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WB article on economic growth and income inequality
Hi folks, I think the article below from WB will shed some lights on your=
=20
discussion. If you don't have time, skim at least the headlines and
read the conclusion.
BTW, I agree with Anh Viet that the info Trung (Poland) quoted is not
sufficient to support the hypothesis that there is no correlation between
economic growth and income inequality. It is unclear if the article Trung
mentioned is the one below.=20
I agree further that in the case of Vietnam the emphasis should be placed o=
n
achieving and sustaining high-rate economic growth, even at the expense=20
of increasing income inequality. We need some superrich guys and need=20
them fast. We don't need a lot of "gia`u co' nu+?a mu`a" folks.
Lam's talking about "golden rate" is nice academically, but is impossible=
=20
to implement in practice.=20
Economic Growth and Income Inequality: Reexamining the Links
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KLAUS DEININGER AND LYN SQUIRE
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Many economists have long believed that income disparities
increase in the early stages of development, making the poor
relatively worse off. Recent research suggests that an unequal
distribution of income can hamper growth. What does the evidence
show?
------------------------------------------------------------------
ECONOMISTS have long sought to understand the links between
economic growth and income distribution. The main issues, listed
below, have important policy implications for developing
countries:
* In countries with low levels of development, does economic
growth result in a more unequal distribution of income, and
is it necessary for per capita income to reach a certain
minimum level before income inequality begins to decrease?
* Do countries with unequal income distributions experience
slower economic growth than more egalitarian countries?
* Should governments consider adopting redistributive policies
to improve the situation of the poor?
Why the links matter
Different assumptions about the links between growth and
inequality produce different outcomes for the poor, as illustrated
in Chart 1. The base scenario, represented by the top line,
assumes an egalitarian economy where the poorest group's share of
total income does not change over a 60-year period. In this case,
economic growth (we assume a rate of 4 percent a year) would raise
the incomes of the poor.
[How do the poor fare in the early stages of development?]
Source: Authors' calculations.
The second scenario (represented by the middle line in Chart 1) is
based on the famous Kuznets hypothesis, first formulated by Simon
Kuznets more than 40 years ago. This hypothesis suggests that, at
low levels of per capita income, inequality increases with rising
per capita income and decreases only in the later stages of
development--resulting in an inverted U-shaped relationship
between per capita income and income inequality--based on a model
where individuals migrate from a low-wage rural sector with little
inequality to an urban sector characterized by high income
inequality and high average income. In this scenario, the poorest
group's share of total income would decrease as economic growth
takes off and would not be restored to initial levels for 60
years; as a result, the poor's per capita incomes are lower by an
average of 10 percent over two generations.
Recent research has also identified a negative relationship
between initial inequality and subsequent growth (see Deininger
and Squire, 1996). The scenario represented by the bottom line in
Chart 1 assumes a significantly higher level of initial
inequality--20 points higher in terms of the Gini coefficient.
(The Gini coefficient, a measure of the extent to which actual
income distribution in a country differs from a hypothetical
uniform distribution, goes from 0, for absolute equality, with
each individual or household receiving an identical share of
income, to 100, which indicates that one person or household
receives all the income.) In this scenario, the rate of annual
income growth would drop to 2.7 percent, and, at the end of our
hypothetical 60-year period, the per capita income of the poor
would be less than half of what it would be in an economy that had
started off with a more egalitarian distribution. This would be
true even if the Kuznets hypothesis did not hold.
Such large differences in outcome have far-reaching implications
for government policies. However, these simulations draw on
available empirical analysis, much of which suffers from an
important shortcoming--it is based on a very limited amount of
data, and these data are often of unacceptably low quality.
The data
To be acceptable, data on income distribution need to satisfy
three criteria.
They should be based on nationally representative surveys rather
than synthetic estimates built up from national accounts data and
general assumptions regarding the distribution of income across
occupations or in other countries at a similar stage of economic
development. Such synthetic estimates, prevalent in early studies,
are unacceptable, since they presuppose the existence of the
relationships that are to be tested in subsequent empirical
analysis.
They should cover the entire population rather than subsets, such
as urban or rural dwellers. Partial coverage, which is often
misleading, is particularly common in Latin America, where many
countries collect information only for the urban population. In
Peru, for example, the Gini coefficient for rural households is
32, compared with 42 for urban households. In South Africa, the
Gini coefficient for the white population is 48, compared with 62
for the whole population.
They should encompass all types of income, including nonwage
income and income from household production. As tax records and
labor force statistics are more commonly available than detailed
data from household surveys, many of the figures used in the
literature refer to wage or taxable income. We found that this
generally overstates the Gini coefficient by about 15 points and,
to the degree that data on wage income in the early years are
complemented with data on total income in later years, may give
the appearance of a spurious decrease in inequality. Own
production is particularly important for low-income groups in
developing countries. Even in Greece, in 1974, household
production (e.g., of vegetables and clothing) accounted for more
than 70 percent of the income of the lowest decile of the
population. Whether or not own consumption is included will,
therefore, have considerable impact on the inequality measure
obtained.
Although the above criteria are easily agreed upon in principle,
applying them consistently to the available data reduces the
number of "acceptable" observations to a point where meaningful
empirical analysis is no longer possible. To overcome these
constraints, we adopted a two-pronged strategy.
On the one hand, we expanded the data set on income distribution
by adding new observations from primary survey data, official
statistical publications, and research papers. This enabled us to
increase the number of acceptable observations. It also yielded 58
countries for which 4 or more consistently defined observations
are available, thus for the first time allowing at least some
inferences regarding changes over time of income distribution
within countries. However, it did not solve the problem of limited
data availability for the 1960s, which makes it difficult to
assess the impact of initial income distribution on subsequent
growth.
To deal with this shortcoming, we complemented our data on income
inequality with information on the distribution of land holdings,
which provides a better measure of initial distribution.
Information on the distribution of land in 1960 is available for a
much larger number of countries (73) than is information on the
initial distribution of income (12). It is attractive also from a
conceptual point of view, because it gives us a solid indication
of asset distribution and thus enables us to make inferences
regarding access to formal credit.
What do the data reveal?
First, income inequality is much greater in Latin America and
sub-Saharan Africa, which have Gini coefficients in the upper 40s,
than in East and South Asia, which have Gini coefficients in the
middle-to-upper 30s. The OECD countries, in general, have
relatively egalitarian distributions of income, with Gini
coefficients around 30, while the Eastern European countries have
historically had very low Gini coefficients. Measures of
inequality tend to be quite different across regions but to remain
relatively stable within regions and individual countries,
regardless of the considerable changes in aggregate income that
have taken place.
Second, land distribution and income distribution are not the
same. India, Indonesia, and Korea are all characterized by Gini
coefficients for income in the 30s, but the coefficients for land
distribution are 63, 55, and 35, respectively. Similarly,
Thailand, Tunisia, and Peru all have Gini coefficients for income
in the 40s, but the coefficients for land distribution are 45, 64,
and 93, respectively. This suggests that tests of the negative
relationship between initial inequality and subsequent growth may
yield different results depending on whether initial inequality is
measured in terms of income or land.
Third, aggregate measures of distribution may hide movements in
the incomes of different groups. Thus, the observation that
overall inequality may remain relatively stable over time can be
consistent with considerable change in the shares of total income
received by individual groups. And since we are primarily
interested in assessing the impact of economic growth on the poor,
it is important to complement the analysis of overall changes in
income with a more detailed assessment of the welfare of the
bottom quintiles of the population.
Results
The new data provide a basis for more detailed research on these
issues and also allow us to answer the three questions posed at
the beginning of this article.
Does inequality increase in the early stages of development and
then decline, as predicted by Kuznets? The Kuznets hypothesis has
spawned a vast empirical literature, much of it driven by concern
that development hurts the poor. Empirical analysis of this issue
has been hampered not only by the quality of the underlying data
but also because what is really a relationship over time has, for
lack of data, usually been tested using cross-country evidence.
Researchers have used variations in per capita incomes across
countries to represent increases in per capita income over time
within a country. Using our data, we are able to test for the
Kuznets curve within countries and find no evidence of it in
almost 90 percent of the cases. Of course, the 30-year period
covered by our data may be too short to produce the full inverted
U. If this is the case, we should still expect to see inequality
increasing in low-income countries and decreasing in countries
with high per capita incomes, but the data confirm the presence of
a linear trend in only a few countries. Even where it exists, the
trend rarely conforms to the Kuznets hypothesis.
We can take the analysis one step further to make more direct
inferences regarding the relationship between growth and poverty.
Examining the relationship between overall growth and changes in
the incomes of the bottom quintile of the population during
10-year periods, we find little systematic relationship between
overall growth and changes in inequality. Periods of growth are
associated with an increase in inequality almost as often (43
cases) as with a decrease in inequality (45 cases). In contrast,
we find a strong systematic relationship between overall growth
and growth in the income of the poorest quintile; the latter
increased in more than 85 percent of 91 cases. This would suggest
that even when inequality has worsened, its negative effect on the
poor has been more than outweighed by the positive effect of
growth.
Do more egalitarian countries grow faster? If economic growth does
benefit the poor, then a focus on factors that increase growth
would be warranted from an equity perspective as well as from a
development perspective. Recent empirical work indicates that
there may be a negative relationship between initial inequality
and future growth. If confirmed, this would imply that unequal
economies will experience lower rates of growth and, in general,
lower rates of poverty reduction.
To investigate the effect of initial inequality on long-term
growth, we look at determinants of growth rates for 1960=AD92.
Because acceptable data on income inequality prior to 1960 are
scarce, we use country averages of observations for the entire
period. We also use the distribution of land, for which more
observations of acceptable quality are available before 1960.
While the results confirm a negative link between initial income
inequality and subsequent growth, they suggest that this
relationship is not very strong. By contrast, initial inequality
of assets, as measured by the distribution of land, exerts a
significant negative effect on subsequent growth (Chart 2). Only 2
of the 15 developing countries with a Gini coefficient for land
distribution in excess of 70 grew more than 2.5 percent annually
during 1960=AD92.
[Countries with more equal land distributions tend to grow faster]
Source: World Bank data.
What are the mechanisms through which an unequal initial
distribution of assets or income might affect subsequent growth?
One possible mechanism is political--that is, poor people may vote
in favor of redistributive taxes that reduce investment
incentives. If this were the case, one would expect higher taxes
and lower investment in democratic--but not in
undemocratic--countries with a more unequal distribution of
income. The evidence does not support this theory, however.
Clearly, other forces are at work.
A second possible mechanism is that the effects of
inequality--primarily of assets-- are transmitted through
financial markets. Access to credit is conditional on ownership of
assets--for example, land--that can be used as collateral. If
certain investments in physical or human capital (for example, in
basic education) are affected by individuals' access to credit
markets, then the distribution of assets in an economy, in
addition to the mean income, will determine how many individuals
are able to undertake such investments. In more unequal economies,
fewer individuals would be able to make such investments,
resulting in lower stocks of human and physical capital and, as a
consequence, lower growth.
Two pieces of evidence provide support for this line of argument.
First, although initial (land) inequality is an important factor
reducing future growth in developing countries, it does not have a
significant effect in OECD countries. In the latter, poverty is
rarely a reason for non- attendance of primary schools; per capita
incomes are higher, so that even relatively poor households can
finance a broader range of investment without recourse to credit;
and land is less important as a form of collateral. Second, we
find that initial (land) inequality is significantly and
negatively related to the average educational attainment in the
population. Thus, the evidence suggests that credit markets, not
the political system, should be seriously considered as a
mechanism through which inequality slows economic growth.
Should policymakers seeking to reduce poverty redistribute
existing assets or create new ones? Our analysis shows that the
poor generally benefit from growth-enhancing policies,
specifically investment. It also suggests that, given the
growth-reducing effect of initial inequality, the poorest groups
in a country may benefit from redistribution. What is the relative
importance of accumulation compared with redistribution?
Initial land inequality has a significant impact on income growth
for all population groups except the top quintile. But investment,
which is associated with significantly higher income growth for
all groups, appears to have an even greater impact on the income
of the poor. Although increased investment coupled with a
redistribution of assets would appear to provide the greatest
benefits to the poor, pursuing a redistributive strategy at the
expense of investment could actually decrease the income of the
poor. Therefore, in situations where redistribution of assets is
either not feasible for political reasons or too costly, creation
of new assets would be a more promising avenue for improving the
welfare of the poor.
Conclusion
Using a new and improved cross-country data set on inequality to
examine the dynamics of growth and poverty reduction, we reached
three main conclusions. First, while policymakers should certainly
pay attention to the distribution consequences of different policy
options, the fear that economic growth on its own will have a
systematic negative effect on the distribution of income is
unfounded. Second, unequal distribution of assets, more than of
income, can be an impediment to rapid growth, implying that
redistributive policies that enhance people's access to credit
markets and, thus, their ability to invest could contribute to
growth. Third, although redistributive policies have the potential
to benefit the poor both directly and indirectly, they will do so
only if redistribution does not jeopardize investment--this may be
one explanation for the observation that, in the past,
redistributive policies such as land reform have often failed to
help the poor. If countries want to implement redistributive
policies, their ability to devise mechanisms that would at the
same time maintain or increase investment incentives may well
determine whether such policies help with poverty reduction. [=B7]
Klaus Deininger, Lyn Squire,
a German national, is an a British national, is
Economist in the World Director of the World
Bank's Policy Research Bank's Policy Research
Department. Department.
References:
Klaus Deininger and Lyn Squire, 1996, "A New Data Set Measuring
Income Inequality," World Bank Economic Review, Vol. 10
(September), pp. 565=AD91.
----, 1996, "New Ways of Looking at Old Issues: Inequality and
Growth" (unpublished; Washington: World Bank).
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