Stalls in Africa’s fertility decline partly result from disruptions in female education

Abstract

Population projections for sub-Saharan Africa have, over the past decade, been corrected upwards because in a number of countries, the earlier declining trends in fertility stalled around 2000. While most studies so far have focused on economic, political, or other factors around 2000, here we suggest that in addition to those period effects, the phenomenon also matched up with disruptions in the cohort trends of educational attainment of women after the postindependence economic and political turmoil. Disruptions likely resulted in a higher proportion of poorly educated women of childbearing age in the late 1990s and early 2000s than there would have been otherwise. In addition to the direct effects of education on lowering fertility, these less-educated female cohorts were also more vulnerable to adverse period effects around 2000. To explore this hypothesis, we combine 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 two million births to some 670,000 women born from 1950 to 1995 by level of education. Statistical analyses indicate clear discontinuities in the improvement of educational attainment of subsequent cohorts of women and stronger sensitivity of less-educated women to period effects. We assess the magnitude of the effect of educational discontinuity through a comparison of the actual trends with counterfactual trends based on the assumption of no education stalls, resulting in up to half a child per woman less in 2010 and 13 million fewer live births over the 1995–2010 period.

Full text

Stalls in Africa’s fertility decline partly result from
disruptions in female education
Endale Kebede
a
, Anne Goujon
a
, and Wolfgang Lutz
a,1
a
Wittgenstein Centre for Demography and Global Human Capital (IIASA, VID/OeAW, WU), International Institute for Applied Systems Analysis, 2361
Laxenburg, Austria
Contributed by Wolfgang Lutz, October 18, 2018 (sent for review October 3, 2017; reviewed by John Casterline and Alex C. Ezeh)
Population projections for sub-Saharan Africa have, over the past
decade, been corrected upwards because in a number of countries,
the earlier declining trends in fertility stalled around 2000. While
most studies so far have focused on economic, political, or other
factors around 2000, here we suggest that in addition to those
period effects, the phenomenon also matched up with disruptions
in the cohort trends of educational attainment of women after the
postindependence economic and political turmoil. Disruptions
likely resulted in a higher proportion of poorly educated women
of childbearing age in the late 1990s and early 2000s than there
would have been otherwise. In addition to the direct effects of
education on lowering fertility, these less-educated female co-
horts were also more vulnerable to adverse period effects around
2000. To explore this hypothesis, we combine 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 two million births to some 670,000 women born from
1950 to 1995 by level of education. Statistical analyses indicate clear
discontinuities in the improvement of educational attainment of sub-
sequent cohorts of women and stronger sensitivity of less-educated
women to period effects. We assess the magnitude of the effect of
educational discontinuity through a comparison of the actual trends
with counterfactual trends based on the assumption of no education
stalls, resulting in up to half a child per woman less in 2010 and 13
million fewer live births over the 1995–2010 period.
fertility
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sub-Saharan Africa
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educational discontinuity
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macro-economic
crisis
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population projections
All human populations have entered the process of de-
mographic transition in which first, death rates start to fall
due to socioeconomic development and improved public health,
and then after some time lag, birth rates start to decline. During
the period when death rates are already low and birth rates are
still high, populations grow rapidly. This was the case in Europe
and North America around 1900, and the process subsequently
spread to Latin America and eastern Asia, and then to southern
and western Asia. In most of these regions, fertility rates have
already fallen to low levels, even when the population still con-
tinues to grow due to the age-structural momentum in which
larger cohorts of young women still enter the reproductive ages
and death rates continue to fall.
Sub-Saharan Africa has been the last world region to enter this
demographic transition. It was only in the 1980s that birth rates
started to fall in most countries, but these declines have been
uneven and have stalled at times. Particularly in the late 1990s
and early 2000s, some sub-Saharan African countries have ex-
perienced a leveling off of their fertility decline and, in some
cases, even saw a reversal, leading to an increase (as shown in
Fig. 1 for selected countries included in our dataset). Much has
been written and speculated about this so-called stalled African
fertility transition (1). The reasons for this interruption of the
fertility decline in many sub-Saharan African countries have
remained a demographic mystery because little consensus exists
on the causes of the stalls (2). Most of the existing studies try to
link the fertility stalls to some specific period factors such as the
slower trends in socioeconomic development prevalent in the
stalling countries (3), the low priority assigned to family planning
programs at the beginning of the 21st century (4, 5), the impact
of HIV/AIDS mainly through its effect on child mortality (2, 6),
and other factors related to public and reproductive health. In
contrast to these explanations, recently, Goujon et al. (7) pro-
posed another plausible explanation, focusing on cohort effects
rather than period effects. These authors linked the fertility stalls
around 2000 to the fact that some cohorts of women were subject
to an education stall, possibly associated with the adverse effects
on education of the structural adjustment programs (SAPs)
launched by the Bretton Woods Institutions in the 1980s.
The analysis presented in this paper provides a more com-
prehensive assessment on the basis of microlevel data from 18
African countries and highlights the cohort and period effects
that, in combination, could have resulted in the slow-down in the
decline of period fertility around 2000 in some countries. The
discontinuity in cohort trends of improving educational attain-
ment (due to the economic and political turmoil in some coun-
tries around the 1980s) is consistent with the higher proportion
of poorly educated women of childbearing age in the late 1990s
and early 2000s than there would have been without these edu-
cation disruptions. This phenomenon coupled with the relatively
higher vulnerability of less-educated women to period effects has
likely contributed to the stalls in the period fertility declines.
Education and Fertility Discontinuities in the Context of
Crises in Africa
The postindependence period was a time of great expectations in
sub-Saharan Africa, as most countries engaged in a process of
expansion of social services (8). However, the initial period of
economic and social bliss was soon replaced by harsher times
Significance
The future pace of fertility decline in sub-Saharan Africa is the
main determinant of future world population growth and will
have massive implications for Africa and the rest of the world,
not least through international migration pressure and diffi-
culties in meeting the sustainable development goals. In this
context, there have been concerns about recent stalls in the
fertility decline in some African countries. Our findings suggest
that these stalls are in part explained by earlier stalls in female
education and that less-educated women are more vulnerable
to adverse period conditions. This has important implications
for setting policy priorities.
Author contributions: E.K., A.G., and W.L. designed research, performed research, con-
tributed new reagents/analytic tools, analyzed data, and wrote the paper.
Reviewers: J.C., Ohio State University; and A.C.E., Center for Global Development.
The authors declare no conflict of interest.
This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
1
To whom correspondence should be addressed. Email: lutz@iiasa.ac.at.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1717288116/-/DCSupplemental.
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related to the consequence of external shocks of oil price in-
creases, declining terms of trade, and increases in interest rates
that had a massive impact on most African economies, increasing
government external debt (9). This resulted in austerity measures
and massive cuts in government budgets, particularly in the social
sectors of health and education, mostly in the framework of
SAPs introduced in Africa by the International Monetary Fund
(IMF) and the World Bank (9). These austerity measures af-
fecting the social sector have been happening recurrently, not
always with the same strength, throughout the last decades in
most African countries. The particularity of the adjustment
programs of the early 1980s was that they were mostly not ac-
companied by compensating programs such as safety net pro-
grams for the most vulnerable population—for example, cash
transfers or school-based food programs—as was the case
thereafter (mostly from the 1990s onward). It is impossible to
disentangle whether the SAP or the initial dire situation (e.g.,
stressed government budgets and diminishing employment op-
portunities) is at the origin of the slow-down in educational
improvement that we observe in our cohort approach. Never-
theless, it is clear that the unfortunate women who, in their
childhood in the 1980s, were deprived of their education op-
portunities will bear the consequences of this over their entire
life cycle. These life-long consequences not only concern health
and income, they could also be relevant for fertility, which in
time would affect population growth and society at large.
In the literature so far, fertility trends in sub-Saharan Africa
have hardly been linked to the slow-down in education progress.
The two phenomena are, indeed, almost two decades apart,
which may have seemed too long for any direct causal effect
from, for example, reduced reproductive health spending on
fertility. This 20-y lag is, however, precisely the timing that would
be expected for an effect on fertility operating through female
education: declining primary school enrolment rates for girls
during the 1980s would result in lower education, and hence
higher fertility, for women in their prime childbearing ages
around 2000. Given the strong differentials in fertility by level of
female education in all African countries and the extensive body
of literature that explains the causal mechanisms behind the
pervasive negative association between the two (10–14), it seems
to be a plausible hypothesis to assume a direct effect from the
stalled trend in female education to the subsequent stall in fer-
tility decline in the countries affected by the former.
In addition to this hypothesis or in combination with it (as we
will show later), the fertility stalls in the early 2000s could also be
linked to some period effects affecting the women of child-
bearing age, as mentioned in the Introduction. Mixed results are
coming out of studies trying to assess the impact of various types
of crises on fertility, especially in the African context in which
increasing economic hardship may not necessarily lead to lower
fertility (as it does in countries further advanced in demographic
transition), because there are still strongly pronatalist norms and
children are typically seen as a way to diversify risk. Also, in-
creased fertility enhances the probability of having a certain
number of surviving children in times of mortality crisis. But there
is also evidence for opposite effects of crises on lowering fertility in
certain cases, such as short-term economic crisis, extreme weather
events, or epidemic diseases (15). Different patterns are also
found in urban areas where household living costs associated with
additional children are substantially higher than in rural areas.
The purpose of this study is to systematically assess the pattern
of period and cohort changes in fertility and educational attain-
ment, as well as other possible drivers of fertility, using the
broadest available datasets. We investigate the link between lon-
gitudinal cohort education trends and longitudinal cohort specific
fertility trends from consecutive Demographic and Health Surveys
(DHSs) conducted in the region. We also apply multivariate sta-
tistical analyses, adding national-level indicators to microlevel
data, to explore alternative explanations and explore the relative
impact of the different potential determinants of fertility.
Data Used and Cohorts Reconstructed
This study is primarily based on a pooled microdataset from a
total of 72 DHSs collected in 18 sub-Saharan African countries
over the years 1990–2016. Within each country, the surveys made
use of a two-stage cluster sampling technique to collect comparable,
reliable, and nationally representative data on living conditions and
demographic characteristics of households. The 18 sub-Saharan
African countries represent about 66% of the population of the
region in 2015 (16). The selected countries are Benin, Burkina
Faso, Côte d’Ivoire, Cameroon, Republic of the Congo, Democratic
Republic of the Congo, Ethiopia, Gabon, Ghana, Guinea, Kenya,
Niger, Nigeria, Malawi, Tanzania, Uganda, Zambia, and Zimbabwe.
The DHSs collect and report full birth histories of women aged
15 to 49 y. The pooled dataset for the 18 countries under study
includes 2,040,664 births to 670,449 women. For each of the
countries studied, we used multiple surveys taken at different
points in time, ranging from two to five surveys per country,
with an average of around four.
DHS individual and household files were used to construct
fertility histories by age and educational attainment of the
mother by single-year birth cohorts. Since DHSs are sample
surveys, the information gathered at different points in time for
the same national-level cohorts of women is not necessarily
identical. Hence, considerable effort was invested in reconstruct-
ing consistent series of age- and cohort-specific data for the period
4
5
6
1985 1990 1995 2000 2005 2010
year
TFR(15-35)
Uganda
Ethiopia
Nigeria
Cameroon
Tanzania
Cote d'Ivorie
Kenya
Ghana
Zimbabwe
Period Total FerƟlity Rate(1985-2010)
Source: Own ComputaƟon
Fig. 1. Reconstructed period TFRs (for women aged 15 to 35 y) for nine selected countries in sub-Saharan Africa for 1985–2010 based on successive DHSs.
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1985–2010 covering the cohorts of women born between 1950 and
1995. Because of these limited time windows of data availability,
we will only consider fertility rates between the ages of 15 and 35 y,
which do cover most of the reproduction time over the life cycle.
In the case that different surveys provided different information
on fertility for the same cohorts, a weighted average of the dif-
ferent surveys was used (more details about the reconstruction
procedures are included in SI Appendix).
Intercohort Changes in Education and Fertility
The reconstructed period fertility trends given in Fig. 1 show
interesting differences in fertility levels and patterns of change.
While Uganda shows stable fertility at a very high level until the
onset of the fertility transition around 2000, Ghana shows a
smooth and uninterrupted fertility decline over the entire period
of 1985–2010. Côte d’Ivoire, on the other hand, shows a clear
reversal of the fertility decline, with increases from 1995 to 2000,
followed by a continuation of the decline. The pattern in
Kenya is similar but somewhat less pronounced. Other
countries such as Nigeria and Cameroon show a flattening of
the curve in the middle of the period but no real increase.
Both Nigeria and Cameroon resumed the declining trend over
the last decade.
For two big countries with stalls in their fertility declines, Fig.
2 shows the reconstructed period fertility trends (1985–2010) by
education group. The trends for all countries are given in SI
Appendix, Fig. S3. We have categorized the educational attain-
ment of women into three broad groups: no education, some
primary education, and completed primary education or more.
These two figures exhibit the well-documented education dif-
ferences in fertility levels, with more-educated women wanting
and actually having fewer children by using contraception more
effectively compared with less-educated women (10). More in-
terestingly, the stall in the period fertility decline and actual in-
creases in some countries are more pronounced among the least
educated groups. It would seem that uneducated women were
more affected by some adverse period conditions prevailing in
the late 1990s and early 2000s than better educated women.
There are thus two different mechanisms in which education
could have influenced fertility in terms of cohort and period
effects. The cohorts of women who had suffered a stall in their
education progress in the context of postindependence economic
and political problems had higher fertility than would have been
the case without such stalls, due to the direct effect of education
lowering fertility. However, in addition, the above-described
impact of period effects on uneducated women around 2000
was also more sizable in terms of affecting more women because
of a higher proportion of uneducated women. To have a deeper
understanding of these patterns, we have taken a closer look at
two examples: Nigeria as the most populous African country and
Kenya as a country classified as “fertility stalled”by the large
majority of previous studies and criteria. Fig. 2 shows the period
fertility trends of those two countries by level of mothers’edu-
cation. In Kenya around 1995, women with completed primary
or higher education had, on average, 3.5 children, whereas
women without any schooling had 5.7 (a difference of 2.2 chil-
dren). By 2005, the gap had significantly widened to 3.3 vs. 6.6
children (a difference of 3.3 children or exactly double that of
women with at least completed primary education). Fig. 3 plots
the trends in the proportions of women who never attended
school by cohorts born between 1950 and 1990 and shows that in
both countries, a clearly declining trend from birth cohort of
1950 to that of the 1970s was discontinued for the subsequent
cohorts. For those born after 1970, something dramatic hap-
pened. The improving trend slowed markedly in Nigeria and
even reversed in Kenya. Such education discontinuities were also
observed in most other countries classified as fertility stalled, as
shown in SI Appendix, Fig. S3.
This combination of a stall or even reversal in the cohort ed-
ucational attainment and the above-described pronounced dif-
ferences in fertility by education suggests a link between education
and fertility stalls. It is not trivial, however, to estimate the size
of this education-related cohort effect in relation to some also-
evident period effects around the time of the stall. We do not
choose a simple decomposition analysis because we should
capture the joint implication of two independent forces: (i) the
fact that without the education stall, more women would be in
the educated category and, since these women would have lower
fertility levels, overall fertility would be lower; and (ii)thefact
that—as shown in Fig. 2—educated women are less affected by
those period influences than women without any education. An
appropriate way to assess the combined effect of these two
different forces is to compare the actually observed fertility
trends with a hypothetical or “counterfactual”trend based on
the assumption of the absence of an education stall.
Kenya Nigeria
1985 1990 1995 2000 2005 2010 1985 1990 1995 2000 2005 2010
4
5
6
7
3
4
5
6
year
TFR(15-35)
no educaon incompl. Primary complete Primary +
TFR by educaon group
Source: Own Computaon
Fig. 2. Reconstructed period TFRs (for women aged 15 to 35 y) for Kenya and Nigeria by level of education (no formal education vs. some formal education).
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The blue dotted lines in Fig. 3 show this counterfactual trend
in the proportions of women with no formal education, which
was extrapolated from an autoregressive moving average model,
based on the empirical trends of cohort educational attainment
up to 1970. In other words, this counterfactual line indicates the
improvement in education by cohorts for the hypothetical case
that education levels would have continued to evolve smoothly
without the stalls and reversals described above. This also closely
resembles the actual education trends in countries such as Ethiopia
that did not experience cohort educational discontinuity.
In a next step, we then applied the empirically observed age-
and education-specific fertility rates (as given in Fig. 2) to the
forecasted proportion of women by education groups and de-
rived a counterfactual period total fertility rate (TFR) plotted in
Fig. 4. Table 1 also lists the empirically observed and counter-
factual TFRs for all African countries included in this analysis,
listing the 10 countries that have been classified as fertility stalled
at the top. For 2005, the biggest difference between the two rates
is observed in Cote d’Ivoire, accounting for 0.50 children per
woman, followed by Nigeria with 0.47. By 2010, the difference
increased to 0.59 in Nigeria, followed by 0.49 in Kenya. Except
for Democratic Republic of the Congo, the fertility difference
induced by the education stall is larger than 0.25. As expected,
for the countries not classified as having a stalled fertility decline,
the difference is much lower or even negative, presumably be-
cause there also had been no discernible education stall.
In sum, the difference between these counterfactual TFRs and
the observed ones lies only in the weights given to the three
education groups when aggregating to total fertility. Over time,
these listed fertility differentials could sum up to large numbers
of additional births due to the education stalls these countries
had experienced in the 1980s. Translated into absolute numbers
of births for all of the 10 fertility-stalled countries, between 1995
and 2010, about 13 million fewer babies would have been born to
Kenya Nigeria
1950 1960 1970 1980 1990 1950 1960 1970 1980 1990
20
40
60
0
10
20
30
40
Cohort
proporon(%)
actual extrapolated
Proporon of women with no formal educaon
Source: Own Computaon
Fig. 3. Reconstructed proportions of women with no formal education by cohorts born between 1950 and 1990 for Kenya and Nigeria (red line) and ex-
trapolated trends for cohorts born after 1970 based on a hypothetical continuation of the trend of the earlier period (blue line).
Kenya Nigeria
1985 1990 1995 2000 2005 2010 1985 1990 1995 2000 2005 2010
4.5
5.0
5.5
4
5
period
TFR(15-35)
actual counterfactual
Period TFR with and with-out educaonal disrupon
Source: Own Computaon
Fig. 4. Reconstructed actual trends in TFRs (for women aged 15 to 35 y) for Kenya and Nigeria (red line) and the counterfactual trends (blue line) calculated
by combining the extrapolated education trends for the cohorts born after 1970 with the observed education-specific fertility rates.
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women aged 15 to 34 y. For Nigeria alone, the difference is 6.5
million births.
Education, Cohort, and Period Effects on Fertility
In addition to the above-described simulations, we conducted
multivariate analyses on the pooled individual-level dataset of all
surveys for all countries and periods. The details of the different
model specifications and numerical results are given in SI Ap-
pendix. As dependent variable for this analysis, we took the cu-
mulative number of children that women had by age 25 y. This
choice was made as a compromise between the need to relate the
fertility experience studied as closely as possible to a specific
time period and the need to capture, as much as possible, the
quantum of fertility rather than differences in timing. The period
conditions associated with this fertility indicator are the ones that
prevailed when the women were 15 to 20 y old, assuming that
there is some lag in the process. In terms of the age at which
cumulative fertility is assessed, we also did sensitivity analysis for
ages 30 and 35 y, which showed qualitatively similar results.
After experimenting with a number of different available in-
dicators of national socioeconomic period conditions that would
also cover possible cuts in reproductive health and general health
care delivery systems, we chose gross domestic product (GDP)
per capita as the most consistently available and most frequently
used such indicator. We operationalized GDP changes over time
in such a way that average growth rates over 5-y intervals were
categorized as normal when they stayed within the band of ±2%,
as negative when they fell below this band, and as positive when
they fell above this band. In addition, the models include three
levels of educational attainment assessed at the level of each
individual woman by age 25 y, as well as the 5-y birth cohorts to
which the women belong, to capture the above-described cohort
effects. All of the results of this multivariate analysis based on
different models are given in SI Appendix.
The results of these multivariate models over the reproductive
experience of over 670,000 women in sub-Saharan Africa clearly
support the above findings based on more descriptive analysis
and aggregate-level simulations. Mothers’education clearly comes
out as the most significant determinant of individual-level fertility,
with the estimated odds ratio indicating that women with com-
pleted primary education have, on average, 64% of the level of
fertility of uneducated women, even after controlling for cohort
membership and period changes in GDP.
Conclusions
Due to the great momentum of population changes, the pros-
pects for future population growth in Africa and consequently
for the world as a whole greatly depend of the fertility trajectory
in Africa over the coming years. For assessing the likely future
fertility trends of Africa, it greatly matters what general approach
is taken to population projections and, in particular, whether
heterogeneity of the population with respect to its changing
education composition is explicitly taken into account. The
projections produced by the United Nations Population Division
only consider the age and sex structure of the population and
base their assumptions about future fertility trends on an ex-
trapolative statistical model of the overall TFR, which is sensitive
to recent trends in the TFR. The medium scenario of the most
recent assessment of 2015 projects that the number of people on
the planet will rise to 9.8 billion in 2050 and that sub-Saharan
Africa will be responsible for more than half of the world’s
population growth over the next 35 y (17). Compared with the
2010 United Nations revision, these recent projections result in
world population that is 0.5 billion larger in 2050. The difference
in the projection outcomes stems in large part from the extrap-
olation of the recent trends in fertility levels in many sub-Saharan
African countries that experienced slowed or stagnating fertility
declines as discussed in this paper. However, the relevance of
these stalls for future fertility trends greatly depends on the nature
and causes of the stalls and on the question of whether they were
just temporary irregularities or more persistent.
In the above-given analysis, we have found strong empirical
support for the hypothesis that this fertility stall aligned in part
with a temporary stall in the education of female cohorts born in
the late 1970s and 1980s. We have suggested that for most of the
countries experiencing fertility stalls around 2000, there have
been stalls in the education improvement of the female cohorts
that entered the prime childbearing ages around that time.
Table 1. Reconstructed actual trends in period TFRs (for women aged 15 to 35 y) and the
counterfactual trends calculated by combining the extrapolated education trends with the
observed education-specific fertility rates
Country
2005 2010
Actual Counterfactual Difference Actual Counterfactual Difference
Côte d’Ivoire 4.51 4.01 0.50 3.67 3.29 0.38
Cameroon 4.43 4.08 0.35 4.03 3.62 0.41
Democratic Republic of the Congo 5.51 5.31 0.20 5.21 5.03 0.18
Republic of the Congo 4.15 3.89 0.26 4.23 3.92 0.31
Kenya 4.17 3.77 0.40 3.75 3.26 0.49
Niger 7.65 7.39 0.26 6.78 6.50 0.28
Nigeria 5.16 4.69 0.47 4.78 4.19 0.59
Tanzania 5.00 4.65 0.35 4.84 4.44 0.40
Zambia 4.86 4.46 0.40 4.46 4.11 0.35
Zimbabwe 3.61 3.38 0.23 3.68 3.41 0.27
Benin 4.59 4.28 0.30 4.02 3.84 0.18
Burkina Faso 4.79 4.95 −0.17 4.13 4.43 −0.30
Ethiopia 4.93 5.02 −0.08 4.33 4.53 −0.20
Gabon 3.55 3.37 0.18 3.55 3.37 0.18
Ghana 3.62 3.63 −0.01 3.32 3.43 −0.10
Guinea 5.94 5.71 0.23 4.72 4.68 0.04
Malawi 4.86 4.94 −0.08 4.25 4.37 −0.12
Uganda 5.37 5.35 0.02 4.82 4.91 −0.09
The 10 countries listed in the top portion of the table have been classified as fertility stalled.
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Detailed analyses of cohort-specific patterns and multivariate
models including possible macrolevel period effects also indicate
that the exceptional education experiences of the cohorts born
around 1980 could indeed be associated with the observed fer-
tility stalls. Because the more recent cohorts of young women
have again picked up in terms of education, this finding suggests
that in the future, we may expect an acceleration of the fertility
decline as the subsequent better-educated cohorts of women
move into the main childbearing ages. This finding is also rele-
vant for the ongoing discussion as to whether population pro-
jections should be carried out by breaking down only by age and
sex or whether educational attainment should be routinely in-
cluded as a third demographic dimension (18, 19). The evidence
discussed in this paper illustrates well that, in the case of edu-
cation discontinuities, the assumed future fertility trajectories
are different when heterogeneity by level of education is ex-
plicitly factored into the model compared with disregarding this
heterogeneity and only observing aggregate TFR trends.
The most recent findings from the Kenya DHS are a point in
case. The TFR in Kenya in the late 1970s was around 8.1 children—
considered to be the highest in the world. After an initial decline, it
remained stagnant for about a decade around 2000 at a level of 4.6
to 4.9 children. But most recently, the TFR experienced a rather
steep dive to 3.9 children for the 3 y preceding the 2014 DHS and
even 3.7 in the 2015 Malaria Indicator Survey (20). No statistical
extrapolation model based on TFR alone could predict this recent
decline. The education-specific analysis discussed in this paper
makes it plausible because the female cohorts that experienced the
stall in education expansion are being replaced in the prime child-
bearing ages by new and better-educated cohorts.
While the evidence for this education–fertility link at the co-
hort level seems to be robust, evidence for directly blaming the
IMF-initiated SAPs for the education stalls seems less certain,
despite the fact that many authors draw clear and strong con-
nections between these programs and worsening health and
education outcomes in the countries affected (21–26). While
such direct connections are not implausible, we want to be more
cautious in drawing conclusions due to the lack of reliable sta-
tistical information about how precisely the SAPs in individual
countries led to cuts in the education budgets and how these cuts
trickled down to effects on school enrolment rates. These aus-
terity programs were implemented in response to economic
turmoil, and it is impossible to sort out whether the SAPs or the
preceding dire situations (or a combination of both) are the
reasons for the evident slow-downs in educational improvement
that we observe in our cohort approach. But whatever the precise
reasons are, the education discontinuities described in this paper
will not only be relevant in terms of their impact on fertility but
also could affect the future health and income of the affected
less-educated cohorts for the rest of their life courses.
Africa’s future population growth will be relevant for the rest
of the world. Whether it will “only”increase to two billion as
shown by optimistic scenarios that assume successful imple-
mentation of the sustainable development goals (27, 28), or by
more-pessimistic scenarios assuming slow or stalled development
and thus resulting in four to five billion Africans by the end of
the century, Africa’s future population growth will first of all
impact on the future of living conditions and quality of life of
Africans. But it will also affect other continents due to out-migration
pressure, global environmental impacts, and, of course, the
efforts needed to live up to the promise to eradicate poverty,
hunger, and premature death in all corners of the planet.
Continued rapid population growth will make this an up-hill
battle. Education is currently underfunded, particularly in
Africa (29, 30). A new effort for massive investment in educa-
tion, particularly of girls, will not only help to slow this growth
but will also empower Africans and create the human capital
needed for rapid social and economic development and sus-
tainable increases in human well-being.
1. Bongaarts J, Casterline J (2013) Fertility transition: Is sub-Saharan Africa different?
Popul Dev Rev 38:153–168.
2. Moultrie TA, et al. (2008) Refining the criteria for stalled fertility declines: An appli-
cation to rural KwaZulu-Natal, South Africa, 1990-2005. Stud Fam Plann 39:39–48.
3. Shapiro D, Gebreselassie T (2008) Fertility transition in sub-Saharan Africa: Falling and
stalling. African Popul Stud 23:3–23.
4. Agyei-Mensah S (2007) New Times, New Families: The Stall in Ghanaian Fertility
(Union for African Studies, Arusha, Tanzania) 24.
5. Bongaarts J (2008) What can fertility indicators tell us about pronatalist policy op-
tions? Vienna Yearb Popul Res 2008:39–55.
6. Westoff CF, Cross AR (2006) The stall in the fertility transition in Kenya (Macro International
Inc, Calverton, MD).
7. Goujon A, Lutz W, Samir KC (2015) Education stalls and subsequent stalls in African
fertility: A descriptive overview. Demogr Res 33:1281–1296.
8. Ezenwe U ( 1993) The African debt crisis and the challenge of development. Intereconomics
28:35–43.
9. Elbadawi IA, Ghura D, Uwuja ren G (1992) World Bank adjust ment lending and
economic performa nce in sub-Sahara n Africa in the 1980s: A co mparison with oth er
low income countr ies (World Bank, Was hington, DC).
10. Bongaarts J (2010) The causes of educational differences in fertility in Sub-Saharan
Africa. Vienna Yearb Popul Res 8:31–50.
11. Cochrane SH (1979) Fertility and Education. What Do We Really Know? (Johns Hop-
kins Univ Press, Baltimore).
12. Castro Martín T (1995) Women’s education and fertility: Results from 26 Demographic
and Health Surveys. Stud Fam Plann 26:187–202.
13. Fuchs R, Goujon A (2014) Future fertility in high fertility countries. World Population
and Human Capital in the Twenty-First Century, eds Lutz W, Butz WP, Samir KC
(Oxford Univ Press, Oxford), pp 147–225.
14. Lutz W, Skirbekk V (2014) How education drives demography and knowledge informs
projections. World Population and Human Capital in the Twenty-First Century, eds
Lutz W, Butz WP, Samir KC (Oxford Univ Press, Oxford), pp 14–38.
15. Eloundou-Enyegue PM, Stokes CS, Cornwell GT (2000) Are there crisis-led fertility
declines? Evidence from central Cameroon. Popul Res Policy Rev 19:47–72.
16. United Nations (2017) World population prospects: The 2017 revision (United Nations
Population Division, Department of Economic and Social Affairs, New York).
17. United Nations (2015) World population prospects: The 2015 revision, key findings
and advance tables (United Nations Population Division, Department of Economic
and Social Affairs, New York). Available at https://www.popline.org/node/639412.
Accessed September 25, 2017.
18. Lutz W, Butz WP, Samir KC (2014) World Population and Human Capital in the
Twenty-First Century (Oxford Univ Press, Oxford).
19. Kc S, Wurzer M, Speringer M, Lutz W (2018) Future population and human capital in
heterogeneous India. Proc Natl Acad Sci USA 115:8328–8333.
20. Kenya National Bureau of Statistics, Ministry of Health/Kenya, National AIDS Control
Council/Kenya, Kenya Medical Research Institute, National Council for Population and
Development/Kenya, and ICF International (2015) Kenya Demographic and Health
Survey 2014. (ICF International, Calverton, MD).
21. Stromquist NP (1999) The impact of structural adjustement programmes in Africa and
Latin America. Gender, Education and Development: Beyond Access to Empowerment,
eds Heward C, Bunwaree S (Zed Books Ltd, London), pp 17–32.
22. Rose P (1995) Female education and adjustment programs: A crosscountry statistical
analysis. World Dev 23:1931–1949.
23. Przeworski A, Vreeland JR (2000) The effect of IMF programs on economic growth.
J Dev Econ 62:385–421.
24. Lockheed ME, Verspoor AM (1990) Improving Primary Education in Developing
Countries (Oxford Univ Press, Washington, DC).
25. Alexander NC (2001) Paying for education: How the World Bank and the Internati onal
Monetary Fund influence education in developing countries. Peabody J Educ 76:285–338.
26. Daoud A, et al. (2017) Impact of International Monetary Fund programs on child
health. Proc Natl Acad Sci USA 114:6492–6497.
27. Casterline JB, Bongaarts JP (2017) Fertility Transition in Sub-Saharan Africa (Pop-
ulation Council, New York).
28. Abel GJ, Barakat B, Kc S, Lutz W (2016) Meeting the Sustainable Development Goals
leads to lower world population growth. Proc Natl Acad Sci USA 113:14294–14299.
29. Lutz W, Klingholz R (2017) Education First! From Martin Luther to Sustainable
Development (Sun Media, Stellenbosch, South Africa).
30. Education Commission (2016) The learning generation: Investing in education for a
changing world (International Commission on Financing Global Education Opportu-
nity, New York).
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