While China successfully leveraged a favorable age structure to fuel decades of economic growth, this age-based demographic dividend is now weakening due to rapid population aging and sustained low fertility. Policy responses have largely focused on reversing fertility decline through pro-natalist measures, yet existing evidence suggests that such interventions have only limited effects (Gauthier & Gietel-Basten, 2025).

This raises a broader question: if the contribution of the age structure to economic growth is diminishing, what factors can sustain future development? To address this, it is useful to distinguish between two dimensions of the workforce. The first is captured by the age support ratio (ASR), which reflects the balance between the working-age population and dependents. The second is related to the skills embodied in the workforce. To measure this second dimension, we introduce the task-based skill ratio (TSR), an index that captures the composition of jobs in terms of the tasks they require, ranging from low- to high-skill activities.

The key challenge, therefore, is to understand how these two dimensions interact. Can improvements in the skill composition of the labor force compensate for the decline in the age-based demographic dividend? And to what extent can skill upgrading sustain economic growth in the context of continued demographic aging?

China provides a particularly relevant case to examine these questions. Its rapid transition from a labor-abundant economy to an aging society, combined with significant investments in education and structural economic transformation, offers a unique opportunity to observe how the sources of economic growth evolve. Insights from this transition are not only important for China, but also for other countries at different stages of the demographic transition.

A skill-based perspective

In a recent study (Gu et al., 2026), we constructed the TSR using an approach comparable in spirit to the ASR, but focused on skills rather than age. Building on the framework of the United States Occupational Information Network, occupations were decomposed into detailed work activities, which were then assigned skill intensities. These task-based scores were aggregated to the city level to produce a consistent measure of local skill composition. This makes the TSR directly comparable to the ASR: both are aggregate indicators defined at city level, both vary over time, and both can be linked empirically to economic outcomes such as per capita GDP. However, they capture different dimensions of the labor force: quantity versus quality. Empirically, we found that the ASR in China reached a turning point around 2010 and has since declined, reflecting demographic aging. In contrast, the TSR has continued to rise, particularly in eastern megacities, indicating sustained improvements in the skill composition of the workforce. 

We then estimated both the independent and joint effects of the ASR and TSR on economic performance, and projected the levels of TSR required to offset future declines in the ASR under different demographic scenarios from 2025 to 2100.

Synergy between age and skills

Both the ASR and the TSR have a positive effect on per capita GDP, but since around 2010, the TSR has become the stronger and more consistent driver of economic performance. Our results also show that expanding the working-age population alone is not sufficient to generate growth. In cities with low skill levels, a larger labor force yields limited economic benefits. By contrast, improvements in skill composition can sustain growth even as the advantage based on age structure declines. This complementarity weakens with population aging. As the workforce ages, the overall contribution of the ASR declines, and the adaptability of the labor force becomes more constrained. Older workers, on average, face greater challenges in adjusting to changing task requirements, which reinforces the shift from a quantity-based to a skill-based driver of economic growth.

Three phases of the demographic-skill transition

The demographic foundations of economic growth in China have undergone a clear transition across three distinct phases (Fig. 1). First, the age-based dividend phase (2000 to 2005) was characterized by rapid growth driven primarily by the expansion of the working-age population. Market reforms and the relaxation of household registration constraints facilitated large-scale labor reallocation from agriculture to industry, allowing China to fully exploit its demographic advantage.

Second, the transition phase (2005 to 2010) marked a turning point. During this period, the contribution of the ASR declined substantially, while the influence of the TSR increased. Third, the skill-based dividend phase (after 2010) saw a fundamental shift in the drivers of economic growth. As labor supply growth slowed and population aging intensified, the marginal effect of the ASR continued to weaken. At the same time, industrial upgrading and rising human capital led the TSR to become the dominant source of growth. Overall, Fig. 1 illustrates that the demographic dividend has not disappeared. Rather, its underlying mechanism has evolved from an extensive model based on labor supply to an intensive model driven by skill accumulation and productivity gains.

Projections and policy implications

To quantify the TSR enhancement required to counteract aging pressure, we constructed a dynamic projection based on the United Nations low fertility variant for the period between 2025 and 2100 (Fig. 2). We selected this low variant because recent fertility trends in China fall below medium-level projections, which makes it a realistic reflection of population dynamics. We then established three adjustment scenarios to evaluate future policy options:

1) Redefining the working-age population: We extended the upper boundary of the working-age population to 69 years and 74 years to simulate delayed retirement policies.

2) Age-dynamic scenario, assuming that the marginal contribution coefficient of the ASR (1) grows at an annual rate of 1% (i.e. becomes stronger over time).

3) Skill-dynamic scenario, assuming that the marginal contribution coefficient of the TSR (2) grows at an annual rate of 1%.

Projections of the compensatory TSR required up to the year 2100 indicate that delayed retirement can partially alleviate the effects of the declining age support ratio. Extending the upper boundary of the working age to 69 or 74 years dilutes the negative impact of age-structure changes by expanding the labor supply, which creates a substantial window for adjustment in the medium term. However, adjusting the labor supply provides only a temporary buffer and cannot reverse the long-term upward trend of ageing. 

The projections highlight that the evolution of economic parameters exerts a decisive influence on future outcomes and, not surprisingly, that combining the enhancement of the TSR with a moderate extension of working life offers the most viable path to navigate the challenges of advanced aging. Economic development relies less on favorable age structures and increasingly depends on the skill composition of the workforce, making skill upgrading a central policy priority.

References

Gauthier A. H. & Gietel-Basten S. (2025). Family policies in low fertility countries: Evidence and reflections, Population and Development Review, 51(1), 125-161. https://doi.org/10.1111/padr.12691

Gu H., Wu Y., Marois G., Lutz W. & Niu T. (2026). China’s demographic dividend has moved from age-based labor supply to skill-based productivity, Proceedings of the National Academy of Sciences, 123 (15) e2532906123. https://doi.org/10.1073/pnas.2532906123

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At the Budapest Demographic Summit in September 2023, Hungary’s President and former Family Minister Katalin Novák told a prominent international audience that Hungary had found a pro-family answer to the demographic crisis: family-related spending amounting to 6% of GDP – presumably the highest level globally – had resulted in a significant fertility increase. Subsequently, Italian Prime Minister Georgia Meloni proclaimed that Italy will now copy these “highly successful” Hungarian policies to increase Italian fertility. International media reported these statements without questioning the underlying demographic evidence. 

Ups and downs in period TFR are often driven by changes in birth timing 

What were the actual fertility trends that elicited these strong statements? Hungarian policy makers pointed at the TFR increasing from a record low of 1.25 in 2010 (when Viktor Orbán returned to power as prime minister) to 1.59 in 2021 (Figure 1). This is indeed a rather impressive increase of 27% – or a third of a child – over a decade. But what do these numbers tell us? 

In Hungary, 2010 was a very specific year due to a spiraling debt crisis. Such economic shocks often accelerate the shift to later parenthood, whereas economic recovery and new family policies frequently contribute to a slowdown in this shift or even a temporary stabilization in fertility timing (e.g., Sobotka, Matysiak and Brzozowska 2019). This is exactly what happened in Hungary. In the decade 2000-2010, the mean age at first birth among Hungarian women jumped from 25.1 to 27.7, rising by almost 2.6 years and strongly deflating the TFR. In the next decade, the mean age at first birth almost levelled off, going up by just 0.7 years to 28.4 in 2020. The observed increases in the TFR were primarily driven by these changing patterns in the timing of births, i.e. a slowdown in the previously strong postponement of childbearing. 

The TFR does not measure the quantum of fertility

How should we assess fertility levels and trends in times of policy changes, economic shocks, and rapid societal shifts? Today most analytical studies and policy-making discussions choose to focus exclusively on the TFR. From a theoretical perspective this seems odd because the TFR is neither an appropriate measure of the direct impacts on population size and age structure nor an appropriate measure of the quantum of fertility. But convenience typically trumps theoretical considerations: TFRs are readily available for almost all countries and regions and often also for specific population groups. And they offer a seemingly straightforward interpretation as the “number of children per woman”.

Two aspects should be of main interest to policy makers and scientists studying longer-term implications of fertility: 

1. The absolute number of births per year is the only variable that directly translates the effect of current fertility on changes in population size and age structures. Together with mortality and migration, it determines the size of cohorts for many decades into the future and should therefore be the key element of any demography-based planning. Due to shifting age structure and continuing outmigration of younger people, the number of women of reproductive age declined in Hungary between 2010 and 2020. Therefore, despite a robust rise in the TFR, the number of live births stagnated in that period, with a total of just 90,000 in 2010 and 92,000 in 2020. This is well below the level required to keep the population size stable in the absence of migration and changes in mortality. A crude estimate of the number of births needed to “replace” expected deaths, computed by dividing total population in 2020 (9.7 million) by its life expectancy (76 years), gives a figure of 127,000 births  in Hungary each year, almost 40% above the actual number.

2. The quantum of fertility refers to the number of live births a woman has on average over her life course. This can only be assessed directly for cohorts of women who have completed their childbearing. For women aged 40 this indicator was steadily declining in Hungary among those born in the 1960s and 1970s, so far with no sign of a turnaround (Figure 1). But computing completed cohort fertility rate (CTFR) implies too long a waiting time for the impatient observer who wants to know how younger women react to changing conditions and stimuli, including family policies. For this reason, many analysts turn to the TFR. But this is a poor proxy for the period fertility quantum and a poor reference indicator in policy discussions because it only eliminates distortions due to age structure but not changes in timing, which played an important role in Hungary.  

There are suitable alternatives to the TFR to measure the period fertility quantum

Over the past 25 years, different methods for calculating estimates of the period fertility quantum have been proposed. A method based on adjusting conventional TFRs for the pace of change in birth timing was proposed by Bongaarts and Feeney in 1998. It is easy to calculate for countries with order-specific data on fertility rates and remains commonly used to date (it is also available in the Human Fertility Database). However, it sometimes shows large fluctuations and may provide implausible results when the variance of fertility by age or the parity composition change rapidly. The indicator of tempo- and parity-adjusted total fertility rate, TFRp* (originally proposed by Bongaarts and Feeney in 2006 and elaborated by Bongaarts and Sobotka in 2012), is less widely used, but provides more robust measures of the true quantum of period fertility. For most European countries, the TFRp* is available in the European Demographic Datasheet (VID 2022) and can be calculated from HFD data.

Hovering around 1.5-1.6, the TFRp* in Hungary did not change much between 2010 and 2020 (Figure 1 above). When we compare TFRp* trends in Hungary and selected other countries in Central and Eastern Europe (Figure 2), three main findings emerge. First, the TFRp* is, as expected, higher and more stable than the conventional TFR. Second, the upswing in the TFR that was apparent for many countries in the 2010s, is largely absent in the TFRp*. Most of the TFR rise in the 2010s can therefore be attributed to the slowing pace of fertility postponement. Third, five out of seven countries featured in the graph show TFRp* levels in 2016-2021 (when the latest data were available) similar to those of 2000. Only Poland and Hungary display considerably lower TFRp* levels, suggesting that their earlier fertility declines did not subsequently recover.  

We need to move beyond the TFR 

Since politicians and journalists cannot be expected to know the difference between fertility indicators, the responsibility falls on the demographers who provide them with the indicators, and whose interpretations all too often uncritically refer to trends in the TFR as trends in the quantum of fertility. Our examples for Hungary illustrate that the TFR can easily send wrong signals about fertility trends and their underlying drivers: the seemingly impressive upturns in the TFR have been largely fueled by a slowdown in the shift to later parenthood and have not been mirrored in corresponding upturns in cohort fertility or in tempo-adjusted period fertility indicators. Demographers should make better use of their toolkit and offer a more differentiated assessment of fertility trends and policy effects, using a broader range of indicators and controlling for the role of tempo effects (Sobotka and Lutz 2011). 

References 

Bongaarts, J. and Feeney, G., 1998. On the quantum and tempo of fertility. Population and Development Review, 24(2): 271-291. DOI: https://doi.org/10.2307/2807974

Bongaarts, J., and Feeney, G.  2006. The quantum and tempo of life-cycle events. Vienna Yearbook of Population Research, 4(2006): 115–151.

Bongaarts, J. and Sobotka, T., 2012. A demographic explanation for the recent rise in European fertility. Population and Development Review, 38(1): 83-120. DOI: https://doi.org/10.1111/j.1728-4457.2012.00473.x

Human Fertility Database. 2023. Max Planck Institute for Demographic Research (Germany) and Vienna Institute of Demography (OeAW) (Austria). Available at www.humanfertility.org (data downloaded 18 December 2023).

Sobotka, T. and Lutz, W., 2010. Misleading policy messages derived from the period TFR: Should we stop using it? Comparative Population Studies, 35(3). DOI: https://doi.org/10.12765/CPoS-2010-15 

Sobotka, T., Matysiak A., and Brzozowska, Z. 2019. Policy responses to low fertility: how effective are they? UNFPA, 2019.  VID. 2022. European Demographic Datasheet 2022. Wittgenstein Centre (IIASA, VID/OEAW, University of Vienna), Vienna. Available at www.populationeurope.org

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The future of global population trends will greatly depend on progress in the universal process of demographic transition. Although the concept of demographic transition, introduced by Warren Thompson (1929) and Alfred Landry (1934), is almost a century old, there is still little consensus about what drives this progressive decrease in death and birth rates from high and largely uncontrolled levels to low and controlled levels. Many partial explanations exist in the literature, focusing on economic costs and benefits, social value changes, or (reproductive) health-related public policies.

Reassessing demographic transition theory

In a recent book that attempts to reassess demographic transition as a theory with predictive power, I highlighted the importance of cognitive empowerment and abstraction skills associated with education as an essential factor in the onset of both mortality and fertility transitions (Lutz 2021). This book points at new evidence concerning the conditions of the first lasting mortality decline in Sweden and fertility decline in France: in both cases, rapidly increasing literacy among the female populations seemed to play a decisive role. In addition to individual-level cognitive empowerment, societal-level consequences of enlightenment also contributed to these behavioral innovations in the form of early public health measures in Sweden and general secularization in France. 

A summary of global fertility and mortality declines around the world over the past half-century also leads to the conclusion that education can explain the trends better than any other driver. Figure 1 illustrates this by revisiting the “Preston curve” that plots life expectancy against GDP per capita for different periods, revealing strongly diminishing returns to income as well as an unexplained upward shift over time (Preston 1975). When replacing income with education on the x-axis the pattern becomes almost linear, with no unexplained shift (Lutz and Kebede 2018).  A press release summarized this by stating: “When it comes to survival, mind matters more than money”.

Three levels of determination

Based on his insights from the comprehensive Princeton European fertility project, Ansley Coale specified three necessary preconditions for a lasting fertility decline, the first of which says that “fertility needs to be within the calculus of conscious choice” (Coale 1973). This is a very clear reference to the decisive cognitive precondition of demographic transition. The other two preconditions are that lower fertility needs to be advantageous, and that acceptable means of birth control need to be available. In this context, the widely used distinction between proximate determinants and socioeconomic determinants, proposed by Bongaarts (1978), has become important: forces operating at these different levels of determination are not in competition with each other. Rather, they come in a sequence, such as contraception being a consequence of not wanting another birth. What I propose is to add a third, still deeper layer of explanation, referring to the changes in the way humans think. 

One can generally distinguish between three different levels of determination: 

1) Cognition (in the mind/brain): degree to which choices are based on rational calculation rather than fatalistic behavioral patterns;

2) External conditions (in the environment): motivation for behavioral choices depending on the costs and benefits implied by social, economic, and environmental living conditions;

3) Proximate determinants: availability and acceptability of physical/technological means of implementation, such as contraceptives for fertility or vaccination/medication for health.

While demographers and social scientists in general tend to be familiar with the second and third level of determination and structure their analyses around them, the first is rarely mentioned. This is surprising, given the prominence of Ansley Coale and the fact that Etienne van de Walle in his PAA presidential address (1992) explicitly focused on this cognitive level. Under the title “Fertility Transition, Conscious Choice and Numeracy” he criticized much of the demographic and economic analysis for assuming that the choice for a particular family size is conscious and rational. He pointed to empirical studies in Africa where he had found high proportions of women who were not able to say how many children they wanted and rather referred to “God’s will”. He convincingly argued, both for historical Europe as well as for contemporary Africa, that numeracy about children and the norm of an ideal family size did not exist traditionally and only appeared shortly before the onset of the fertility transition. He concluded that there cannot be modern family limitation without such conceptualization of family size, which is indeed a deeply cognitive process. It requires abstraction skills that tend to be associated with numeracy and literacy. Several other influential papers on demographic transition – such as Cleland and Wilson (1987) – also point to ideational changes as drivers of the fertility transition rather than changing economic conditions or contraceptive methods.

Education as a proxy for cognition

In most demographic studies, education is seen merely as a proxy for socioeconomic status (SES). This is not entirely wrong since more education typically leads to more income and higher SES. But it is incomplete since there is much more to education than its impact on SES. Education literally changes our brains for the rest of our life. Every repeated learning experience makes us anatomically different people – as stressed by the Nobel laureate in medicine Erik Kandel (2007) – by building new synapses in our brains. All further learning and all choices made will build on these established synaptic structures.  In developmental psychology, it has also been shown beyond doubt that education alters the way we perceive the world, changes our planning horizon, the degree to which we think abstractly and can imagine counterfactual scenarios, all things that matter for our choices and our behavior. This is true in general, but also specifically for choices in reproductive and health-related behavior. 

Postscript: While economists have started to acknowledge bounded rationality in their models, to my knowledge there is still little work that tries to distinguish quantitatively between different degrees of rationality as captured by education categories, accounting for the changing proportions of individuals in the different education categories over time. Maybe, demographers could help with such efforts.

References

Bongaarts, J. (1978). A framework for analyzing the proximate determinants of fertility. Population and Development Review, 4(1), 105–132. https://doi.org/10.2307/1972149

Cleland, J. and C. Wilson (1987). Demand Theories of the Fertility Transition: An iconoclastic view. Population Studies 41:5-30.

Coale, A. J. (1973). The demographic transition reconsidered. Proceedings of the International Population Conference, 1, 53–72.

Kandel, E. R. (2007). In search of memory: The emergence of a new science of mind. W.W. Norton & Co.

Landry, A. (1934). La révolution démographique. Études et essais sur les problèmes de la population. Recueil Sirey.

Lutz, W. (2021). Advanced introduction to demography. Edward Elgar Publishing.

Lutz, W., & Kebede, E. (2018). Education and health: Redrawing the Preston curve. Population and Development Review, 44(2), 343–361. https://doi.org/10.1111/padr.12141

Preston, S. H. (1975). The changing relation between mortality and level of economic development. Population Studies, 29(2), 231–248. https://doi.org/10.2307/2173509

Thompson, W. (1929). Population. American Journal of Sociology, 34(6), 959–975.

van de Walle, E. (1992). Fertility transition, conscious choice, and numeracy. Demography, 29(4), 487–502.

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The notion of a demographic dividend (conventionally defined as the potential economic benefits derived from having a small share of economically dependent individuals) stands as the dominant paradigm for conceptualizing and communicating why population matters for development. The United Nations Population Fund, for instance, has prominently adopted such an approach, focusing on reproductive health services to alleviate unmet need for family planning and moderate fertility (UNFPA 2016).

However, a new look at the demographic dividend (Lutz et al. 2019) shows that education improvement rather than a change in age structure is the gatekeeper for accessing economic growth. The econometric model that we used separates education from age-structure effects on development outcomes, finding that gains from a rising share of the working-age population are prevalent only when a relatively high share of the population is educated beyond primary schooling (Figure 1).

Age-structural change in itself does not open a specific window of opportunity: it can help strengthen the dividend, but only when combined with an improvement in human capital, which is the primary agent of change in low-income countries. Development goals, especially for early demographic transition countries, should therefore set education as a priority from the beginning, as the Sustainable Development Goals (SDGs) already postulate. Such a strategy also works in moderating fertility (Kebede et al 2019), which is a useful complement in the process of economic development.

What constitutes demographic change and its dividend?

While demographic change is sometimes narrowly viewed as only referring to changing age structure, both common usage and authoritative scientific definitions often take a broader view that includes changes in several demographic dimensions. For example, when the media write about the changing demographics of the USA in the context of voting behaviour, they refer to the changing proportions of Hispanics, changing proportions in urban, semi-urban and rural areas and changing education structures, among others. This broad usage is also in line with the definition used by the International Union for the Scientific Study of Population (IUSSP) and key textbooks (Shryock et al 1973) as the scientific study of changing population size and structures, meaning multiple structures. In this sense, both changes, in the education structure and in the age structure, can be labelled “demographic”, and both can influence the demographic dividend. Which of these demographic structural changes is more important for economic growth is a matter of empirical assessment rather than ex ante assumptions.

A counterfactual: what if Nigeria had followed a South Korean path?

The coefficients estimated in (Lutz et al. 2019) can be used to simulate alternative paths of per capita GDP. To do this, we will take Nigeria as an example. We can model the country’s evolution by “inserting” aspects of the South Korean experience, characterized by marked fertility decline, rapid progress in education and economic growth (Table 1). Of course, these simulations are not to be taken at face value: they are meant solely to disentangle the drivers of development and ultimately show that paired together, age structure and progress in education produce the best results.

Figure 2 presents the results of these simulations as log-deviations from the observed path of per capita GDP in Nigeria, between 1970 and 2015. Zero means “no difference”, whereas positive (or negative) values indicate that the economic development in Nigeria would have been better (or worse) than it actually was.

We consider four scenarios. Scenario 1 simulates what would have happened in Nigeria, had education improved as fast as it did in South Korea. Scenario 2, instead, freezes Nigeria’s education at its 1970 level, letting other variables change. Comparison between the two unbroken lines gives an idea of the economic impact of educational progress.

Under Scenario 3, the Korean education expansion is combined with the Korean age structure trend (traditional demographic “bonus”). Finally, Scenario 4 combines the actual education expansion of Nigeria with the very rapid increase in the working-age share of the population observed in Korea (an “education-free” demographic transition). The results of this scenario may appear surprising, but they are consistent with the parametric estimates shown in Figure 1. As the initial education level is very low in Nigeria (which is associated with negative parameters in Figure 1), a massive increase in the working-age share (same as in Korea) has a strong depressing effect on per capita GDP. Only after education increases beyond a critical point (which happens around 2000 in Nigeria) does the “traditional” demographic dividend turn positive.

This same interaction between age structure and education also explains the cross-over of the two blue curves. In Scenario 3, a strong (Korean style) education expansion, which has a positive effect in itself (see Scenario 1), is initially partly cancelled out by the rapid age-structure change, which is unfavourable to economic growth when education levels are very low (see again Figure 1). It is only later, when education levels increase sufficiently, that the two trends (age structure and education transition) positively reinforce each other and lead to an economic take-off.

Implications

The results underscore the importance of human capital accumulation for economic growth: a higher share of working-age population in itself is not enough, and it can even be detrimental when education levels are low. Admittedly, the causal path behind this remains unclear and is certainly worth investigating. A plausible conjecture is that young adults are not always employed productively, and this may lead to violence and conflict in the absence of the proper institutional settings (Cincotta 2011). Another explanation could be that recent strong declines in child mortality in some African countries, leading to relatively larger youth dependency ratios, have occurred in countries with strong economic growth, resulting in a positive association between youth dependency and GDP growth for least developed countries.

Of course, a counterfactual simulation that isolates age structure and education trends must be interpreted with caution, since in reality the two are not independent of each other: for instance, a strong fertility decline is unlikely to take place in the absence of education. But this study leaves no doubt as to the importance of education for economic growth. A full and rapid implementation of Sustainable Development Goals 4 (universal high-quality primary and secondary education) and 3 (health including reproductive health) is therefore a development priority. Otherwise, missed opportunities for the demographic dividend will persist.

References

Cincotta, R. P. (2011) Half a Chance: Youth Bulges and Transitions to Liberal Democracy. Wilson Center, Washington, D.C.

Kebede E., Goujon A., Lutz W., Gailey N. (2019) Education disruptions in the 1980s contributed to the stalled fertility decline in Africa around 2000. N-IUSSP

Lutz, W., Crespo Cuaresma, J., Kebede, E., Prskawetz, A., Sanderson, W., Striessnig E. 2019. “Education rather than age structure brings demographic dividend”. Proceedings of the National Academy of Sciences, 116 (26) 12798-12803; DOI: 10.1073/pnas.1820362116.

Shryock, H. S., Siegel, J. S. and Larmon, E. A. (1973) The Methods and Materials of Demography. US Bureau of the Census.

UNFPA (2016) The State of the World Population Report 2016. United Nations Population Fund. New York.

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The future pace of fertility decline in Sub-Saharan Africa will likely be the chief determinant of world population growth, with massive implications for Africa itself as well as the rest of the world. Sub-Saharan Africa stands out as the world’s last region to enter the demographic transition. It was only in the 1980s that birth rates started to fall in most Sub-Saharan African countries, and still, these declines have been uneven and have stalled at times. Particularly in the late 1990s and early 2000s many of these countries experienced a slowdown of their fertility decline and in some cases even a reversal, the reasons for which have remained largely unexplained (Kebede, Goujon, Lutz 2019). Most existing studies try to link stalls in fertility decline to some specific factors, such as slower socio-economic development (Shapiro and Gebreselassie 2008), the low priority assigned to family planning programs at the beginning of the 21st century (Bongaarts 2008) and other factors related to public and reproductive health.

Fertility stalls echo earlier education stalls for some cohorts in sub-Saharan Africa

In contrast to the above-mentioned explanations, Goujon, Lutz and Kc (2015) proposed another plausible explanation focusing on cohort effects. They linked the fertility stalls around 2000 to the fact that some cohorts of women were subjected to an education stall, possibly associated with post-independence economic and political turmoil as well as structural adjustment. To explore this hypothesis, we combined individual level data from Demographic and Health Surveys for 18 African countries – with and without fertility stalls – thus creating a pooled dataset of more than 2 million births to some 670,000 women born between 1950 and 1995 by level of education. Our results highlighted that disruptions in educational attainment among cohorts around the 1980s are consistent with populations having higher proportions of poorly educated women of childbearing ages in the late 1990s and early 2000s, than would otherwise have been the case, had educational improvements continued to progress. This phenomenon, coupled with the relatively higher vulnerability of less educated women to period effects, has likely contributed to stalls in the period fertility declines (Kebede, Goujon and Lutz 2019).

A two-decade delay, linking changes in education with fertility

The post-independence period was a time of great expectations in Sub-Saharan Africa as most countries engaged in a process of expanding social services (Ezenwe 1993). However, the initial period of economic and social optimism was soon replaced by harsher times linked to the external shocks of oil price increases, declining terms of trade, and increasing government external debt (Elbadawi, Ghura and Uwujaren 1992). These changes resulted in austerity measures and massive cuts in government budgets – particularly in the social sectors of health and education – which were mostly in the framework of Structural Adjustment Programs (SAP) introduced in Africa by the Bretton Woods institutions. While it is impossible to say whether the SAP or initial dire situations are behind the slow-down in cohort educational improvement, the children of the 1980s were deprived of educational opportunities, with life-long consequences.

Figure 1 displays the proportions of women who never attended school from the cohorts born between 1950 and 1990 in two populous countries with stalls in their fertility declines (Kenya and Nigeria),. In both countries, the clear trend towards a declining cohort birth rate from 1950 to the 1970s was stopped (Nigeria) or even reversed (Kenya; Figure 2). Such education discontinuities were also observed in most other countries with fertility stalls.

The fertility stalls in Sub-Saharan Africa have rarely, if ever, been linked to the slowdown in educational progress. Indeed, the two phenomena occurred almost two decades apart, which may have seemed too long for any direct causal effects. However, they may be linked by two mechanisms. First, given that education has a direct effect on lowering fertility, the cohorts of girls who were deprived of schooling had higher fertility than they would otherwise have had. Second, period effects on uneducated women around 2000 were more sizable because these women were more numerous than they would have been without the earlier education disruptions.

To assess the combined effect of these two different forces, we compared actual fertility trends with a counterfactual, i.e., a hypothetical scenario of what would have happened without this educational stall. This scenario (blue dotted line) refers both to the proportion of women with no formal education (Figure 1) and to the period total fertility rate (Figure 2). The difference between the counterfactual TFRs and the observed ones lies only in the weights given to the three educational groups (no formal education, incomplete primary, and completed primary education or more).

Our simulation results show a sizable difference between the actual and counterfactual TFR around the year 2000 in the stalled-fertility countries. In absolute numbers for all ten stalled-fertility countries, between 1995 and 2010, this translates into about 13 million fewer births to women aged 15-34. For Nigeria alone, the difference is about 6.5 million births.

Conclusions and policy implications

The delayed consequences of educational disruptions on fertility levels two decades later have gone largely unnoticed in the scientific literature about Sub-Saharan Africa. An under-appreciation of this connection – between girls’ education and fertility in the following years – could be ruinous for the societies in question, as they attempt to maintain their living standards amid growing demands from prolonged rapid population increases. Shifts toward sustainable fertility and development depend on the reliable expansion of quality education, and accordingly, all relevant initiatives should embrace this as a priority.

References

Bongaarts J (2008) Fertility transitions in developing countries: Progress or stagnation? Studies in family planning, 39(2):105–110.

Elbadawi IA, Ghura D, Uwujaren G (1992) World Bank adjustment lending and economic performance in sub-Saharan Africa in the 1980s. Country Economics Department WPS, 1000.

Ezenwe U (1993) The African debt crisis and the challenge of development. Intereconomics, 28(1):35–43.

Goujon A, Lutz W, Kc S (2015) Education stalls and subsequent stalls in African fertility: A descriptive overview. Demographic Research, 33:1281–1296.

Kebede E, Goujon A, Lutz W (2019) Stalls in Africa’s fertility decline partly result from disruptions in female education. PNAS, Jan 30;201717288.

Shapiro D, Gebreselassie T (2008) Fertility transition in sub-Saharan Africa: falling and stalling. African Population Studies, 23(1).

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