Download Are SubSaharan African countries in a Malthusian trap? The case of

PED401. Applications and Cases in International Development
Teaching Notes1
Are SubSaharan African countries in a
Malthusian trap? The case of Mali
In this case we look at interactions between growth and population. This takes us to the central
questions of unified growth—how to explain in a consistent framework the last few millennia of
economic experience, including the recent period of “modern growth”. It also takes us to a
different angle over debates on the contemporary problems of SubSaharan Africa. Is stagnation
in much of the region due to a Malthusian problem of too much population for available
resources? What does this mean? And what does this imply for policy choices?
Take Mali. Mali is an extraordinarily beautiful country, with rich cultural traditions and some of
the best contemporary music in the world. (Or in terms of our recent discussions, in music Mali
is at the frontier.) It is also landlocked and is mainly desert—see the map. Things are only
likely to get worse for agriculture if global warming leads to less and more uncertain water
availability. It has been growing in recent years, owing in part to a gold boom, but has a
population growth rate of some 3 percent per annum, a total fertility rate that has only recently
dropped just below 7 expected births per woman, and a contraceptive prevalence of 8 percent
amongst women of childbearing age. Surely population dynamics matter here?
Long-run history, the Great Divergence and the condition of
Sub-Saharan Africa
The essence of the story to be explained is given in Figure 1, taken from Galor (2004). For most
of history there was essentially no growth in income per capita. But after around 1800
something starts happening, and by the end of the 20th Century there had been a historically
unprecedented transformation of income per capita in parts of the world, with an associated
enormous expansion of well-being. But the expansion has been extraordinarily uneven, in what
has become known as the Great Divergence. In particular, some countries—including many in
SubSaharan Africa—have sustained a growth path that doesn’t look much different from the
history of stagnation in previous centuries.
1 These were prepared by Alexander Culiuc and Michael Walton and are solely for teaching purposes.
2
Figure 1 Long-run patterns of regional income per capita
Source: Galor (2004)
Underlying the stylized facts in Figure 1 is another: in the long period before the transition, total
output was not stagnant. But population adjusted. This is why this period is termed the
Malthusian phase. Malthus is most remembered for population expansion being a source of
problems: growing population would put pressure on resources, income per capita would decline
and eventually so would population. But the core idea of their being an equilibrating mechanism
between population and income applies on the upside too. Expansion in aggregate income—
induced by technological change or increased efficiency—largely translated into growing
populations, leaving income per capita broadly unchanged (and vice versa in periods of decline).
Figure 2 Two patterns of relationship between output and population: “Western Offshoots”
and Africa
Source: Galor (2004) based on work by Maddison.
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This pattern raises two types of question:
What theory of growth can both explain the long period of near-stagnation and the (staggered)
takeoff? This is the quest for “unified” theory, that extends the primary preoccupation of growth
theory of the past few decades in in explaining the recent period.
Do the mechanisms that historically explain long-run stagnation and the transition to modern
growth help in interpreting the growth malaise of poor countries today? And does this have
implications for policy?
This will get us into another debate: if population dynamics make a difference to income per
capita and well-being, is there a case for focusing policy on the determinants of population
change—and especially fertility? And if so, what is likely to be most efficacious in the
conditions prevailing in poor countries today?
Analytical context
We start with the simplest possible representation of the Malthusian trap in the framework of the
Solow model which we have already introduced in the Big Push discussion.
Figure 3 Malthusian trap in the Solow model
The problem with the representation is that it gives us no intuition behind this strangely shaped
population growth line. It only provides us with a mechanical picture of the trap.
Galor (2004) reviews a number of theories that attempt an explanation of the very long-term
historical trends in income levels. For most of human history, until the early 18th century, the
notion of “economic growth” belonged squarely in the realm of fiction (we can’t use “science
fiction” because in those days there was neither “science fiction”, nor “science” in the present
meaning of the word), with income per capita increasing at less than 0.05% per year. Any
increases in output were offset by increased population, a property which defines a Malthusian
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economy. Then something happened in Western Europe around the border of the 18th and 19th
centuries, and growth took off around the globe. Some take-offs (Japan in the end of the 19th and
then again the middle of 20th century, Korea in the second half of the 20th) were more impressive
than others (Latin America in early 20th century), some are uncertain or yet to happen (Africa).
However, the general pattern of the take-off is roughly the same everywhere:
Decreased mortality rates (esp. infant and child mortality) were offset by even sharper
decreases in fertility rates, resulting in lower population growth rates.
 Rapid increase in education attainment levels.
 Urbanization and industrialization with a decreased role of agriculture in overall output

Since then, “economic growth” got a new meaning. The world economy grows today nearly 100
times faster than two centuries ago. The change is so dramatic, that it’s hard to imagine that the
two types of economies could be driven by the same underlying economic forces. And that has
been exactly the way most economists reacted: it is hard to envision a growth theory that would
explain both worlds. Malthus’s theory explains why economies didn’t grow for most of history.
New growth theories explain why modern economies grow today. But each approach fails to
explain the other historical epoch. This matters for theory. It also matters for contemporary
interpretations.
Unified Growth Theory
Galor to the rescue. Together with Weil, he developed a complicated model that can explain both
periods (Malthusian and modern), as well as the transition phase between the two. The general
story goes as follows (this is a slightly abridged quote from Galor’s paper).
In early stages of development the economy was in a stable Malthusian steady
state equilibrium. Technology advanced […] slowly, and generated proportional
increases in output and population. The inherent positive interaction between
population and technology in this epoch, however, gradually increased the pace of
technological progress and the delayed adjustment of population permitted output
per capita to creep forward at a miniscule rate. The slow pace of technological
progress in the Malthusian epoch provided a limited scope for human capital in
the production process and parents therefore had no incentive to reallocate
resources towards child quality during this era.
The Malthusian interaction between technology and population accelerated the
pace of technological progress permitting a take-off. […] The expansion of
resources was partially counterbalanced by the enlargement of population and the
economy was characterized by rapid growth rates of income per capita and
population. The acceleration in technological progress increased the demand for
human capital, while having two opposing effects on population growth. On the
one hand, it eased households’ budget constraints, allowing the allocation of more
resources for raising children. On the other hand, it induced a reallocation of these
additional resources toward child quality. In the Post-Malthusian regime, due to
the limited demand for human capital, the first effect dominated and the rise in
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real income permitted households to increase their family size as well the quality
of each child.
[…] The interaction between investment in human capital and technological
progress generated a virtuous circle: human capital generated faster technological
progress, which in turn further raised the demand for human capital, inducing
further investment in child quality, and ultimately initiating a demographic
transition. The offsetting effect of population growth on the growth rate of income
per capita was eliminated and the interaction between human capital accumulation
and technological progress permitted a transition to a state of sustained economic
growth.
The building blocks
The story above is based on the dynamics of an overlapping generations model (OLG) developed
by Galor and Weil. You’ll learn OLG later in the semester (in Macro), so we’ll only explain the
basics. Instead of having infinitely living individuals, like in the Ramsey model, we are dealing
with “mortals”, whose life is composed of two periods. In this case the periods are “child” and
“adult”. The adult generation is indexed by t (so children of generation t are considered
generation t+1). The only decision maker is the “Adult”, who derives utility from consumption c
and a combination of children quantity (n) and quality (h, measured by human capital):
There exists a minimum subsistence level of consumption, so the adult will not allocate any time
to making/raising children as long as this minimum consumption is not satisfied. Notice that the
adult decides how much time to allocate to work (which provides income for consumption), the
rest being dedicated to either to making or educating children. The output (and hence
consumption) generated from work depends on two factors of production: land X and human
capital H.2 Land is fixed, but its marginal product depends on the level of technology used:
In per-capita terms, this becomes
(where xt=AtX/Lt is the effective resources per
adult).
The human capital embodied in people is determined by their quality (education) as well as by
the technological environment. Technological progress reduces the adaptability of existing
human capital for the new technological environment (the ‘erosion effect’). Education, however,
lessens the adverse effects of technological progress. That is, skilled individuals have a
comparative advantage in adapting to the new technological environment. In particular, the time
required for learning the new technology diminishes with the level of education and increases
2
The authors don’t model include physical capital in their model, since that would complicate things by adding a
savings decision.
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with the rate of technological change.3 Therefore the level of human capital is increasing in their
parental time investment in education, et+1, and decreasing in the rate of technological progress,
gt+1 (where gt+1 ≡ (At+1 − At)/At):
ht+1 = h(et+1, gt+1)
The last building block that needs to be endogenized is the rate of technological progress g. In
Galor and Weil (2000), g depends positively on the size of the population (as in the Kremer
model – revenues from a fixed cost technology investment depends on the size of the market, i.e.
there is a scale effect) and the level of education: g(e, L).
Finally, we have to state the starting conditions of the economy. We assume that the world
comes into existence with (i) a small population, for whom (ii) the subsistence consumption
constraint is binding, which is due to (iii) low technological level.
The maximization problem
Members of generation t choose the number and quality of their children, and therefore their own
consumption, so as to maximize their intertemporal utility function subject to the subsistence
consumption constraint. The maths is messy, so we won’t present it here. The results are as
follows (you can check them on pages 60-61 of the Galor paper):
a. An increase in the rate of technological progress reduces the number of children and
increases their quality.
b. If the subsistence consumption constraint is binding, an increase in parental potential income
raises the number of children, but has no effect on their quality. This is the essence of the
Malthusian trap – all additional input is eaten away by increased population.
c. If the subsistence consumption constraint is not binding, an increase in parental potential
income does not affect the number of children or their quality (i.e. past a certain level of
income, the Malthusian effect disappears).
The dynamics of the system
The key component of the model is the circular relation between education and technological
growth. Notice that in order to provide a given level of human capital to their children, the adult
must invest more in education for higher rates of technological growth. On the other hand, the
higher the education level, the higher the technological progress. So technology is a concave
function of education and population gt+1(et, Lt), and education is a concave function of
technological growth, et+1 = e(gt+1)
We can put these two functions in a single e-g graph. There are several potential scenarios: e(g)
and g(e, L) don’t intersect at all, or intersect one or more times. The Malthusian epoch
corresponds to the first scenario.
3
Yes, this assumption of the model is debatable.
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Figure 4. The Malthusian trap
How do we interpret such a graph? Let’s say that the adult is pondering whether to give their
child a certain level of education e0. The desired level of technology would then be g0. However,
if that will be the actual technological progress in the next period, the adult would actually prefer
to provide children only with e1, since that’s what his utility maximization solution tells him to
do (determined by the e(g) function). But for this new e, the technological growth will be even
smaller (g1). Continuing this logical sequence, we quickly conclude that there is only one
possible outcome – the child will receive zero education. The arrows that go between the two
curves take us towards this equilibrium. You can think of this graph as being the mathematical
representation of the vicious circle.
However, there will be still some technological progress due to the population effect (remember,
population affects technological progress). As technology slowly creeps ahead, population
slowly increases, which is due to result (b) from the maximization problem. This in turn
increases technological progress for any given level of education, which gradually shift the
g(e, L) curve up. So the vicious circle between e and g is gradually being eroded by the effect of
the virtuous circle between g and L. At some point in time, the two curves will intersect, as
depicted in figure 3.
We now have three equilibria: (0, gl), (eh, gh) and (eu, gu). The second one is unstable – notice
that the arrows go away from it (you can see how this graph is similar in spirit to the Solow
representation of the Malthusian trap and our big push discussion). So, depending on
expectations, policies and luck, the economy can go to either the high “modern” (eh, gh) or the
low “Malthusian” equilibrium (0, gl).
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Figure 5. Multiple equilibrium scenario
However, even if the country is inhabited by unlucky pessimists ruled by the worst government
and the economy gets stuck at the bad equilibrium, the situation is not hopeless. The reinforcing
effects of population and technology growths will continue to slowly push the g(e, L) curve up
until the bad equilibrium disappears, in which the economy progresses to the “modern”
equilibrium, as depicted in figure 4.
Figure 6. A modern economy equilibrium
A final note on the model. In the original paper, these graphs are supplemented by complex
phase diagrams that link education with effective resources x (pages 67-68). You may want to
test your understanding of phase diagrams by trying to interpret them (good luck!).
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Additions to the model
In the model presented above, the rise in the demand for human is an outcome of the acceleration
in technological progress, underlying the role of educated individuals in coping with a rapidly
changing technological environment. The mechanism is founded on the premise that the
introduction of new technologies increases the demand for skilled labor in the short-run. In his
review study, Galor (2004) briefly discusses other possible mechanisms for the rise of human
capital, that he argues are complementary influences:

A technology-life expectancy interaction. Improvements in medical technology lower
mortality rates and increases life expectancy, which can increase the utility that parents
derive from better educated children. The lower mortality means that the education will not
go to waste, whereas long life expectancy means that the life-long return on this education
will be larger. (This interaction will be an important part of our discussion on HIV-AIDS
next week)

Capital-skill complementarities. “The accumulation of physical capital in the early
stages of industrialization enhanced the importance of human capital in the production
process and generated an incentive for the capitalists to support the provision of public
education for the masses. […] The accumulation of physical capital by the capitalists in the
first phase of the Industrial Revolution increased the importance of human capital in
sustaining the rate of return to physical capital inducing capitalists to support the provision
of public education for the masses.” (p. 72) You have seen this model in your
macroeconomics class, in the opening lecture on endogenous growth, where the production
function exhibited CRS to the combination of human and physical capital: y=Akαh1-α.

Policy on education. While the demand side is interpreted as central to the transition,
supply-side interventions can play a complementary role in changing the incentives for
households to send their kids to school, in particular pushes to lower the cost of schooling
or change the household demand for education through socialization processes.
Historically two aspects of this were the support for education policy from industry, just
noted, and—for present purposes—religious or nationalist drives for education.

Decline in the gender gap. Extending the discussion to account for gender differences
can provide an additional explanation of the human capital accumulation and demographic
transition.
“…technological progress and capital accumulation complemented mental-intensive tasks and
substituted for physical-intensive tasks in the industrial production process. In light of the
comparative physiological advantage of men in physical-intensive tasks and women in mentalintensive tasks, the demand for women’s labor input gradually increased in the industrial sector,
decreasing monotonically the wage deferential between men and women. In early stages of
industrialization, wages of men and women increased, but the rise in female’s relative wages was
insufficient to induce a significant increase in women’s labor force participation. Fertility,
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therefore increased due to the income effect that was generated by the rise in men’s absolute
wages. Ultimately, however, the rise in women’s relative wages was sufficient to induce a
significant increase in labor force participation, increasing the cost of child rearing
proportionally more that households income and triggering a demographic transition and a shift
from stagnation to growth.” (p. 75)
This argument can be strengthened further by the fact that work and the number of children are
(traditionally) substitutes for women. However, in the presence of an educational system, work
and quality of children no longer need to be substitutes, since education can be [partially]
outsourced. In fact, greater female labor participation can further increase education of children,
by providing additional financing for educational expenditures. The particularly strong influence
of female labor participation on the demographic transition and education points towards an
important policy implication: female education is, in some sense, more important than male
education. The graph below4 puts some of these links together, though note that this starts from
the contemporary focus on more education for women, rather than this being a product of shifts
in demand for female labor.
Figure 7. The decline in the gender gap, the demographic transition and growth
Contemporary patterns and policy choices
We set up the discussion of contemporary issues with some illustrative data from Africa and
Asia, and a brief sketch of the policy options. The assessment of these alternatives will be
discussed further in class.
Patterns of growth and fertility change in Africa and Asia
Let’s start with an overview at a regional level, comparing East Asia, South Asia and
SubSaharan Africa. In the 30-year period between 1975 and 2005, SubSaharan Africa
essentially stagnated in terms of income per capita (though with growth of both output and
population, as seen in Figure 2), while East Asia and South Asia, both initially poorer, grew at
rapid and modest rates, respectively(Figure 8) . This was associated with a very slow fall in
fertility rates in SubSaharan Africa, both absolutely and in comparison with East and South Asia.
4
Borrowed from Ricardo Hausmann’s PED-309 lecture slides.
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Figure 8. GDP per capita in East Asia, South Asia and SubSaharan Africa5
(in PPP at 2000 prices)
Figure 9. Fertility rates in East Asia, South Asia and SubSaharan Africa
As a consequence of this fertility transition, East Asia and South Asia have been experiencing a
fall in the dependency ratio (the ratio of young and old to the working age population), and fall
that will eventually be reversed as the old age population rises (Figure 10). And while schooling
has risen in all periods, it remains significantly lower in SubSaharan Africa.
5
All data from World Bank, World Development Indicators.
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Figure 10. Dependency ratios in East Asia, South Asia and SubSaharan Africa
Figure 11. Primary and secondary school enrollment East Asia, South Asia and SubSaharan
Africa
The following figures provide selected numbers for Mali, in comparison with two Asian
countries, Bangladesh and Indonesia, both highly populous that—some 30-40 years ago—
pessimists would undoubtedly have characterized as being caught in a Malthusian trap.
First, in terms of GDP per capita trends broadly reflect the regional patterns shown above.
Though when we look at growth rates, we see a slight acceleration in growth in population in
Mali in the 1990s. Fertility rates in Mali remain extraordinarily high, while there have been
dramatic declines in both Bandladesh and Indonesia (Figure 14)
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Figure 12. GDP per capita in Bangladesh, Indonesia and Mali
(at 2000 PPP)
Figure 13. GDP and population growth in Bangladesh and Mali
Figure 14. Fertility rate in Bangladesh, Indonesia and Mali
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Figure 15 compares education enrollment in the three countries, while Figure 16 shows the stark
differences in contraceptive prevalence—with Mali still way below 10 percent of adult women.
Figure 15. Secondary education enrollment in Bangladesh, Indonesia and Mali
Figure 16. Contraceptive prevalance Bangladesh, Indonesia and Mali
(%, women aged 15-49)
Policy options
Do population dynamics matter to the expansion of welfare? Let’s put aside some deep ethical
issues for now, at least for these notes. Or more specifically, we could follow the position of one
of the founders of the Enlightenment, the Marquis Marie-Jean-Antoine-Nicolas Caritat
Condorcet who looked forward to a time in which people “will know that, if they have a duty
towards those who are not yet born, that duty is not to give them existence but to give them
happiness” (cited in Sen, 1999 p. 214). Given this position, we are interested in the interaction
between population and human well-being of those who are born. Then there are (at least) four
interesting policy positions for a country in Mali’s position.
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Population push. The contemporary world has the benefit of contraceptive technologies
unavailable in the 19th century, that opens up a whole new control variable (though one that
Malthus would undoubtedly have favored): proactive policy to directly reduce fertility at
low levels of income.
 Education and women’s empowerment push. Fertility choices are fundamentally driven by
the decisions of young women, so what really drives a demographic transition is the variety
of determinants of their power, with girl’s education and outside work key drivers of
change.
 Growth and technological change pull. The (desirable) shifts in education and women’s
position will only occur if, as in 19th Century Europe and Western Offshoots, there is a
major change in the economic demand for skills. The question is how to get Mali on to
such a growth path.
 Malthus redux: migration. Mali simply can’t get rich given its geographic endowment, and
the only way to transform living standards of Malians is through substantial outmigration.
This would not even be controversial if Africa’s national boundaries weren’t created by
19th Century European interests.

Note that the second, and then the third, position don’t deny a role for the first (and then the
second). Take the third position, that is closest to Galor’s interpretation of history. This can
surely be helped by government action on provisioning of schooling and social mobilization for
women. And access to contraceptives can make it easier for women to implement their
preferences on family size. But the central question is what the primary driver of change is.
Since these notes are already long, we sketch the arguments, in order to frame the discussion we
will have on Mali’s option, rather than discuss the evidence in detail.
On population push there are two big issues, whether it is ever likely to be significant, and over
coercion.
In the development literature of the early 1970s, some economists argued that family planning
had just terrific cost-benefit ratios. Condoms and pills were cheap, they had a big impact on the
denominator of income per capita (and released resources for public provisioning), with minor,
or even positive effects on the numerator6, so could have large impacts on welfare. The key
assumption in this line of reasoning is that families had more kids then they wanted—that there
was unmet demand for contraception. Pritchett (1994) directly attacks this, arguing that, to first
order, all the variation in actual fertility is driven by differences in desired fertility. This is, of
course, entirely consistent with Galor’s interpretation of history: the fertility transition was an
endogenous consequence of shifts in the economy-wide factors that in turn influenced both
household preferences and government policy—as discussed above. Yes, contraceptive
prevalence rose dramatically in countries such as Bangladesh and Indonesia (Figure 16), and this
was welfare-improving, but to attribute causation from the family planning programs to the
fertility decline is like attributing to tonic the inebriating effects of gin and tonic. (Or to put it a
less colourfully, there’s an identification problem). Not all family planning specialists agree
6
We postpone to a future session discussion of whether or not there is a “demographic bonus”.
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with this, and for those interested there is a comment and response in the Population and
Development Review a year later. See also Caldwell et al (1999) who argues the relevance of
social change in affecting household choices over children.
The second issue concerns coercion. Here China is the reference case. It is argued that China,
with its one-child policy, did successfully make population policy a control variable, and while
this had unattractive elements, it could be justified in the grander (Malthusian) scheme of things.
Sen (1999) argues against this perspective (and against continued, if less pervasive, examples of
coercion that he cites in India in the 1990s), both on ethical grounds, and—more immediately
relevant for these notes—on practical grounds. He in particular suggests that the experience of
Kerala and Tamil Nadu shows an alternative path is feasible, that is based on expanding the
freedoms (“capabilities” in his technical terminology) of young women in particular through
education and outside earnings opportunities. To illustrate, he points out that “in 1979, when the
one-child policy was introduced in China, Kerala had a higher fertility rate than China: 3.0 as
opposed to China’s 2.8. By 1991 its fertility rate of 1.8 is as much below china’s 2.0 as it had
been above in 1979. Despite the added ‘advantage’ of the one-child policy and other coercive
measures, the fertility rate seems to have fallen much more slowly in China then in Kerala.”
(Sen, 1999 p. 222.)
This takes us already deeply into whether the second or third position is a better description of
contemporary processes. Sen also has a view on this. He adduces cross-sectional evidence from
India (Murthi et al. 1995) that finds that the substantial inter-district variation in fertility across
India is only significantly associated with female literacy and female labor force participation.
Income has no statistically significant influence. If this interpretation is correct, it would suggest
there are different causal dynamics at work across India than in the historical transitions. But
there is still room for debate over this. And it would still leave us with the question of what mix
of developments would sustain a fertility transition in the very different conditions of Mali.
Finally, let’s go to “Malthus redux”—the view that, whatever your interpretation of causal
dynamics of change in the past, some countries simply don’t have the natural resources and
geographic location to effect a major increase in GDP per capita—at least in a reasonable space
of time. An advocate of this position is Lant Pritchett again (see Pritchett, 2006), who argues
that outmigratoin was precisely how historically many ghost towns, or unproductive countries
(Ireland in the 19th Century?) did adjust, with positive welfare consequences both for those who
moved and those who stayed. This is a controversial position, but one that is important to
discuss. Note that it has two alternative visions. The first (that Pritchett argues) is of much
greater mobility to rich countries. The second is a vision of Africa in which urbanization and
industrialization takes off in coastal areas—or areas with either major resource potential or lowcost access to the global economy—and that within Africa migration will be an important
mechanism whereby all Africans can participate in this process.
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The policy question
You are a Malian economist, just returned from graduate training, and you have joined the
planning office. There is a debate on Mali’s growth process. By Mali’s own historical
standards, and by the standards of most countries in SubSaharan Africa, Mali’s growth has been
quite good over the past decade or so—six percent per annum for 1995-2005. But it comes from
an extraordinarily low level, is at least in part driven by gold, and expansion in income per capita
is only about half this, owing to continued rapid population growth. Fertility rates remain very
high, and there is concern that as the currently very high mortality falls, population growth will
remain high for some time. What should be done? Is there a case for action to reduce population
growth? If so, how? Or should the whole focus be on determinants of growth? Or, is the
transformation of Mali’s economy impossible at current and projected population levels, so that
migration has to be a major part of any solution? The President has asked the planning office to
come up with an answer. You have been asked to give a briefing to the team responsible for this
to frame the discussion, making use of your learning on growth, history and international
experiences.
Notes: (i) you may pick another country if you prefer; (ii) for this question, the focus is not on
HIV/AIDS, that will be at the center of a future case.
References
Caldwell, John, Barkat-e-Khuda, Bruce Caldwell, Indrani Pieris and Pat Caldwell. 1999. “The
Bangladesh Fertility Decline: An Interpretation.” Population and Development Review, 25(1)
Galor, Oded. 2004. “From Stagnation to Growth: Unified Growth Theory.” In Philippe Aghion
and steven Dulauf eds. The Handbook of Economic Growth.
Murthi ,Mamta, Anne-Catherine Guio and Jean Drèze. 1995. “Mortality, Fertility, and Gender
Bias in India: A District-Level Analysis.” Population and Development Review 21.
Pritchett, Lant. 1994. “Desired Fertility and the Impact of Population Policies”. Population and
Development Review, 20(1).
Pritchette, Lant. 2006. Let Their People Come: Breaking the Gridlock on Global Labor
Mobility. Washington DC: Brookings Institution Press.
Sen, Amartya. 1999. Development as Freedom. New York: Alfred A. Knopf.
Sturzenegger, Federico. 2007. Teaching notes API119
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Map of Mali
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