WJPS 2S5 8I
POLIcy RESEARCH WORKING PAPER 2587
Growth is Good When average incomes rise,
the average incomes of the
for the Poor poorest fifth of society rise
proportionately. This holds
across regions, periods,
David Dollar
income levels, and growth
Aart Kraay
rates. But relatively little is
known about the broad
forces that account for the
variations across countries
and across time in the share
of income accruing to the
poorest fifth.
The World Bank
Development Research Group
Macroeconomics and Growth
April 2001
POLICY RESEARCH WORKING PAPER 2587
Summary findings
When average incomes rise, the average incomes of the have little systematic effect on the share of income that
poorest fifth of society rise proportionately. This is a accrues to the bottom quintile. Consequently, these
consequence of the strong empirical regularity that the factors benefit the poorest fifth of society as much as
share of income accruing to the bottom quintile does not everyone else. There is some weak evidence that
vary systematically with average income. Dollar and stabilization from high inflation and reductions in the
Kraay document this empirical regularity in a sample of overall size of government not only increase growth but
92 countries spanning the past four decades and show also increase the income share of the poorest fifth in
that it holds across regions, periods, income levels, and society. Finally, Dollar and Kraay examine several factors
growth rates. commonly thought to disproportionately benefit the
Dollar and Kraay next ask whether the factors that poorest in society, but find little evidence of their effects.
explain cross-country differences in the growth rates of The absence of robust findings emphasizes that relatively
average incomes have differential effects on the poorest little is known about the broad forces that account for
fifth of society. They find that several determinants of the cross-country and intertemporal variation in the
growth-such as good rule of law, openness to share of income accruing to the poorest fifth of society.
international trade, and developed financial markets-
This paper-a product of Macroeconomics and Growth, Development Research Group-is part of a larger effort in the
group to study growth and poverty reduction. Copies of the paper are available free from the World Bank, 1818 H Street
NW, Washington, DC 20433. Please contact Emily Khine, room MC3-347, telephone 202-473-7471, fax 202-522-3518,
email address kkhine@worldbank.org. Policy Research Working Papers are also posted on the Web at http://
econ.worldbank.org. The authors maybe contacted atddollar@worldbank.org or akraay@worldbank.org. April2001. (50
pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The
papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this
paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the
countries they represent.
Produced by the Policy Research Dissemination Center
Growth Is Good for the Poor
David Dollar
Aart Kraay
Development Research Group
The World Bank
1818 H Street N.W., Washington, DC, 20433 (ddollar@worldbank.org,
akraay@worldbank.org). We are grateful to Dennis Tao for excellent research
assistance. This paper and the accompanying dataset are available at
www.worldbank.ora/research/qrowth.
Globalization has dramatically increased inequality between and within nations...
--Jay Mazur
"Labor's New Intemationalism," Foreign Affairs (Jan/Feb 2000)
We have to reaffirm unambiguously that open markets are the best engine we know of to
lift living standards and build shared prosperity.
--Bill Clinton
Speaking at World Economic Forum (2000)
1. Introduction
The world economy has grown well during the 1 990s, despite the financial crisis
in East Asia. However, there is intense debate over the extent to which the poor benefit
from this growth. The two quotes above exemplify the extremes in this debate. At one
end of the spectrum are those who argue that the potential benefits of economic growth
for the poor are undermined or even offset entirely by sharp increases in inequality that
accompany growth. At the other end of the spectrum is the argument that liberal
economic policies such as monetary and fiscal stability and open markets raise incomes
of the poor and everyone else in society proportionately.
In light of the heated popular debate over this issue, as well as its obvious policy
relevance, it is surprising how liKtle systematic cross-country empirical evidence is
available on the extent to which the poorest in society benefit from economic growth. In
this paper, we define the poor as those in the boftom fifth of the income distribution of a
country, and empirically examine the relationship between growth in average incomes of
the poor and growth in overall incomes, using a large sample of developed and
developing countries spanning the last four decades. Since average incomes of the
poor are proportional to the share of income accruing to the poorest quintile times
average income, this approach is equivalent to studying how a particular measure of
income inequality -- the first quintile share - varies with average incomes.
We find that incomes of the poor rise proportionately with average incomes.
Figure 1 illustrates this basic point. In the top panel, we plot the logarithm of per capita
incomes of the poor (on the vertical axis) against the logarithm of average per capita
1
incomes (on the horizontal axis), pooling 418 country-year observations on these two
variables. The sample consists of 137 countries with at least one observation on the
share of income accruing to the bottom quintile, and the median number of observations
per country is 3. There is a strong, positive, linear relationship between the two
variables, with a slope of 1.07. Since both variables are measured in logarithms, this
indicates that on average incomes of the poor rise equi-proportionately with average
incomes. In the bottom panel we plot average annual growth in incomes of the poor (on
the vertical axis) against average annual growth in average incomes (on the horizontal
axis), pooling 285 country-year observations where we have at least two observations
per country on incomes of the poor separated by at least five years. The sample
consists of 92 countries and the median number of growth episodes per country is 3.
Again, there is a strong, positive, linear relationship between these two variables with a
slope of 1.19. In the majority of the formal statistical tests that follow, we cannot reject
the null hypothesis that the slope of this relationship is equal to one. This indicates that
on average, within countries, incomes of the poor rise equi-proportionately with average
incomes. This is equivalent to the observation that there is no systematic relationship
between average incomes and the share of income accruing to the poorest fifth of the
income distribution. Below we examine this basic finding in more detail and find that it
holds across regions, time periods, growth rates and income levels, and is robust to
controlling for possible reverse causation from incomes of the poor to average incomes.
Given the strong relationship between incomes of the poor and average incomes,
we next ask whether policies and institutions that raise average incomes have
systematic effects on the share of income accruing to the poorest quintile which might
magnify or offset their effects on incomes of the poor. We focus attention on a set of
policies and institutions whose importance for average incomes has been identified in
the large cross-country empirical literature on economic growth. These include
openness to intemational trade, macroeconomic stability, moderate size of government,
financial development, and strong property rights and rule of law. We find little evidence
that these policies and institutions have systematic effects on the share of income
accruing to the poorest quintile. The only exceptions are that there is some weak
evidence that smaller government size and stabilization from high inflation
disproportionately benefit the poor by raising the share of income accruing to the bottom
quintile. These findings indicate that growth-enhancing policies and institutions tend to
2
benefit the poor and everyone else in society proportionately. We also show that the
distributional effects of such variables tend to be small relative to their effects on overall
economic growth.
We next examine in more detail the popular idea that greater economic
integration across countries is associated with increases in inequality within countries.
We first consider a range of measures of international openness, including tariffs,
membership in the World Trade Organization, and the presence of capital controls, and
ask whether any of these has systematic effects on the share of income accruing to the
poorest in society. We find little evidence that they do so, and we find that this result
holds even when we allow the effects of measures of openness to depend on the level of
development and differences in factor endowments as predicted by the factor
proportions theory of international trade. We conclude from this that, on average,
greater economic integration benefits the poorest in society as much as everyone else.
In recent years there has been a great deal of emphasis in the development
community on making growth even more "pro-poor." Given our evidence that neither
growth nor growth-enhancing policies tend to be systematically associated with changes
in the share of income accruing to the poorest fifth of societies, we interpret this
emphasis on "pro-poor" growth as a call for some other policy interventions that raise the
share of income captured by the poorest in society. We empirically examine the
importance of four such factors in determining the income share of the poorest: primary
educational attainment, public spending on health and education, labor productivity in
agriculture relative to the rest of the economy, and formal democratic institutions. While
it is plausible that these factors are important in bettering the lot of poor people in some
countries and under some circumstances, we are unable to uncover any systematic
evidence that they raise the share of income of the poorest in our large cross-country
sample.
Our work builds on and contributes to two strands of the literature on inequality
and growth. Our basic finding that (changes in) income and (changes in) inequality are
unrelated is consistent with the findings of several previous authors including Deininger
and Squire (1996), Chen and Ravallion (1997), and Easterly (1999) who document this
same regularity in smaller samples of countries. We build on this literature by
3
considering a significantly larger sample of countries and by employing more elaborate
econometric techniques that take into account the possibility that income levels are
endogenous to inequality as suggested by a variety of growth models. Our results are
also related to the small but growing literature on the determinants of the cross-country
and intertemporal variation in measures of income inequality, including Li, Squire and
Zou (1998), Gallup, Radelet and Warner (1998), Barro (1999), Spilimbergo et. al. (1999),
Leamer et. al. (1999), and Lundberg and Squire (2000) . Our work expands on this
literature by considering a wider range of potential determinants of inequality using a
consistent methodology in a large sample of countries, and can be viewed as a test of
the robustness of these earlier results obtained in smaller and possibly less
representative samples of countries. We discuss how our findings relate to those of
these other papers throughout the discussion below.
The rest of this paper proceeds as follows. In the next section we provide a brief
non-technical overview of the results. Section 3 describes the data and empirical
specification. Section 4 is presents our main findings. Section 5 concludes.
4
2. The Story in Pictures
Income of the poor has a very tight link with overall incomes. The top panel of
Figure 1 shows the logarithm of average income in the poorest fifth of the population
plotted against the logarithm of average income for the whole economy (per capita
GDP). The graph includes 418 observations covering 137 countries, and multiple
observations for a single country are separated by at least five years over time. The
slope of this relationship is very close to one, and all of the observations are closely
clustered around this regression line. This indicates that as overall income increases, on
average incomes of the poor increase equiproportionately. For 285 of these
observations, we can relate growth of income of the poor over a period of at least five
years to overall economic growth, as shown in the bottom panel of Figure 1. Again, the
slope of the relationship is slightly larger than one, and although the fit is not quite as
tight as before, it is still impressive.' There are 149 episodes in which per capita GDP
grew at a rate of at least 2% per year: in 131 of these episodes, income of the poor also
rose. Thus, it is almost always the case that the income of the poor rises during periods
of significant growth. There are a variety of econometric problems with simple estimates
of the relationship between incomes of the poor and overall income, which we take up in
the following section. Even after addressing these, the basic result that growth in the
overall economy is reflected one-for-one in growth in income of the poor turns out to be
very robust.
One can use the data in Figure 1 to ask a closely-related question: what fraction
of the variation across countries and over time in (growth in) incomes of the poor can be
explained by (growth in) overall income? In terms of levels of per capita income, this
fraction is very large. The data in the top panel of Figure 1 imply that over 80 percent of
the variation in incomes of the poor is due to variation in overall per capita incomes, and
only 20 percent is due to differences in income distribution over time and/or across
countries. To us, this reflects nothing more than the commonsense observation that
poor people in a middle-income country like Korea enjoy much higher living standards
than poor people in a country like India, not because they receive a significantly larger
share of national income, but simply because average incomes are much higher in
5
Korea than in India. So far, this discussion has focused on cross-country differences in
income levels, which reflect growth over the very long run. Over shorter horizons such
as those captured in the bottom panel of Figure 1, growth in average incomes still
explains a substantial fraction of growth in average incomes: just under half of the
growth of incomes of the poor is explained by growth in mean income.2
Having seen the importance of growth in overall income for incomes of the poor,
we tum to the remaining variation around the general relationship in Figure 1. The main
point of this paper is to try to uncover systematic pattems in those deviations - that is,
what makes growth especially pro-poor or pro-rich? We consider two types of
hypotheses. First, we consider hypotheses that essentially involve dividing the data
points into different groups (poor countries versus rich countries, crisis periods versus
normal growth, and the recent period compared to earlier times). Second, we introduce
other institutions and policies into the analysis and ask whether these influence the
extent to which growth benefits the poor.
A common idea in the development literature is the "Kuznets hypothesis" that
inequality tends to increase during the early stages of development and then decrease
later on. In our framework, exploring this hypothesis requires that, in trying to explain
growth of income of the poor, we need to interact growth of per capita income with the
initial level of income. We find this interaction term to be zero. In other words, in our
large sample of countries and years, there is no apparent tendency for growth to be
biased against low-income households at early stages of development.
Another popular idea is that crises are particularly hard on the poor. Our growth
episodes are all at least five years long. Hence, an episode of negative per capita GDP
growth in our sample is a period of at least five years in which per capita incomes fell on
average: we feel comfortable labeling these as 'crisis" periods. We introduce a dummy
variable to investigate whether the relationship between growth of income of the poor
2 The figures in this paragraph are based on the following standard variance decomposition. The logarithm
of per capita income of the poor is equal to the logarithm of the share of income accruing to the bottom
quintile, plus the logarithm of overall per capita income, plus a constant. Given an observation on per capita
income of the poor that is x0/o above the mean, we would expect that 80% of this deviation is due to higher
per capita income, and only 20% due to lower inequality. The figure 80% is the covariance between per
capita income and incomes of the poor divided by the variance of incomes of the poor. The calculation for
growth rates is analogous.
6
and overall growth is different during crisis periods. We find no evidence that crises
affect the income of the poor disproportionately. Of course, it could still be the case that
the same proportional decline in income has a greater impact on the poor if social safety
nets are weak, and so crises may well be harder on the poor. But this is not because
their incomes tend to fall more than those of other segments of society. A good
illustration of this general observation is the recent financial crisis in East Asia in 1997.
In Indonesia, the income share of the poorest quintile actually increased slightly between
1996 and 1999, from 8.0% to 9.0%, and in Thailand from 6.1 percent to 6.4 percent
between 1996 and 1998, while in Korea it remained essentially unchanged after the
crisis relative to before.
A third idea is that growth used to benefit the poor, but that the relationship is no
longer so robust. We test this by allowing the relationship between income of the poor
and overall income to vary by decades. We find no significant evidence that growth has
become less pro-poor than it was in the past. In fact, our point estimates indicate that, if
anything, growth has become slightly more pro-poor in recent decades, although this
trend is not statistically significant. In summary, none of the efforts to distinguish among
the poverty-growth experiences based on level of development, time period, or crisis
situation changes the basic proportional relationship between incomes of the poor and
average incomes.
We next turn to the second set of hypotheses concerning the role of various
institutions and policies in explaining deviations from this basic relationship between
incomes of the poor and growth. A core set of institutions and policies (notably,
macroeconomic stability, fiscal discipline, openness to trade, financial sector
development, and rule of law) have been identified as pro-growth in the vast empirical
growth literature. However, it is possible that these policies have a systematically
different impact on income of the poor. For example, the popular idea that
"globalization" increases inequality within countries - as expressed in the opening quote
from Jay Mazur - can be examined by asking whether measures of openness can help
explain negative deviations in the relationship between income of the poor and mean
income. Alternatively, there may be institutions and policies that have not been
established as robust determinants of growth, but are often thought to be good for the
poor, notably democracy and social spending. These hypotheses can be considered by
7
asking whether these variables explain positive deviations in the relationship between
income of the poor and mean income.
We use Figure 3 to summarize the results of introducing these policies and
institutions into the analysis. We decompose the effects of each of these variables on
mean incomes of the poor into two components. The first, labeled "growth effect",
shows direct effects of the indicated variable on incomes of the poor that operates
through its effect on overall incomes. The second, labeled 'distribution effect" captures
the indirect effect of that variable on incomes of the poor through its effects on the
distribution of income. Openness to international trade raises incomes of the poor by
raising overall incomes. The effect on the distribution of income is tiny and not
significantly different from zero. The same is true for improved rule of law and financial
development, which raise overall per capita GDP but do not significantly influence the
distribution of income. Reducing government consumption and stabilizing inflation are
examples of policies that are "super-pro-poor". Not only do both of these raise overall
incomes, but they appear to have an additional positive effect on the distribution of
income, further increasing incomes of the poor. In the case of reducing government
consumption, this additional distributional effect is statistically significant in some of our
specifications, and the pro-poor effect of reducing high inflation is also close to
significant.3 From this we conclude that the basic policy package of private property
rights, fiscal discipline, macro stability, and openness to trade increases the income of
the poor to the same extent that it increases the income of the other households in
society. This is not some process of "trickle-down," which suggests a sequencing in
which the rich get richer first and eventually benefits trickle down to the poor. The
evidence, to the contrary, is that private property rights, stability, and openness directly
and contemporaneously create a good environment for poor households to increase
their production and income.
Finally, we also examine a number of institutions and policies for which the evidence
of their growth impacts is less robust, but which may have an impact on the material
well-being of the poor. Most notable among these are government social spending,
8
formal democratic institutions, primary school enrollment rates, and agricultural
productivity (which may reflect the benefits of public investment in rural areas). None of
these variables has any robust relationship to either growth or to income share of the
poor. Social spending as a share of total spending has a negative relationship to
income share of the poor that is close to statistical significance. That finding reminds us
that public social spending is not necessarily well targeted to the poor.4 The simple
correlations between all of these variables and income share of the poor, in both levels
and differences, are shown in Figures 2, 4, and 5. Those simple correlations reflect
what we find in multivariate analysis: it is not easy to find any robust relationships
between institutions and policies, on the one hand, and income share of the poor, on
the other.
To summarize, we find that contrary to popular myths, standard pro-growth
macroeconomic policies are good for the poor as they raise mean incomes with no
systematic adverse effect on the distribution of income. In fact, there is weak evidence
that macro stability, proxied by stabilization from high inflation and a reduction in
government consumption, increases income of the poor more than mean income as
they tend to increase the income share of the poorest. Other policies such as good rule
of law, financial development, and openness to trade benefit the poor and the rest of the
economy equally. On the other hand, we find no evidence that formal democratic
institutions or a large degree of government spending on social services generally affect
income of the poor. Finally, the growth-poverty relationship has not changed over time,
does not vary during crises, and is generally the same in rich countries and poor ones.
In the remainder of this paper we provide details on how these results are obtained.
This is not to say that growth is all that is needed to improve the lives of the poor.
Rather, we simply emphasize that growth generally does benefit the poor as much as
3This result is consistent with existing evidence in smaller samples. Agenor (1998) finds an adverse effect
of inflation on the poverty rate, using a cross-section of 38 countries. Easterly and Fischer (2000) show that
the poor are more likely to rate inflation as a top national concem, using survey data on 31869 households
in 38 countries. Datt and Ravallion (1999) find evidence that inflation is a significant determinant of poverty
using data for Indian states.
9
anyone else in society, and so the growth-enhancing policies of good rule of law, fiscal
discipline, and openness to international trade should be at the center of any effective
poverty reduction strategy.
4 Existing evidence on the effects of social spending is mixed. Bidani and Ravallion (1997) do find a
statistically significant impact of health expenditures on the poor (defined in absolute terms as the share of
the population with income below one dollar per day) in a cross-section of 35 developing countries, using a
different methodology. Gouyette and Pestiau (1999) find a simple bivariate association between income
inequality and social spending in a set of 13 OECD economies. In contrast Filmer and Pritchett (1997) find
little relationship between public health spending and health outcomes such as infant mortality, raising
questions about whether such spending benefits the poor.
10
3. Empirical Strategy
3.1 Measuring Income and Income of the Poor
We measure mean income as real per capita GDP at purchasing power panty in
1985 intemational dollars, based on an extended version of the Summers-Heston Penn
World Tables Version 5.6.5 In general, this need not be equal to the mean level of
household income, due to a variety of reasons ranging from simple measurement error
to retained corporate eamings. We nevertheless rely on per capita GDP for two
pragmatic reasons. First, for many of the country-year observations for which we have
information on income distribution, we do not have corresponding information on mean
income from the same source. Second, using per capita GDP helps us to compare our
results with the large literature on income distribution and growth that typically follows
the same practice. In the absence of evidence of a systematic correlation between the
discrepancies between per capita GDP and household income on the one hand, and per
capita GDP on the other, we treat these differences as classical measurement error, as
discussed further below.6
5 We begin with the Summers and Heston Penn World Tables Version 5.6, which reports data on real per
capita GDP adjusted for differences in purchasing power parity through 1992 for most of the 156 countries
included in that dataset. We use the growth rates of constant price local currency per capita GDP from the
World Bank to extend these forward through 1997. For a further set of 29 mostly transitfon economies not
included in the Penn World Tables we have data on constant price GDP in local currency units. For these
countries we obtain an estimate of PPP exchange rate from the fitted values of a regression of PPP
exchange rates on the logarithm of GDP per capita at PPP. We use these to obtain a benchmark PPP GDP
figure for 1990, and then use growth rates of constant price local currency GDP to extend forward and
backward from this benchmark. While these extrapolations are necessarily crude, they do not matter much
for our results. As discussed below, the statistical identification in the paper is based primarily on within-
country changes in incomes and incomes of the poor, which are unaffected by adjustments to the levels of
the data.
6 Ravallion (2000) provides an extensive discussion of sources of discrepancies between national accounts
and household survey measures of living standards and finds that, with the exception of the transition
economies of Eastem Europe and the Former Soviet Union, growth rates of national accounts measures
track growth rates of household survey measures fairly closely on average.
11
We use two approaches to measuring the income of the poor, where we define
the poor as the poorest 20% of the population.7 For 796 country-year observations
covering 137 countries, we are able to obtain information on the share of income
accruing to the poorest quintile constructed from nationally representative household
surveys that meet certain minimum quality standards. For these observations, we
measure mean income in the poorest quintile directly, as the share of income earned by
the poorest quintile times mean income, divided by 0.2. For a further 158 country-year
observations we have information on the Gini coefficient but not the first quintile share.
For these observations, we assume that the distribution of income is lognormal, and we
obtain the share of income accruing to the poorest quintile as the 20th percentile of this
distribution.8
Our data on income distribution are drawn from four different sources. Our
primary source is the UN-WIDER World Income Inequality Database, which is a
substantial extension of the income distribution dataset constructed by Deininger and
Squire (1996). A total of 706 of our country-year observations are obtained from this
source. In addition, we obtain 97 observations originally included in the sample
designated as 'high-quality" by Deininger and Squire (1996) that do not appear in the
UN-WIDER dataset. Our third data source is Chen and Ravallion (2000) who construct
measures of income distribution and poverty from 265 household surveys in 83
7An altemative would be to define 'the poor' as those below a fixed poverty line such as the dollar-a-day
poverty line used by the World Bank. We do not follow this approach for two reaons. First, constructing this
measure requires information on the shape of the entire lower tail of the income distribution, and we only
have at most five points on the Lorenz curve for each country. Second, even if this information were
available or were obtained by some kind of interpolation of the Lorenz curve, the relationship between
growth in average incomes and growth in this measure of average incomes of the poor is much more difficult
to interpret. For example, if the distribution of income is very steep near the poverty line, distribution-neutral
growth in average incomes will lift a large fraction of the population from just below to just above the poverty
line with the result that average incomes of those below the poverty line fall. Ali and Elbadawi (2001)
provide results using this measure of incomes of the poor and unsurprisingly find that incomes of the poor
according to this measure rise less than proportionately with average incomes. Another altemative would
not be to examine average incomes of the poor, but rather the fraction of the population below some pre-
specified poverty line. In this case, it is well-known that the elasticity of the poverty headcount with respect
to average income varies widely across countries and depends among other things on the level and
distribution of income.
8 If the distribution of income is lognormal, i.e. log per capita income - N(p,a), and the Gini coefficient on a
scale from 0 to 100 is G, the standard-deviation of this lognormal distribution is given by
o = a 2_.(_1+GI100) where o(.) denotes the cumulative normal distribution function. (Aitcheson and
Brown (1966)). Using the properties of the mean of the truncated lognormal distribution (e.g. Johnston, Kotz
and Balakrishnan (1994)) it can be shown that the 20' percentile of this distribution is given by
0(q-1 (0. 2) - c).
12
developing countries. As many of the earlier observations in this source are also
reported in the Deininger-Squire and UN-WIDER database, we obtain only an additional
118 recent observations from this source. Finally, we augment our dataset with 32
observations primarily from developed countries not appearing in the above three
sources, that are reported in Lundberg and Squire (2000). This results in an overall
sample of 953 observations covering 137 countries over the period 1950-1999. To our
knowledge this is the largest dataset used to study the relationship between inequality,
incomes, and growth. Details of the geographical composition of the dataset are shown
in the first column of Table 1.
This dataset forms a highly unbalanced and irregularly spaced panel of
observations. While for a few countries continuous time series of annual observations
on income distribution are available for long periods, for most countries only one or a
handful of observations are available, with a median number of observations per country
of 4. Since our interest is in growth over the medium to long run, and since we do not
want the sample to be dominated by those countries where income distribution data
happen to be more abundant, we filter the data as follows. For each country we begin
with the first available observation, and then move forward in time until we encounter the
next observation subject to the constraint that at least five years separate observations,
until we have exhausted the available data for that country.9 This results in an
unbalanced and irregularly spaced panel of 418 country-year observations on mean
income of the poor separated by at least five years within countries, and spanning 137
countries. The median number of observations per country in this reduced sample is 3.
In our econometric estimation (discussed in the following subsection) we restrict the
sample further to the set of 285 observations covering 92 countries for which at least two
spaced observations on mean income of the poor are available, so that we can consider
within-country growth in mean incomes of the poor over periods of at least five years.
The median length of these intervals is 6 years. When we consider the effects of
additional control variables, the sample is slightly smaller and varies across
specifications depending on data availability. The data sources and geographical
9 We prefer this method of filtering the data over the alternative of simply taking quinquennial or decadal
averages since our method avoids the unnecessary introduction of noise into the timing of the distribution
data and the other variables we consider. Since one of the most interesting of these, income growth, is very
volatile, this mismatch in timing is potentially problematic.
13
composition of these different samples is shown in the second and third columns of
Table 1.
As is well known there are substantial difficulties in comparing income distribution
data across countries.'0 Countries differ in the coverage of the survey (national versus
subnational), in the welfare measure (income versus consumption), the measure of
income (gross versus net), and the unit of observation (individuals versus households).
We are only able to very imperfectly adjust for these differences. We have restricted our
sample to only income distribution measures based on nationally representative surveys.
For all surveys we have information on whether the welfare measure is income or
consumption, and for the majority of these we also know whether the income measure is
gross or net of taxes and transfers. While we do have information on whether the
recipient unit is the individual or the household, for most of our observations we do not
have information on whether the Lorenz curve refers to the fraction of individuals or the
fraction of households." As a result, this last piece of information is of little help in
adjusting for methodological differences in measures of income distribution across
countries. We therefore implement the following very crude adjustment for observable
differences in survey type. We pool our sample of 418 observations separated by at
least five years, and regress both the Gini coefficient and the first quintile share on a
constant, a set of regional dummies, and dummy variables indicating whether the
welfare measure is gross income or whether it is consumption. We then subtract the
estimated mean difference between these two altematives and the omitted category to
arrive at a set of distribution measures that notionally correspond to the distribution of
income net of taxes and transfers.'2 The results of these adjustment regressions are
reported in Table 2.
3.2 Estimation
10 See Atkinson and Brandolini (1999) for a detailed discussion of these issues.
" This information is only available for the Chen-Ravallion dataset which exclusively refers to individuals
and for which the Lorenz curve is consistently constructed using the fraction of individuals on the horizontal
axis.
12 Our main results do not change substantially if we use three other possibilities: (1) ignoring differences in
survey type, (2) including dummy variables for survey type as strictly exogenous right-hand side variables in
our regressions, or (3) adding country fixed effects to the adjustment regression so that the mean
differences in survey type are estimated from the very limited within-country variation in survey type.
14
In order to examine how incomes of the poor vary with overall incomes, we
estimate variants of the following regression of the logarithm of per capita income of the
poor (yP) on the logarithm of average per capita income (y) and a set of additional control
variables (X):
(1) yPt = (XO + CC1 * Yct + a2'Xct + Pc + £ct
where c and t index countries and years, respectively, and 1.c + Ect is a composite error
term including unobserved country effects. We have already seen the pooled version of
Equation (1) with no control variables Xct in the top panel of Figure 1 above. Since
incomes of the poor are equal to the the first quintile share times average income
divided by 0.2, it is clear that Equation (1) is identical to a regression of the log of the first
quintile share on average income and a set of control variables:
(2) ln(Qt) = ao+ (ci -1).Yt+ c2'Xct+l4c+8et
Moreover, since empirically the log of the first quintile share is almost exactly a linear
function of the Gini coefficient, Equation (1) is almost equivalent to a regression of a
negative constant times the Gini coefficient on average income and a set of control
variables.13
We are interested in two key parameters from Equation (1). The first is a,, which
measures the elasticity of income of the poor with respect to mean income. A value of
ct1=1 indicates that growth in mean income is translated one-for-one into growth in
income of the poor. From Equation (2) this is equivalent to the observation that the
share of income accruing to the poorest quintile does not vary systematically with
average incomes (ai-1=0). Estimates of a1 greater or less than one indicate that growth
more than or less than proportionately benefits those in the poorest quintile. The second
parameter of interest is a2 which measures the impact of other determinants of income
13 In our sample of spaced observations, a regression of the log first quintile share on the Gini coefficient
delivers a slope of -23.3 with an R-squared of 0.80.
15
of the poor over and above their impact on mean income. Equivalently from Equation
(2), a2 measures the impact of these other variables on the share of income accruing to
the poorest quintile, holding constant average incomes.
Simple ordinary least squares (OLS) estimation of Equation (1) using pooled
country-year observations is likely to result in inconsistent parameter estimates for
several reasons.'4 Measurement error in average incomes or the other control
variables in Equation (1) will lead to biases that are difficult to sign except under very
restrictive assumptions.'5 Since we consider only a fairly parsimonious set of right-hand-
side variables in X, omitted determinants of the log quintile share that are correlated with
either X or average incomes can also bias our results. Finally, there may be reverse
causation from average incomes of the poor to average incomes, or equivalently from
the log quintile share to average incomes, as suggested by the large empirical literature
which has examined the effects of income distribution on subsequent growth. This
literature typically estimates growth regressions with a measure of initial income
inequality as an explanatory variable, such as:
(3) YC D PO+P'Y,- I ( I Q1Ctk ' + P2)- 13 ' 9 Z-V
lct o1WYc.tk~P~1I 0.2 )_
This literature has found mixed results using different sample and different econometric
techniques. On the one hand, Perotti (1996) and Barro (1999) find evidence of a
negative effect of income inequality on growth (i.e. 01>0). On the other hand, Forbes
(2000) and Li and Zou (1998) both find positive effects of income inequality on growth,
(i.e. 13i<0). Finally, Bannerjee and Duflo (1999) modestly, and perhaps most
appropriately, conclude that there is at best weak evidence of a U-shaped correlation
between income inequality and growth and that very little can be said about causation in
14 It should also be clear that OLS standard errors will be inconsistent given the cross-observation
correlations induced by the unobserved country-specific effect.
'5 While at first glance it may appear that measurement error in per capita income (which is also used to
construct our measure of incomes of the poor) will bias the coefficient on per capita income towards one in
Equation (1), this is not the case. From Equation (2) (which of course yields identical estimates of the
parameters of interest as does Equation (1)) it is clear that we only have a problem to the extent that
measurement error in the first quintile share is correlated with average incomes. Since our data on income
distribution and average income are drawn from different sources, there is no a priori reason to expect such
a correlation. When average income is taken from the same household survey, under plausible
assumptions even measurement error in both variables will not lead to inconsistent coefficient estimates
(Chen and Ravallion (1997)).
16
either direction. Whatever the true underlying relationship, it is clear that as long as D1I is
not equal to zero, OLS estimation of Equations (1) or (2) will yield inconsistent estimates
of the parameters of interest. For example, high realizations of A. which result in higher
incomes of the poor relative to mean income in Equation (1) will also raise (lower) mean
incomes in Equation (3), depending on whether P, is greater than (less than) zero. This
could induce an upwards (downwards) bias into estimates of the elasticity of incomes of
the poor with respect to mean incomes in Equation (1).
A final issue in estimating Equation (1) is whether we want to identify our
parameters of interest using the cross-country or the time-series variation in the data on
incomes of the poor, mean incomes, and other variables. An immediate reaction to the
presence of unobserved country-specific effects p in Equation (1) is to estimate it in
first differences.16 The difficulty with this option is that it forces us to identify our effects
of interest using the more limited time-series variation in incomes and income
distribution.17 This raises the possibility that the signal-to-noise ratio in the within-
country variation in the data is too unfavorable to allow us to estimate our parameters of
interest with any precision. In contrast, the advantage of estimating Equation (1) in
levels is that we can exploit the large cross-country variation in incomes, income
distribution, and policies to identify our effects of interest. The disadvantage of this
approach is that the problem of omitted variables is more severe in the cross-section,
since in the differenced estimation we have at least managed to dispose of any time-
invariant country-specific sources of heterogeneity.
Our solution to this dilemma is to implement a system estimator that combines
information in both the levels and changes of the data.18 In particular, we first difference
Equation (1) to obtain growth in income of the poor in country c over the period from t-
k(c,t) to t as a function of growth in mean income over the same period, and changes in
the X variables:
le Alternafively one could enter fixed effects, but this-requires the much stronger assumption that the error
terms are uncorrelated with the right-hand side variables at all leads and lags.
17 Li, Squire, and Zou (1998) document the much greater variability of income distribution across countries
compared to within countries. In our sample of irregularly spaced observations, the standard deviation of
the Gini coefficient pooling all observations in levels is 9.4. In contrast the standard deviation of changes in
the Gini coefficient is 4.7 (an average annual change of 0.67 times an average number of years over which
the change is calculated of 7).
18 This type of estimator has been proposed in a dynamic panel context by Arellano and Bover (1995) and
evaluated by Blundell and Bond (1998).
17
(4) Yct -Yct-k(c,t) =Cac* (Yct - Y ct-k(ct) ) + a2'(Xct-X -c,t(ct) )+ (C ct- cA-k(c,t) )
We then estimate Equation (1) and Equation (4) as a system, imposing the restriction
that the coefficients in the levels and differenced equation are equal. We address the
three problems of measurement error, omitted variables, and endogeneity by using
appropriate lags of right-hand-side variables as instruments. In particular, in Equation
(1) we instrument for mean income using growth in mean income over the five years
prior to time t. This preceding growth in mean income is by construction correlated with
contemporaneous mean income, provided that p is not equal to zero in Equation (3).
Given the vast body of evidence on conditional convergence, this assumption seems
reasonable a priori, and we can test the strength of this correlation by examining the
corresponding first-stage regressions. Differencing Equation (3) it is straightforward to
see that past growth is also uncorrelated with the error term in Equation (1), provided
that Ect is not correlated over time. In Equation (4) we instrument for growth in mean
income using the level of mean income at the beginning of the period, and growth in the
five years preceding t-k(c,t). Both of these are by construction correlated with growth in
mean income over the period from t-k(c,t) to t. Moreover it is straightforward to verify
that they are uncorrelated with the error term in Equation (4) using the same arguments
as before.
In the version of Equation (1) without control variables, these instruments provide
us with three moment conditions with which to identify two parameters, ao and a1. We
combine these moment conditions in a standard generalized method of moments (GMM)
estimation procedure to obtain estimates of these parameters. In addition, we adjust the
standard errors to allow for heteroskedasticity in the error terms as well as the first-order
autocorrelation introduced into the error terms in Equation (4) by differencing. Since the
model is overidentified we can test the validity of our assumptions that the instruments
are uncorrelated with the error terms using tests of overidentifying restrictions.
When we introduce additional X variables into Equation (1) we also need to take
a stand on whether or not to instrument for these as well. On a priori grounds, difficulties
with measurement error and omitted variables provide as compelling a reason to
18
instrument for these variables as for income. Regarding reverse causation the case is
less clear, since it seems less plausible to us that many of the macro variables we
consider respond endogenously to relative incomes of the poor. In what follows we
choose not to instrument for the X variables. This is in part for the pragmatic reason that
this further limits our sample size. More importantly, we take some comfort from the fact
that tests of overidentifying restrictions pass in the specifications where we instrument
for income only, providing indirect evidence that the X variables are not correlated with
the error terms. In any case, we find qualitatively quite similar results in the smaller
samples where we instrument, and so these results are not reported for brevity.
19
4. Results
4.1 Growth is Good for the Poor
We start with our basic specification in which we regress the log of per capita
income of the poor on the log of average per capita income, without other controls
(Equation (1) with a2=0). The results of this basic specification are presented in detail in
Table 3. The five columns in the top panel provide altemative estimates of Equation (1),
in turn using information in the levels of the data, the differences of the data, and finally
our preferred system estimator which combines the two. The first two columns show the
results from estimating Equation (1) in levels, pooling all of the country-year
observations, using OLS and single-equation two-stage least squares (2SLS),
respectively. OLS gives a point estimate of the elasticity of income of the poor with
respect to mean income of 1.07, which is (just) significantly greater than 1. As
discussed in the previous section there are reasons to doubt the simple OLS results.
When we instrument for mean income using growth in mean income over the five
preceding years as an instrument, the estimated elasticity increases to 1.19. However,
this elasticity is much less precisely estimated, and so we do not reject the null
hypothesis that al=1. In the first-stage regression for the levels equation, lagged growth
is a highly significant predictor of the current level of income, which gives us some
confidence in its validity as an instrument.
The third and fourth columns in the top panel of Table 3 show the results of OLS
and 2SLS estimation of the differenced Equation (4). We obtain a point estimate of the
elasticity of income of the poor with respect to mean income of 0.98 using OLS, and a
slightly smaller elasticity of 0.91 when we instrument using lagged levels and growth
rates of mean income. In both the OLS and 2SLS results we cannot reject the null
hypothesis that the elasticity is equal to one. In the first-stage regression for the
differenced equation (reported in the second column of the bottom panel), both lagged
income and twice-lagged growth are highly significant predictors of growth. Moreover,
the differenced equation is overidentified. When we test the validity of the
overidentifying restrictions we do not reject the null of a well-specified model for the
differenced equation alone at conventional significance levels.
20
In the last column of Table 3 we combine the information in the levels and
differences in the system GMM estimator, using the same instruments as in the single-
equation estimates reported earlier. The system estimator delivers a point estimate of
the elasticity of 1.008, which is not significantly different from 1. Since the system
estimator is based on minimizing a precision-weighted sum of the moment conditions
from the levels and differenced data, the estimate of the slope is roughly an average of
the slope of the levels and differenced equation, with somewhat more weight on the
more-precisely estimated differenced estimate. Since our system estimator is
overidentified, we can test and do not reject the null that the instruments are valid, in the
sense of being uncorrelated with the corresponding error terms in Equations (1) and (4).
Finally, the bottom panel of Table 3 reports the first-stage regressions underlying our
estimator, and shows that our instruments have strong explanatory power for the
potentially-endogenous income and growth regressors.
We next consider a number of variants on this basic specification. First, we add
regional dummies to the levels equation, and find that dummies for the East Asia and
Pacific, Latin America, Sub-Saharan Africa, and the Middle East and North Africa
regions are negative and significant at the 10 percent level or better (first column of
Table 4). Since the omitted category consists of the rich countries of Western Europe
plus Canada and the United States, these dummies reflect higher average levels of
inequality in these regions relative to the rich countries. Including these regional
dummies reduces the estimate of the elasticity of average incomes of the poor with
respect to average incomes slightly to 0.91, but we still cannot reject the null hypothesis
that the slope of this relationship is equal to one (the p-value for the test of this
hypothesis is 0.313, and is shown in the fourth-last row of Table 4). We keep the
regional dummies in all subsequent regressions.
Next we add a time trend to the regression, in order to capture the possibility that
there has been a secular increase or decrease over time in the share of income accruing
to the poorest quintile (second column of Table 4). The coefficient on the time trend is
statistically insignificant, indicating the absence of systematic evidence of a trend in the
share of income of the bottom quintile. Moreover, in this specification we find a point
estimate of al=1.00, indicating that average incomes in the bottom quintile rise exactly
21
proportionately with average incomes. A closely related question is whether the
elasticity of incomes of the poor with respect to average incomes has changed over
time. To capture this possibility we augment the basic regression with interactions of
income with dummies for the 1970s, 1980s and 1990s. The omitted category is the
1 960s, and so the estimated coefficients on the interaction terms capture differences in
the relationship between average incomes and the share of the poorest quintile relative
to this base period. We find that none of these interactions are significant, consistent
with the view that the inequality-growth relationship has not changed significantly over
time. We again cannot reject the null hypothesis that c1=1 (p=0.455).
In the next two columns of Table 4 we examine whether the slope of the
relationship between average incomes and incomes of the poorest quintile differs
significantly by region or by income level. We first add interactions of each of the
regional dummies with average income, in order to allow for the possibility that the
effects of growth on the share of income accruing to the poorest quintile differ by region.
We find that two regions (East Asia/Pacific and Latin America/Caribbean) have
significantly lower slopes than the omitted category of the rich countries. However, we
cannot reject the null hypothesis that all of the region-specific slopes are jointly equal to
one. We also ask whether the relationship between income and the share of the bottom
quintile varies with the level of development, by interacting average incomes in Equation
(1) with real GDP per capita in 1990 for each country. When we do this, we find no
evidence that the relationship is significantly different in rich and poor countries, contrary
to the Kuznets hypothesis that inequality increases with income at low levels of
development."'
In the last column of Table 4 we ask whether the relationship between growth in
average incomes and incomes of the poor is different during periods of negative and
positive growth. This allows for the possibility that the costs of economic crises are
borne disproportionately by poor people. We add an interaction term of average
incomes with a dummy variable which takes the value one when growth in average
incomes is negative. These episodes certainly qualify as economic crises since they
correspond to negative average annual growth over a period of at least five years.
However, the interaction term is tiny and statistically indistinguishable from zero,
22
indicating that there is no evidence that the share of income that goes to the poorest
quintile systematically rises or falls during periods of negative growth.
4.2 Growth Determinants and Incomes of the Poor
The previous section has documented that the relationship between growth of
income of the poor and overall economic growth is one-to-one. That finding suggests
that a range of policies and institutions that are associated with higher growth will also
benefit the poor proportionately. However, it is possible that growth from different
sources has differential impact on the poor. In this section we take a number of the
policies and institutions that have been identified as pro-growth in the empirical growth
literature, and examine whether there is any evidence that any of these variables has
disproportionate effects on the poorest quintile. The five indicators that we focus on are
inflation, which Fischer (1993) finds to be bad for growth; government consumption,
which Easterly and Rebelo (1993) find to be bad for growth; exports and imports relative
to GDP, which Frankel and Romer (1999) find to be good for growth; a measure of
financial development, which Levine, Loayza and Beck (2000) have shown to have
important causal effects on growth; and a measure of the strength of property rights or
rule of law The particular measure is from Kaufmann, Kraay, and Zoido-Lobat6n (1999).
The importance of property rights for growth has been established by, among others,
Knack and Keefer (1995). In Figure 2 we plot each of these measures against the log
share of income of the poorest quintile as a descriptive device. Variable definitions and
sources are reported in Table 9. A quick look at Figure 2 suggests that there is little in
the way of obvious bivariate relationships between each of these variables and our
measure of income distribution, and what little relationship there is is often driven by a
small number of influential observations. This first look at the data suggests to us that it
will be difficult to find significant and robust effects of any of these variables on the share
of income accruing to the poorest quintile - and we confirm this in the regressions which
follow.
First, we take the basic regression from the first column of Table 4 and add these
variables one at a time (shown in the first five columns of Table 5). Since mean income
is included in each of these regressions, the effect of these variables that works through
19 Using a quadratic term to capture this potential nonlinearity yields similarly insignificant results.
23
overall growth is already captured there. The coefficient on the growth determinant itself
therefore captures any differential impact that this variable has on the income of the
poor, or equivalently, on the share of income accruing to the poor. In the case of trade
volumes, we find a small, negative, and statistically insignificant effect on the income
share of the bottom quintile. The same is true for government consumption as a share
of GDP, and inflation, where higher values of both are associated with lower income
shares of the poorest quintile, although again insignificantly so. The point estimates of
the coefficients on the measure of financial development and on rule of law indicate that
both of these variables are associated with higher income shares in the poorest quintile,
but again, each of these effects is statistically indistinguishable from zero. When we
include all five measures together, the coefficients on each are similar to those in the the
simpler regressions. However, government consumption as a share of GDP now has an
estimated effect on the income share of the poorest that is negative and significant at the
10% level. In addition, inflation continues to have a negative effect, which just falls short
of significance at the 10% level.20
Our empirical specification only allows us to identify any differential effect of
these macroeconomic and institutional variables on incomes of the poor relative to
average incomes. What about the overall effect of these variables, which combines their
effects on growth with their effects on income distribution? In order to answer this
question we also require estimates of the effects of these variables on growth based on
a regression like Equation (3). Since Equation (3) includes a measure of income
inequality as one of the determinants of growth, we estimate Equation (3) using the
same panel of irregularly spaced data on average incomes and other variables that we
have been using thus far.21 Clearly this limited dataset is not ideal for estimating growth
regressions, since our sample is very restricted by the relative scarcity of income
distribution data. Nevertheless it is useful to estimate this equation in our data set for
consistency with the previous results, and also to verify that the main findings of the
cross-country literature on economic growth are present in our sample.
20 This particular result is primarily driven by a small number of very high inflation episodes.
21 Since our panel is irregularly spaced, the coefficient on lagged income in the growth regression should in
principle be a function of the length of the interval over which growth is calculated. There are two ways to
address this issue. In what follows below, we simply restrict attention to the vast majority of our
observations which correspond to growth spells between 5 and 7 years long, and then ignore the
dependence of this coefficient on the length of the growth interval. The alternative approach is to introduce
this dependence explicity by assuming that the coefficient on lagged income is pk(c't) Doing so yields very
similar results to those reported here.
24
We include in the vector of additional explanatory variables a measure of the
stock of human capital (years of secondary schooling per worker) as well as the five
growth determinants from Table 5. We also include the human capital measure in order
to make our growth regression comparable to that of Forbes (2000) who applies similar
econometric techniques in a similar panel data set in order to study the effect of
inequality on growth. In order to reduce concerns about endogeneity of these variables
with respect to growth, we enter each of them as an average over the five years prior to
year t-k. We estimate the growth regression in Equation (3) using the same system
estimator that combines information in the levels and differences of the data, although
our choice of lags as instruments is slightly different from before.22 In the levels
equation, we instrument for lagged income with growth in the preceding five years, and
we do not need to instrument for the remaining growth determinants under the
assumption that they are predetermined with respect to the error term v t. In the
differenced equation we instrument for lagged growth with the twice-lagged log-level of
income, and for the remaining variables with their twice-lagged levels.
The results of this growth regression are reported in the first column of Table 6.
Most of the variables enter significantly and with the expected signs. Secondary
education, financial development, and better rule of law are all positively and significantly
associated with growth. Higher levels of government consumption and inflation are both
negatively associated with growth, although only the former is statistically significant.
Trade volumes are positively associated with growth, although not significantly so,
possibly reflecting the relatively small sample on which the estimates are based (the
sample of observations is considerably smaller than in Table 5 given the requirement of
additional lags of right-hand side variables to use as instruments). Interestingly, the log
of the first quintile share enters negatively (although not significantly), consistent with the
finding of Forbes (2000) that greater inequality is associated with higher growth.
We next combine these estimates with the estimates of Equation (1) to arrive at
the cumulative effect of these growth determinants on incomes of the poor. From
22 See for example Levine, Loayza, and Beck (2000) for a similar application of this econometric technique
to cross-country growth regressions.
25
Equation (1) we can express the effect of a permanent increase in each of the growth
determinants on the level of average incomes of the poor as:
(5) aycp = at +_(aI-1)_-a _ +a2
aXct aXct axct
where 2X.ct denotes the impact on average incomes of this permanent change in X. The
axct
first term captures the effect on incomes of the poor of a change in one of the
determinants of growth, holding constant the distribution of income. We refer to this as
the ugrowth effect" of this variable. The second term captures the effects of a change in
one of the determinants of growth on incomes of the poor through changes in the
distribution of income. This consists of two pieces: (i) the difference between the
estimated income elasticity and one times the growth effect, i.e. the extent to which
growth in average incomes raises or lowers the share of income accruing to the poorest
quintile; and (ii) the direct effects of policies on incomes of the poor in Equation (1).
In order to evaluate Equation (5) we need an expression for the growth effect
term. We obtain this by solving Equations (1) and (3) for the dynamics of average
income, and obtain:
(6)
yct = P + , ao + (p + , a,) .YCt-k + (P, a2 + 2)XC,t-k + llc + 1 .: + Vct +I sEct
Iterating Equation (6) forward, we find that the estimated long-run effect on the level of
income of a permanent change in one of the elements in X is:
(7) 'Yt PI ' * 2 + P2
ax't 1 -(P + Pl -2)
The remaining columns of Table 6 put all these pieces together. The second
column repeats the results reported in the final column of Table 5. The next column
26
reports the standard deviations of each of the variables of interest, so that we can
calculate the impact on incomes of the poor of a one-standard deviation permanent
increase in each variable.23 The remaining columns report the growth and distribution
effects of these changes, which are also summarized graphically in Figure 3. The main
story here is that the growth effects are large and the distribution effects are small.
Improvements in rule of law and greater financial development of the magnitudes
considered here, as well as reductions in government consumption and lower inflation all
raise incomes in the long run by 15-20 percent. The point estimate for more trade
openness is at the low end of existing results in the literature: about a 5% increase in
income from a one standard deviation increase in openness. This should therefore be
viewed as a rather conservative estimate of the benefit of openness on incomes of the
poor. In contrast, the effects of these policies that operate through their effects on
changes in the distribution of income are much smaller in magnitude, and with the
exception of financial development work in the same direction as the growth effects.
4.3 Globalization and the Poor
One possibly surprising result in Table 5 is the lack of any evidence of a
significant negative impact of openness to international trade on incomes of the poor.
While this is consistent with the finding of Edwards (1997) who also finds no evidence of
a relationship between various measures of trade openness and inequality in a sample
of 44 countries, a number of other recent papers have found evidence that openness is
associated with higher inequality. Barro (1999) finds that trade volumes are significantly
positively associated with the Gini coefficient in a sample of 64 countries, and that the
disequalizing effect of openness is greater in poor countries. In a panel data set of 320
irregularly spaced annual observations covering only 34 countries, Spilimbergo et. al.
(1999) find that several measures of trade openness are associated with higher
inequality, and that this effect is lower in countries where land and capital are abundant
and higher where skills are abundant. Lundberg and Squire (2000) consider a panel of
119 quinquennial observations covering only 38 countries and find that an increase from
zero to one in the Sachs-Wamer openness index is associated with a 9.5 point increase
in the Gini index, which is significant at the 10% level.
23 The only exception is the rule of law index which by construction has a standard deviation of one. Since
perceptions of the rule of law tend to change only very slowly over time, we consider a smaller change of
27
Several factors may contribute to the difference between these findings and ours,
including (i) differences in the measure of inequality (all the previous studies consider
the Gini index while we focus on the income share of the poorest quintile, although given
the high correlation between the two this factor is least likely to be important); (ii)
differences in the sample of countries (with the exception of the paper by Barro, all of the
papers cited above restrict attention to considerably smaller and possibly non-
representative samples of countries than the 76 countries which appear in our basic
openness regression, and in addition the paper by Spilimbergo et. al. uses all available
annual observations on inequality with the result that countries with regular household
surveys tend to be heavily overrepresented in the sample of pooled observations); (iii)
differences in the measure of openness (Lundberg and Squire (2000) for example focus
on the Sachs-Warner index of openness which has been criticized for proxying the
overall policy environment rather than openness per se24); (iv) differences in
econometric specification and technique.
A complete accounting of which of these factors contribute to the differences in
results is beyond the scope of this short section. However, several obvious extensions
of our basic model can be deployed to make our specification more comparable to these
other studies. First, we consider several different measures of openness, some of which
correspond more closely with those used in the other studies mentioned above. We first
(like Barro (1999) and Spilimbergo et al. (1999)) purge our measure of trade volumes of
the geographical determinants of trade, by regressing it on a trade-weighted measure of
distance from trading partners, and a measure of country size and taking the residuals
as an adjusted measure of trade volumes.25 Since these geographical factors are time
invariant, this will only influence our results to the extent that they are driven by the
cross-country variation in the data and to the extent that these geographical
determinants of trade volumes are also correlated with the share of income of the
poorest quintile. Second, we use the Sachs-Wamer index in order to compare our
results more closely with those of Lundberg and Squire (2000). Finally, we also consider
0.25, which still delivers very large estimated growth effects.
24 See for example the criticism of Rodriguez and Rodrik (1999), who note that most of the explanatory
power of the Sachs-Wamer index derives from the components that measure the black market premium on
foreign exchange and whether the state holds a monopoly on exports.
25 Specifically, we use the instrument proposed by Frankel and Romer (1999) and the logarithm of
population in 1990 as right-hand side variables in a pooled OLS regression.
28
three other measures of openness not considered by the above authors: collected import
taxes as a share of imports, a dummy variable taking the value one if the country is a
member of the World Trade Organization (or its predecessor the GATT), and a dummy
variable taking the value one if the country has restrictions on intemational capital
movements as reported in the International Monetary Fund's Report on Exchange
Arrangements and Exchange Controls. Figure 4 reports the simple correlations between
each of these measures and the logarithm of the first quintile share, in levels and in
differences analogously to Figure 2. Variable definitions and sources are reported in
Table 9. As was the case in Figure 2, there is little obvious evidence of any kind of
systematic bivariate relationship between each of these measures of openness and the
share of income of the poorest quintile.
We also consider two variants on our basic specification. First, in order to
capture the possibility that greater openness has differential effects at different levels of
development, we introduce an interaction of the openness measures with the log-level of
real GDP per capita in 1990 for each country. Given the high correlation in levels
between per capita income and capital per worker, this interaction may be thought of as
capturing in a very crude way the possibility that the effects of trade on inequality
depend on countries' relative factor abundance. The second elaboration we consider is
to add an interaction of openness with the logarithm of arable land per capita, as well as
adding this variable directly. This allows a more general formulation of the hypothesis
that the effects of openness depend on countries' factor endowments.
The results of these extensions are presented in Table 7. Each of the columns of
Table 7 corresponds to a different measure of openness, and the three horizontal panels
correspond to the three variants discussed above. Two main results emerge from this
table. First, in all of the specifications considered below, we continue to find that
average incomes of the poor rise proportionately with average incomes: in each
regression, we do not reject the null hypothesis that the coefficient on average incomes
is equal to one. This indicates that our previous results on the lack of any significant
association between average incomes and the log first quintile share are robust to the
inclusion of these additional control variables. Second, we find no evidence whatsoever
of a significant negative relationship between any of these measures of openness and
average incomes of the poor. In all but one case, we cannot reject the null hypothesis
29
that the relevant openness measure is not signficantly associated with the income share
of the bottom quintile, holding constant average incomes. The only exception to this
overall pattem is the measure of capital controls, where the presence of capital controls
is significantly (at the 10 percent level) associated with a lower income share of the
poorest quintile. Overall, however, we conclude from this table that there is very little
evidence of a significant relationship between the income share of the poorest quintile
and a wide range of measures of exposure to the international economy. The only other
finding of interest in this table is unrelated to the question of openness and incomes of
the poor. In the bottom panel where we include arable land per capita and its interaction
with openness measures, we find some evidence that countries with greater arable land
per worker have a lower income share of the poorest quintile. This is consistent with
Leamer et. al. (1999) who find that cropland per capita is significantly associated with
higher inequality in a cross-section of 49 countries.
4.4 Other Determinants of Incomes of the Poor
Finally we consider a number of other factors that may have direct effects on
incomes of the poor through their effect on income distribution. We consider four such
variables: primary educational attainment, social spending, agricultural productivity, and
formal democratic institutions. Of these four variables, only the primary education
variable tends to be significantly correlated with economic growth, and even here recent
evidence suggests that much of this correlation reflects reverse causation from growth to
greater schooling (Bils and Klenow (2000)).
However, these policies may be especially important for the poor. Consider for
example primary enrollment rates. Most of the countries in the sample are developing
countries in which deviations from complete primary school enrollments are most likely
to reflect the low enrollment among the poorest in society. This in turn may be an
important factor influencing the extent to which the poor participate in growth. Similarly,
depending on the extent to which public spending on health and education is effective
and well-targeted towards poor people, a greater share of social spending in public
spending can be associated with better outcomes for poor people. Greater labor
productivity in agriculture relative to the rest of the economy may benefit poor people
disproportionately to the extent that the poor are more likely to live in rural areas and
30
derive their livelihood from agriculture. And finally, formal democratic institutions may
matter to the extent that they give voice to poor people in the policymaking process.
Figure 5 reports the simple correlations between each of these measures and the
income share of the poorest quintile, in levels and in differences as before. Variable
definitions and sources are reported in Table 9. In the case of differences, there is again
little in the way of even a simple bivariate relationship between changes in these
variables and changes in the income share of the poorest quintile. In levels, there is a
weak positive relationship between the income share of the poorest quintile and primary
education, agricultural productivity, and the democracy index, and a negative
relationship with the share of social spending in total spending. However, as we see in
Table 8, these relationships are generally not robust to the inclusion of average income.
In particular, while years of primary education and relative productivity in agriculture both
enter positively, neither is significant at conventional levels. In the regression with social
spending, we also include overall government consumption in order to capture both the
level and compositional effects of public spending. Overall government spending
remains negatively associated with incomes of the poor, and the share of this spending
devoted to health and education does not enter significantly. This may not be very
surprising, since in many developing countries, these social expenditures often benefit
the middle class and the rich primarily, and the simple share of public spending on the
social sectors is not a good measure of whether government policy and spending is
particularly pro-poor. Finally, the measure of formal democratic institutions enters
positively and significantly (although only at the 10% level). However, this result is not
very robust. In our large sample of developed and developing countries, measures of
formal democratic institutions tend to be significantly correlated with other aspects of
institutional quality, especially the rule of law index considered earlier. When we include
the other growth determinants in the regression, the coefficient on the index of
democratic institutions is no longer significant.
31
5. Conclusions
It should come as no surprise that the general relationship between growth of
income of the poor and growth of mean income is one-to-one. What is new here is that
we show that a number of popular ideas about the poverty-growth nexus are not
supported by empirical evidence in a very large sample of countries spanning the last
four decades. In particular,
* The poverty-growth relationship is not different in negative growth (crisis) episodes
and normal growth periods;
* The poverty impact of growth has not declined in recent decades;
* Growth spurred by open trade or other macro policies (good rule of law, low
government consumption, macro stability, financial development) benefits the poor
as much as it does the typical household; and
- Growth of income of the poor does not appear to respond systematically to a number
of supposedly 'pro-poor" policies including formal democratic institutions and public
expenditure on health and education.
This does not imply that growth is all that is needed to improve the lives of the poor.
Rather, these findings leave plenty of room for further work, because they emphasize
the fact that we know very little about what systematically causes changes in the
distribution of income. What we do learn is that growth generally does benefit the poor
as much as everyone else, so that the growth-enhancing policies of good rule of law,
fiscal discipline, and openness to international trade should be at the center of
successful poverty reduction strategies.
32
References
Ali, Abdel Gadir Ali and Ibrahim Elbadawi (2001). "Growth Could Be Good for the Poor".
Manuscript, Arab Planning Institute and the World Bank.
Aitchinson, J. and J.A.C. Brown (1966). The Lognormal Distribution. Cambridge:
Cambridge University Press.
Agenor, Pierre-Richard (1998). "Stabilization Policies, Poverty, and the Labour Market."
Manuscript, International Monetary Fund and the World Bank.
Arellano, M. and 0. Bover (1995). "Another Look at the Instrumental-Variable
Estimation of Error-Components Models". Journal of Econometrics. 68:29-52.
Atkinson, A.B. and A. Brandolini (1999). "Promise and Piffalls in the Use of "Secondary"
Data-Sets: Income Inequality in OECD Countries". Manuscript. Nuffield College,
Oxford and Banca d'ltalia, Research Department.
Beck, Thorsten, Asli Demirguc-Kunt, and Ross Levine (1999). "A New Database on
Financial Development and Structure". World Bank Policy Research Department
Working Paper No. 2146.
Banerjee, Abhijit V. and Esther Duflo (1999). "Inequality and Growth: What Can the
Data Say?" Manuscript, MIT.
Barro, Robert J. (1999). "Inequality, Growth and Investment." Manuscript, Harvard
University.
Barro, Robert J. and Jong-wha Lee (2000). "International Data on Educational
Attainment: Updates and Implications". Harvard University Center for
International Development Working Paper No. 42.
Bidani, Benu and Martin Ravallion (1997). "Decomposing Social Indicators Using
Distributional Data." Journal of Econometrics, 77:125-139.
Bils, Mark and Peter Klenow (2000). "Does Schooling Cause Growth?". American
Economic Review. 90(5):1160-1183.
Blundell, Richard and Stephen Bond (1998). "Initial Conditions and Moment Restrictions
in Dynamic Panel Data Models." Joumal of Econometrics, 87:115-143.
Chen, Shaohua and Martin Ravallion (1997). "What Can New Survey Data Tell Us
about Recent Changes in Distribution and Poverty?" The World Bank Economic
Review, 11(2):357-382.
Chen, Shaohua and Martin Ravallion (2000). "How Did the World's Poorest Fare in the
1990s?". Manuscript, the World Bank. Data and paper available at
http://www.worldbank.org/research/povmonitor/.
33
Daft, Gaurav and Martin Ravallion. "When is Growth Pro-Poor?". Manuscript, The
World Bank.
Deininger, Klaus and Lyn Squire (1996). "A New Data Set Measuring Income
Inequality." The World Bank Economic Review, 10(3):565-591.
Easterly, William (1999). "Life During Growth." Joumal of Economic Growth, 4:239-276.
Easterly, William and Stanley Fischer (2000). "Inflation and the Poor." World Bank
Policy Research Department Working Paper No. 2335.
, and Sergio T. Rebelo (1993). "Fiscal Policy and Economic Growth: An
Empirical Investigation." Journal of Monetary Economics, (December) 32(3),
417-58.
Edwards, Sebastian (1997). "Trade Policy, Growth, and Income Distribution". American
Economic Review. 87(2):205-210.
Filmer, Deon, and Lant Pritchett (1997). "Child Mortality and Public Spending on Health:
How Much Does Money Matter?" Policy Research Working Paper No. 1864, The
World Bank.
Fischer, Stanley (1993). "The Role of Macroeconomic Factors in Growth." Joumal of
Monetary Economics, (December) 32(3), 485-512.
Forbes, Kristin J. (2000). "A Reassessment of the Relationship between Inequality and
Growth." American Economic Review. 90(4):869-97.
Frankel, Jeffrey A. and David Romer (1999). "Does Trade Cause Growth?" The
American Economic Review, (June) 379-399.
Gallup, John Luke, Steven Radelet and Andrew Warner (1998). 'Economic Growth and
the Income of the Poor. Manuscript, Harvard Institute for International
Development.
Gouyette, Claudine and Pierre Pestieau (1999). "Efficiency of the Welfare State."
Kyklos. 52:537-553.
Johnston, N, S. Kotz, and N. Balakrishnan (1994). Continuous Univariate Distributions
(Second Edition, Volume 2). New York: Wiley.
Kaufmann, Daniel, Aart Kraay, and Pablo Zoido-Lobat6n (1999). "Govemance Matters".
World Bank Policy Research Department Working Paper No. 2196.
Knack, Stephen, and Phillip Keefer (1995).- "Institutions and Economic Performance:
Cross-Country Tests Using Alternative Institutional Measures." Economics and
Politics, (November), 7(3), 207-227.
Kuznets, Simon (1955). "Economic Growth and Income Inequality." The American
Economic Review, 45(1):1-28.
34
Leamer, Edward, Hugo Maul, Sergio Rodriguez, and Peter Schott (1999). "Does Natural
Resource Abundance Increase Latin American Income Inequality?". Journal of
Development Economics. 59:3-42.
Levine, Ross, Norman Loayza, and Thorsten Beck (2000). "Financial Intermediation and
Growth: Causality and Causes". Journal of Monetary Economics. 46:31-77.
Li, Hongyi and Heng-fu Zou (1998). "Income Inequality is not Harmful for Growth:
Theory and Evidence." Review of Development Economics, 2(3):318-334.
, Lyn Squire and Heng-fu Zou (1998). "Explaining International and
Intertemporal Variations in Income Inequality." The Economic Joumal, 108:26-
43.
Lundberg, Mattias and Lyn Squire (2000). 'The Simultaneous Evolution of Growth and
Inequality." Manuscript, The World Bank.
Perotti, Roberto (1996). "Growth, Income Distribution and Democracy: What the Data
Say." Journal of Economic Growth, 1: 149-187.
Quah, Danny (1999). '6x109: Some Dynamics of Global Inequality and Growth."
Manuscript, LSE Economics Department.
Ravallion, Martin (2000). "Do National Accounts Provide unbiased estimates of Survey-
Based Measures of Living Standards?". Manuscript, The World Bank.
Rodriguez, Francisco and Dani Rodrik (1999). "Trade Policy and Economic Growth: A
Skeptic's Guide to the Cross-National Evidence." Mimeo, Department of
Economics, University of Maryland.
Sachs, Jeffrey D. and Andrew Warner (1995). "Economic Reform and the Process of
Global Integration." Brookings Papers on Economic Activity, (1), 1-1 18.
Spilimbergo, Antonio, Juan Luis Londono, and Miguel Szekely (1999). "Income
Distribution, Factor Endowments, and Trade Openness". Journal of
Development Economics. 59:77-101.
Summers, Robert and Alan Heston (1991). 'The Penn World Table (Mark 5): An
Expanded Set of International Comparisons, 1950-88." Quarterly Journal of
Economics, (May) 106(2), 327-68.
United Nations University - World Institute for Development Economics Research
(2000). World Income Inequality Database. Available online at
(http://www.wider.unu.edu/wiid/wiid.htm).
35
Table 1: Sources for Income Distribution Data
Number of Observations
Total Spaced Sample Changes
By Source
UN-WIDER World Income Inequality Database 706 289 199
Deininger and Squire High Quality Sample 97 45 28
World Bank Poverty Monitoring Website 118 68 45
Lundberg and Squire (2000) 32 16 13
By Region
East Asia and Pacific 178 77 22
E. Europe and Central Asia 172 52 66
Latin America and Caribbean 160 88 95
Middle East/North Africa 41 31 24
South Asia 73 28 18
Sub-Saharan Africa 90 59 29
Other 239 83 31
Total 953 418 285
Notes: This table shows the four sources of data on income distribution on which we rely to construct
estimates of mean incomes of the poor. Total refers to the total number of annual observations. Spaced
sample refers to observations separated by at least five years from each other within countries. Changes
refers to the source of the final year for each pair of observations for which it is possible to construct a
five-year change within countries in incomes of the poor.
36
Table 2: Adjustments to Gini Coefficients and Income Shares
Gini Coefficient Income Share of Bottom Quintile
Coefficient Std Err Coefficient Std Err
Constant 31.160 0.664 0.072 0.002
Gross Income Dummy 4.046 1.011 * -0.011 0.003
Expenditure Dummy -1.397 1.412 0.002 0.003
East Asia and Pacific 4.673 1.088 * -0.001 0.003
E. Europe and Central Asia -2.656 1.502 * 0.022 0.004
Middle EasVNorth Africa 9.095 1.625 -0.007 0.004
Latin America and Caribbean 15.550 1.015 *** -0.023 0.003
South Asia 3.519 1.502 ** 0.009 0.004 **
Sub-Saharan Africa 16.186 1.772 -0.018 0.005
Notes: This table reports the results of a pooled OLS regression of the indicated inequality measures on
the indicated variables. Standard errors are White-corrected for heteroskedasticity.
37
Table 3: Basic Specification
Estimates of Growth Elasticity
(1) (2) (3) (4) (5)
Levels Differences System
No Inst Inst No Inst Inst
Intercept -1.762 -2.720 -1.215
0.210 1.257 0.629
Slope 1.072 1.187 0.983 0.913 1.008
0.025 0.150 0.076 0.106 0.076
P-Ho: a1=1 0.004 0.213 0.823 0.412 0.916
P-OID 0.174 0.163
T-NOSC -0.919
# Observations 269 269 269 269 269
First-Stage Regressions for System
Dependent Variable:
In(lncome) Growth
Intercept 8.238
0.064
Lagged Growth 0.956
0.293
Lagged Income 0.011
0.002
Twice Lagged Growth 0.284
0.094
P-Zero Slopes 0.007 0.001
Notes: The top panel reports the results of estimating Equation (1) (columns 1 and 2), Equation (3)
(columns 3 and 4), and the system estimator combining the two (column 5). OLS and IV refer to ordinary
least squares and instrumental variables estimation of Equations (1) and (3). The bottom panel reports
the corresponding first-stage regressions for IV estimation of Equations (1) and (3). The row labelled P-
Ho: ai=1 reports the p-value associated with the test of the null hypothesis that a1=1 .The row labelled P-
OlD reports the P-value associated with the test of overdentifying restrictions. The row labelled T-NOSC
reports the t-statistic for the test of no second-order serial correlation in the differened residuals.
Standard errors are corrected for heteroskedasticity and for the first-order autocorrelation induced by first
differencing using a standard Newey-West procedure.
38
Table 4: Variants on the Basic Specification
Regional Dummies Regional Dummies Regional Dummies Regional Dummies Regional Dummies Regional Dummies
Common Trend Slopes Differ by Decade Slopes Differ by Region Slopes Differ with Income Slopes Differ +/- Growth
Coef Std. Err Coef Std. Err Coef Std. Err Coef Std. Err Coef Std. Err Coef Std. Err
Constant -0.114 0.876 -0.050 4.824 -0.465 0.698 -4.308 1.421 -0.762 0.815 -1.254 0.647
ln(Per Capita GDP) 0.905 0.094 1.003 0.139 0.941 0.079 1.355 0.153 0.988 0.196 1.027 0.070
EAP -0.168 0.102 -0.079 0.143 -0.127 0.088 3.733 1.568 -0.103 0.064 -0.050 0.081
ECA -0.023 0.147 0.085 0.202 0.003 0.131 2.965 3.944 0.050 0.115 0.132 0.109
LAC -0.618 0.121 -0.512 0.166 - -0.572 0.101 8.244 3.083 -0.542 0.095 -0.490 0.095
MENA -0.275 0.140 -0.152 0.199 -0.246 0.118 2.213 2.380 -0.189 0.100 -0.127 0.109
SA -0.079 0.208 0.128 0.311 0.000 0.166 2.615 1.616 0.055 0.135 0.185 0.154
SSA -0.685 0.288 -0.369 0.355 -0.550 0.243 2.111 2.008 -0.422 0.170 -0.384 0.210
Time 0.000 0.003
y x 1970s -0.001 0.008
yx 1980s 0.003 0.010
y x 1990s 0.005 0.010
y x EAP -0.413 0.173
yx ECA -0.290 0.474
y x LAC -1.019 0.368
y x MENA -0.243 0.285
y x SA -0.239 0.188
yxSSA -0.230 0.256
y x y90 -0.001 0.013
y x (Dummy Negative Growth) 0.009 0.008
P-Ho: a1=1 0.313 0.983 0.455 0.020 0.949 0.694
P-OID 0.390 0.240 0.126 0.133 0.209 0.174
T-NOSC -0.948 -0.921 -0.938 -1.571 -0.932 -0.907
# Observations 269 269 269 269 269 269
Notes: The row labelled P-Ho: a,=1 reports the p-value associated with the test of the null hypothesis that a1=1. The row labelled P-OID reports the P-value
associated with the test of overidentifying restrictions. The row labelled T-NOSC reports the t-statistic for the test of no second-order serial correlation in the
differened residuals. Standard errors are corrected for heteroskedasticity and for the first-order autocorrelation induced by first differencing using a standard
Newey-West procedure.
39
Table 5: Growth Determinants and Incomes of the Poor
Trade Government log(1+lnflation Financial Rule of Law All Growth
Volumes Consumption/GDP Rate) Development Index Variables
Coef Std. Err. Coef Std.Err. Coef Std.Err. Coef Std.Err. Coef Std. Err. Coef Std.Err.
ln(Per Capita GDP) 1.094 0.108 *** 1.050 0.085 1.020 0.089 0.995 0.119 0.914 0.105 1.140 0.100
(Exports+lmports)/GDP -0.039 0.088 0.023 0.056
Government Consumption -0.571 0.419 -0.746 0.386 *
/GDP
ln(1+lnflation) -0.136 0.103 -0.163 0.107
Commercial Bank Assets 0.032 0.257 -0.209 0.172
(Total Bank Assets
Rule of Law 0.084 0.069 -0.032 0.060
P-Ho: a1=1 0.386 0.555 0.825 0.968 0.412 0.164
P-OID 0.257 0.168 0.159 0.350 0.279 0.393
T-NOSC -0.751 -0.506 -0.261 -0.698 -0.945 -0.762
# Observations 223 237 253 232 268 189
Notes: All regressions include regional dummies. The row labelled P-Ho: a,=1 reports the p-value associated with the test of the null hypothesis that acl=l.
The row labelled P-OID reports the P-value associated with the test of overidentifying restrictions. The row labelled T-NOSC reports the t-statistic for the test
of no second-order serial correlation in the differened residuals. Standard errors are corrected for heteroskedasticity and for the first-order autocorrelation
induced by first differencing using a standard Newey-West procedure.
40
Table 6: Growth and Distribution Effects
Growth Regression Income of Poor Regression Standard Growth Distribution
Coef Std.Err. Coef Std.Err. Deviation Effect Effect
Income 1.140 0.101 ***
Lagged Income 0.668 0.169
Lagged Inequality -0.089 0.062
Secondary Education 0.097 0.057 *
Trade Volumes 0.045 0.074 0.024 0.056 0.280 0.035 0.012
Inflation -0.145 0.131 -0.162 0.107 0.275 -0.104 -0.059
Government Consumption -0.973 0.415 ** -0.744 0.387 * 0.054 -0.143 -0.060
Financial Development 0.374 0.167 ** -0.208 0.172 0.153 0.175 -0.007
Rule of Law 0.180 0.082 ** -0.032 0.060 0.250 0.133 0.011
Notes: The first column reports the results of estimating the growth regression in Equation All regressions include regional dummies. The row labelled P-Ho:
aj=1 reports the p-value associated with the test of the null hypothesis that a=1. The row labelled P-OID reports the P-value associated with the test of
overidentifying restrictions. The row labelled T-NOSC reports the t-statistic for the test of no second-order serial correlation in the differened residuals.
Standard errors are corrected for heteroskedasticity and for the first-order autocorrelation induced by first differencing using a standard Newey-West
procedure.
41
Table 7: Openness and Incomes of The Poor
Trade Adjusted Trade Sachs-Wamer Import Taxes Dummy for Dummy for
Volumes Volumes Trade Policy Index As Share of Imports WTO Membership Capital Controls
Coef Std.Err. Coef Std.Err. Coef Std,Err. Coef Std.Err. Coef Std.Err. Coef Std.Err
Basic
In(Per Capita GDP) 1.094 0.108 * 1.047 0.133 1.077 0.092 0.936 0.136 *^* 0.917 0.104 0.869 0.116
Openness Measure -0.039 0.088 -0.038 0.167 -0.071 0.065 -0.161 0.358 0.021 0.043 -0.090 0.051
P-Ho: (x1=1 0.386 0.724 0.407 0.638 0.428 0.259
P-OID 0.257 0.135 0.431 0.074 0.425 0.183
T-NOSC -0.751 -0.767 -0.677 1.263 -0.998 -1.084
# Observations 223 213 234 137 269 208
Interaction with Per Capita GDP
In(Per Capita GDP) 1.102 0.092 0.991 0.126 1.066 0.076 * 1.013 0.082 ** 1.012 0.078 ** 0.969 0.084
Openness Measure -0.323 1.363 1.188 1.601 0.237 0.573 0.604 3.133 -0.026 0.558 -0.515 0.587
Openness Measurex In(Per Capita GDP) 0.030 0.146 -0.123 0.169 -0.036 0.072 -0.085 0.396 0.002 0.070 0.052 0.064
P-Ho: a1=1 0.267 0.942 0.386 0.873 0.876 0.708
P-OID 0.218 0.144 0.567 0.126 0.226 0.121
T-NOSC -0.742 -0.816 -0.696 1.253 -0.905 -1.005
# Observations 223 213 234 137 269 208
Interaction with Per Capita GDP and Land
In(Per Capita GDP) 1.120 0.105 0.901 0.099 1.046 0.084 1.063 0.083 1.101 0.072 1.009 0.081
Openness Measure 0.304 1.780 1.161 1.485 0.109 0.605 2.552 2.858 0.513 0.569 -0.574 0.607
ln(Arable Land/Worker) -0.090 0.031 *** -0.086 0.023 -0.018 0.032 -0.037 0.029 -0.054 0.039 -0.038 0.025
Openness Measure x In(Per Capita GDP) -0.036 0.198 -0.074 0.170 -0.024 0.075 -0.378 0.385 -0.066 0.072 0.050 0.066
Openness Measure x ln(Arable Land Per Worker) 0.061 0.070 0.245 0.111 -0.041 0.035 -0.366 0.262 0.016 0.039 -0.023 0.031
P-Ho: a1=1 0.253 0.322 0.582 0.443 0.163 0.915
P-OID 0.030 0.062 0.267 0.082 0.208 0.095
T-NOSC -0.755 -0.896 -1.134 0.421 -1.019 -1.492
# Observations 207 207 219 131 243 193
Notes: All regressions include regional dummies. The row labelled P-Ho: x,l=1 reports the p-value associated with the test of the null hypothesis that x1=1 . The row
labelled P-OID reports the P-value associated with the test of overidentifying restrictions. The row labelled T-NOSC reports the t-statistic for the test of no second-order
serial correlation in the differened residuals. Standard errors are corrected for heteroskedasticity and for the first-order autocorrelation induced by first differencing
using a standard Newey-West procedure.
42
Table 8: Other Determinants of Incomes of the Poor
Years Primary Social Agricultural Voice Voice with
Education Spending Productivity Macro Controls
Coef Std.Err. Coef Std.Err. Coef Std.Err. Coef Std.Err. Coef Std.Err.
ln(Per Capita GDP) 1.067 0.088 *** 1.025 0.101 *** 0.985 0.104 *** 0.933 0.095 *** 1.117 0.098
Years Primary Education 0.014 0.031
Government Consumption/ -1.553 0.547 ***
GDP
Social Spending -0.664 0.429
/Total Public Spending
Agricultural Relative 0.060 0.081
Productivity
Voice 0.095 0.053 * 0.029 0.058
P-Ho: al=l 0.448 0.803 0.886 0.480 0.233
P-OID 0.213 0.028 0.166 0.302 0.419
T-NOSC -0.384 0.594 -0.837 -0.970 -0.767
# Observations 222 111 197 265 207
Notes: All regressions include regional dummies. The row labelled P-Ho: a,1 reports the p-value associated with the test of the null hypothesis that cXl=l. The row
labelled P-OID reports the P-value associated with the test of overidentifying restrictions. The row labelled T-NOSC reports the t-statistic for the test of no second-order
serial correlation in the differened residuals. Standard errors are corrected for heteroskedasticity and for the first-order autocorrelation induced by first differencing
using a standard Newey-West procedure.
43
Table 9: Variable Definitions and Data Sources
Variable Source Comments
Real GDP Per Capita Summers and Heston Penn World Constant 1985 US dollars. Extended
Tables, World Bank Data to 1998 using constant price local
currency growth rates. Extended cross-
sectionally as described in Kraay,
Loayza, Serven and Ventura (2000).
First Quintile Share UN-WIDER (2000), Deininger and Combination of data from different
Squire (1996), Ravallion and Chen sources described in text.
(2000), Lundberg and Squire (2000)
Gini Coefficient UN-WIDER (2000), Deininger and Combination of data from different
Squire (1996), Ravallion and Chen sources described in text.
(2000), Lundberg and Squire (2000)
(Exports + Imports)/GDP World Bank Data, Summers and Exports and imports are in constant
Heston Penn World Tables 1985 US dollars at market exchange
rates. Denominator is in constant 1985
dollars at PPP.
Govemment Consumption / GDP World Bank Data Numerator and denominator are in
current local currency units.
In(1+inflation) World Bank Data Inflation is CPI-based where available,
otherwise use growth of GDP deflator.
Commercial Bank Assets/Total Bank Beck, Demirguc-Kunt, and Levine
Assets (1999)
Rule of Law Kaufmann, Kraay and Zoido-Lobaton Index, greater values indicate better
(1999) rule of law
Secondary Education Barro and Lee (2000) Stock of years of secondary education.
Frankel-Romer Distance Measure Frankel and Romer (1999) Trade-weighted average of distance
from trading partners.
Population World Bank Data
Sachs-Wamer Index Sachs and Wamer (1995)
Import Taxes/Total Imports World Bank Data Data on import taxes in numerator
originally from IMF Govemment
Finance Statistics. Numerator and
denominator in current local currency
units.
WTO Membership Dummy wwwmto.ora
Capital Controls Dummy International Monetary Fund Report
on Exchange Arrangements and
Exchange Controls, various issues.
Years Primary Education Barro and Lee (2000) Stock of years of primary education
Social Spending/Total Public Spending Government Finance Statistics
Arable Land Per Worker World Bank Data Total arable land in hectares divided by
population aged 15-64.
Agricultural Relative Labour Productivity World Bank Data Current price share of agriculture in
GDP divided by share of workforce in
agriculture.
44
Average Annual Change in Iog(Per Capita Income In Poorest
Quintile) Log(Per Capita Income In Poorest Quintile)
C Ca~~~~~~~~~C
EL~ ~ ~ L
C, CD 0~* )
U 0 0 0 0* CI U
0 C~~~.* *~~ *0 0 0 0 c C)
0 #* ~~~~~~~~~~~0
4 -4 ~~~~~~0
p~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-
41 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ *
CO94
0n )
Figure 2: Growth Determinants and the Income Share of the Poor
Levels Differences
(Exports + Imports)/GDP
0t > 1 1.5 2 2.5 3 315-- 5o
0
~~ 0.5 1 1.5 2 2.5 3 35~~~~~~~~~~~~~1 .4 *6 ob
.2.5 y 0. 1602x - 1.2489 ~ - .0 -0 *4 . 0
| -2.5 *t y=O.t602x-1.2489 y y0.3097xy-0.015
1 -3 | * R2 = 0.0198 -15 R' =.0238
2 ] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-2
Govemment Consumption/GDP
0
0.1 0.2 0.3 0.4 0.5
1.5
415 -ai 44- ~~~~~~~~~~0,15
-2. ._15 r _o
-3 y=Q.0107x -1.883 | .5 y=-1.0114x 0.0022
351 R2=2E.06 -2- R2=0.01t
Ln(1 +inflation)
I'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. I .
'-0.5 0.5 1 1.5 2 2S5 3!
*0.5 t*U*I..
t -2.5 * * 1 * 4~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.
. 31+ -1.5 *
g ~~~~~~~~~y =0 41987x -1i. 1394 y = 0.24lx - 0.0011
-3.5 R2 = e 0318 -2 R2 = 0 0554 _
Notes: Each panel shows the simple correlation in levels and in differences between the logarithm of the
first quintile share (on the vertical axis) and the indicated growth determinant (on the horizontal axis).
46
Figure 2, cont'd: Growth Determinants and the Income Share of the Poor
Levels Differences
Deposit Money Bank Assets/Total Bank Assets
-0.5 > 0.2 0'4 O.S OS 1 1'2
4* 4~~~~~~~~~~~~06 -A ..4 0.2 0.4 0.6
-2.5- -
4 ~~~~~~~~~~~~~~~~~y =0.3004x - 0.0076
,3 y 0.3566x - 1I4845 * R2=00154
Fe =0.0322
-3.5 --2
Rule of Law
i3 02 1 1 2 3
0 t ~~~~~~~~~~~~~~(n.a.)
-3.5
Notes: Each panel shows the simple correlation in levels and in differences between the logarithm of the
first quintile share (on the vertical axis) and the indicated growth determinant (on the horizontal axis).
47
Figure 3: Growth and Distribution Effects of Policies
Rule of Law
oGrowth
* Distribution S D Velocma | ~Financila
Trade Volumes
-25% -20% -15% -10% -5% 0% 5% 10% 15% 20%
Percent of Per Capita GDP
48
Figure 4: Openness Measures and the Income Share of the Poor
Levels Differences
Sachs-Warner Index
0 1.5
-0.5 0.5 1 1.5 1 *
-1 As 0= !0.
-1.5
-2 -1.5 4 -0.5 -0.5 %5* 1.5
-2.5 y 0.1363x - 1.2573 -1 -0.0684x - 0.003
-3 ^> R2= 0.0331 -2 R2 =0.0052
-3.5 __
Import Taxes / Imports
0 ,
-0.5 .AA1 0.2 0.3 0.4 0.5
-2- * -0.3 -0.2 -0 -0 7 0.1 0.2
-2.5 - *i * 5 0633x + 0.001 1
-3- * y -0.8322x- 1.0935 -2 0.0001
-3.5 R2 =0.0366
WTO Membership Dummy
0 - ~---- ~1.5
j 0.5 A 0.5 1 1.5 1
2 * | -1 0.5
-2 -1.5 -1 -0.5-0.5 404t 1.5
-2 y 0.4399x - 1.2006 1.' -9E-05x - 0.0285
R2 =0.0025 * -2 R = 7E-09
-3.5-
Capital Controls Dummy
0 1.5
-0.5 0 .5, ~1.5
I -'-'| . S i ** ~~~~~~~~~~~1.5 -0.X 5- +. 1.5l
-3'~~~~~~~~~~~~~~~~~~~~~~~~-
-2.5 .5 .0872x -0.0233
-3 y =-0.0774x - 1. 1492 R2=008l
-3 ~ R2 =0.0068 *-2 008
-3.5ll
Notes: Each panel shows the simple correlation in levels and in differences between the logarithm of the
first quintile share (on the vertical axis) and the indicated growth determinant (on the horizontal axis).
49
Figure 5: Other Determinants of Income Share of the Poor
Levels Differences
Years of Primary Education
0 , 5
-0.5 0 2 4 6 8 10
-1 - . *i
-1.5~~~~~~~~~~~~~~~~~~~~~~'O
-2 1 ** ++ t ~~~~~~~~~-4 -3 -2 -1 2oS
-2.5 X = 0.0545x - 1.3815 =- 5 .0Q6x - 0.0015
-3 * R2=0 007 | -2R270
_~~~~= 0
-3.5
Health and Education Spending/Total Public Spending
0 --1.5
-05 0 .2 0.4 0.6 1
-1.5 * *
-2 + + + + -0.4 -0.2 -0.. * 0.2 0.4
-25 .
t -2.5 , * Y - i8S 02x+0.0193
-3 0.0352
35 ~~~y -1.748lx - 08286 __ _ _ _ _ _ _ _ _ _ _ _ _ _ _
-35 ~~~~R2 _0.1878l
Relative Productivity in Agriculture
0 0-- 1.5
05 2
1 -2s 0 05 5 2
-1
-1.5
-2
-2.5 y .04't16x - 1.4829 yx-000
-3 R2~~ 0.0796 _y0.01 49
Voice and Accountability Index
._ __ _ __-I
-2t -2
n.a.
i 0 .095x- 1.2056
* + -3 x, R2= 0.0423
-3.5
Notes: Each panel shows the simple correlation in levels and in differences between the logarithm of the
first quintile share (on the vertical axis) and the indicated potential determinant of the income share of the
poor (on the horizontal axis).
50
Policy Research Working Paper Series
Contact
Title Author Date for paper
WPS2566 Convexity and Sheepskin Effects Norbert R. Schady March 2001 T. Gomez
in the Human Capital Earnings 32127
Function: Recent Evidence for
Filipino Men
WPS2567 Policy Reform, Economic Growth, Susmita Dasgupta March 2001 Y. d'Souza
and the Digital Divide: An Somik Lall 31449
Econometric Analysis David Wheeler
WPS2568 Ownership Structure and the Robert Cull March 2001 Z. Kranzer
Temptation to Loot: Evidence from Jana Matesova 38526
Privatized Firms in the Czech Mary Shirley
Republic
WPS2569 From Users to Custodians: Changing Liz Alden Wily March 2001 M. Yue
Relations between People and the Peter A. Dewees 34102
State in Forest Management in Tanzania
WPS2570 Asymmetries in Union Relative Wage Niels-Hugo Blunch March 2001 M. Clarke
Effects in Ghanaian Manufacturing: Dorte Verner 31752
An Analysis Applying Quantile
Regressions
WPS2571 Stock Market Responses to Bank Daniela Klingebiel March 2001 R. Vo
Restructuring Policies during the Randy Kroszner 33722
East Asian Crisis Luc Laeven
Pieter van Oijen
WPS2572 Nonfarm Income, Inequality, and Richard H. Adams, Jr. March 2001 N. Obias
Poverty in Rural Egypt and Jordan 31986
WPS2573 The Gender Implications of Public Martin Rama March 2001 H. Sladovich
Sector Downsizing: The Reform 37698
Program of Vietnam
WPS2574 How Adverse Selection Affects the Paolo Belli March 2001 H. Sladovich
Health Insurance Market 37698
WPS2575 The Quality of Bureaucracy and Chong-En Bai March 2001 H. Sladovich
Capital Account Policies Shang-Jin Wei 37698
WPS2576 Trade Policy, Standards, and Gary Clyde Hufbauer March 2001 L. Tabada
Development in Central America Barbara Kotschwar 36896
John S. Wilson
Policy Research Working Paper Series
Contact
Title Author Date for paper
WPS2577 Developing Rainfall-Based Index Jerry Skees April 2001 P. Kokila
Insurance in Morocco Stephanie Gober 33716
Panos Varangis
Rodney Lester
Vijay Kalavakonda
WPS2578 How Accession to the European Bartlomiej Kaminski April 2001 L. Tabada
Union Has Affected External Trade 36896
and Foreign Direct Investment in
Central European Economies
WPS2579 Public Health and Education Sudharshan Canagarajah April 2001 M. Clarke
Spending in Ghana in 1992-98: Xiao Ye 31752
Issues of Equity and Efficiency
WPS2580 On "Indirect" Trade-Related Research Olivier Lumenga-Neso April 2001 L. Tabada
and Development Spillovers Marcelo Olarreaga 36896
Maurice Schiff
WPS2581 Securities Clearance and Mario Guadamillas April 2001 H. Issa
Settlement Systems: A Guide to Robert Keppler 30154
Best Practices
WPS2582 Development Financing during a Suhas Ketkar April 2001 S. Crow
Crisis: Securitization of Future Dilip Ratha 30763
Receivables
WPS2583 How the Location of Roads and Maureen Cropper April 2001 V. Soukhanov
Protected Areas Affects Jyotsna Puri 35721
Deforestation in North Thailand Charles Griffiths
WPS2584 Structural Adjustment and Forest Kiran D. Pandey April 2001 Y. D"Souza
Resources: The Impact of World David Wheeler 31449
Bank Operations
WPS2585 Law, Politics, and Finance Thorsten Beck April 2001 A. Yaptenco
Asli Demirgu,-Kunt 31823
Ross Levine
WPS2586 On the Urbanization of Martin Ravallion April 2001 C. Cunanan
Poverty 32301