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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.
Stalls in Africas 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 19952010 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 populationfor example, cash
transfers or school-based food programsas 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 (1014), 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 19902016. 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 dIvoire, 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 19852010 based on successive DHSs.
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19852010 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 19852010. Côte dIvoire, 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 (19852010) 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 stalledby the large
majority of previous studies and criteria. Fig. 2 shows the period
fertility trends of those two countries by level of mothersedu-
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
thatas shown in Fig. 2educated 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 counterfactualtrend 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 dIvoire, 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. Motherseducation 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 worlds
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 dIvoire 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 educationfertility 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 (2126). 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.
Africas future population growth will be relevant for the rest
of the world. Whether it will onlyincrease 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, Africas 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 38:153168.
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 39:3948.
3. Shapiro D, Gebreselassie T (2008) Fertility transition in sub-Saharan Africa: Falling and
stalling. African Popul Stud 23:323.
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 2008:3955.
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 33:12811296.
8. Ezenwe U ( 1993) The African debt crisis and the challenge of development. Intereconomics
28:3543.
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 8:3150.
11. Cochrane SH (1979) Fertility and Education. What Do We Really Know? (Johns Hop-
kins Univ Press, Baltimore).
12. Castro Martín T (1995) Womens education and fertility: Results from 26 Demographic
and Health Surveys. Stud Fam Plann 26:187202.
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 147225.
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 1438.
15. Eloundou-Enyegue PM, Stokes CS, Cornwell GT (2000) Are there crisis-led fertility
declines? Evidence from central Cameroon. Popul Res Policy Rev 19:4772.
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 115:83288333.
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 1732.
22. Rose P (1995) Female education and adjustment programs: A crosscountry statistical
analysis. World Dev 23:19311949.
23. Przeworski A, Vreeland JR (2000) The effect of IMF programs on economic growth.
J Dev Econ 62:385421.
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 76:285338.
26. Daoud A, et al. (2017) Impact of International Monetary Fund programs on child
health. Proc Natl Acad Sci USA 114:64926497.
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 113:1429414299.
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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www.pnas.org/cgi/doi/10.1073/pnas.1717288116 Kebede et al.
... Some researchers have linked fertility stalls to a leveling off of contraceptive use and desired family size (Bongaarts 2006;Ezeh, Mberu, and Emina 2009) or flattening trends in age at marriage (Staetsky 2019). Others have argued that socioeconomic conditions have led to fertility stalls, including stagnation in women's educational attainment (Goujon, Lutz, and Samir 2015;Kebede, Goujon, and Lutz 2019;Shapiro and Gebreselassie 2013) and employment opportunities (Al Zalak and Goujon 2017;Krafft 2020), as well as persistent infant and child mortality (Shapiro and Gebreselassie 2013). Nevertheless, no consistent drivers of fertility stalls across countries and time periods have been identified. ...
... Much of the existing literature on fertility stalls focuses on sub-Saharan Africa (Ezeh, Mberu, and Emina 2009;Goujon, Lutz, and Samir 2015;Kebede, Goujon, and Lutz 2019;Moultrie et al. 2008;Schoumaker 2019;Shapiro and Gebreselassie 2013). Yet the dynamics of the fertility transition in the Middle East and North Africa (MENA) region are also important for understanding when and why fertility stalls occur. ...
... Among the background determinants, particular attention has been paid to women's education. Studies have argued that fertility stalls in sub-Saharan Africa were associated with the proportion of women with no education, particularly as progress in schooling rates slowed during the 1980s (Ezeh, Mberu, and Emina 2009;Goujon, Lutz, and Samir 2015;Kebede, Goujon, and Lutz 2019). These arguments highlight how fertility stalls at the national level may be driven by certain subpopulations, such as educational (Kebede, Goujon, and Lutz 2019) or ethnic groups (Grace and Sweeney 2016), whether through differential fertility behaviors or changing composition of the population overall. ...
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BACKGROUND Fertility stalls have been observed in numerous African and Middle Eastern countries. From the late 1990s until 2011 the fertility transition in Jordan was stalled, with the total fertility rate (TFR) well above replacement level. OBJECTIVE This paper demonstrates a resumption of fertility decline in Jordan since 2012 and investigates the background and proximate determinants behind the decline. METHODS Fertility trends among Jordanians are analyzed using the Jordan Labor Market Panel Survey (JLMPS) 2010 and 2016 waves and the Jordan Population and Family Health Survey (JPFHS) 2002 to 2017/2018 rounds. We estimate age-specific and total fertility rates over time and conduct a proximate-determinants decomposition. We also examine the evolution of fertility by age, education, and parity, testing for meaningful changes over time in a multivariate framework. RESULTS Fertility among Jordanians declined from a TFR of 3.8 in 2009/2010 to 3.3 in JLMPS 2016 and 2.6 in JPFHS 2017/2018. Vital statistics data are more consistent with the JLMPS estimate. Declines in fertility occurred across age groups and education levels and have parity-specific components. The proximate-determinants decomposition does not identify a clear driver of resumed fertility decline. Age at marriage increased steadily but slowly over time, yet contraceptive use among currently married women declined over time. The ideal number of children decreased less than observed fertility. CONTRIBUTION This paper discusses one of the first cases of a country in the Middle East and North Africa coming out of a fertility stall. It is an important contribution to understanding future demographic trajectories in the region.
... Demographers have, however, observed that fertility transition began much later in Africa than it did in other developing regions although this process is not consistent across all African countries despite significant economic progress. They note that this phenomenon is rare in other developing regions, which begs the question why fertility transition in Africa is different (Bongaarts & Casterline, 2013;Bongaarts, Mensch, & Blanc, 2016;Kebede, Goujon, & Lutz, 2019). ...
... The foregoing discussions beg the question of why adult and adolescent fertility remains persistently high in African countries. From the standpoint of economic research, the stall in fertility decline can be pinned on a variety of factors including under-development, low level of education, and the failure of political leaders to prioritize the provision of reproductive health services (Bongaarts, 2011;Bongaarts, 2017;Kebede et al., 2019;Shapiro, 2015). The conventional theory of fertility transition does not, however, completely explain the nature and causes of the sluggish decline in fertility in Sub-Saharan Africa. ...
Preprint
Recent UN data show that the lifetime fertility of women in developed countries has fallen below 2.1 live births. By contrast, fertility rates in most developing countries have remained quite high despite falling mortality rates. This paper examines the effect of culture on fertility outcomes in developing countries, using the norms of premarital sexual behaviour as a measure of culture. Three types of norms are identified viz., the emphasis on female early marriage, the emphasis on female virginity at marriage, and weakly censuring premarital sexual behaviour. These differences in premarital rules are a source of identifying variation in the age at first birth and the number of children. Using a sample of women aged 15 to 49 from Africa and Turkey, the study shows that premarital sexual norms significantly affect the age at first birth and the number of children per woman. It finds that the cultural emphasis on early marriage significantly lowers a woman's age at first birth while it raises her fertility level relative to the culture which weakly censures female premarital sexual relations. Conversely, the emphasis on female virginity at marriage increases the age at first birth and lowers fertility relative to the comparison group.
... While the average number of children per woman has now reached relatively low levels in most Asian, South American, and North African countries, in most sub-Saharan countries, fertility rates remain higher. Considering the stalls in fertility decline observed recently in several sub-Saharan countries (Kebede et al., 2019), the number of people intending to migrate from those countries to Europe could increase strongly, particularly in countries with already large populations such as Nigeria or the Democratic Republic of the Congo. Our sample of migration scholars further predicted that decreased political stability could strongly increase the pressure to migrate from less developed countries. ...
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Scenario planning has been gaining popularity during the last decade as a tool for exploring how international migration flows might be affected by changing future circumstances. Using this technique, scholars have developed narratives that describe how flows might change depending on different developments in two of their most impactful and uncertain drivers. Current applications of scenario planning to migration however suffer from limitations that reduce the insights that can be derived from them. In this article, we first highlight these limitations by reviewing existing applications of scenario planning to migration. Then, we propose a new approach that consists in specifying different pathways of change in a set of six predefined drivers, to then ask migration scholars how each of these pathways might impact both migration flows and the other five drivers. We apply our approach to the case of migration pressure and demand from less developed countries to Europe until the year 2050. Results from our survey underscore the importance of a wide array of drivers for the future of migration that have so far not been considered in previous applications of scenario planning. They further suggest that drivers do not change independently from each other, but that specific changes in some drivers are likely to go hand in hand with changes in other drivers. Lastly, we find that changes in similar drivers could have different effects in sending and receiving countries. We finish by discussing how enhanced, quantified scenarios of migration between less developed countries and Europe can be formulated based on our results.
... Liu and Raftery (2020) analyse how women's education and family planning had accelerated fertility decline, showing that women's education and contraceptive prevalence both had significant effects, with contraceptive prevalence having a substantially larger effect size. Some researchers use education and contraception in fertility projections (Vollset et al. 2020), with Abel et al. (2016) and Kebede, Goujon, and Lutz (2019) concluding that women's education has a major impact on past and future fertility trends in sub-Saharan Africa. ...
... After an initial strong drop in fertility, many countries experience a gradual reduction in the pace of the transition once fertility levels approach the replacement rate 44 . While the overall pattern of the fertility transition is often similar, there are large differences between countries and regions 45,46 . ...
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Access to electricity and modern cooking fuels, especially for women, leads to time savings in the home, improved health and better access to information. These factors increase women’s well-being and enhance their ability to make reproductive choices, which is empirically expressed by falling birth rates. This study provides an international analysis of the relationship between access to modern energy and fertility, based on panel data synthesized from 155 Demographic and Health Surveys over 26 years. Controlling for other determinants, we find that access to electricity and modern cooking fuels, along with education, negatively affects fertility. Energy and education effects are complementary and strongest in regions with initially high fertility rates. Expanded access to modern energy and education would accelerate the demographic transition. Therefore, the energy demand and carbon emissions needed to achieve the Sustainable Development Goal of energy access while ensuring gender equality and climate action would be lower in the long term than currently assumed.
... The causes of African fertility stalls-which have been identified for contexts in different stages of development and the fertility transition, with variable patterns of urban and rural distribution-currently are not well-understood (25,26). Researchers have variously pointed to contemporaneous trends in socioeconomic development (27), declining national and international support for FP programmes leading to greater unmet need and lower contraceptive use (28), high levels of desired fertility related to socioeconomic uncertainty (29,30), as well as disruptions to female education linked to the effects of economic crises (and ostensibly structural adjustment programmes) of the 1980s and 1990s (31,32). Regardless of their precise drivers, if fertility stalls continue then existing rates of urban growth will be sustained in future decades, potentially undermining the influence of urbanization in driving wider fertility and demographic transitions (23). ...
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Health agendas for low- and middle-income countries (LMICs) should embrace and afford greater priority to urban family planning to help achieve a number of the global Sustainable Development Goals. The urgency of doing so is heightened by emerging evidence of urban fertility stalls and reversals in some sub-Saharan African contexts as well as the significance of natural increase over migration in driving rapid urban growth. Moreover, there is new evidence from evaluations of large programmatic interventions focused on urban family planning that suggest ways to inform future programmes and policies that are adapted to local contexts. We present the key dimensions and challenges of urban growth in LMICs, offer a critical scoping review of recent research findings on urban family planning and fertility dynamics, and highlight priorities for future research.
... Gender equality in education has been linked to a great variety of favorable outcomes for women, their households, and for society as a whole. Such positive outcomes include women's economic and political participation later in life (World Bank 2017), lower fertility and reduced incidence of early marriage (Lloyd et al. 2000;Duflo, Dupas, and Kremer 2015;Boahen and Yamauchi 2018;Kebede, Goujon, and Lutz 2019), reduced child mortality (Makate and Makate 2016;Keats 2018; Andriano and Monden 2019), improved family well-being (Abuya et al. 2012;Pratley 2016), and economic growth (Klasen 2002;Baliamoune-Lutz and McGillivray 2009;Klasen and Lamanna 2009). It is thus crucial to understand the origins and drivers of African women's educational attainment relative to men's. ...
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To what extent did sub-Saharan Africa's twentieth century schooling revolution benefit boys and girls equally? Using census data and a cohort approach, we examine gender gaps in years of education over the twentieth century at world region, country and district levels. First, we find that compared to other developing regions, Africa had a small initial educational gender gap but subsequently made the least progress in closing the gap. Second, in most of the 21 African countries studied, gender gaps increased during most of the colonial era (ca. 1880–1960) and declined, albeit at different rates, after independence. At the world region and country level, the expansion of men's education was initially associated with a growing gender gap, and subsequently a decline, a pattern we refer to as “educational gender Kuznets curve.” Third, using data from six decadal cohorts across 1,177 birth districts, we explore subnational correlates of educational gender inequality. This confirms the inverse-U relationship between the gender gap and male education. We also find that districts with railroads, more urbanization and early twentieth century Christian missions witnessed lower attainment gaps. We find no evidence that cash crop cultivation, agricultural division of labor or family systems were linked to gender gaps.
... Iran, Bangladesh, South Korea and China have all successfully reduced fertility in pursuit of improved welfare. However, changes in government policy and the priorities of NGOs have already been shown as a cause of stalling fertility decline (see Sinding, 2009;Kebede et al. 2019). Reducing population as part of tackling ecological overshoot requires more than a reliance on the supposedly autonomous logic of demographic transition. ...
Chapter
Demographers, national planners, and others interested in future trends want to know how fertility will develop in the future. Will fertility continue to decline in the high fertility countries? Will fertility rebound in the low fertility countries, or will it decline to the lowest levels as observed in some Asian countries? In this chapter, I discuss projections of future fertility, how projections are made, and why some projections are probably wide off the mark. I consider whether fertility in all countries around the world will eventually converge around two children per woman and the implications of the dramatic differences in fertility across countries and regions.
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Sub-Saharan Africa has entered the early stage of the demographic transition with differences in and between countries. The relation between fertility preference and actual fertility is at the core of the demographic changes during the demographic transition in sub-Saharan Africa. At the current pace of the demographic transition, overachieved fertility (actual fertility being higher than fertility preference) is more prevalent in sub-Saharan Africa although some women do achieve their fertility preference. Our aim is to assess the trends and identify factors that consistently influence women with completed fertility to achieve their fertility desires in Ghana over a 10-year period. We used secondary data from the 2003, 2008 and 2014 Ghana Demographic and Health Surveys for the analysis. The sample size was restricted to currently married/living in union women aged 45–49 years. The results indicate that underachieved fertility has increased from 25.1% in 2003 to 35.8% in 2014. Similarly, achieved fertility has also increased from 23.8% in 2003 to 26.0% in 2014. On the contrary, overachieved fertility has decreased from 51.1% in 2003 to 38.2% in 2014. The most persistent determinants of achieved fertility relative to overachieved fertility in Ghana during the last three rounds of the Ghana Demographic and Health Surveys are child survival status, ethnicity and couple’s fertility preference. The study provides support for programmatic interventions targeting improving child survival and regulating men’s fertility preference.
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Within the next decade India is expected to surpass China as the world’s most populous country due to still higher fertility and a younger population. Around 2025 each country will be home to around 1.5 billion people. India is demographically very heterogeneous with some rural illiterate populations still having more than four children on average while educated urban women have fewer than 1.5 children and with great differences between states. We show that the population outlook greatly depends on the degree to which this heterogeneity is explicitly incorporated into the population projection model used. The conventional projection model, considering only the age and sex structures of the population at the national level, results in a lower projected population than the same model applied at the level of states because over time the high-fertility states gain more weight, thus applying the higher rates to more people. The opposite outcome results from an explicit consideration of education differentials because over time the proportion of more educated women with lower fertility increases, thus leading to lower predicted growth than in the conventional model. To comprehensively address this issue, we develop a five-dimensional model of India’s population by state, rural/urban place of residence, age, sex, and level of education and show the impacts of different degrees of aggregation. We also provide human capital scenarios for all Indian states that suggest that India will rapidly catch up with other more developed countries in Asia if the recent pace of education expansion is maintained. Link: https://doi.org/10.1073/pnas.1722359115 (CC BY-NC-ND 4.0)
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BACKGROUND Recent stalls in fertility decline have been observed in a few countries in sub-Saharan Africa, and so far no plausible common reason has been identified in the literature. This paper develops the hypothesis that these fertility stalls could be associated with stalls in the progress of education among the women of the relevant cohorts, possibly resulting partly from the Structural Adjustment Programs (SAPs) of the 1980s. METHODS We descriptively link the change in the education composition of successive cohorts of young women in sub-Saharan Africa and the recent fertility stalls. We use reconstructed data on population by age, gender, and level of education from www.wittgenstein-centre.org/dataexplorer, and fertility rates from the United Nations. RESULTS In most sub-Saharan African countries, we observe that the same countries that had fertility stalls had a stall in the progress of education, particularly for young women who were of primary school age during the 1980s, when most of the countries were under structural adjustment. Conversely, stalls in fertility are less common in countries that did not have an education stall, possibly in relation to SAPs. CONCLUSION The results point to the possibility of a link between the recent fertility stalls and discontinuities in the improvement of the education of the relevant cohorts, which in turn could be related to the SAPs in the 1980s. This descriptive finding now needs to be corroborated through more detailed cohort-specific fertility analysis. If the education-fertility link can be further established, it will have important implications for the projections of population growth in affected countries.
Article
Parental education is located at the center of global efforts to improve child health. In a developing-country context, the International Monetary Fund (IMF) plays a crucial role in determining how governments allocate scarce resources to education and public health interventions. Under reforms mandated by IMF structural adjustment programs, it may become harder for parents to reap the benefits of their education due to wage contraction, welfare retrenchment, and generalized social insecurity. This study assesses how the protective effect of education changes under IMF programs, and thus how parents' ability to guard their children's health is affected by structural adjustment. We combine cross-sectional stratified data (countries, 67; children, 1,941,734) from the Demographic and Health Surveys and the Multiple Indicator Cluster Surveys. The sample represents ∼2.8 billion (about 50%) of the world's population in year 2000. Based on multilevel models, our findings reveal that programs reduce the protective effect of parental education on child health, especially in rural areas. For instance, in the absence of IMF programs, living in an household with educated parents reduces the odds of child malnourishment by 38% [odds ratio (OR), 0.62; 95% CI, 0.66-0.58]; in the presence of programs, this drops to 21% (OR, 0.79; 95% CI, 0.86-0.74). In other words, the presence of IMF conditionality decreases the protective effect of parents' education on child malnourishment by no less than 17%. We observe similar adverse effects in sanitation, shelter, and health care access (including immunization), but a beneficial effect in countering water deprivation.
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Here we show the extent to which the expected world population growth could be lowered by successfully implementing the recently agreed-upon Sustainable Development Goals (SDGs). The SDGs include specific quantitative targets on mortality, reproductive health, and education for all girls by 2030, measures that will directly and indirectly affect future demographic trends. Based on a multidimensional model of population dynamics that stratifies national populations by age, sex, and level of education with educational fertility and mortality differentials, we translate these goals into SDG population scenarios, resulting in population sizes between 8.2 and 8.7 billion in 2100. Because these results lie outside the 95% prediction range given by the 2015 United Nations probabilistic population projections, we complement the study with sensitivity analyses of these projections that suggest that those prediction intervals are too narrow because of uncertainty in baseline data, conservative assumptions on correlations, and the possibility of new policies influencing these trends. Although the analysis presented here rests on several assumptions about the implementation of the SDGs and the persistence of educational, fertility, and mortality differentials, it quantitatively illustrates the view that demography is not destiny and that policies can make a decisive difference. In particular, advances in female education and reproductive health can contribute greatly to reducing world population growth.
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Past demographic transitions have been observed with and without economic progress, but there is little empirical record of crisis-driven fertility transitions. In recent years, several authors have argued that conditions for such transitions are met in African countries under economic crisis and structural adjustment. Using retrospective family histories, this study examines fertility responses to crisis in Cameroon, a country with a particularly abrupt economic reversal. The thesis of a crisis-led decline is tested on the basis of five criteria including timing of the decline, statistical and substantive significance, rural-urban response differentials and social salience. Findings are consistent with a crisis-led effect.