Income Inequality in the Developing World

Abstract

Should income inequality be of concern in developing countries? New data reveal less income inequality in the developing world
than 30 years ago. However, this is due to falling inequality between countries. Average inequality within developing countries
has been slowly rising, though staying fairly flat since 2000. As a rule, higher rates of growth in average incomes have not
put upward pressure on inequality within countries. Growth has generally helped reduce the incidence of absolute poverty,
but less so in more unequal countries. High inequality also threatens to stall future progress against poverty by attenuating
growth prospects. Perceptions of rising absolute gaps in living standards between the rich and the poor in growing economies
are also consistent with the evidence.

Full text

Income Inequality in the Developing World
Martin Ravallion
1
Abstract: Should income inequality be of concern in developing countries? New data reveal less
income inequality in the developing world than 30 years ago. However, this is due to falling
inequality between countries. Average inequality within developing countries has been slowly
rising, though staying fairly flat since 2000. As a rule, higher rates of growth in average incomes
have not put upward pressure on inequality within countries. Growth has generally helped reduce
the incidence of absolute poverty, but less so in more unequal countries. High inequality also
threatens to stall future progress against poverty by attenuating growth prospects. Perceptions of
rising absolute gaps in living standards between the rich and the poor in growing economies are
also consistent with the evidence.
Development economics emerged as a sub-discipline of economics in the 1950s and its
initial focus was on economic growth, with inequality as a secondary concern. The prevailing
orthodoxy for many decades was that a period of rising inequality was to be expected in growing
poor countries. Rising inequality was seen to be more-or-less inevitable and not something to
worry about, particularly if the incidence of poverty was falling. Another commonly held view
was that policy efforts to reduce inequality were likely to impede growth and (hence) poverty
reduction.
The existence of high inequality within many developing countries, side-by-side with
persistent poverty, started to attract attention in the early 1970s. Nonetheless, through the 1980s
and well into the 1990s, the mainstream view in development economics was still that high
and/or rising inequality in poor countries was a far less important concern than assuring
sufficient growth, which was the key to poverty reduction. The policy message for the
developing world was clear: you can’t expect to have both lower poverty and less inequality
1
Science, forthcoming. Department of Economics, Georgetown University, Washington DC, and National Bureau
of Economic Research, USA. Correspondence: mr1185@georgetown.edu.

2
while you remain poor, and if you choose to give poverty reduction highest priority then focus
on growth.
Other objections could still be raised to high income inequality. The classical utilitarian
formulation—whereby social welfare is judged by the sum of “utilities,” assuming diminishing
marginal utility of income—pointed to social welfare losses from high inequality at a given
mean. But that did not persuade those who believed that there was a trade-off between equity and
growth. A moral defense could also be mounted for the view that inequality is not an important
issue for a growing developing country by appeal to John Rawls’s (1971) “difference principle”
that (subject to assuring liberty and equal opportunity) higher inequality can be justified as long
as it benefits the worst off group in society.
The period since 2000 has seen a deeper and more widespread questioning of this
longstanding view of “pro-poor inequality.” New concerns have emerged about the instrumental
importance of equity to other valued goals, including poverty reduction and human development
more broadly. It appears more likely today that high inequality will be seen as a threat to future
development than as an inevitable and unimportant consequence of past progress. The long-
standing idea of a significant “growth-equity trade-off” has come to be seriously questioned.
This paper reports new estimates of the levels and changes in income inequality measures
for the developing world. The new estimates take us up to 2010, embracing the period of higher
growth rates in the developing world since the turn of the millennium. In the light of these new
data, the paper revisits the arguments and evidence pertaining to past and ongoing debates on
inequality in developing countries and the trade-offs with growth and poverty reduction.
Income Inequality Measures
To measure inequality in the developing world as a whole one ignores country borders—
pooling all residents and measuring inequality amongst them. This overall measure will naturally
depend on the inequality between countries as well as that within them. Thus its evolution over
time will depend on whether poorer countries are seeing lower growth rates as well as the things
happening within countries—economic changes and policies—that impact on inequality.
If we are comparing country or regional performance then we will want to isolate the
within-country component of inequality, as distinct from that between countries. While there are
many inequality measures, not all of them allow a clean separation of the “between” and

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“within” components. For example, such a decomposition is not generally possible for the Gini
index—a popular inequality index based on the average absolute difference between all random
pairs of incomes normalized by the mean. (The exception is when the distributions of different
countries do not share any common support, which is unlikely.)
The mean-log deviation (MLD) offers an elegant solution. This is given by the
(appropriately weighted) mean across households of the log of the ratio of the overall mean
income to household income per person. When all incomes are equal MLD=0; the higher the
inequality the higher the MLD. Like the Gini index, MLD is a relative inequality measure, in that
it depends on ratios of incomes to the mean; this is implied by the scale invariance axiom of
inequality measurement, which says that when all incomes are multiplied by a constant the
inequality measure does not change. (We will return to this axiom, which can be questioned.)
Also, both measures satisfy the standard “transfer axiom” for inequality measurement, namely
that a small transfer to someone with a lower income will reduce inequality. However, unlike the
Gini index, MLD is exactly decomposable by population sub-groups.
Inequality is measured using household surveys. The estimates presented here are based
on around 900 surveys spanning 1980-2010 and 130 countries. The calculations have been done
on an updated version of the dataset used by Chen and Ravallion (2010) for measuring poverty
and (when relevant) the same methods have been used here. For over half the surveys, household
consumption expenditure (including imputed values for consumption from own-production) was
used rather than income, although for brevity the word “income” is used for both. For a given
economy, income inequality measures are expected to be somewhat higher than for consumption
given the scope for smoothing consumption in the presence of income shocks.
While household surveys are the only source of data on inequality across households,
there are two sources of data on the growth rates in average household consumption or income.
The same surveys used to measure inequality comprise one source, while the other is the
household consumption component of domestic absorption in the national accounts. There is no
clear way of ranking these measures; in some developing countries the measures based on
national accounts are considered quite unreliable, while elsewhere there are serious concerns
about the survey-based measures.
It is acknowledged that household surveys may well underestimate the extent of
inequality, notably through either the rich under-reporting their incomes or through selective

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compliance in the randomized assignments of the survey instrument, whereby richer households
are less likely to participate. (The latter problem does not imply that inequality will be
underestimated; see Korinek et al. 2006, who nonetheless find that selective compliance leads to
an underestimation of inequality in the U.S.) While there are methods that can be used to address
these concerns, they have so far tended to be confined to research studies, and mostly in rich
countries. No corrections for these data problems have been made in the primary data used here.
The overall MLD for the developing world in 2010 is estimated to be 0.578. To help
interpret this number, imagine a distribution with three incomes (1, 2, x); for this distribution to
have MLD=0.578, x would need to be 12.73, i.e., the richest of three people would need to have
over 12 times that of the poorest and 6 times that of the middle-income.
Fig. 1: Inequality in the developing world
Inequality has fallen over this 30 year period. For the earliest year that the data permit an
estimate, 1981, MLD was 0.651. (This is equivalent to x=14.79 in the distribution (1, 2, x).)
Figure 1 plots MLD for the developing world as a whole and its between-country component.
(The supplementary notes give detailed estimates.) We see that the overall decrease came with
ups and downs, and an increase over 2005-10. The variance over time is largely accountable to
inequality between countries. Over the period as a whole, the between-country component has
.0
.1
.2
.3
.4
.5
.6
.7
1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010
Inequality in the developing world (MLD)
Total
Between
country
Within
country

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fallen while the within-country component has risen. The latter accounted for 31% of total
inequality in the developing world in 1981, but 47% in 2010. Strikingly, however, this pattern
has reversed since 2000, with inequality rising between countries but falling on average within
countries. Something different seems to be happening since the turn of the millennium. The next
section will consider the role played by the higher growth rates since then.
Fig. 2: Evolution of average inequality within countries
Figure 2 plots the within-country component by region. Latin America and the Caribbean
(LAC) have persistently had the highest average inequality of any region. (The mean difference
between LAC and other regions falls only slightly when one controls for income surveys, which
have been more popular in LAC; see the Addendum.) Inequality in LAC was rising until around
2000, but has fallen noticeably since then. Eastern Europe and Central Asia (EECA) saw a sharp
rise in average inequality in the 1990s but has seen generally falling inequality since then. Sub-
Saharan Africa (SSA) has the second-highest average inequality, though with no clear trend.
South Asia has been a region of low inequality, though rising somewhat since the early 1990s.
East Asia started out as the region with the lowest inequality, but has seen a steady rise. The
Middle-East and North Africa (MENA) region has seen steadily falling average inequality. The
next section will take a closer look at the pattern of changes over time.
.0
.1
.2
.3
.4
.5
.6
.7
1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010
Developing world as a whole
East Asia
Eastern Europe and Central Asia
Middle East and North Africa
South Asia
Population-weighted mean log deviation
Latin America and the Caribbean
Sub-Saharan Africa

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Inequality and Growth across Countries
As is well known, the developing world has seen substantially higher growth rates since
2000. Have the processes of stronger economic growth in developing countries, and the
acceleration of growth seen since the turn of the century, put upward pressure on inequality?
The recent signs of rising inequality in the developing world as a whole have coincided
with the higher growth rates since 2000 (Figure 3). However, when we un-pack the relationship
(exploiting the properties of the MLD) we find two opposing forces: the higher growth rates
have come with higher between-country inequality but lower average within-country inequality
(Figure 4). Across the developing world as a whole, and taking 3-year periods back to 1981, the
periods of higher growth have come with higher between-country inequality (r=0.70), while
inequality within developing countries has been falling with higher growth (r=-0.63). But the
latter relationship is clearly rather flat; going from zero growth rate to a 5% annual rate is
associated with a move from an expected rise in MLD of 0.005 (about one third of the standard
deviation in the annualized change in MLD) to no change on average. Growth in average
household income appears to be close to inequality-neutral on average.
These are aggregates across countries. I also assembled a data set of the longest spells
between two household surveys, and I obtained complete data for about 100 countries. The
median year of the first survey is 1989 and it is 2008 for the latest survey. The (un-weighted)
mean MLD is unchanged at 0.318. (Population-weighted values are slightly lower.) However,
the variance has fallen, from a standard deviation of 0.195 to 0.171 for the latest year. Inequality
fell over time in 11 of the 13 countries that had MLD greater than 0.576—the aggregate value
(including the between country component) in 1990. The annualized change in MLD is
negatively correlated with its initial value (r=-0.33, n=122) and the same is true for the Gini
index (r=-0.55; n=122). This pattern of inequality convergence is consistent with other evidence
(Bénabou, 1996; Ravallion, 2003). While measurement errors exaggerate the signs of
convergence, it remains robust to the use of an econometric estimator that addresses this concern
(Ravallion, 2003).

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Fig. 3: Growth in average income and changes in inequality for the developing world as a
whole
Note: The year labels refer to the end year of three-year periods (two years for 2010)
Fig. 4: Growth rates and changes in inequality between versus within countries
-.024
-.020
-.016
-.012
-.008
-.004
.000
.004
.008
.012
.016
-1 0 1 2 3 4 5 6
Growth rate in household income per capita (% per annum)
Change in inequality in the developing world (MLD)
2010
2008
2005
2002
1987
1996
1984
1993
1990
1999
r=0.57
-.03
-.02
-.01
.00
.01
.02
-1 0 1 2 3 4 5 6
Growth rate in household income per capita (% per annum)
Change in within-country inequality (annualized)
Change in between-country inequality (annualized)
Change in inequality (MLD)
r=-0.63
r=0.70

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These data suggest a small negative correlation between changes in inequality and growth
rates amongst developing countries. The correlation coefficient between the growth rate
(annualized log difference) in the survey mean and the annualized change in MLD is -0.24,
which is significant at the 5% level. Using growth rates from the national accounts instead of
surveys the correlation drops to -0.04. Using the Gini index rather than MLD gives similar
results. Nor did I find evidence of nonlinearity in the relationship; regressing the change in
inequality on both the growth rate and the annualized change in the squared log mean, neither
coefficient was significant. (See the supplementary notes for details.) So, again, inequality
increases about as often as it falls during spells of growth. This finding is consistent with past
evidence using cross-country comparisons (as surveyed in Ferreira and Ravallion, 2009).
It is not then surprising that there is a strong negative correlation between growth rates
and changes in absolute poverty. Across the regional averages used above, the correlation
coefficient is -0.75 between growth rates in mean household consumption or income and the
annualized changes in the log of the poverty rate for a poverty line of $1.25 a day at purchasing
power parity. Across countries the corresponding correlation coefficient is -0.85. This is
consistent with a large body of cross-country evidence back to around 1990, as reviewed by
Ferreira and Ravallion (2009).
There are a number of caveats that should be noted about this negative correlation. There
are measurement errors to consider. There are some rather extreme values in some of the country
data that may well reflect such errors. However, the correlation is still high at -0.70 (n=88) if one
trims extreme values (dropping absolute log differences per year over 0.5). The correlation is
also robust to the use of an econometric correction for correlated measurement errors between
the poverty measures and the survey means (Addendum). The correlation is lower if one uses
growth rates from national accounts instead of those from surveys. (The correlation between
annualized log differences in the $1.25 a day poverty rate and the growth rates in private
consumption per capita from national accounts is -0.56.) Some of the things that are included in
the national accounts do not get passed on quickly (or at all) to household living standards. Also,
survey dates do not cover entire years as do national accounts. Another caveat is that the
correlation tends to be much weaker using relative poverty measures, whereby the poverty line
rises with average income (consistently with the cross-country relationship between national
poverty lines and average income); see Chen and Ravallion (2013).

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An important caveat is that the fact that we do not see rising inequality on average in
growing economies does not imply that inequality can safely be ignored. The rest of this paper
points to a number of reasons why inequality should be considered a central development issue.
Inequality Matters to Progress against Poverty
Does higher inequality in poor countries permit faster progress against poverty through
economic growth? While most development economists would probably have answered “yes”
even 10 years ago, recent development theories and evidence are more suggestive of a negative
answer.
The performance of developing countries against poverty is quite diverse. Inequality
comes back into the story in efforts to explain this diverse performance. We have learnt that
inequality plays three important roles in influencing the pace of progress against poverty. First,
changes in inequality during the growth process have implications for how much that growth
impacts on poverty. Using the country level data set discussed in the last section, I find that
amongst growing developing countries in terms of mean household income based on the surveys,
those experiencing falling inequality see the $1.25 a day poverty rate falling at a median rate of
about 1.30% points per year versus a median fall of only 0.42 % points per year for countries
with rising inequality. Either way, poverty incidence tends to fall, but at very different rates.
Second, initial inequality reduces the growth elasticity of poverty reduction—the
responsiveness of poverty measures to growth in mean income. This is intuitive: the more
unequal the original distribution, the smaller the share of the growth accruing to the poor, and the
lower the poverty reduction arising from that growth; this was demonstrated empirically by
Ravallion (2007). The converse holds too: in more unequal societies the poor tend to be more
protected from aggregate economic contractions.
Third, even if inequality does not rise during a period of economic growth, a high initial
level of inequality can mean less growth and (hence) less progress against absolute poverty. In
the 1990s, we started to see various theoretical arguments that high levels of inequality stifled
investment, innovation and reform. Here I shall only provide a sketch of the arguments that have
been made; Voitchovsky (2009) and Ravallion (2014) survey the arguments and evidence in the
literature on how the initial level of inequality influences the subsequent growth rate.

10
An influential strand of this literature points to the implications of borrowing constraints
associated with asymmetric information and the inability to write binding enforceable contracts.
Credit market failure that disproportionately affects poor people leaves unexploited opportunities
for their investment in physical and human capital, and it is assumed that there are diminishing
returns to capital, such that poor people have higher marginal products of capital. (This idea can
be extended to also embrace technical innovation, assuming that everyone gets new ideas, but
that the poor are more constrained in responding.) Then higher current inequality implies lower
future mean wealth at a given value of current mean wealth.
Other sources of economic distortions can create costly inequalities. This can happen if a
relatively privileged sub-group is able to restrict entry to economic opportunities (including jobs)
and thus set the returns to those activities above the market clearing level. Labor-market failures
in the form of persistent unemployment can also have lasting adverse consequences for both
equity and efficiency. Human capital is developed in part by working; thus longer spells of
unemployment create a de-skilling that makes it harder to get a job. Unemployment can also be
associated with psychological distress and depression. This psychological scarring may also
make it harder to get a job.
Another class of models is based on the idea that high inequality restricts efficiency-
enhancing cooperation, such that key public goods are underprovided, including legally-secure
property rights, or desirable economic and political reforms are blocked. High initial inequality
can also induce costly political economy responses.
Some of the literature has pointed to other aspects of the initial distribution of income
besides inequality. It has been argued that a larger “middle-class” helps assure a more diversified
economy (especially though greater demand for consumer goods) and that the middle class also
tends to be a stronger political force for pro-growth economic reforms. Others have argued that
higher current “wealth poverty” (of which access to land is a key factor) impedes growth, such as
through access to credit.
A strand of this new empirical literature on economic growth has tested for inequality as
an initial condition and the results have generally supported the view that higher initial inequality
impedes growth. And the effect is quantitatively large, as well as statistically significant; two
recent examples are Berg et al. (2012) and Herzer and Vollmer (2012).

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We have seen that the literature has pointed to various aspects of initial distribution—
inequality, the size of the middle-class or poverty—that matter to progress against poverty.
These distributional measures tend to be correlated with each other, yet very few studies have
tested encompassing models that try to see if one measure is more important than another. In
one exception, Ravallion (2012) finds that high poverty at a given initial mean matters more to
rates of growth in mean consumption in developing countries than inequality, or measures of the
middle class or polarization. This does not imply that inequality is unimportant, but rather it tells
us that inequality matters to growth in poor countries mainly via its bearing on the extent of
initial poverty.
So the arguments and evidence from modern development economics do not suggest that
we should expect any significant aggregate trade-off between progress against absolute poverty
and progress in reducing inequality. Indeed, the evidence suggests that falling inequality tends to
come with falling poverty incidence (Ravallion, 2005).
Differing Concepts of “Inequality”
The value judgments made in measuring inequality carry weight for the position one
takes on whether economic growth tends to be inequality increasing or not (Ravallion, 2004). So
far, this paper has followed the long-standing practice of relying on relative inequality measures,
defined in terms of ratios of incomes or consumptions. By contrast, “absolute inequality”
depends on the absolute differences in levels of living. If the distribution of income amongst two
people changes from ($1,000, $10,000) to ($2,000, $20,000) then relative inequality is
unchanged, yet the absolute gap between the “rich” and “poor” has doubled.
Perceptions on the ground that “inequality is rising” appear often to be referring to the
absolute concept. Amiel and Cowell (1999) report experiments to identify which concept of
inequality is held by people. They found that 40% of the university students they surveyed (in
the UK and Israel) thought about inequality in absolute rather than relative terms. (Harrison and
Seidl, 1994, report similar findings for a large sample of German university students.) For the
purpose of this paper I fielded a similar (confidential) survey to my class of undergraduates at
Georgetown University. From the 130 responses (out of 150 students), the class was roughly
evenly split between those who thought about inequality in relative terms and those who thought
about it in absolute terms.

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It is not that one concept is “right” and one “wrong.” They simply reflect different value
judgments. The relative inequality concept is implied by the scale invariance axiom, while the
absolute concept is implied by an alternative axiom called translation invariance. The former
axiom has dominated practice in the measurement of inequality by economists and statisticians
but the “axiom” is hardly a self-evident truth.
In this light, let us return to the longstanding debates on growth and equity. Finding that
the share of income going to the poor does not change on average with growth does not mean
that “growth raises the incomes (of the poor) by about as much as it raises the incomes of
everybody else,” as claimed by the Economist magazine (May 27, 2000, p.94). Given existing
inequality, the absolute income gains to the rich from inequality-neutral growth will of course be
greater than the gains to the poor. For example, for the richest decile in India, the income gain
from aggregate growth will be about four times higher than the gain to the poorest quintile; it
will be 15-20 times higher in Brazil or South Africa.
The empirical finding in the literature that growth tends to be inequality-neutral within
developing countries will carry little weight for those concerned about absolute inequality, who
prefer translation invariance to scale invariance. One expects an absolute measure to rise with
growth, and fall with contraction. I confirmed this on the aforementioned country-level data set.
Changes in the absolute Gini index show a significant positive correlation with growth rates in
either survey means (r=0.51, r=123) or consumption per capita from the national accounts
(r=0.75, n=114). Figure 5 plots the relationships.
It may well be that past and ongoing debates about the distribution of the gains from
growth in the developing world rest in no small measure on this (rarely discussed) conceptual
difference in how inequality is defined. Unlike those who see inequality as relative, those who
view it in absolute terms will expect to see a trade-off between reducing inequality and reducing
poverty. That does not mean that any policy that is good for one is necessarily bad for the other,
or that it is impossible to have both; the correlation is just that—a correlation. However, it does
help us understand why some growth-promoting and poverty-reducing policy reforms may well
come in for serious criticism and even be blocked by a non-negligible number of observers
concerned about widening gaps in living standards between the rich and the poor. How policy
makers deal with that critique may matter greatly to progress against poverty.

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Fig. 5: The concept of inequality matters to assessing whether inequality rises in growing
developing economies
Note: Absolute Gini indices scaled by the mean of initial and final years.
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