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WORKING PAPER SERIES
ECONOMIC DEVELOPMENT AND POPULATION GROWTH:
AN INVERTED-U SHAPED CURVE?
Vittorio Valli and Donatella Saccone
Dipartimento di Economia “S. Cognetti de Martiis”
Working paper No. 05/2011
Università di Torino
Economic development and population growth: an inverted-U
Vittorio Valli, Donatella Saccone
Department of Economics “Cognetti de Martiis”
University of Torino
There has been a large debate on the relations between demography and economic development.
Our paper discusses the possibility that there exists an inverted-U curve, similar in shape to Kuznets’s
curve, between the growth rate of population and the growth rate of the per-capita GDP. The cross-
country empirical analysis, carried out on over 90 countries in the period 1980-2010, seems to confirm
the existence of this kind of curve. The main reasons behind this phenomenon are discussed. First, it is
difficult to sustain a high economic growth either with a low (lower than 0.5%) or high (higher than 2-
2.5%) growth rate of population. In the first case, an excessive ageing of population causes the well-
known negative consequences. In the second case, the possibilities of large households of providing
children with adequate nourishment, education and health are reduced. Moreover, without a perfect
capital market, it is difficult to promote new firms and innovation unless adequate personal or family
resources are available.
The aim of this paper is to revisit the relation between population growth and economic development
starting from the observation that in the period 1980-2010, using cross-country data, the relation seems
to exhibit an inverted-U shaped form. This form, similar to the one utilized in various versions of
, led us to assume that several low-income countries, which usually have a high rate of
growth of population, have encountered great problems breaking the vicious circles of poverty and
establishing a good pattern of economic growth. On the other hand in several mature countries,
where population growth is close to zero or even negative, economic development tends to be very
In this paper we try to discuss the main determinants of these complex relationships and to wonder
whether there is an intermediate zone in which the rate of growth of population is likely to be
associated with a higher rate of economic growth.
2. Demography and economic growth
Francesco Botero in his Delle cause della grandezza delle città (1558 ) was probably the first major author
systematically treating of the limits of population growth in urban areas and the economy. However
his contribution was almost completely overlooked in the prevailing economic literature
analyses on the relations between population growth and economic development start from the seminal
contribution of Malthus. Malthus is generally considered to have introduced malthusianism, an utterly
pessimistic view on the relationship between population growth and economic development. However,
while in 1798 ‘s edition of his “Essay on population”
his conclusions were indeed very pessimistic, in
his 1803 enlarged edition of the “Essay” they were somewhat softer and in his 1820 “Principles of
Political Economy” Malthus held a much more optimistic view
.In his 1798 contribution Malthus
maintained that resources are limited and land has diminishing returns so that an increase of income
will determine a rise in population, which will lead to starvation, deaths and population decline until a
new equilibrium will be restored. In the “Principles” Malthus recognized instead the importance of
technical progress in agriculture and of fertility restraint. He also introduced, in the short run, the
possibility of increasing in some occasions effective demand thereby rising overall production,
anticipating for some aspects the analysis of Keynes.
Malthusianism was more or less influential for over a century, but many authors criticized several parts
of Malthus’ arguments in the first edition of the “Essay”. In particular his assertion that population will
grow at a geometric rate while food will grow at an arithmetic rate was found empirically wrong.
Technical progress in agricultural and industrial production, some restraints in fertility and, in some
occasions, emigration to America or Australia made possible the persistence of a delicate balance
between population rise and economic growth even in a supposedly overcrowded Europe.
Moreover, the history of industrialized countries in the XIX and XX century revealed that beyond a
certain level of development, health and hygiene progress, industrialization, urbanization, the rise of
The original Kuznets curve associates income inequality and per capita income and the
“environmental Kuznets curve”, associates some pollution indicator and per capita income.
Notice that our inverted U shaped curve differs from Kuznets curves both for the variables used and
for the positions on the axes.
One notable exception is Perlman (1975), p. 248, in his contribution to a Symposium on Population
in the “Quarterly Journal of Economics”.
3 See Malthus (1798).
On the two Malthus, the malthusian author of the“Essay” and the “economist” of the“ Principles”,
see for example, Spengler (1957), Paglin (1964), Barucci (1972), pp XXVI-XXVII.
education and in particular of female education, the increase in the cost of rising children and in some
case, change in values and family planning policies tend to determine a ”demographic transition”,
namely a passage from high birth and death rates to low birth and death rates, that finally can lead to a
progressive decline in population growth
. As graph 1 and table 1 and 2 show, there has been in the
long run a sharp acceleration of the rate of growth of world population and then a gradual decline, at
first in industrialized countries and more recently in several emerging and developing countries.
While in Malthus approach population was endogenous in his economic development view, most
growth studies of the 1930s and the 1940s, as Harrod’s and Domar’s models assumed population as an
exogenous variable. Leibenstein tried to show the risks of this approach, emphasizing the complex
interrelationships between economic development and population trends
. He argued that poor
countries had to make a “critical minimum effort” in order to be able to break the vicious circles of
poverty and to reach a sustainable rate of economic growth capable of reducing the motivations to
have large families typical of most poor subsistence economies.
Figure 1. Annual rate of growth of world population and world per-capita GDP: 1950- 2008.
Source: Maddison (2010).
See, for example, Thompson (1929), Notenstein (1945), United Nations (1953). A recent contribution
(Myrskyla, Kohler and Bollari, 2009) has introduced the possibility that beyond a certain very high level
of the human development index (HDI) the decreasing trend of the fertility rate will reverse.
See Leibenstein (1954), (1957), chapters 8 and 10, and (1975).
Table 1. Average annual rates of growth of population in main countries and regions: 1913-2030
Areas or countries 1913-50 1950-73 1973-2003 2003-2030*
World 0.93 1.93 1.59 0.98
Western Europe 0.42 0.71 0.32 0.05
US 1.21 1.45 1.06 0.84
Eastern Europe 0.26 1.01 0.32 - 0.21
Former USSR 0.38 1.44 0.47 0.27
Latin America 1.96 2.72 1.90 0.97
Japan 1.32 1.14 0.53 -0.33
China 0.61 2.10 1.27 0.46
India 0.45 2.11 2.00 1.12
Total Asia (escl. Giappone) 0.92 2.19 1.76 0.95
Africa 1.65 2.36 2.64 1.98
* Forecasts. Source: Maddison (2007), pp. 377 and 336.
Table 2. Rates of growth of per capita GDP in main countries and regions: 1950-2030
Areas or countries 1913-50 1950-73 1973-2003 2003-2030*
World 0.88 2.91 1.56 2.2
Western Europe 0.76 4.05 1.87 1.7
US 1.61 2.45 1.86 1.7
Eastern Europe 0.60 3.81 0.87 2.0
Former USSR 1.76 3.35 - 0.38
Latin America 1.41 2.60 0.83 1.5
Japan 0.88 8.06 2.08 1.3
China -0.56 2.76 5.99 4.5
India -0,22 1.40 3.14 4.5
Total Asia (excl. Japan) -0.08 2.87 3.88
Africa 0.91 2.02 0.32 1.0
* Forecasts. Source: Maddison (2007), pp. 382 and 337.
However, in the late 1950s several scholarly studies such as an influential book by Coale and Hoover
re-introduced the possible existence of a negative relationship between population growth and
economic development in low-income countries. Very large families, as the ones prevailing in India in
those years, would lead to lower national saving and investment rates. Moreover the higher
expenditure on education and health required by the rapidly growing population would reduce the
financial resources available for productive investment. While the book had an important impact on
academic debate and policies, it was not confirmed by several empirical studies. Moreover, it badly
overlooked the importance of human capital and technical progress on economic development
the new growth theories came to emphasize.
See Coale, Hoover (1958)
See for these critical remarks Kelley ( 2001), p. 5. See also for other good surveys Kelley (1988),
Kelley, Schmidt (2005), Lee (2009).
In the 1960s also influential popular pamphlets, such as Paul and Anne Ehrlich’s book “Population
bomb” (1968), were supporting neo- malthusian pessimistic views about the consequences of the rapid
world population growth. Not only such a fast growth of population, mainly due to the substantial
reduction of mortality in developing countries, could lead to starvation and death for large masses of
population, but also it could contribute to badly deteriorate the world environment and to exhaust
several limited natural resources. Here again academic debate and history revealed that most of authors’
forecasts were wrong and their worries about a global lack of food were largely exaggerated, although
severe damages on environment have become more and more evident.
Also the celebrated, and widely discussed, Club di Roma contribution of MIT scientists “Limits to
growth” (1972) held a pessimistic view on the relations between high population growth, limited
natural resources, and the economic growth pattern prevailing in the world, but several economists
criticized the rather rigid assumptions of the MIT model, although many authors acknowledged the
existence of severe and growing risks for global environment.
As Figure 1 and table 1 show, in the second part of the XX° Century and in the first decade of this
Century there has indeed been an explosive growth of world population, but there has also been, a
substantial acceleration of economic growth and, since 1972, a gradual, but steady decline of the rate
of growth of world population. Economic growth was also accompanied by a certain increase in per
capita availability of food. It is, however, true that even now large masses of population, mainly
concentrated in developing countries are under-nourished and suffer of hunger and severe deprivation
of basic needs, because food and the benefits of growth have been unevenly spread between countries
and among families and regions.
As the academic debate is concerned, in the 1970s some influential authors, such as Simon Kuznets
(1973), Boserup ( 1976) and Perlman (1975) raised some doubts on the theoretical and empirical bases
of the pessimistic view. While the (US) Commission on Population Growth and the American Future
(1972-3) held in its conclusions the traditional pessimistic anti-natalistic and anti-migration view, a UN
1973 report concluded in a much less pessimistic way, stating that the rise of population led to both
negative and positive effects and that some price and institutional feedbacks might partly compensate
the impact of negative effects.
A stronger reaction to the pessimistic view came through the contributions by Boserup (1965),
(1976), (1981) and Simon (1981). The former emphasized the fact the many technological advances in
agriculture were made when population pressure determined high land densities. As Kelley has noted
“Simon extended this notion to observe that major social overhead projects (roads, communication,
irrigation) benefitted from expanded population and scale”
. Moreover he illustrated the importance of
population pressures on various forms of long –run “feedbacks”, as price induced substitutions in the
use of natural resources, as well as the role of density and size of population. Simon concluded that
population growth could have a net positive effect on economic growth in developing countries. His
optimistic view heavily influenced the economic debate in the 1980s. Many contributions led to a more
balanced view if compared with the traditional pessimistic one
. Although it was maintained that
slower population growth might contribute to economic growth of many developing countries, its
quantitative impact was considered weak and much attention was given to country-specific factors.
From an empirical point of view, the Nineties marked a turning point in the econometric techniques
and specifications used to investigate the relationship between demography and economic growth. In
particular, it reflected the necessity to explain the diverging results of empirical analyses carried out over
See Kelley (2001), p. 10
See National Research Council (1986), Birdsall (1988), Kelley (1988), Srinivasan (1988).
the previous 30 years. While in the 1960s and 1970s the correlation between population growth and
per-capita GDP growth was found weak and statistically insignificant, in the 1980s the empirical
evidence showed a negative relation between the two variables (see Kelley 2000 and Lee 2009). As a
consequence, in the 1990s the empirical efforts aimed at better formulating the theoretical framework
and going beyond the simple correlation between population growth and economic growth.
Moreover, the world was rapidly changing. As pointed out in the Report of the UN International
Conference on Population and Development (ICPD), held in Cairo in 1994, “the decline in fertility
levels, reinforced by continued declines in mortality levels, is producing fundamental changes in the age
structure of the population of most societies. […] The majority of the world’s countries are converging
towards a pattern of low birth and death rates, but since those countries are proceeding at different
speeds, the emerging picture is that of a world facing increasingly diverse demographic situations” (UN
1995, pp. 32 and 53). However, “despite recent declines in birth rates in many countries, further large
increases in population size are inevitable” (UN 1995, p.15). One of the core issue emerging from the
UN ICPD was the awareness that what determines the demographic effect on development and
economic growth is the structure rather than the growth rate of population, recognizing that each
country should integrate demographic issues into economic and development strategies according to
its population composition.
These academic and historical changes gave rise to two main directions of research, often
. First, the new convergence models inspired by Barro’s pioneering work (Barro 1997)
took into account short and long run impact of demography on economic growth and the possibility of
reversing effects. Second, the total population growth rate was split and decomposed into fertil
mortality components or into different age-cohorts (Barro and Lee 1994; Barlow 1994; Brander and
Dowrick 1994; Kelley and Schmidt 1995 and 2005, Barro 1997; Bloom and Williamson 1998;
Azomahou and Mishra 2008). Barro (1997) found a long-run positive effect of a decreasing fertility
rate, while Kelley and Schmidt (1995) provided evidence for reversing effects of birth-rate reduc
promoting economic growth in the short run and affecting it in the long run. From then on, the mai
idea has been that what affects economic growth is the change in the working-age population rather
than the total population growth. In other words, what matters is the evolution in the age compos
In general, what emerged from these studies is that fertility rate has a negative and significant impact on
economic growth in the short period. This acts by increasing the share of unproductive population.
However, in the long period a greater share of population will enter the productive working force,
fostering economic growth. Countries in which working-age population is swelling could indeed benefit
from the so-called “demographic dividend”, i.e. the increase in the added productivity leading by the
maturing of formerly young population (Bloom, Canning and Sevilla 2001).
An important theorization of this perspective was provided by Bloom and Williamson (1998) and
further developed by Bloom, Canning and Sevilla (2001), Bloom, Canning and Finlay (2008) and
Cervellati and Sunde (2009). These studies modeled the impact of the working-age population growth
and individuated three channels by which age-structured population and the relative economic behavior
can affect economic growth. The first relies on the endowment of labor inputs per-person, defined as
the working hours per-capita and depending on the working-age population share. The second is based
on different saving behavior across age cohorts: the young and the elderly are used to save less, while
people aged between 40 and 65 save more. Third, investment in human capital change along with the
life expectancy. When life expectancy improves, educational investment should increase and then the
labor force productivity. Bloom and Williamson (1998) empirically tested and confirmed the
Along with these macro-studies, it is worth mentioning an important series of micro-analyses aiming
to explain family behavior in terms of fertility (see Lee 2009 for a review) .
theoretical hypothesis according to which age distribution rather than overall population growth affects
economic growth. A full demographic transition could require more than 50 years and is characterized
by three phases. At first, young cohorts swell and decelerate economic growth; then, after around 20
years, these cohorts become active and productive, promoting economic growth; finally, they become
elderly and their dependency burden limits economic growth again.
Recently, this logical framework was further tested by Choudhry and Elhorst (2010). Using data from
70 countries over the period 1961-2003 and alternative econometric specifications, authors regressed
the GDP per-capita growth rate on three demographical variables: the growth differential between
working-age and total population, the child dependency ratio and the old-dependency ratio. Results
confirm that the increasing relative importance of working-age population has a positive impact on
economic growth, while a high child dependency ratio hinders the growth rate. Conversely, the effect
of the old-cohorts is ambiguous and not significant, confirming the findings of Bloom et al. (2008).
However, these results could reflect the composition of the sample, under-representing countries that
have completed the demographic transition. This suggest that empirical and theoretical analyses should
deeply investigate the effects of demographic variables by disjointing factors differently acting in young
and mature countries.
Our contribution is twofold. On the one hand, we explore the possibility of quadratic effects. The
hypothesis we test is that growth rates are negatively affected both by low and high growth rates of
population. In other words, we aim to understand if there exists a range of population growth favorable
to the economic growth. On the other hand, we discuss the effects of population growth dividing
countries in three categories: young poor, vibrant emerging and ageing mature countries.
3. An empirical assessment: does there exist an inverted-U shaped curve?
Our cross-country analysis covers a sample of 93 countries over the period 1980-2010
. We regress the
average annual growth rate of per-capita GDP for the period 1980-2010 on five main variables: the
average annual growth rate of population for the same period and its squared, the average young-age-
dependency ratio and the average young-old-dependency ratio over the analyzed period and the average
annual growth rate of educational attainment. The square of the average annual growth rate of
population is introduced to take into account the possibility of non-linear effects. To test the effects of
age-cohorts depending on the working-age population, we use the total-age-dependency ratio and,
alternatively, we split it into its young and old components. The annual growth rate of educational
attainment is included to typify two potential effects. First, as well known, it could have a direct effect
on the per-capita GDP growth as a proxy for human capital. Second, it is a proxy for the average age of
entrance in the labor market. Indeed, the more young people study, the later they enter the work-force
unless they work and study at the same time. Actually, this effect would be better captured by the
activity and employment rates by age-cohorts. However, this is unfeasible because of the lack of reliable
cross-country data for the full set of countries. Data on per-capita GDP and population are based on
the Total Economy Database provided by Groningen Growth and Development Centre (GGDC),
while the age-dependency ratios are taken from the World Development Indicators (WB). Educational
attainment data are from the Barro-Lee dataset (2010) and refer to population aged 15 and over.
Results are shown in table 3 and validate our hypothesis. The average annual growth rate of population
and its squared are significant and of opposite sign, the former positive and the latter negative. This
suggests that countries with either low (lower than 0.5%) or high population growth rates (higher than
2-2.5%) are characterized by a lower pace of economic development. In other words, it seems that
there exists a inverted-U curve representing the empirical relationship between economic and
Countries with a population lower than 1 million are excluded from the sample.
population growth. This is shown in figure 2, in which the two average annual rates of growth are
plotted by country. The interpolation line is based on the results reported in table 3, column 1.
Mechanisms behind this relationship are discussed in the next section.
Figure 2. Inverted-U curve between the population growth rate and the per-capita GDP growth rate.
-5 0 5 10
average rate of growth of per-capita GDP
-1 0 1 2 3 4
average rate of growth of population
The importance of the ratio between depending and working-age population is confirmed. The
coefficients of total-age-dependency ratio and its two old and young components are significant and
negative. However, it seems that countries presenting a high average old-dependency ratio over the
period 1980-2010 have been more penalized in terms of economic growth than countries with a high
young dependency ratio. On the contrary, the effect of the educational attainment growth is not
significant although of the expected sign.
Our findings suggest that the equation better representing the relationship between economic growth
and population growth has the following form. We discuss it in the next section.
Table 3. OLS regression results
Dependent variable: average annual growth rate of per-capita GDP (1980-2010)
(1) (2) (3) (4) (5) (6)
93 92 92 86 92 86
0.21 0.14 0.24 0.28 0.26 0.29
Notes: Values of standard errors in brackets. *, **, *** and **** mean coefficients are significant respectively at 90%,
95%, 99% and 99.9%.
4. Young poor countries, vibrant emerging ones and ageing mature countries.
The observation of world demographic and growth trends leads to distinguish at least three kinds of
countries. There is a number of poor countries, with a very high, though generally decreasing, fertility
rate, a high, though decreasing, mortality rate and a large rate of growth of population. The number of
children and young people on total population is very high, but overall poverty and lack of
industrialization and modern tertiary activities make it difficult to several young people to find a decent
employment or even any employment. Although these countries often exhibit significant migration
outflows and consistent remittances from abroad, their rate of development is rather slow. This partly
depends on the fact that real investment tend to grow very slowly and provide an insufficient labor
demand. Even educated people have difficulty in finding an adequate labor position, and this hampers
the rise of human capital. Moreover the State has limited financial means and so generally devotes
scarce funds to public education, while poor families with many children have difficulties in providing
adequate nourishment, health and education to all their children and in renouncing to their possible
work contribution. Moreover, with imperfect capital markets, most poor and middle class families are
usually unable to provide funds to start new enterprises or to realize important innovations eventually
conceived by gifted members of their families.
At the opposite there are rich mature countries, such as most Western European countries and Japan,
which have very low fertility rates and long life expectations. In the last two or three decades these
countries have experienced very low growth rates of population. The natural rate of the local
population is around zero and in most Western European countries the slow rate of growth of
population is substantially due to consistent immigration inflows. Also these countries have difficulties
to rapidly grow. The ageing of population leads to increased public and private expenditure on
pensions, health and care services. As paragraph 3 has shown, the dependency rate for older people is
in these countries very high. The dependency rate is, however, relatively low for children, but in
industrialized countries this possible advantage is largely offset by the prolongation of the studies to
higher education and by the fact that in several countries a consistent percentage of young people
remains unemployed. Moreover a older population means three other negative implications. In
democratic countries the high average age of population tends to lead to economic policies relatively
favorable to people over 40 and less favorable to younger people, which represent a minority in the
total number of voters. Moreover, high average age leads to less dynamism in innovating and in
starting entrepreneurial activities. Finally old-age people tend to behave differently from younger
people in their decisions to save, to consume and to invest. They tend to consume more services than
goods and they usually prefer to employ their savings in housing and relatively safe financial assets
rather than in risky productive investment.
Also most Eastern European countries have experienced very low or negative rates of growth of
population and low average annual rates of economic growth. This is partly due to the great difficulties
of the transition period and to large emigration flows to richer countries, but also, in several countries,
to a low fertility rate and a rapid growing ageing of the population.
There is, finally, a third kind of countries mainly composed by emerging countries like China, India and
Brazil and by some developing countries
, which have been able to exploit the “demographic
dividend”: China is now approaching the risk of an excessive ageing of population, due to their strict
“one child” demographic policy, while India and Brazil will be able to enjoy the advantage of the
demographic dividend for the next two-three decades.
The presence of a inverted-U curve seems to suggest demographic, immigration and development
policies which would in the long-run gradually lead to a convergence towards rates of growth of the
population capable of maintaining a sustainable increase of population and reduce both the problems
associated to the polar cases of an excessive population growth and an excessive ageing of the
population. However, institutions, cultural factors and social and economic problems can deeply differ
among countries, so that there will probably remain a considerable variety of demographic and
Also the United States have been able to maintain, partly through a robust immigration inflow, a
consistent rate of growth of population (around 1% per year) and so escape the negative effects of
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Table A1. Average annual growth rate of population and per-capita GDP by country: 1980-2010
COUNTRIES average annual growth rate of
average annual growth rate of
Bulgaria -0.71 1.32
Estonia -0.46 2.21
Latvia -0.43 1.68
Ukraine -0.32 -0.63
Hungary -0.23 1.02
Czech Republic -0.03 -0.77
Romania -0.03 0.33
Russian Federation 0.01 -1.58
Croatia 0.08 1.08
Italy 0.10 1.25
Lithuania 0.11 1.49
Belgium 0.19 1.63
Denmark 0.25 1.50
Poland 0.26 2.11
Japan 0.27 1.70
Austria 0.28 1.83
Sweden 0.29 1.70
Slovenia 0.30 2.37
Portugal 0.31 1.94
Finland 0.32 1.99
Slovak Republic 0.32 2.36
United Kingdom 0.34 1.85
Greece 0.36 1.73
Albania 0.38 2.76
Norway 0.45 2.11
France 0.54 1.30
Netherlands 0.57 1.64
Switzerland 0.59 0.99
Uruguay 0.60 1.78
Spain 0.72 2.03
South Korea 0.82 5.69
Taiwan 0.85 5.05
New Zealand 0.98 1.34
Ireland 1.03 3.37
China 1.04 7.59
United States 1.04 1.70
Canada 1.06 1.45
Hong Kong 1.13 3.60
Thailand 1.19 4.29
Sri Lanka 1.20 3.65
Argentina 1.26 0.95
Australia 1.30 1.97
Chile 1.38 2.90
Indonesia 1.63 3.14
Zimbabwe 1.63 -1.55
Brazil 1.65 0.90
Tunisia 1.67 2.61
Mexico 1.67 0.65
Vietnam 1.72 4.93
Morocco 1.73 1.94
South Africa 1.74 0.48
Turkey 1.84 2.39
Peru 1.84 0.90
India 1.86 4.31
Bangladesh 1.91 2.85
Mozambique 2.02 2.48
Bolivia 2.03 0.59
Venezuela 2.06 -0.09
Ecuador 2.10 0.59
Iran 2.17 1.36
Angola 2.23 2.04
Singapore 2.25 3.82
Egypt 2.25 2.45
Philippines 2.27 0.78
Israel 2.28 1.85
Mali 2.31 1.33
Malaysia 2.36 3.43
Nigeria 2.40 1.40
Kuwait 2.40 -0.65
Pakistan 2.48 2.62
Cambodia 2.49 3.57
Cameroon 2.67 -0.04
Ghana 2.68 1.31
Iraq 2.73 -4.45
Tanzania 2.73 1.20
Senegal 2.84 0.56
Sudan 2.84 1.36
Zambia 2.96 -0.48
Ethiopia 3.00 1.43
Madagascar 3.03 -1.60
Côte d'Ivoire 3.03 -1.75
Kenya 3.04 0.27
Malawi 3.05 0.50
Oman 3.10 2.95
Syria 3.12 0.57
Burkina Faso 3.20 1.60
Yemen 3.20 1.09
Saudi Arabia 3.20 -0.90
Niger 3.24 -1.76
DR Congo 3.26 -3.07
Colombia 3.29 0.13
Uganda 3.35 2.33
Jordan 3.68 0.72
Average for 93
Source: GGDC, Total Economy Database.
Table A2. Age dependency ratio by country
total old young
Albania 60.88 10.66 50.22
Angola 97.87 5.03 92.85
Argentina 61.87 15.46 46.41
Australia 50.19 17.62 32.57
Austria 48.71 22.80 25.90
Bangladesh 76.29 5.67 70.62
Belgium 50.97 23.76 27.20
Bolivia 79.40 7.27 72.13
Brazil 60.57 8.06 52.51
Bulgaria 48.14 21.53 26.61
Burkina Faso 96.89 4.72 92.17
Cambodia 81.83 5.29 76.54
Cameroon 90.23 6.77 83.45
Canada 46.21 17.16 29.05
Chile 55.02 10.56 44.46
China 50.46 9.25 41.22
Colombia 65.31 7.43 57.87
Croatia 47.57 20.09 27.49
Czech Republic 48.03 19.57 28.45
Cote d'Ivoire 86.25 5.61 80.64
Congo, Dem. Rep. 99.72 5.45 94.27
Denmark 50.33 22.83 27.50
Ecuador 72.05 8.22 63.83
Egypt, Arab Rep. 75.32 6.97 68.35
Estonia 50.01 20.60 29.41
Ethiopia 92.20 5.61 86.58
Finland 48.86 21.07 27.79
France 53.46 23.10 30.36
Ghana 84.86 5.84 79.03
Greece 49.37 22.85 26.52
Hong Kong 40.72 13.51 27.21
Hungary 48.78 21.09 27.69
India 67.71 6.82 60.90
Indonesia 61.58 7.07 54.51
Iran, Islamic Rep. 73.24 6.84 66.40
Iraq 90.86 6.60 84.26
Ireland 56.91 17.36 39.55
Israel 65.51 15.69 49.81
Italy 49.17 24.81 24.36
Japan 47.06 21.66 25.40
Jordan 85.51 5.90 79.61
Kenya 98.00 5.47 92.53
Kuwait 48.72 2.10 46.62
Latvia 49.01 20.79 28.21
Lithuania 50.05 18.96 31.09
Madagascar 92.10 6.12 85.98
Malawi 97.63 5.72 91.91
Malaysia 65.04 6.42 58.62
Mali 90.43 5.13 85.30
Mexico 70.95 8.16 62.78
Morocco 70.33 7.53 62.80
Mozambique 91.06 6.00 85.06
Netherlands 47.09 19.33 27.76
New Zealand 52.70 17.28 35.42
Niger 103.63 4.03 99.61
Nigeria 90.59 5.67 84.92
Norway 54.33 23.76 30.57
Oman 74.16 3.80 70.36
Pakistan 84.35 7.10 77.25
Peru 69.20 7.51 61.68
Philippines 74.32 5.97 68.35
Poland 49.10 16.68 32.42
Portugal 50.92 21.89 29.03
Romania 49.03 17.85 31.18
Russian Federation 45.89 16.79 29.09
Saudi Arabia 73.57 4.46 69.11
Senegal 93.94 4.80 89.14
Singapore 40.03 9.22 30.80
Slovak Republic 49.22 15.87 33.35
Slovenia 45.66 18.56 27.10
South Africa 66.59 5.80 60.79
Korea, Rep. 44.46 9.15 35.31
Spain 49.55 21.88 27.67
Sri Lanka 55.50 9.13 46.37
Sudan 85.48 5.87 79.62
Sweden 55.02 27.01 28.01
Switzerland 47.56 22.11 25.45
Syria 89.61 5.36 84.25
Tanzania 93.26 5.41 87.85
Thailand 52.40 8.27 44.13
Tunisia 65.41 8.76 56.65
Turkey 63.29 7.77 55.52
Uganda 105.38 5.51 99.87
Ukraine 48.03 19.86 28.17
United Kingdom 53.20 24.09 29.10
United States 50.94 18.43 32.51
Uruguay 59.98 19.62 40.37
Venezuela, RB 66.67 6.91 59.76
Vietnam 71.14 8.91 62.23
Yemen, Rep. 106.24 4.64 101.60
Zambia 95.12 5.54 89.58
Zimbabwe 91.86 6.27 85.60
Source: World Bank, World Development Indicators.