Atelier 1, Article 19
© Bob Sutcliffe :
(Dollars & Sense, November 2002)
A more or less unequal world?
World income
distribution in the 20th century
Bob Sutcliffe
Contents
1. Partial evidence about the poor and the
rich
2. What to measure: integral measures versus
ratios of groups
3. How
to compare incomes: exchange rates versus purchasing power parity
4. Difference
sources of ppp income data
5. Inter-country
versus global distribution
6. Difference
sources of distribution data
7. Inter-country
studies compared
8. Global
studies compared
9. Two
additional calculations
10. More disaggregated measures
11. Agreements and disagreements
12. Ironies of the debate
Statistical studies
are hardly needed to prove the existence of immense material inequality between
human
beings. It is evident to anyone who walks down the
street in most major cities or watches a television newscast which jumps from
images of famine in
1. Partial evidence about the poor and the
rich
According to the
World Bank’s frequently quoted figures, 56 percent of the world’s population
were living below the poverty line of $2 a day in 1998. This estimate is based
on household surveys conducted between 1985 and 1998, the results being
compared using purchasing power parity prices of 1993 and the figures updated
in accordance with aggregate consumption figures. This means that in countries
where income has become more unequally distributed this method will
underestimate the number of poor people (and vice versa). The latest
calculations estimate that both the poverty and the extreme poverty ($1 a day)
rate have fallen during the years 1987–1998 (from 61 per cent to 56 per cent
and from 28 to 23 per cent respectively) but that the absolute numbers of poor
people grew during this period by about 260 millions (World Bank 2001). While
the Bank’s estimates are evidence that poverty is the norm for around half of
the world’s population it is not easy to use them in the form in which they are
published. This is because they do not give estimates of incomes but only of
the numbers of people living below a
given level of income; and they provide no information about the incomes of
those who are not poor. The kind of household survey data on which they are
based, however, will be seen later in this article to play a central role in
reaching quantitative estimates of inequality on a world scale.
We know less about
the very rich and their income. This is partly because they are able to hide
their wealth and partly because fewer research resources are devoted to
studying extreme wealth since it is not officially regarded as socially
pathological. In some countries, however, surveys of the relative incomes of
the rich have been conducted. In the United States, for instance, it is
estimated that between 1960 and 1999 the average real pay of chief executive
officers of large corporations rose by 11 times while that of real production
workers remained almost unchanged (Sutcliffe 2001, derived from data on EPI
website). Forbes magazine and various other publications regularly list the
very wealthy of the world and a group of financial companies has recently
started to produce an annual World Wealth
Report (Merrill Lynch and Cap Gemini Ernst & Young 2002). While this
estimates that in the year 2001 there were 7.1 million people in the world with
assets of more than one million dollars (‘high net-worth individuals’ or HNWIs) and that these owned $26.2 trillion in assets, it
provides no estimates of their incomes. Such information contributes even less
to estimating the overall worldwide distribution of income than that the
available information on the poor.
Yet, when we place
such disparate information together, although it is only a few pieces of the
jigsaw, a picture of extreme and possibly rising inequality is suggested. Facts
of this kind have fed a conviction, almost universal among journalists and political
critics of the status quo that world inequality has recently (especially during
the years of neoliberal ‘globalization’ since, say,
1980) been rising fast and has reached unprecedented levels. Yet at the same
time, with few exceptions, the opinion of most academic economists who have
carried out quantitative studies of the question is that the opposite has
occurred and that recent decades have been ones of diminishing world
inequality. Is this a difference based on misunderstanding, on different conceptual
visions, or on differences about the facts and how to interpret them? This
article seeks to clarify these questions by looking at the figures, their types
and sources and then to see how much the differences are apparent or real.
Another kind of
more general information has also helped to convey the impression that world
inequality has grown and is growing: estimates of the income or product per
head of individual countries of groups of countries. The three following graphs
show the level of GDP per head, measured at purchasing power parity (the
significance of which will be discussed later) for continents or parts of
continents relative to the figure for the world: Figure 1, derived from the
recent work of Angus Maddison, is for the years 1820
to 1998 (with an expansion of the scale of the graph after 1950); and Figure 2
(using World Bank statistics) shows more detail for the years 1980 to 2000.
There have evidently been many phases in the continental patterns of equality
and inequality; up to 1900
Figure 1a. Income levels relative to the world
average 1820–1998
Source: Author’s calculations based on Maddison 2001
Figure 1b: Income levels relative to
the world, 1980–2000
Source: Author’s calculations based on World Bank, World
Development Indicators 2002, online edition
What is constant is
that for two centuries
This paper is mainly
concerned not with such particular inequalities but with the question of
whether, by using available economic statistics, it is possible to obtain an
overall assessment of the degree of world inequality and say definitively how
it has changed. Some systematic comparison of recent estimates is needed as a
guide to an increasingly studied subject which must
produce great confusion in an uninitiated reader who sees some of the
statements in the two lists in
The apparent
inconsistency of these two lists has three causes: the use of different
concepts of what equality and inequality are; the way in which those concepts
should be measured; and inconsistencies in data obtained from different
sources. In the hope of clearing the ground of all this undergrowth, this paper
proposes to outline the problems of method, measurement and data in assessing
the movement of global inequality. It then surveys and compares a considerable
number of existing studies and adds its own additional calculations in the hope
of clarifying the differences and of suggesting some new lines for research. It
ends by commenting on the ideological and political meaning of the debate.
2. What to
measure: integral measures versus ratios of groups
Two common ways of
looking at world distribution (or any distribution for that matter) are to
compare the extremes of the distribution (the ratio of the incomes of the rich
to the incomes of the poor), or to use all the data and produce an integral
measure of distribution, of which the Gini
coefficient is by far the most widely used. Both these methods can be used to
calculate either distribution which takes into account only the differences
between countries (referred to here as inter-country distribution) or
distribution which also takes into account differences within countries
(referred to here as global distribution). This gives us the four possible
approaches to world distribution shown in Table 1.
Table 1:
Different concepts of world distribution
|
|
Integral measure |
Ratio of extremes |
Inter-country
|
A
|
C |
|
Global |
B |
D |
Is an integral
measure better than a ratio of extremes? The ratio of extremes has the
advantage that it can be understood much more intuitively while integral
measures, such as the Gini coefficient, are more
abstract and require more explanation. On the other hand the ratio of extremes
only compares two parts of the available data and so at best can give a limited
view of the distribution. Measures of the ratio of extremes can in some cases
use all the available data (for instance by measuring the ratio of the income
of the top to that of the bottom half of the population, sometimes called the
Robin Hood index); but even this gives no more than a relation of two summary
figures. On the other hand the ratio of extremes may be a better approximation
to the level of social justice than integral measures. This point can be
illustrated with an example: suppose that we observe the following levels of
income per head by quintiles of the same population in years 1 and 2.
Table 2: A hypothetical example of two distributions
|
|
Quintile I |
Quintile II |
Quintile III |
Quintile IV |
Quintile V |
|
Distribution 1 |
1 |
1 |
1 |
1 |
15 |
|
Distribution 2 |
1 |
16 |
16 |
16 |
16 |
Which of these two
distributions is more egalitarian? In this example, which, as we shall see, is
not too far removed from some aspects of world reality, the two types of measure
give completely different answers. In Distribution 2 shows a higher ratio of
extremes (the top divided by the bottom quintile) and so greater inequality
than Distribution 2 (16 to 1 as opposed to 15 to 1). The Gini
coefficient, however, shows a spectacular reduction in inequality, falling from
0.589 to 0.185.
There could be a
long debate about which of these distributions shows more social justice. But
it is at least arguable that a society where four fifths of the people were
rich and one fifth poor is morally worse than one where four fifths are poor
and one fifth is rich. This is on the grounds that the exclusion of a small
minority in conditions of general plenty is worse than great riches for a few
amid general poverty, since only in the first case could everyone be made
comfortable with only a small amount of redistribution. In other words, extreme
poverty can be considered more unjust in a generally rich than in a generally
poor society.
This point is not
just a formality but, as will be seen later, is relevant to the interpretation
of the conclusions about the course of income distribution during the last
century. It suggests that it would be wise to look at both kinds of measures in
order to judge the changes in equality and inequality.
3. How to
compare incomes: exchange rates versus purchasing power parity
A very large amount
of the disagreement and confusion about what has been happening to world income
inequality has been due to the fact that two different ways of comparing the
incomes of different countries are in common use – the exchange rate method and
the purchasing power parity method. They both start from the same income
figures, taken from the national accounts or from household surveys or other
sources. These are, of course, in the first instance in national currencies.
For countries to be compared, and world calculations made, they must be
converted to a common currency. This has traditionally been done by converting
them via the ruling exchange rate to dollars. The problem with this is that, as
nearly everyone accepts, exchange rates very often fail to reflect equivalence
of purchasing power. A person from one country going to another and changing
currency will often find his or her purchasing power increased or reduced. The
exchange rate-converted figures for income, therefore, produce false
comparisons. The general solution proposed is the use of purchasing power
parity, a calculation, based on an exhaustive exploration of prices in
different countries, of what is the real equivalence of a quantity of one
currency when converted to another. In practice, it appears that countries
whose exchange rate underestimates purchasing power are mostly poor countries
and those with the opposite characteristic are mostly rich countries. This
means that when calculations are made using ppp the
numerical measure of inequality between the richer and poorer countries tends
to be lessened. In principle, however, this is a real comparison of material
living standards which the figures converted with exchange rates are not. The ppp method is, therefore, overwhelmingly favoured by economists. It enables income levels between
counties (over space) to be compared in the same way that in each country
adjustment for inflation produces real figures which can be compared over time.
This space and time comparability constitutes the great breakthrough of ppp figures which have recently become available in
abundance.
The difference in
methods produces enormous differences in calculations about inequality, as shown
in Table 3.
Table 3: Calculated world inequality in 2000
|
Measure |
ppp |
exchange rate |
|
1. Inter-country Gini coefficient 2000 ppp (163
countries) 2. Inter-country
5%/5% ratio 2000 3. Inter-country
10%/10% ratio 2000 4. Inter-country 20%/20%
ratio 2000 5. Inter-country
50%/50% ratio 2000 |
0.543 47.95 31.37 15.99 5.38 |
0.753 175.31 126.08 67.03 20.09 |
Sources: World Bank, World Development Indicators 2002 online version. The exchange rate
conversion uses the World Bank’s Atlas method (using exahcnge
rates averaged over a year).
These figures are
all based on the same 163 countries (the maximum for which the quoted source
gives both exchange rate based and purchasing power parity based estimates of
income). So the two columns show only the difference produced by the type of
income conversion used. The exchange rate converted figures used are those
described by the World Bank as the Atlas method, in which the exchange rate
used is an average for the year rather than the rate on a particular date.
Comparing the two columns it is obvious that the exchange rate method gives
much higher measures of inequality than the ppp
method, although of course the reality they are attempting to describe is identical.
The Gini coefficient is nearly half as high again and
the ratios of the extremes show indices of inequality around 4 times greater
than the ppp method. In addition, as shown in Table
4, when observed over time the two methods give very different results. In
general over the past two decades the exchange rate method shows the level of
world inequality rising and the ppp method shows it
falling. Later some exceptions and nuances to this generalization will be
discussed but for now the figures in Table 4 show a very simple calculation
based this time on 113 countries (those which have data for both dates) to
clarify the problem. Not only is the exchange rate based Gini
higher in both years but it rises from 1980 to 2000 indicating greater
inequality while the ppp based Gini
falls indicating greater equality. This fundamental difference is the result
only of the difference in the basis of conversion since the basic data are the
same in both cases.
Table 4. Changes in Gini coefficient 1980–2000, exchange rate and ppp methods of comparison
|
|
Exchange rate
(Atlas) |
ppp (World Bank) |
|
1980 |
0.7053 |
0.6137 |
|
2000 |
0.7449 |
0.5422 |
Source: Author’s calculations based on World Bank, World
Economic Indicators 2002, online edition; the same 113 countries are common
to all four calculations.
Exchange rate
figures do not necessarily give higher values for the level and growth of
inequality. The basic reason for the differences shown in Tables 3 and 4 is
that exchange rates in poor countries have tended to be undervalued in foreign
exchange markets in relation to their domestic purchasing power (a phenomenon
well known to tourists). In addition during the years 1980 to 2000 the relative
undervaluation in many poor countries increased and the relative overvaluation
of the all important currency of the
Since they give
very different levels of inequality and opposite trends it is obviously of
fundamental importance to decide which method is correct. It seems completely
clear that in principal the correct measurement for comparing living standards
(and so the real levels of international inequality) is given by the ppp method. This is based on the conversion of incomes
using an index (a kind of shadow exchange rate) calculated on the basis of
detailed comparison of the price levels of the same commodities between
countries. In this way the effect of changes in exchange rates on the apparent
distribution of world income is eliminated in a similar way to that in which
comparisons between dates are made real by adjustment for price differences
over time. So in principle the ppp figures allow a
matrix in which the figure for the income per head of each country over time is
comparable both vertically (over time) and horizontally (over space), in other
words the figures are both temporally and spatially real. Since the measurement
of inequality is concerned with real differences in living standards this is
surely the correct procedure. Nearly all writers on the subject accept this;
indeed it is the recent multiplication of ppp income
estimates which has permitted the rise in the analysis of world income
disparities. A few writers nonetheless claim that exchange rate conversions
produce a more accurate picture of relative economic power which countries can
only obtain by converting their undervalued currencies into high valued
currencies (for example, to spend on renting an office in New York or Geneva
from which to lobby international organizations).This argument may have some
small merit in relation to the international power of countries but has none in
relation to the measurement of inequality in the standard of living. Most use
of exchange rate based calculations of world inequality, however, are not based
on such arguments but on an desire to produce a particular result. This will be
discussed further but in the meantime it should be made clear that from now on
all calculations made and referred to in this paper use ppp
methods. These methods, however, have their own problems.
4. Different
sources of ppp income data
All ppp estimates of incomes come ultimately, though not
directly, from the same source – the International Comparisons Program, a joint
venture of the United Nations and the Center for International Comparisons at
the
If in principle ppp converted figures are much better reflections of real
differences in living standards, in practice there are three separate sources
of ppp estimates which are by no means identical. One
of these is from the World Bank data bank, World Development Indicators (WDI),
the second from the latest version (number 6) of the Penn World Tables,
produced by Heston, Summers and Aten
and their colleagues (PWT6.1) and the third produced by Angus Maddison working under the auspices of the OECD (Maddison 2001).
The work of Maddison and of Heston and
Summers and their associates, in producing a continuous series of figures for
income per head (and other variables) since distant dates and in figures which
are in principle comparable over both time and space is what has made possible
a debate on the history of distribution between countries. Maddison’s
data begin in 1820 for some countries and have recently been updated to 1998
for most countries, while Heston and Summers’ series
for a growing number of countries covers the period from 1950 to 1998. The
World Bank’s ppp data begin in 1975.
While all three
estimates use the price data produced by the World Comparisons Project, they
adjust in various ways so that considerable differences emerge between the
different estimates. As we shall see, the differences are great enough to imply
different conclusions about the recent course of movement of world inequality.
Each person or
group who has analysed the basic ppp
data has added his or her own eccentricities. To take a single case which is
bound to have major effects of international calculations, that of China:
between 1980 and 1990 the real income per head of China, measured at ppp, increased by 36 percent according to the Penn World
Tables version 5.6, 63 per cent according to the Penn World Tables version 6,
by 85 per according to Maddison’s 1995 study and by
70 per cent according to Maddison’s 2001 revision; it
is not possible to give a comparative figure for the World Development Indicators
since it gives the data only in current prices. In the face of differences of
that degree about the second largest economy in the world it is evident that
any conclusions must be treated with extreme caution. Where possible, different
estimates should be tested to see the degree of robustness of the conclusions
to different versions of the income data. I have tried to do this in most of my
later calculations.
Table 5 gives some
details of the differences between estimates made by the three sources. For Maddison 2001 and PWT6.1 I have taken the 92 countries for
which both versions have estimates, and almost the same group of countries for
the World Development Indicators; to make them comparable the figures for all
countries have been normalized as a proportion of the estimate for the USA
(since the Maddison 2001 and PWT6.1 base years are
different and WDI is in current prices). The comparisons between them appear in
Table 5. This shows large enough variations between the three sources to feed
doubts about the use of these figures.
Table 5: Variability
of estimates of GDP per head, 1998
|
|
% within 10% |
range of difference % |
|
PWT6.1–Madd2001 |
45.7 |
62–291 |
|
Madd2001–WDI2002 |
34.4 |
36–326 |
|
PWT6.1–WDI2002 |
48.9 |
70–202 |
|
Based on figures for 1998 in each case for about
90 countries; the countries used in each comparison are the same for both
measures compared |
||
Source:
Author’s calculations based on Maddison 2001 and Heston, Summers and Aten 2002 and
World Bank 2002
The first column
shows the percentage of the country income estimtates
of the second mentioned source which are within 10 percent (above or below) of
the country estimates of the first mentioned source (so, for example, only 45
percent of the Maddison 2001 values are withing 10 percent of the PWT values); the second column
shows the range of the country estimates of the second mentioned source as a
percentage of the first (so, for example, the Maddison
2001 values vary between 62 percent and 291 percent of the PWT values). These
divergences seem very large indeed.
The three sources
produce estimates of the Gini coefficient which are
rather closer than the differences in estimates of individual countries’ GDP
per head might suggest. This is partly because many of the biggest differences
are for small and poor countries and because some of the differences cancel
each other out. The upper half of Table 6 compares the Gini
coefficients given by the three sources using the same 92 countries for PWT6.1
and Maddison 2001 and nearly the same for the WDI.
The differences in the Gini coefficients are surely
small enough to be within any reasonable margins of error. All three show a
falling Gini coefficient for the years 1980 to 2000
and the differences are not large; it is significant, as we shall see, that Maddison 2001 shows the lowest fall in the coefficient.
When the calculation is made not for the same group of countries in each case
but for the maximum for which they respectively provide estimates in the years
1950 to 1988 the differences are more striking. The result is shown in the
lower half of the table. Both PWT6.1 and WDI still show a falling Gini coefficient (that is, falling inequality) but Maddison 2001 shows scarcely any fall at all. These
calculations are done here merely to illustrate the differences in the data.
Later we shall see that the difference is significant for conclusions about
world inequality.
Table 6: Comparing
Gini coefficients produced by 3 income sources
|
|
PWT6.1 |
Maddison
2001 |
WDI 2002 |
|||
|
|
Gini |
countries |
Gini |
countries |
Gini |
countries |
|
1980 |
0.582 |
92 |
0.587 |
92 |
0.618 |
87 |
|
1990 |
0.563 |
92 |
0.569 |
92 |
0.587 |
90 |
|
1998 |
0.523 |
92 |
0.539 |
92 |
0.542 |
90 |
|
Maximum number of countries |
||||||
|
1950 |
0.523 |
53 |
0.551 |
198 |
n.a. |
n.a. |
|
1973 |
0.591 |
115 |
0.574 |
217 |
*0.612 |
*117 |
|
1990 |
0.564 |
134 |
0.569 |
219 |
0.577 |
161 |
|
1998 |
0.519 |
140 |
0.564 |
219 |
0.543 |
167 |
|
|
|
|
|
|
* figures for 1975 |
|
Note: in the case of PWT6.1 and WDI 2002 the rising
number of countries reflects the existence of data for an increasing number of
countries. In the case of Maddison the data is for
the same countries which change in number due to political changes (fusions and
breakups)
Source:
author’s calculations based on Heston, Summers and Aten 2001, Maddison 2001 and
World Bank 2002
5. Inter-country
versus global distribution
An obvious limitation
of all the results mentioned in the previous section is that they only estimate
distribution between countries as a whole (weighted, of course, by
populations). They do not take into account the distribution of income within
countries. Included this is like considering the whole world as a single
economic unit and I refer to such a concept as global (as opposed to
inter-country) distribution. It is evident that the objective of studies of
world distribution must be to produce global and not inter-country estimates.
We can hardly be confident in information about the world which assumes that
1,200 million Chinese citizens, or 280 millions
The most
fundamental problem in calculating global inequality is the inadequacy of
national data about distribution. In particular very few long-term consistent
series for distribution exist. So global, as opposed to inter-country
inequality can only be observed over comparatively short periods, although
Williamson has recently pioneered the use of historical wage data to reach
conclusions about changes in inequality (Williamson and Lindert
2001).
Two methods have
been used to try to assess the level and changes in global inequality in recent
decades. One is to begin with the national income data used in the
inter-country calculations and apply to it available estimates of distribution
thus deriving the income per head of distributional groups (usually quintiles,
occasionally deciles and rarely smaller percentiles). These figures (weighted
by the appropriate population figures) are then pooled to calculate global
inequality. The only attempt I have found to do this for a long historical
period has been the study by Bourguignon and Morrison for the period 1910 to
1992. They use the Maddison 1995 income estimates
weighted by data on distribution from a variety of sources, some of it based
largely on plausible surmise. A recent study by Sala-i-Martin
applies the same principle to a shorter time period (1970–1998), using for
income the estimates in PWT6.1 and for distribution the Deininger–Squire
database, to be discussed in the next section. Later I describe in detail my
own study using the same principle in which I apply the Deininger–Squire
distribution data to two sets of income data – the World Bank’s World
Development Indicators and Maddison 2001.
A recent study by Milanovic uses a second method. Instead of applying
distribution data to independently obtained income data as in the three studies
mentioned above he bases his whole analysis on household survey data which
produce his distribution and income figures simultaneously. The consequences of
this different method are discussed in section 8.
6. Different
sources of distribution data
When it comes to
comparisons over time and between countries the figures for GDP per head are
certainty itself compared with those for the distribution of income. While the
number of estimates for distribution are growing fast they are still much less
systematically available than those for GDP per capita. For very few countries
are long series available and it is by no means certain that estimating methods
in different countries or at different dates are consistent with each other.
The study of international inequality has been given a big stimulus by the
publication of the dataset produced by Klaus Deininger
and Lyn Squire at the World Bank and the WIDER International Inequality
Database (WIID) which takes the Deininger–Squire
dataset as its basis. Deininger and Squire produce
two sets of data for the years 1950 to about 1995: the total available and a
reduced version of what they regard as the most reliable figures, called high
quality or “accept”. The criteria which they use for inclusion in this category
are: income or expenditure data covering the whole national population from
national household studies which use all income sources, including
self-consumed production. The application of these criteria seems to give some
coherence to the whole data set. But major reservations about its validity have
been made by Atkinson and Brandolini (2001) as part
of a critique of large international “secondary” data sets in general. Those
authors point to significant inconsistencies between the Deininger
and Squire high-quality data and other, more intensively researched, sources of
data on income distribution in the OECD countries and, due to the use of
different definitions at different dates, they even conclude, using the case of
the Netherlands as an example, that “it would be highly misleading to regard
the DS [Deininger–Squire] “accept” estimates as a
continuous series” (p. 780). If this is the case in a country where economic
statistics are highly developed, the situation must be even worse in the
majority of countries where they are not.
A perfectly
understandable conclusion from the arguments of Atkinson and Brandolini, and many other criticisms of inconsistencies
and unreliability in international income and distribution data, is that any
attempt to calculate a figure for world distribution with distribution data for
many countries over a considerable time-period must be completely unreliable
and should perhaps be abandoned. Once data of this kind exists, however,
whatever its limitations, the temptation to analyse
it to see what it implies is too great to resist. The question of what is
happening to distribution is too important for us to ignore even the inadequate
evidence which we may have about it. And drawing provisional conclusions from
the data we have, comparing them with other studies and observing
inconsistencies could help the task of improving the future quality of the
data. While Atkinson’s and Brandolini’s warnings are
important, I have not let them stop me using our inadequate data to explore
tentative conclusions. About the past there is virtually no hope that we shall
ever have better data. So, as in the case of the income estimates, we should
use it in a spirit of great caution.
7. Inter-country studies compared
I now turn from the
problems of method in studying world inequality to a comparison of some of the
studies which have been done, comparing the method, the data used, aspects of
the treatment, the results obtained and the significance of the conclusions.
This section discusses the results of inter-country studies and the next looks
at global studies.
The number of
countries included in each study is affected by the dates and the type of
calculation. Inter-country studies require population and income per head
figures for each country. Maddison provides such
information since 1900 for 49 countries (for most of which the figures also go
back to 1820). Unless extra estimates are made, century-long studies are thus
confined to these countries. For more recent dates more countries can be
included, using any of the three sources of estimates discussed above in
section 5, namely the two versions of Maddison,
various versions of PWT and the WDI. All three now provide annual estimates of ppp income covering countries which contain well over 90
percent of the world’s population.
7i. Long term studies
The time periods
covered by all the studies surveyed in this and the next section range from 98
years to five years. Both long and short term comparisons have alternative
disadvantages relating to the data. In the case of long term comparisons the
quality and completeness of the data is liable to change considerably over the
period of the comparison. And in the case of short term comparisons a change in
apparent distribution may easily be within the margins of error of the data.
For this reasons long-term comparisons must be treated with general caution;
and short-term changes should not be weighed very heavily.
There is no
disagreement with the conclusion that during the twentieth century as a whole
the world’s distribution of income has become considerably more unequal. Maddison’s 1995 data for 49 countries between 1900 and 1998
(as analysed by Boltho and Toniolo) shows an overall rise in the Gini
coefficient from 0.393 to 0.496. Maddison’s data also
show that this polarization between the richest and poorest countries has been
a characteristic of the period since 1820. Using the same data and adding their
own historical estimates of distribution changes Bourguignon and Morrison in
their global study produce a pattern of change of the long term evolution of
the global Gini coefficient which is broadly
consistent with the Maddison 1995 inter-country
distribution. And other quantitative and qualitative data supports the
conclusion that current inequality is much greater than historical inequality
(Williamson 1997, O’Rourke 2001). It seems that there is general agreement,
based on the estimates available, that the world’s countries became
considerably more unequal between the Industrial Revolution and at least the
end of the great post-Second World War boom in about 1973.
7ii. Medium term studies
While the long-term
conclusion is not challenged, a large amount of disagreement, alluded to in
section 2, has recently emerged on the
question of what happened to world distribution during the last two decades.
This rapidly developing debate was partly generated by the study in which Boltho and Toniolo calculated the
long term Gini coefficient from Maddison’s
data. They showed that although inequality had grown during the twentieth
century as a whole it had, using the same data, distinctly fallen since 1980,
the Gini falling from 0.544 to 0.496 in 1998 (see
Table 7, row 2).
How secure is the
conclusion reached by Boltho and Toniolo?
The first possible problem with it is that, since their aim was to view changes
in distribution over the whole century, the calculations only contain the 49
countries which have the appropriate figures for that period. What happens if
more countries are included? I repeated the same calculation based on World
Bank GDP per head figures (ppp) for the 121 countries
for which figures exist for the controversial shorter period from 1980 to 1998.
The inclusion of 72 more countries (many of them relatively poor countries)
actually reinforces the earlier conclusion: while the Gini
has a higher value in 1980 it nonetheless falls relatively slightly more up to
1998 (from 0.610 to 0.538, line 4). There are two reservations to this
conclusion: first that the effect of
Substituting the
Penn World Tables data for the Maddison 1995 does not
change the direction of the result. Summers and Heston
find a slightly smaller fall in the Gini between 1980
and 1990 (compare 1 and 2); their data (PWT5.6) at the time of writing did not
yet allow the calculation of the Gini beyond 1992.
Firebaugh and Melchior and Telle,
both using PWT5.6 (in the latter case updated by the World Bank), both produce
fairly similar results (lines 3 and 6). My own calculation based on PWT6.1 also
shows a comparable fall in the Gini coefficient (line
7).
What does make a
real difference to the inter-country estimates is using Maddison’s
more recent figures (Maddison 2001) instead of the
earlier ones, used by Boltho and Toniolo.
The differences in the new series are: more countries are included (which means
especially including very poor countries previously omitted); the estimates for
many countries have been changed somewhat; and, most important, the estimates
for three countries –
Table 7: Inter-country Gini
coefficients, 1950–1998 (also see
Figure 2a)
|
Author
and income data source |
1950 |
1973 |
1980 |
1990 |
1998 |
|
1.
Summers & Heston (PWT 5.6) |
|
|
0.552 |
0.547 |
n.a. |
|
2.
Boltho & Toniolo (Maddison 95) |
|
|
0.544 |
0.526 |
0.496 |
|
3.
Firebaugh (PWT 5.6) |
|
|
0.550 |
*0.543 |
n.a. |
|
4.
Author’s calculation (WDI 2002) |
|
|
0.610 |
0.584 |
0.538 |
|
5. as above omitting |
|
|
0.555 |
0.562 |
0.561 |
|
6.
Melchior & Telle**
(PWT 5.6 updated) |
|
0.59 |
0.57 |
0.56 |
0.52 |
|
7.
Author’s calculation (PWT 6) |
0.523 |
0.591 |
0.581 |
0.564 |
0.519 |
|
8.
Author’s calculation (Maddison 01) |
0.550 |
0.573 |
0.555 |
0.569 |
0.564 |
|
9. as above omitting |
|
|
0.532 |
0.568 |
0.582 |
*
= 1989
** = figures approximate (read-off from graph)
Sources: see bibliography
Figure 2a: Inter-country Gini
coefficients, 1950–1998
Figure 2b: Global
Gini coefficients, 1980–2000
The conclusion from comparing
these calculations (all using ppp figures, but with
differing numbers of countries) is that the Maddison
1995 income estimates, the Penn World Tables and the World Bank WDI figures
give consistent results, all showing either a slightly or moderately declining Gini coefficient, in other words less inter-country
inequality, during the two decades following 1980. The exception is the revised
Maddison 2001 income estimates. These produce an a
slightly fluctuating Gini coefficient. The key
changes in Maddison’s data have been revisions of the
figures for
Evidently no
calculation of the world’s income distribution can be performed without the
inclusion of its most populous country,
I have also
calculated various ratios of extremes using WDI (ppp)
figures and Maddison’s 2001 study several ratios of
extremes. The results are shown in Table 8. The 50/50 ratio according to Maddison’s figures and the 20/20 ratio according to both
these estimates became less unequal during the whole period. But the 10/10
ratio behaved very differently. In the case of the World Bank figures it
declined in the first decade but then becomes more unequal again in the second,
leaving it at about the same level as it started. But according to Maddison’s income estimates the difference was much more
significant: the 10/10 ratio showed a strong increase in inequality at the extremes.
Table 8: Inter-country
ratios of extremes 1980–1998
|
|
1980 |
1990 |
1998 |
|
Richest/poorest
50% WDI |
10.91 |
7.37 |
5.27 |
|
Richest/poorest
20% WDI |
26.68 |
15.94 |
14.81 |
|
Richest/poorest
10% WDI |
27.94 |
23.54 |
25.75 |
|
Richest/poorest
5% WDI |
29.01 |
31.73 |
39.91 |
|
|
|
|
|
|
Richest/poorest
50% Maddison 2001 |
8.35 |
6.55 |
5.49 |
|
Richest/poorest
20% Maddison 2001 |
16.82 |
28.19 |
18.58 |
|
Richest/poorest
10% Maddison 2001 |
23.09 |
34.62 |
40.01 |
|
Richest/poorest
5% Maddison 2001 |
30.03 |
45.40 |
61.14 |
Source:
author’s calculations from World Bank 2002 and Maddison
2001
Figure 3: Ratios
of extremes, 1990–2000
Source: World Bank, World
Development Indicators 2002, online version
A similar result
(using Penn World Tables figures updated by the World Bank) was obtained by Melchior (2000). And Figure 3, using annual calculations
based on WDI, shows the divergence in the behaviour
of the 20/20 ratio which slowly declines while the 10/10 ratio appreciably
falls during the 1990s then very slowly begins to rise again. Looking at all of
these figures together, therefore, begins to suggest that to say that
inter-country inequality in the last two decades of the 20th century
either fell of was on a ‘plateau’ (Firebaugh 1999) or ‘was roughly stable’ (Bourguinon and Morrison 2001) is too simple. As well as
being affected by the number of countries included and by the source of the
income data, the overall conclusion about inequality depends on the statistic
which is used to measure it. The contrast between the integral measure and the
ratio of extremes suggests anything but stability or constancy. It looks more
as if there were strong equalising forces in the
middle sections of the distribution (which influence the Gini
coefficient) combined with equally strong disequalizing
ones at the extemes. There will be more evidence of
this when we move to look at global, as opposed to inter-country, inequality.
8. Global studies compared
Of the four global
studies, three apply distribution estimates to independently obtained income
estimates and the fourth (that of Milanovic) derives
both distribution and income at the same time from World Bank household
surveys. While some of the studies use various statistics to test changes in
inequality, I have restricted comparisons to the Gini
coefficient. Partly this is to save space and simplify the argument. But also
it is because none of the alternative measures used by other authors
substantially changes the conclusions of any of their studies, although it
sometimes allows them to be more sophisticated.
Bourguignon and
Morrison’s estimates based on Maddison’s 1995 income
figures and various sources for distribution, have already been mentioned. For
the period after 1980 they provide only two observations, thirteen years apart.
They are identical and lead the authors to argue that overall inequality has
been stable in the recent period. Sala-i-Martin uses
PWT6.1 income figures and the Deininger–Squire
database for distribution. He produces annual figures by deriving trend lines
for the distribution data. If there is only one estimate of distribution then
he applies it through the whole period. He concludes that from 1980 there has
been a significant downward reduction in inequality, the Gini
coefficient falling from 0.662 to 0.633 between 1980 and 1998.
My own
calculations, which are described in detail in the following section of the
paper were done in a way similar in principle to Sala-i-Martin’s,
though they are statistically much less intricate. They involve applying
distribution estimates from the Deininger-Squire high
quality dataset to two different income estimates: the World Bank WDI data and Maddison’s 2001 data. Each of these two calculations was
done with two sets of countries: a pure set of
35 countries in which a distribution estimate existed for all three
years compared (or a year fairly close; see Appendix) and a second much larger
hybrid set of countries some with very incomplete, sometimes non-existent,
distribution figures. The exact methods are set out in the next section.
Table 9: Values
of Gini coefficients in global studies (also see
Figure 2b)
|
Authors
(income, distribution) |
1970 |
1975 |
1980 |
1985 |
1988 |
1990 |
1993 |
1995 |
1998 |
2000 |
|
1.
Bourguignon/Morrison (Maddison 95, various) |
|
|
0.657 |
|
|
|
0.657 |
|
|
|
|
2.
Sala-i-Martin (PWT 6, D/S) |
0.657 |
0.661 |
0.662 |
0.650 |
|
0.654 |
|
0.635 |
0.633 |
|
|
3.
Milanovic (Household surveys) |
|
|
|
|
0.628 |
|
0.66 |
|
|
|
|
4.
Sutcliffe pure (WDI 2002, D/S) |
|
|
0.697 |
|
|
0.658 |
|
|
|
0.619 |
|
5.
Sutcliffe pure (Maddison 01, D/S) |
|
|
0.661 |
|
|
0.636 |
|
|
0.617 |
|
|
6.
Sutcliffe hybrid (WDI 2002, D/S) |
|
|
0.667 |
|
|
0.650 |
|
|
|
0.627 |
|
7.
Sutcliffe hybrid (Maddison 01, D/S) |
|
|
0.638 |
|
|
0.633 |
|
|
0.628 |
|
Sources: see bibliography and section 9 of this
paper. D/S = Deininger/Squire
As in the case of
the inter-country studies all the estimates based on PWT6.1 and WDI (Table 9,
rows 2,4 and 6), income data show an appreciable fall in inequality between
1980 and 1998/2000. The Bourguignon and Morrison estimate(1) based on Maddison 95 shows no change. My own (5,7), based on Maddison 01, shows an appreciable fall in the case of the
pure study but very little change when a lot more countries are included. There
seem to be two reasons for these differences: the number of countries included
and the actual estimates. As mentioned in relation to the inter-country
studies, Maddison in producing the latest version of
his income study made major changes to his earlier estimates for a number of
important countries, especially
The other global study,
by Milanovic (3), produces a very different result, a
rise in the Gini coefficient over a period of 5
years. His method is to begin not with aggregate national income estimates but
with detailed household income or consumption survey data. Distribution and
income data are thereby obtained together. It is, as he says, a much more
natural way to conduct a study of global distribution than the two stage
methods used by others. Such data has been collected for a number of years by
the World Bank in their household surveys and Branko Milanovic has used these to calculate what he calls the
first ‘true’ measure of global inequality (Milanovic
2002). His study is remarkable not only for its pioneering methodology but for
the fact that, unlike most of the other studies so far reviewed it concludes
that the global Gini coefficient (and other measures
of inequality) has been rising fast in exactly the period during which other
writers have concluded that it has been falling or steady. Another study by Nikhanov and Ward (2001), using the same household
distribution data as Milanovic reaches an almost
identical conclusion.
Interesting and
innovative as it is, Milanovic’s study has a number
of limitations. The first is that it covers a very brief time span (1988 and
1993) which makes it impossible to draw from it a conclusion even about the two
decades from 1980. The dates have been chosen because they were benchmark years
for international price level comparisons (used in the production of ppp income and consumption data). But the dates of the
household surveys do not always correspond to these years. Sometimes they
related to nearby years which means that for a study based on two years which
are only five years apart the range of years used in the surveys can overlap.
The figures for 1988 are based on surveys made between 1980 and 1991 and those
for 1993 on surveys made between 1990 and 1998. This limitation can be partly
answered by saying that, assuming that changes in national distributions have
generally moved in one direction, the observed rise in the global Gini coefficient suggests a general tendency towards rising
global inequality between the 1980s and 1990s. But this problem nonetheless
means that, despite its interesting conclusion, the study does not provide a
definitive answer to the question of what happened to global distribution
during the last two decades.
Milanovic decomposes the influences on overall global
inequality into inter-country and intra-country components. He finds, like
almost all others who have used such a technique, that the vast majority of
global inequality is accounted for by differences between and not within
countries. But his conclusion that growing inter-country differences are the
main cause of the rise of the Gini coefficient over
the five years of his study, seems in this case a rather eccentric one since he
emphases the special contribution of the growth in rural/urban inequality in
both China and India but he treats the rural and urban sectors in both cases as
two separate “countries”.
The studies which
use the two stage methodology (starting with national income or product per
head and then using quintile distribution data to produce income per head or
quintile groups) give fairly consistent results while the one-stage method used
by Milanovic (using household surveys directly to
calculate income and distribution) gives a contrasting result. It is possible
that this difference may be explained by the difference in method. Milanovic discusses why the two methods might be expected
to give different results. But what he does not explain is why for 1988 his
method produces a Gini coefficient which is
(implicitly) slightly lower than any of those produced by the two stage method
while for 1993 it produces one which is higher. It is not clear why the factors
which can cause a two stage method to produce a different result from the one
stage method should have changed over this five-year period so as to produce
this reversal.
The final problem
with Milanovic’s study is that the comparability and
reliability of household surveys may be no better than the comparability and
reliability of the other statistics used in all the studies quoted. And, unlike
the case of two-stage studies which use ppp estimates
of product or income per head, there is in the case of the World Bank household
studies only one source. This is not meant as a reservation about Milanovic’s study in particular since in the end all
studies of global equality depend on data of questionable reliability and many
of them end up by using estimates of intra-country inequalities which are at
least in part derived from the same World Bank household surveys used by Milanovic. But the difference between conclusions reached
by his method and those produced by other methods (even where the underlying
data are partly the same) does underline the importance of further work on
comparing and improving data on income and distribution.
I am not, however,
suggesting that no work is valid until the data improves. Rather that the
problems of data mean that differing results arise due to differences in both
data and method and that these cannot be considered definitive conclusions but
rather hypotheses for which further support needs to be sought.
9. Two additional calculations, 1980–2000
I have carried out
two additional calculations for the three years 1980, 1990 and 1998/2000, some
of the results of which were already set out in the previous section but which
are here described in detail. These are sufficiently different in method, data
selected and results to make it worth describing them and comparing them to the
other calculations mentioned above. I have calculated global values for both
ratios of extremes and Gini coefficients and the most
interesting conclusion is the different pictures which emerge from comparing
these measures. The income data used was taken both from the World Bank’s WDI
and from Maddison 01 and so allow a direct comparison
of the differences resulting from using different data sources. Quintile
distribution figures are from the Deininger–Squire
distribution dataset supplemented for more recent years by the table on
distribution which appears in World Development Indicators 2002 (book).
The two
calculations use these same sources but treat them differently. The first
(which I call the pure study) includes only those countries which have values
for GDP (ppp) per head and estimates of
inter-quintile distribution in each of the three years; no extra data has been
extrapolated. The ideal country for the sample was one with figures for
quintile shares in 1980, 1990 and 1998 (the most recent year possible). This strong stipulation means, of course,
that the number of countries for which all this data is available in the cited
sources is very limited; only 5 countries fulfilled these criteria. So
countries were included if they had observations for inter-quintile
distribution in years close to the three benchmark years. I had to be quite
generous in interpreting this rule (as can be seen in the appendix note) in
order to include 35 countries which accounted for 70% of the world population
in 1980 and 1990 and 69% in 2000 and for a share of world GDP which rises from
61 percent in 1980 to 67 percent in 2000. The means that collectively the 35
countries have a lower than world average GDP per head but that they have
improved relative to the world average
during the years studied.
The second
calculation (which I call the hybrid study) is based on the same data sources
but extrapolates values where they do not exist in order to maximise
the number of countries which included. The two changes made were:
a. where GDP per
head figures were lacking for one or more of the years in the WDI dataset they
were interpolated using one of two methods. In some cases the country was
assumed to have the same relationship to the average for its continent as the
nearest year in which a GDP per head estimate is available, except in specific
cases where the same ratio was assumed to be maintained between the country
lacking data and some other individual country (Mongolia with the Russian
Federation; and Oman, Qatar and Kuwait with Saudi Arabia).
b. the distribution
data was extrapolated by assuming that, when not available for a particular
year, inter-quintile distribution remained the same as in the previous
available year. Where no distribution data at all are available each quintile
of the population is allocated one fifth of the income (i.e. total equality is
assumed). Hence no assumptions have been made about what distribution might be
or about how it might have moved. Missing estimates are all supplied by
applying these simple rules and not by efforts to divine what happened.
These procedures
allow the number of countries in the sample to be increased from 35 to 163
containing about 96 percent of world population. Of the extra 128 countries 4 (
Table 10: Results of the ‘pure’ study
Using WDI 2002 figures
|
|
1980 |
1990 |
2000 |
|
Global Gini coefficient |
0.6977 |
0.6582 |
0.6192 |
|
Richest/poorest
50% |
12.6 |
9.5 |
8.0 |
|
Richest/poorest
20% |
42.2 |
30.4 |
24.5 |
|
Richest/poorest
10% |
74.6 |
69.9 |
47.7 |
Using Maddison 2001 GDP figures
|
|
1980 |
1990 |
1998 |
|
Global Gini coefficient |
0.6607 |
0.6359 |
0.6173 |
|
Richest/poorest
50% |
9.9 |
8.37 |
7.9 |
|
Richest/poorest
20% |
40.0 |
25.8 |
25.2 |
|
Richest/poorest
10% |
54.4 |
51.1 |
48.7 |
Source:
author’s calculations based on World Bank 2002a and 2002b and Maddison 2001
In the pure study
(Table 10) the percentage fall in the global Gini
coefficient for the global figure in the case of both income sources is in fact
proportionately greater than the fall for the inter-country figure in the Boltho and Toniolo calculation
using Maddison 95. For this sample of countries the
inter-country Gini falls by much more that the Boltho and Toniolo figure. But in
1998 the global Gini, according to this calculation,
is still higher than for any single country in the world with the exception of
The results point
to a growing gap between global and inter-country Ginis,
suggesting that an increasing share of inequality between the inhabitants of
the world is caused by internal rather than inter-country inequalities. A
similar conclusion is reached by Bourguignon and Morrison and several other
writers.
Table 11: Results
of the hybrid study
Using
WDI 2002 figures
|
|
1980 |
1990 |
2000 |
|
Gini coefficient |
0.6667 |
0.6504 |
0.6272 |
|
Richest/poorest
50% |
13.62 |
10.21 |
8.83 |
|
Richest/poorest
20% |
45.73 |
33.85 |
29.49 |
|
Richest/poorest
10% |
78.86 |
64.21 |
57.41 |
|
Richest/poorest
5% |
120.75 |
101.02 |
116.41 |
|
Richest/poorest
1% |
216.17 |
275.73 |
414.57 |
Using
Maddison 2001 figures
|
|
1980 |
1990 |
1998 |
|
Gini coefficient |
0.6385 |
0.6331 |
0.6285 |
|
Richest/poorest
50% |
10.4 |
9.1 |
8.9 |
|
Richest/poorest
20% |
33.0 |
30.5 |
23.1 |
|
Richest/poorest
10% |
58.2 |
54.9 |
61.1 |
|
Richest/poorest
5% |
139.4 |
98.6 |
123.1 |
|
Richest/poorest
1% |
214.3 |
290.6 |
359.6 |
Source:
see Table 10
The hybrid study
(Table 11) in effect adds data on inter-national distribution for 128 countries
to the global data for 35 countries in the pure study. Perhaps surprisingly
(since many African countries are now included) the overall Gini
coefficient, for both income sources, is
slightly lower in 1980 than that of the pure study. But, although it still
registers a decline during the 18 years, the fall is very much less than in the
pure study or in the inter-country Gini calculated
using Maddison’s 1995 data. Maddison
01 produces a falling Gini coefficient but the fall
is much smaller than that shown by the WDI data. More surprisingly in the case
of the WDI figures there is a rather considerable decline in the 20/20 and
10/10 ratios. The latter is particularly surprising given that, as observed
above, there is some evidence from other sources of an increase in inequality
according to this statistic during the 1990s. But, if even smaller extremes are
compared, the result does change and rather dramatically. The 5/5 ratio falls
during the 1980s and then rises during the 1990s. Using the Maddison
01 figures this occurs for both the 10/10 ratio as well. And in the case of
both data sources there is an enormous increase in the ratio of the income of
the richest to that of the poorest 1 per cent of the world’s population, in
other words about 60 million people at each end of the distribution. This ratio
has very nearly doubled over the two decades studied.
From all the above
global data, as in the case of the inter-country calculations, what seems to
emerge is that within a decline or stabilization of inequality in one sense
there is a growth of inequality in other senses. First, a small group at the
top of the distribution has been separating itself off from the rest of the
world distribution, and another group at the bottom have been suffering incresasingly extreme privation, producing the ratios of
extremes we have just seen. The largest component of the top group is the top
quintile of the
10. More disaggregated measures
Measures of world
inequality are the net outcome of rises and falls of relative income for
thousands of different groups of the world’s inhabitants; we can perhaps,
therefore, conclude more about the way in which inequality has changed by looking
at more disaggregated measures. Although the number of countries for which
internal distribution data are available over time is limited we can take
countries in different parts of the world and see how they fared in relation to
each other. That is done in Table12 which shows the ratio of the top 10 percent
of the population of the
Table 12: Ratio of income per head of the richest
|
Country Year |
|
|
|
|
|
1980 |
46 |
157 |
96 |
152 |
|
1990 |
75 |
106 |
79 |
215 |
|
2000 |
94 |
67 |
83 |
402 |
Note:
for all countries the income figures for the years specified are used in
combination with internal distribution figures for the nearest available year
(see Appendix note).
Sources:
World Bank 2002a and 2002b (for income in all three years and for distribution
in latest year); Deininger and Squire 1996 (for
distribution in 1980 and 1990)
These figures show
that the poor in China have become somewhat less poor in relation to rich United
Statesians during the last two decades, the poor of
India have fluctuated and are now marginally less relatively poor; the poor of
Brazil are twice as relatively poor as in 1980 and those of Nigeria between two
and three times as relatively poor. Whatever the overall single measures of
distribution show, these figures dramatically underline how behind global
figures is a complex and contradictory process of convergence and divergence.
It is worth noting that, on the same method of calculation between 1990 and
2000 the highest quintile in
The same process is
illustrated by the evolution of the income of the richest
Table 13: Relation of the top
|
|
1980 |
1990 |
2000 |
|
US top
quintile/world median |
34 |
31 |
26 |
|
US top
quintile/world mean |
8.5 |
9.5 |
10.5 |
|
World mean/world
median |
4.0 |
3.3 |
2.5 |
Source: see Table 13
11. Agreements and disagreements
The above
comparisons of various studies, using different methodology and data, lead to a
number of tentative conclusions. First, several estimates, using partially
differing data and methods, all place the global Gini
coefficient in the 1990 as about 0.65 or a little below. It is possible that
that is not a coincidence but approximates to its really value. If so, then it
allows the conclusion that the distribution of income among the world’s
population is more unequal than for any individual country, even
Second, in relation
to trend, the estimates of Milanovic seem to show a
very different picture from the others, though no exact comparison is possible.
While the years compared are not the same, this study in finding a sharp
increase in global inequality between 1988 and 1993 seem to be quite
inconsistent with my calculation and that of Bourguignon and Morrison, although
the latter authors rather inexplicably say that they regard the Milanovic study as consistent with theirs. But if they are
incompatible, as I suspect, the reason for the difference must be the
difference in the data. Milanovic and Dikhanov and Ward use the World Bank’s data set on
household spending while Bourguignon and Morrison, Sala-i-Martin
and I use estimates of GDP per head, weighted by distribution estimates. If it
is indeed this difference in income data which produces such apparently
radically different results, then the debate about calculated results should
obviously be replaced by a much more detailed one about the validity of these
household spending estimates as compared with GDP estimates (a question which Milanovic addresses in his article (2002)). One obvious
question relating to this is that of government income and spending. Total
household spending will necessarily be less than total national income and a major
source of the difference will be government spending. If government spending is
inegalitarian then the GDP figures will underestimate
the degree of inequality; if it is egalitarian then the calculations based on
household spending will exaggerate the degree of inequality. Until this kind of
question has been resolved the present disagreements between different
economists about the trend of global inequality in the last 20 years cannot
resolve very much. The differences are really differences about the data and
its appropriateness.
Third, the studies
present a range of different outcomes for the last two decades of the twentieth
century. Together they cast doubt on the idea that inequality has sharply and
unambiguously declined during the epoch of neoliberalism.
Nor do they seem to offer comfort to those who claim that it has sharply and
unambiguously increased.
Fourth, it is
striking that the most recent version of Maddison’s
widely used historical income estimates does not show the considerable decline
in the Gini coefficient which was noted in the
earlier version. In fact, in the whole world excluding
Fifth, calculations
based on ppp estimates give lower estimates of
inequality and show slower growth of inequality than estimates which convert
national incomes using exchange rates. I have given data in exchange rate
based-comparisons only for comparative purposes because they are so widely
quoted. In my opinion an estimate of world distribution, inter-national or
global, in exchange rate terms is in principle meaningless and should never be
done. To give an example: between 1999 and 2001 the Euro/US Dollar exchange
rate fell by around 30 percent. Does that mean that US real incomes have risen
30 percent compared with European real incomes? Evidently not. Calculations in
exchange rate terms should really be banished from this debate. But they will
persist because they fuel conclusions which many people want to reach. This is
not to say that ppp estimates are anywhere near
perfect. They have many defects. Different sources provide widely different
estimates and they can only be produced by devoting a large quantity of
resources to the necessary price surveys. But at least they provide in
principle a coherent basis of comparison. We cannot say as much of exchange
rate based estimates, especially in a world of greater exchange rate
instability.
Sixth, my own
calculations suggest that inequality is growing between the extremes of rich
and poor while the intermediate sections of the world population move close
together (see section 9). This result is consistent with a similar one produced
by Melchior (2001) using annual inter-country income
data.
Seventh, in
comparing all these results an important debate has emerged about whether the
world is characterised increasingly by a bi-modal or
a uni-modal distribution. Quah
has argued that what has been emerging is a “twin-peaks” form of distribution. Milanovic (2002b) also concludes that we are approaching a
“world without a middle class”. Applying to the world the rule of thumb that
the middle class is statistically defined as those with between 75 and 125
percent of the median income he calculates than only 14.5 percent of the
population belong to it. According to Maddison’s
figures it was only about 12 percent in 1998, scarcely changed since 1980. By
contrast, Sala-i-Martin concludes that the situation
is one of “vanishing twin-peaks” and “emergence of a world middle class”. Not
only is this difference important in relation to interpreting the statistics,
but also it is relevant to the kind of class structure a more globalized capitalist world is assuming, and this would, of
course, have many implications for future political development. There is
plenty of scope for more work and debate on this issue both on the statistical
and the political plane.
12. Ironies of the debate
The end of the
twentieth century has produced a spate of economic assessments of it by
scholars and by international institutions. While there is much aggreement, except among ecological economists, that the
century has been extraordinarily successful in terms of increased productivity
and output, there is more doubt about the question of distribution. Many are
worried that it has been a century of divergence rather than convergence. The
fact that the gap between the income per head of countries has widened during
the century has been observed in reports from the UNDP, the OECD, the IMF, the
World Bank and other institutions and publications.
A little over a
decade ago the UNDP began to denounce the use of conventional national income
per head figures as an appropriate measure of development. In inventing its
influential Human Development Index it argued that income per head should be
converted at purchasing power parity not with exchange rates, that its value
should be sharply attentuated (by using its logarithm
rather than actual value) and that it should be only one third of an index of
development, the other two thirds being life expectancy and education. This
index, however, produces a world in which countries are considerably more
quantitatively equal than when they are compared using conventional income per
head. And a recent study has shown that over the long period, during which they
have diverged according to income per head, they have converged according to
the HDI (Crafts 2000). Bourguignon and Morrison also look not only at the
long-term divergence of distribution of income but also at the convergence of
the distribution of years of life. On this variable only inter-country data are
available. But like income, life expectancy is unequally distributed within
national populations (Sutcliffe 2001) although there is generally still very
little data about this.
During the 1990s
the UNDP continue to publish the HDI but put growing propagandistic importance
on measures of world inequality during the last 30 years based on figures which
its reports (and most economists) had previously claimed was inappropriate –
namely income per head converted into dollars using the current exchange rate.
These, of course, showed sharply increasing income inequality and the UNDP’s claims on this subject have been central to the
spread of this idea. This irony was has recently been compounded since in the
late 1990s the relation between exchange rate conversions and ppp measures went partially into reverse. For example,
between 1995 and 2000, due to a reverse in exchange rate trends, the ratio of
the richest to the poorest 10 percent of the population (based on
population-weighted inter-country figures) fell for the exchange rate
comparison and rose for the ppp based comparison, the
opposite of the relation which had existed in previous years and which had been
exploited by those who wished to overstate the inequality.
At the turn of the
century the IMF, disturbed that its upbeat assessment of the twentieth century
was tainted by the rise in inequality, suddenly discovered in the 2000 issue of
its Global Economic Outlook that maybe income is not the most important
measure of welfare and that the Human Development Index may be a better measure
to use.
The UNDP and many
other participants in the debate (some of them innocently) makes liberal use of
statistics which almost everyone (including themselves) otherwise reject as
seriously misleading measures of comparative welfare of development, only, it
seems, because they show inequality which is quantitatively greater and growing
faster. The UNDP have been criticised for it by
members of the UN Statistical Commission. And the IMF extols the convergence
suggested by the HDI without mentioning that, because of the way in which the
index is constructed (with a maximum attainable level and based on variables
which have upper limits which most developed countries are close to), it is
almost bound to show convergence. In the HDI all progress, however slow or
rapid, expresses itself as convergence. The important discussion of world
inequality therefore is being seriously harmed by uncritical and opportunistic
use of statistics by these organizations.
The tendency to
choose the figures which best suit ones conclusions is, of course, not confined
to international bureaucracies. The reason for the extraordinary diffusion of
the exchange rate based estimates of international distribution is that they
seem to support already reached conclusions, especially that neoliberalism and globalization considerably worsen the
distribution of income. The recent tendencies of global distribution are
clearly difficult to establish and depend very much on the insufficiently
discussed quality of different types of data. But the changes in inequality
over the last few decades is a comparatively trivial question compared with the
actual degree of that inequality during all of the modern period. Inequality in
the distribution of income in the world is in the modern epoch as a whole
higher than in any previous period of world history; and it is greater than the
inequality which exists in any single one of the world’s component countries.
(correct?) Those are the important and undeniable facts. Those of us who
believe that this is a manifestation of massive social injustice should not
automatically deny all evidence of lessening inequality because it might weaken
our argument; we should be concerned to arrive at the best and most coherent
numerical estimates, whatever those may show. Unfortunately, we can be safe in
the knowledge that an egalitarian world is not at hand.
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Appendix note
Countries in my “pure” study with exact dates
of distribution estimates:
First year Second
year Third year
Australia 79 90 94
Bangladesh 78 86 95/6
Brazil 80 89 98
Bulgaria 80 90 97
Canada 79 90 94
China 80 90 98
Colombia 78 88 96
Costa Rica 81 89 97
Czech Rep/Czecho. 80 88 96
Dominican Rep 84 89 98
India 77 90 97
Indonesia 80 90 99
Italy 80 89 95
Jamaica 75 90 00
Jordan 80 91 97
Morocco 84 91 98/9
Netherlands 79 91 94
Nigeria 86 92 96/7
Norway 79 91 95
Pakistan 79 88 96/7
Panama 79 89 97
Poland 80 90 98
Russian Fed/USSR 80 89 98
Share of world pop 70 70 69
Share of world GDP 61 63 67
Sources: Deininger
and Squire, World Bank 2002b