chapter 2 development

Economic Development: Overview
By the problem of economic development I mean simply the problem of accounting for the observed pattern, across
countries and across time, in levels and rates of growth of per capita income. This may seem too narrow a definition, and
perhaps it is, but thinking about income patterns will necessarily involve us in thinking about many other aspects of societies
too, so I would suggest that we withhold judgement on the scope of this definition until we have a clearer idea of where it
leads us.
—R. E. Lucas [1988]
[W]e should never lose sight of the ultimate purpose of the exercise, to treat men and women as ends, to improve the human
condition, to enlarge people’s choices. . . . [A] unity of interests would exist if there were rigid links between economic
production (as measured by income per head) and human development (reflected by human indicators such as life
expectancy or literacy, or achievements such as self-respect, not easily measured). But these two sets of indicators are not
very closely related.
—P. P. Streeten [1994]
2.1. Introduction
Economic development is the primary objective of the majority of the world’s nations. This truth is
accepted almost without controversy To raise the income, well-being, and economic capabilities of
peoples everywhere is easily the most crucial social task facing us today. Every year, aid is
disbursed, investments are undertaken, policies are framed, and elaborate plans are hatched so as to
achieve this goal, or at least to step closer to it. How do we identify and keep track of the results of
these efforts? What characteristics do we use to evaluate the degree of “development” a country has
undergone or how “developed” or “underdeveloped” a country is at any point in time? In short, how
do we measure development?
The issue is not easy to resolve. We all have intuitive notions of “development.” When we speak
of a developed society, we picture in our minds a society in which people are well fed and well
clothed, possess access to a variety of commodities, have the luxury of some leisure and
entertainment, and live in a healthy environment. We think of a society free of violent discrimination,
with tolerable levels of equality, where the sick receive proper medical care and people do not have
to sleep on the sidewalks. In short, most of us would insist that a minimal requirement for a
“developed” nation is that the physical quality of life be high, and be so uniformly, rather than being
restricted to an incongruously affluent minority.
Of course, the notion of a good society goes further. We might stress political rights and freedoms,
intellectual and cultural development, stability of the family, a low crime rate, and so on. However, a
high and equally accessible level of material well-being is probably a prerequisite for most other
kinds of advancement, quite apart from being a worthy goal in itself.1 Economists and policy makers
therefore do well (and have enough to do!) by concentrating on this aspect alone.
It is, of course, tempting to suggest that the state of material well-being of a nation is captured
quite accurately in its per capita gross national product (GNP): the per-head value of final goods and services produced by the people of a country over a given year. Indeed, since economic development
at the national level was adopted as a conscious goal,2 there have been long phases during which
development performance was judged exclusively by the yardstick of per capita gross domestic
product (GDP) growth. In the last few decades, this practice increasingly has come under fire from
various quarters. The debate goes on, as the quotations at the beginning of this chapter suggest.
We must be careful here. No one in their right mind would ever suggest that economic
development be identified, in a definitional sense, with the level or growth of per capita income. It is
perhaps universally accepted that development is not just about income, although income (economic
wealth, more generally) has a great deal to do with it. For instance, we noted previously that
economic advancement should not be restricted to a small minority. This means, in particular, that
development is also the removal of poverty and undernutrition: it is an increase in life expectancy; it
is access to sanitation, clean drinking water, and health services; it is the reduction of infant mortality;
it is increased access to knowledge and schooling, and literacy in particular. There is an entire
multitude of yardsticks. Paul Streeten’s thoughts, summarized in the quotation at the beginning of this
chapter, capture this “multidimensionality” very well.
Far more intriguing is the sharp focus of Robert Lucas’ words (see quotation). At first they appear
narrow, perhaps even missing the point, whereas the more holistic scenario sketched in the foregoing
paragraphs seems pretty much the way to go. In thinking this we would be wrong. Neither Lucas nor
any intelligent person believes that per capita income is development. What’s hidden in these words
is actually an approach, not a definition. It is really a belief about the world, which is that the
universal features of economic development—health, life expectancy, literacy, and so on—follow
in some natural way from the growth of per capita GNP, perhaps with the passage of time. Implicit
here is a belief in the power of aggregate economic forces to positively affect every other
socioeconomic outcome that we want to associate with “development.” This outlook may be
contrasted with the view that a correlation between GNP and other desired features is not automatic,
and that in many cases such connections may not be present at all. According to this view, per capita
GNP fails as an adequate overall measure and must be supplemented by other indicators directly.
The debate implicit in the two quotations is not about what development means, on which there is
possibly widespread agreement. It is really about a view of the world—about the possibility of
finding a smaller set of variables that correlates well with the multifaceted process of development.
Note well that, in a way, saying too much is saying too little. It may be that per capita income does not
capture all aspects of development, but a weighty assertion that no small set of variables ever
captures the complex nature of the development process and that there are always other
considerations is not very helpful. In this sense, the view that economic development is ultimately
fueled by per capita income may be taking things too far, but at least it has the virtue of attempting to
reduce a larger set of issues to a smaller set, through the use of economic theory.
This book implicitly contains a reduction as well, although not all the way to per capita income
alone. In part, sheer considerations of space demand such a reduction. Moreover, we have to begin
somewhere, so we concentrate implicitly on understanding two sets of connections throughout this
book. One is how average levels of economic attainment influence development. To be sure, this must
include an analysis of the forces that, in turn, cause average levels (such as per capita GNP) to grow.
The other connection is how the distribution of economic attainment, across the citizens of a nation or
a region and across the nations of the world, influences development. The task of understanding these
two broad interrelationships takes us on a long journey. In some chapters the relationships may be hidden in the details, but they are always there: levels and distribution as twin beacons to guide our
inquiry.3
This is not to say that the basic features of development will be ignored. Studying them is our
primary goal, but our approach to them lies through the two routes described in the previous
paragraph.
We begin, then, with a summary of the historical experience of developing countries over the past
few decades. We pay attention to per capita income, then to income distribution, and then consider
other indicators of development. We then try to understand how these manifold characteristics of
development correlate with the smaller set of features: income levels and distribution. This chapter
ends with an overview of the structural characteristics of developing countries. We describe the
occupational distribution of the population, the share of different sectors (such as agriculture and
services) in national income, the composition of imports and exports, and so on.
2.2. Income and growth
2.2.1. Measurement issues
Low per capita incomes are an important feature of economic underdevelopment—perhaps the
most important feature—and there is little doubt that the distribution of income across the world’s
nations is extraordinarily skewed. Per capita incomes are, of course, expressed in takas, reales, yuan,
and in the many other world currencies. To facilitate comparison, each country’s income (in local
currency) is converted into a common currency (typically U.S. dollars) and divided by that country’s
population to arrive at a measure of per capita income. This conversion scheme is called the
exchange rate method, because it uses the rates of exchange between the local and the common
currencies to express incomes in a common unit. The World Development Report (see, e.g., World
Bank [1996]) contains such estimates of GNP per capita by country. By this yardstick, the world
produced $24 trillion of output in 1993. About 20% of this came from low- and middle-income
developing countries—a pittance when we see that these countries housed 85% of the world’s
population at that time. Switzerland, the world’s richest country under this system of measurement,
enjoyed a per capita income close to 400 times that of Tanzania, the world’s poorest.
Figure 2.1 displays per capita income figures for selected countries. The figure contrasts per
capita incomes in different countries with the populations of these countries. No comment is
necessary.
The disparities are enormous, and no amount of fine-tuning in measurement methods can get rid of
the stark inequalities that we live with. Nevertheless, both for a better understanding of the degree of
international variation that we are talking about and for the sake of more reliable analysis of these
figures, it is best to recognize at the outset that these measures provide biased estimates of what is
actually out there.
(1) For one thing, underreporting of income is not uncommon in developing countries. Because tax
collection systems are not as efficient as those prevailing in the industrialized market economies,
there is a greater incentive to underreport income or output for tax purposes. The national accounts
may not be comprehensive as well.4
In addition, the proportion of income that is actually generated for self-consumption is relatively
high in developing countries. As we shall soon see, the proportion of the population living in the rural
sector in developing countries is large. Many of these individuals are subsistence farmers who grow
crops that they themselves consume. Such outputs may not be reported adequately.
Although we can make educated guesses about the degree of underestimation involved, there is
really very little that we can do about correcting for this problem.
(2) A far more serious issue comes from the fact that prices for many goods in all countries are
not appropriately reflected in exchange rates. This is only natural for goods and services that are not
internationally traded. Exchange rates are just prices, and the levels of these prices depends only on
commodities (including capital) that cross international borders. The prices of nontraded goods, such
as infrastructure and many services, do not affect exchange rates. What is interesting is that there is a
systematic way in which these nontraded prices are related to the level of development. Because poor
countries are poor, you would expect them to have relatively low prices for nontraded goods: their lower real incomes do not suffice to pull these prices up to international levels. However, this same
logic suggests that a conversion of all incomes to U.S. dollars using exchange rates underestimates
the real incomes of poorer countries. This can be corrected to some extent, and indeed in some data
sets it has been. The most widely used of these is the Heston-Summers data set (see box). Recently,
the World Bank started to publish income data in this revised format.
Briefly (see box for more details), international prices are constructed for an enormous basket of
goods and services by averaging the prices (expressed, say, in dollars) for each such good and
service over all different countries. National income for a country is then estimated by valuing its
outputs at these international prices. In this way, what is maintained, in some average sense, is parity
in the purchasing power among different countries. Thus we call such estimates PPP estimates, where
PPP stands for “purchasing power parity.”
PPP estimates of per capita income go some way toward reducing the astonishing disparities in
the world distribution of income, but certainly not all the way. For an account of how the PPP
estimates alter the distribution of world income, consult Figure 2.3.

The direction of change is quite clear and, from the foregoing discussion, only to be expected.
Measured in PPP dollars, developing countries do better relative to U.S. per capita GNP, although the
fractions are still small, to be sure. This situation reflects the fact that domestic prices are not
captured adequately by using exchange-rate conversions, which apply correctly only to a limited set
of traded goods.
(3) There are other subtle problems of measurement. GNP measurement, even when it accounts for
the exchange-rate problem, uses market prices to compare apples and oranges; that is, to convert
highly disparate goods into a common currency. The theoretical justification for this is that market
prices reflect people’s preferences as well as relative scarcities. Therefore such prices represent the
appropriate conversion scale to use. There may be several objections to this argument. Not all
markets are perfectly competitive; neither are all prices fully flexible. We have monopolies,
oligopolistic competition, and public sector companies6 that sell at dictated prices. There is
expenditure by the government on bureaucracy, on the military, or on space research, whose monetary
value may not reflect the true value of these services to the citizens. Moreover, conventional measures
of GNP ignore costs that arise from externalities—the cost of associated pollution, environmental
damage, resource depletion, human suffering due to displacement caused by “development projects”
such as dams and railways, and so forth. In all of these cases, prevalent prices do not capture the true
marginal social value or cost of a good or a service.
Figure 2.3. PPP versus exchange rate measures of GDP for ninety-four countries, 1993. Source: World Development Report
(World Bank [1995]).
All these problems can be mended, in principle, and sophisticated measures of GDP do so to a
large extent. Distortions in prices can be corrected for by imputing and using appropriate “shadow
prices” that capture true marginal values and costs. There is a vast literature, both theoretical and
empirical, that deals with the concepts and techniques needed to calculate shadow prices for
commodities. An estimated “cost of pollution” is often deducted in some of the measures of net GDP,
at least in industrialized economies. Nevertheless, it is important to be aware of these additional problems.
With this said, let us turn to a brief account of recent historical experience.
2.2.2. Historical experience
Over the period 1960–85, the richest 5% of the world’s nations averaged a per capita income that
was about twenty-nine times the corresponding figure for the poorest 5%. As Parente and Prescott
[1993] quite correctly observed, interstate disparities within the United States do not even come
close to these international figures. In 1985, the richest state in the United States was Connecticut and
the poorest was Mississippi, and the ratio of per capita incomes worked out at around 2!
Of course, the fact that the richest 5% of countries bear approximately the same ratio of incomes
(relative to the poorest 5%) over this twenty-five year period suggests that the entire distribution has
remained stationary. Of greatest interest, and continuing well into the nineties, is the meteoric rise of
the East Asian economies: Japan, Korea, Taiwan, Singapore, Hong Kong, Thailand, Malaysia,
Indonesia, and, more recently, China. Over the period 1965–90, the per capita incomes of the
aforementioned eight East Asian economies (excluding China) increased at an annual rate of 5.5%.
Between 1980 and 1993, China’s per capita income grew at an annual rate of 8.2%, which is truly
phenomenal. For the entire data set of 102 countries studied by Parente and Prescott, per capita
growth averaged 1.9% per year over the period 1960–85.
In contrast, much of Latin America and sub-Saharan Africa languished during the 1980s. After
relatively high rates of economic expansion in the two preceding decades, growth slowed to a crawl,
and in many cases there was no growth at all. Morley’s [1995] study observed that in Latin America,
per capita income fell by 11% during the 1980s, and only Chile and Colombia had a higher per capita
income in 1990 than they did in 1980. It is certainly true that such figures should be treated cautiously,
given the extreme problems of accurate GNP measurement in high-inflation countries, but they
illustrate the situation well enough.
Similarly, much of Africa stagnated or declined during the 1980s. Countries such as Nigeria and
Tanzania experienced substantial declines of per capita income, whereas countries such as Kenya and
Uganda barely grew in per capita terms.
Diverse growth experiences such as these can change the face of the world in a couple of decades.
One easy way to see this is to study the “doubling time” implicit in a given rate of growth; that is, the
number of years it takes for income to double if it is growing at some given rate. The calculation in
the footnote7 reveals that a good approximation to the doubling time is seventy divided by the annual
rate of growth expressed in percentage terms. Thus an East Asian country growing at 5% per year
will double its per capita income every fourteen years! In contrast, a country growing at 1% per year
will require seventy years. Percentage growth figures look like small numbers, but over time, they
add up very fast indeed.
The diverse experiences of countries demand an explanation, but this demand is ambitious.
Probably no single explanation can account for the variety of historical experience. We know that in
Latin America, the so-called debt crisis (discussed more in Chapter 17) triggered enormous
economic hardship. In sub-Saharan Africa, low per capita growth rates may be due, in large measure,
to unstable government and consequent infrastructural breakdown, as well as to recent high rates of
population increase (on this, see Chapters 3 and 9). The heady successes of East Asia are not fully
understood, but a conjunction of farsighted government intervention (Chapters 17), a relatively equal domestic income distribution (Chapters 6 and 7), and a vigorous entry into international markets
played an important role. As you may have noted from the occasional parentheses in this paragraph,
we will take up these topics, and many others, in the chapters to come.
Thus it is quite possible for the world distribution of income to stay fairly constant in relative
terms, while at the same time there is plenty of action within that distribution as countries climb and
descend the ladder of relative economic achievement. Indeed, the few countries that we have cited as
examples are no exceptions. Figure 2.4 contains the same exercise as Chart 10 in Parente and Prescott
[1993]. It shows the number of countries that experienced changes in income (relative to that of the
United States) of different magnitudes over the years 1960–85.
Figure 2.4 indicates two things. First, a significant fraction (well over half) of countries changed
their position relative to the United States by an average of one percentage point or more per year,
over the period 1960–85. Second, the figure also indicates that there is a rough kind of symmetry
between changes upward and changes downward, which partly accounts for the fact that you don’t see
much movement in the world distribution taken as a whole. This observation is cause for much hope
and some trepidation: the former, because it tells us that there are probably no traps to ultimate
economic success, and the latter, because it seems all too easy to slip and fall in the process.
Economic development is probably more like a treacherous road, than a divided highway where only
the privileged minority is destined to ever drive the fast lane.
Figure 2.4. Annual percentage change in PPP income of different countries relative to U.S. levels, 1960–85. Source: Penn
World Tables.
This last statement must be taken with some caution. Although there appears to be no evidence that
very poor countries are doomed to eternal poverty, there is some indication that low incomes are very
sticky. Even though we will have much more to say about the hypothesis of ultimate convergence of
all countries to a common standard of living (see Chapters 3–5), an illustration may be useful at this
stage. Quah [1993] used per capita income data to construct “mobility matrices” for countries. To
understand how these matrices work, let’s start by converting all per capita incomes to fractions of
the world’s per capita income. Thus, if country X has a per capita income of $1,000 and the world average is $2,000, we give country X an index of 1/2. Now let’s create categories that we will put
each country into. Quah used the following categories (you can certainly use others if you like): 1/4,
1/2, 1, 2, and ∞. For instance, a category with the label 2 contains all countries with indexes between
1 and 2; the category 1/4 contains all countries with indexes less than 1/4; the category oo contains all
countries with indexes exceeding 2, and so on.
Now imagine doing this exercise for two points in time, with a view to finding out if a country
transited from one category to another during this period. You will generate what we might call a
mobility matrix. The diagram in Figure 2.5 illustrates this matrix for the twenty-three year period
1962–84, using the Summers–Heston data set. The rows and columns of the matrix are exactly the
categories that we just described. Thus a cell of this matrix defines a pair of categories. What you see
is a number in each of these cells. Look, for instance, at the entry 26 in the cell defined by the
categories 1 (row) and 2 (column). This entry tells us the percentage of countries that made the
transition from one category to the other over the twenty-three year period. In this example, therefore,
26% of the countries who were between half the world average and the world average in 1962
transited to being between the world average and twice the world average. A matrix constructed in
this way gives you a fairly good sense of how much mobility there is in relative per capita GNP
across nations. A matrix with very high numbers on the main diagonal, consisting of those special
cells with the same row and column categories, indicates low mobility. According to such a matrix,
countries that start off in a particular category have a high probability of staying right there.
Conversely, a matrix that has the same numbers in every entry (which must be 20 in our 5 × 5 case,
given that the numbers must sum to 100 along each row) shows an extraordinarily high rate of
mobility. Regardless of the starting point in 1962, such a matrix will give you equal odds of being in
any of the categories in 1984.
Figure 2.5. The income mobility of countries, 1962–84. Source: Quah [1993].
With these observations in mind, continue to stare at Figure 2.5. Notice that middle-income
countries have far greater mobility than either the poorest or the richest countries. For instance,
countries in category 1 (between half the world average and the world average) in 1962 moved away to “right” and “left”: less than half of them remained where they were in 1962. In stark contrast to
this, over three-quarters of the poorest countries (category 1/4) in 1962 remained where they were,
and none of them went above the world average by 1984. Likewise, fully 95% of the richest countries
in 1962 stayed right where they were in 1984.8 This is interesting because it suggests that although
everything is possible (in principle), a history of underdevelopment or extreme poverty puts countries
at a tremendous disadvantage.
This finding may seem trite. Poverty should feed on itself and so should wealth, but on reflection
you will see that this is really not so. There are certainly many reasons to think that historically low
levels of income may be advantageous to rapid growth. New technologies are available from the
more developed countries. The capital stock is low relative to labor in poor countries, so the
marginal product of capital could well be high. One has, to some extent, the benefit of hindsight: it is
possible to study the success stories and avoid policies that led to failures in the past. This account is
not meant to suggest that the preceding empirical finding is inexplicable: it’s just to say that an a
priori guess does not yield straightforward answers. We will have much more to say on this topic
throughout the book.
There is actually a bit more to Figure 2.5 than lack of mobility at the extremes. Look at the next-topoorest
category (those with incomes between one-quarter and one-half of the world average in
1962). Note that 7% of these countries transited to incomes above the world average by 1984.
However, over half of them dropped to an even lower category. Thus it is not only the lowest-income
countries that might be caught in a very difficult situation. In general, at low levels of income, the
overall tendency seems to be movement in the downward direction.
To summarize, then, we have the following observations.
(1) Over the period 1960–1985, the relative distribution of world income appears to have been
quite stable. The richest 5% of the world’s nations averaged a level of per capita income that was
about 29 times the corresponding figure for the poorest 5%. By any standards, this disparity is
staggering, and especially so when we remember that we are talking about incomes that have been
corrected for purchasing power parity.
(2) The fact that the overall distribution has remained stationary does not mean that there has been
little movement of countries within the world distribution. Of particular interest in the 1980s is the
rise of the East Asian economies and the languishing of other economies, particularly those of sub-
Saharan Africa and Latin America. Diverse growth experiences such as these can change the
economic composition of the world in the space of a few decades. Nonetheless, a single explanation
for this diversity remains elusive.
(3) The observation that several countries have changed relative positions suggests that there are
no ultimate traps to development. At the same time, a history of wealth or poverty does seem to partly
foretell future developments. The mobility of countries appears to be highest somewhere in the
middle of the wealth distribution, whereas a history of underdevelopment or extreme poverty appears
to put countries at a disadvantage.
(4) That history matters in this way is an observation that requires a careful explanation. Poor
countries do seem to have some advantages. They can use, relatively free of charge, technologies that
are developed by their richer counterparts. Scarce capital in these countries should display a higher
rate of profit, because of the law of diminishing returns. They can learn from mistakes that their predecessors have made. In this way differences across countries should iron themselves out over the
longer run. Thus the observation that history matters in maintaining persistent differences needs more
of a justification than might be obvious at first glance.
2.3. Income distribution in developing countries
The international disparity of national income is only one indication that something is fundamentally
askew with global development. Add to this the astonishing inequalities observable within each of
the vast majority of developing countries. It is commonplace to see enormous wealth coexisting with
great poverty, and nowhere is this more evident than on the streets of Bombay, Rio de Janeiro,
Manila, Mexico City, and the other great urban conglomerates of the developing world. It isn’t that
such inequalities do not exist in the developed world—they certainly do—but coupled with the low
average income of developing countries, these disparities result in an outcome of visible poverty and
destitution.
We will have much more to say on the topic of income distribution later in this book (see
especially Chapters 6 and 7). As an overview, however, it is useful to get a feel for the magnitude of
the problem by looking at some data.9 Figure 2.6 summarizes recent information on inequality for
selected countries, spanning the range between poorest and richest.10 The figure records the income
share of the poorest 40% of the population as well as the income share of the richest 20% of the
population. By simply eyeballing the data, you can see that the poorest 40% of the population earn, on
average, around 15%—perhaps less—of overall income, whereas the richest 20% earn around half
of total income. Even though there is plenty of variation around these averages (see subsequent
discussion), this is a large discrepancy. Remember, moreover, that to understand how these
inequalities affect the poorest people in each country, we must compound this intracountry inequality
with the intercountry differences that we already discussed. The poor are twice cursed: once for
living in countries that are poor on average, and then again for being on the receiving end of the high
levels of inequality in those countries.
Figure 2.6 also plots tentative trends in these shares as we move from poor to rich countries.
There appears to be a tendency for the share of the richest 20% to fall, rather steeply in fact, as we
cross the $8,000 per capita income threshold (1993 PPP). However, there is also a distinct tendency
for this share to rise early on in the income scale (mentally shut out the patch after $8,000 and look at
the diagram again). The overall tendency, then, is for the share of the richest 20% to rise and then fall
over the cross section of incomes represented in the diagram. The share of the poorest 40% displays
the opposite relationship, although it is somewhat less pronounced. At both extremes of the income
scale, the share is relatively high, and falls to a minimum around the middle (in the cluster
represented by $4,000–9,000 of per capita income).
The two trends together suggest, very tentatively indeed, that inequality might rise and then fall as
we move from lower to higher incomes. This is the essence of a famous hypothesis owing to Kuznets
[1955] that is known as the inverted U (referring to the shape traced by rising and then falling
inequality). We will take a closer look at this relationship in Chapter 7. For now, nothing is really
being said about how inequality in a single country changes over time: what we have here is a
snapshot running over different countries.
South Asian countries, such as India, Bangladesh, and Sri Lanka, many African countries, such as
Tanzania, Uganda, Kenya, Senegal, Nigeria, and Ghana, and a few of the poorer Latin American
countries, such as El Salvador, Nicaragua, and Bolivia, populate the first stretch in this diagram. Then
come the middle-income countries, with a large concentration of Latin American nations—
Guatemala, Peru, Brazil, Colombia, Costa Rica, Mexico, Chile, Panama—as well as fast-growing
Asian countries such as Thailand and Malaysia. At the $9,000 mark we hit countries such as Korea,
Puerto Rico, Portugal, and Mauritius, and this is the approximate region in which we see a drop in the
income share of the richest 20%. We then move into the rich countries, mainly European and North
American, with a sprinkling of East Asian nations—Singapore, Japan, and Hong Kong—among them.
Specific data on income and inequality are provided for a subsample of countries in Table 2.1.
The data presented here suggest that economic development is an inherently uneven process. At
very low levels of income, average levels of living are very low, and so it is very difficult to squeeze
the income share of the poorest 40% below a certain minimum. For such countries the income share
of the rich, although high, is nowhere close to the extraordinarily high ratios observed in middleincome
countries. This indicates the possibility that as economic growth proceeds, it initially benefits
the richest groups in society more than proportionately. This situation is reflected in a rise in the income share of the upper quintile of the population. The share of the poorest groups tends to fall at
the same time, although this does not mean that their income goes down in absolute terms. At higher
levels of per capita income, economic gains tend to be distributed more equally—the poorest
quintiles now gain in income share.
Table 2.1. Shares of poorest 40% and richest 20% for selected countries.
Source: World Development Report (World Bank [1995]) and Deininger and Squire [1996a].
It is worth noting (and we will say this again in Chapter 7) that there is no inevitability about this
process. Countries that pursue policies of broad-based access to infrastructure and resources, such as
health services and education, will in all likelihood find that economic growth is distributed
relatively equally among the various groups in society. Countries that neglect these features will show
a greater tendency toward inequality. Indeed, matters are actually more complicated than this. These
policies may in turn affect the overall rate of growth that a country can sustain. Although many of us
might want to believe that equity and growth go hand in hand, this may well turn out to be not true, at
least in some situations. The need to discuss this crucial interaction cannot be overemphasized.
The combination of low per capita incomes and the unequal distribution of them means that in
large parts of the developing world, people might lack access to many basic services: health, sanitation, education, and so on. The collection of basic indicators that makes up the nebulous concept
of progress has been termed human development, and this is what we turn to next.
2.4. The many faces of underdevelopment
2.4.1. Human development
Income is distributed unequally within all countries, and especially so in developing countries.
We also noticed a fair degree of variation in inequality across countries: middle-income countries
have significantly higher inequality. This variation suggests that excessive reliance on GNP per capita
as a reliable indicator of overall development might well be dangerous. A relatively prosperous
country may fare poorly on some of the commonsense indicators of development, such as literacy,
access to drinking water, low rates of infant mortality, life expectancy, and so on. In part, this is
because income is distributed unequally, but other features may be at work as well. The social and
economic empowerment of women may serve to significantly reduce infant mortality and (more
generally) raise the health and nutritional status of children, yet neither income nor its equal
distribution across households fully guarantees the empowerment of women. Likewise, a country that
promotes popular science and health education programs might be a welcome outlier in the health
category, even though income may be low or poorly distributed. Later in this section, we will
emphasize the overall correlation of “human development” with per capita income, but it is
worthwhile to be sensitive to the outliers, because they tell a different story.
Consider the countries of Guatemala and Sri Lanka. The income and income distribution data for
these two countries have been extracted from Table 2.1 and reproduced in Table 2.2.
Table 2.2 informs us that in 1993, Guatemala had per capita income that exceeded that of Sri
Lanka, but the distribution of this income speaks for itself. In Guatemala, the poorest 40% of the
population had access to a bit less than 8% of national income. The corresponding share for Sri
Lanka is almost three times as large.
Now look at some of the “human development” indicators for these two countries, compiled in
Table 2.3. Except for access to safe water, these indicators are very different indeed. Life expectancy
is a good seven years higher in Sri Lanka. Much of this difference stems from the huge difference in
the infant mortality rate, defined as the number of children (per thousand live births) who die before
the age of 1. In Sri Lanka this figure is eighteen per thousand; in Guatemala it is more than two and a
half times higher. Sri Lanka has an adult literacy rate of close to 90%; that of Guatemala is only 54%.
Looking at these two tables, it is hard to escape the conclusion that the highly unequal distribution
of income in Guatemala is responsible, at least in part, for these differences in some natural
yardsticks of development.
However, that isn’t the whole story. Even a relatively equal distribution of income may not be
enough. Of course, one reason for this is that per capita income is not high. For instance, however
stunning the efforts of Sri Lanka are, countries such as Hong Kong do better simply because there are
more resources to play with. However, what about a country such as Pakistan? The 1995 World
Development Report lists Pakistan with a 1993 per capita GDP of $2,170. The poorest 40% of the
population earn 21% of the total income. These overall figures are similar to those of Sri Lanka, but
Pakistan has a life expectancy of only 62 and an infant mortality rate of ninety-one per thousand, five times that of Sri Lanka. The literacy rate for Pakistan was only 36% in 1992—significantly less than
half that of Sri Lanka. Clearly, government policies, such as those concerning education and health,
and the public demand for such policies, play significant roles.
Table 2.2. Shares of poorest 40% and richest 20% for Sri Lanka and Guatemala.
Source: World Development Report (World Bank [1995]) and Deininger and Squire [1996].
Table 2.3. Indicators of “human development” for Sri Lanka and Guatemala.
Source: Human Development Report (United Nations Development Programme [1995]).
Note: All data are for 1992, except for access to safe water, which is the 1988–93 average.
2.4.2. An index of human development
Many of the direct physical symptoms of underdevelopment are easily observable and
independently measurable. Undernutrition, disease, illiteracy—these are among the stark and
fundamental ills that a nation would like to remove through its development efforts. For quite some
time now, international agencies (like the World Bank and the United Nations) and national statistical
surveys have been collecting data on the incidence of malnutrition, life expectancy at birth, infant
mortality rates, literacy rates among men and women, and various other direct indicators of the health,
educational, and nutritional status of different populations.
As we have seen, a country’s performance in terms of income per capita might be significantly
different from the story told by these basic indicators. Some countries, comfortably placed in the
“middle-income” bracket, nevertheless display literacy rates that barely exceed 50%, infant mortality
rates close to or exceeding one hundred deaths per thousand, and undernourishment among a
significant proportion of the population. On the other hand, there are instances of countries with low
and modestly growing incomes, that have shown dramatic improvements in these basic indicators. In
some categories, levels comparable to those in the industrialized nations have been reached.
The United Nations Development Programme (UNDP) has published the Human Development
Report since 1990. One objective of this Report is to coalesce some of the indicators that we have
been discussing into a single index, which is known as the human development index (HDI). This is
not the first index that has tried to put various socioeconomic indicators together. A forerunner is
Morris’ “physical quality of life index” (Morris [1979]), which created a composite index from three indicators of development: infant mortality, literacy, and life expectancy conditional on reaching the
age of 1.
The HDI has three components as well. The first is life expectancy at birth (this will indirectly
reflect infant and child mortality).11 The second is a measure of educational attainment of the society.
This measure is itself a composite: it takes a weighted average of adult literacy (with weight 2/3) and
a combination of enrollment rates in primary, secondary and tertiary education (with weight 1/3). The
last component is per capita income, which is adjusted somewhat after a threshold (around $5,000 in
PPP dollars, 1992) is crossed. Less weight is given to higher incomes after this point, on the grounds
that there is diminishing marginal utility to higher incomes. The HDI is calculated by defining some
notion of a country’s achievements in each of these three components, and then taking a simple
average of the three indicators.
The creation of composites from such fundamentally different indicators as life expectancy and
literacy is a bit like adding apples and oranges. It is arguable that rather than create composites, the
reader should view the different indicators (as we will do presently) and then judge the overall
situation for herself. The advantage of a composite index is its simplicity and, of course, its political
power: in this era of sound bites, it is far easier and appears to be more “scientific” to say that
country X has an “index” of 8 out of 10, rather than laboriously to detail that country’s achievements
(or lack of them) in five different spheres of development.12 The HDI might look scientific and the
formulae used to create the final average might look complex, but that is no reason to accept the
implicit weighting scheme that it uses, because it is just as ad hoc as any other. It cannot be otherwise.
Nevertheless, the HDI is one way to combine important development indicators, and for this reason it
merits our attention.
The HDI creates, for each country, a final number that takes a value somewhere between 0 and 1.
The number is to be (tentatively) interpreted as the “fraction of ultimate development” that has been
achieved by the country in question. Because these notions of “ultimate bliss” are embodied in the
HDI, it is not surprising that the indicator is relatively varied among the poorer countries, but then
flattens out sharply as we move into richer countries.13 Thus statements made in the Report, such as
“the HDI of industrial countries (0.916) is only 1.6 times higher than that of developing countries
(0.570), even though their real GDP per capita (PPP$) is 6 times higher,” are meaningless.14
Although such comparisons of ratios simply do not make sense, the rankings generated by the HDI
are of some interest because they illustrate how it is possible for a relatively high-income country to
fare so badly in meeting basic socioeconomic goals that its HDI index falls behind that of a relatively
poor country. One way to show how this happens is to present the HDI ranking for different countries
as well as the rankings induced by per capita GDP. It is then possible to study the difference in the
two rankings induced by these two measures. A positive difference means that the country has done
better in “human development” relative to its position in the GDP rankings; a negative ranking means
the opposite. What about the examples of our previous section: Sri Lanka, Guatemala, and Pakistan?
The ranking approach justifies what we already saw on the basis of specific indicators. Sri Lanka has
a positive rank differential of +5. Guatemala and Pakistan have negative rank differentials of -20 and
-28, respectively.
2.4.3. Per capita income and human development
There is little doubt, then, that per capita income, or even the equality of its distribution, does not serve as a unilateral guarantee of success in “human development.” This sentiment is captured very
well in one of the views of development with which we started this chapter.
At the same time, the apparently narrow perspective of mainstream economists, with its hardnosed
focus on per capita income as a summary statistic of development, may not be too out of line. It
is arguable that although taking a wider and multidimensional view of development is conceptually
correct, per capita GDP still acts as a fairly good proxy for most aspects of development.15 For
instance, it can be argued that rising income levels ultimately and inevitably translate into better
health, nutritional, and educational standards in a population. It is, therefore, a useful exercise to see
from cross-country data, how much “explanatory power” per capita GDP has over other basic
indicators.
One way to go about this exercise is to collect data on per capita income as well as on some other
facet of development that we might be interested in, and then connect the two by means of a scatter
diagram (see Appendix 2). In brief, a scatter diagram allows us to eyeball possible relationships
between a dependent variable whose variation we are seeking to explain (such as infant mortality or
life expectancy) and one or more independent variables whose variation presumably “explains” the
changes of the dependent variable. In the present situation, our independent variable is per capita
income.
In this section, we chose three indicators of development that are of interest: life expectancy at
birth, the infant mortality rate, and the adult literacy rate. To be sure, these indicators are not entirely
independent of each other. For instance, life expectancy includes the possibility of dying before the
age of 1, which is infant mortality. Nevertheless, these are common indicators that enter into indexes
of development, such as the HDI or the physical quality of life index.
Figures 2.7–2.9 simply plot the relationship between these variables and per capita income, by
looking at a cross section of countries. It is only to be expected that as we move into the range of
countries with very high per capita income, these indicators will be at high levels as well, and they
are. So as not to dwarf the entire exercise by these extremes, we leave out all countries with per
capita income exceeding $9,000 PPP (1993). In principle, this makes the case against per capita
income stronger. Variation in income here is somewhat smaller, and there is therefore much room for
other policies or characteristics to affect the outcome.
Each of these diagrams has a cross-bar that is drawn at the average values of the data in that
sample. For instance, the cross-bar in Figure 2.8 is located at an average per capita income of $3,500
and a literacy rate of around 72% (which are the averages for the sample). The scattered dots are the
observations, of course. The idea of the diagram is to check the correlation between the two
variables concerned (see Appendix 2). If the correlation is expected to be positive, for instance, we
would expect most of the dots to lie in the northeast and southwest quadrants of the cross-bar. This is
our expectation for literacy and life expectancy. For infant mortality, we expect the relationship to be
negative, so the observations should lie in the northwest and southeast quadrants of the cross-bar.
By and large, the relationship between per capita income alone and each of these variables is
strikingly strong. In each of the three cases, the great majority of the observations lie within the
expected quadrants. The figures speak for themselves to express the idea that per capita income is a
powerful correlate of development, no matter how broadly we conceive of it. Thus we must begin,
and we do so, with a study of how per capita incomes evolve in countries. This is the subject of the
theory of economic growth—a topic that we take up in detail in the chapters to come.

A further point needs to be stressed. By looking at the actual levels of achievement in each of
these indicators, rather than just the ranking across countries that they induce, I have actually made
life more difficult for the argument in favor of per capita income. In an influential book, Dasgupta
[1993] showed that per capita income is correlated even more highly with other indicators of
development if we consider ranks rather than cardinal measures. In other words, if we rank countries
according to their per capita GDP levels and then compute similar ranks based on some other index
(such as adult literacy, child mortality, etc.), then we find a high degree of statistical correspondence
between the two sets of ranks if the set of countries is sufficiently large and wide ranging. Because I
have already carried out cardinal comparisons, I will skip a detailed discussion of these matters and
simply refer you to Dasgupta’s study for a more thorough reading.16
The point of this section is not to discredit human development, but only to show that we must not
necessarily swing our opinions to the other extreme and disregard per capita income altogether. To be
more emphatic, we must take per capita income very seriously, and it is in this spirit that we can
appreciate the seemingly narrow quotation from Robert Lucas at the beginning of this chapter.
To complete this delicate balancing act, note finally that the relationship between per capita
income and the other indicators is strong but far from perfect (otherwise the data would all lie on
some smooth curve linking the two sets of variables). The imperfect nature of the relationship is just a
macroreflection of what we saw earlier with countries such as Sri Lanka, Pakistan, and Guatemala.
Inclusion of the distribution of per capita income would add to this fit, but even then matters would
remain undecided: social and cultural attitudes, government policy, and the public demands for such
policies, all would continue to play their role in shaping the complex shell of economic development.
Thus it is only natural that we concentrate on economic growth and then move on to other pressing
matters, such as the study of income distribution and the operation of various markets and institutions
2.5. Some structural features
Our final objective in this chapter is to provide a quick idea of the structural characteristics of
developing countries. We will examine these characteristics in detail later in the book.
2.5.1. Demographic characteristics
Very poor countries are characterized by both high birth rates and high death rates. As
development proceeds, death rates plummet downward. Often, birth rates remain high, before they
finally follow the death rates on their downward course. In the process, a gap opens up (albeit
temporarily) between the birth and death rates. This leads to high population growth in developing
countries. Chapter 9 discusses these issues in detail.
High population growth has two effects. It means that overall income must grow faster to keep per
capita growth at reasonable levels. To be sure, the fact that population is growing helps income to
grow, because there is a greater supply of productive labor. However, it is not clear who wins this
seesaw contest: the larger amount of production or the larger population that makes it necessary to
divide that production among more people. The negative population effect may well end up dominant,
especially if the economy in question is not endowed with large quantities of capital (physical or
human).
A second effect of high population growth (or high birth rates, to be precise) is that the overall
population is quite young. It is easy to get an intuition for this: high birth rates mean that a
proportionately larger number of children are always entering the population at any given point of
time. This means that the population is heavily weighted in favor of children. This may be quite
delightful, as any of us who has grown up with several brothers, sisters, and cousins knows, but it
does not change the grim reality of utter economic dependence, especially for those in poverty. There
are many untoward consequences of an abnormally young population, and these include poverty, child
labor, and low education.
Figure 2.10 shows us how population growth rates vary with per capita income. The thin line
plots annual growth rates of population for 1970–80; the thick line does the same for 1980–93. In
both cases the horizontal axis records 1993 percapita income (PPP). The variation is substantial
(remember: per capita income isn’t everything!), but there is a clear downward trend in the growth
rate, both with per capita income and over time (for the same country).
2.5.2. Occupational and production structure
Agriculture accounts for a significant fraction of production in developing countries. Indeed, given
that substantial agricultural output is produced for self-consumption and so may not be picked up in
the data, the proportion is probably higher than that revealed by the published numbers. For the
poorest forty-five countries for which the World Bank publishes data, called the low-income
countries, the average proportion of output from agriculture is close to 30%. Remember that the
poorest forty-five countries include India and China and therefore a large fraction of the world’s
population. Data for the so-called middle-income countries, which are the next poorest sixty-three
countries and include most Latin American economies, is somewhat sketchier, but the percentage
probably averages around 20%. This stands in sharp contrast to the corresponding income shares
accruing to agriculture in the economically developed countries: around 1–7%.
Even more striking are the shares of the labor force living in rural sectors. For the aforementioned
low-income category, the share averaged 72% in 1993 and was as high as 60% for many middleincome
countries. The contrast with developed countries is again apparent, where close to 80% of the
labor force is urbanized. Even then, a large fraction of this nonurban population is so classified
because of the “commuter effect”: they are really engaged in nonagricultural activity although they
live in areas classified as rural. Although a similar effect is not absent for developing countries, the
percentage is probably significantly lower.
Figure 2.11 displays the share of the labor force in agriculture as we move over different
countries indexed by per capita income. The downward trend is unmistakable, but so are the huge
shares in agriculture for both low- and middle-income countries.
Figure 2.11. Fractions of the labor force in agriculture. Source: World Development Report (World Bank [1996]).
Clearly, agricultural activity forms a significant part of the lives of people living in developing
countries. We therefore devote a good part of this book to agricultural arrangements: the hiring of
labor, the leasing of land, and the operation of credit markets. The overall numbers for production and
occupational structure suggest that agriculture often has lower productivity than other economic
activities. This is not surprising. In many developing countries, capital intensity in agriculture is at a
bare minimum, and there is often intense pressure on the land. Add to this the fact that agriculture,
especially when not protected by assured irrigation and ready availability of fertilizer and pesticides,
can be a singularly risky venture. Many farmers bear enormous risks. These risks may not look very
high if you count them in U.S. dollars, but they often make the difference between bare-bones
subsistence (or worse) and some modicum of comfort.
2.5.3. Rapid rural–urban migration
With the above-mentioned features, it is hardly surprising that an enormous amount of labor moves from rural to urban areas. Such enormous migrations deserve careful study. They are an outcome of
both the “push” from agriculture, because of extreme poverty and growing landlessness, and the
perceived “pull” of the urban sector. The pulls are reinforced by a variety of factors, ranging from the
comparatively high wages and worker protections offered in the organized urban sectors to the effect
of the media in promoting the urban lifestyle as a desirable end in itself. The media is often
misleading and so are the benefits of the organized sector, which are often accessible only to a lucky
minority of workers.
Consider the rates of growth of the urban sector in developing countries. For the forty-five lowincome
countries covered by the World Bank, the average rate of urban population growth over the
period 1980–93 was 3.9% per year. Compare this with an average rate of population growth of 2%
per year for the same countries over the same period of time. Urban growth was simply double that of
overall population growth for these countries. Imagine, then, the pressure on the cities of these
countries. For the sixty-three countries classified as middle-income by the Bank, the urban growth
rate was 2.8% per annum over the period 1980–93, to be compared with a population growth rate of
1.7% per year. Once again, we see evidence of a pressure on the urban sector that is just not captured
by the overall population growth figures. On the other hand, the high-income developed countries
exhibit near balance: urban populations grew at 0.8% per year, while overall population grew at
0.6% per year.
This is not to say that such migrations are somehow undesirable. Indeed, how did developed
countries get to the point that they are now at? The fact of the matter, however, is that all these
processes are accelerated in modern-day developing countries, and the speed-up imposes enormous
strains.
One piece of evidence that reveals these strains is the fact that an unusually large fraction of the
population in developing countries is classified as being in the tertiary or “services” sector. Before
we take a look at the data, it is useful to conceptualize matters a bit. Think of what we consume as our
income increases. Our first needs are for food and clothing. As we have more income to spare we
switch to industrial products: radio, television, bicycles, automobiles, and the like. At a still higher
level of income we begin to register a high demand for services: banking, tourism, restaurants, and
travel. It is not surprising, then, that the developed countries allocate a large fraction of their
nonagricultural labor force to the services sector. Countries such as Australia, the United States, the
United Kingdom, Norway, and Sweden have about 70% of the total labor force in the services sector;
the corresponding figures for some other developed countries such as Japan are somewhat lower.
That isn’t odd at all. What is odd is that many developing countries exhibit large fractions of the labor
force in “services” as well!
Figure 2.12 illustrates the general point and Table 2.4 provides data for particular countries.
Expressed as a fraction of the nonagricultural labor force, the proportion in the services sector is not
at all different from what we see in developed countries. At the same time, the proportion of people
in agriculture does vary a great deal, as we have already seen. What we are seeing, then, for
developing countries, is a classification of a large part of the labor force into “services” simply
because such services are waiting positions or fallback options for laborers lacking an industrial job.
That is, the enormous services sector in developing countries is symptomatic of the development of
the unorganized or informal sector, on which we will have more to say in Chapter 10. This sector is
the home of last resort—the shelter for the millions of migrants who have made their way to the cities
from the rural sector. People who shine shoes, petty retailers, and middlemen: they all get lumped under the broad rubric of services because there is no other appropriate category. It is fitting that the
World Bank Tables refer to this sector as “Services, etc.” The large size of this sector in developing
countries is, in the main, a reflection of the inability of industry in these countries to keep up with the
extraordinary pace of rural–urban migration.
Figure 2.12. Nonagricultural labor in services. Source: World Development Report (World Bank [1996]).
2.5.4. International trade
By and large, all countries, rich and poor, are significantly involved in international trade. A quick
plot of the ratio of exports and imports to GNP against per capita income, does not reveal a
significant trend. There are large countries, such as India, the United States, and Mexico for which
these ratios are not very high—perhaps around 10% on average. Then again, there are countries such
as Singapore and Hong Kong for which these ratios attain astronomical heights—well over 100%.
The modal ratios of exports and imports to GNP are probably around 20%. Trade is an important
component of the world economy. The differences between developing and developed countries are more pronounced when we look
at the composition of trade. Developing countries are often exporters of primary products. Raw
materials, cash crops, and sometimes food are major export items. Textiles and light manufactured
items also figure on the list. In contrast, the bulk of exports from developed countries is in the
category of manufactured goods, ranging from capital goods to consumer durables. Of course, there
are many exceptions to these broad generalizations, but the overall picture is broadly accurate, as
Figure 2.13 shows. This figure plots the share of exports that comprise primary products against per
capita income. We have followed the now-familar method of using cross-bars at the mean levels of
per capita income and primary share (unweighted by population) to eyeball the degree of correlation.
It is clear that, on the whole, developing countries do rely on primary product exports, whereas the
opposite is true for the developed countries.
Notice that there are some developing countries that have a low ratio of primary exports.
Countries such as China, India, the Philippines, and Sri Lanka are among them. These countries and
many of their compatriots are attempting to diversify their exports away from primary products, for
reasons that we indicate subsequently and discuss at greater length later in the book. At the same time,
there are developed countries that export primaries to a great degree. Australia, New Zealand, and
Norway are among them.
The traditional explanation for the structure of international trade comes from the theory of
comparative advantage, which states that countries specialize in the export of commodities in which
they have a relative cost advantage in production. These cost advantages might stem from differences
in technology, domestic consumption profiles, or the endowment of inputs that are particularly
conducive to the production of certain commodities. We review this theory in Chapter 16. Because
developing countries have a relative abundance of labor and a relative abundance of unskilled labor
within the labor category, the theory indeed predicts that such countries will export commodities that
intensively use unskilled labor in production. To a large extent, we can understand the aforementioned
trade patterns using this theory.
At the same time, the emphasis on primary exports may be detrimental to the development of these
countries for a variety of reasons. It appears that primary products are particularly subject to large
fluctuations in world prices, and this creates instability in export earnings. Over the longer run, as
primary products become less important in the consumption basket of people the world over, a
declining price trend might be evident for such products as well.
The definite existence of such a trend is open to debate. At the same time, we can see some broad
indication of it by studying how the terms of trade for different countries have changed over recent
decades. The terms of trade for a country represent a measure of the ratio of the price of its exports to
that of its imports. Thus an increase in the terms of trade augers well for the trading prospects of that
country, whereas a decline suggests the opposite. Figure 2.14 plots changes in the terms of trade over
the period 1980–93 against per capita income. There is some indication that the relationship is
positive, which suggests that poor countries are more likely than richer ones to face a decline in their
terms of trade. Primary exports may underlie such a phenomenon.
In general, then, activities that have comparative advantage today might not be well suited for
export earnings tomorrow. The adjustment to a different mix of exports then becomes a major
concern. Finally, technology often is assimilated through the act of production. If production and
exports are largely limited to primary products, the flow of technology to developing countries may
be affected. We discuss these issues in Chapter 17.
The import mix of developing countries is more similar to that of developed countries. Exporters
of primary products often need to import primary products as well: thus India might be a major
importer of oil and Mexico a major importer of cereals. Primary exports for each country are often
concentrated in a handful of products, and there is no contradiction in the fact that primaries are both
exported and imported. A similar argument establishes that although developed countries might export
manufactured items, there is always a need for other manufactures that are in relatively short supply.
Trade patterns in this aggregated form are therefore quite similar over countries, as Figure 2.15
reveals.
We summarize: developing countries are likely to have a high ratio of primary goods in their total
exports, but as far as imports are concerned, there is significantly less variation.
2.6. Summary
We began with a discussion of what the term economic development might mean. It is a multifaceted
concept, embodying not just income and its growth, but also achievements on other fronts: reductions
in infant mortality, higher life expectancy, advances in literacy rates, widespread access to medical
and health services, and so on. Per capita income is sometimes used as an (incomplete) indicator for
overall economic development, but should not be identified conceptually with development in the
broader sense.
We turned next to per capita income data for countries. Using exchange rates to convert local
currencies into dollars, we obtained per capita income evaluated according to the exchange rate
method. The disparities across countries is enormous. Some of this disparity is due to underreporting
of income, but a far more serious problem arises from the fact that price levels are systematically
different across countries: dollar prices for nontraded goods and services tend to be lower in
developing countries. The purchasing power parity method attempts to correct for this by constructing international prices that are used to estimate national incomes. Cross-country disparities
in per capita income are then smaller, but still large: the richest 5% of the world’s nations averaged a
per capita income that was about twenty-nine times the corresponding figure for the poorest 5%, over
the period 1960–85.
There have been substantial changes in incomes for many countries. The meteoric rise of East
Asia is a case to be noted. This case is contrasted with the fact that much of Latin America and sub-
Saharan Africa languished during the 1980s. Thus, although the world distribution of income
remained fairly unchanged in relative terms, there was plenty of movement within that distribution.
However, there is evidence that a history of underdevelopment or extreme poverty feeds on itself.
Using mobility matrices, we noted that middle-income countries have significantly higher mobility
than either the poorest or the richest countries.
Next, we studied income distribution within countries. By and large, income is more unequally
distributed in developing countries than in their developed counterparts, which suggests that the poor
in developing countries are twice hit: once at the level of distribution across countries and then at the
level of distribution within countries. Income distribution is particularly bad for middle-income
countries, and most of this extreme inequality appears to be located in Latin America.
With income and income distribution out of the way, we then returned to the broader notion of
development. The Human Development Index is the name given to a set of indicators developed by
the United Nations Development Programme. It combines three indicators—life expectancy at birth,
educational attainment, and per capita income—with weights to arrive at a combined index. We noted
that just because an overall index is provided does not mean it should be necessarily taken seriously:
the weights are, of course, quite arbitrary. Nevertheless, the overall idea of human development is a
laudable attempt to conceptually go beyond per capita income as an operational measure of
development.
Nevertheless, per capita income isn’t a bad predictor of human development. We showed that the
correlations between per capita income and other variables that describe “human development” are
high, even if attention is restricted only to the subsample of developing countries.
Finally, we described some structural characteristics of developing countries. We looked at
demographic characteristics and showed that there is a general tendency for population growth rates
to decline with increased per capita income. We discussed very briefly some of the effects of
population growth on per capita income. We then studied occupational and production structure:
agricultural activity accounts for a significant fraction of occupations in developing countries. At the
same time, the rates of rural–urban migration are very high indeed. In part, this is reflected in the
observation that a large fraction of the nonrural labor force is engaged in a nebulous activity called
“services.” This category includes all sorts of informal activities with low setup costs, and in
developing countries is a good indicator of urban overcrowding. At the end, we discussed patterns of
international trade. Developing countries are largely exporters of primary products, although this
pattern shows change for middle-income countries. Primary product exports can be explained using
the theory of comparative advantage, although we note that primary product exports have intrinsic
problems, such as a strong tendency for their international prices to fluctuate, which creates
instability in export revenues. The import mix of developing countries is, however, more similar to
that of developed countries.

Purchasing Power Parity Measurement of Income:
The International Comparison Program
According to GDP estimates calculated on an exchange-rate basis, Asia’s weight in world output fell from 7.9% in 1985 to 7.2%
in 1990—and yet Asia was by far the fastest growing region during this period5. This same period also witnessed a sharp decline in
some Asian countries’ exchange rates against the dollar. Now does that tell us something about the shortcomings of GDP
exchange-rate estimates? In an attempt to correct for such anomalies, two economists at the University of Pennsylvania, Alan
Heston and Robert Summers, created a new data set called the Penn World Tables (PWT; also called the Heston-Summers data
set). It consists of a set of national accounts for a very large set of countries dating from 1950 and its unique feature is that its
entries are denominated in a set of “international” prices in a common currency. Hence, international comparisons of GDP can be
made both between countries and over time.
Actually, the trouble with market exchange rates for GDP calculations is not so much that they fluctuate, but that they do not
fluctuate around the “right” average price, if “right” is to be measured by purchasing power. Even if exchange rates equalize the
prices of internationally traded goods over time, substantial differences remain in the prices of nontraded goods and services such as
housing and domestic transportation. These prices need to be corrected for as well. The most ambitious effort, to date, toward
estimating the “correct” international prices is the United Nations International Comparison Program (ICP), which carried out
detailed price comparisons for a set of benchmark countries every fifth year between 1970 and 1985. Apart from domestic price
data, the procedure also involves the use of national accounts expenditure data. The PWT were constructed using the ICP data.
As a first step, the ICP gathers detailed data on prices of 400–700 items in each of a set of benchmark countries. The price of
each item is then divided by its corresponding price in the United States, thus yielding a relative price. These items are then
classified into one of 150 expenditure categories (110 consumption, 35 investment, and 5 government expenditure categories). By an
averaging procedure, the average relative price for each category is obtained, which makes 150 relative prices (or “price parities”)
available for each country.
Next, national currency expenditure pijqij (i.e., price times quantity for each item i in each benchmark country j) on each of the
150 categories is obtained from each country. This is used to estimate the quantities involved in national output. How is this done?
Dividing the expenditure for each category by its relative price, that is, (Pijqij)/(Pij/PUs) yields an estimate of the quantity in the
category, valued at its corresponding U.S. price, qijpUs. Note that it is possible to make international comparisons of output by
simply using these quantities valued at U.S. prices. However, U.S. prices alone do not reflect the tastes of all countries, so we still
have to construct international prices to evaluate these quantities.
For this, recall that we have 150 categorywise relative prices for each country. For each category, the international relative price
is obtained by aggregating the relative price for this category over all benchmark countries, based on a method suggested by
statistician R. C. Geary. The method is such that the international relative price obtained for any item is a specialized weighted
average of the relative price of that item in all the countries in the set. Thus the international price for any item may differ from a
country’s domestic price. For instance, because food is cheaper in a rich country than in a poor country, the international price of
food tends to be higher than its domestic price in a rich country. At the same time, the international price of investment is lower than
in a rich country. The quantities obtained earlier from expenditure data are now valued at the international prices, which yields the
value of national output at these prices. The purchasing power parity (PPP) for any country is the ratio of its domestic currency
expenditures to the international price value of its output.
From the set of benchmark countries, PPPs for other countries are extrapolated using capital city price surveys conducted by
other agencies. Once a complete set of PPPs is available, extrapolations are made for the value of GDP of the entire set of
countries for other years between 1950 and 1988. For instance, RGDP (i.e., real GDP for other years, using 1985 international
prices as the base year prices) is extrapolated on the basis of growth rates of different economies, and CGDP (calculated nominal
GDP for other years at international prices in those years) is calculated using price indexes and current price national accounts data
for those years.
Apart from GDP data, the PWT also offers data on selected countries’ capital stocks and demographic statistics. In the revised
GDP calculations based on PPP, Asia’s share in world output in 1990 jumped from 7 to 18%. China emerges as the world’s third