stating something that goes against the expressed needs of the nation.

References

Barát, E. (2022). “Paradoxes of the Right-Wing Sexual/Gender Politics in Hungary: Right-Wing Populism and the Ban of Gender Studies.” In Paradoxical Right-Wing Sexual Politics in Europe, edited by Cornelia Möser, Jennifer Ramme, and Judit Takács, 173–99. London: Palgrave Macmillan.

Erát, D. (2026). “National Identity and Fertility Intentions: Evidence from Hungary.” Population and Development Review, 52: 135–166. https://doi.org/10.1111/padr.70051

Goetz, J., Oláh, L., and Pető, A. (2022). Discourses On Demography in the EU Institutions. Berlin: Heinrich-Böll-Stiftung, Gunda-Werner-Institut. Online: https://eu.boell.org/sites/default/files/2022-03/Demography_EU_institutions_FIN_web.pdf

Melegh, A. (2016). On the East-West Slope: Globalization, Nationalism, Racism and Discourses on Eastern Europe. Budapest: Central European University Press.

Stephan, W. G., Ybarra, O., and Morrison, K. R. (2009). “Intergroup Threat Theory.” In Handbook of Prejudice, Stereotyping, and Discrimination, edited by Todd D. Nelson, 43–59. New York: Routledge.

Tajfel, H. (1981). Human Groups and Social Categories: Studies in Social Psychology. Cambridge: Cambridge University Press.

Waterbury, M. A. (2020). “Populist Nationalism and the Challenges of Divided Nationhood: The Politics of Migration, Mobility, and Demography in post-2010 Hungary.” East European Politics and Societies and Cultures, 34(4), 962–983. https://doi.org/10.1177/0888325419897772

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Australia is one of the world’s major migrant-receiving countries. More than three in ten residents were born overseas (Australian Bureau of Statistics, 2025) and migration has, for decades, played a central role in shaping the country’s workforce, cities, families and cultural life. It has also influenced the health profile of the population. One of the widely documented findings in migration research is the so-called “healthy migrant effect”: migrants often arrive in their destination country in better health and tend to live longer than people born in the destination country (Anikeeva et al., 2010, Cunningham et al., 2008).

Healthy migrant? At first, yes

Australia broadly fits this pattern, but only in part. In our recent study of life expectancy and healthy life expectancy among overseas-born and Australian-born populations, migrants exhibited a clear longevity advantage (Huang et al., 2026). In 2016, life expectancy at birth was 84.8 years for the overseas-born population, compared with 82.4 years for the Australian-born population (Table 1). Similar differences were evident in 2006 and 2011: Australia’s migrant population does indeed live longer than the Australian-born population.

The reasons for this advantage are not difficult to understand. Migration is often a selective process: healthier individuals are more likely to migrate, and Australia’s migration system has long incorporated health checks and, particularly in recent decades, a strong emphasis on skilled migration (Martinez et al., 2015). Part of the observed advantage may also reflect so-called “salmon bias”, whereby migrants in poor health return to their country of origin and are therefore less likely to be captured in destination-country statistics. In addition, many migrants bring with them protective health behaviours, family networks and community ties that can contribute to better health, particularly in the early years after arrival. 

When longer lives do not mean healthier lives

Living longer does not necessarily mean living healthier. A person may live longer, but spend more of those years with disability, chronic illness or limitations in daily life. This is why healthy life expectancy matters, and this is where the story becomes more complex. In 2016, although overseas-born Australians enjoyed more healthy years at younger ages, this advantage diminished steadily through adulthood and reversed in later life. By ages 55–64, the overseas-born population had lower healthy life expectancy than the Australian-born population. At older ages, this produced a notable divergence: migrants retained a survival advantage yet experienced a disadvantage in health. Globally, immigrants spend a lower share of their life in good health: 91.7% vs 92.5% for natives (Table 1).

Between 2006 and 2016, life expectancy increased for both overseas-born and Australian-born Australians, but healthy life expectancy barely moved. Among overseas-born Australians, for instance, life expectancy at birth rose from 83.4 to 84.8 years, while healthy life expectancy increased only from 77.5 to 77.7 years, and, in comparative terms, things worsened for immigrants (Figure 1). Longer lives were not accompanied by equally large gains in healthy years.

The gendered aspect of migrant ageing

The most striking differences were observed among women. Overseas-born women lived longer than overseas-born men, but they spent a smaller proportion of life in good health. Compared with Australian-born women, their healthy-life advantage disappeared earlier than it did for men. By age 35, overseas-born women had a lower healthy life expectancy than Australian-born women, while overseas-born men retained an advantage over Australian-born men across almost the whole life course.

This matters because migrant women may face a double burden (Donato et al., 2014). Some arrive through family rather than employment pathways, and may therefore be less positively selected in terms of health and labour market advantage. After arrival, they may experience language barriers, unpaid caregiving responsibilities, weaker labour-market attachment, limited social networks, and difficulties accessing culturally appropriate healthcare. These pressures may accumulate over time and become visible in health outcomes in later life.

Discussion

Our findings suggest that migrants’ longer lives should not be taken as evidence that their health is secure. In Australia, the central challenge is increasingly about healthspan, not just lifespan. A multicultural ageing society requires services that recognise the intersecting influences of language, culture, gender, migration history and social disadvantage. This includes culturally safe primary care, stronger efforts in chronic disease prevention, better support for older migrant women, and improved data to identify which migrant communities are most at risk.

The healthy migrant effect remains real, but it is neither permanent nor evenly distributed. Australia’s migrants live longer: the next task is to ensure that those longer lives are also lived in good health. 

References

ANIKEEVA, O., BI, P., HILLER, J. E., RYAN, P., RODER, D. & HAN, G.-S. 2010. The health status of migrants in Australia: a review. Asia Pacific Journal of Public Health, 22, 159-193.

AUSTRALIAN BUREAU OF STATISTICS. 2025. Australia’s population by country of birth [Online]. Available: https://www.abs.gov.au/statistics/people/population/australias-population-country-birth/latest-release [Accessed 4 June 2024].

CUNNINGHAM, S. A., RUBEN, J. D. & NARAYAN, K. V. 2008. Health of foreign-born people in the United States: a review. Health & place, 14, 623-635.

DONATO, K. M., PIYA, B. & JACOBS, A. 2014. The double disadvantage reconsidered: Gender, immigration, marital status, and global labor force participation in the 21st century. International Migration Review, 48, 335-376.

HUANG, G., GUO, F., TAKSA, L., CHENG, Z., LIU, L., ZIMMERMANN, K. F., TANI, M. & FRANKLIN, M. 2026. The ‘Healthy Migrant Effect’and Ageing: Life Expectancy and Healthy Life Expectancy of the Migrant and Native-born Populations in Australia. Social Indicators Research, 182, 38.

MARTINEZ, J. N., AGUAYO-TELLEZ, E. & RANGEL-GONZALEZ, E. 2015. Explaining the Mexican-American health paradox using selectivity effects. International Migration Review, 49, 878-906.

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In many parts of the globe, having a child as an unmarried woman comes with a host of social and economic challenges, and can affect the timing and the quality of a woman’s eventual marriage (Smith-Greenaway and Clark 2018; Smith-Greenaway, Koski, and Clark 2021). Despite focusing on high-mortality contexts, like contemporary Africa, research on this question often makes a rather heroic assumption: that the out-of-wedlock child survives. In reality, however, child mortality remains high in Africa (Smith-Greenaway et al. 2021), and children born outside marriage are especially vulnerable to premature death (Clark and Hamplová 2013). 

What happens when an unwed mother’s child dies?  

This raises the question of whether a child’s survival alters unmarried mothers’ lives, including their eventual entry into marriage. How does the death of a premaritally born child affect the mother’s future marital outcomes? Does the child’s death deepen any observed disadvantage or can it “remove” the stigmatizing status of single motherhood, and improve her chances of marrying? Alternatively, given the high frequency of child fostering in Africa (Cotton 2021), are unwed mothers’ life trajectories unaffected by the loss of a child? 

In a recent paper, we tested these competing hypotheses by examining unmarried mothers’ subsequent marital trajectories after a first birth as a function of their child’s survival (Lin et al. 2025). We used the most recent round of Demographic and Health Surveys, collected between 2011 and 2023, spanning 26 countries in sub-Saharan Africa. On average, 17% of women of reproductive age (15–49) across these countries experienced a premarital birth, yielding a study sample of 69,800 women. Among them, 3% experienced the death of their firstborn during infancy, allowing us to assess how marital timing and union quality differ between women whose premaritally born child died and those whose child survived. 

Premarital child loss and mothers’ marital outcomes 

Unwed mothers whose first child died during infancy marry sooner relative to their unmarried counterparts whose first child survived (Figure 1). This pattern persists after controlling for mothers’ socioeconomic characteristics, including education. However, these analyses offer no qualitative distinctions as to the nature of these women’s marital unions , a topic that has been largely overlooked in the literature. 

Closer scrutiny reveals that following their child’s death, women tend to marry partners with 0.3 fewer years of schooling, are 16% more likely to enter polygynous unions, and 26% more likely to experience domestic violence. 

As child mortality declines, do these patterns change? 

While the broader question concerns women’s marital trajectories following child loss, a natural follow-up is whether these patterns persist as deaths, including child deaths, become rarer over time. 

In our main analyses, we focused on all reproductive-age women in the Demographic and Health Surveys. These include women born in different cohorts who have navigated romantic relationships, childbearing, and marriage under distinct circumstances. As such, we analyzed whether our findings varied across birth cohorts—including women born in the 1970s (or earlier), 1980s, or 1990s (or later).  

We found that inequalities in both marital timing and quality between unmarried mothers by children’s vital status are largest for the youngest cohort in our data, the cohort with the lowest rates of child mortality. Specifically, experiencing an infant death is associated with substantially faster entry into marriage among women born in or after the 1990s, compared to those born in or before the 1980s (Figure 2). Moreover, the spousal educational gap between mothers whose child died versus survived is considerably larger among women born in the 1980s and 1990s than among those born in or before the 1970s.

This suggests that the death of a firstborn child before marriage has become more consequential for women’s subsequent marriage outcomes, with respect to both marriage timing and quality, despite secular decreases in child mortality.

Final remarks 

Our work demonstrates that a child’s death is not only an outcome of inequality but may also be a future source of disadvantage. Future research should examine more closely how child loss reshapes women’s life trajectories and highlights the unique double burden of unwed mothers who may not only bear a child outside marriage but also, in some cases, lose that child. Although child mortality has been declining in Africa, our findings suggest that it is becoming a more consequential event for women, attesting to the need to further understand the impact of child loss on women’s lives and wellbeing. 

References 

Clark, Shelley, and Dana Hamplová. 2013. “Single Motherhood and Child Mortality in Sub-Saharan Africa: A Life Course Perspective.” Demography 50 (5): 1521–1549. https://doi.org/10.1007/s13524-013-0220-6. 

Cotton, Cassandra. 2021. “An Enduring Institution? Child Fostering in Sub-Saharan Africa.” Population and Development Review 47 (4): 1179–1206. https://doi.org/10.1111/padr.12439. 

Lin, Yingyi, Emily Smith-Greenaway, Camila Cortes Rodriguez, and Shelley Clark. 2025. “From Birth to Death: The Marital Consequences of Child Loss for Unmarried Mothers.” Studies in Family Planning. Advance online publication. https://doi.org/10.1111/sifp.70041 

Smith-Greenaway, Emily, Diego Alburez-Gutierrez, Jenny Trinitapoli, and Emilio Zagheni. 2021. “Global Burden of Maternal Bereavement: Indicators of the Cumulative Prevalence of Child Loss.” BMJ Global Health 6: e004837. https://doi.org/10.1136/bmjgh-2020-004837. 

Smith-Greenaway, Emily, and Shelley Clark. 2018. “Women’s Marriage Behavior following a Premarital Birth in Sub-Saharan Africa.” Journal of Marriage and Family, 80(1): 256–270. https://doi.org/10.1111/jomf.12433.

Smith-Greenaway, Emily, Alissa Koski, and Shelley Clark. 2021. “Women’s Marital Experiences Following Premarital Fertility in Sub-Saharan Africa.” Journal of Marriage and Family 83 (2): 394–408. https://doi.org/10.1111/jomf.12704.

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Why it is plausible that health affects fertility

Mental or physical health problems are related to fertility intentions, childlessness and birth rates (e.g., Lazzari and Beaujouan 2025; Liu et al. 2024; Kravdal, Flatø and Torvik 2025). While, to some extent, such relationships are a result of joint determinants of health and fertility that have not been adequately controlled for, they may also reflect effects of health on fertility through a variety of causal pathways. For example, some chronic and severe health problems may adversely affect education and income, with implications for partnership formation and dissolution, and for a couple’s interest in having children.

Women and men with serious health issues may fear that having a child, or an additional child, would not be a joyful experience, that they would not be good enough parents, or that the strains of parenthood could make their condition worse. Additionally, there may be effects on fecundity and even on the effective use of contraception.

Even for individuals in good mental and physical health, childbearing can be a daunting experience. Some women experience particularly challenging pregnancies with, for example, severe nausea, health risks due to eclampsia, particularly difficult deliveries, or adverse mental or physical postnatal implications even after a relatively normal pregnancy and delivery. The latter include, for example, postnatal depression, pelvic pain, and perineal tears. These women and their partners may be less interested than others in having an additional child: even short-lived mental and physical health problems may be perceived as heavy burdens, and women may not want to take the risk of assuming them a second time. Some may not be physically able to have more children either.

Similarly, while it is often exhausting to care for very young children, some infants are more challenging for parents than others, for example because of their sleep patterns or colic. These issues may persist for several months, or even years, sometimes leading to health problems for the parents themselves. In such situations, some parents may not be very eager to have an additional child, who may affect them in the same way. 

New empirical evidence

Two studies based on nationwide Norwegian register data (Grundy and Kravdal 2025) and on the large Mother, Father and Child survey with added register data (Kravdal, Grundy and Hart 2026) provide evidence for these hypotheses. The latter investigation was based on information about health status around the time of the first birth for about 37,000 women, and around the time of the second birth for about 29,000. It was shown, for example, that mothers who experienced nausea in their first pregnancy were less likely than others to ever have a second child, and they also waited longer if they had one (see Table 1). Women who had pre-eclampsia or (more rarely) eclampsia in their second pregnancy had a reduced probability of progressing to a third birth, as did those who were on sick leave after week 30 of the second pregnancy. 

Furthermore, second- and third-birth probabilities were substantially reduced among women whose previous birth was difficult, as indicated by an emergency caesarean section, and more markedly so among those who had a planned caesarean section (but not when performed for a breech presentation). This situation may signal problems in pregnancy or underlying health problems not captured by other covariates. (The link between delivery mode and second births was investigated thoroughly in Grundy and Kravdal 2025.) Additionally, the probability of having a second child was low among women who had postnatal depression after the first birth or whose first child had colic or slept poorly. Sleep problems were also related to a reduced third-birth probability.  

Certain chronic somatic or mental health problems experienced by the mother or father before the most recent previous pregnancy were also associated with the probability of having additional children (not shown in the table).  

The value of this research

How useful is such knowledge about associations between health and fertility, which may partly reflect causal effects? In principle, it may help us predict the fertility implications of certain types of health deterioration or improvement. Having a more reasonable picture of future fertility levels may in turn be valuable from a planning perspective and for political discussions. Current evidence suggests that the apparent rise in mental disorders among young Norwegians in recent years – and the possible continuation of this trend – has had and will have a modest impact on the national fertility level (Kravdal, Flatø and Torvik 2025). However, it is also useful to be aware of a lack of aggregate-level implications, and there may be other implications relating to other settings and other types of health changes. 

Clearly, when steps are taken to improve the prevention or treatment of various diseases, or increase the quality of patient care, better health and well-being is a goal in itself. However, the evidence referred to above indicates that there may be spin-offs in the form of higher fertility. This means that, if there is concern about low fertility (Kravdal 2025), attempts to prevent or cure diseases, or to make life easier for individuals with chronic or temporary health problems – including during pregnancy and for some time after birth – may serve a dual purpose. This is in line with the conclusions of a recent White Paper to the Norwegian government about the causes and consequences of low fertility (NOU 2026). 

References

Grundy, Emily, and Øystein Kravdal. 2025. “Mode of delivery of the first child and progression to a second birth in Norway.” Population-E 80 (3-4), 381-404. https://shs.cairn.info/journal-population-2021-0-page-381?lang=en

Kravdal, Øystein. 2025. “Should we be concerned about low fertility? A discussion of six possible arguments.”  Demographic Research 53(14), 373-418. https://doi.org/10.4054/DemRes.2025.53.14

Kravdal, Øystein, Martin Flatø, and Fartein A. Torvik. 2025. “Fertility among Norwegian women and men with mental disorders.” European Journal of Population 41 (1): 17. https://doi.org/10.1007/s10680-025-09739-5

Kravdal, Øystein, Emily Grundy, and Rannveig K. Hart. 2026. “Health influences in second- and third-birth probabilities in Norway”. Population and Development Review. http://doi.org/10.1111/padr.70066

Lazzari, Ester, and Éva Beaujouan. 2025. “Self-assessed physical and mental health and fertility expectations of men and women across the life course.” Demography 62 (2): 543-569.https://doi.org/10.1215/00703370-11873109

Liu, Aoxing, Evelina T. Akimova, Xuejie Ding, Sakari Jukarainen, Pekka Vartiainen, Tuomo Kiiskinen, et al. 2024. “Evidence from Finland and Sweden on the relationship between early-life diseases and lifetime childlessness in men and women.” Nature Human Behaviour 8 (2): 276-287. https://doi.org/10.1038/s41562-023-01763-x

NOU 2026. “Policy for new generations – Causes, consequences and measures related to low birth rates. English summary”. NOU 2026:2. Oslo: Ministry of Families and Children.

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Race is a social construct subject to variation not only across historical epochs but also within an individual’s lifetime. The fluidity of racial classification has been extensively documented in cases such as the United States and in Latin America (Davenport 2020, Loveman 2014, Nobles 2000), with growing attention to other parts of the world (Saperstein 2025). Despite this recognition, the incorporation of racial fluidity into demographic research has been notably sparse.
In a recent study (Muniz, Saperstein and Queiroz 2024), we estimated race-specific mortality and transition probabilities in Brazil, aiming to capture the variability inherent in racial classification over the life course using multistate life tables. We later slightly revised our estimates, using a technique that more accurately reflects classification fluidity over the life course (Muniz, Saperstein and Queiroz 2026). This note describes our most recent results. 
Brazil offers an illustrative case because of its long history of official racial classification in the census and major surveys, along with its pronounced racial dynamism (Muniz and Bailey 2022). In the most recent census (2022), the Brazilian population predominantly self-identified as Brown (i.e., as mixed-race or pardo, 45.3%), followed by White (43.5%), Black (10.2%), Indigenous (0.8%), and Asian (0.4%). This represents a notable rise of 16% in the number of people identifying as Black and Brown since 2010, while the share of the White population has declined by 3%. Our analysis focuses on changes in classification among these three largest categories.
We estimated age and race-specific multistate life tables referring to the second half of the 2010 decade using nine-month longitudinal data from the Continuous National Household Sample Survey (PNAD-C), conducted by the Brazilian Institute of Geography and Statistics (IBGE), and death registration information from the Ministry of Health’s Mortality Information System, alongside census population counts. In PNAD-C, one person provides information for the entire household, so race can be either self-identified or reported by a family member.

Transition to Brown is the most common racial reclassification

Our findings unveil varying degrees of stability in racial classification, with Black categorization exhibiting the least stability, followed by White and Brown. Notably, transitions from Black to Brown emerge as the most common racial shifts, while transitions from White to Black are relatively rare. In particular, Brazilians initially classified as Black are more than twice as likely to be reclassified as Brown than White (Figure 1).

Overall, transitions to the Brown category are the most common pattern of racial shifting. This has contributed to recent increases in the share of negros (Blacks + Browns) in Brazil, making it the largest racial population in the country. These racial reclassification patterns likely derive from recent darkening among Brazilians with higher socioeconomic status and incentives from affirmative action policies in universities and the labor market (Miranda 2015, Senkevics 2022).

On average, people will experience 6.07 years in the Black category

According to IBGE, life expectancy at birth was 76.4 years in 2023. Given current patterns of racial fluidity, the results show Brazilians will spend 39% of their lives as White, 53% as Brown, and 8% as Black, regardless of their racial classification at birth. These percentages correspond to about 40.5 years spent in the Brown category, on average, and 6.07 years lived as Black (Figure 2).
These figures emphasize the dynamic nature of racial classification, challenging fixed notions of race and highlighting the importance of contextual and temporal dynamics in shaping the distribution of racial populations.

Implications for demographic projections

Racial reclassification has several implications for how populations, identities, and inequalities are defined and measured. When a person’s race is reclassified, either by themselves or others, it affects not only the size but also the composition and estimated growth rates of racial populations. 
Given the level of racial fluidity in Brazil, in particular, ignoring racial reclassification in population forecasts can have social, political, and economic consequences, from resource allocation to political representation, making it critical to address this issue in demographic studies. Acknowledging temporal racial fluidity, preferably through multistate demographic projections, should be integrated into the practice of demographers and policy planners.

Conclusions

By employing multistate life tables, our study not only illuminates the fluidity of racial classification over the life course but demonstrates avenues for investigating temporal and regional variations in racial dynamics. Future research endeavors should explore the spatiotemporal dimensions of racial fluidity, shedding light on the evolving nature of racial classification and its intersection with broader social phenomena. Multistate life tables offer a powerful tool for revealing the complexities of racial classification, paving the way for more nuanced analyses of race and its implications for social inequality and public policy.
More broadly, our research underscores the importance of conceptualizing race as a mutable attribute rather than a static construct. As research continues to unravel the intricacies of race and racism, it becomes increasingly clear that understanding racial fluidity is essential for advancing the comprehension of social stratification and inequality in contemporary societies.

Funding

J. Muniz (308909/2022-4) and B. Queiroz (303928/2022-0) received financial support from the National Council for Scientific and Technological Development (CNPq).
*The views expressed in this article are those of the authors and not those of the sponsoring agencies.

References

Davenport, L. (2020). The fluidity of racial classifications. Annual Review of Political Science 23: 221–240. doi:10.1146/annurev-polisci-060418-042801.

Loveman, M. (2014). National colors: Racial classification and the State in Latin America. New York: Oxford University Press. doi:10.1093/acprof:oso/9780199 337354.001.0001.

Miranda, V. (2015) A resurgence of black identity in Brazil? Evidence from an analysis of recent censuses. Demographic Research 32(59): 1603-1630. doi: https://doi.org/10.4054/DemRes.2015.32.59.

Muniz, J. and Bailey, S. (2022). Does race response shift impact racial inequality? Demographic Research 47(30): 935–966. doi:10.4054/DemRes.2022.47.30.

Muniz, J., Saperstein, A., and Queiroz, B. (2024). Racial classification as a multistate process. Demographic Research 50(17):457-472. doi:10.4054/DemRes.2024.50.17.

Muniz, J., Saperstein, A., and Queiroz, B. (2026). Letter in response to Racial classification as a multistate process. Demographic Research 50(17):457-472.

Nobles, M. (2000). History counts: A comparative analysis of racial/color categorization in US and Brazilian censuses. American Journal of Public Health 90: 1738–1745. doi:10.2105/AJPH.90.11.1738.

Saperstein, A. (2025). Recognizing Identity Fluidity in Demographic Research. Population and Development Review 51(1): 519-538. doi: 10.1111/padr.12670.

Senkevics, A. (2022). De brancos para negros? Uma análise longitudinal da reclassificação racial no Enem 2010-2016. Dados 65(3): e20190088. doi:10.1590/dados.2022.65.3.268.

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Studies of various wars repeatedly show that women who live in a war zone face not only immediate violence but also elevated risks long after conflicts end. In a recent study (Lin and Smith-Greenaway 2026), we revisited this finding from a new angle. We asked: “Does women’s war exposure increase their risk of experiencing domestic violence because of their own exposure to war, or because they marry men who were exposed to war?” Answering this question is critical for designing effective interventions. To date, most programs to reduce domestic violence in post-conflict settings have focused primarily on women (Spangaro et al. 2021).

The Nigerian Civil War (1967–1970)

The Nigerian Civil War, also known as the Biafran War, began in 1967 when the Igbo-dominated Eastern region declared itself independent, and the federal government responded with military force to suppress the secessionist movement, leading to war between the Nigerian government and the secessionist state of Biafra. Sparked by ethnic tensions, political instability, and the fight for control over oil resources (Uche 2008), the conflict led to devastating humanitarian crises, including widespread famine and mass casualties, as well as economic devastation. 

Given that the war was a relatively short–lived (July 1967–January 1970), geographically concentrated ethnic conflict, ethnicity (Igbo) and birth cohort (conceived or born during the war years) function as fixed markers of war exposure, allowing us to analyze wives’ and husbands’ war exposure separately in nationally representative couple data. Specifically, we analyzed the 2008 Demographic and Health Survey couple data, allowing us to account for both wives’ and husbands’ experiences and traits when studying intimate partner violence. As the survey focuses on reproductive age women (15–49 years old) and their spouses, and given the timing of the war, these data allow us to capture wives and husbands who were exposed to war at some point between being in utero and early adolescence (age 12 for wives) and late adolescence and early adulthood (age 22 for husbands), respectively. Figure 1 shows that, in the data, 11% of wives and 19% of husbands were exposed to war in early life.

Whose war exposure matters: hers or his, and why? 

Women are significantly more likely to experience intimate partner violence in their marriage if they or their husband were exposed to war in early life (Figure 2), with a larger difference in this probability by husbands’ war exposure than by wives’ own exposure. By age 49, the probability of experiencing domestic violence is about 15% higher for war-exposed wives than for those not exposed. However, by the same age, the probability is more than 20% higher for wives whose husbands were exposed to war in early life compared to those whose husbands were not.

This descriptive pattern, suggesting that husbands’ war exposure may matter more, persists in multivariate models. Indeed, wives’ war-related risk of experiencing domestic violence appears to be driven entirely by their husbands’ war exposure. That is, the apparent link between women’s exposure to the Nigerian Civil War and their risk of experiencing intimate partner violence disappears once we take into account their husbands’ exposure to war. For women with a war-exposed husband, the risk of experiencing intimate partner violence is increased by 45%.

Among a range of potential explanations – including husbands’ experience of war-related family loss, their human capital accumulation (such as education and employment), and their marital characteristics (including fertility, child mortality, and polygamy) – men’s risk behaviors emerge as particularly important. Alcohol use helps explain part of the association linking husbands’ war exposure and their perpetration of violence against their partners.

Where do we go from here?

These findings highlight the importance of targeting men in post-conflict interventions, contrary to current efforts that have focused primarily on war-exposed women, as men’s wartime experiences play a central role in shaping their risk of perpetrating violent behavior years later. The results also point to alcohol consumption as a key driver of intimate partner violence, suggesting that interventions addressing substance use may be especially effective.

At the same time, important questions remain. Alcohol use only partially explains the link between men’s war exposure and their violent behavior; further research is needed to identify additional mechanisms. Understanding these pathways will be critical for developing more effective policies and interventions. Taken together, the findings underscore the potential for the collateral damage of war to reverberate within families and intimate relationships long after a conflict has ended.

References

Ajefu, Joseph B., and Daniela Casale. 2025. “Long-Term Effects of Childhood Exposure to War on Domestic Violence.” The European Journal of Development Research 37: 124–51. https://doi.org/10.1057/s41287-024-00659-4. 

Torrisi, Orsola. 2023. “Young-Age Exposure to Armed Conflict and Women’s Experiences of Intimate Partner Violence.” Journal of Marriage and Family 85: 7–32. https://doi.org/10.1111/jomf.12876.

Lin, Yingyi, and Emily Smith-Greenaway. 2026. “War, Gender, and Family Dynamics: A Couple Analysis,” Population and Development Review https://doi.org/10.1111/padr.70062 

Spangaro, Jo, Chye Toole-Anstey, Catherine L. MacPhail, Delia C. Rambaldini-Gooding, Lynne Keevers, and Claudia Garcia-Moreno. 2021. “The Impact of Interventions to Reduce Risk and Incidence of Intimate Partner Violence and Sexual Violence in Conflict and Post-Conflict States and Other Humanitarian Crises in Low and Middle Income Countries: A Systematic Review.” Conflict and Health 15 (1): 86. https://doi.org/10.1186/s13031-021-00417-x. 

Uche, Chibuike. 2008. “Oil, British Interests and The Nigerian Civil War.” The Journal of African History 49 (1): 111–35. https://doi.org/10.1017/S0021853708003393.

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Across most of the world, women today are having fewer children than their mothers or grandmothers did. This much is well known. Less examined is how the timing of those births has shifted: not just when women start having children, but how compressed or spread-out childbearing has become across the reproductive years.

Since 1950, the median total fertility rate has fallen from nearly six births per woman to under two (United Nations 2024). As fertility levels converged worldwide, so too did the analytical toolkit used to monitor them: the total fertility rate (TFR) summarizes the quantum of fertility, and the mean age at childbearing (MAC) captures its central timing. Both indicators are indispensable, but both reduce a complex distributional process to a single number. They cannot reveal whether births cluster tightly around the mean age or spread across the full reproductive lifespan. Two countries can share identical TFR and MAC values yet exhibit very different fertility schedules: one concentrated between ages 25 and 35, another stretching from adolescence into the forties.

What existing approaches do and don’t capture

Efforts to go beyond averages have a long history in demography. An influential line of research quantifies how concentrated reproduction is among women, i.e. what share of women account for what share of all births. Drawing on Lorenz-curve principles, Vaupel and Goodwin (1987) constructed such indices for the United States, and Lutz (1989) extended them globally; Shkolnikov et al. (2007) later applied similar tools to European and American cohorts. A separate strand examines the shape of the fertility age schedule through its mean and variance, though these parameters assume a symmetric distribution and struggle when fertility schedules are skewed or multi-peaked (Chandola et al. 1999). More recently, percentile-based metrics have proven their value in measuring inequality in ages at death (Shi et al. 2022), pointing toward an approach that is robust to irregular distributions and straightforward to interpret.

What has been missing is a measure focused on the temporal spread of fertility by age: not who bears children, but over how many reproductive years most births occur in a given population. This is the gap the childbearing span is designed to fill (Spoorenberg 2026). It offers a direct and globally comparable lens for tracking this distributional dimension of fertility.

The childbearing span: concept and intuition

The childbearing span measures the number of years separating the age at which a population reaches 10% of its total fertility from the age at which it reaches 90%. It captures the width of the fertility schedule (i.e. how many years of reproductive life account for the bulk of childbearing) rather than simply its level or central timing. A span of 13 years, as observed in India today, means that 80% of births are concentrated into just 13 years. A span of 19 years, as in Peru, reflects a much more spread-out pattern. Both countries share nearly identical total fertility rates, yet their reproductive age distributions look strikingly different.

This distinction matters for several reasons. The spread of fertility affects demographic momentum, the overlap of generations, and intergenerational support structures. It is also closely tied to the social transformations, such as in education, employment, contraception, and gender norms, that reshape when and how reproduction is organized within societies.

A worldwide compression, unfolding unevenly

Applying this approach to a global dataset from 1950 to 2025 reveals a clear global trend: childbearing has been compressed into steadily shorter reproductive windows over the past 75 years. The global average childbearing span declined from about 19 years in 1950 to under 17 years in 2025, meaning the world’s reproductive age distributions have narrowed by roughly two years on average (Figure 1).

Behind this overall trend lie sharply divergent regional trajectories. Eastern and South-Eastern Asia underwent the most dramatic compression: spans fell from around 19.5 years in 1950 to approximately 13.5 years by the early 1990s, driven by rapid fertility transitions, strong government policies, and normative convergence around socially sanctioned ages for childbearing. Today, this region stands at the extreme end of concentration globally.

Sub-Saharan Africa tells a different story. Despite substantial fertility decline from over six births per woman to around 4.3, the region’s childbearing spans have remained consistently broad, barely narrowing over seven decades. This persistence suggests that wide age distributions are maintained by structural and cultural factors such as the continued social importance of motherhood, plural marriage systems that extend fertility into older ages and, possibly, risk-diversification strategies in high-mortality and high-uncertainty environments.

Europe, Northern America, Australia, and New Zealand display yet another pattern. After early compression through the 1970s, spans temporarily widened between 1980 and 2010, when fertility postponement intensified, before narrowing again. This U-shaped trajectory illustrates how period measures are sensitive to ongoing timing shifts: when large numbers of women delay births simultaneously, fertility appears more dispersed in any given year than it actually is across cohorts.

Concentration is not simply a function of fertility level

One of the most striking findings concerns the relationship between fertility levels and concentration patterns (Figure 2). In 1950, despite vast differences in how many children women had, the width of fertility schedules was remarkably similar across countries: most childbearing spanned an interval of between 45% and 55% of the 35-year reproductive period. By 2025, that uniformity has dissolved. Countries with identical total fertility rates of around two births per woman now display concentration shares ranging from 30% to 50%, representing a near two-fold difference in the proportion of reproductive life devoted to childbearing.

This decoupling of level from distribution signals that fertility concentration has become an independent demographic dimension, shaped by cultural norms, institutional contexts, and socioeconomic structures that operate separately from the quantum of fertility. Understanding why countries at similar fertility levels have such different age distributions requires looking beyond the number of children to the social organization of reproduction itself.

A simple tool with broad analytical reach

The childbearing span requires only standard age-specific fertility rates (i.e. data that are relatively easily available) and yields an immediately interpretable quantity: a number of years. It can be disaggregated by region, income group, or social subpopulation whenever data allow, and it can complement the total fertility rate and mean age at childbearing in routine demographic monitoring.

As fertility continues to decline globally, differences in concentration patterns may become increasingly consequential, reflecting distinct experiences of reproductive timing across populations and raising questions about equity, autonomy, and the conditions under which women make childbearing decisions. The childbearing span offers a straightforward entry point for tracking these distributional dynamics across time and place.

* The views expressed herein are those of the author and do not necessarily reflect the views of the United Nations.

Reference

Chandola, T., Coleman, D.A. and Hiorns, R.W. 1999. “Recent European fertility patterns: Fitting curves to ‘distorted’ distributions.” Population Studies 53(3): 317–329.

Lutz, W. 1989. Distributional Aspects of Human Fertility: A Global Comparative Study. Academic Press.

Shi, J., Aburto, J., Martikainen, P., Tarkiainen, L. and van Raalte, A. 2022. “A distributional approach to measuring lifespan stratification.” Population Studies 77(1): 15–33.

Shkolnikov, V.M., Andreev, E.M., Houle, R. and Vaupel, J.W. 2007. “The concentration of reproduction in cohorts of women in Europe and the United States.” Population and Development Review 33(1): 67–100.

Spoorenberg, T. 2026. “Global trends in the compression of childbearing spans, 1950–2025: A new approach to the age distribution of fertility.” Journal of Population Research 43:12. https://doi.org/10.1007/s12546-026-09420-6 

United Nations. 2024. World Population Prospects 2024. Department of Economic and Social Affairs, Population Division. https://population.un.org/wpp/ 

Vaupel, J.W. and Goodwin, D.G. 1987. “The concentration of reproduction among U.S. women, 1917-1980.” Population and Development Review 13(4): 723–730.

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