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Realizing the Demographic Dividend: Is Africa any different?
David E. Bloom
David Canning
Günther Fink
Jocelyn Finlay
Program on the Global Demography of Aging
Harvard University
May 2007
2
Introduction
Because people’s economic behavior varies at different stages of life, changes in a
country’s age structure can have significant effects on its economic performance. Nations
with a high proportion of children are likely to devote a high proportion of resources to their
care, which tends to depress the pace of economic growth. The effects are similar if a large
share of resources is needed by a relatively less productive segment of the elderly. By
contrast, if most of a nation’s population falls within the working ages output per capita will
be high all other factors being equal.
As countries move through the demographic transition from a high fertility and high
mortality to a low fertility and low mortality equilibrium, the size of the working age
population mechanically increases. This can create virtuous cycles of economic growth
commonly referred to as the “demographic dividend”. Bloom, Canning and Sevilla (2003)
explore this concept of the demographic dividend in detail and compare the variation in the
age distribution across countries and regions.
Less developed countries have a large proportion of their population in the younger
age groups as fertility rates are high and life expectancy is low. More developed countries
have lower fertility rates and higher life expectancy and thus a large proportion of their
population is at higher ages. Lee (2003) and Weil (1999) examine the projected demographic
transition and the effect of this transition on economic outcomes respectively. While most
regions around the world are evolving through the demographic transition Africa stands as an
outlier. Fertility rates are high and falling only slightly and life expectancy is actually falling
in some countries due to the impact of HIV/AIDS. Given these trends Bongaarts and Bulatao
3
(1999) have argued that Sub-Saharan African countries are not likely to earn the
demographic dividend. We draw a more differential picture for Sub-Saharan Africa, and
present a positive outlook for those countries with the right set of institutions in place.
The goal of this paper is to test whether the determinants of growth in general, and
the effects of demography in particular, are different in Africa than for the rest of the world.
We show that most Sub-Saharan countries have the potential to reap the benefits of the
demographic dividend, but that solid institutional settings will be imperative for its
realization. Lee, Lee and Mason (2006) along with Bloom et al. (2003) acknowledge the
ineffectiveness of the demographic transition in realizing the demographic dividend when
quality institutions are not in place. We refer to institutions as a general term to include rule
of law, efficiency of the bureaucracy, corruption, political freedom and expropriation risk,
openness (political system, trade barriers, black market premium), freedom of political
representation and freedom of speech. Despite this list of measures that we capture in the
institutions measure, a broader measure would include infrastructure (health care systems,
schooling, roads, transport), and a formal labor market with unions and laws protecting both
employees and employers.
Without the right policy environment, countries will be too slow to adapt to their
changing age structure and, at best, will miss an opportunity to secure high growth. At worst,
where an increase in the working-age population is not matched by increased job
opportunities, they will face costly penalties, such as rising unemployment and perhaps also
higher crime rates and political instability. With no policies in place to provide for rising
numbers of old people, many will face destitution in their final years. Having a larger,
healthier and better-educated workforce will only bear economic fruit if the extra workers
4
can find jobs. Solid institutions that can gain the confidence of the population and markets
alike may help countries to reap the potential benefit created by their demographic
transition.
1
Poor rule of law discourages investment as contracts are not reliably enforceable.
Corruption and inefficient bureaucracy create difficulties and uncertainties in establishing
enterprise or gaining and maintaining employment. The lack of political freedom and high
expropriation risk lead to short sighted behavior and undermine long term investment. Thus,
a poor institutional environment critically affects the potential gains from the demographic
transition.
A healthy degree of flexibility in labor markets is also vital if a country is to
accommodate a burgeoning working-age population. Flexibility means that employers are
able to rapidly expand and contract their businesses, to shift workers from one area of the
business to another, and to raise and lower pay more easily. Flexibility also means a
workforce that is able to adapt its working patterns as the business environment shifts.
Flexibility can be difficult to sell to a workforce, as employers are commonly thought to reap
the benefits while employees bear the costs. However, the provision of adequate safety nets
and generous re-training programs can help persuade workers to become less risk-averse.
Although recent history shows that designing and implementing effective programs along
1
We stress that changes in population age structure do not automatically promote economic
growth. In the absence of policies that successfully facilitate the absorption of considerable numbers of
people into productive employment, large cohorts of working-age people can impede economic growth and
be socially and politically destabilizing, a "demographic penalty" of sorts. In practice, this may well be a
significant factor for some countries in slowing the pace of economic growth. In addition, it is precisely in
the countries that have long had slow economic growth that rapid institutional improvement would be most
difficult and that therefore these countries may be the ones most likely to experience a demographic
penalty.
5
these lines is a challenging task in low- and middle-income countries, the incentives for
proceeding in this direction are substantial. Many wealthy industrial countries have
successful programs that provide a good starting point for thinking both conventionally and
imaginatively along these lines.
The difficulty in changing institutions (both those related to labor flexibility and
many others) must not be underestimated. In the example of labor flexibility, it is likely that
not only will changes be widely resisted but that the substantial incentives for action will be
unlikely to carry the day, since the requisite compensatory measures for the losers in any
such reform would be so costly. Thus, pointing out the importance of institutional change is
quite different from showing whether such change is possible, economically, socially, and
politically. Addressing this matter is worthy of a great deal of further attention but is certainly
beyond the scope of this paper.
Comparing key economic and demographic statistics of the Sub-Saharan African
countries to the rest of the world (ROW) highlights the particularity of the region. Given
these structural differences, it is not clear whether the African countries follow the global
growth dynamics. Sachs and Warner (1997) find that the determinants of economic growth in
Africa are no different from those for the rest of the world. Moreover, they find that the lack
of openness and poor economic policies are at the root of Africa’s dismal economic
performance within the 1965-1990 sample period they use. Since the publication of the Sachs
and Warner (1997) article, many African countries, such as in Mali, South Africa, Niger,
Mozambique, Cameroon, Madagascar, and Uganda, have improved their institutional quality,
and fertility rates in many African countries have begun to fall, potentially initiating the
demographic transition.
6
However, despite some recent decreases, fertility rates in many African countries
remain high relative to the rest of the world. There are various reasons for high fertility. With
limited financial infrastructure in rural areas offering little incentive or means to save,
children are still viewed as insurance against old age. They are also a key source of labor.
Furthermore, and despite medical advances, infectious disease is still widespread, particularly
in rural areas, so cultural norms and policies encouraging high fertility in order to achieve
desired family sizes (such as child fosterage, polygyny and the distribution of land according
to family size) are changing only slowly.
In the last 40 years Africa has faced a series of prolonged and debilitating wars. Wars
not only kill and injure soldiers and civilians alike; they also destroy infrastructure and social
structures, thus destroying the foundation on which the demographic transition can be of
benefit to the economy.
Another aspect of the problems facing Sub-Saharan Africa is the prevalence and
virulence of infectious diseases. Despite some impressive health gains over the last century,
malaria, HIV/AIDS, and TB are just three of the big killers that are not yet successfully
controlled. Malaria and HIV alone currently account for 3-4 million of Sub-Saharan Africa’s
roughly 10 million annual deaths. HIV is particularly prevalent in Sub-Saharan Africa, where
many countries have ten or more people living with HIV for each person who has already
died from the disease. Between 1985 and 1995, more than 4 million Sub-Saharan Africans
died of AIDS. By 2005 it was estimated that fifteen million more deaths had occurred, with
70% of the world’s new infections and 80% of AIDS deaths in Sub-Saharan Africa.
7
Furthermore, in addition to children and the elderly as dependents, many will be
suffering the ravages of HIV in adulthood. Heterosexual sex is the dominant means of
transmission, and the majority of people dying of AIDS are between 20 and 59 years of age.
In other words, it is a disease that particularly hits those who should be economically
productive, and threatens not only health, but also the economic stability and potential of a
country.
Despite the long list of challenges to be faced, our outlook on economic growth in
Sub-Saharan countries over the next 20 years is rather positive. Given our past estimates of
economic growth, current institutional settings and demographers’ population forecasts, we
argue that Ghana, Ivory Coast, Malawi, Mozambique, and Namibia have a very high
potential to profit from the demographic dividend over the next 20 years. Our growth outlook
is also very positive for South Africa and Botswana as current regional leaders in terms of
their institutional quality, even though their prospects for profiting from a demographic
dividend over the next two decades are rather small.
Senegal, Cameroon, Tanzania, Togo and Nigeria are projected to have very strong
growth of the share of the working age population, but still suffer from institutional
deficiencies. Given the importance of institutional quality as a catalyst for converting growth
of the working age share into a demographic dividend, it is hard to tell the degree to which
these countries will be able to gain from the demographic dividend.
In the section that follows we explain the model and data used to identify whether the
determinants of economic growth are different in Sub-Saharan Africa compared to the rest of
the world. Then we decipher the role of institutions coupled with the demographic transition
8
that can lead to a realization of the demographic dividend. We then use projections of the
growth of the working age share to identify which countries stand to benefit from the
demographic dividend. In the last section of the paper we discuss the results and provide
conclusions.
Model, Data and Empirical Results
Our empirical strategy consists of three parts. In the first part of the empirical section,
we estimate the basic relation between demography and economic growth. Denoting income
by Y and the total population by P we can express output per capita as
YYWA
PWAP
= (0.1)
where
WA
is the number of working age people. Taking logs
log,log,log
YYWA
yzw
PWAP
=== (0.2)
we can express the steady state level of income per capita as
**,
yzwxw
β
=+=+
(0.3)
where
x
is the matrix containing the variables determining steady state income per working
age person. Following Barro and Sala-i-Martin (2003) economic growth occurs as each
country converges from its initial position to its steady state. In our case, this is conditional
on the variables
x
and
w
, so that growth in every period is given by:
11
(*)()
yyyxwy
λλβ
−−
∆=−=+− (0.4)
9
The steady state determines the end of period equilibrium of the economy and can
change during the growth period considered. Let us suppose that we can write a structural
model as:
11231213
,
xxwywxwy
αααγγγ
−−
=++=++ (0.5)
Then we can derive the reduced form
1112131
(*)
yyyxwy
λδδδ
−−−−
∆=−=++ (0.6)
where the reduced form coefficients
δ
are combinations of the structural coefficients
from equations (0.7) and (0.8). Taking five year growth rates as our dependent variable we
first estimate a standard growth model as described in equation (0.9) for the full sample of
countries. In a second step, we divide our sample into Sub-Saharan and non Sub-Saharan
countries, and test whether there are significant differences in the estimated coefficients.
In the second part of the empirical section, we use the estimated coefficients in
combination with population forecasts from the United Nations to gauge the magnitude of the
demographic dividend in Sub-Saharan countries over the coming 20 years.
The Data
We use a five year panel covering the years 1960 to 2000. We limit our data set to
those countries where all explanatory variables are available, which leaves us with a sample
of 85 countries, out of which 19 are located in the Sub-Saharan zone
2
. We then use our
estimates for out-of-sample economic growth projections using an extended set of countries.
2
For a full list of countries, please see appendix.
10
Data on national income are from the Penn World Tables mark 6.2, data on working
age share from the United Nations’ World Population Prospects 2004. We add additional
variables from different data sources: schooling data are from Barro and Lee (2000), life
expectancy from the World Development Indicators (World Bank 2006), ethnic
fractionalization data from Alesina, Devleeschauwer, Kurlat and Wacziarg (2003), and
institutional quality data from Knack and Keefer (1995), Sachs and Warner (1997), Wacziarg
and Welsh (2003) and the World Bank (2007)
In Table 1, we provide separate descriptive statistics for Sub-Saharan countries and
the rest of our sample (which we refer to as rest of the world or ROW). Pronounced
differences are visible across all dimensions.
The average income per capita in Sub-Saharan countries over the sample period was
US$ 1,850, and the average annual growth rate a bleak 0.5% per year. Comparing this to the
average income of US$ 9,393 and an average growth rate of 2.3% for the rest of the world
3
portrays a rather bleak picture of the average Sub-Saharan economic development over the
last decades. The differences between Sub-Saharan countries and the rest of our sample are
visible across all explanatory variables typically applied in growth regressions: on average,
Sub-Saharan countries display significantly lower levels of schooling and life expectancy,
have poorer institutions, higher degrees of ethnic fractionalization, a lower degree of
openness, and have a higher chance of being land-locked and in a tropical zone than the
average non Sub-Saharan country in our sample.
Table 1: Descriptive Statistics Sub-Samples
3
Note that the economic growth figures are five year averages.
11
Sub-Saharan Countries Non-Sub-Saharan
Countries
Variable Obs. Mean
Std. Dev. Obs. Mean
Std. Dev
Year 124
1985
10.6
486
1983
11.2
GDP per capita 124
1850
1760
486
9393
7348
5 year growth in GDP per
capita
124
.026
.229
486
.116
.136
Openness 124
.209
.409
486
.591
.492
Institutions 124
19.47
4.60
486
26.25
7.62
Ethnic Fractionalization 124
.728
.141
486
.336
.224
Landlocked 124
.395
.491
486
.078
.269
Tropical 124
.916
.242
486
.388
.460
Average years of schooling 124
2.29
1.55
486
5.72
2.76
Life expectancy 124
48.74
7.15
486
68.41
7.91
Working Age Share 124
51.63
2.71
486
59.67
6.14
Growth in Working Age
share (5 year)
124
.001
.019
486
.014
.024
Most importantly for the purpose of this paper, the average working age share of the
total population is 8 percentage points lower in Sub-Saharan countries than in the rest of the
world, with the average growth rate of the being close to zero over the sample period.
Part I: Estimating the Demographic Dividend
We start by estimating the basic growth model outlined in section 2. To capture
different steady states across countries, we introduce an extended set of control variables,
including, institutional quality, ethnic fractionalization, schooling, life expectancy and two
dummy variables that indicate whether a country lies in a tropical zone and is landlocked.
12
The main variables of interest are the log of the size of the working age population (WAS) as
well as its growth.
The main results are summarized in Table 2 below. In column 1, we estimate our
basic model with OLS. Out of the set of control variables our measures of institutional
quality, life expectancy and ethnic fractionalization appear to have the most significant effect
on growth. The lagged level of income enters with a negative and highly significant
coefficient, consistent with our basic convergence assumption. As to the demographic
variables, our results strongly confirm our priors: both the level of the working age share and
its growth enter the growth equation with a positive and highly significant sign.
The negative and non-significant effect of schooling is orthogonal to our prior. We
try alternative specifications where we interact the schooling variable with institutional
quality and growth of the working age share - without different results. One possible
explanation for the insignificance of schooling in our empirical work may be measurement
error in the schooling data. Another explanation may lie in the high degree of collinearity in
the data; the correlation between initial income, initial life expectancy and institutional
quality are all larger than 0.75.
To control for the interdependence of the growth in working age share and economic
growth, we use instrument for the growth in working age share in columns 2 and 3 of the
Table. The instrument we use is lagged growth in WAS, which works well as a predictor of
WAS growth due to the slow moving character of the variable, and can be considered
predetermined in the main equation. As shown in column 2 of Table 2, the IV estimates on
the growth in working age share are significantly larger than the ones obtained by OLS. This
13
could reflect measurement error, or may be interpreted as evidence for a negative causal
effect from economic growth to the growth in WAS.
In column 3, we test whether the positive growth effects of the demographic dividend
are conditional on the institutional quality of a country as suggested by Bloom and Canning
(2003); our results strongly confirm this hypothesis. Interacting the growth in working age
population with institutional quality, we find the interaction term to have a positive and
highly significant effect, while the growth in working age share itself is no longer significant.
This is an important result, as it implies that only countries featuring high institutional quality
are able to receive a demographic dividend. The estimated size of the institutional effect on
growth is large. Taking a non Sub-Saharan country with an average growth of working age
population (0.014), a two standard deviation increase in institutional quality (15.2) increases
economic growth by about 2% per year. Three quarters of this effect is due to the direct effect
of institutions on growth; one quarter, or half a percent growth per year, is due to the
interaction of institutions with the growth in the working age share.
Given the strong differences between Sub-Sahara and ROW, one may question the
applicability of this basic result to the Sub-Saharan region, and argue that Sub-Saharan
countries follow different patters. To test this hypothesis, we separately estimate the model
specified in column 3 for the two sub samples, and test whether the estimated coefficients
differ significantly between the two sub samples. We perform both Wald tests for each single
coefficient being the same across sample, and a Chow test for all coefficients being the same
across the two samples. The results of this test are displayed in the last column of Table 2.
According to our analysis, the coefficient on initial GDP per capita is the only one
significantly different across samples. The absolute value of the coefficient on initial GDP is
14
higher in Sub-Saharan Africa than the rest of the world, implying more rapid income
convergence in this region. For all other variables, the null hypothesis of equal coefficient
can not be rejected at standard confidence intervals.
15
Table 2: Economic Growth and the Demographic Dividend
Dependent Variable: 5 Year Economic Growth
Rate
Wald Test
1)
(1) (2) (3)
Openness 0.036** 0.026 0.018 1.43
(2.37) (1.52) (1.00) (0.23)
Institutions 0.006*** 0.007*** 0.005*** 0.02
(3.36) (3.38) (2.61) (0.88)
Ethnic Fractionalization -0.064*** -0.068*** -0.080*** 1.55
(2.64) (2.66) (3.02) (0.21)
Land Locked Country 0.005 0.016 0.013 0.66
(0.28) (0.88) (0.72) (0.42)
Tropical Country -0.007 -0.006 -0.005 2.38
(0.37) (0.28) (0.28) (0.12)
Avg. Years of Schooling -0.007 -0.007 -0.008 0.05
(1.61) (1.57) (1.62) 0.82
Life Expectancy 0.007*** 0.006*** 0.007*** 2.00
(4.82) (3.79) (4.00) (0.16)
Initial GDP -0.117*** -0.128*** -0.130*** 3.21*
(5.29) (5.44) (5.51) (0.07)
Log(Working Age Share) 0.378*** 0.540*** 0.505*** 0.19
(3.59) (4.64) (4.23) (0.67)
Growth in WAS 0.804*** 1.538*** -2.002 0.43
(2.94) (3.20) (1.02) (0.51)
Growth in WAS *Institutions 0.139** 0.40
(1.98) (0.53)
Constant -0.919** -1.437*** -1.299*** 0.00
(2.30) (3.30) (2.88) (0.96)
Time Fixed Effects Yes Yes Yes Chow Test:
Country Fixed Effects No No No 49.85***
Estimation OLS 2SLS 2SLS (0.00)
Sample Full Sample Full Sample Full Sample Full Sample
Observations 610 554 554 554
R-squared 0.32 0.37 0.37 0.37
Notes:
Robust t statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
1) Wald test H0: Coefficient identical for Sub-Saharan sub sample and non Sub-Saharan sample. We
report the chi
2
statistics, with p-values in parenthesis.
16
Part II: Implications
Two main results emerge from our empirical analysis: First, the effects of
demographic change on economic growth are sizeable, but contingent on good institutions.
Second, the estimated relation between demographic variables and economic growth appears
to be constant across samples, and thus to apply to Sub-Saharan countries as much as to any
other country in the world. A closer look at the Sub-Saharan countries in our sample confirms
this finding. As shown in Figure 1, the relationship between demographic developments and
economic growth over the last 20 years appears quite robust. With the exception of Uganda
and Mali, which showed high growth rates despite negative growth in the working age share
due to excellent policy reforms, all countries with positive economic growth rates have also
seen positive growth rates in their working age population.
Figure 1: Growth in Working Age Share and Economic Growth 1980 - 2000
Botswana
Cameroon
Congo, Dem. Rep.
Congo, Rep.
Gambia, The
Ghana
Kenya
Liberia
Malawi
Mali
Mozambique
Niger
Senegal
Sierra Leone
South Africa
Togo
Uganda
Zambia
Zimbabwe
-.3 -.2 -.1 0 .1 .2
Average Annual Economic Growth 1980-2000 (%)
-.01 0 .01 .02 .03 .04
Average Annual Growth in Working Age Share 1980 - 2000 (%)
17
What are the implications for the growth prospects in Sub-Saharan countries? Given
our results, the answer to this question depends on two country specific factors: future
developments in population structure and institutional quality. Institutional quality is hard to
define, and even harder to measure; to provide the most complete measure possible, we
display four different measures of institutional quality in Table 3 below. Column 1 shows the
average combined score each country reached in the International Country Risk Guide over
the period 1982-1997 (Knack and Keefer 1995). The total score is the aggregation of the
scores of five different subcategories: rule of law, efficiency of the bureaucracy, government
stability, corruption and expropriation risk. Column 2 shows the institutional quality measure
as determined by the Polity IV project. The score ranges from -10 (worst) to +10 (best).
Column 3 shows the position of each country in the World Bank’s recent business
environment ranking (World Bank 2007). The ranking is based on business surveys, and
measures the general difficulty of doing business in a country. Column 4 shows the latest
version of the openness variable originally introduced by Sachs and Warner (1997), and
updated by Wacziarg and Welsh (2003). A country is considered closed if average tariff rates
are above 40%, non-tariff barriers cover 40% or more of trade, black market exchange rates
depreciated by 20% or more relative to the official exchange, if the country has a state
monopoly on major exports, or the country has a socialist economic system. Although there
is some variation in the absolute rankings, the relative position of countries looks relatively
similar across the categories. South Africa, Namibia and Botswana appear to have the best
institutions independent of the measure applied, while Sudan and the Republic of the Congo
get the worst evaluations.
18
Table 3: Institutional Development: Sub-Saharan Countries
Country
ICRG Score
(1982-1997,
Avg.)
Polity
1)
Score
Business
Environment
Rank
2)
Sachs-Warner
Openness
3)
Average Ranking
Within Sub-
Saharan Group
4)
South Africa 28.57 9 29 1 1.25
Botswana 27.21 9 48 1 2
Namibia 23.8 6 42 - 7.75
Cote d'Ivoire 23.86 4 141 1 8
Mozambique 21.64 6 140 1 8
Ghana 19.98 2 94 1 8.25
Kenya 22.28 -2 83 1 8.75
Madagascar 17.65 7 149 1 11.25
Malawi 20.47 7 110 0 11.5
Gambia, The 23.08 -5 132 1 12.5
Niger 19.96 4 160 1 13.25
Gabon 22.14 -4 31 0 13.75
Senegal 19.18 8 146 0 14
Cameroon 21.87 -4 152 1 14.25
Ethiopia 17.84 1 97 0 14.75
Mali 12.4 6 155 1 14.75
Tanzania 21.27 2 142 0 14.75
Nigeria 16.11 4 108 0 15.5
Uganda 14.99 -4 107 1 15.5
Guinea 19.54 -1 157 1 15.75
Zambia 17.21 1 102 0 15.75
Guinea-Bissau 13.62 5 173 1 17
Burkina Faso 19.22 -3 163 1 17.5
Liberia 8.98 0 - 0 19.75
Somalia 12.06 0 - 0 19.75
Togo 17.67 -2 151 0 20
Zimbabwe 20.57 -5 153 0 20
Angola 17.75 -3 156 0 21.5
Sierra Leone 16.05 0 168 0 23
Congo, Dem. Rep. 9.84 0 175 0 24.75
Sudan 11.96 -7 154 - 25.25
Congo, Rep. 16.99 -6 171 0 25.5
Non-Sub-Saharan 25.39 3.88 80 0.78 -
United States 36.25 10
3 1 -
Notes:
1) Source: The Polity IV Project, http://www.cidcm.umd.edu/polity/. Reference year is 2000.
2) The World Bank Business Environment database. http://www.doingbusiness.org/. Reference year is 2005.
3) Source: Wacziarg and Horn Welsh (2003). Openness measure reflect the average score 1990 to 1999.
4.) Average ranking in columns 2-5.
19
To get a simple measure of a country’s overall evaluation, we show the average
ranking (among Sub-Saharan countries in our sample) in the four categories in column 5.
Table 4: Forecasted Growth in the Working Age Population; Institutional Ranking
Country
Working-age
Share 2000
1)
Average Annual
WAS Growth 2005-
2015
2)
Average Annual
WAS Growth 2005-
2025
2)
Average
Institutional
Ranking
3)
Senegal 52.19 0.64 0.68 14
Cameroon 53.54 0.66 0.63 14.25
Tanzania 52.94 0.59 0.62 14.75
Togo 52.39 0.55 0.62 20
Nigeria 51.76 0.52 0.59 15.5
Madagascar 52.17 0.58 0.57 11.25
Cote d'Ivoire 53.72 0.57 0.56 8
Gabon 53.51 0.77 0.54 13.75
Gambia, The 55.34 0.46 0.52 12.5
Sudan 56.18 0.51 0.52 25.25
Ghana 55.37 0.54 0.51 8.25
Namibia 53.09 1.06 0.51 7.75
Ethiopia 51.62 0.48 0.50 14.75
Malawi 50.81 0.45 0.46 11.5
Zambia 50.67 0.38 0.46 15.75
Mozambique 52.61 0.40 0.45 8
Mali 48.8 0.35 0.44 14.75
Burkina Faso 48.65 0.33 0.43 17.5
Guinea 52.66 0.26 0.41 15.75
Kenya 53.1 0.04 0.39 8.75
Somalia 53.38 0.19 0.36 19.75
Zimbabwe 54.09 0.50 0.35 20
Niger 49.02 0.22 0.27 13.25
Angola 50.52 0.19 0.26 21.5
Uganda 47.08 -0.03 0.20 15.5
Congo, Rep. 50.39 -0.02 0.18 25.5
Congo, Dem. Rep. 50.23 -0.12 0.15 24.75
Guinea-Bissau 50.07 -0.04 0.14 17
Sierra Leone 53.95 0.00 0.14 23
Liberia 50.86 -0.15 0.07 19.75
Botswana 58.13 0.25 0.05 2
South Africa 62.84 0.09 0.02 1.25
US 66.10 -0.11 -0.25
Non-Subsaharan 63.37 0.24 0.07
Notes:
1) Fraction of the population in the age group 15-64. World Population Prospects 2004.
2) Forecast from World Populations Prospects 2005, medium scenario.
3) Average ranking within Sub-Saharan countries, see previous table.
20
The lower the aggregate score, the better the overall evaluation of the country’s
institutions, and the more positive is thus the growth outlook for the given country. Cohort
specific population growth rates are calculated on a regular basis by the United Nations
and published in the World Population Prospects (2004). Table 4 summarizes the
forecasts for the intermediate growth scenario. Column 2 of Table 4 shows the current
share of the working age population, while columns 3 and 4 show the expected growth in
working age population share over the period 2005-2015 and 2005-2025, respectively. The
countries with the highest expected growth in working age share are Senegal, Cameroon,
Tanzania; the countries with the lowest rates are Botswana and South Africa, whose working
age population is expected to grow only very moderately over the next 20 years. In the last
column, we show the aggregate institutional score. Quite strikingly, the five countries with
the highest expected rate of growth of working-age share are all characterized by medium to
low quality institutions. The countries that do relatively well on both dimensions are
Madagascar, Cote d’Ivoire, Ghana and Namibia, which are thus most likely to earn the
demographic dividend.
Discussion and Conclusion
The descriptive statistics in Table 1 illustrate the dismal economic, social and
geographic predicament of Africa compared to the rest of the world. Current GDP per capita
amounts to one fifth of that in ROW, average years of schooling are at 40% of ROW levels,
and average life expectancy is 20 years lower. Moreover, the average institutional quality in
Africa lags significantly behind the average in ROW. Given this pronounced la g one might
argue that Africa is simply different from ROW and claim that the factors determining
21
economic development in Africa diverge from those of ROW. We find no evidence for this
claim. Our results imply that the standard economic growth model equally applies to Sub-
Saharan countries as it does to other regions in the world in general and with respect to the
demographic dividend in particular.
As discussed in the introduction, the demographic transition from high fertility and
low life expectancy to low fertility and high life expectancy does not guarantee a
demographic dividend. A stable and transparent political and economic environment is
required for individuals in the working-age population to be productive. Efficiency losses due
to poor institutional quality will outweigh any gains that a high proportion of working-age
population can bring. Our results show that the demographic transition does have an effect on
economic growth, but only when coupled with institutional quality.
From a demographic perspective, the prospects for earning the demographic dividend
and spur economic growth look good. As shown in Table 4, the United Nations predict
significant increases in the working age share for close to all countries in the Sub-Saharan
region. Africa stands on the cusp of the demographic transition, but good institutions will be
needed to earn the demographic dividend. As of today, our results make us most optimistic
for Ghana, Ivory Coast, Malawi, Mozambique and Namibia , who have done relatively well
on the institutional side and significant increases in working age share coming up over the
next 20 years. Cameroon, Nigeria , Senegal, Tanzania and Togo have the greatest potential for
increased economic growth from a demographic point of view, but will likely have to
significantly improve their institutional framework to fully reap the demographic dividend.
22
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23
Appendix
Sub-Saharan Sample Country List
Botswana, Cameroon, Congo(Dem. Rep.), Congo (Rep.), Gambia, Ghana, Kenya, Liberia,
Malawi, Mali, Mozambique, Niger, Senegal, Sierra Leone, South Africa, Togo, Uganda,
Zambia, Zimbabwe.
Non-Sub-Saharan Sample Country List
Algeria, Argentina, Australia, Austria, Bangladesh, Belgium, Bolivia, Brazil, Canada, Chile,
China, Colombia, Costa Rica, Cyprus, Denmark, Dominican Republic, Ecuador, Egypt (Arab
Rep.), El Salvador, Finland, France, Greece, Guatemala, Haiti, Honduras, Hungary, Iceland,
India, Indonesia, Iran, Islamic Rep., Ireland, Israel, Italy, Jamaica, Japan, Jordan, Korea,
Rep., Malaysia, Malta, Mexico, Netherlands, New Zealand, Nicaragua, Norway, Pakistan,
Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Singapore,
Spain, Sri Lanka, Sweden, Switzerland, Syrian Arab Republic, Thailand, Trinidad and
Tobago, Tunisia, Turkey, United Kingdom, United States, Uruguay, Venezuela.
