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Demographic transition in Sub Saharan Africa: accounting and economics
Robert Eastwood and Michael Lipton
1. Introduction: the background
Sub-Saharan Africa (SSA) has experienced, and is projected to experience, rapid population growth –
from 183m in 1950 to 863m in 2010 and 1753m in 2050. Yet both the rate of natural increase (crude
birth-rate minus crude death-rate, i.e. population growth net of migration) and the dependency ratio
peaked around 1985. Projected falls in these variables in 1985-2025 exceed, by about a third and a quarter
respectively, the preceding rises from 1950. This mirrors a comparable trajectory in much of Asia, where
the peak was some twenty years earlier, and where the fall in the dependency ratio has been linked to a
large ‘demographic dividend’ in the form of more rapid economic growth (Bloom and Williamson 1998;
Bloom et al. 2000).
This paper asks whether such a dividend can be expected in SSA during 1985-2025, as natural increase
slows and the dependency ratio falls. At the outset, one important difference must be noted. While natural
increase and the dependency ratio in both regions exhibit hump-shaped trajectories (Figures 1-2), SSA’s
levels lie markedly above Asia’s. And the pace of development may depend not only on the trajectories of
these variables but also on their levels: for instance, high youth dependency may slow development by
reducing savings.
Figure 1: Dependency ratios: Asia and SSA
0
50
60
1
70
%
%
2
80
90
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Figure 2: Natural increase: Asia and SSA
1950
1970
1990
2010
2030
2050
1950
date
Dependency ratio: Asia
1970
1990
2010
2030
2050
date
Dependency ratio: SSA
Natural increase: Asia
Natural increase: SSA
Demographic transition
Demographers (e.g. Coale 1973, Montgomery 2009) identify four stages, of which only the middle two
are part of transition as such: pre-modern equilibrium, with crude birth and death rates (CBR and CDR)
both around 35-45; urbanising/industrialising, with CBR unchanged but CDR falling towards 15; mature
industrialising, with both rates falling towards 10; and post-industrial equilibrium, with the rates roughly
equal at 10 or less. The recent emphasis given by economists to the effects of changes in the dependency
ratio – and the balance within it between young and old dependents – has led some to break the transition
into three phases (Lee 2003, fig.6):
Phase 1 (c. 25 years): Rising dependency ratio driven by rising young dependency.
Phase 2 (c. 40 years): Falling dependency ratio driven by falling young dependency.
Phase 3 (c. 50 years): Rising dependency ratio driven by rising old dependency.
We adopt this scheme, noting that the start of Phase 2, defined by peak dependency, also approximately
coincided with peak natural increase in Asia and SSA (Figures 1, 2). We concentrate on the possible dividend that SSA may reap in Phase 2.
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Crucial for the start, and speed, of Phase 2 - i.e. for the move to falling dependency and natural increase is the shift to falling TFR and hence birth-rates. Malthus (1824), from regional census data from Switzerland and Norway, suggested that ‘extreme healthiness’ accelerated ‘prudential checks’: in today’s language, that lower mortality (we might add: especially among children) induced behavioural change that
reduced total fertility and hence CBR. That remained the standard view of demographic transition theory
at its formulation (Thompson 1929) and during modern development (Coale and Hoover 1958, pp.12-13).
Of the proximate determinants of TFR (Bongaarts 1978, 1982), falling child mortality - CMR, used in this
paper to mean per-thousand live-born children who die before age 5 - may quantifiably affect three:
post-partum infecundability, use of contraception, and proportion married (Preston 1978, but see Montgomery and Cohen 1998).
This view of fertility transition has been challenged by theory and evidence that marital age and fertility
are determined jointly with other variables, notably female labour supply, within some kind of household
optimizing framework (Becker 1960, 1981; Schultz 1981, 2007). This view suggests that (a) factors other
than falling child mortality cause fertility decline, notably (prior) female education and female wages (reflecting opportunity-cost of motherhood); (b) without such factors, child mortality decline - which on its
own raises natural increase - may fail to induce enough fertility decline to reduce this, let alone to a postindustrial equilibrium of zero (Preston 1978, Doepke 2005). Whether falling young-end mortality raises
or lowers the expected (or average) number of surviving children is unclear. The reduced expected cost of
rearing a new-born to adulthood induces couples to plan for more surviving children (Tzannatos and Symons 1989; Birchenall and Soares 2009;). Working the other way is the ‘dynastic’ motive: if couples
wish to have a given chance of attaining a fixed number (e.g. 2) or more of adult offspring, then a reduction in risk will cause them to plan for fewer surviving children on average (for example, with zero risk,
they would plan to have exactly 2). Econometric attempts to resolve such issues have been bedevilled by
the need to control for other determinants of fertility, such as female education, and for the endogeneity of
infant mortality (either because IMR and TFR are jointly determined by other variables, or because less
frequent births change the risk of child mortality). Nevertheless, recent literature using best-practice
econometric methodology concludes that a fall in young-end mortality is the main driver of TFR decline
(section 2).
Impact of phase 2 demographic transition on growth of income per person: accounting
Projection of this impact in developing countries was pioneered in 1957 for India (Coale and Hoover
1958). Losses in Phase 1, and especially gains in Phase 2, seemed small, because both peaks and subsequent falls in the dependency ratio were under-projected. It was projected to fall only from 52 to 50 from
1986 to 2006, if post-1986 TFR stayed at half the 1956 rates, (ibid. pp.233, 322). In fact India's TFR fell
more slowly than projected, not reaching half of its 1956 value until about 2000. This raised the dependency peak (82 in 1965) and the subsequent rate of fall, to 73 in 1985 and 60 in 2005 (UN 2009), so that
much larger economic effects from age-structure change now seemed plausible. Kelley and Schmidt
(1995) and Bloom and Williamson (1998), disaggregating fertility and mortality, claimed that the Asian
tigers, and to some extent South Asia too, had gained a large demographic dividend as a result of agestructure change during Phase 2 and, moreover, that this was the total effect of demographic change.
To set this age-structure hypothesis in context, an accounting approach is useful. Figures 1 and 2 show
two features of Phase 2 of transition: age-structure change and falling natural increase. Each can be held
to yield growth benefits from a simple accounting standpoint. For age structure, a falling dependency ratio implies arithmetically that a given rate of growth of output per worker translates into a faster rate of
growth of output per person. For natural increase, the slower it is, the less an economy needs to save in
order to maintain total (natural plus reproducible) capital per person, so the higher is sustainable consumption per person. In an economy where natural increase is falling through time, therefore, the amount
that has to be saved in order to equip extra workers is also falling, so that the amount left over and available for (sustainable) consumption is growing through time. Expressing this somewhat differently, any
natural increase means that a given stock of capital is ‘diluted’ by having to be shared among more workers: so falling natural increase delivers a demographic dividend via reduced dilution.
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Accounting thus allows us to calculate two types of demographic dividend, one from improved age structure and one from slower natural increase. These dividends are not strictly commensurate, as one comprises extra growth of output per person, and the other extra growth of sustainable consumption per person. Nevertheless we estimate and compare the dividends for a number of countries in section 4: in SSA
the age-structure gains are likely to be some two to three times as large as those from falling capitaldilution.
This accounting, however, has severe limitations. For the age-structure accounting to be the whole story,
there must be no effects of Phase 2 on the time path of output per worker. However there may be positive
effects either from age-structure itself, if for instance a fall in youth dependency raises the savings rate, or
from slower natural increase, since this implies that given savings are spread less thinly, tending to raise
capital per worker and therefore output per worker. The first effect fits into the age-structure hypothesis,
simply implying that the effects are more than arithmetical, while the second does not. Similarly, for the
natural-increase accounting to be the whole story, we must assume that sustainability can be identified
with constant capital per person, and thus must neglect technological advance, in particular the possibility
that age-structure change might stimulate such advance. The natural-increase accounting also has a normative character, calculating extra savings ‘needed’ for sustainability under given circumstances, but
silent on whether transition might make these savings either more or less likely to materialize.
To assess the effect of demographic change on growth in output per person, there is an alternative to the
accounting approach: econometric estimation. This literature, reviewed in section 4, strongly supports the
age-structure hypothesis. Not only does falling dependency increase growth arithmetically, as described
above, but the effect - i.e. the demographic dividend – is more than arithmetical. Moreover, no link from
natural increase to growth is found. These findings cannot, however, be taken as final, because of doubts
over the robustness of the econometric methodology employed.
Overshadowing the demographic dividend in Phase 2 in SSA, and highly pertinent to any comparison
with Asia, are the consequences of SSA’s combination, throughout transition, of much more rapid natural
increase (Figure 2) and much lower savings rates. In Asia, not only did the slowing of natural increase
raise sustainable consumption per head during 1965-2005, but savings were high enough that the path of
development was itself broadly sustainable. This is very unlikely to be the case in SSA over 1985-2025
(section 4). Slower natural increase will indeed raise sustainable consumption per head by reducing the
savings that would be needed to sustain capital per person, but this pales into insignificance beside the
likelihood that savings will fall far short of the necessary level.
Outline and contributions of this paper
Section 2 states the paths of main demographic variables during the three phases of transition. We analyse
natural increase rather than the rate of growth of population, because the causes and consequences of migration would require a major digression. We then enquire whether UN middle projections, used in this
paper, correctly predict Phase 2 transition. Being global and univariate, they do not allow for regionspecific impacts on fertility of causal variables, such as female literacy and young-end mortality. In particular, where the latter is higher, or falling more slowly, than the global norm, these projections may
overstate future fertility decline, and therefore reductions in both the dependency ratio and the rate of natural increase.
Section 3 tracks transition data in SSA, its regions, and its most populous countries. In most cases, natural
increase and dependency peaked (bringing in Phase 2) at higher levels than in Asia, and about twenty
years later, around 1985. This sets the stage for larger total declines in dependency and natural increase in
Phase 2. However, UN middle projections to 2030 show annual declines lower in most of SSA than comparable falls twenty years earlier in Asia and even those projections look optimistic. But the future can be
changed. Parts of Asia and Africa experienced fast reductions in young-end mortality, and measures to
transmit these rapidly into less pro-natalist behaviour. It is medically possible for much of SSA to do the
same. Yet total fertility decline has been sluggish in some large West and Middle African countries, and
has stalled in many, but not all, groups and regions of East Africa (Ezeh et al. 2009). Groups and areas
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slow to reduce natural increase come to represent growing proportions of populations. Most groups and
regions will need faster reduction of child mortality, plus measures influencing fertility decisions more
directly, for Africa as a whole to reap a large demographic dividend.
Section 4 moves to estimates of the demographic dividend. Giving appropriate Asian comparisons, we
evaluate econometric evidence as it affects SSA’s dividend so far, and present alternative accounting estimates for 1985–2025. As is evident from Figures 1 and 2, both the dependency ratio and natural increase
in SSA reach a higher peak and then decline more slowly in SSA compared to Asia. Therefore the accounting dividends are for SSA larger in total but smaller per year. Yet, as noted above, arguably more
important than the dividends from demographic transition as such are the effects of continuing natural
increase, when set in the context of national savings rates. We estimate these underlying rates of capital
dilution for populous SSA countries, with comparative estimates for some Asian countries.Section 5 concludes.
The main contribution is to bring together the elements needed for an assessment of the demographic dividend in Sub-Saharan Africa, reviewing past estimates and presenting some new calculations. Since all
this must rest on demographic projections, we first set UN middle projections in the context of some determinants of fertility, and of the extent to which policy might modify them. Given the demography, we
calculate two sorts of accounting dividend for the region and its populous countries, and assess the influential view that the dividend arises entirely from changes in dependency. We show that, under current
policies, adverse effects of ongoing rapid natural increase in much of Sub-Saharan Africa exceed likely
gains from its reduction during transition.
2. Three phases of demographic transition
Defining the phases
In the stylised ‘demographic equilibrium’ before transition, births equal deaths, typically at high levels.
Age-specific mortality and fertility rates are trendless, as is age-structure, and therefore the dependency
ratio. Phase 1 begins with a sustained fall in the crude death-rate. This raises the rate of natural increase.
The reduced deaths (e.g. from malnutrition, dysentery and malaria) are a much larger proportion of under15s than of persons aged 15-64. So the dependency ratio rises substantially. Towards the end of Phase 1,
total fertility starts to fall. In Phase 2, more and more ‘survivors’ - beneficiaries from earlier falls in under-five mortality - reach age 15. Therefore, even though young-end mortality is still falling somewhat,
dependency ratios fall. Meanwhile the fall in TFR continues. ‘Momentum’, viz. rising numbers of women
‘survivors’ reaching childbearing age, slows the consequent falls in CBR, but after some years these falls
accelerate, leading to steady slowing of natural increase. In Phase 3, as ‘survivors’ reach age 65, old-end
dependency starts to rise, and natural increase has little further to fall, having approached zero.
All three phases, like the preceding equilibrium, are stylised (Table 1). Fluctuations and crucial simultaneities, notably between the start of falls in natural increase and dependency, are neglected. As we shall
see, this is not too damaging for assessing transition in most parts of Africa. All three phases are visible in
Figures 1 and 2 for Asia, while only Phase 1 and a prolonged Phase 2 are visible for SSA as a whole;
however, Phase 3 starts much sooner in some countries, notably South Africa.
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TABLE 1. DEMOGRAPHIC TRANSITION: THREE PHASES, AND TRENDS IN KEY VARIABLES
Crude death rate
Young-end mortality
Total fertility rate
Phase 1
Fall
Fall, becoming rapid
No trend or slow rise, then fall
Phase 2
Fall
Fall
Fall
Phase 3
Slow fall
Slow fall
Fall
Crude birth rate
Natural increase
Dependency ratio
No trend or slow rise
Rapid rise
Rapid rise
Fall
Fall
Rapid fall
Slow fall
Fall, then no trend
Rise
Diversity within Asia and sub-Saharan Africa
Though it is of interest to compare SSA with Asia, both are regionally diverse, demographically and economically. In Africa, Phase 2 has progressed fastest in the Southern region, with Eastern SSA slower and
West and Central SSA slowest. South-Central Asia’s transition has lagged well behind East Asia’s, with
West and South-East Asia in between. Table 2 also shows some of the country diversity, with data for
India, China, Indonesia and Thailand, and for the 16 most populous countries (out of 51) in SSA. These
housed 764m people in 2005, 77 per cent of SSA’s population. Just four countries - Nigeria, Ethiopia, DR
Congo and S Africa - contained 42 per cent.
Diversity within countries also affects transition. Each phase arrives earlier, and proceeds faster, for urban, richer, more economically integrated, and less gender-unequal groups and places. So child mortality,
total fertility, and hence natural increase and dependency all fell later and more slowly, and are now much
higher, in North-Central India, above all Bihar, than in Southern India, especially Kerala (Cassen et al.
2004). Arguably Bihar has emerged from phase 1 as slowly and recently as Middle Africa, while Kerala,
like Southern Africa, is in phase 3. For thirteen SSA countries in the late 1990s, between the two most
recent Demographic and Health Surveys ‘unweighted average rural TFR declines by about 0.3’ but by 0.9
in urban areas – ‘[usually] with fertility initially declining in urban areas [only], then … in both settings
but more rapidly in urban places, and finally … more in rural than in urban areas’ (Shapiro and Tambashe
2002; cf. Garenne and Joseph 2002). This can retard national trends in total fertility, natural increase and
dependency during Phase 2 but we do not look at sub-national trends, except where overlap of phases
within a country has major policy importance.
UN estimates of the past and projections of the future: impact of young-end mortality on fertility
To estimate demographic dividends in section 4, we use past estimates and middle projections from UN
(2009), based on censuses and Demographic and Health Surveys. The last census round was 2000-1. All
the countries in Table 2 except China also have a post-2000 DHS except for Thailand and Sudan (1989).
In sub-Saharan Africa, demographic estimates vary from relatively good (Kenya) to very rough-and-ready
(DR Congo), with past mortality less disputed [Hill and Amouzou 2006] than past total fertility; the UN
fertility estimates appear to be in the middle range [Bulatan 2006:62]. However, UN middle projections
of total fertility in many countries are probably biased downwards. The UN model assumes that a country’s future total fertility depends only on:
(1) its TFR in the latest year when it was measured (typically in 2000-08 for the most recent projections);
(2) past trends in TFR in other countries after reaching that country’s latest measured TFR, except:
(3) if this ‘has stalled or where there is no evidence of [its] decline’, is it ‘projected to remain constant for
[5-10] years’ after which ‘the model projection takes over’ and TFR falls toward 1.85 (UN 2009a).
(1) and (3) mean that UN projections are univariate, i.e. predict a country’s TFR by past TFRs alone. (2)
means that they are global, i.e. based on past TFR paths world-wide (except when a national TFR is as-
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sumed briefly stationary, as in (3)). Being univariate and global, UN projections cannot allow for possible
country-specific determinants of future TFR such as its child mortality, female education and earnings,
and access to contraception. If these are worse, or slower-improving, than the global norm, the UN model
will project implausibly rapid acceleration of that country’s fertility decline. For example, compared with
global experience of Phases 1-2, infant (and, by inference, child) mortality in Nigeria is far higher, and
has fallen far more slowly (Table A2). If these drive subsequent total fertility, its fall in Nigeria will not
accelerate steeply, as the UN projects: in Nigeria from 1985-90 to 2000-5 TFR fell by only 0.32-0.39 per
quinquennium, but from 2000-5 to 2025-30 the UN middle projection is a fall of 0.60 per quinquennium
(UN 2009). Where young-end mortality has fallen more slowly, or stalled at higher levels, than elsewhere, such fertility projections are too high (UN high projections do not help, because they rely on the
same global univariate model). But how important is the mortality-to-fertility effect?
Recent literature, using best-practice econometrics to reduce problems of reverse and joint causation, confirms earlier demographic-transition theory: ‘the biggest driver quantitatively [of TFR decline] remains
child survival’ (Conley et al. 2007). In India in 1981-91, a fall in child mortality of 50 was estimated to
have reduced total fertility by 0.20 (out of a total fall in TFR of 0.7), slightly more than the response to
higher female literacy (Drèze and Murthi 2001; Murthi 2002). Turning to multi-country panels, Lorentzen
and Wacziarg (2008) imply that a fall of 50 in infant mortality cuts total fertility by 0.73. Angeles (2010;
Table 3) estimates an immediate fall of 0.21 in total fertility due to a fall of 50 in CMR; he indicates that
this is a good deal more if one allows for the lagged effect. Conley et al. (2007, pp. 29-30) find that a fall
of 50 in CMR cuts TFR by 0.5-0.9; the effect is ‘robust across all specifications, and holds if CMR is replaced with IMR’. Group disaggregations support such effects. Rural-urban gaps in fertility (and its decline) among main child-bearing age-groups, especially 20-24, are significantly wider where child mortality gaps are greater (Shapiro and Tambashe 2000, Table 3). Also HIV/AIDS incidence was related to
higher TFR in South Africa (Kalemli-Ozcan 2009); HIV/AIDS lowers libido and fecundability (Keogh et
al 2009: S35, refs 15-18), suggesting that it was higher AIDS-related child mortality, anticipated or actual, that increased TFR.
The relative impacts of infant, child and youth (0-15) mortality on fertility decisions are unclear. There
are few comparable data based on reliable life tables for youth mortality, yet it is the whole span 0-15 that
affects both rearing costs and prospects that children will continue the line. Some local findings are based
on CMR, but world-wide data acceptable to UN (2009) are recent and few (1995, 2000 and 2005 only), so
in time-series estimation reliance on IMR data is inevitable. In any event, IMR, CMR, and the little we
know of youth mortality suggest a similar hierarchy of country disadvantage. Countries with higher IMR
tend to have even higher post-1995 child mortality. UN (2009) estimates that, for every 100 infant
deaths, there are 163 child deaths in sub-Saharan Africa (Angola, Ethiopia, Kenya, Mozambique, Nigeria
174-5), but fewer where IMR rates are lower (138 in Asia; China 129, Thailand 152) (UN 2009; see also
Hill and Amouzou 2006 for West Africa). Countries with high infant mortality show even more child
deaths (per hundred infant deaths) in local life tables, which are more reliable: the average of local surveys is Ethiopia 182, Gambia 251, Guinea-Bissau 203, Ghana 170, Senegal 153, Zambia 173 (IDEC
2002). As for youth mortality, the excess over IMR in higher-risk countries looks even larger: the best
estimate for SSA is 220 for boys and 206 for girls in 1990-1995 (projected 186 and 174 in 2000-05:
Jamison et al. 2001, Table 1), but 111 for both sexes in India in 2000
(http://apps.who.int/ghodata/?vid=720).
3. Transitions, 1920/50-2030: African dependency and natural increase in Asian context
Phase 1
Mortality decline: This led to rising natural increase in most of Africa and Asia by 1930. In the 1930s
population rose annually by 0.6 per cent in China, 1.4 per cent in India, 1.5 per cent in Indonesia and over
1 per cent in Congo, Kenya, Nigeria and South Africa (Visaria and Visaria 1982, p.488; Fage et al. 1986,
p.49; Iliffe 1995; Dawson 1987; Brass and Jolly 1993, p.29; http://www.populstat.info/Asia/chinac.htm;
http://en.wikipedia.org/wiki/DutchEastIndies; http://www.populstat.info/Africa/safricac.htm).
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So by 1950-5, when UN (2009) data series begin, Phase 1 had been under way for some time. From 19505 infant mortality declined through Phase 1, into Phase 2, in all regions, but much more slowly in subSaharan Africa, so that by 2000-5 it was still 95, compared to 47 in Asia. Southern Africa did better than
the rest of sub-Saharan Africa until 1985-90, after which infant mortality rose. Taken on its own, the relatively slow decline of IMR in sub-Saharan Africa should have slowed natural increase relative to Asia.
That this did not happen was because the path of fertility was so different in the two regions.
Fertility trajectories: In Asia, TFR fell dramatically, from 5.7 to 3.5, between 1950-5 and 1985-90; in
SSA it hardly fell at all, with an Asian-scale fall in Southern Africa and a modest fall in East Africa offset
by rises in Middle and West Africa (Cohen 1993, 1998; Table A-1). Even by 2000-5, TFR was above
1950-5 levels in Burkina Faso and DR Congo and almost the same in Uganda. Due to population momentum, the TFR downturn in late Phase 1 is translated into a fall in CBR and natural increase only after a
time lag. In many cases (India, Ghana, Kenya, Sri Lanka; Southern Africa) the fall in TFR was fast
enough that CBR started to fall, albeit slowly, almost at once. In E and SE Asia, the fall in TFR was precipitate and led to early and sharp falls in CBR.
Dependency and natural increase outcomes: Old-age dependency in most of Asia and Africa was very
stable throughout Phases 1 and 2. Hence the dependency ratio was largely determined by young-end dependency and its changes. Asia’s dependency ratio rose from 67 in 1950 to a peak of 80 in fifteen years,
in 1965; SSA’s rose from 82 in 1950 to a peak of 94 in thirty-five years. There was substantial country
variation (Table A-3): at the extremes, Sudan’s dependency ratio rose negligibly while Kenya’s rose from
78 in 1950 to a peak of 113, due to very high, and at first rising, total fertility. SSA’s natural increase in
1950-5 was 0.3 per cent per year above Asia’s, and peaked at 0.6 per cent above the Asian peak, but
twenty years later.
Dating the peak: How uniform across countries was the 1985 switch from Phase 1 to Phase 2 in subSaharan Africa? In 1980/5, natural increase peaked in Cameroon, Kenya and Sudan; Nigeria, S. Africa
and four other ‘populous’ countries were within 0.1 per cent per year of the peak, and DR Congo and two
others within 0.25 per cent/year. The only populous outliers, where natural increase peaked well after
1980/5, were Ethiopia (2.64 per cent/year 1980/5; peak 3.01 per cent 1990/5), Madagascar (2.57 per cent;
3.05 per cent 1990/5) and Burkina Faso (2.99 per cent; 3.47 per cent 2005/10). As for dependency, it
peaked in 1985 in Nigeria, Sudan and four other populous countries of SSA. In four more, it peaked in
1980 (and in Angola in 1995) at 1-3 points above 1985 level. Ethiopia, Uganda and DR Congo peaked in
1995 - at 5-7 points above 1985 dependency, not much compared to the 21-25-point (UN-projected) decline for 1995-2025. Only South Africa is an outlier, with peak dependency early: 84 in 1970. Despite
variations, it is reasonable to see 1985 as sub-Saharan Africa’s switch point from Phase 1 to Phase 2.
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TABLE 2. NATURAL INCREASE AND DEPENDENCY RATIO: 1950/5 TO PEAK RATES, BY REGIONS
Region
Natural increase
1950-55
Natural increase
peak
Natural increase
peak years
Dependency
ratio 1950
Dependency ratio
peak
Dependency ratio
peak year
SSA
East
Middle
South
West
2.15
2.29
1.94
2.29
2.02
2.89
2.99
3.07
2.52
2.87
1980-85
1985-90
1985-95
1970-75
1980-85
82
85
82
74
81
94
97
99
85
94
1985
1980
1995
1965-70
1985-90
Asia
East
(China)
S. Central
S. East
West
1.88
1.81
1.87
1.84
2.08
2.52
2.38
2.42
2.60
2.43
2.52
2.71
1965-70
1965-70
1965-70
1975-80
1960-70
1960-65
67
63
61
70
73
74
80
76
80
83
88
89
1965
1965
1965
1965
1970
1965
Source: UN (2009)
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Phase 2
Natural increase: In which countries of sub-Saharan Africa is the Phase 2 slowdown in natural increase
comparable to the earlier slowdown in Asian countries? Thirty years after the country peak, natural increase had fallen by 1.7 percentage points in China, 0.8 in India and Indonesia and 1.5 in Thailand. In
only six populous SSA countries did natural increase peak by 1980, so we can track thirty years of its fall.
This was of ‘Asian’ size in South Africa (1.03), Côte d’Ivoire (0.98), Ghana (0.76) and Sudan (0.70), but
only 0.11 in Uganda and 0.20 in Tanzania.
In the other ten populous countries in sub-Saharan Africa, natural increase peaked after 1980, so thirtyyear trajectories for Phase 2 are partly projections. As late as 2005-2010, natural increase in four of the
six most populous countries - DR Congo, Ethiopia, Kenya and Nigeria - ranged from 2.36 to 2.79 per
cent, well above India’s 2.21 per cent peak thirty-five years previously. On the UN middle projection,
these four countries will slow their natural increase after 2010 more than did India after 1975. Note that,
unlike India 15-35 years ago, these countries so far have: little decline in total fertility in DR Congo, and
stalled decline in Kenya, i.e. no convergence to any global path of fertility decline - and slow young-end
mortality decline in DR Congo, Ethiopia and Nigeria, i.e. weakness of the main driver of fertility decline.
Modelled, not empirical, age-specific death-rates in many African countries including DR Congo, Kenya
and Nigeria, make it precarious to project demographic change from past birth-and death-rates (IDEC
2002, ch. 6).
In several African countries, HIV/AIDS clouds any projections for natural increase. In South Africa, the
crude death rate troughed at 8.8 in 1990-5, but then rose to 15.1 in 2005-10, automatically reducing annual natural increase by 0.63 per cent. Plainly, this 0.63 per cent is not part of transition. In UN (2009a) ‘the
impact of HIV/AIDS on mortality is modelled explicitly ... where HIV prevalence among persons aged
15 to 49 was [over] 1 per cent during 1980-2007’, using adult HIV prevalence rates from DHS surveys,
and evidence on spread, treatment, and survival prospects: best practice, but projections based on such
evidence are inevitably speculative. The impact of HIV/AIDS on natural increase is further complicated
by effects on fertility, and interactions between HIV-related fertility and mortality change.
Dependency ratio: SSA's Phase 2 fall in dependency is so far even slower, relative to Asia's, than was the
Phase 1 rise. Among the populous countries, only in Kenya and South Africa has dependency fallen almost as fast in Phase 2 as it did in China, India and Indonesia. The all-SSA dependency ratio, in 20 years
since its 1985 peak, fell only from 94 to 86. However, the UN middle projection is an accelerated fall to
65 by 2030. In most of Africa, fertility trends have been sluggish. Remarkable acceleration seems required to drive the projected declines in dependency. As an editor points out, that is especially the case in
countries heavily affected by HIV/AIDS, which 'hollows out' the age-group 30-50: thus from 2000 to
2005, despite ongoing falls in total fertility, the dependency ratio actually rose in Malawi from 96 to 99,
and in Mozambique from 88 to 90 (Tables A-1, A-3).
Fertility: Among populous countries of SSA, only South Africa provides a precedent for an absolute fall,
from peak, in total fertility comparable to Asia’s. It has also fallen by over two points in Côte d’Ivoire,
Ghana and Kenya (where it has stalled: Ezeh et al. 2009). Only in Ghana, South Africa, Cameroon and
Sudan is TFR now below 5
Actual and projected fertility change: an Afro-Asian parallel?
A parallel between South-central Asia and sub-Saharan Africa makes it useful to compare their fertility
behaviour. In South-central Asia, total fertility started to fall significantly from 1965-70, and in most of
sub-Saharan Africa from 1985-90. In SSA it then fell by 16 per cent in fifteen years. UN (2009) projects
the fall to accelerate to 22 per cent in the subsequent fifteen years, cutting TFR to 4.2 in 2015-20. Indeed,
the Phase 2 path of total fertility in sub-Saharan Africa has quite precisely paralleled that of South-central
Asia twenty years earlier. In each quinquennium, the absolute falls in TFR in South-central Asia from
10
1965/70 (5.86) to 1980/85 (4.88) are almost identical to those in SSA from 1985/90 (6.42) to 2000/2005
(5.41). Why, then, might one doubt the UN middle projection that this parallelism will continue?
First, global projections of a decline in TFR to the same endpoint in all countries rest, implicitly, on the
assumption that replacement fertility is much the same everywhere (2.1 lifetime births per woman). Yet
it ranges ‘from less than 2.1 to nearly 3.5 … due almost entirely to cross-country differences in mortality
… the current replacement TFR for [East Africa] is 2.94’ (Espenshade et al. 2003). Given high projected
mortality, UN fertility projections therefore imply that Ethiopia, Madagascar, Mozambique, Kenya and
Sudan reduce TFR implausibly close to replacement level by 2030-35.
Second, one proximate determinant of fertility, contraceptive prevalence, lags well behind levels seen at a
comparable point in transition in Asia. For example, among women aged 15-49, prevalence was over 35
per cent in India in the early 1980s (Visaria 2009, p. 188; Ross et al. 1980, p.41), but only 23 per cent in
sub-Saharan Africa in 2003-8 (UNICEF 2010a: DR Congo 21, Ethiopia 15, Kenya 39, Nigeria 15, S Africa 60). Contraceptive use (given availability, not considered here) depends on demand. This, like age of
marriage and frequency of cohabitation, depends partly on political attitudes and cultural norms. Many
allege that these are more pro-natalist in SSA than elsewhere. However, they vary greatly, just as they do
across Asia, and seem unlikely to explain why contraception (and probably other proximate determinants
of fertility decline) lags well behind Asian experience in most of the populous countries of SSA.
Might this (and, more generally, weaker prospects for fertility decline than in Asia) arise because women’s advancement in SSA lags behind Asia’s 20-25 years previously? It is well established that later marriage, lower marital fertility, and contraception are advanced by better opportunities for women. However, the main proxy for these, female adult literacy, is not worse in most of Africa than in India 20-25 years
earlier: India’s 1981 rate of 30 per cent (RGCC 2001) was exceeded in 2003-6 by many populous African
countries, including, of the few with reliable DHS data, Nigeria (55 per cent) and Uganda (49 per cent),
though not Burkina Faso (12 per cent) (Huebler 2008).
Third, we saw that by Asian standards most of Africa has seen slow, or (in several countries most affected
by HIV/AIDS) reversed, falls in young-end mortality. To the extent that such falls drive fertility decline,
the UN’s projection model will overstate it in much of Africa.
Finally, within-country variation in Africa impedes rapid national fertility decline arithmetically. As in
India (Cassen et al. 2004:98, 105-6), total fertility is increasingly influenced by regions (and rural sectors)
where it is persistently high. Big falls in TFR, and therefore in dependency and natural increase in Phase
2, increasingly depend on progress among SSA’s poor, rural people. Yet a 2002 study, using the most
recent DHS surveys for 24 SSA countries, found that average rural TFR was 6.4 compared to an urban
average of 4.6; for the thirteen countries with repeat surveys, all but two showed a widening rural-urban
gap (Shapiro and Tambashe 2002). Urban-rural and overall inequality is greater in most African countries
than in most of Asia (Eastwood and Lipton 2004), impeding female education, contraceptive availability,
and child nutrition and preventive medicine in lagging areas. Assuming roughly similar prospects for urbanisation, this leaves most of Africa worse placed than was Asia to provide preconditions for reduced
TFR among their rural majorities.
In Middle Africa, slow past fertility decline makes UN middle projections of dependency especially vulnerable. In 1950-95 it rose, to peak at 99. In 1995-2005 it fell to 95. Yet to 2025-30 it is projected to fall
sharply to 63, because fertility, 6.0 in 1950-5 and even higher, at 6.2, in 2000-5, is projected to fall to 3.7
by 2025-30. There is no sign of the sharp fall in total fertility without which Middle Africa will not approach UN middle projections of declining dependency. For these to happen, big changes in policy or
institutions seem needed.
Improving the complex of young-end mortality and fertility in SSA: prospects and policies
The mortality prospect. To a considerable extent, fertility decline depends on prior falls in young-end
mortality. Here too, most of SSA has lagged far behind Asia, yet UN (2009) projects rapid catch-up ‘on
11
the basis of [regional] models of change of life expectancy [with] smaller gains the [higher] the life expectancy reached’(UN 2009a). In other words, regional and national age-specific mortality - like fertility is projected on a univariate model imposing convergence to global trends: where mortality remains high
accelerating decline is projected.
Thus Asia cut IMR by 51 per cent in 1965-95; the projection for 1995-2025 is 46 per cent. Yet for the
same periods SSA achieved only 27 per cent; the projection is 41 per cent. Some country projections
would be attained with resumption of past falls in IMR (as in Kenya) or consolidation or modest improvement (Burkina Faso, Cameroon, Côte d’Ivoire, Malawi, Sudan), but most look high. Infant mortality
in DR Congo, having fallen 10 per cent (from 143) from 1965 to 1995, is projected to fall three times as
fast from 1995 to 2025, and in Nigeria the rate of decline is projected to double. Does recent evidence
support such acceleration?
During 1990-2000, seventeen countries in SSA had two DHS surveys. Infant mortality rose in nine (significant at 5 per cent in four), with significant falls only in Malawi, Niger and Nigeria (Mahy 2003, p.31).
South Africa (among others) has slipped back, mainly due to HIV/AIDS. In the 21 st century, Kenya reversed a similar setback: IMR decreased from 119 in 1969 to 66 in 1989, increased to 77 in 1999 and
2003, but then resumed its decline to 52 in 2008-9 (KNBS and ICF Macro 2010, pp.xv, 3). As regards
child mortality, UNICEF (2010) shows it falling at 1.8 per cent per year in 2000-5, and at the same rate in
2005-8 (to 144). Other careful recent reviews of censuses and quality-controlled micro-data (UNICEF,
WHO, World Bank and UN Population Division 2007; UNICEF 2010a; UN 2009 (revised 1.8.2010);
Macro International Inc, for Demographic and Health Surveys, 2010) also find slow, non-accelerating
falls in CMR (and IMR). A dissenting view, apparently based on the use - and trust - of a very wide variety of data sources, is Rajaratnam et al. (2010).
Whatever the past trends, Asian and some African experiences show that rapid improvements in child
mortality are feasible (Table A-2; Conley et al. 2007). In sub-Saharan Africa, child mortality is due mainly to the interaction of malnutrition with gastro-intestinal infections and malaria, and in some countries to
HIV/AIDS. For the 60 per cent of SSA population at high malaria risk, P. Falciparum alone directly
causes 28 per cent of all child deaths. Finding and delivering a vaccine against all forms of malaria would
cut child mortality by over a third. Even with today’s medical knowledge, West and Middle African infant mortality can be pushed far below its 2000-5 level of 100-115 (Snow and Omumbo 2006, p.202), as
Ghana’s rate (70) shows. In Benin between the 2000 and 2006 DHS rounds, ‘increased possession of bed
nets led to a decrease of about 21 percent in what [under-five] mortality would have been without the increased possession’ (Rutstein et al. 2009: Abstract, and pp.16-17). SSA has seen huge rises in public
health expenditure to combat malaria, tuberculosis and HIV/AIDS. However, this is mostly aid-financed;
lasting impact requires embedding the largely preventive measures in national politico-administrative systems. This may not be swift. South-central Asia last saw today’s West African infant mortality around
1980, and took 25 years to get it down to a still-high 60. As noted earlier, even maintaining past rates of
fertility decline in most African countries (let alone accelerating them on the Asian pattern) will require
falls in fertility among areas and groups where it is particularly high.To facilitiate this, measures to reduce
young-end mortality – principally, affordable access to preventive and primary health care - must increasingly be spread to poorer rural and slum areas and groups.
A paradox: There is a paradox about expecting, let alone requiring, such policy to strengthen Phase 2.
Especially where child mortality is still very high, effective action to cut it - though needed for big medium-term cuts in natural increase and dependency - will for some years increase them. Can this time lag be
reduced? Thompson (1959) thought 25 years was the normal lag in Asia. The raw data, and the econometric literature reviewed above, hint at a shorter lag, but do not resolve the issue. We can see big country
differences ex post, and can be sure that policy matters. Crucial is integrated policy on young-end mortality and total fertility. They are mutual causes, seeking joint solutions. Data ‘from all 52 countries [with
DHS surveys] between 2000 and 2005 [except] India’ show that ‘if all women would wait at least 24
months to conceive again, under-five deaths would fall by 13 per cent[, and if] 36 months [by] 25 per
cent’ (Rutstein 2008, pp.14, 70-71). The processes of lowering under-five mortality and fertility in Africa
are well under way, even if mostly slower and fainter than in Asia at a comparable stage of transition.
12
Africans are used, therefore, to mortality declines, and to reviewing the consequences for their fertility
decisions. It is reasonable to expect a shorter lag between falling young-end mortality and the fertility
response – and hence a quicker, more vigorous Phase 2 – if couples know that access to contraception,
improved education and prospects for women and measures to reduce child mortality are being tackled
jointly. Put another way, these three (expensive) objectives are complementary: spending on each is usually more cost-effective if accompanied by spending on the others. Putting family planning in context of
child health has much more than rhetorical importance.
We now set aside the above concerns. We assume that middle UN projections of falls in dependency and
natural increase, for sub-Saharan Africa and each region and populous country, are correct – or will be
made so by changing policies and institutions. What demographic dividend can be expected from 1985
(the posited start of Phase 2) to 2025, via lower dependency ratios and slower natural increase?
4. Economic effects of demographic changes
Causal channels from demography to output per head
Dilution of natural capital: If the constraint represented by natural resources, such as (quality-adjusted)
land or water, is binding, then a rise in the number of people will reduce steady-state GDP per head. Niger, whose population is projected by the UN to grow from 13 million in 2005 to 58 million in 2050, is an
example of an agrarian economy with a fast-growing population, where such a mechanism may seem potentially important. Another example appears to be Rwanda (André and Platteau 1998). Also, a sufficient
rise in population (density) might trigger progressive environmental degradation, in which case there
would be a link to the growth rate as well as the level of GDP/head. An example is the account of environmental collapse on Easter Island in Diamond (2005, p.118), which he describes as ‘the clearest example of a society that destroyed itself by overexploiting its own resources’. The population is believed to
have declined by the end of the 18th century by some 70 per cent from the peak reached in 1400-1600;
deforestation of the island reached a peak in about 1400 and was complete by the end of the 1600s.
Increasing returns to population via the rate of technical advance or the sharing of infrastructure overheads: Extra people may raise long-run income per person, via higher density. This may accelerate research and invention [Kremer 1993, Simon 1996]: ‘exogenous’ advances can be shared, and incentives to
invent and innovate are increased by the prospect of returns in larger markets. Higher density may induce
land-saving agricultural innovations [Boserup 2005]. In SSA it has sometimes stimulated environmental
improvement [Tiffen et al. 1994; see however Zaal and Oostendorp 2002], some forms of mechanisation
[Pingali et al. 1987], and rural roads in isolated regions [Tiffen et al. 1994, pp.102-4]. However, evidence
is lacking that such mechanisms are widespread in Sub Saharan Africa.
Dilution of reproducible capital (Solow model): In economic models that allow only for reproducible
capital, rises in the number of people lead only temporarily to capital dilution (falls in capital per head)
and lower output per head. To see this in an idealized way, consider a once-for-all instantaneous 10% rise
in population (and workforce). Capital per head falls by 10% at once, but the extra workers are productive, generating extra savings which cause capital per head to begin to rise again. This compensating capital deepening is, however, self-limiting, because – given diminishing returns - rises in capital per head
raise the proportion of savings per head that must be devoted to replacing worn-out machines (and equipping new workers if there is an underlying rate of population growth greater than zero). So, in the long
run, capital per head and output per head return to their initial equilibrium values. Similar reasoning implies that, as opposed to a one-off rise in population, a sustained rise in its rate of growth does permanently lower capital per head and output per head. In practice the distinction made here is less sharp than it
appears, since simulations suggest that ‘temporarily’ may mean many decades (Sato 1963).
Age-structure effects: The fall in the dependency ratio in Phase 2 of the demographic transition yields a
straightforward economic benefit as follows. Denote output by Y, population by N, the population of
working age by WA and the percentage growth rate of any variable x by g(x). Then by definition:
13
g(Y/N) = g(Y/WA) + g(WA/N)
(1)
Assume, just in this paragraph, that the given demographic change has no impact on g(Y/WA). Then, if
the demographic transition causes the working-age share (WA/N) to grow by an extra 1% (e.g. at 1% instead of 0% per year), there will be an equal rise of 1% in the rate of growth of output per person. We
refer to this as the arithmetic age-structure dividend. It can be linked directly to the change in dependency, since g(WA/N)= minus g(1+dependency ratio). A falling dependency ratio implies a rising workingage share and, arithmetically, a faster rate of growth of output per person.
Going beyond this is the age-structure hypothesis, which in what we call its strong form says that all the
effect of demographic change (of any kind) on output per person comes through changes in the workingage share (equivalently, through the dependency ratio): whether the magnitude of this effect is only
arithmetic, as above, or larger (or smaller) than that is to be determined econometrically. The weak form
of the hypothesis says that all the effect comes through the age structure, allowing for the possibility that
– for instance – the effects of young and old dependency might be different. What the age-structure hypothesis excludes are any effects of population growth per se on output per person.
To explore this hypothesis from the standpoint of a model in which all capital is reproducible, it is helpful
to decompose the sources of growth of output per person using an aggregate production function.
Let Y = T.F(K,L), where
T.F(K,L) is a constant returns to scale production function with capital K, employed labour L, and technology parameter T: technological advance is represented by a rise in T. Denote the labour force by LF.
Then we can decompose g(Y/N), in a slightly different way from (1), as follows:
g(Y/N) = g(Y/L) + g(L/N)
= [g(T) + α.g(K/L)] + [g(L/LF) + g(LF/WA) + g(WA/N)]
(2)
(3)
α>0 is another technological parameter: the elasticity of output per head with respect to capital per head.
Equation (2) breaks down growth in GDP/head into growth in labour productivity (Y/L) and growth in
the ratio of employment to population (L/N). Equation (3) breaks down each of these components. Labour
productivity growth comes either from technological advance (g(T)) or capital deepening (g(K/L)). The
ratio of employment to population can rise from a fall in the unemployment rate (L/LF rises), a rise in the
participation rate (LF/WA), or a rise in the working-age share in the population (WA/N). For simplicity,
this assumes that all labour force participants are of working age.
To bring out the meaning of the strong form of the age-structure hypothesis, consider any demographic
change whatsoever and use ‘Δ’ to represent its effects on any variable. Then the hypothesis can be written
in proportional form as:
Δg(Y/N) = γ Δg(WA/N)
(4)
where γ is a parameter, inserted to leave open the question whether the effects of changes in the workingage share are just arithmetical (γ =1) or more or less than that. For instance, as above, if the working-age
share has been constant (g(WA/N)=0) and the demographic transition causes it to start rising at 1% per
year, then if γ =1 the growth of GDP/head is raised by 1% per year.
Pursuing this further, we can write (3) in ‘changes’ form thus:
Δg(Y/N) = [Δg(T) + α. Δg(K/L)] + [Δg(L/LF) + Δg(LF/WA) + Δg(WA/N)]
(5)
14
which implies, after substitution of (4) and rearrangement:
[Δg(T) + α. Δg(K/L)] + [Δg(L/LF) + Δg(LF/WA)] = (γ-1).Δg(WA/N)
(6)
Equivalently
[Δg(T) + α. Δg(K/L)] + [Δg(L /WA)] = (γ-1).Δg(WA/N)
(7)
Continuing with the special case γ =1, implying that the right-hand sides of (6) and (7) are zero, the strong
form of the age-structure hypothesis is then equivalent to the claim that the terms on the left-hand sides
add to zero (with γ >1 they would have to add to more than zero, if the demographic change was improving the age structure, i.e. Δg(WA/N)>0).
For example, suppose there is a rise in the rate of population growth accompanied by a rise, not necessarily the same, in the rate of growth of the working-age population. From (6) we see that in this case, the net
effects via (a) productivity growth (technological advance + increased capital per worker) and (b) unemployment and participation rates, have to add to zero. (a) and (b) correspond to the two square-bracketed
terms in both (6) and (7).
Might the effects via each of (a) and (b) be zero? Using (7), for (b), this would mean that employment
growth was just keeping up with faster growth in the working age population (Δg(L /WA)=0 ). Then for
(a), if there were no effect on technological advance, there would have to be faster capital accumulation,
financed either by higher domestic savings or capital inflow, so that faster employment growth was not
reducing capital per worker. The effects could add up to zero in other ways: for instance, there might be
some capital dilution as a result of insufficient savings that was being offset by faster (Boserupian) technological advance. Or else female labour market participation might be raised: if the unemployment rate
was unaffected, then, from (6), the contribution from (b) would be positive. Summing up, for growth of
output per person not to fall as a result of faster population growth, something has to offset the extra capital dilution that this brings about.
Nevertheless, the econometric literature has claimed not merely that the age-structure hypothesis is true,
but that the effects are greater than arithmetical: γ>1. As noted above, there are mechanisms that might
account for this. A fall in the dependency ratio – especially the fall in the young dependency ratio that a
fall in fertility will bring about - may well induce higher female labour force participation and raised savings. As regards savings, if the population is considered as consisting of dissaving dependents at both
ends of the age spectrum plus saving workers in the middle, then the early consequences of a fall in fertility will be to raise savings ratios, by reducing the weight of young dissavers in the population.
If falls in fertility simultaneously raise female participation and savings, then an appealing scenario presents itself: at the same time as a rise in female participation is swelling the labour force, both financial
and labour-market conditions will be tending to favour the investment that will facilitate gainful employment of the extra labour. The extent to which raised domestic savings are a necessary ingredient of this
naturally depends on how important are barriers to the international mobility of capital.
What can the data tell us about the consequences of demographic change for SSA development?
Evidence from cross-country regressions: The mainstream economics literature on this issue has been
dominated by cross-country regression analysis. Cross-country growth regressions, founded in the ‘convergence’ framework described below, have sought to quantify both capital-dilution and age-structure
effects of demographic change on the growth rate of output per head. Some of this work has used a pure
cross-section of countries, with the dependent variable being – say – their growth of output per head over
1965-90; other work has also sought to exploit within-country variation over time, so has used panel data,
with the dependent variable being growth over several five- or ten-year periods. The cross-country regression method has not been confined to growth regressions. The finding by some of large age-structure effects has motivated other work to shed light on the mechanisms, looking especially for age-structure ef-
15
fects on savings. Cross-country regression methods have also been used to look for effects on poverty,
conditional on growth (Eastwood and Lipton 1999).
One reservation should be noted. The research aims to give evidence of causal links from demography to
development, but ultimately what is exhibited is correlation. Correlation between X and Y may reflect
causation from X to Y, but also possible are reverse causation from Y to X and incidental association,
reflecting causal links from some omitted variable to both Y and X. In practice, as shown below, these
possibilities are very hard to exclude.This has led to scepticism among economists about the usefulness of
the methodology.
Cross-country growth econometrics has, overwhelmingly, been based on the so-called ‘convergence’ or
‘technology-gap’ framework (Barro 1991, 1997, Kelley and Schmidt 2005, 2007). The underlying idea is
that economic growth is partly a matter of ‘catch-up’. Even if a developing country has low capital per
worker, it is rising through time – and taking productivity up with it – for two reasons. First, domestic
savings, plus inflow of capital from abroad, is more than enough to merely equip new entrants to the labour force with as much capital as existing workers have. Second, the country may be using technology
well inside the global technology frontier, and deriving some growth from closing this technology gap.
From this comes the idea that a given country at a given time has a steady-state equilibrium value of (labour) productivity, defined as what productivity would be if the gaps were closed, and that productivity
growth depends partly on the gap between actual and steady-state productivity. In spite of its name, the
convergence framework does not imply that convergence will necessarily be observed, since international
gaps in steady-state productivities could exceed those in actual productivities, perhaps because of low
savings rates or rapid natural increase in some poor countries (on divergence in practice, see Pritchett
1997). Evidence on this for SSA is explored in the last part of this section.
A demography-oriented elaboration of the convergence framework is as follows:
g(Y / L)  (( Y / L)*  (Y / L))
(8)
This is the ‘convergence’ hypothesis: the rate of growth of productivity, g(Y/L), depends on how far
productivity falls short of its ‘steady-state’ value, (Y/L)*. δ is the speed of convergence, assumed constant
across time and space.
(Y / L)*  X
(9)
β is a vector of coefficients and X is a vector of variables which are held to influence steady-state productivity. Those employed by Barro (1997) include education (male secondary and tertiary), health (e.g. life
expectancy), the share of government consumption in GDP, and proxies for the rule of law and the robustness of democratic institutions. Demographic variables may also be included. The Solow model implies, as noted above, that faster population growth lowers (Y/L)*; higher population density might raise
it.
g(Y / N)  g(Y / L)  g(L)  g( N)  g(Y / L)  g( WA)  g( N)
(10)
The first part of (10) is an identity; the second part replaces g(L) with g(WA), by assuming that unemployment and participation rates are constant through time.
g(Y / N)    X  (Y / L)  1g(WA)   2 g( N)  
(11)
Equation (11), used by Bloom and Williamson (1998) and Bloom and Sachs (1998), is derived by eliminating (Y/L)* and g(Y/L) between (8)-(10) and making three amendments:
16
(i) a constant  is added to allow for labour-augmenting technical progress at the same rate in all countries
(ii) coefficients γ1 and γ2 are inserted, allowing the age-structure hypothesis to be tested rather than imposed
(iii) an error term ε is added
In Bloom and Williamson and Bloom and Sachs the dependent variable is the annual growth rate of
GDP/capita over 1965-1990, and the sample of countries is all those (developed and developing) for
which data can be obtained. Bloom and Williamson ask how far the relatively rapid growth in East Asia is
traceable to the demographic transition in that region; Bloom and Sachs ask whether relatively slow
growth in Africa can be explained by a combination of geographic and health variables, included in vector X, together with the lack of a demographic transition.
The first empirical question is whether age-structure effects and capital-dilution effects are both discernible. The strong version of the age-structure hypothesis is represented by γ1 = γ2 in (11), since equal rises in
the rates of population and labour force growth then have no net effects on per capita GDP growth. If
‘Solow-model’ capital dilution matters as well, then (γ1 - γ2) should be significantly positive. Most studies
employing cross-country growth regressions do not have age-structure effects as a prime focus, so they
suppress g(L) (in effect imposing γ1=0) and include g(N) to test for capital-dilution: generally speaking
significant capital-dilution effects are not found (Kelley and Schmidt 1995). Bloom and Sachs and Bloom
and Williamson similarly find no evidence of capital dilution, but they do find a more-than-arithmetical
age structure effect. Thus the hypothesis that γ1 = γ2 is not rejected, and when γ1 = γ2 = γ is imposed, the
estimate of γ is significantly greater than unity. Bloom and Williamson attribute about a third of the ‘economic miracle’ in East Asia) to the age-structure demographic dividend that East Asian countries enjoyed
during 1965-90.
In their paper on Africa, Bloom and Sachs begin with a version of (11) which excludes the demographic
variables, so that only initial GDP/worker, three X variables (trade openness, a proxy for the quality of
institutions and the central government deficit) and an Africa dummy are included. One might expect the
savings rate to be included as an X variable, but it is excluded on the ground that it should be considered
endogenous, that is, dependent on the other X variables. The coefficient on the Africa dummy indicates
an unexplained shortfall of growth in Africa of about 2.2% per annum. Then the X vector is expanded to
include variables representing geography and health, and g(L) and g(N) are also included. Geography is
proxied by the percentage of land in the tropics and coastal and inland population densities, and health by
life expectancy at birth in 1965. Expanding the X vector in this way works, in that the ‘Africa effect’, as
measured by the dummy, is eliminated. Once the hypothesis that γ1 = γ2 is accepted, the regression is rerun to impose this and the estimated age structure effect is represented by a gamma of between 1.5 and
3.5, with the higher of these figures being obtained if the sample is restricted to African countries (ibid
Table 6). As in BW, γ can be interpreted straightforwardly as a multiplier to be applied to the ‘arithmetic’
dividend in (10), potentially explicable via the savings and participation effects noted earlier.
In Table 3 we give estimates of this ‘arithmetic’ dividend for sub-Saharan Africa and its sixteen most
populous countries, with some comparative data for Asia. Projected dependency ratios are UN ‘medium
variant’ ones (so that projected falls may be exaggerated, as argued earlier) and the dividends are calculated using equation (2) above with gamma equal to unity. A comparison of SSA 1985-2025 with Asia
1965-2005 shows a projected SSA arithmetic dividend of 0.32% per annum, compared with 0.41% for
Asia and 0.52% for East Asia. South Africa’s early transition is visible – it is projected to enjoy a dividend equal to that of Asia over 1965-2005. Among the populous SSA countries, those with the largest
projected dividends are those where Phase 2 demographic transition is well-established – Cameroon,
Ghana, Kenya and Sudan are all projected to enjoy a dividend of 0.4% or more. Clearly, if gamma really
were as high as 3.5 for African countries, then in these countries at least, the age-structure demographic
dividend in prospect would be substantial (e.g. 1.9% per annum for Kenya over 1985-2025).
17
TABLE 3. AGE-STRUCTURE DIVIDENDS (γ=1)
1950
1965
Dependency Ratios
1985
2005
SSA
E SSA
Ethiopia
Kenya
Madagascar
Malawi
Mozambique
Sudan
Tanzania
Uganda
M SSA
Angola
Cameroon
DRC
S SSA
South Africa
W SSA
Burkina Faso
Cote d'Ivoire
Ghana
Nigeria
82
85
89
78
71
95
80
89
93
85
82
79
76
90
74
73
81
75
83
91
81
88
92
85
108
92
93
84
89
93
97
86
92
82
88
85
84
85
81
85
89
87
94
96
90
112
94
101
92
91
96
102
96
99
96
96
80
77
94
100
93
93
95
86
90
92
83
90
99
90
79
91
108
95
95
83
101
58
56
86
92
82
76
86
71
74
70
71
67
86
73
58
82
88
75
76
66
78
54
53
70
79
66
62
67
Dividends
1965-2005 1985-2025
% per year % per year
0.03
0.32
0.03
0.30
-0.09
0.28
0.32
0.54
0.03
0.37
-0.08
0.19
-0.08
0.26
0.14
0.47
0.03
0.19
-0.14
0.18
-0.12
0.28
-0.04
0.31
-0.01
0.42
-0.17
0.24
0.39
0.39
0.41
0.36
-0.01
0.33
-0.15
0.28
0.04
0.38
0.18
0.44
0.01
0.39
China
India
Indonesia
Thailand
61
68
76
83
80
82
81
93
56
73
72
63
42
60
51
43
46
47
43
48
0.59
0.32
0.45
0.75
0.17
0.41
0.46
0.24
Asia
E Asia
SC Asia
SE Asia
W Asia
67
63
70
73
74
80
76
83
87
89
67
55
77
74
81
53
43
61
53
61
48
47
49
46
50
0.41
0.52
0.32
0.50
0.40
0.30
0.13
0.43
0.44
0.47
2025
Source: UN(2009)
By including population densities in X, Bloom and Sachs allow for both the positive effects via increasing
returns and the negative ones via the dilution of natural capital discussed earlier. While, in the model, X
variables are supposed to affect growth only via the steady-state level of GDP/worker, (Y/L)*, and thereby the productivity ‘gap’, their estimating equation would evidently also pick up unmodelled countryspecific effects on the rate of technical advance (i.e. on  ). They find a significantly positive coefficient
on coastal density and an insignificantly negative one on interior density, results which (at face value)
give evidence for ‘increasing returns’, but not ‘natural capital dilution’.
How credible are these cross-country findings? Unfortunately, such models are subject to important
econometric reservations. Consistency of the parameter estimates requires that the error term in (11) is
independent of the included regressors. There are reasons to doubt this here (Udry 1998). First, the error
term amounts to the total effect on the growth of GDP/capita of all relevant variables that are not included
18
as regressors. It is implausible that such variables are uncorrelated with the level of GDP/capita in 1965,
which is one of the regressors. There will surely be omitted serially-correlated variables that have affected
national growth rates both before and after 1965, creating an incidental association between error term
and regressor. Problems of endogeneity will also affect other regressors. In particular, causation is likely
to run in both directions between growth in GDP/capita and population growth; cross-section regressions
cannot tell us how much of the negative association between these two variables is attributable to reverse
causation from GDP/capita growth to population growth.
One way of alleviating such endogeneity problems has been to use a panel of data, allowing a particular
application of the method of instrumental variables, described below. Kelley and Schmidt (2005, 2007)
use the same data period as Bloom and Williamson and Bloom and Sachs but split the data into three decades plus a quinquennium (for 1990-95). Their estimating equation is the same as (11), although its conceptualization is a little different. Their X vector includes a set of politico-economic variables (derived
from Barro’s work, noted earlier), alongside four demographic variables: youth and old-age dependency
ratios, population and population density. The authors argue persuasively that the right way to model age
structure effects is to isolate the arithmetical effect in the term [g(W)-g(N)], while hoping to pick up effects on savings – effects that may, as they note, affect not only the steady state but the speed of convergence towards it – via disaggregated dependency ratios, so they test the age-structure hypothesis in its
weak form . The inclusion of population and population density means that they are also taking account
of the possibilities of increasing returns or dilution of natural capital.
Kelley and Schmidt’s findings, like those of Bloom and Sachs and Bloom and Williamson, fit their model
well if judged by the correspondence between the signs of estimated coefficients and theoretical expectation (ibid. 2005, Table 1). There is support for the hypothesis that γ1 = γ2=1 once the demographic X variables are included in the model. Of these four variables, only the youth dependency ratio is significant,
but its estimated effects are very large compared to the arithmetic dividends reported in Table 3. Those
dividends were calculated against a benchmark of unchanged age structure. On the same basis, the projected decline in the youth dependency ratio in sub-Saharan Africa from 88 in 1985 to 64 in 2025 would,
using the parameter estimate given in Kelley and Schmidt (2007) Table 3.1, raise average growth over
1985-2025 by 0.59% p.a, nearly double the arithmetic dividend. The corresponding figure for Asia 19652005 is 1.14% p.a, nearly three times the arithmetic dividend.
It is worth noting some contrasts between the Bloom and Sachs and Kelley and Schmidt approaches.
While it is true that some of the variation across time in the data is exploited by Kelley and Schmidt, they
do include dummy variables for each time period other than the first, and – as the authors acknowledge –
these dummies swallow up most of the aggregate variation in growth from decade to decade (ibid. 2005,
p. 294 and Table 2). As regards variation across space, Kelley and Schmidt include regional dummy variables and, given the limited range of their included regressors, are not able to eliminate a strong ‘Africa
effect’: African growth, ceteris paribus, is estimated to be between 0.7% and 1.3% per annum slower than
growth in Asia, Europe and Latin America (ibid. 2005, table 1, col 5).
The use of the instrumental variables method in the estimation of the model is designed to circumvent
endogeneity issues of the kind discussed earlier. Space precludes extensive discussion (an introduction is
Angrist and Krueger 2001), but the essential requirement is that one can identify an ‘instrument’ Z for an
endogenous regressor X, such that Z is (a) highly correlated with X, (b) independent of the error term ε.
The normal practice, followed by Kelley and Schmidt, is to use lagged Xs as instruments, but this is controversial, since it has to assume that - while the included causal variables, the Xs, are serially correlated,
as may be expected with slow-moving X variables, the omitted causal variables, whose aggregate influence comprise ε, are not (Weil 2007, p.1271). It is difficult to see how this assumption could ever be convincingly defended, since, whatever properties lead some variables to be included as regressors and others
to be excluded, that of being serially correlated or not can hardly be one of them. Further, the assumption
cannot be tested, in view of the small number of time periods, typically four or five.
The argument for an age-structure effects that are more than arithmetical rests in part on the hypothesis
that a fall in the dependency ratio raises savings. What is the evidence for this? For East Asia, there is
19
certainly a strong negative association between youth dependency and aggregate savings as a share of
income. Between the mid-1960s and the early 1990s, youth dependency rates in some East Asian countries fell from about 40% to 25% or less, while the savings ratio rose from about 20% to about 35%.
Empirical work to test whether this association can be interpreted as a causal consequence of life-cycle
savings has two strands. The first consists of panel studies on country-level data using the methodology
above. Thus Higgins and Williamson (1997), on the basis of a panel of annual observations over 40 years
on 16 Asian countries, claim that nearly all (about 13 percentage points) of the rise in the savings rate can
be attributed to the life-cycle mechanism via reduced dependency burdens. However it must be questionable whether year-on-year variations in noisy age structure and savings rates can be informative, especially given what are likely to be complex dynamics. In a reconsideration of this work Schultz (2004) suggests an effect that is only one tenth as great and not statistically significant.
A second strand of research (Deaton and Paxson 1997, 2000; Lee, Mason and Miller 2000) uses household data to identify age and cohort effects on savings, using simulation methods to estimate the effects of
demographic change on aggregate savings. This research is based on the life-cycle model of savings, according to which individuals follow a consistent pattern of smoothing consumption through the life span,
periods of dissaving early and late in life being balanced by a hump of saving in between. Demographic
change affects the aggregate savings rate in this model via changes in age-structure. In particular, fertility
decline should raise the savings rate by reducing the proportion of young dissavers in the population.
Deaton and Paxson (2000), using Taiwanese data over 1976-1995, test the life-cycle hypothesis rather
than imposing it, and their critical finding is that the hypothesis in its simplest form fails, a result confirmed in their work on other countries (Deaton and Paxson 1997). Successive cohorts of Taiwanese individuals at every age are found to save higher fractions of their permanent incomes, a result which the lifecycle model would have to attribute to a trend strengthening of the bequest motive. The household saving
rate in Taiwan rose from about 10% in 1970 to about 30% in 1990, but the authors can only attribute, at
most, one fifth of this rise to the effect of the demographic transition (ibid. 2000 p.167).
Summing up, what does the cross-country econometric literature claim about the importance in practice
of the four causal channels identified earlier? Population density is included in some of the research, acting as an imperfect proxy for the dilution of natural capital and/or increasing returns, but does not provide
strong evidence for either causal channel, although BS’s finding that coastal density raises per-capita
growth provides some weak support for increasing returns. The strongest claim in the literature relates to
the relative importance of the dilution of reproducible capital and benefits from improved age structure,
coming out in favour of the latter and arguing that greater-than-arithmetical age-structure effects arise, in
particular, because reduced youth dependency raises savings. However neither conclusion is firmly established. Both rest on a controversial macro-econometric methodology, and investigations of the savings
channel using microeconomic data suggests for East Asia that most of its rise in savings cannot be attributed to the demographic transition.
Capital stocks, savings and sustainability
One response to doubts over the use of aggregate econometric methods to identify links from demography to development has been to use relationships estimated on household data as inputs to macroeconomic simulation models, with parameters mostly obtained by calibration rather than estimation. For example,
Young (2005) uses micro-level estimates of the effects of HIV infection on fertility as an input to a longrun macro simulation model of the effects of HIV on development in South Africa. Such models involve
strong simplifying assumptions and have attracted powerful critiques (e.g. Bell et. al. 2006, who emphasize adverse effects of HIV on human capital accumulation via the diversion of public resources from education towards care of the sick and the effects of both parental deaths and reduced child life expectancy
on private incentives to educate children).
Going further away from estimation than this is recent work (World Bank 2006), which eschews the
strong assumptions required for cross-country econometric analysis in favour of strong theoretical as-
20
sumptions and country-by-country calculation. The primary aim of this work is to investigate at country
level whether savings are consistent with sustainability, defined as non-decreasing wealth per capita. It is
assumed that output per capita is a function of wealth per capita, with no exogenous technical change.
Slower population growth, ceteris paribus, lowers the savings share consistent with sustainability and
therefore raises sustainable per-capita consumption – a demographic dividend defined slightly differently
from that found in the age-structure literature. For a given deceleration of population growth over, say, a
40-year interval, we can represent the gain in per-capita consumption in the form of an annual growth
rate, making it broadly (but not exactly) commensurate with the per-capita output growth identified in the
age-structure approach and shown in Table 3.
Before estimating such ‘reduced-dilution’ dividends, we consider the World Bank’s estimates of dilution
itself. These are interesting in themselves and offer an important contrast between SSA and Asia. The
algebra is as follows. With W denoting wealth and other notation as before (Y now stands for income rather than GDP), we can write the growth rate of W/N as:
g( W / N)  g( W)  g( N)  W / W  g( N)
(12)
But the change in wealth, ΔW, is equal to adjusted net savings, i.e. the product of the net savings rate and
income, with an adjustment, explained below. To keep W/N constant, the required savings rate, denoted
sR, can therefore be obtained by setting equation (12) equal to zero, substituting ΔW=sRY, and rearranging:
s R  (W / Y ) g ( N )
(13)
Then the ‘savings gap’ is defined as the gap between this ‘required’ savings rate (to keep consumption per
person just sustainable, i.e. without depleting capital per person) and the adjusted net savings rate. Table 4
shows estimated savings gaps for the eleven out of the sixteen most populous countries in SSA for which
the data are available. What is striking is how large these gaps are - over 10% of income for all but South
Africa, Kenya and Ghana - in spite of the fact that gross savings rates in most of these countries already
exceed 10%.
21
TABLE 4. SAVINGS GAPS IN SUB- SAHARAN AFRICA AND ASIA
Burkina Faso
Cameroon
Cote d'Ivoire
Ethiopia
Ghana
Kenya
Madagascar
Malawi
Mozambique
Nigeria
South Africa
China
India
Indonesia
Thailand
Net saving Adjusted net saving
% of income % of income
Reproducible capital
Tangible wealth Natural
share of tangible wealth to income ratio increase, %
2000-05
Required net saving Savings gap
% of income
% of income
4.0
5.7
–0.7
4.5
8.4
5.7
1.7
–3.8
3.8
17.3
2.4
29.8
14.6
15.4
15.9
0.4
0.27
0.24
0.18
0.34
0.39
0.19
0.41
0.31
0.14
0.68
0.55
0.45
0.42
0.32
27.5
27.6
16.6
26.1
18.2
16.9
24.1
23.8
21.0
38.7
5.4
4.5
10.7
12.8
4.0
6.3
-1.3
-0.9
-4.0
6.5
11.6
3.5
-0.9
7.6
-32.5
8.7
28.0
14.1
3.1
17.6
Sources: UN(2009), World Bank(2006)
8.87
11.83
6.59
9.63
7.93
6.52
8.47
8.19
7.88
15.85
3.76
6.14
6.47
9.08
5.81
3.1
2.33
2.52
2.71
2.29
2.59
2.85
2.91
2.66
2.44
1.43
0.74
1.65
1.41
0.68
21.2
28.9
17.5
30.1
11.7
5.3
20.6
24.7
13.4
71.2
-3.3
-23.4
-3.4
9.7
-13.7
22
To grasp these results, note two features of the World Bank’s methodology. The first is the measurement
of national wealth, which comprises ‘tangible wealth’ and ‘intangible wealth’ (for details on ‘intangible
wealth’, see World Bank 2006, p.XIV). What matters here is ‘tangible wealth’, W, defined to include not
only reproducible capital but also land and natural resources, defined as arable land, pasture, timber and
non-timber forest resources, protected areas and subsoil resources (fish stocks and subsoil water are excluded). Reproducible capital averages only 29% of tangible wealth in the eleven large SSA countries
considered, so the dilution of wealth resulting from population growth is, on average, over three times as
great as would be the case if only reproducible capital were included. As equation (13) shows, a high value of W/Y (i.e. low capital productivity) raises the required savings rate: inclusion of natural capital in the
analysis tends to raise W/Y in SSA relative to Asia, where natural capital is generally a lower fraction of
wealth, and so contributes to the relatively high required savings rates for SSA that are shown in Table 4.
The second feature is the definition of adjusted net saving. This is obtained from net saving conventionally defined (i.e. gross saving minus the depreciation of reproducible capital) by adding education expenditure and deducting the consumption of natural capital: petroleum resources, mineral resources and forest
resources (excluding soil degradation and the depletion of fish stocks). For some SSA countries the consumption of natural capital is important, as indicated by the difference between columns 1 and 2 in table
4: among our eleven countries, oil depletion has a very large impact for Nigeria, and a significant one for
Cameroon, while net deforestation looms large for Ethiopia.
The idea behind these calculations is that reproducible capital can substitute for natural capital ($ for $ at
the margin), e.g. enough extra roads, factories or irrigation can make up for excessive depletion of mineral resources or the crowding of land resources that population growth entails. However, population
growth rates of 2% per year or more require very high savings rates if wealth per capita is not to fall. Table 4 shows that in Burkina Faso, where depletion of exhaustible resources is not a significant factor and
where adjusted saving is over 6% of national income, required saving is 27.5% and so there is a savings
gap of 21%. Since raising the savings share by an amount of this order is hard to contemplate, the implication might appear to be that a marked fall in the rate of population growth is a sine qua non of a transition to sustainability. This argument cannot be transplanted without caveats to countries, such as Nigeria,
where the counterpart of a high savings gap is rapid oil depletion. To the extent that oil or other minerals
rents are accruing to foreigners or national elites, it may be that the gap between consumption and sustainable consumption for the mass of the population is much smaller than the calculation would indicate.
Working the other way, some countries have high annual rates of depletion of soil nutrients (and fish
stocks), probably due in part to population increase, but not accounted for here (Henao and Baanante
2006, Haileselassie et. al. 2005).
What are the results of similar calculations for Asian countries? If it could be shown that Asia in 1965 or
1985 had similar savings gaps to SSA in 2000, then the credibility of this exercise would be weakened.
Estimates of pre-2000 wealth levels are not available, so that equation (13) cannot be applied historically,
but for 2000, gaps can be calculated for nine populous Asian countries (China, India, Indonesia (Table 4),
Korea, Malaysia, Nepal, Philippines, Sri Lanka and Thailand). In all of these countries with the exception
of Indonesia (an oil producer to which arguments parallel to those for Nigeria apply) another example of
the Nigeria caveat above) the gaps are negative: net savings rates are high and easily sufficient to outweigh dilution. Estimated net savings rates over 1970-2000 averaged about 20% for East Asia and the
Pacific, little more than zero for SSA (ibid. Fig. 3.3, p.41). This gap may be exaggerated owing to the under-recording of non-monetized savings in SSA, as appears to have happened in Asia in the 1950s and
1960s (Lipton 1977, ch.10).
How robust are these results? Two reservations may be noted, pulling in opposite directions. First, technological advance - whether exogenous or via the closing of an international technology gap - is excluded. Otherwise, constant wealth per capita would allow rising, not constant, consumption per capita, undermining the sustainability calculations given here (Weitzmann and Lofgren 1997). Second, natural and
reproducible capital are assumed perfectly substitutable. If they are not, then $ for $ substitution of natural capital with reproducible capital will fail to sustain output and, as a result, the numbers in Table 4 will
underestimate savings gaps (compare Weil and Wilde 2009).
23
We now turn to the effects of of the fall in natural increase during Phase 2. For SSA, we take the projected change in annual natural increase between a reference period representing the end of Phase 1, taken as
1970-1985 and the first forty years of Phase 2, taken as 1985-2025. For comparisons with Asia, the two
periods chosen are 1950-1965 and 1965-2005. Falls in natural increase between the two forty-year periods (cols. 2 and 3 of Table 5) imply from equation (13) that required savings rates fall, raising sustainable
consumption per head by given amounts, (col 5). These rises are quite substantial, 5.8% for Ghana and
6.8% for Kenya, for example, but when they are converted to rates of increase over 40 years, so as to
make them roughly comparable with the age-structure dividends in Table 3, much smaller numbers result.
So the message of Table 5 is simple. Whether or not natural increase is acting as a drag on SSA development, as the World Bank research suggests, changes in it seem in most cases to have a small projected
effect on sustainable consumption per capita. The projected SSA age-structure dividends over 1985-2025
in Table 3 range from 0.18% p.a. to 0.54% p.a, while those from reducing the dilution of capital in Table
5 range from minus 0.07% p.a. to plus 0.17% p.a. (negative numbers indicate rises in natural increase in
countries that remain in Phase 1 for much of the period). Estimates for Asian countries – inaccurate to the
extent that they are based on tangible wealth-income ratios for 2000 – are similarly small in comparison
with the estimated (arithmetic) age-structure dividends. The small numbers in Table 5 are, of course, an
artefact of the chosen counterfactual. If fertility were to decline so much faster than UN middle projections that Phase 2 ended with the rate of natural increase equal or close to zero, then dividends five or six
times larger would, on average, be the result.
24
TABLE 5. REDUCED DILUTION DIVIDENDS: SUB-SAHARAN AFRICA (1970-85 TO 1985-2025), ASIA (1950-65 TO 1965-2005)
Burkina Faso
Cameroon
Cote d'Ivoire
Ethiopia
Ghana
Kenya
Madagascar
Malawi
Mozambique
Nigeria
South Africa
Natural Increase (0)
SSA 1970-85
Asia 1950-65
2.73
2.84
3.54
2.63
3.01
3.72
2.72
3.31
2.45
2.71
2.42
Natural Increase (1)
SSA 1985-2025
Asia 1965-2005
3.03
2.31
2.50
2.63
2.28
2.68
2.69
2.92
2.25
2.29
1.16
Tangible wealth:
multiple of income
8.87
11.83
6.59
9.63
7.93
6.52
8.47
8.19
7.88
15.85
3.76
Change in sustainable
consumption/head:
share of national income
-2.7
6.2
6.8
0.0
5.8
6.8
0.2
3.2
1.5
6.8
4.7
Change in sustainable
consumption/head:
annual change, per cent
-0.07
0.15
0.17
0.00
0.14
0.16
0.01
0.08
0.04
0.16
0.12
China
India
Indonesia
Thailand
1.83
1.93
1.99
2.97
1.52
2.07
1.91
1.70
6.14
6.47
9.08
5.81
1.9
-0.9
0.8
7.4
0.05
-0.02
0.02
0.18
Sources: UN(2009), World Bank(2006)
5. Conclusions
We first reviewed the demographic transition in SSA. In Phase 1, compared with Asia, dependency and
natural increase peaked some 20 years later - and higher, by about 14 points and 0.6 per cent respectively.
UN middle projections suggest that in Phase 2 both will fall more slowly in SSA than they did in Asia,
but even that depends on accelerated falls in African total fertility. This is driven mainly by earlier falls in
young-end mortality, but this continues to fall relatively slowly in sub-Saharan Africa. Since that is not
taken into account in UN projections, these probably overstate future TFR declines. Yet there is sufficient
national diversity to suggest possible policy-driven acceleration of falls in young-end mortality, though
the immediate effect would be to raise both dependency and natural increase. Even in countries where
total fertility has fallen overall, it has remained stubbornly high in some regions, and in rural areas. Despite urbanization, for some time the demographics of such areas will increasingly dominate national outcomes. This strengthens the case for policy to cut young-end mortality, alongside fertility, in rural areas.
We then turned to the economic consequences of the demographic transition, specifying the channels
through which demographic change might have macro-economic consequences. Phase 2 transition entails
falls in both dependency and natural increase, each of which may deliver a demographic dividend. In each
case, accounting can be used to calculate an ‘arithmetic’ dividend which, under strong assumptions specific to that case, constitutes the total dividend. For dependency falls, the arithmetic dividend for SSA
over 1985-2025, defined in terms of the growth of GDP per person, is 0.32 per cent per year. For falls in
natural increase, data gaps prevents us from calculating a figure for SSA as a whole, but for eleven of its
populous countries the median arithmetic dividend - here defined in terms of growth of sustainable consumption per person - is 0.12 per cent per year.
An influential body of econometric work has supported an ‘age-structure hypothesis’ that all of the macroeconomic effects of the transition are mediated through age-structure change: natural increase as such is
irrelevant. In its strong form, the hypothesis claims that the working-age share (equivalently, the dependency ratio) is a sufficient statistic for the age structure; the econometrics then suggests a growth dividend
1.5 to 3.5 times greater than the arithmetic dividend. In its weak form, the hypothesis allows for differential effects from young and old dependency, and strong effects from young dependency, perhaps via savings behaviour, are found. Reservations about these results arise, however, both for technical reasons and
because of doubts whether findings that depend a good deal on Asian experience can be projected onto
SSA’s future.
On the latter point, realizing even the arithmetic age-structure dividend requires that demographic transition does not reduce labour productivity, in spite of a continuing rise in the labour force. That this was
achieved in Phase 2 in much of Asia was due both to the sharp rise in the savings rate, only about a fifth
of which can be convincingly attributed to dependency falls, and to rapid, labour-intensive agrotechnical
progress on smallholdings, especially in 1965-85. Such achievements have proved more elusive in Africa.
Falling natural increase delivers a demographic dividend in terms of sustainable consumption, by reducing the proportion of income that has to be saved to prevent capital per person from falling. Borrowing
data and methods from the World Bank, we have shown that falls in natural increase during Phase 2 yield
rather small dividends of this form. Far more significant, and in sharp contrast to Asia, is the alarming
implication that continuing high levels of natural increase, in combination with low savings rates and low
capital productivity, render current consumption per person in most of SSA unsustainable. Matters are
worse if man-made capital cannot readily be substituted for natural capital, or if an environmental resource is progressively degraded (perhaps below a critical threshold) because used by more and more
people.
One escape from such predicaments could be via inflows of savings from abroad, attracted by the high
marginal returns to capital that one might expect to be available in capital-scarce economies. Contrary to
this, recent work which takes proper account of natural capital suggests that the marginal product of reproducible capital is not high in SSA: this may help to account both for low private capital inflows and
low domestic savings rates (Caselli and Feyrer 2007). If this is correct, then the prospects for stemming
26
capital dilution in sub-Saharan Africa in the coming decades will rest on technological advances raising
the marginal product of capital, unless greater falls in natural increase than currently projected can be
achieved. Other things equal it will be advances in the least capital-intensive sectors (i.e. agriculture)
where the greatest potential benefits are likely to lie, simply because a given investment will be complemented with more extra labour in such sectors. Such advances are not impossible: capital pessimism was
also a feature of debates on Asian economic development in the 1950s and 1960s (e.g. Higgins 1959), and
a series of key technological advances in agriculture (the Green Revolution) helped to prevent its predictions from being realized.
Finally, neither arithmetic nor econometrics determines the future of demographic variables anywhere,
nor the resulting economic gains in Phase 2. We have hinted at relevant policy choices, but a general issue arises. Might mutual causation (e.g. between reduction in fertility and in mortality), and interaction
among determinants of labour-productivity, suggest an efficient route to higher demographic dividend:
complementary policies to cut youth mortality, raise availability of contraception, improve girls’ education and earning prospects, and absorb extra workers productively?
Demographic transition in Sub Saharan Africa
27
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