Between February and April 2024, I conducted a qualitative study in six purposively selected communities in Ghana’s Greater Accra Region—Dansoman, Osu, Adenta (urban), Sege, Korle Gonno, and Bawjiase (rural/peri-urban). Using focus group discussions and semi-structured interviews, 52 older adults aged 60 and above were engaged. Participants were recruited through the Social Security & National Insurance Trust Pensioners Association and interviewed in English or local languages. 
The study sought to understand the lived experiences of older people ageing alone or at risk of doing so. Thematic analysis followed Clarke and Braun’s (2013) six-phase process, and findings were interpreted through three sociological lenses (“social determinants of health”, “contemporary life course perspective”, and “feminist gerontology”), to allow for gender-sensitive and structural insight into ageing in solitude.

An emerging but under-researched problem

In Ghana, the share of the population aged 60 and over had risen to 6.4% by 2021 and is projected to double by 2050. Yet this quiet demographic shift has received little structured policy or scholarly attention. While the ageing population grows, the number of older persons living alone is also rising, a reality that challenges the long-standing norms of communal and intergenerational living in African societies (Aboderin & Hoffman 2015; Apt 2012). Most studies on ageing in Ghana and sub-Saharan Africa have focused primarily on poverty, chronic illness, or pension coverage, overlooking the emotional, spiritual, and psychosocial implications of living and ageing in solitude (Cattell 2001; Mba 2010). Widowhood, youth out-migration, and the breakdown of extended family systems have quietly restructured daily life for many older adults, but these patterns remain insufficiently documented or addressed. As a result, existing social protection and health policies fail to account for the relational dimensions of ageing, such as loneliness, anxiety, and social disconnection, especially among those ageing in place with no regular household support.

Lived realities of solitary ageing in Ghana

In urban Accra, a 74-year-old woman lives alone in a single-room compound house. “Weeks pass without a visitor”, she explained. Her children have migrated, and her spouse died ten years ago. Her situation reflects the new reality; declining fertility, rising longevity, and fragmented kinship networks are leaving many older adults, especially women, to age alone. Women are disproportionately affected. They tend to outlive men, experience higher rates of widowhood, and lack formal pensions or income streams. “I don’t need much”, said a 72-year-old woman in Bawjiase. “But I need to know someone remembers I am here.” Men also expressed deep emotional strain. A 68-year-old widower in Adenta shared, “I talk to myself sometimes. It is the only way to break the silence”. Even those with informal work or community visibility felt emotionally disconnected. Solitary ageing in Ghana is thus not merely a matter of residence, but a deeper experience of being socially, spiritually, and emotionally forgotten.

Solitary ageing as a public health concern

Elders living alone face heightened risks of depression, nutritional neglect, untreated illness, and even premature death. Many are not enrolled in targeted programmes, and neighbours may remain unaware of their isolation until a crisis occurs. Shame, pride, or fear of burdening others often prevents older adults from seeking help. This growing vulnerability calls for recognition of solitary ageing as a public health issue, not only in terms of material deprivation but of psychosocial decline.

Policy gaps and a tentative look beyond Ghana

Ghana’s National Ageing Policy offers useful protections, but implementation is weak. Community nurses are under-resourced, and social protection schemes such as LEAP¹ often fail to reach those living alone. Few local governments have elder-sensitive housing or visitation systems. While faith-based groups and pensioner associations offer some relief, their impact is small and uncoordinated. Ageing alone is not only about economic deprivation but also about disconnection. Where ageing was once a shared, communal journey, it is now becoming an individual and often silent struggle. The absence of children, regular contact, and supportive neighbours exacerbates both the physical and emotional decline that comes with age. While this study is focused on Ghana, similar patterns may be emerging elsewhere in sub-Saharan Africa. Urbanisation, outmigration, longer survival, and the weakening of traditional familial structures are continent-wide trends. Although contexts vary, the warning signs are clear: the solitary elder is not an isolated case, but a signal of changing times.

Conclusions

Solitary ageing is no longer a distant or marginal issue. It is a pressing demographic reality that demands urgent, targeted, and humane responses. As urbanisation, outmigration, increased longevity, and the weakening of traditional kinship structures continue to erode intergenerational cohabitation, more older adults are left to navigate the final stages of life in profound isolation. This phenomenon must be named, measured, and addressed. Governments, local assemblies, faith institutions, and civil society must act decisively. Routine elder outreach, intergenerational community programmes, and inclusive housing policies must reflect the emerging profile of older adults who age without daily familial contact. Technology and social innovations must also be harnessed not as substitutes for human touch, but as bridges to connection. As one 73-year-old woman in urban Accra said, “I am old, but I have not died. My ears still want to hear a human voice”. That voice must now be society’s, not echoing in pity or policy promises, but responding with a consistent, compassionate presence. Ageing in solitude should not be Ghana’s quiet legacy. 

Footnote

¹The Livelihood Empowerment Against Poverty (LEAP) programme is Ghana’s national cash transfer initiative targeting extremely poor households. Beneficiaries include the elderly poor (65+), orphans, persons with severe disabilities, and vulnerable pregnant women. In addition to modest bimonthly stipends, LEAP enrollees receive free access to Ghana’s National Health Insurance Scheme. Despite its potential, many isolated older adults remain unreached due to registration gaps and weak community-level implementation.

References

Aboderin, I., & Hoffman, J. (2015). Families, intergenerational care and ageing in sub-Saharan Africa: Policy challenges and responses. International Social Work, 58(2), 275–290. https://doi.org/10.1177/0020872813517320

Apt, N. A. (2012). Ageing in Ghana: Meeting the challenges of a changing society. Accra: Sub-Saharan Publishers.

Clarke, V. & Braun, V. (2013) Teaching thematic analysis: Overcoming challenges and developing strategies for effective learning. The Psychologist, 26(2), 120-123

Cattell, M. G. (2001). African widows, culture and social change: Case studies from Kenya. The International Journal of Ageing and Human Development, 52(4), 321–337. https://doi.org/10.2190/C7G2-8TNN-W7E2-0GVJ

Mba, C. J. (2010). Population ageing in Ghana: Research gaps and the way forward. Journal of Ageing Research, 2010, 672157. https://doi.org/10.4061/2010/672157

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After the great success of their most famous generalization, the demographic transition, demographers have been constantly looking for regularities. For a long time, strong convergence, or laggards catching up with forerunners, appeared to be a very convincing one: fertility and mortality in poor countries were (correctly) believed to be following the same pattern of change as previously observed in rich countries. What it is less clear, however, is what should happen after the end of the demographic transition, when fertility is close to or below replacement level, infectious diseases are mostly under control, and life expectancy at birth is high, at above 65-70 years.

Weak convergence, or a reduction of variability in a supposedly homogeneous group of countries, is clearly the prevailing hypothesis in many of the UN World Population Prospects Revisions, including that of 2017 (the one that we analyzed in Castiglioni, Dalla Zuanna and Tanturri, 2020), and the most recent one, that of 2019, not explicitly considered here. For example, the 2017 edition suggests that during the 21^st century, all of the 112 countries around the world where the total fertility rate (TFR) was below 2.5 in 2010-15 will converge to a sort of quasi-stable population (Bourgeois-Pichat 1994) with declining mortality, constant fertility slightly below replacement (TFR close to 1.8), and a net migration rate approaching zero (±0.1 per 1,000).

By comparing actual data with the fertility, mortality, and migration trends forecasted in the UN World Population Prospects over the last half-century (revisions of 1980, 1990, 2000, and 2017), our aim here is to look for evidence of weak convergence over the recent past. To do this, we will focus on the 38 countries that had already reached relatively low fertility (TFR<2.5) in 1975-85: Austria, Belgium, Bulgaria, Cyprus, Czechoslovakia, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Italy, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Spain, Sweden, Switzerland, UK, USSR, Yugoslavia (Europe); Barbados, Canada, Cuba, Martinique, Puerto Rico, USA (America); Hong Kong, Japan, Singapore, South Korea (Asia); Australia, New Zealand (Oceania).

The lack of weak convergence: fertility

The UN Prospects Revisions of 1980 and 1990 suggested that the TFRs of countries having completed their fertility transition would quickly converge towards similar values, of around 1.8-1.9. Figure 1 shows that this did not happen. Instead, variability in fertility remained approximately constant between 1980 and 2015. In its 2000 release, the UN Population Division accurately predicted both average fertility trends and variability, i.e. a lack of convergence across the 38 countries. In 2017, however, (and likewise in 2019) they seem to have changed their minds (Table 1), but it is difficult to understand why fertility convergence should occur among post-transitional countries in the coming years and decades.

The lack of weak convergence: mortality

The 1980-based and 1990-based World Population Prospects were unsuccessful in predicting either the spectacular increase in e_0, life expectancy at birth (eight years of life gained in just 35 years), or the persistence of profound differences between countries (Figure 2). Meanwhile, the 2000-based World Population Prospects confirmed the pace of e₀ increase in the early years of the new century projected in the 1990-based World Population Prospects, while significantly underestimating (by two years) the actual increase in survival. They correctly predicted the lack of convergence between countries, however. Again, given these results, the progressive convergence of e₀ over the coming decades as projected in the UNPD forecasts of 2017 should perhaps be reconsidered.

The lack of weak convergence: migration

Migration rates are by far the most difficult population parameters to forecast as they are more closely related to largely unpredictable external shocks, such as economic downturns. Neither the 1980 nor the 1990 World Population Prospects correctly predicted the level or the variability of the net migration rate (NMR) in the first 15 years of the new century (Figure 3). Likewise, the 2000-based forecasts were also unable to predict the immigration boom of the first decade of the new century and the further increase in cross-country differences.

Concluding remarks

The paradigm of the demographic transition has provided an exceptionally useful tool for describing a common path of demographic change across countries, and their strong convergence over time from a regime where mortality and fertility are high to a new regime where they are low. On the basis of this well-known pattern, forecasters projected that mortality and fertility trends would follow a common path as modernization and economic development spread across the world. However, we have shown that for countries that have already completed their demographic transition, the idea of a general weak convergence also seems to inform the hypotheses and/or the outcomes of population projections elaborated by the UN Population Division. This conjecture, however, does not seem to hold: there are no clear signs of a convergence in fertility, mortality or net migration for the 38 countries that had practically completed their demographic transition by 1985 (i.e. whose fertility was below 2.5 by that time).

While the 2000 Revision of World Population Prospects correctly did not predict convergence for mortality and fertility (although it did so for migration) in the period 2000-2015, it was reintroduced in later revisions. However, in light of our results, it is hard to believe that in the years 2020-2100 the 112 countries with a TFR below 2.5 children per woman in 2015 will converge towards similar demographic behaviours, as suggested by the 2017 and 2019 Revisions of World Population Prospects.

The problem is that in a post-modern (post-transition) world, the components of demographic change depend on a variety of causes, which can differ by country or groups of countries (for fertility, for instance, see Rindfuss and Choe 2015; 2016). For forecasters, this greatly complicates the search for the correct underlying assumptions, but a better understanding of this complexity may help to improve forecasting accuracy.

References

Bourgeois-Pichat, J. (1994) La dynamique des populations. Populations stables semi-stables et quasi stables, Travaux et Documents, INED, Paris.

Castiglioni M., Dalla Zuanna G., Tanturri M.L. (2020) “Post-transitional Demography and Convergence: What Can We Learn from Half a Century of World Population Prospects?”. In: Mazzuco S., Keilman N. (eds.) Developments in Demographic Forecasting. The Springer Series on Demographic Methods and Population Analysis, vol 49. Springer, Cham..

Rindfuss, R.R., Choe, M.K. (eds.) (2015), Low and Lower Fertility: Variations across Developed Countries, Springer.

Rindfuss, R.R., Choe, M.K. (eds.) (2016), Low Fertility, Institutions and Their Policies: Variations across Industrialized Countries, Springer.

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Most estimates of homelessness are based on point-in-time counts, such as the night or consecutive nights of a homelessness count. These are important because they provide critical information on the daily need for housing and homelessness services such as crisis accommodation. However, because homelessness is often experienced temporarily or episodically (Culhane et al. 2007; Link et al. 1995), more people experience it than are ever captured at a single point‑in-time.

Capturing the effects of these dynamics is difficult. Administrative data collected from homelessness service providers is an important source of longitudinal information (Culhane et al. 2007), but only capture data on those who come into contact with services. Household surveys sometimes ask retrospective questions about past episodes of homelessness, from which estimates of annual and lifetime rates of homelessness have been calculated (Chamberlain and Johnson 2015). However, these surveys invariably miss the potentially large population who are experiencing homelessness at the time of the survey. Thus, we do not really know how many people experience homelessness in any given year.

Estimating the currently homeless

In new research, I propose a method for estimating the size of the population missed in retrospective household surveys (O’Donnell 2020). Inspired by Capture‑Recapture techniques, the approach sets out to establish a relationship between the population whose homelessness over a 12 month period is ‘observed’ in a household survey to the population whose homelessness episodes are ‘unobserved’.

Observed episodes are those that conclude in the 12 months prior to a household survey, making individuals eligible for selection into the survey sample (barring death, imprisonment or emigration). Unobserved episodes are those that are ongoing at the time of the survey, making individuals ineligible for sample selection (see Figure 1). Dividing the number of unobserved episodes by the observed episodes gives what I call the k ratio.

There are in fact a set of k ratios specific to the duration of homelessness episodes. To understand their rationale, take the example of an individual who is homeless for one month in the year. This person will have a one-in-12 chance on being homeless on an average day. Under a certain set of assumptions (e.g. homelessness occurs uniformly throughout the year), this could also be taken to be the probability of the episode occurring at the time of a survey and therefore being unobserved. Dividing it by its complementary probability of the episode being observed (i.e. 11‑in‑12) gives a k ratio specific to episodes lasting one month. Calculating ratios for each duration and multiplying them by the number of observed homelessness episodes gives an estimate of the non‑sampled population experiencing homelessness at the time of the survey.

Applying the approach using Australian data

I use retrospective data from Australia’s General Social Survey (GSS) 2014 to operationalise the approach (ABS 2015). The GSS was a survey representing almost all private households in Australia. A homelessness module in the survey asked responding adults (aged 15 years and over) several questions about past experiences of homelessness, including the timing and duration of their most recent episode.

Respondents were recorded as having experienced homelessness when they reported not having a permanent place to live for reasons outside of their personal control and choice. Living circumstances included those often described as ‘literal’ and ‘hidden’ homelessness. Literal homelessness includes people sleeping rough on the streets and in makeshift dwellings (e.g. tents, cars, abandoned buildings and train stations) and people staying in homeless shelters. Hidden homelessness or ‘housing exclusion’ includes people staying with family and friends (e.g. ‘couch surfing’) or in caravans/mobile homes, motels, hostels and boarding/rooming houses.

A microsimulation model was used to account for several aspects in calculating the k ratios. Ratios were calculated by simulating homeless episode start and end dates and survey sample selection dates for a synthetic population. Episodes that overlapped selection dates were considered unobserved, while episodes that finished in the prior 12 months were observed. Microsimulation allows for the incorporation of auxiliary data on seasonal variation in the onset of homelessness, the size of the population who experience long term homelessness, or the occurrence of multiple homelessness episodes. It also takes account of people who die or otherwise move out of the sample frame (e.g. due to imprisonment and migration).

Annual estimates of homelessness

According to the GSS, 351,000 adults experienced a completed episode of literal or hidden homelessness in Australia in the 12 months to 30 June 2014. On top of this, I estimate that 148,400 adults were experiencing homelessness at the time of the survey and a further 8,900 episodes may have been otherwise missed (e.g. due to people dying before the survey). Thus, 508,300 adults or 2.7 per cent of the Australian population were estimated to have experienced homelessness in the 2013-14 financial year.

The impact of homelessness and housing deprivation in society is considerably larger than existing measures suggest. The population who experience homelessness over a one-year period is predicted to be 3.4 times larger than the number counted on any single night. Further, only one-in-five homeless people are predicted to seek help from housing and homelessness services, suggesting that administrative data from such services substantially understate the scale of the problem.

The experience of homelessness is more diverse than commonly measured. Of the 508,300 adults who experienced homelessness in 2013-14, only 52,300 people (0.28 per cent of the population) experienced literal homelessness on the streets or in a homeless shelter. Approximately 90 per cent of episodes involved at least one period staying with family or friends. And while 25 per cent of episodes are predicted to last 12 months or longer, approximately 20 per cent last less than two weeks. As shown in Figure 2, this contrasts strongly with point‑in‑time counts which tend to overrepresent long-term homelessness.

Implications

Estimating homelessness is difficult and the results are imprecise. In addition to sampling and non-sampling error, estimates in this article are the product of how homelessness is defined and how data are collected and measured in Australia. Nevertheless, two key implications have broader application. Firstly, homelessness and housing deprivation impact a larger and more diverse population than conventional measures suggest, highlighting the position of literal homelessness within a much larger dynamic of housing and financial disadvantage and deprivation.

Secondly, much of the burden for preventing and responding to homelessness falls on interpersonal support networks. While staying with family and friends is often not considered a form of homelessness, it appears to be the most common arrangement for people without their own permanent home and is likely to be an important staging and landing point for people entering and exiting street and sheltered homelessness (O’Donnell 2019).

Such implications highlight the importance of public policy in rectifying housing market deficiencies and providing a safety net when interpersonal supports fail or no longer exist. They also underscore the value of annual estimates, not just to measure the scale of homelessness, but also to reveal something of its nature and experience.

References

ABS (2015). General Social Survey: Summary Results, Australia, 2014. Cat no. 4159.0. Canberra: Australian Bureau of Statistics.

Chamberlain, C. and Johnson, G. (2015). How many Australians have slept rough? Australian Journal of Social Issues, 50(4), 439-456.

Culhane, D.P., Metraux, S., Park, J.M., Schretzman, M., and Valente, J. (2007). Testing a typology of family homelessness based on patterns of public shelter utilization in four U.S. jurisdictions: Implication for policy and program planning. Housing Policy Debate, 18(1), 1‑28.

Link, B.G., Phelan, J., Bresnahan, M., Stueve, A., Moore, R.E., and Susser, E. (1995). Lifetime and Five-Year Prevalence of Homelessness in the United States: New Evidence on an Old Debate. American Journal of Orthopsychiatry 65(3), 347-354.

O’Donnell, J. (2020). Estimating annual rates of homelessness. Demographic Research, 43(1), 1-34.

O’Donnell, J. (2019). Does social housing reduce homelessness? A multistate analysis of housing and homelessness pathways. Housing Studies, DOI: 10.1080/02673037.2018.1549318, 1-27.

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Demographic variables often occupy a difficult space in global debates about poverty. The United Nations’ 17 Sustainable Development Goals, for instance, do not consider demographic policies as a priority in their own right and instead focus on more specific elements of the population agenda, such as maternal and child health and women’s empowerment. This approach is justified by a desire to move away from the alarmism of neo-Malthusian perspectives and the disruptive legacies of highly coercive birth control programs in the past. However, it also contributes to the fragmentation of population policies and makes it harder to establish the management of demographic change as a cross-cutting development priority. Economic research and forecasting of global poverty trends similarly tends to omit demographic information. In most cases, this literature only relies on population growth projections to estimate the expected number of extreme poor in the future. But demographic processes rarely feature as explanatory variables in their own right.

Fertility and poverty are closely linked …

In a recent article (Wietzke, 2020), I argue that this non-consideration of population variables can be a serious omission. For example, we know from cross-national research that demographic change, and notably fertility reduction, is at least correlated with, and probably in part responsible for, accelerated economic growth in developing regions like Asia (Bloom and Williamson, 1998). As such, it can contribute to structural transformations that have been linked to widespread movements out of poverty in the developing world. Within countries, similarly, countless studies have shown that people’s socio-economic status, poverty risks, and demographic variables like fertility are typically closely connected. Although exact causal relationships are often hard to identify, this suggests again that demographic processes affect poverty, by influencing how evenly benefits of economic growth are shared within societies.

… between and within societies

To understand what happens within societies, a useful starting point is the so-called ‘leader-follower model’ of demographic transitions (Bongaarts, 2003; Eloundou-Enyegue et al., 2017). This model suggests that shifts towards smaller family sizes typically begin in wealthier and better educated groups, while it is often rational for poorer households to postpone fertility reductions (e.g. because of imperfections in insurance markets, lack of access to modern family planning technologies, etc.). Building on earlier research by economists Eastwood and Lipton (1999), I argue that these uneven fertility transitions can slow down the pace of poverty reduction in the early stages of demographic change, when the ‘burden’ of larger family sizes is shifted disproportionally onto poorer households. The result is the counterintuitive prediction that, controlling for average economic development, the risk of poverty increases at the onset of fertility decline, when the gap in birth rates between the poor and non-poor tends to expand. Faster rates of poverty reduction are only expected at more advanced stages of the demographic transition, when new birth control techniques and behaviors become more widely disseminated among the population.

The overall outcome is an inverted U-shaped relationship between average fertility and poverty during the demographic transition that is summarized in Figure 1. The Figure uses an inverted scale for average fertility on the horizontal axis, to trace countries as they move from high to low birth rates.

Empirical results

The data consulted for this analysis support these predictions. In a first step, I use information from the Demographic and Health Surveys (DHS) to illustrate that fertility differentials between the poor and non-poor indeed increase in the early stages of the demographic transition (late 1970s to 2016; Figure 2). Because of data limitations, observations are plotted as country-years, with inverted average fertility rates on the horizontal axis and fertility inequality on the vertical axis (the latter is measured by the ratio of the fertility rate of the poorest 40% of the population over the wealthiest 60%, measured on an index of asset ownership).

Despite considerable variation in the data and fewer observations in post-transition countries, the results provide strong support for the expected change in fertility inequality during the demographic transition. The fitted regression line increases initially in countries where average fertility is beginning to decrease from high levels. It then turns downward, signalling fertility convergence between richer and poorer populations in countries that are at, or close to, replacement level.

In the second step of the analysis I show that the expected effects of evolving fertility inequality on poverty can also be observed using simpler but more readily available data from UN population statistics that do not capture explicitly the level of fertility inequality within societies. To do so, I turn to standard growth-poverty regressions from the economic literature on global poverty and insert a term for average and squared average fertility. The latter term, in particular, is intended to capture the predicted inverted U-shaped relationship between average fertility and poverty described in Figure 1. The detailed results reported in the article bear out this effect in a sample of 140 developing countries that covers the period from the late 1970s to 2016 (unbalanced sample). They also hold up in a wide range of robustness and specification tests.

Towards a new alignment between poverty and demographic research?

Although cross-country regressions of this kind should always be treated with some caution, my analysis points to several potential ways forward in the debate about population and poverty. In particular, the evidence of a distributional effect of fertility on poverty helps to reconcile concerns about population dynamics with the rights- and justice-based approaches that have come to dominate the field in recent decades. This applies especially to the current focus on reproductive and women’s rights which, if accompanied by effective and well-targeted interventions towards the poorest, can help speed up the transformation of economic growth into poverty reduction.

In addition, the strong and significant effect of demographic variables in my analysis provides further justification for the move toward a more multidimensional conceptualization of poverty. In my study, neither economic nor demographic processes alone adequately predict global poverty numbers. It is the interaction between these variables that makes the difference.

Acknowledgement: Research reported here was supported by the Spanish Ministry of Science, Innovation and Universities (Ministerio de Ciencia, Innovación y Universidades) through its Academic Excellence and Societal Challenges initiative (Grant CSO2017-87350-P).

References

Bongaarts, J. (2003). “Completing the Fertility Transition in the Developing World: The Role of Educational Differences and Fertility Preferences.” Population Studies 57(3):321–335.

Eastwood, R. and M. Lipton (1999). “The Impacts of Changes in Human Fertility on Poverty.” Journal of Development Studies 36(1):1–30.

Eloundou-Enyegue, P., S. Giroux, and M. Tenikue (2017). “African Transitions and Fertility Inequality: A Demographic Kuznets Hypothesis.” Population and Development Review 43(S1):59–83.

Wietzke, F.B. (2020). “Poverty, Inequality, and Fertility: The Contribution of Demographic Change to Global Poverty Reduction.” Population and Development Review. doi:10.1111/padr.12317.

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Introduction

The WHO recommends that women wait between two and three years after the birth of a child to become pregnant again, yet about one-third of non-first-born children in developing countries are born within two years of their older sibling. Decades of research, mostly from low-income countries, have shown that children born at least two to three years after their older sibling have a significantly lower probability of infant mortality, premature birth, and low birth weight (Conde-Agudelo, Rosas-Bermúdez and Kafury-Goeta 2006; Conde-Agudelo et al. 2012). This is because short intervals do not allow women to fully recuperate from previous pregnancies, and closely spaced children may be more at risk from the same sets of infectious diseases. Some recent studies of wealthy populations have found no relationship between birth spacing and perinatal outcomes, however, and have argued that the current consensus may be a result of flaws in previous research designs (Class et al. 2017; Hanley et al. 2017).

Most research on birth spacing has studied if children born following shorter intervals were more likely to experience adverse outcomes, such as infant mortality, while controlling for observable individual and family characteristics. This kind of analysis can be problematic, however, if short birth intervals are also more common among women with characteristics that are both hard to measure and possibly related to the risk of infant mortality, like infant feeding practices or poor health. Without adjusting for these unobserved characteristics, it is impossible to know if birth spacing really does directly influence infant mortality. Some recent studies on high-income countries have compared differences in birth spacing and other infant health outcomes between siblings, which can rule out confounding by unobserved family characteristics. However, the application of a different methodology makes it difficult to compare the findings across different studies.

My own previous work on birth spacing and infant mortality in modern Bangladesh and historical Stockholm had shown that birth spacing can matter even when applying sibling models (Molitoris 2017, 2018), which led me to suspect that birth spacing may be important for infant health, but not equally across all contexts.

This hypothesis was tested in a recent publication using over 200 waves of Demographic and Health Survey (DHS) data on 4.5 million births in 77 low- and middle-income countries covering the years 1986 to 2016 (Molitoris, Barclay and Kolk 2019). Our study had two main goals. First, we wanted to use the same statistical models that were used in wealthy populations to test whether the spacing between children predicts infant mortality. Second, we wanted to test whether the importance of birth spacing varies by level of development. Although the DHS is focused on relatively poor countries, the overall levels of development vary widely both across countries and over time.

Short birth intervals increase infant mortality

We found that children who were born less than two years after the birth of their older sibling were significantly more likely to die in their first year of life (Figure 1). In our data, 33% of children were born following an interval shorter than two years, but they made up over 50% of all infant deaths. Compared to a child born following an interval of three years, children born after one or two years had three and two times the risk of infant mortality, respectively. After approximately four years, longer birth intervals continued to reduce the risk of infant mortality, but to a much smaller degree. Our findings therefore supported the WHO (World Health Organization) recommendations to space births at least three years. The WHO also suggests that women should not wait more than five years to conceive their next child, but we did not find any evidence that birth intervals longer than this were related to increased risks for children.

Birth spacing matters more at lower levels of development

To evaluate how the importance of birth spacing varies by level of development, we calculated the percentage of deaths that could be avoided if birth intervals increased from 12 to 24 months and then plotted them against indicators of development, such as the national infant mortality rate, for every combination of country and birth year (Figure 2).

The message from Figure 2 is clear: when the overall level of infant mortality is high, there are large benefits to spacing births further apart. When infant mortality decreases, however, birth spacing becomes less and less effective at reducing infant deaths. If we extrapolate from these findings, we would expect, just as recent studies in high-income contexts have shown, that birth spacing should have little to no influence on infant health when populations are even healthier than those with the lowest infant mortality rates in the DHS data. Given the theoretical links between birth spacing and infant mortality, birth intervals should have a greater influence on infant health when populations are faced with higher burden of malnutrition or infectious disease, and our results are in line with those expectations.

Conclusion

Public health organizations and family planning programs should continue to place emphasis on longer birth spacing to accelerate the decline in infant mortality. Our findings are in agreement with current recommendations of spacing births at least three years apart. While there will always be children born after shorter intervals than this, extending the time between births by even just a few months can still drastically reduce the risk of infant mortality for those born below the three-year threshold. This is especially true for populations facing high levels of infant mortality. The incidence of short birth intervals in low-income settings could be reduced both by encouraging exclusive breastfeeding for the first six months postpartum, and by reducing the high level of unmet need for contraception in low-income contexts. Though accomplishing these goals is no simple task, these changes have the potential to shift the distribution of birth intervals upward and reduce the overall risk of infant mortality in the most vulnerable populations.

References

Class, Q.A., M.E. Rickert, A.S. Oberg, A.C. Sujan, C. Almqvist, H. Larsson, P. Lichtenstein, and B.M. D’onofrio. 2017. “Within-Family Analysis of Interpregnancy Interval and Adverse Birth Outcomes.” Obstetrics and Gynecology 130(6):1304-1311.

Conde-Agudelo, A., A. Rosas-Bermudez, F. Castaño, and M.H. Norton. 2012. “Effects of Birth Spacing on Maternal, Perinatal, Infant, and Child Health: A Systematic Review of Causal Mechanisms.” Studies in Family Planning 43(2):93-114.

Conde-Agudelo, A., A. Rosas-Bermúdez, and A. Kafury-Goeta. 2006. “Birth Spacing and Risk of Adverse Perinatal Outcomes: A Meta-Analysis.” JAMA 295(15):1809-1823.

Hanley, G.E., J.A. Hutcheon, B.A. Kinniburgh, and L. Lee. 2017. “Interpregnancy Interval and Adverse Pregnancy Outcomes: An Analysis of Successive Pregnancies.” Obstetrics and Gynecology 129(3):408-415.

Molitoris, J. 2017. “The Effect of Birth Spacing on Child Mortality in Sweden, 1878–1926.” Population and Development Review 43(1):61-82.

Molitoris, J. 2018. “Heterogeneous Effects of Birth Spacing on Neonatal Mortality Risks in Bangladesh.” Studies in Family Planning 49(1):3-21.

Molitoris, J., K. Barclay, and M. Kolk. 2019. “When and Where Birth Spacing Matters for Child Survival: An International Comparison Using the DHS.” Demography 56(4):1349-1370.

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In a recent study (Jalovaara and Andersson 2018) we examined family forms and family transitions in Finland, from the perspective of children. We studied socio-economic differences in terms of both the type of family children are born into, and subsequent experiences of family events, such as parental separation and stepfamily formation. Our analyses are based on register data on the childbearing and union histories of women in Finland and on the family events that 64,162 children of these women experienced during their childhood (0–15 years) in 2003–2009. We present separate calculations for children of mothers with different levels of education. Our study shows that there are remarkable disparities in family forms and family transitions for different groups of children.

Family forms, at birth and later

Family situations already differ widely when children are born. Compared to children of highly educated mothers, children of mothers with low levels of education are almost twice as likely to be born to a cohabiting mother and four times as likely to be born to a lone mother, with no father living in the household (Figure 1). Most children of highly educated mothers are born in marriage. Moreover, if a highly educated mother is cohabiting when the child is born, she is more likely to marry later on.

Children of low-educated mothers are also much more likely to experience further changes in their family structure as they grow up. This holds for parental separation in particular. Of all children born into a household with a mother and father, 41% see their parents separate before their fifteenth birthday. Family dissolution rates are lower for children born to two married parents. Among children born in marriage, one-third see their parents separate before turning fifteen, as compared to half of children born to cohabiting parents. However, the disparities in outcomes by mother’s education are much more striking than those related to the parents’ marital status. Differences in outcomes are particularly stark at the youngest child ages (Figure 2). Children of low-educated mothers are much more prone to experience parental separation, and separations occur faster and at younger ages than for children of mothers with more education. For instance, 43 percent of children with low-educated mothers experience parental separation by age six, and two-thirds by age fifteen. Among children of highly educated mothers, these proportions are 12 and 29 percent, respectively.

Figure 3 shows a summary of the proportion of time that children in Finland spend in different family forms as they grow up. On average, children of low-educated mothers spend just half of their childhood years with both parents, whereas those of high-educated mothers spend four-fifths of their childhood with both parents (Figure 3). Besides, the children of low-educated mothers tend to live in a lone-parent family more often than others.

Outlook

A well-known argument from the literature claims that socioeconomic differentials in children’s family forms have become increasingly important in shaping child outcomes and the resources that are available to children in developed societies (McLanahan 2004; McLanahan and Jacobsen 2015). Children of low-educated mothers are increasingly likely to experience family changes and situations associated with a loss of resources, including early parental family formation outside marriage, parental separation, lower levels of paternal involvement, and weaker parental labour market attachment. The opposite holds for children born to highly educated parents. These unevenly distributed family experiences of children born into disadvantaged and advantaged families have the potential to increase differences in children’s well-being and life chances, and thus to contribute to the accumulation of advantage or disadvantage across the life course and across generations.

Many agree that welfare states could and should reduce inequalities in children’s opportunities. McLanahan (2004; see also McLanahan and Jacobsen 2015) claims that income-tested benefits to single parents, widely used in the United States, discourage marriage and employment among low-educated women and thereby contribute to wider socioeconomic disparities. One may expect a weaker link between maternal education and childhood family structure in the Nordic welfare regime, where many policies are targeted at promoting social and gender equality, and social policies are guided by the principle of universalism. In our study, we show that this link is anything but weak in Finland, where large proportions of children grow up in families that are headed by a lone mother with low education and, in many cases, a precarious labour market position.

Based on the Gini index, the Nordic countries are still among the most economically equal nations in the world (World Bank 2019), and social policies support parents and children regardless of family type. Still, our study provides little evidence of Nordic equalities in terms of family demographic outcomes. Of course, modern welfare states cannot impose a predefined family demographic behaviour. But a realistic aim for policymakers could be to reduce any detrimental consequences of differential family dynamics on children and their life chances (Cohen 2015). Our findings highlight how critical it is to maintain the institutions and practices that help compensate for the losses of resources related to the family arrangement that children experience in their early years.

References

Cohen P.N. (2015). Divergent responses to family inequality. In Families in an era of increasing inequality (pp. 25–33). New York: Springer.

Jalovaara M. & Andersson G. (2018). Disparities in children’s family experiences by mother’s education: The case of Finland. Population Research and Policy Review.

McLanahan S. (2004). Diverging destinies: How children are faring under the second demographic transition. Demography, 41(4), 607–627.

McLanahan S. & Jacobsen W. (2015). Diverging destinies revisited. In Families in an Era of Increasing Inequality, pp. 3–23. Springer International Publishing.

World Bank (2019). The World Bank Indicators. Retrieved March 1, 2019 from.

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What has happened to the prevalence of female-headed households in Africa?

Nearly one in four households in Africa, comprising 21% of the continent’s population, are headed by a woman. To be sure, prevalence is not the same in all countries: households in Southern Africa have substantially higher rates, at an overall 43%, while those in West African countries are least likely to be headed by a woman (15%) (Milazzo and van de Walle, 2017). The latter reflects the practice of polygamy, together with high remarriage rates following marital dissolutions that continue to be widespread in predominantly Muslim countries (Lardoux and van de Walle, 2003; Tabutin et al., 2004). What is true in all countries is that female headship has been increasing, as has the share of the population living in female-headed households, since the early 1990s. The data show quite clearly that the probability that a woman aged 15 or older heads a household has been rising over time in all sub-regions and at every age (Figure 1).

What explains this notable change in household structure?

Using data from the last 25 years and covering 89 percent of Africa’s population, we investigated this Africa-wide shift in a recent paper (Milazzo and van de Walle, 2017). The analysis suggests that higher gross domestic product is associated with lower female headship — presumably in part due to a decline in work-related migration by men associated with a growing local economy. So what explains the seeming paradox that female headship is rising during a period of economic growth? Subtle transformations in a number of other factors across Africa are related to this fundamental feature of household structure, including changes in demographic and population characteristics, social norms, marriage behavior and education levels.

An extra year of schooling produces a three percentage point increase in the share of the population living in female-headed households (or FHHs for short). On average, a one-year rise in women’s age at first marriage produces a 2.5 percentage point increase—an effect almost as strong as that of an extra year of schooling. Life expectancy’s positive effect―equal to a 0.5 percentage point boost per extra year―presumably reflects the natural survival advantage of women that is revealed with higher overall life expectancy. This, in turn, results in an increase in widow-headed households. Conflict and HIV also raise a country’s share of population living in households headed by women.

What has happened to the living standards of female-headed households during this period?

How does this change in a key feature of household structure in Africa connect with poverty? We have seen that the share of FHHs is increasing while poverty has been falling. The discourse about FHHs has generally been that these households are poorer compared to those headed by men (Buvinic and Rao Gupta, 1997). So have FHHs been left behind by recent improvements in average household living standards? Maybe not. The women who head households in Africa are a diverse group. Some, such as married women with a nonresident husband (these could be polygynous marriages or a husband who has migrated for work) or women who choose not to be married or remarried (because they are educated and have social and economic opportunities to enable that decision), can be expected to be relatively well-off. Others, such as war or AIDS widows, repudiated, separated or abandoned women, and single mothers who have not ‘chosen’ headship but simply have no options, are frequently found to head disadvantaged households.

So what has happened to the living standards of FHHs in the aggregate? Taking the set of countries where we have comparable survey pairs, which covers approximately 80% of Africa’s current population, we find that poverty declined for both male and female headed households. But in most countries, poverty fell faster for FHHs. This pattern holds when one allows for the diversity among FHHs. For example, it is true when comparing households with widowed and non-widowed heads, or married (or non-married) heads with and without a male adult household member. The finding that poverty is falling faster for FHHs is robust to adjusting our poverty measure for household size and economies of scale in consumption (FHHs are, on average, smaller). Looking at the different types of FHHs (widowed, nonresident husband, etc.), we find that they follow dissimilar paths across countries and time periods, with no one type consistently experiencing faster poverty reduction than the others, yet with at least one type usually outpacing male-headed households. There is little discernible pattern across countries. One type of FHH does well in one country or time period, while another category does best elsewhere.

Decomposing the change in poverty shows that, rather than putting a break on poverty reduction, FHHs are contributing appreciably to the overall decline in poverty. This is impressive given that they are a minority (at 25 percent of all households).

But why has poverty fallen faster for FHHs? Perhaps poor FHHs face relatively high economic returns to the new opportunities unleashed by growth; or perhaps they have benefited disproportionately from the expansion of social protection in the region; or perhaps the group of people living in FHHs is fundamentally changing over time. Noting that we have limited data to study this topic, a first, superficial examination does not support any of these explanations. This new stylized fact about poverty in Africa warrants a closer look.

References

Buvinic Mayra, Rao Gupta Geeta (1997) “Female-headed Households and Female-maintained Families: Are they Worth Targeting to Reduce Poverty in Developing Countries?” Economic Development and Cultural Change 45(2): 259-280.

Chen Shaohua, Ravallion Martin (2013) “More Relatively Poor People in an Absolutely Poor World”, Review of Income and Wealth, 59:1-28.

Lardoux Solene, van de Walle Etienne (2003) “Polygyny and Fertility in Rural Senegal”, Population 58: 807-836.

Milazzo Annamaria, van de Walle Dominique (2017) “Women Left Behind? Poverty and Headship in Africa”, Demography, 54(3): 1119–1145.

Tabutin Dominique, Schoumaker Bruno, Rabenoro Mireille (2004) “The Demography of Sub-Saharan Africa from the 1950s to the 2000s”, Population, 59(3): 521-622.

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Single motherhood and poverty

In countries where most children of single mothers are economically disadvantaged relative to children in two-parent families, accounting for family socioeconomic status shrinks the estimated cost of being reared by a lone mother. That is, children of lone mothers would have worse health outcomes even if their mothers were partnered, so part of their disadvantage has nothing to do with maternal union status. But a little-known fact about mothers who have never been in a marital or cohabiting union in Africa, Latin America and the Caribbean, and Asia is that they have more education (Figure 1) and are less likely to be poor (Figure 2) than mothers whose children were born during their first and only union.

Why would advantaged women in the Global South be more likely to bear children before entering a union than their poorer and less educated counterparts? The simple answer is that socioeconomic development tends to increase the average age that first unions are formed. Where unions are formed close to puberty — in the least developed countries, 13 per cent of girls marry before age 15 (UNICEF 2017) — there is little opportunity to have a birth before first union. In contrast, there is much more opportunity where entry into unions has become delayed until the later teens, 20s, or even past 30. The oldest average marriage ages are in the world’s most developed countries, and many of the countries with the earliest average marriage ages cluster around the equator (United Nations 2015; see map here).

It is also the case that more advantaged women are more likely to divorce in much of the Global South. Access to divorce can be limited by patriarchy, personal resources, and the strength of traditional norms: Educated women are more likely to overcome these obstacles. Overall, we did not find divorced mothers to be as consistently advantaged as mothers who had never been in union, but they still had more education in Africa and Latin America, plus they experienced lower rates of poverty in Latin America (Figure 2). When single motherhood is concentrated among socioeconomically advantaged women, poor health outcomes among children of single mothers cannot be explained solely by socioeconomic disadvantage.

Union transitions matter much more than union status

So far, I have highlighted how different Northern and Southern countries are with respect to who becomes a single mother, but our work also uncovered an important similarity across the globe: Poor health outcomes are more strongly associated with union transitions than with union status. We found higher childhood death rates among children of divorced mothers in all three major regions of the Global South, but higher childhood death rates among never partnered mothers only in Africa (Figure 3). Moreover, children of currently partnered women had significantly lower risk of remaining untreated when suffering symptoms of one of the three leading causes of childhood death (acute lower respiratory infection, diarrhea, and malaria) in the developing world if their mother was in her first union than if she was in a subsequent one in all three regions (Figure 4). Together these findings show elevated mortality risk among children whose mothers have been through union transitions—regardless of union status.

How can union transitions before a child was born matter?

Our work found elevated mortality risk among children of mothers with unstable union histories. Our data came from a single point in time; in most cases, we did not know whether women had changed their union status during their child’s lifetime (or since their child’s lifetime ended). Many children of repartnered women were, however, known to be living with their biological fathers: These children were significantly more likely to suffer symptoms of one of the three leading causes of childhood death in the Global South (acute lower respiratory infection, diarrhea, and malaria) and remain untreated—even though they lived in two biological parent families. In other words, children’s higher mortality risk did not depend on whether maternal union transitions occurred during their own lifetimes.

Why would a mother’s prior union transitions put a child at risk? Prior transitions may have compromised the mother’s physical and mental health, plus caregiving is more complicated in blended families. Also, given that more educated women in the Global South are more likely to be single parents, those who have repartnered may have been motivated in part by economic necessity (again see Figure 2).

Therefore, our work did not establish that union transitions themselves decreased children’s survival. We nevertheless argue that maternal union instability is a marker for childhood mortality risk that warrants policy attention. Characteristics of previous partners that cause both union dissolution and childhood death (like being physically abusive) would not explain why rates of recent untreated illness are elevated among children of divorced and repartnered women, unless it were somehow a consequence of union transitions. Further, in cases where something about the mother is the common cause of both union transitions and child health outcomes (e.g., she is mentally ill, addicted, selfish, HIV-positive, or repeatedly chooses lousy partners), then she would carry those traits into subsequent unions, and her children would still require health interventions. Finally, the similar outcomes between children of divorced and repartnered women argue against poverty as a common cause of both union disruption and childhood mortality risk. Targeting children of mothers who have experienced union instability—regardless of current union status—may augment ongoing efforts to reduce childhood mortality in the Global South, especially in Africa and Latin America where union transitions are common.

References

DeRose, L, Salazar-Arango, A, Corcuera Garcia, P, Gas-Aixendri, M, and Rivera, R (2017), Maternal Union Instability and Childhood Mortality Risk in the Global South, 2010-2014, Population Studies, 76(2):211-228   

UNICEF (2017)

United Nations (2015). World Marriage Data.

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The proportion of children experiencing parental divorce or separation has increased across Western countries. As a result, in 2014 about 16% of children aged 0 to 17 lived in single-parent household across OECD countries, with considerable cross-national variation ranging from 6% in Turkey to about 23% in the UK and 27% in the United States (OECD, 2015).

A rich literature has shown that divorce and separation are often accompanied by a drop in income, particularly for women and children (Aassve et al., 2007, Brewer et al., 2014, Fisher and Low, 2015), increasing their risk of poverty. However, a separate strand of literature tells us that family income does not always reflect living conditions and well-being, especially for children (UNICEF, 2013). This may be especially true following a separation, when parents may prioritize scant resources towards children to maintain their living standards and home environments. Arguably, therefore, monetary measures may not fully capture the practical impact of parental separation on children’s daily lives.

Accounting for changing needs and multiple spheres of deprivation.

In a recent paper (Leturcq, Panico 2016), we used rich data from the UK Millennium Cohort Study (MCS), which follows over 18,000 children born in 2000-2001 and living in the UK. The Study collects detailed information on a number of dimensions of children lives, starting shortly after birth, and will do so regularly across their life course. MCS families were interviewed when their children were aged about 9 months, 3 years, 5 years, 7 years and 11 years. Data collection is complete for age 16 but these data are not yet available. We used this information to construct indicators capturing children’s experience in different dimensions of their lives, measuring different domains of child deprivation. Therefore, alongside classic measures of monetary poverty (a household is considered as poor if its equivalent income¹ is below 60% of the median equivalent income), we explore how parental separation affects a diverse set of dimensions of childhood deprivation, including access to material goods and housing (such as living in a damp or overcrowded house, not having central heating, etc.), leisure deprivation (including not being able to afford holidays or activities that must be paid for, such as music lessons), and parenting, tapping into the home learning environment (reading and helping with homework), participation in free family activities (going to the park or the library), and home routines.

Children’s needs change as they grow. Multi-dimensional indicators of deprivation have already been created separately for adults and children, but they do not vary according to the children’s ages, and tend to focus on primary-school-aged children, ignoring the early childhood period. We deemed it preferable to adapt existing methodologies, notably that of Alkire and Foster (2015), to study the same dimensions of deprivation over the life course of children, but taking into account how the needs of children change with age, from shortly after birth until the beginning of secondary school. So, for example, when we looked at parenting, we included indicators such as whether parents sing songs and read books to their children when they are 3 years old, while at ages 5 and 7, we analyzed whether parents help them with reading. By age 11, we looked at whether parents help them with their homework or attend school meetings. Another example is lack of access to the internet or a computer at home: this may not be relevant in early childhood, but becomes very important by the age of 11, when it is a strong marker of deprivation.

In our sample, we found that about 18% of children are monetarily poor at any given point, and about 20% of children are deprived on two or more of the deprivation domains that we studied. We consider these children as being “generally deprived”. These two groups do not always overlap: only 9% of children in our sample are both monetarily poor and deprived.

So, how do children experience parental separation?

Studying the impact of separation on childhood poverty and deprivation is difficult because parents who separate are usually different from those who do not, even before the divorce or separation occurs. Parents who eventually separate are on average less educated, have lower incomes, and are less likely to be employed before separation. However, even after controlling for all the differences that we could observe (that is, that were available in the dataset, such as parental education, age, and employment status),² we still registered a notable increase in the risk of monetary poverty associated with parental separation, with a sharp rise in income poverty in the short term, and only a partial recovery in the long term (Figure 1).

Looking at the different dimensions of deprivation explained above, we found a strong increase in leisure deprivation following parental separation (for example, being able to afford holidays, music lessons, etc.). However, we found only a small short-term effect on material deprivation, perhaps because parents prioritize their (reduced) incomes towards their children’s basic needs, such as providing them with a safe, warm home. We also did not find any impact of parental separation on parenting involvement and children’s day-to-day routines. This suggests that, even during the chaotic time around separation, parents prioritize maintaining parenting activities and routines with their children. However, let us remind our readers that the families who eventually experience a breakdown tend to start out with fewer material or cultural resources. Their children, therefore, even if not particularly affected by parental separation, remain a relatively deprived group throughout their lives.

We also found that children’s post-separation trajectories differ according to their mother’s level of education, suggesting that the parents’ pre-separation social and economic capital may play an important role. In fact, we found that the negative long-term effects of parental separation on child poverty and deprivation are only found in less advantaged groups. For more advantaged groups, while the risk of deprivation increases slightly in the short term, the long term impact of parental separation on children’s lives is negligible.

Bibliography

Aassve Arnstein, Betti Gianni, Mazzuco Stefano, Mencarini Letizia (2007) Marital disruption and economic well-being: A comparative analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society), 170(3):781-799,.

Alkire Sabina, Foster James, Seth Suman, Santos Maria Emma, Roche José Manuel, Ballón Paola (2015) Multidimensional Poverty Measurement and Analysis, New York, Oxford University Press.

Brewer Mike, Nandi Alita, et al. (2014) Partnership dissolution: how does it affect income, employment and well-being? Institute for Social and Economic Research, University of Essex, n. 30.

Fisher Hayley, Low Hamish (2015). Financial implications of relationship breakdown: does marriage matter? Review of Economics of the Household, 13(4):735-769.

Leturcq Marion, Panico Lidia (2016) The long-term effect of parental separation on childhood financial poverty and multidimensional deprivation: a lifecourse approach, Documents de travail INED, n° 228, 44 pages.

OECD – Organization for Economic Co-Operation and Development. Family Database (2015)

Unicef (2013) Child Well Being in Rich Countries: A Comparative Overview. UNICEF Office of Research-Innocenti.

Foot note

¹The equivalent income transforms monetary income into a value that takes the household dimension into account. The resulting figure is therefore (more easily) comparable between large and small households.

² If the controls are introduced properly, and if there is no other unobserved but systematic effect, the differences in the changes in the risk of deprivation or poverty due to these initial conditions are eliminated or, at least, greatly reduced.

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The demographic dividend comes and goes

Readers of N-IUSSP will probably be familiar with this effect: high fertility countries have very high proportions of children who are not economically active, so that as fertility falls, normally in conjunction with infant and child mortality, the proportion of ‘working age’ adults increases, at least for a few decades, until the rise in proportion of old-age retirees begins to outpace the decline in proportion of children. The demographic dividend refers to this temporary ‘window’ in which working-age proportion is unusually high.

Intuitively, a high proportion in ‘working age’ seems likely to mean greater economic activity per capita and a higher savings rate. But the dependency ratio itself, assuming that the expanded workforce is productively employed, can account for only a fifth (Wang and Mason 2007) to a third (Bloom and Canning 2008) of the economic stimulus observed in east Asian countries. The story is evidently a little more complicated.

Much emphasis is now being placed on demographic dividend as an opportunity, which requires a major expansion of human and industrial capacity to be realised. (Canning, Raha and Yazbeck 2015). What is rarely mentioned is that trying to increase education and employment opportunities are not new priorities, nor uniquely applicable to countries with shifting age structures. They have been the priorities of development forever. What is pertinent is not that ‘demographic dividend’ countries did more on this front, but that what they did was enough to get ahead, while similar efforts in high fertility countries generally failed to keep pace with the tide of population growth.

Population growth hinders economic and social development

The population growth rate itself, not merely the change in age structure, has a great impact on countries in fertility-transition. The dead weight burden of ‘capital widening’, to deliver the currently prevailing rate of educational, industrial and institutional capacity to each year’s additional people, is a massive constraint on economic betterment. (O’Sullivan 2012). This masquerades in the national accounts as ‘investment’, but is in practice merely a maintenance cost, bringing no additional benefit per person. The burden can be demonstrated though its impact in high-immigration developed countries, where population growth is associated with little change in age structure. Data from the UK (O’Sullivan 2013) and Australia (O’Sullivan 2014a) found that each 1% of population growth rate ‘uses up’ around 6.5–7% of GDP for capital widening. This amount of spending is thus not available for improving the welfare of existing people. If a similar proportion prevails in sub-Saharan African countries, population growth may be consuming 20% or more of GDP, just running to stand still. Reducing this impost, by reducing fertility, represents an ‘infrastructure dividend’, which complements and might surpass the more favourable age structure.

Too much population growth (especially in Africa)

It is therefore disturbing to hear increasingly frequent claims that Africa’s burgeoning population offers an economic bonanza due to the ‘demographic dividend’. The implication is that Africa will have the last laugh, as its growing, youthful population will become the economic powerhouse as developed economies senesce due to demographic ageing. The ageing fearmongering is so far unsubstantiated (Kluge et al 2014): ageing has not yet been demonstrated to constrain workforce or per capita wealth in any country (Betts 2014), but rather to reduce unemployment and inequality (O’Sullivan 2014b). So the advantage of youthfulness may be a mirage.

More importantly, the message that a bigger proportion of working age people may be a good thing has been confused with the idea that a bigger number is an unmitigated good. At the 7^th African Population Conference last December, a South African Minister said, “What was the big fuss on population? Western Europe and the UNFPA made such a fuss about absolute numbers of population and today this discourse has been blighted by what is called a demographic dividend, suggesting that a big and youthful population after all is not a bad thing as Malthus made us believe.” He added, “A demographic dividend is brought about by expanding the thinking capabilities of the human mind and brain.” These words echo Julian Simon’s 1981 thesis that humans are ‘the ultimate resource’, dismissing any possibility that crowding of the natural resource base will ever (let alone already does) cause deprivation.

More recently, an International Monetary Fund blog (Thakoor and Wakeman-Linn 2016), picked up by African business media, announced, “Sub-Saharan Africa could reap significant benefits from its growing population—if the transition is well managed.” The article, by IMF advisers to Africa, does eventually concede that some countries will have to reduce their fertility rates. But not before extolling the increasing proportion of the world’s workforce that will be African. Never mind the limited capacity of other regions to generate demand for additional labour, the scarcity of resources available to be converted to exportable products, and the already rapidly rising food import-dependence of African countries.

It is good that the demographic dividend discourse is highlighting the need to invest in youth. Some see it as a way to incentivise better family planning programs without mentioning the word “population”. But does this strategy risk backfiring, by distracting attention from fertility decline and implying that, once the ‘demographic window’ opens (demographic dividend, reputedly at a working-age proportion around 60%), then further declines in fertility and population growth are not necessary? High population growth is the main engine of impoverishment in Africa, and ending population growth is an absolute requirement for sustainable prosperity. Why must these simple facts be mired in such subterfuge?

References

Betts K. (2014) The ageing of the Australian population: triumph or disaster? Report for the Monash Centre for Population and Urban Research

Bloom D.E. and Canning D. (2008) Global Demographic Change: dimensions and economic significance. Population and Development Review, Vol. 34, Population Aging, Human Capital Accumulation, and Productivity Growth (2008), pp. 17-51.

Bloom D. Canning D. and Sevilla J. (2001) Economic Growth and the Demographic Transition. NBER Working Paper No. 8685, National Bureau of Economic Research.

Canning D., Raha S. and Yazbeck A.S. (Eds) (2015) Africa’s Demographic Transition: Dividend or Disaster? Africa Development Forum series. Washington, DC: World Bank.

Kluge F., Zagheni E., Loichinger E. and Vogt T. (2014) The advantages of demographic change after the wave: fewer and older, but healthier, greener, and more productive? Plos One 9(9): e108501.

O’Sullivan J.N. (2012) The burden of durable asset acquisition in growing populations. Economic Affairs 32(1), 31-37.

O’Sullivan J.N. (2013) The cost of population growth in the UK. Report for Population Matters.

O’Sullivan J. (2014a) Submission to the Productivity Commission Inquiry into Infrastructure provision and funding in Australia.

O’Sullivan, J. (2014b) Ageing paranoia, its fictional basis and all too real costs. In “Sustainable Futures: Linking Population, Resources and the Environment.” (Eds J Goldie and K Betts). CSIRO Publishing, Melbourne. ISBN: 9781486301898 pp 47-60

Simon J.L. (1981) The ultimate resource. Princeton University Press, New Jersey USA

Thakoor V. and Wakeman-Linn J. (2016) Surf the Demographic Wave. Finance & Development, March 2016, Vol. 53, No. 1.

Wang F. and Mason A. (2007) Population Aging in China: Challenges, Opportunities and Institutions. In: Zhao Z and Guo F. (Eds.), Transition and Challenge: China’s Population at the Beginning of the 21st Century. Oxford University Press, pp. 177–196.

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