metadata: authors: 0: author: Dollar, David
1: author: Kraay, Aart
docna: 0: docna: Growth is good for the poor
docty: Policy Research Working Paper
subtopic: Judicial System Reform,Poverty Reduction Strategies,International Trade and Trade Rules,Inflation,Inequality
teratopic: Poverty Reduction
abstracts: cdata!: When average income rises, the average
            incomes of the poorest fifth of society rise
            proportionately. This is a consequence of the strong
            empirical regularity that the share of income accruing to
            the bottom quintile does not vary systematically with
            average income. The authors document this empirical
            regularity in a sample of 92 countries spanning the past
            four decades and show that it holds across regions, periods,
            income levels, and growth rates. The authors next ask
            whether the factors that explain cross-country differences
            in the growth rates of average incomes have differential
            effects on the poorest fifth of society. They find that
            several determinants of growth--such as good rule of law,
            opennness to international trade, and developed financial
            markets--have little systematic effect on the share of
            income that accrues to the bottom quintile. Consequently,
            these factors benefit the poorest fifth of society as much
            as everyone else. Thee is some weak evidence that
            stabilization from high inflation and reductions in the
            overall size of government not only increase growth but also
            increase the income share of the poorest fifth in society.
            Finally, the authors examine several factors commonly
            thought to disproportionately benefit the poorest in
            society, but find little evidence of their effects. The
            absence of robust findings emphasizes that relatively little
            is known about the broad forces that account for the
            cross-country and intertemporal variation in the share of
            income accruing to the poorest fifth of society.
repnme: repnme: Growth is good for the poor
display_title: Growth is good for the poor
listing_relative_url: /research/2001/04/1089579/growth-good-poor
topicv3: Justice m1326106 840,Poverty m1326687 695,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Trade m1326924 698,Income Inequality m1503891 2668
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Poverty Reduction
origu: Development Research Group (DECRG)
no_of_pages: 56
model_extractions: POLICY RESEARCH WORKING PAPER 2587

# Summary findings

When average incomes rise, the average incomes of the poorest fifth of society rise proportionately. This is a consequence of the strong empirical regularity that the share of income accruing to the bottom quintile does not vary systematically with average income. Dollar and Kraay document this empirical regularity in a sample of 92 countries spanning the past four decades and show that it holds across regions, periods, income levels, and growth rates.

Dollar and Kraay next ask whether the factors that explain cross-country differences in the growth rates of average incomes have differential effects on the poorest fifth of society. They find that several determinants of growth-such as good rule of law, openness to international trade, and developed financial marketshave little systematic effect on the share of income that accrues to the bottom quintile. Consequently, these factors benefit the poorest fifth of society as much as everyone else. There is some weak evidence that stabilization from high inflation and reductions in the overall size of government not only increase growth but also increase the income share of the poorest fifth in society. Finally, Dollar and Kraay examine several factors commonly thought to disproportionately benefit the poorest in society, but find little evidence of their effects. The absence of robust findings emphasizes that relatively little is known about the broad forces that account for the cross-country and intertemporal variation in the share of income accruing to the poorest fifth of society.

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.<sup>2</sup> 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 apparen **t** endency 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 x<sup>0</sup> /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.

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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.<sup>3</sup> 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.<sup>4</sup> 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.

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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.

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# **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 withincountry 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.

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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 2 0 th percentile of this distribution.<sup>8</sup> 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

> 7 An 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 ofthe 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, ifthe 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 resul **t** hat 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 prespecified 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 isgiven 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).

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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.<sup>9</sup> 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 average **s** ince 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.

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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.'<sup>0</sup> 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.'<sup>2</sup> 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 isonly 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+ c 2 '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 equivalen **t** o a regression of a negative constant times the Gini coefficient on average income and a set of control variables.<sup>13</sup> 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 equivalen **t** o 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.

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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.'<sup>4</sup> 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-handside 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: 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. 1<sup>3</sup> i<<sup>0</sup> ). 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)).

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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 withincountry 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 timeinvariant 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.<sup>18</sup> 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 inlevels 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).

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(4) _Yct_ -Yct-k(c,t) =Cac* (Yct - Y ct-k(ct) ) + a 2'(Xct-X -c,t(ct) )+ (C ctcA-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.

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# **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 a 2=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.

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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 singleequation 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 c 1=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.

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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.<sup>20</sup> 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.<sup>21</sup> 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.

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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.

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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: 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: 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

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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 nonrepresentative 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 se<sup>24</sup> ); (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.

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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 conventiona **l** evels. 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.

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# **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.

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