inequality and poverty essay

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24 Poverty and Inequality: The Global Context

Francisco H. G. Ferreira is a Lead Economist with the World Bank's Research Department, and a co-editor of the Journal of Economic Inequality. He holds a Ph.D. from the London School of Economics, and has taught at PUC-Rio de Janeiro.

Martin Ravallion is the director of the Development Research Group at the World Bank.

• Published: 18 September 2012
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This article summarizes the recent evidence on global poverty and inequality, including both developed and developing countries. Section 1 discusses poverty and inequality data and presents evidence on levels and recent trends in poverty and inequality around the world. Section 2 turns to the issues involved in aggregating inequality indices across countries, in order to construct a meaningful measure of global inequality. Section 3 discusses the empirical relationship between economic growth, poverty, and inequality dynamics. Section 4 turns to the likely economic determinants of poverty and inequality changes. Section 5 offers some conclusions, and points to some promising research themes within this general topic.

The previous chapters in this Handbook have focused primarily on inequality in developed countries. The approximately five billion people who live in low and middle-income countries figured only fleetingly in the plot, as a huge (and possibly a little frightening) cast of extras, who produce cheap internationally tradable goods (Chapter 23 ) and are potential migrants to richer countries (Chapter 19 ). Yet, developing countries account for over 80% of the world's population, and experience levels of absolute poverty—and often of inequality too—much greater than those found in developed countries.

This chapter summarizes the recent evidence on global poverty and inequality, including both developed and developing countries. It draws on two main compilations of distributional data created at the World Bank, both of which are built up from country-specific nationally representative household surveys, generally fielded by national statistical offices. First is the PovcalNet data set, which comprises some 560 surveys for 100 low and middle-income countries, representing some 93% of the developing world's population. 2 Where necessary, the PovcalNet data set is complemented with information from the World Development Report 2006 household survey database, which has a somewhat broader geographical coverage (including many developed countries), but a more limited time-span.

In the first part of the chapter we discuss our poverty and inequality data and present evidence on levels and recent trends in poverty and inequality around the world. Global and regional poverty aggregates are also discussed here. Section 2 turns to the issues involved in aggregating inequality indices across countries, in order to construct a meaningful measure of global inequality. It reviews the main results from the literature that has sought to measure global income inequality, and briefly summarizes some of the evidence on global inequalities in health and education. Section 3 discusses the empirical relationship between economic growth, poverty, and inequality dynamics. Here we present what we see as the three key stylized facts to emerge from these data: the absence of a correlation between growth rates and changes in inequality among developing countries; the strong (positive) correlation between growth rates and rates of poverty reduction, and the importance of inequality to that relationship. In Section 4, in a more speculative mode, we turn to the likely economic determinants of poverty and inequality changes. Section 5 offers some conclusions, and points to some promising research themes within this general topic.

1. Poverty and Inequality around the World: A Bird's Eye View

There has been a remarkable expansion in the availability of household surveys in developing countries over the last 25 years. These surveys, which are typically designed and fielded by national statistical agencies, have the measurement of living standards in the population as one of their key objectives. Although clearly there are measurement errors in such data, it is also widely accepted that these data generally represent the best available source of information on the distribution of living standards for any country where they have been conducted.

Our poverty and inequality measures are constructed for the distributions of household income or consumption per capita, as captured by these surveys. This choice of indicator prompts three caveats. First, by focusing on income or consumption, we end up effectively taking a one-dimensional approach to measuring welfare. It would clearly be desirable to include other important dimensions of welfare not already included in consumption or income (at least directly), such as health status, cognitive functioning, civil and personal freedoms, and environmental quality. 3 Even short of a fully multidimensional approach to welfare, it might well be desirable to include in the aggregate indicator of well-being some measure of the value of access to public and publicly provided goods (such as education and health services, personal security, and access to local infrastructure). But extending welfare measurement in either of these two directions in a manner that allows international comparisons is impossible on the basis of the information available to date. As in most of the preceding chapters in this Handbook, we restrict our attention to the narrow realm of people's ability to consume private goods, as measured by their income or consumption expenditures.

Second, income is not the same thing as consumption. Although over the long run consumption should come quite close to permanent income (except for the limited number of lineages where bequests are important), there can be considerable deviations in the short run, as households either save or dissave. Consumption is thus generally considered a better measure of current welfare than income. 4 In addition, and perhaps of greater practical importance, the questionnaires for income and consumption are perforce quite different, and yield different types of measurement error; see Deaton 1997 . As a result of both higher measurement error and of the variance of the transitory component, 5 income inequality tends to be higher than inequality in consumption expenditures in a given population. In the description that follows, we use consumption distributions to construct our poverty and inequality measures wherever possible. Only when consumption data are unavailable in the survey do we report income-based indicators. The type of indicator is noted for each country in Table 24.1 .

Third, by looking at the distribution of income or consumption per capita, we are effectively making two strong assumptions, neither of which is likely to hold perfectly. First, we ignore intrahousehold inequality. Following common practice, such inequality is simply assumed away from our computations. Secondly, even if one is forced to use a single indicator for each household, it is not clear that the per capita definition is the most appropriate. There are differences in needs across age groups (and possibly genders), and there may well be certain fixed costs or ‘household public goods’ that generate economies of scale in consumption at the household level. 6 Both of these considerations have led many analysts to use some measure of ‘equivalent income’ as their welfare indicator for each household. However, these variables turn out to be quite sensitive to the different assumptions made in identifying specific equivalence scales from observed demand behavior, and there is no agreement on which particular scale should be used. 7 There is likely to be more agreement, in fact, with the statement that different scales may be appropriate for different settings (such as, say, South Korea and Togo). All this implies that seeking to introduce sensitivity to household size and composition in the context of international comparisons is, given the present state of knowledge, likely to contribute to less, not more, clarity.

Notes : N The World Bank classifies countries regionally and among income groups according to 2006 nominal GNI per capita, calculated using the World Bank Atlas method. High-income countries have GNI per capita of $11,116 or more. ECA = Eastern Europe and Central Asia; MNA = Middle-East and North Africa; EAP = East Asia and the Pacific; SAR = South Asia; SSA = Sub-Saharan Africa; LAC = Latin America and Caribbean; HI = High Income. y = income; c = consumption;

1 = PovcalNet; 2 = WDR 06; 3 = WDI;

Source : the World Bank Indicators, reference year 2006.

Having agreed on the choice of welfare indicator, the next challenge is the aggregation of the national distributions into scalar poverty or inequality indices. This is a much easier problem in the case of relative inequality measures that are, by construction, scale-invariant. 8 Since these measures do not depend on mean incomes or on the currency in which income is expressed, a number of vexing issues to do with Purchasing Power Parity (PPP) exchange rates and with the relevance of national account means to welfare measurement (to which we return below when discussing poverty measures) can be safely ignored. The inequality indices reported in Table 24.1 are therefore simple Gini indices and mean log deviations (MLD), computed over the original distribution of household consumption (or income) per person in each country's nominal currency, in each year. Unlike the Gini index, MLD is additively decomposable into between-group and within-group inequality components (Bourguignon, 1979 ).

Absolute poverty measures, on the other hand, summarize the extent of deprivation in a distribution with respect to a specific welfare threshold, given by the poverty line. This implies that scale matters, and so does the choice of mean (e.g. mean income from a household survey, or GDP per capita) and exchange rate when making inter-country comparisons or aggregations. It has been argued that misreporting of incomes in household surveys would justify scaling up the income distribution so that its mean equaled per capita consumption in the Private Consumption account in the National Accounts System (NAS). 9 But such an approach ignores the fact that the Private Consumption account includes components of institutional consumption as well as personal consumption, which could introduce a systematic overstatement of household welfare levels. Things are even worse if the scaling up is to GDP per capita itself, rather than only to per capita consumption from the NAS.

In addition, in economies with substantial subsistence agriculture and other forms of production for own consumption, it is not clear that the national accounts system provides a more accurate portrayal of real consumption than the surveys, which typically include information on consumption from own production at the household level. Finally, it is unlikely that income underreporting or selective compliance in surveys is distribution-neutral. 10 If richer households underreport more than middle-income or poorer households, then the uniform re-scaling that is proposed would result in an unwarranted under-estimation of poverty. It appears likely that richer households are also less likely to participate in surveys. This has theoretically ambiguous implications for inequality, although there is evidence (for the USA) that it entails a non-negligible under-estimation of overall inequality (Korinek et al ., 2006 ). In what follows we do not use National Accounts information to re-scale mean incomes or consumption from the surveys (although NAS data are used in the interpolation method of Chen and Ravallion, 2004a , which is used for ‘lining up’ household surveys with the reference years used in Tables 24.2 and 24.3 ).

In this chapter, we report poverty measures with respect to the World Bank's ‘standard’ international poverty line of about $1 a day (or, more precisely, $32.74 per month, at 1993 international PPP exchange rates). 11 This is a deliberately conservative definition of ‘poverty’, being anchored to the poverty lines typical of low-income countries. It is also one that has acquired considerable currency in international policy discussions: The first Millennium Development Goal (MDG1), for example, is to halve the 1990 ‘$1 a day’ poverty rate by 2015. To gauge sensitivity, we also use a line set at twice this value, $65.48 per person per month. Following common practice we refer to these as the ‘$1 a day’ and ‘$2 a day’ lines ($1.08 and $2.15 would be more precise). The higher line is more representative of what ‘poverty’ means in middle-income developing countries.

These international lines are converted to local currencies using the Bank's 1993 PPP exchange rates for consumption, and each country's consumer price index (CPI). PPP exchange rates adjust for the fact that non-traded goods tend to be cheaper in poorer countries. There is more than one way to calculate PPP exchange rates. The Geary-Khamis (GK) method used by the Penn World Tables (PWT) uses quantity weights to compute the international price indices. For our purposes, this method gives too high a weight to consumption patterns in richer countries when measuring poverty globally. The Elteto-Kones-Sculc (EKS) method—a multilateral extension of the usual bilateral Fisher index—attempts to correct for this bias. Since 2000, the World Bank's global poverty and inequality measures have been based on the Bank's PPP rates, which use the EKS method. 12 At the time of writing, new PPP rates, based on 2005 prices, are about to become available. While existing poverty and inequality measures have not yet been revised accordingly, we comment later on some of the likely implications.

Once the international poverty lines have been appropriately converted into local currency, and local CPI has been used to inflate the line to the nominal currency of the survey year, poverty measures are calculated for each survey year. Naturally, different countries do not all field their household surveys (which are rarely annual) in the same year. In Table 24.1 , we report the year(s) in which the latest surveys available to us were conducted in each country, and report poverty measures for those years. In Tables 24.2 and 24.3 , where we seek to describe regional and global poverty aggregates, the poverty measures are lined up in time for each of a set of ‘reference years’ using the interpolation method described in Chen and Ravallion (2004a) .

We will focus on the most common poverty measure, namely the headcount index ( H ), which gives the proportion of the country's population that lives in households with per capita incomes below the poverty line. Other measures are the poverty gap index ( PG ), which gives the average shortfall of income from that line, where the average is taken over the entire population (with the gap set to zero for incomes higher than the poverty line); the squared poverty gap index (Foster et al ., 1984 ); and the Watts (1968) index. The latter two measures penalize inequalityamongst the poor, and so are better at picking up differences in the severity of poverty. PovcalNet provides all these measures. In some of the discussion, we also multiply H by the country's population, to yield the absolute number of poor people.

Table 24.1 presents the two inequality measures (Gini and MLD) and H for the two poverty lines for every country for which we have household-survey data. 13 Wherever possible, we present results for two periods: (i) the 1990s (centered on 1994), and (ii) the 2000s (centered on 2004). Since most surveys have less-than-annual frequency and since countries field their surveys on different schedules, for each country we use the survey nearest to the two period centers, and indicate the year in the table.

The range of inequality measures across the 130 countries in Table 24.1 is very large indeed. The Gini index ranges from 0.20 in the Slovak Republic, to 0.74 in Namibia. The MLD ranges from 0.12 in Hungary to 0.71 in Bolivia using data for the 2000s; using data for the 1990s, the range is from 0.07 in the Slovak Republic to 1.13 in Namibia. In terms of country groupings, the high-income economies (including the OECD) and Eastern Europe and Central Asia (ECA) record the lowest inequality measures, and Sub-Saharan Africa (SSA) and Latin America and the Caribbean (LAC) have the highest. The predominance of measures using income, rather than consumption, in LAC is a contributing factor to the high-inequality measures for that region. The high level of inequality in SSA thus deserves special mention, as many of the indices refer to distributions of consumption expenditures. The commonly held view that LAC is unambiguously the most unequal region in the world needs to be qualified accordingly.

Income levels and inequality around the world

Figure 24.1 plots inequality (measured by the latest available Gini coefficient) against GDP per capita for each country listed in Table 24.1. The figure reveals a negative correlation between inequality and mean incomes (measured by GDP per capita). The correlation coefficient is -0.44 (statistically significant at the 1% level). In addition, the variance of inequality is higher among poorer countries, but much smaller among richer ones. Above $20,000 per capita per annum, all Gini indices lie in the relatively narrow interval of (0.25, 0.45). The implication is that no country has successfully developed beyond middle-income status while retaining a very high level of inequality in income or consumption. High inequality (a Gini above 0.5, say) is a feature of underdevelopment. We do not explore the difficult issue of causality here: is it that high-inequality prevents growth, or is it that growth tends to reduce inequality? These issues are the subject of a large literature, which is summarized in Chapter 22 We simply note the significant negative correlation in levels, and that very high levels of inequality are not observed among rich countries in the present-day cross-section.

In terms of changes over time, there is no universal or common trend in inequality between the 1990s and 2000s. Out of the 49 countries in Table 24.1 that have inequality measures for both periods, 30 (29) record an increase in the Gini (MLD 14 ) index, 13 (16) record declines, and in 6 (3) countries there has been little or no change, which we (somewhat arbitrarily) define as being in the range (−2.5%, 2.5%). These numbers are consistent with the evidence of rising within-country inequality discussed in Chapter 23 , but we caution against over-interpreting results in a selected sample of some 50 countries for which data were available on both periods.

Income levels and poverty around the world

The situation is somewhat different with regard to poverty: there is even greater variation in levels, the correlation with mean incomes is more pronounced, and there is a clearer pattern in the recent changes. Two important facts can be gleaned from Figure 24.2 , which plots H (for the $1-a-day threshold) against GDP per capita. The first is that absolute poverty incidence decreases markedly with mean income, as one would expect. The simple correlation coefficient is −0.57 and statistically significant at the 1% level. Above a GNP per capita of approximately $15,000 p.a., this extreme kind of absolute poverty essentially vanishes. 15 In fact, dollar-a-day poverty is not even estimated for the high-income countries listed in Table 24.1 , and they are not included in Figure 24.2. The second fact is that this relationship between mean income and poverty is not statistically ‘tight’. The points in Figure 24.2 do not lie neatly along a specific curve or line. Below a per capita GDP of around $12,000, there is considerable variation in the incidence of extreme poverty for each level of mean income. In fact, at around $2,000, one can find countries with the same per capita income levels reporting poverty rates in a range from zero to 65%. Latent country-level heterogeneity may well be confounding the ability to detect the true relationship; we will return to this point. However, as we will see in the next section, this heterogeneity in poverty levels conditional on mean incomes has a lot to do with between-country differences in the level of inequality.

To look at poverty trends over time, we resort to a longer time series than the one presented in Table 24.1 Chen and Ravallion 2007 compile poverty time-series indicators for 560 surveys from 100 countries (essentially the same sample of countries used by PovcalNet ). Since poverty incidence at the $1-a-day threshold is effectively zero in high-income economies (which accounts for the main differences between the PovcalNet data set and that presented in Table 24.1 ) we restrict our attention to the Chen-Ravallion sample of countries.

Tables 24.2 and 24.3 present the world and regional average poverty levels, both as incidence ( H ) and in absolute numbers of the poor for selected reference years spanning 1981–2004. Table 24.2 uses the $1-a-day poverty line, while Table 24.3 uses the $2-a-day line. There is clear evidence of a decline in absolute poverty in the developing world over the last quarter-century. The incidence of $1-a-day poverty, as a proportion of the developing world's population, fell from 40% in 1981 to 18% in 2004. By 2004, the developing world as a whole was only four percentage points short of attaining MDG1 (a poverty rate of 14.3% by 2015). The corresponding proportions for the total population of the world are 34% and 15%, assuming that nobody lives below $1 a day in the high-income countries. Although the rapid reduction of poverty in China (from 63% to 10%) accounts for much of this global decline, there has clearly been progress elsewhere too: global poverty incidence excluding China falls from 31% to 21% over 1981–2004.

The rates of poverty reduction have been quite disparate in different countries. If one partitions the country sample into the broad regions defined by the World Bank, we see clear heterogeneity in poverty reduction across regions (Table 24.2 ). The most pronounced decline was registered in East Asia (from 58% to 9%). South Asia came second, with a fall from 50% to 31%. At the other end of the spectrum, poverty incidence actually rose in ECA during the period of transition from socialism to market economies, though showing encouraging signs of progress since the late 1990s. In Sub-Saharan Africa, poverty was essentially the same in 2004 and 1981, having first grown during the 1980s, and then declined slowly since the late 1990s. Such a small decline in poverty rates, combined with a growing population, translates into a rise in the absolute number of people living in households below the $1-a-day poverty line, as can be seen from panel (b) in Table 24.2 . In fact, the number of poor people rose not only in Africa and Eastern Europe and Central Asia, but also in Latin America, where economic stagnation and persistent inequality in the last decades prevented substantial progress against poverty. These regional trends in poverty reduction are summarized in Figure 24.3 , which is also taken from Chen and Ravallion 2007 . The dominant role of poverty reduction in East Asia is immediately apparent.

Trends in the incidence of absolute poverty in less developed countries, by region

Note : For region identifiers see Table 24.2.

Trends are somewhat more muted for the $2-a-day poverty line. Global incidence in the developing world fell from 67% to 48% (59% to 52% if China is excluded). Poverty also fell markedly in the Middle East and North Africa (MENA), and South Asia, but doubled in ECA. Because of population growth, the absolute number of poor people (under $2-a-day) rose in every region other than East Asia. Given a very substantial decline in East Asia, the world total grew only slightly, from 2.45 billion to 2.55 billion. This is in contrast to a decline in the absolute number of poor (under $1-a-day), from 1.47 billion to 0.97 billion in the same period. See Tables 24.2 and 24.3 . 16

The 1993 PPP exchange rates on which these calculations were based are known to have a number of problems. In particular, the two most populous countries, China and India, did not participate in the 1993 price surveys, so their PPPs are subject to larger margins of error. This will be corrected in the 2005 PPPs, in which both countries participated. The preliminary release of the new estimates at the time of writing indicates higher price levels in both China and India than implied by the 1993 PPPs, so the poverty rates in these two countries will rise relative to the rest of the developing world. Aggregate poverty counts will then rise, although the rates of aggregate progress over time will actually be higher than implied by Tables 24.2 and 24.3 , given that India and (especially) China had high rates of poverty reduction over time. (Note that, while the new PPPs change the level comparisons, the real growth rates in a given country are unaffected.)

2. Global Inequality

If constructing internationally comparable poverty measures is harder than computing comparable inequality measures (because the latter are scale-, and thus exchange-rate-invariant), aggregation into a single global measure is more difficult for inequality than for poverty. Standard poverty measures are immediately decomposable by population subgroups and, therefore, easy to aggregate up from subgroups. The numbers of poor can simply be added across countries, while poverty incidences and poverty gaps are first weighted by the country's population share and then summed. This simple procedure underlies the global poverty incidence and the global absolute numbers of the poor that are reported in the previous section.

The analogous procedure for inequality indices is more involved for two reasons. First, it has to contend with the fact that global inequality is not merely an aggregation of within-country inequalities. It also contains a component that corresponds to inequality between countries. Second, once the world is treated as a single entity, with a well-defined distribution of living standards, then the scale in which each individual national distribution is expressed matters again. While PPP exchange rate calculations are not needed if one simply wants to compare national levels of inequality, they are crucial for the construction of a global inequality index.

By ‘global inequality’ we shall mean inequality amongst all people of the world, ignoring where they live. This is calculated by combining the surveys from all the different countries (at the appropriate PPP exchange rates) into a single world distribution of income, and then computing inequality indices for this distribution. As long as the inequality index is additively decomposable (such as MLD), it will be possible to separate this overall measure into a component corresponding to inequality between countries, and one that aggregates the inequality within all the different countries. Only recently have household surveys been available for a sufficient number of countries for this approach to be feasible. Since then, this approach has become dominant among researchers interested in global interpersonal inequality—for the simple reason that it does not ignore inequality within countries.

The earlier literature contains two (simpler) approaches to measuring overall inequality in the world. The first takes each country as the relevant unit of observation, and computes inequality between these ‘country means’. This is what Milanovic 2005 calls Concept 1 inequality, and what World Bank (2005) calls inter-country inequality. Second, it is possible to take account of different population sizes by weighting each country mean by its share of world population—giving Milanovic's Concept 2 inequality, or what World Bank (2005) calls international inequality. Both of these approaches are unsatisfactory since they ignore inequality within countries, and capture only the between-country differences.

In the last few years, a number of studies have sought to quantify global inequality, and to investigate its dynamics. One of the most ambitious was a paper by Bourguignon and Morrisson 2002 , who constructed a time series of world inequality estimates for the period from 1820 to 1992. For all but the last 10 to 20 years of that series, disaggregated household survey data are not available for many countries. The authors thus grouped countries into 33 ‘blocs’, the composition of which changed over time, depending on data availability (see Bourguignon and Morrisson, 2002 , for details). The distributions are constructed in such a manner that all the members of a ‘bloc’ are assumed to have the same distribution as a country for which data are actually available in the relevant time period. The authors construct a distribution based on decile (and some ventile) shares, and on GDP per capita figures. Individuals are assumed to have the same incomes within 10ths (or 20ths) of the distribution, where that income corresponds to the group's share of GDP per capita. This set of strong assumptions allowed the authors to construct a long time series covering most of the 19th and 20th centuries. 17

The main finding of the study is that world inequality rose almost continuously from the onset of the industrial revolution until the First World War. During that period, the world's Gini index rose from 0.50 to 0.61. Although inequality was also rising within most countries for which data were available, the real driving force for this increase in global disparity was inequality between countries, that is, international inequality (see Figure 24.4 ).

Global inequality and its components, 1820–1992

Between the two world wars, and until around 1950, a decline in within-country inequality was observed, but the rise in inequality across countries continued apace and proved to be the dominant force. 18 The world Gini index rose further to 0.64. From the middle of the 20th century onwards, the rise of global inequality slowed, as Japan and parts of East Asia started growing faster than Europe and North America. This process became particularly pronounced after the take-off of China in the 1980s. Broadly speaking, global inequality changes in the second half of the last century are much less significant than in the 130 years that preceded it: there was certainly a reduction in the rate of growth of inequality and, towards the end of the period, the level actually started to decline.

When considering the last decades of the 20th century, however, better and more comprehensive data are available, enabling researchers to work with approximations to the world income distribution based on (and only on) fully disaggregated household surveys. Looking at the second half of the century with these new data, three interesting regularities emerge. First, even as (unweighted) inter-country inequality continued to grow between 1950 and 2000, international inequality (when population-weighted) began to fall. The disparate behavior in these two inequality concepts has been one of the reasons behind the discordant discourse on globalization and inequality. The continuing rise in inter-country inequality (to which Pritchett, 1997 , refers as ‘divergence, big time’) was due largely to slow growth in most poor (and small) countries, relative to some middle-income and richer countries. The decline in international inequality, which refers to a population-weighted distribution, was due fundamentally to rapid growth in two large nations that started out very poor: China and, to a lesser extent, India. As Figure 24.5 suggests, once China and India are excluded from the international distribution, the post-1980 trend in that inequality concept changes dramatically, and becomes much closer to the rising trend in inter-country inequality.

Inter-country inequality and international inequality, 1950–2000

The second regularity is that the last two decades in the 20th century saw resumption in the upward trajectory of aggregate within-country inequality, defined as the contribution of within-country inequality to total inequality. The rise in within-country inequality prevented the decline in international inequality (which began, slowly, around the 1960s) from translating immediately into a decline in global inequality. Recall that global inequality is the sum of (appropriately aggregated) within-country inequality and international inequality. Indeed, Milanovic ( 2005   2002 ) finds that global income inequality between people was still rising between 1988 and 1993, but appears to have fallen between 1993 and 1998. This is confirmed by World Bank (2005) , which extends Milanovic's data set by a couple of years, and is consistent with the findings reported in Chapter 23 .

The third regularity is that there are signs of inequality convergence over time, whereby inequality has a tendency to rise in low-inequality countries, and fall in high-inequality ones. This was first noticed by Bénabou (1996) , although his tests did not deal with the concern that the signs of convergence may stem solely from measurement error. Subsequent tests by Ravallion 2003 indicate that convergence is still evident when one uses better data and an econometric method that allows for classical measurement errors in the inequality data.

Bénabou interprets inequality convergence as an implication of a neoclassical growth model. Ravallion points instead to an explanation in terms of the policy and institutional convergence that has occurred in the world since about 1990. Low-inequality socialist economies have become more market-oriented, which has increased inequality. On the other hand, non-socialist economies have adopted market-friendly reforms. In some of these economies pre-reform controls benefited the rich, keeping inequality high (Brazil is an example), while in others the controls had the opposite effect, keeping inequality low (India is an example). Thus liberalizing economic policy reforms can entail sizeable redistribution between the poor and the rich, but in opposite directions in the two groups of countries. However, as Ravallion also notes, the process of convergence toward medium inequality implied by his finding is not particularly rapid, and it should not be forgotten that there are deviations from these trends, both over time and across countries.

The foregoing discussion has been about relative inequality. What about the competing concept of absolute inequality, which depends on the absolute gaps in levels of living between the ‘rich’ and the ‘poor’? 19 As Figure 24.6 shows, the two concepts give rise to completely different trends for international inequality: whereas relative inequality measures (such as the Gini and the MLD) fall from around 1980 onwards, absolute measures record substantial increases. 20 This figure is drawn for (population-weighted) international inequality, but the difference is as important when considering global inequality.

Although this chapter (and the broader debate) has focused on income inequality and poverty trends, there should be no presumption that it is the only inequality that matters. Indeed, from some perspectives, international disparities in health status and educational achievement may matter inherently just as much (in addition to being instrumentally important in shaping income inequality and poverty). Since around 1930 there has been convergence in the inter-country and international distributions of life expectancy at birth (LEB). As (weighted) mean world LEB rose from 53.4 years in 1960 to 64.8 years in 2000, its distribution moved from bimodality to unimodality and the coefficient of variation fell from 0.233 to 0.194 (World Bank, 2005 ). This heartening trend was partly reversed, however, during the 1990s, when LEB fell precipitously in some of the world's poorest countries, due largely to the spread of HIV/AIDS. 21

Absolute and relative inequality in the world, 1970–2000

Educational inequality, measured for the distribution of years of schooling, has also fallen substantially over the last four decades or so. As mean years of schooling in the world rose from 3.4 in 1960 to 6.3 in 2000, the coefficient of variation fell from 0.739 to 0.461. (Note that inequality measures for variables like life expectancy or years of education have to be interpreted with care, as both variables are effectively bounded from above.) This pattern of rising means and falling inequality in attainment was common to all regions of the world and, in addition, all regions also saw a reduction in gender disparities, as measured by the male to female schooling ratio (World Bank, 2005 ). 22

Unfortunately, this reduction in attainment inequality has not always meant a reduction in the disparities in true educational achievement . Indeed, internationally comparable test score data suggest that these disparities remain strikingly large with, for example, the reading competence of the average Indonesian student in 2001 being equivalent to that of a student in the 7th percentile of the French distribution.

These changes in the distribution of health and education should be taken into account when assessing global inequality in a broad sense. While this chapter provides only a very brief summary of the existing evidence along each dimension, a number of scholars have attempted to explore the correlations among the different dimensions. Because increases in longevity have been greater in poorer countries, for instance, Becker et al . (2005) argue that inequality in measures of well-being that account for the quantity, as well as quality, of life have been declining throughout the post-war period.

3. The Growth-Poverty-Inequality Triangle

Given the negative correlation between mean incomes and inequality levels across countries that is illustrated in Figure 24.1 , it is not surprising that there is an even stronger correlation between mean incomes and poverty rates. Given the mathematical relationship that must always hold between mean income, poverty, and inequality, the first correlation more or less automatically implies the second. To see why, we can assume (without loss of generality) that the shape of the Lorenz curve can be fully captured by a vector of (functional form) parameters π, such that L ( p, π ) is the share of consumption (or income) held by the poorest p proportionof the population, ranked by household consumption per person. It is well known that the slope of the Lorenz curve L ( p, π ) with respect to p (denoted L p ( p, π )) is simply the ratio of the quantile function ( y ( p )) to the mean μ. 23 By evaluating that derivative at p = H , we can write the following equation for the headcount index of poverty, given a poverty line z :

Equation (1) is an identity that relates the incidence of poverty at any given (real) poverty line to two aspects of the distribution: the mean μ and inequality or, more precisely, the Lorenz curve. From (1) it can be seen that the partial derivative of poverty with respect to the mean (holding the Lorenz curve parameters fixed) is always negative so that, if the poverty line is fixed and inequality is constant, poverty must fall as the mean rises. 24 In the scatter-plot of Figure 24.2 , the poverty line is the same across all countries. If Lorenz curves did not differ systematically with GDP per capita, poverty should be lower as GDP rises. This association is only strengthened by the negative correlation between GDP and inequality levels in the cross-section: higher income levels are associated with lower poverty both because of the direct effect of a higher mean at a given Lorenz curve, and because there exists an inverse empirical relationship between income levels and inequality. 25

But the cross-country correlation between mean incomes and inequality need not be informative of the growth process of a particular country, since there may well be country-specific idiosyncrasies that cloud temporal patterns in the cross-section. So, what happens to inequality as a particular country grows over time? The first careful attempt to answer that question, by Simon Kuznets 1955 , has become so influential that it still guides a great deal of thinking on the topic. Building on the Lewis 1954 model of development as a transfer of resources from a low-productivity, low-inequality sector (say, traditional agriculture) to a higher-productivity, higher-inequality sector (say, manufacturing or modern commercial agriculture), Kuznets hypothesized that inequality would rise during an initial phase of the process (as labor begins to move across sectors), and then eventually decline (as most workers are already in the modern sector, and the intersectoral gap loses significance). Kuznets found empirical support for this inverted-U inequality trajectory in the data he had available at the time, for the USA, England, and Germany. Some cross-sectional studies have found evidence consistent with an inverted-U relationship between inequality and mean income, and there is a hint of this relationship in Figure 24.1 . 26

As data on changes in inequality over time have accumulated for many more countries, however, it has become apparent that the inverted-U relationship hypothesized by Kuznets does not hold in general. It does not hold systematically for individual countries for which there are long time series of inequality measures. Bruno et al . (1998) compiled time-series data on inequality measures amongst growing developing countries and found almost no cases that conformed to the prediction of the Kuznets Hypothesis. And its ‘dynamic version’, which postulates a relationship between rates of GDP growth and changes in inequality, does not seem to hold on average either. Using all countries in the PovcalNet data set for which there are more than one survey, Ravallion 2007 plots proportional changes in the income Gini against proportional changes in mean income for 290 observations, representing 80 countries. (This can be thought of as a re-estimation of the relationship in Figure 24.1 in which we restrict the sample to developing countries and allow for the existence of country-level fixed effects, potentially correlated with mean income.) A small negative correlation (r = −0.15) is found in the data, which is insignificant at the 10% level. Among growing economies, inequality tends to rise as often as it falls. 27 Thus we have:

Stylized Fact 1: Economic growth tends to be distribution-neutral on average in developing countries, in that inequality increases about as often as it decreases in growing economies .

Growth in poverty headcount against growth in survey mean consumption or income in less developed countries, 1981–2004

It is not then surprising that there is a strong correlation between growth rates and changes in absolute poverty. This is evident in Figure 24.7 , which plots the proportionate changes in the poverty rate (using the $1-a-day line) against the growth rates in the survey mean; the correlation coefficient is −0.44 and the regression coefficient is −1.76 with a White standard error of 0.24; n = 290 after trimming likely outliers due to measurement error. Thus we have: 28

Stylized Fact 2: Measures of absolute poverty tend to fall with economic growth in developing countries .

In discussing Figure 24.2 we had noted that, although there is a clear negative correlation between GDP per capita and poverty levels, there is also considerable heterogeneity around the average relationship. Figure 24.7 shows that a similar relationship holds after we take proportional differences: growth in GDP is strongly associated with poverty reduction, but there is considerable variation in the size of the effect. An illustration is provided by Ravallion (2001) , who estimated a regression coefficient on a scatter-plot very much like that in Figure 24.7 . The 95% confidence interval on that coefficient implies that a 2% rate of growth in mean income (which is about the average rate for developing countries in the 1980s and 1990s) will bring anything from a 1% to a 7% annual decline in poverty incidence.

Why are there such large differences across countries (and time periods) in the impact of growth on poverty? Given equation (1), it is unsurprising that the answer has to do with inequality. Interestingly, though, it has to do both with the initial level of inequality (i.e. how unequal a country is before a given growth spell) and with changes in that level (i.e. on the ‘incidence’ of economic growth). Taking the differential of equation (1) yields two terms, 29 one of which accounts for the impact of changes in the mean (i.e. growth), holding the initial distribution constant, while the other captures the change in the distribution (i.e. the Lorenz curve), holding the mean constant:

The first term is the growth component of poverty reduction, while the second term is the distributional component (the weighted sum of all changes in the distributional parameters). 30 Given the convexity of the Lorenz curve, equation (2) shows that the partial growth elasticity of poverty reduction

is always negative. This result conforms to intuition: holding the poverty line and the Lorenz curve constant, poverty must fall when the mean rises. But the sign of the second term is ambiguous, since it depends on the marginal change in the Lorenz curve—in other words, it depends on the incidence of economic growth: on how the new income from growth is distributed.

The two ways in which inequality affects the impact of growth on poverty can be seen clearly in equation (2). First, initial inequality reduces the growth component of poverty reduction (in absolute value), because

tends to be higher in more unequal distributions. This stands to reason: the growth component captures how a given amount of growth would affect poverty if there were no change in the Lorenz curve. In other words: how it would affect poverty if the gains from growth were distributed proportionately to existing household incomes. Clearly, the more unequal the original distribution, the smaller the share of the growth accruing to the poor, and the lower the poverty reduction arising from that given growth; this was first demonstrated empirically by Ravallion (1997b) . 31

Empirical growth elasticities of poverty reduction against initial Gini index: less developed countries in 1981–2004

Figure 24.8 , which is also taken from Ravallion 2007 , plots the total growth elasticity of poverty reduction against initial inequality, for a sample of countries during 1981–2005, when poverty is defined by the $1-a-day line. 32 It can be seen that the average empirical (total) elasticity is higher (in absolute value) the lower the initial inequality. The correlation coefficient of 0.26 is statistically significant at the 1% level. Whereas the elasticity averaged −4 for countries with Gini indices in the mid-20s, it was very close to zero for countries with a Gini index of about 0.60. To illustrate the important role played by initial inequality, Ravallion 2007 uses a parsimonious parametric model, based on essentially the same data, to simulate the rate of poverty reduction with a 2% rate of growth and a headcount index of 40%. In a low-inequality country—a Gini index of 0.30 (say)—the headcount index will be halved in 11 years. In a high-inequality country—a Gini index of 0.60 (say)—it will take about 35 years to halve the initial poverty rate. 33

A second mechanism through which inequality affects the impact of growth on poverty is through changes in inequality during the growth process. If the aggregate changes in the Lorenz curve in the second term on the right-hand side of equation (2) are poverty increasing then the effect of growth on poverty will be less than the partial effect, holding distribution constant. Figure 24.8 also suggests that changes in initial inequality have considerable empirical importance, since this (and measurement error) accounts for the spread around the regression line.

We can summarize these observations as:

Stylized Fact 3: The higher the initial level of inequality in a country or the greater the increase in inequality during the growth spell, the higher the rate of growth that is needed to achieve any given (proportionate) rate of poverty reduction .

We can thus sum up the analysis of the empirical inter-relationships between growth, poverty, and inequality as follows. Despite some evidence that this might be changing in the 1990s, the balance of the evidence for the last quarter-century suggests that there is no systematic empirical relationship between economic growth rates and changes in inequality (Stylized Fact 1). Given the relationship that must hold between poverty, inequality, and mean income in levels, Stylized Fact 1 implies that there must be a negative correlation between changes in poverty incidence and economic growth. This is indeed the case empirically: growth is good for the poor (Stylized Fact 2). But the relationship between mean income and poverty is mediated by the Lorenz curve, so that the power of growth to reduce poverty depends on inequality. In fact, that power tends to decline both with the initial level of inequality, and with increases in inequality during the growth process (Stylized Fact 3).

4. Exploring the Economics behind these Stylized Facts

How can we go beyond the mathematical relationship between mean income, poverty, and inequality to gain a deeper understanding of the economic forces behind changes in inequality and poverty, and their relationship with aggregate growth? In this section, we review some of the insights from three branches of the literature that has tried to explore these determinants.

The first branch seeks to exploit spatial variation in the geographic and sectoral patterns of growth and in initial demographic and distributional conditions within countries to shed light on what makes growth more or less ‘pro-poor’, that is, to examine its incidence within a country. Datt and Ravallion 1998 and Ravallion and Datt 2002 for India, Ravallion and Chen 2007 and Montalvo and Ravallion (2008) for China, Ravallion and Lokshin 2007 for Indonesia and Ferreira et al . (2007) for Brazil all follow this approach. In essence, these studies computea panel of poverty rates across states (or provinces) and over time, and regress the changes against sector-specific rates of growth in each spatial unit. Control variables typically include differences in initial conditions across states, including pre-sample differences in land or income inequality, literacy, and the like. There may also be time-varying state-level controls, such as changes in various types of public spending in each state.

These studies require relatively long series of repeated cross-section household surveys, and are easiest to conduct in large countries, where spatially disaggregated subsamples retain statistical representativeness. Looking across the studies carried out so far, a few lessons emerge. First, the sectoral composition of growth does seem to matter for poverty reduction. In all four countries, the growth elasticities of poverty reduction varied substantially and significantly across sectors. But the relative sector ranking varied across countries: agricultural growth was by far the most effective in reducing poverty in China, while growth in the services sector had a higher impact on poverty in Brazil and India. In these three countries, the effect of manufacturing growth on poverty reduction seemed to vary significantly across states, suggesting that diverse geographic, distributional, or institutional conditions can affect the growth elasticity of poverty reduction, even within a single country.

It was generally found that less ‘initial’ (i.e. pre-sample) inequality was associated with a greater effectiveness of growth in reducing poverty (as the previous section would suggest). Greater literacy and better initial health conditions (often measured inversely by infant mortality rates) also help make growth more poverty-reducing. In India, about half of the range in long-term rates of poverty reduction across India's states (between the best performer, Kerala, and the worst one, Bihar) can be attributed to the difference in initial literacy rates (Datt and Ravallion, 1998 ). The elasticity of poverty to non-farm economic growth in India was particularly sensitive to differences in human resource development (Ravallion and Datt, 2002 ). In Brazil, one interesting finding was that a greater level of voice or ‘empowerment’—proxied by the rate of unionization more than 10 years before the sample started—also raised the elasticity of poverty reduction with respect to growth (in manufacturing).

Other policies can also affect the pattern of distributional change (and thus of poverty reduction), even after one controls for differences in the pattern of growth. A repeated finding is that higher rates of inflation result in lower rates of poverty reduction (in Brazil, China, and India). The Brazilian case study revealed two important changes in the policy environment which contributed to greater success against poverty: a dramatic reduction in the country's previously massive rate of inflation (in 1994), and a substantial increase in the amount of social security and social assistance payments, accompanied by some improvements in targeting, during the period 1988–2004.

A second branch of literature is even more micro-oriented, and takes the individual household, rather than a state or province, as the unit of observation. This approach is exemplified by the various chapters in Bourguignon et al . (2005) and can be thought of as a set of statistical decompositions of the growth incidence curve , as given by g (p) = d ln y (p) (where it will be recalled that y (p) is the quantile function). 34   g (p) is the income growth rate at percentile p of the distribution (for example, g (0.5) is the growth rate of the median income). In these studies, a small set of models for key economic relationships—such as earnings regressions, participation equations, or education demand functions—is estimated for both the initial and terminal years of the period under study. Then various counterfactual income distributions can be simulated by importing sets of parameters from either date into the corresponding models for the other date. The spirit of the exercise follows that of Oaxaca 1973 and Blinder 1973 and the results, like the original Blinder—Oaxaca decomposition, are best interpreted as a statistical decomposition of changes in the distribution, rather than as measures of causal effects.

Nevertheless, some of the empirical regularities arising from the studies of Latin America and East Asia in Bourguignon et al . (2005) are quite interesting. First, the increase in the returns to schooling that accompanied rapid growth in countries like Taiwan (China) or Indonesia tended to contribute to increases in inequality. This effect was also present in countries that grew less rapidly, like Mexico, and is reminiscent of the so-called ‘Tinbergen Race’ between increases in the demand for schooling (arising from technological progress) and the rising supply of skilled workers (brought about by expansions in the educational system). In most countries in the sample, the demand side dominated, leading to increased earnings inequality; the only exceptions were Brazil and Colombia.

Greater earnings inequality often led to higher inequality in household incomes, but not always. An interesting example is provided by Taiwan, where a marked increase in labor force participation by women led to a divergence between the earnings and income distributions. While the entry of relatively skilled women into the labor force reduced earnings inequality (as they entered roughly in the middle of the distribution), it contributed to an increase in the dispersion of household incomes: most of these new workers were married to skilled men, and lived in households that were already relatively well-off. The importance of changes in labor force participation and occupational structure is not an isolated characteristic of the Taiwanese experience. In Brazil, too, between 1976 and 1996, a substantial increase in extreme poverty was associated primarily with an increase in unemployment, informality, and underemployment. In Indonesia, a large share of the overall increase in inequality was associated with large movements of labor away from wage employment (in agriculture) towards (predominantly urban) self-employment.

This approach also illustrates the ambiguous effect of rising levels of education on inequality. In Colombia, Indonesia, and Mexico, substantial increases in the average level of schooling of the population did not lead to lower inequality. On the contrary, when one controls for the changes in returns, it seemed to be associated with higher inequality levels. This result was due to two effects: increases in the education stock that raised inequality in educational attainment itself (i.e. where most of the increase is accounted for by rises among the better-educated), but also the fact that when returns to education are convex, even a distribution-neutral increase in schooling can lead to higher earnings inequality. Of course, educational expansions can offset this effect if they lower returns to schooling, but this is less likely to happen in countries experiencing sharp increases in demand for skills.

By its very nature, this generalized Blinder-Oaxaca approach is, in isolation, incapable of attributing the causal origin of any of these changes to specific exogenous or policy shocks. This is particularly true when broad policy changes, such as a large-scale liberalization of trade, or a permanent change in the exchange rate, are expected to have substantial general equilibrium effects, affecting many variables at the same time. Wide-ranging changes in tariffs, for instance, can affect the distribution of income or consumption through changes in consumer prices, changes in relative wage rates, and changes in employment levels across industries. All of these variables will be changing in the micro-simulations that generate counterfactual growth incidence curves, but which share of the changes is due to the trade liberalization policy is anyone's guess.

To address this point, a third branch of the literature has sought to combine macroeconomic or general equilibrium models with micro-simulations on household survey data. Examples include Bourguignon et al . (2002) for the Indonesian crisis, Chen and Ravallion (2004b) for China's accession to the WTO, and Ferreira et al . (2004) for Brazil's devaluation in 1998–9. These models are still in theirearly, experimental phase, and are subject to the usual criticisms leveled against computable general equilibrium models (CGEs). Nevertheless, when the model is run on a single household survey, and its predictions are checked against a separate, ex-post survey (as in the case of Brazil), its distributional prediction performance is superior to those of the previous generation of representative-agent CGEs. 35

A common finding in these exercises concerns the importance of worker and employment flows across sectors, in response to shocks or policy changes that affect relative prices. Developing country labor markets are often de facto very flexible (despite sometimes significant de jure rigidities), because of the existence of large informal sectors. When relative goods prices change in response to a change in the exchange rate (as in Brazil, in 1998) or policy change (as in China's accession to the WTO), different industries contract and expand in response, and workers move across these sectors.

5. Conclusions

Absolute poverty is clearly a bigger problem in developing countries—where over four-fifths of the world's population lives—than in developed ones. Virtually all of the one billion people subsisting on per capita incomes less than $1 per day live in developing countries. Perhaps more surprisingly, inequality is also a bigger problem in developing countries. Looking at the world as a whole, there is a clear negative correlation between average levels of inequality and the level of development, and all countries with really high income inequality—a Gini index of (say) 0.50 or higher—are developing economies.

However, the evidence from the available cross-section of developing countries suggests that there is little aggregate tendency for these inequality levels to fall with economic growth. Although there are no developed countries today with inequality levels above a Gini index of 0.50, growth rates among developing countries are virtually uncorrelated with changes in inequality levels. This is our first stylized fact.

The absence of a robust cross-country correlation between changes in inequality and growth necessarily implies that there must be a strong negative correlation between growth and changes in poverty. This is confirmed empirically: on average, economies that grow faster reduce absolute poverty much more rapidly—our second stylized fact.

But this does not mean that policymakers in developing countries can ignore inequality. There are a number of reasons why persistently high inequality is a concern. Two primary reasons were not discussed here, namely the fact that higher inequality may be ethically objectionable in its own right, and the possibility that greater inequality may generate certain inefficiencies that could actually reduce the future rate of economic growth. World Bank (2005) contains summary discussions of both points; on the second also see Chapter 22 . In this chapter, we have focused on a third reason why persistent inequality may be undesirable in developing economies: the fact that, even for a given growth rate, inequality tends to reduce the growth elasticity of poverty reduction—our third stylized fact. Other things equal, one percentage point of growth leads to a smaller reduction in poverty in a very unequal country than in a less unequal one. And if inequality rises during the growth process, things are worse yet.

While these three stylized facts can be identified from a macro, cross-country perspective, an understanding of the economic factors behind changes in distribution (or behind the levels and incidence of growth) in developing countries requires a more microeconomic approach which exploits differences in conditions within countries. Changes in income distribution respond to so many different stimuli—in a general equilibrium environment—that no single method has yet been developed to fully identify the causes of all observed changes. Instead, researchers have relied on a variety of different approaches. Sub-national regression analysis (using geographical panel data) sheds light on the relative importance of sectoral growth patterns, and of initial differences in the distribution of land or human capital. Micro-simulation-based decompositions of growth incidence curves can help us understand the relative roles of changes in household endowments; changes in returns to those endowments; and changes in participation and occupational choices. Finally, combining such micro-simulations with models capable of capturing the general equilibrium transmission of initial shocks can help us understand the distributional impact of broad, economy-wide policy changes.

As we move forward, more research is needed on all of these fronts, and in their integration. It is only from such research that we can hope to learn what enables some countries (such as Vietnam) to grow rapidly with little or no rise in inequality, and thus to enjoy dramatic rates of poverty reduction. The diversity of country experience has established that equitable growth is possible, and that it is particularly pro-poor. But much remains to be learned about both the general economic conditions and the policy context within which it is achievable.

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We are grateful to the editors (Brian Nolan, Wiemer Salverda, and Tim Smeeding) and to Branko Milanovic, Berk Ozler, and participants in a symposium at the Russell-Sage Foundation in New York City for helpful comments on an earlier version. Thanks are also due to François Bourguignon, Shaohua Chen, and Branko Milanovic for kindly allowing us to draw on earlier work that we (either one or both of us) co-authored with them in the past, as well as to Phillippe Leite and Prem Sangraula for able assistance and very useful suggestions. However, we alone are responsible for any errors. Furthermore, the opinions expressed in this chapter are those of the authors, and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.

See 〈 http://iResearch.worldbank.org/PovcalNet/jsp/index.jsp 〉.

The Human Development Index (HDI) is a well-known example of how one can construct an aggregate index that combines income and certain ‘non-income’ dimensions of welfare. The HDI does not directly reflect inequality within countries and also imposes some questionable aggregation conditions (including trade-offs); for further discussion see Ravallion (1997a) . Grimm et al. (2006) provide an ambitious attempt to differentiate the HDI by income groups.

It is sometimes claimed that this argument carries less weight in developed countries, but for a counter-argument see Slesnick 1998 .

There tend to be more people dissaving than saving at the bottom of the income distribution, and more people saving than dissaving at the top.

See Lanjouw and Ravallion 1995 .

See Coulter et al . (1992) and Chapter 3 of this Handbook.

Absolute inequality measures, which may well be relevant for the discussion of global trends, are scale-sensitive. We return to absolute measures of inequality in Section 2 below.

See e.g. Sala-i-Martin 2006 .

See e.g. Banerjee and Piketty 2005 and Korinek et al . (2006) .

See Chen and Ravallion 2001 for a detailed description of how this line was constructed.

For further discussion of the difference between these two methods and the bearing on poverty measurement see Ackland et al . (2006) .

An extended version of Table 24.1 is available from the authors giving PG for both poverty lines.

Forty-nine countries report Gini coefficients in both periods. Forty-eight report MLDs in both periods.

Which may explain why researchers looking at developed countries tend to be more concerned with inequality than with poverty and, even when addressing the latter, usually rely on alternative concepts of poverty, such as relative poverty, social exclusion (see Chapter 13 ), or ‘low pay’ (see Chapter 11 ).

For a more detailed discussion, including their recent estimates when accounting for cost-of-living differences between rural and urban areas, see Chen and Ravallion 2007 .

Given the long-run perspective of this exercise, however, it is likely that some of the problems associated with using means from the National Accounts had only limited importance. In particular, the estimated evolution of GDP per capita over such a long period is likely to be very strongly correlated with any measure of household welfare.

The increase in inter-country inequality between 1914 and 1950 took place during each of the two world wars, and most markedly during the Second World War. The inter-war period properly defined (1919–39) actually saw a reduction in inter-country inequality. On the association between wars and rising international inequality, and between crises and its decline, both during this period and in 1890–5, see Milanovic 2006 .

For further discussion of the role played by the concept of absolute inequality in debates about the distributional impacts of economic growth and trade openness see Ravallion 2004 .

Although we include only two relative and one absolute measure, the opposing trends between relative and absolute measures over this period are robust to the choice of index. See Atkinson and Brandolini 2004 .

See Deaton 2003 on the relationship between health outcomes and inequality more broadly.

See also Castello and Domenech 2002 on international inequality in education.

The quantile function is the inverse of the cumulative distribution function, p = F (y) .

This is a general result because the Lorenz curve is always (by construction) an increasing and convex function of the percentiles of the income distribution.

It is interesting to note that the negative correlation between GDP and inequality levels is much weaker if the sample is restricted to developing countries only.

Following the most common specification in the literature on testing the Kuznets Hypothesis, we regressed the Gini index on a quadratic function of log GDP per capita using the data in Figure 24.1. The coefficient on log GDP was positive and that on its squared value was negative, and both coefficients were significant at the 1% level. The turning point was within the range of the data.

Among economies experiencing contractions during the spells used by Ravallion 2007 , inequality increases are somewhat more frequent than inequality reductions.

This second stylized fact was noted by Ravallion 1995 , Ravallion and Chen 1997 , Fields 2001 , Dollar and Kraay 2002 amongst others.

This is true if we hold the poverty line constant in real terms. If that is allowed to change over time (giving a relative poverty measure), there will be a third term for the change in the poverty line.

For further discussion of this decomposition see Datt and Ravallion 1992 and Kakwani 1993 .

For an update see Ravallion 2007 .

Period elasticities are smoothed by taking the simple average over two contiguous spells, and 15 extreme elasticities (lower than −20 or above +20) are excluded.

The opposite also holds: high inequality protects the poor from the adverse impact of aggregate economic contraction. For example, high-inequality districts of Indonesia experienced less dramatic rates of increase in poverty during the 1998 financial crisis than did low-inequality districts (Ravallion and Lokshin, 2007 ).

On the properties of the growth incidence curve see Ravallion and Chen 2003 . When making distributional comparisons over time, the growth incidence curve can be calculated from any two cross-sectional surveys (which do not need to be panel surveys, given the usual anonymity assumption). Alternatively, one of the two quantile functions can be a counterfactual distribution. It can also be shown that the changes in most commonly used poverty and inequality measures can also be written as functionals of the corresponding growth incidence curve, usually with weights that can be interpreted as the sensitivity of the particular measure to changes in the distribution at each percentile. This is particularly simple for the Watts index of poverty; it can be readily shown that the change in this index is given by the area under the growth incidence curve up to the headcount index of poverty (Ravallion and Chen, 2003 ).

An intermediate approach seeks to identify the causal effects of policy changes econometrically, and then estimate their share within the different components of a micro-simulation-based decomposition. Ferreira et al . (2007) regress changes in wages and employment levels disaggregated by sectors on (arguably exogenous) changes in tariffs and exchange rates. These trade-mandated changes are then used to generate counterfactual growth incidence curves which can be interpreted alongside other micro-simulation results.

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• 1 0000000404811396 https://isni.org/isni/0000000404811396 International Monetary Fund

Contributor Notes

Is there a tradeoff between raising growth and reducing inequality and poverty? This paper reviews the theoretical and empirical literature on the complex links between growth, inequality, and poverty, with causation going in both directions. The evidence suggests that growth can be effective in reducing poverty, but its impact on inequality is ambiguous and depends on the underlying sources of growth. The impact of poverty and inequality on growth is likewise ambiguous, as several channels mediate the relationship. But most plausible mechanisms suggest that poverty and inequality reduce growth, at least in the long run. Policies play a role in shaping these relationships and those designed to improve equality of opportunity can simultaneously improve inclusiveness and growth.

• I. Introduction

The most commonly used measure of a country’s economic activity and the overall well-being is gross domestic product (GDP). It gauges the magnitude of economic production, which in turn affects the payments to factors of production such as capital and labor. GDP growth is therefore an estimate of how the aggregate income of a country increases over time. A country’s aggregate income, in turn, provides resources that can increase the incomes of families and individuals. 2 Given these relationships, economists have long been concerned about explaining the determinants of economic growth and formulating policies to elevate it.

But whether economic growth is sufficient to improve the welfare of every individual depends on how the benefits of growth are spread across the society. If all individuals benefit proportionately, then studying growth through the device of a “representative agent” would be sufficient to determine the economic forces at work and the policy options needed to improve welfare of each individual. However , if growth does not raise everyone’s incomes proportionately, then an analysis of the economic welfare of an individual requires studying aggregate economic growth in conjunction with the distribution of income within the economy. 3

So, what is the relationship between growth and measures of the inclusion of individuals in the economy and society, such as inequality and poverty? Does growth help pull people out of poverty? And how does growth affect inequality, if at all? What about the reverse relationship: that is, how do poverty and inequality affect growth?

This paper studies the nexus of growth, poverty, and inequality, seeking answers to these questions. The relationship between inequality and economic activity has been a subject of interest throughout the history of economic thought. In the Wealth of Nations , Adam Smith (1776) noted that wealth inequality could lead to social unrest and that the government had a role in protecting property rights and preventing the poor from seizing the property of the rich. From a different perspective, in the mid-nineteenth century, Karl Marx saw capitalism as exacerbating inequality, making capital owners richer and workers poorer over time. He thought that this polarization of income could lead to a revolution, where a communist system eventually would replace capitalism ( Marx 1867 ). The complex relationship between income distribution and growth has continued to receive attention from many other economists, including the seminal works of Simon Kuznets (1955) and Nicholas Kaldor (1957) . Furthermore, the study of inequality and growth has been facilitated by developments in data collection on poverty, wealth, and labor market conditions. For instance, Charles Booth (1891) , in Life and Labour of the People in London , published maps describing wealth and poverty levels street by street in the city of London. About the same time in the United States, Carroll Wright, the first US Commissioner of Labor, was a pioneer in the collection of labor market statistics. He initiated the collection of data on wages and labor conditions of women and also published studies describing how the adoption of new machinery affected wages and employment. These advances in data collection continued over the twentieth century and made it possible to conduct a systematic analysis on the links between growth and inclusiveness.

Multiple channels link growth to inclusion and inclusion to growth, making it difficult to determine causation. Moreover, many factors affect growth and inclusion simultaneously. Compounding these issues, data on poverty and inequality have been difficult to compile, are collected and measured infrequently, and are often unreliable. Estimates are sensitive to assumptions on factors such as capital gains and untaxed income ( Cerra et al. 2021 , Chapter 1) and alternative measures may show different trends ( Blotevogel et al. 2020 ). Empirical studies, especially those exploring the link between growth and inequality, sometimes find inconsistent results, no doubt due to these multiple channels, endogenous relationships, and poor data quality. As a starting point, the next section presents key stylized facts and trends of inequality, poverty, and economic growth across different world regions and over time. Sections III and IV then discuss the channels linking the variables on this nexus, drawing on the theoretical and empirical literature. Section V concludes with the key takeaways and policy implications.

• II. Trends in Inequality, Poverty, and Growth

Market-based income inequality has risen steadily in advanced economies and some large emerging market economies. Figure 1 shows the evolution across country groups of income inequality, measured by the Gini coefficients for market-based income (before taxes and transfers) and disposable income (after taxes and transfers). The key distinctive feature of the evolution of income inequality has been the large and sustained increase in the market-based

Inequality across Country Groups, 1980s–2010s

(Market and Disposable Income Gini Coefficients)

Citation: IMF Working Papers 2021, 068; 10.5089/9781513572666.001.A001

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Gini coefficient in advanced economies in each decade from the 1980s through the 2010s. 4 In contrast, income inequality for emerging market and developing economies (EMDEs) as a group has been broadly unchanged since the 1980s. 5 As a result of these contrasting trends, market inequality in advanced economies has surpassed that of EMDEs, on average, in recent decades, from a lower relative level in the 1980s ( Table 1 ). Despite the relatively stable trend for EMDEs, some of the largest emerging market countries—notably China, Russia , India, South Africa, and Indonesia—have experienced increasing market inequality ( Table 1 ). In addition, inequality varies considerably more across emerging markets and low-income countries—especially the former, where outliers range from a low Gini coefficient in the range 20 to 30 to nearly 70 ( Figure 2 , left panel). The variation in inequality across countries is especially pronounced when comparing the ratio of income of the top decile relative to the bottom decile of each country’s income distribution (right panel). For emerging markets and low-income countries, the ratio exceeds 20 for several countries.

Inequality and Poverty in the 2010s Compared to the 1980s, Selected Countries and Country Groups

Indicators of Inequality across Country Groups, 2000s and 2010s

Fiscal redistribution through taxes and transfers reduces income inequality, especially in advanced economies. The disposable income (or net) Gini coefficient (after taxes and transfers) drops to an average of 30 points from nearly 50 points for advanced economies, bringing net inequality much below that of other income groups. In contrast, redistribution is very limited in emerging markets and low-income countries, where the tax base and resources available for redistribution tend to be much smaller than in advanced economies.

Poverty rates are low in advanced economies and have been declining in developing countries from a high level. Figure 3 illustrates the dynamics of the poverty rate, measured as the fraction of the population that earns less than $3.20 a day in purchasing power parity (PPP) terms. Not surprisingly, the poverty rate in advanced economies has been low and stable during the sample period (top left panel), given that most people in those countries have an income level substantially higher than the poverty threshold ( Table 1 ). Most of the dynamics in poverty reduction since the 1970s has been concentrated in emerging markets and low-income countries (top right and bottom left panels), with emerging markets experiencing the largest reduction in poverty rates.

Poverty across Country Groups, 1980s–2010s

(percent of population)

While GDP per capita growth in advanced economies has been slowing down every decade since the 1980s, growth has accelerated in emerging markets and low-income countries, particularly since the 2000s ( Duttagupta and Narita 2017 ). 6 Figure 4 shows recent trends in GDP per capita growth across different groups of countries. Globalization allowed a large pool of the workforce in emerging markets and low-income countries to participate in the global markets through international trade, which arguably increased growth and reduced poverty rates (top right and bottom left panels) ( Dollar and Kraay 2004 ). During the same period, advanced economies experienced a slowdown in GDP per capita growth rates, which worsened in the 2010s as a consequence of the global financial crisis (top left panel). Some of the long-term structural factors that might be behind the slowdown in per capita income growth are related to aging ( Bloom, Canning, and Fink 2010 ) and a generalized slowdown in productivity growth ( Gordon 2018 ).

Average Growth in GDP per capita across Country Groups, 1980s–2010s

With these facts and trends on inequality, poverty, and growth examined, the rest of the paper will comprehensively review the multiple dimensions through which inclusiveness and growth are related.

III. How Does Growth Affect Poverty and Inequality?

• A. Empirical Estimates of the Impact of Growth on Poverty and Inequality

The impact of growth on poverty and inequality depends on how income growth at each percentile of the distribution compares with average income (GDP) growth. Figure 5 shows that the income of the poor is strongly correlated with GDP per capita, both in levels (top left panel) and in growth rates (middle left panel). This clearly illustrates the adage that a “rising tide lifts all boats,” in the sense that when average GDP per capita rises, income in the lowest decile also increases and poverty falls.

Relationships among GDP per capita, Growth, Inequality, and Poverty

The poverty-reducing effect of growth has been corroborated in several studies. Dollar and Kraay (2002) investigate the systematic relationship between economic growth and poverty reduction for a sample of 92 countries from 1950 to 1999. These authors find a robust pattern across countries where the share of income of the first quintile of the population varies proportionally to average incomes. They uncover a strong and positive relationship between these two variables, with a correlation coefficient that is not statistically different from one. Dollar and Kraay also evaluate the extent to which policies and institutions that have been identified in the literature as promoting growth can play a role in reducing poverty by increasing the share of income of the poorest quantile. The main conclusion of this analysis is that growth-enhancing policies and institutions do benefit the poor and the rest of the society in equal proportions.

Building on this work, using data from a panel of 80 countries, Kraay (2006) decomposes the changes in absolute poverty into three potential sources: the growth rate of average income; the sensitivity of poverty to growth; and a poverty-reducing pattern of growth (changes in relative income). In the short term, growth in average income accounts for 70 percent of the variation in poverty changes, while in the long term, it accounts for 97 percent. This study reemphasizes that growth-enhancing policies and institutions are central to alleviating poverty.

Dollar, Kleineberg, and Kraay (2016) update their analysis on the systematic relationship between average growth and growth of the poorest groups, examining 151 countries from 1967 to 2011. Similar to the result in Dollar and Kraay (2002) , they find that the income in the poorest deciles varies in equal proportions with average incomes ( Figure 5 , bottom left panel). They also find that on average, the shares of income accruing to the poorest 20th percentile and 40th percentile are fairly stable over time. These results emphasize the idea that policies aimed directly at increasing economic growth rates are indeed “pro-poor,” in the sense that they lift the average income in the lowest deciles of the income distribution.

More recent literature has corroborated the importance of economic growth in reducing poverty. Analyzing the dynamics of the extreme poverty rate (PPP $1.90 per day poverty line) in 135 countries from 1974 to 2018, Bergstrom (2020) finds that 90 percent of the variation of poverty rates can be explained by changes in GDP per capita, while much of the rest is accounted for by changes in inequality. 7 At the same time, a 1 percent decline in inequality (measured as the standard deviation of log income) reduces poverty more than a 1 percent increase in GDP per capita for most countries in the sample. These results are reconciled by the fact that changes in mean growth have been substantially larger than observed changes in inequality. The study confirms that although growth has been the dominant force in poverty reduction, reductions in inequality have great potential in reducing poverty rates.

While both economic growth and inequality have an impact on social welfare, growth has been the dominant force. Dollar, Kleineberg, and Kraay (2015) construct social welfare functions that are sensitive to the bottom deciles, where welfare depends positively on income growth and negatively on inequality. Focusing on five decades of data for 151 countries, they find that most of the variation in welfare across countries is driven by the average growth of income. The role played by inequality is relatively minor—again because changes in inequality have been small and generally uncorrelated with growth. These results imply that policies aimed at reducing inequality will improve welfare as long as they are not detrimental to growth but may reduce social welfare if they reduce growth. Complementary results from Jones and Klenow (2016) show that GDP per capita is a good indicator of welfare for most countries, as these two variables have a correlation of 0.98. Moreover, they find that welfare inequality is greater than income inequality across countries. The mortality rate is the most important factor driving the dispersion in welfare.

In contrast to poverty, there is no significant systematic relationship between a country’s income level and its market inequality ( Figure 5 , top right panel). The simple cross-country evidence is not consistent with the Kuznets curve model that postulates an inverse U-shaped relationship between development and inequality. 8 Likewise, per capita GDP growth is uncorrelated with contemporaneous changes in inequality, measured in the middle right panel of Figure 5 by the market Gini coefficient. The same lack of correlation is observed if inequality is measured by the change in the income ratio of the top to bottom deciles (not shown). Part of the explanation for the weak correlation between growth and inequality lies in the strong correlation between per capita GDP growth and each of the income deciles. As shown in the bottom right panel of Figure 5 , the correlation coefficient ranges between 0.6 to nearly 1.0. In addition, the change in inequality depends on the relative growth in incomes in each decile across the distribution, called the “growth incidence curve” (as discussed in Cerra et al. 2021 , Chapter 1). For the sample of all countries, the income of the bottom and top deciles grew slightly faster than middle deciles over 1993–2008. Fast growth of the bottom would decrease inequality, while fast growth at the top would increase it, for an ambiguous overall impact.

In short, the impact of growth on poverty and inequality depends on how growth is distributed across the rich and poor. The discussion that follows describes the various channels by which growth can result in differential income growth rates for different socioeconomic groups.

B. Channels from Growth to Poverty and Inequality

• 1. The Neoclassical Growth Model

What does growth theory predict for the impact of growth on inclusion? The standard workhorse theory is the neoclassical growth model ( Solow 1956 ), in which output is a function Y=F(A,K,L) of factors of production, including capital (K), labor (L), and total factor productivity or TFP (A). Investment leads to capital accumulation, which increases the marginal product of labor and the wage paid to workers. In addition, growth arising from increases in TFP raises the marginal products of both capital and labor and therefore the income payments that they receive. Higher investment and/or higher technological progress imply higher production and higher incomes for everyone in the economy. In addition, because of diminishing returns to capital, capital-po or countries are expected to grow faster and eventually converge to capital-rich countries.

This simple model has been the cornerstone of much of growth theory. Given its one-sector structure in which both capital owners and workers benefit from growth, the policy implication is to focus on improving incentives for investment for economies to grow and converge more quickly to the (higher-than-initial) steady state capital stock. The model does not account for any heterogeneity in capital ownership and labor supply within a country but predicts a decline in global poverty and inequality as poor countries catch up. Implicitly, this analytical framework is centered on aggregate growth, rather than on distributional issues.

Drawing on the neoclassical framework, Hausmann, Rodrik, and Velasco (2005) develop a general framework, “growth diagnostics,” designed to inform policymakers on how to prioritize growth policies in a context of multiple distortions by targeting the most binding constraints. As in the neoclassical framework, with its emphasis on investment, economic growth depends on three elements: the returns to capital accumulation, their private appropriability, and the cost of financing capital investment. Distortions that can lower the return on capital include high taxes or expropriation risk, large negative externalities, low productivity, or insufficient investment in infrastructure or human capital. Distortions that increase the cost of financing investment include underdeveloped domestic financial markets due to lack of banking competition or a poor regulatory framework, and impediments to international financing due to high country-risk premium, excessive regulation of the capital account, or external debt vulnerabilities. However, the growth diagnostics analysis relies on a representative agent approach, which, like the Solow model, does not illuminate the distributional impacts of growth policies.

The basic neoclassical paradigm features a number of assumptions including: no government sector activities and redistribution; fully employed factors; a fixed and undifferentiated supply of labor; a competitive market structure; and balanced growth (no differential growth across sectors/industries/regions/firms, and so on). Relaxing each of these assumptions creates channels through which growth can have distributional effects, including for inequality and poverty. Each channel is considered in turn next.

• 2. The Government: Public Goods and Redistribution

Public goods and services

Growth increases aggregate resources, including the tax base and the public sector’s capacity to collect taxes. A higher tax ratio facilitates the provision of public goods such as health and education that can be pro-poor. The extent to which growth leads to an expansion of pro-poor public services depends on the society’s preferences for private versus public goods and the composition of public goods. As shown in Figure 6 , it is an empirical regularity that as countries become richer, the government is capable of raising more fiscal revenue and increase the capacity of providing public goods. This stylized fact is better known as the Wagner’s Law ( Wagner 1893 ) and captures a channel through which growth leads to an increase in the size of the government, which can reduce poverty and improve the income distribution provided spending is efficient and its composition benefits the poor.

Tax Revenues and Spending on Health and Education, by Country Group

(percent of GDP, 2010–19 average)

Redistribution

As with public goods, the impact of growth on poverty and inequality through redistribution depends on social preferences. If poverty and inequality are considered social ills, people may be willing to “purchase” reductions in poverty and inequality through redistribution policies as overall incomes rise (that is, poverty and inequality reduction function as “normal goods,” in which demand increases with income). Indeed, cross-country evidence shows that higher-income countries engage in more redistribution than developing countries ( Figure 7 ), where redistribution is measured as the difference between the Gini before and after taxes and transfers. But the composition and incidence of taxes and transfers is important. For example, developing countries have high energy subsidies. This policy may be intended to support the poor, but instead largely benefits the rich who spend more on energy products (see Cerra et al. 2021 , Chapter 12 and 13 for elaboration on taxation and spending policies).

Income Redistribution by Country Group, 1980s–2010s

(difference in Gini points before and after taxes and transfers)

• 3. Factors and Markets

Employment of factors

In the short and medium term, factors of production such as labor and capital are not necessarily fully employed. Recessions resulting from a variety of shocks, including financial distress and pandemics, can reduce long-term output ( Cerra, Fatás, and Saxena 2020) and generate large spikes in unemployment and inequality and declines in capacity utilization ( Heathcote, Perri, and Violante 2020 ). Unemployment creates income losses in the short term, especially for those in lower-income groups such as people with lower educational attainment, ethnic minorities, and women ( Hoynes, Miller, and Schaller 2012 ). Unemployment often results in scarring effects on incomes over the longer term. As shown by von Wachter, Song, and Manchester (2009) , 15 to 20 years after a layoff, earnings can be depressed by as much as 20 percent, as workers’ skill set becomes outdated and they lose skills that are specific to the jobs lost in a specific industry. As described in Okun’s law (discussed in Cerra et al. 2021 , Chapter 3), unemployment varies inversely with cyclical growth ( Ball, Leigh, and Loungani 2017 ). Higher growth generates employment, which improves inclusion. In general, economic volatility is associated with both lower growth and higher inequality ( Cerra et al. 2021 , Chapter 11).

Another reason for unemployed or underemployed factors could be poverty traps that entail the inability of low-income individuals to pay any fixed costs of education, move to a booming region, or obtain collateral to obtain credit. Such individuals can be excluded from more remunerative productive activities or remain unable to meet a threshold of productivity. Those stuck in a poverty trap may not be able to benefit from growth in the absence of government intervention such as the provision of microcredit (see Banerjee et al. 2019 ).

Labor supply response

Growth that generates higher returns to labor would induce more work effort. If leisure is a normal good, then higher-income people would increase their work less than low-income people. Bick, Fuchs-Schündein, and Lagakos (2018) show empirically that this is the case across countries, where the average adult worker in a low-income country works 50 percent more hours than the adult workers in high-income countries. Moreover, within countries, on average, the number of hours worked decreases with the level of wages. The exception to these stylized facts occurs in very high -income countries, including the United States, where the number of hours increases with the wage rate.

Growth also leads to demographic changes, notably a decline in the number of children and investment in the upbringing of children (through parental efforts to educate them). Growth may induce women to enter the labor force, raising family incomes (and reducing poverty if women of poor families did not previously work outside the home). Becker (1992) analyses the interaction between fertility and growth. His economic framework shows how economic growth can result in a lower fertility rate, which reduces the labor supply and thus increases the return to labor.

Differentiated labor

Labor is not homogeneous in practice. Educational attainment and skills vary across individuals. Technological progress has generally been more complementary to skilled and educated workers than to the unskilled and uneducated, leading to a higher demand for the former and a reduction in the demand for the latter. As a result of economic growth associated with skilled-biased technological change, the rising wage skill premium has increased inequality of labor income ( Krusell et al. 2000 ).

In the United States, the observed increase in wage inequality since the 1980s can be attributed, at least partially, to the increase of the wage premium of college education. Autor (2014) and Autor, Katz, and Kearney (2008) show that the college wage premium roughly doubled between 1980 and 2012 for both male and female workers, in part due to skill-biased technological change that increased the demand for college-educated workers. 9 The relationship between growth and inequality through skill-biased technical change is not necessarily linear. Since the late 1980s, skill-biased technological change has led to job market polarization due to an increased demand for skilled and unskilled workers at expense of middle-class jobs, as new technologies are capable of performing routine tasks traditionally done by middle-wage workers ( Goldin and Katz 2007 ).

Analyzing cross-country evidence, Brueckner, Dabla Norris, and Gradstein (2015) find that national income and inequality are positively related, with education as a possible channel. For a sample of 80 countries, the authors use two instruments for within-country variation of real GDP per capita, including international oil price fluctuations and countries’ trade-weighted world income. The instrumental variables regressions show that, on average, a 1 percent increase in real GDP per capita reduces the Gini coefficient by around 0.08 percentage points. However, the importance of national income in explaining inequality is significantly reduced when education proxies are introduced, making education a probable channel.

Market structure

Contrary to the assumptions in the Solow neoclassical growth model, many industries do not have perfectly competitive market structures. Natural monopolies, policy-induced monopolies, or industries supported by rents (particularly in the natural resource sectors) lead to high returns to owners without a commensurate rise in payments to labor. Returns to certain factors— entrepreneurship, capital, land, and resource ownership—rise faster than returns to labor (especially unskilled labor). Scale of market can be important—bigger markets provide higher returns to owners if competition can be avoided. There can also be network effects (such as in high-tech and communications sectors) and tournament effects (for instance, the best sport star earns much more than the second best; singers/actors benefit more from brand in large markets).

Diez, Leigh, and Tambunlertchai (2018) document that a generalized increase in market concentration (associated with higher markups) occurred across advanced economies and across industries. At high levels of markups and profitability, an increase in market concentration leads to lower investment and lower wages, which directly influences the income distribution and growth. De Loecker and Eeckhout (2018) also analyze the global evolution of market power from 1980 to 2016, based on data from Worldscope covering more than 60,000 firms located in 134 countries. They corroborate that the recent trend of rising markups and market power has been predominantly concentrated in advanced economies, while markups in most emerging economies have been either stable or declining.

For the United States, De Loecker, Eeckhout, and Unger (2020) show that markups nearly tripled between 1980 and 2016, increasing from 21 percent above marginal cost to 61 percent. The rise in markups was greatest for firms in the upper tail of the distribution: that is, with markups that were already high compared to the average. Those firms expanded at the expense of firms with low markups. This rise in markups can account for recent macroeconomic trends such as the secular decline in labor shares and the wage reduction of low-skilled workers. For a cost-minimizing firm, the labor share is inversely related to the markup. Greater market power also implies fewer firms, lower output, and reduced aggregate demand for labor, negatively affecting real wages and income inequality. Autor et al. (2020) also analyze the consequences of firm size on the labor market share by developing a framework for superstar firms characterized by a “winner takes most” feature. They provide evidence for the United States that industries that exhibited the largest increase in market concentration have also experienced larger declines in the labor market share. Cerra et al. (2021 , Chapter 6) discusses the role market structure plays in shaping inclusive growth in more detail.

• 4. Unbalanced Growth

For a variety of reasons, different sectors, industries, regions, and firms may grow at different rates. Many of the sources of growth, including technology and trade, could improve growth in some economic sectors more than in others. Uneven growth produces uneven returns. When some sectors boom but others lag, growth is not likely to raise incomes proportionately. Payments to factors may fall in some cases. As some industries emerge and others disappear in a process of “creative destruction” ( Schumpeter 1942 ), some workers could be displaced or face stagnant wages. In addition, pecuniary externalities can cause an increase in market prices, such as housing rents, that may reduce real incomes of poor. 10

Economic development may entail unbalanced growth that affects inequality. For example, Kuznets (1955) postulated that inequality evolves as an inverted “U” shape function where inequality initially increases and eventually declines. In the initial stages of development, some workers migrate from rural agriculture to the fast-growing urban manufacturing sector. Workers in the manufacturing sector experience an increase in income, while the ones staying in the traditional sector remain with low wages, resulting in higher income inequality. As a larger share of workers shift to the manufacturing sector, inequality eventually declines at later stages of development.

Sectoral Composition

Empirical studies confirm that the sectoral composition of growth is important in determining poverty reduction. Loayza and Raddatz (2010) study a cross-section of 55 developing countries and find that growth in sectors that rely more intensively on unskilled labor have the greatest contribution to reducing poverty rates. The empirical results show that agriculture is the most effective poverty-reducing sector, followed by construction and manufacturing. Mining, utilities, and services do not have a statistically significant impact on poverty alleviation. These results highlight that in some countries, growth might be insufficient to reduce poverty if it is concentrated in sectors that are not intensive in unskilled labor, such as oil and mining.

Studies conducted for individual countries support the results of Loayza and Raddatz (2010) . Ravallion and Datt (1996) find that for India in the second half of the 20 th century, growth in agriculture and services was correlated with declines in poverty in both rural and urban areas, while industrial growth did not have a systematic impact on poverty. Ravallion and Chen (2007) find that agriculture growth was the most important driver for poverty alleviation in China. For Indonesia, Suryahadi, Suryadarma, and Sumarto (2009) find that growth in the service sector was strongly correlated with poverty reduction in rural and urban areas, while agriculture growth was correlated with poverty declines in rural areas. Ivanic and Martin (2018) find that in poor countries, productivity gains in agriculture are generally—although not always—more effective in reducing global poverty than the productivity gains in industry or services of equivalent size. However, the effectiveness of the former fades as average income rises.

Capital intensity

If growth is generated in sectors that are intensive in capital or innovative skill, such growth could provide higher returns to capital and entrepreneurs than to labor. Indeed, in recent years, the labor share of output across advanced and emerging market economies has fallen as a result of capital deepening and technological progress ( Dao, Das, and Koczan 2019 ). Moreover, Piketty (2015) finds that the return on capital is higher than the growth rate of GDP in many country episodes, leading to higher inequality, as capital owners tend to be at the top of the income distribution. Using historical data from the United States and Europe, Piketty provides evidence that the difference between the return to capital (r) and the growth rate of GDP (g) has the effect of amplifying wealth inequality over time. Sin c e wealth is highly concentrated at the top of the income distribution, the high return to capital relative to GDP growth increases the ratio of wealth to GDP, increasing the extent of inequality.

However, even if the driving sector is capital-intensive, it could have positive spillovers to the poor, provided it simulates enough growth in more labor-intensive sectors. Conversely, under some circumstances, strong productivity growth in labor-intensive agriculture could reduce demand for rural labor, thereby increasing poverty and the number of urban unemployed.

Technology and innovation

The prospect of obtaining rents from new products drives innovation, and innovation contributes to growth. The rents created by successful innovations lead to a rising share of the top 1 percent of the distribution. However, innovations appear to have limited impact on inequality in the bottom 99 percent of the population, and there is some evidence that innovation is positively correlated with social mobility ( Aghion et. al. 2019 ). This may be consistent with the findings of Galor and Tsiddon (1997) . They distinguish between “invention,” which they assume draws on ability and leads to higher inequality and higher intergenerational mobility, versus a more accessible category of “innovation,” which they model as depending on human capital correlated with parental human capital, and which thus leads to lower inequality but also lower intergenerational mobility.

The empirical evidence shows that investment in new technologies—such as information and communication technologies (ICT)—has important effects on the income distribution. Relying on a sample of 11 member-countries of the Organisation for Economic Co-operation and Development (OECD) from 1980 to 2004, Michaels, Natraj, and Van Reenen (2014) find that industries that experienced the highest growth in the use of ICT technologies increased the demand for highly educated workers (such as physicians or engineers) at the expense of middle-educated workers (such as administrative or clerical occupations). The demand for low-skilled workers was not affected, since many of the tasks performed by these workers (such as janitors or farmworkers) are difficult to replace with new technologies. As a result, investment in ICT results in polarization of labor markets across OECD economies, as tasks of middle-educated workers are replaced by new technologies. ICT could also increase the bargaining power of large, financially strong and politically influential entities that are capable of collecting, storing and analyzing large amounts of individual data, to the detriment of individuals and smaller enterprises, raising inequality.

More recently, Graetz and Michaels (2018) study the impact of the adoption of robots across industries in 17 OECD countries from 1993 to 2007. As opposed to new ICT technologies, robots can perform a wide array of repetitive tasks typically done by low-skilled workers, such as wielding, painting, or packaging, with very little human intervention. The increased use of robots contributed to an increase in labor productivity and average wages and a decline in output prices that benefited consumers but reduced the employment shares of low-skilled workers. For th e US labor markets, Acemoglu and Restrepo (2020) find that adopting robots has led to higher productivity gains, but lower aggregate employment and wages. The authors estimate that, on average, one robot displaces three workers, even after accounting for the positive effects via higher productivity and lower output prices. For the French manufacturing sector, Aghion et al. (2020) find net positive effects from automation technologies (including the adoption of robots) on employment, including of unskilled workers, and no discernible impact on wages. Cerra et al. (2021 , Chapters 3 and 5) look into the links between technology, labor markets, and inequality in more detail.

The simplest framework for understanding the impact of trade liberalization on inequality is the Stolper-Samuleson theorem ( Stolper and Samuelson 1941 ) derived in the context of the Hecksher-Ohlin model of trade. In this framework of two countries, two goods, and two factors, a reduction of tariffs in a developing country abundant in unskilled labor will lead to an increase in exports of the good that uses labor intensively and higher labor compensation of unskilled workers in that country. Conversely, opening up to trade leads to higher imports of products from developed countries that use skills or capital intensively and a reduction in wages for high-skilled workers in the importing country. For developed countries that are abundant in skilled labor, the reverse will be true: trade liberalization will reduce the wages of unskilled workers relative to skilled ones. Consequently, trade liberalization will lead to lower inequality in developing countries and higher inequality in advanced economies. In practice, however, the skill premium, or the gap between the wages of skilled and unskilled workers, has increased in both advanced and developing countries, mainly due to skill-biased technological change (see Cerra et al. 2021 , Chapter 7). This suggests that additional factors besides trade might be playing a role in driving inequality.

Financial liberalization

Financial globalization can also influence income distribution through different channels ( Cerra et al. 2021 , Chapter 8). For instance, foreign direct investment (FDI) typically flows to high -skilled sectors of the host economy ( Cragg and Epelbaum 1996 ), which might raise the skill premium and increase inequality in that country. The impact of other capital f lows (portfolio debt and equity flows) in principle can have an ambiguous impact on inequality. Some authors argue that higher global financial integration can improve financial intermediation and help the poor by providing funds that can be used to accumulate human and physical capital. On the other hand, capital account liberalization might increase the frequency of financial crises ( Kaminsky and Reinhart 1999 ). Governments may also increase debt following financial market integration ( Azzimonti, de Francisco, and Quadrini 2014 ), raising the likelihood of a debt crisis. Financial and debt crises often lead to severe recessions that disproportionately affect the poor and raise inequality ( Cerra et al. 2021 , Chapter 11). The quality of institutions might also shape the direction in which financial flows influence income distribution. With strong institutions, financial flows might be channeled to the most productive uses and also would allow the poor to smooth consumption to better insure themselves against macroeconomic volatility. On the other hand, with weak institutions, those well connected to financial institutions might have disproportionate access to the financial flows to the detriment of the poor, which can exacerbate inequality. 11

• 5. Empirical Estimates of Multiple Drivers of Growth and Inequality

Various empirical studies have estimated the impact of several factors mentioned above that concurrently affect growth and inequality. For instance, Jaumotte, Lall, and Papageorgiou (20 13) focus on two important drivers of economic growth in recent decades—technological change and globalization— and evaluate their joint impact on inequality. Relying on a panel data set of 51 countries covering 1981 to 2003, they find that technological change has a greater impact on income inequality than globalization does. The overall impact of globalization on inequality is limited, reflecting two off setting effects. Trade globalization reduces inequality by raising the income of the bottom four quintiles, while financial globalization—manifested through an expansion in FDI flows—increases inequality. Technological innovation is the key channel increasing inequality: it increases the demand for skilled workers and the returns to capital, and disproportionally boosts the income in the top quintile of the income distribution. The authors also find that an increase in access to education could offset the negative effects of technological change and financial globalization, thus reducing inequality.

More recently, Furceri and Ostry (2019) have corroborated the different roles of technological change and globalization in driving inequality. Using model-averaging techniques in a sample of 108 countries covering the more recent period of 1980 to 2013, they find econometric results consistent with Jaumotte, Lall, and Papageorgiou (2013) : namely , that financial globalization and technological improvements contribute to a rise in inequality while trade globalization is associated with lower inequality, especially in developing countries. 12

IV. How Does Poverty and Inequality Affect Growth?

A. empirical estimates of the impact of poverty and inequality on growth.

• 1. From Poverty to Growth

The empirical evidence shows that poverty is detrimental to long-term economic growth. Using panel data of 85 countries covering 1960 to 2000, López and Servén (2015) find that a 10 percentage-point increase in the poverty rate reduces the GDP per capita growth rate by 1 percentage point. In particular, an increase in the poverty rate reduces the investment rate for countries with low levels of financial development. There is also evidence that the negative impact of poverty on growth depends on the initial level of poverty. In a sample of 156 countries covering 1960 to 2010, Marrero and Servén (2018) find that for low levels of poverty (below the median), poverty has an insignificant impact on growth ( Figure 8 ). In contrast, when the poverty rate is high, a 10 percentage-point decrease in headcount poverty is associated with an increase in economic growth ranging from 1 to 2 percent per year.

Growth in GDP per capita vs Initial Poverty, 1960–2010

Related evidence comes from the observation that despite the global reduction in poverty rates, cross-country evidence indicates a lack of convergence in poverty rates. Studying 90 developing countries during the 1991–2004 period, Ravallion (2012) finds that two distinctive effects prevented the convergence of poverty rates. First, poverty reduces growth , consistent with the results from López and Servén (2015) . Second, high initial poverty dulls the impact of growth in reducing poverty. The combination of these two channels makes it more difficult for the poorest countries to reduce their poverty rates.

• 2. From Inequality to Growth

As an illustration of the relationship from inequality to growth, Bénabou (1996) compares the growth outcomes of East Asian and Latin America economies conditional on the initial levels of income inequality. According to Bénabou (1996) , the conventional wisdom among development economists is that the relatively equal distribution of income and land in East Asian economies contributed to their observed high economic growth rates. By the same token, the lack of a similar economic dynamism in Latin America has been attributed to the consequences of high concentration of wealth and income in that region. 13

The left panel of Figure 9 reports the correlation between income inequality in 1980 and the average GDP per capita growth in the subsequent 30 years for selected Latin American and Asian economies. Consistent with Bénabou (1996) , on average countries that exhibited lower levels of initial inequality also experienced higher rates of economic growth. While there are many other factors that might explain the economic dynamism of these Asian economies, such as the quality of institutions and high rates of saving and investment ( Collins and Bosworth 1996 ), this figure illustrates that income distribution might be one key element for understanding differences in economic performance. An extended sample of advanced and developing countries (right panel) confirms the relationship between initial income inequality and subsequent growth. 14

Growth in GDP per capita vs Initial Inequality

The empirical relationship between inequality and growth has been investigated formally in a number of cross-country growth studies, following Barro and Sala-i-Martin (1995) . Many of these studies find that inequality, typically measured by a Gini coefficient, enters with a negative and statistically significant sign in cross-country growth regressions, indicating that an increase in inequality leads to lower economic growth. In a survey of 23 different empirical studies on inequality and growth, for instance, Bénabou (1996) finds that despite differences in data sets, sample periods, and measures of income distribution, the studies consistently find that initial inequality is negatively associated with growth. In particular, the quantitative effects of inequality are quite robust across studies: a one-standard-deviation decrease inequality raises the annual growth of GDP in the range of 0.5 percentage points to 0.8 percentage points.

Various studies examine different dimensions of the relationship. An early work by Alesina and Rodrik (1994) finds that income and land inequality are statistically significant variables that decrease long-term growth in a sample of 70 advanced and developing countries. Perotti (1996) finds a negative and robust association between inequality, inversely related to the share of the middle class (third and fourth quantiles of the income distribution), and growth. He finds that social political instability and fertility rates could be driving the relationship between inequality and growth.

The impact of inequality on growth can also depend on the initial level of development. Barro (2000) estimates the impact of inequality on growth by splitting a sample of 100 countries into high – and low-income samples. In that specification, there is a negative relationship between inequality and growth for poor countries, similar to previous studies, while the relationship is positive for richer countries. The empirical results suggest that in the presence of credit constraints, inequality prevents low-income households from accumulating human and physical capital, resulting in lower growth in poor countries. On the other hand, the positive relationship observed in richer economies is consistent with the traditional growth-enhancing effects of inequality emphasized by Kaldor (1957) .

The effects of inequality on output might also differ across economic sectors. For instance, Erman and te Kaat (2019) identify the effect of inequality on industry-level value added growth. The authors use a data set that includes 22 industries in 86 countries for the period between 1980 and 2012. They find that that higher income inequality increases the growth rates of industries that use physical capital intensively, while it decreases the growth rates of industries that use skilled labor intensively. Thus, the lower human capital stock associated with inequality drives its negative effect on growth. At the country level, these results are consistent with the theoretical predictions by Galor and Moav (2004) .

Studies based on panel data techniques find conflicting results regarding the impact of inequality on economic growth. Forbes (2000) estimates the impact of inequality on growth in a panel of 45 advanced economies and emerging markets for the period between 1966 and19 95 . Contrary to the cross-country results, she finds that higher inequality leads to higher economic growth in the short and medium term. These results are robust to alternative samples and model specifications. Forbes mentions several theoretical models that are consistent with a positive relationship between inequality and growth. For example, Galor and Tsiddon (1997) find that a concentration of high-skilled workers in technologically advanced sectors allows a higher rate of technological innovation, promoting higher growth rates but also increasing inequality. Mo re recently, using fixed effects panel data techniques, Cingano (2014) finds a negative effect of inequality on growth for a sample of 30 OECD countries for the period between 1970 and 2010. Berg et al. (2018) find that net inequality has a negative effect on growth in a sample of advanced and developing countries, and moderate redistribution through taxes and transfers does not have statistically significant effects on growth.

Evidence from panel data studies also indicates that the effect of inequality on growth might depend crucially on the level of the development and the time horizon of the growth spells (short term vs long term). Brueckner and Lederman (2018) find that income inequality may be beneficial for transitional growth in poor countries but becomes harmful for growth in economies with high average income, contradicting the results by Barro (2000) . Regarding the time horizon, Halter, Oechslin, and Zweimüller (2014) find that higher inequality is beneficial for economic performance in the short term, but in the long term the net effect of the relationship tends to be negative. Inequality reduces the duration of growth spells ( Berg, Ostry, and Zettelmeyer 2012 ; Berg and Ostry 2017 ), with most of the results coming from cross-country differences rather than changes over time.

Banerjee and Duflo (2003) find a nonlinear relationship between changes in inequality and growth. In particular, growth is an inverted U-shaped function of changes in inequality such that a change in the Gini coefficient in either direction is correlated with lower future growth. This empirical result strongly rejects the standard linear specification of cross-country growth regressions and suggests an explanation for the seemingly contradictory results obtained in the literature. However, the non-linear relationship could also reflect omitted variables in the empirical model. For instance, Aiyar and Ebeke (2020) show that the negative effect of inequality on growth largely depends on the degree of intergenerational mobility. In countries with higher intergenerational mobility, the negative impact of income inequality can be more easily reversed because the poor have more opportunities to improve their living standards. In particular, they show that in their specification, the nonlinear term proposed by Banerjee and Duflo (2003) is not statistically significant, suggesting that intergenerational mobility could be capturing the nonlinear relationship between inequality and growth. 15

In sum, the mixed evidence of the impact of inequality on growth arises primarily based on whether the study used a cross-country approach (which includes between-country inequality) or a panel data approach (which includes only within-country variation over time). Given that some of the key mechanisms linking inequality to growth—such as institutional quality, credit constraints, and redistribution policies—do not change much over time, the influence of those channels are greater in the cross-country than the time series dimension. Given that channels such as political economy and credit constraints generate a negative impact of inequality on growth, this may explain the stronger negative results in cross-country regressions relative to the mixed results of panel data studies. In general, with many potential channels affecting the relationship, inconsistent findings may be expected with differences in country coverage, sample period, time horizon, model specification, and econometric method.

B. Channels from Poverty and Inequality to Growth

• 1. Channels by which Inequality Can Boost Growth

Inequality provides incentives to work, save, and invest—those who do will receive higher returns than those who do not. Differential returns incentivize good behaviors that promote growth. Milton Friedman ( Friedman 1962 ; Friedman and Friedman 1980 ) based his opposition to redistributive policies aimed at reducing inequality of outcomes on the grounds of efficiency, arguing that they could distort incentives and induce an inefficient allocation of resources. In a capitalist system, the distribution of income is consistent with the ethical principle, “To each according to what he and the instruments he owns produce.” This implies that in a free market economy, people should be rewarded according to their marginal productivity, resulting in some inequality of outcomes. Friedman emphasized that this inequality of outcomes could be necessary to provide incentives to perform certain types of tasks that could be risky or tedious (Friedman and Friedman 1980 ). Moreover, compensation schemes that reward relative performance and thus generate inequality can provide incentives for workers to invest in skills and exert strong efforts ( Lazear and Rosen 1981 ).

Different savings rates between rich and poor can affect growth. Kaldor (1957) hypothesized that since the richer save more of their income, higher income inequality can lead to a higher national savings rate, a higher investment rate, and greater accumulation of capital, and consequently higher economic growth. Evidence for the United States ( Dynan, Skinner, and Zeldes 2004 ), for instance, supports the notion that both saving rates and the marginal propensity to save are positively correlated with the level of income, suggesting that higher income inequality can lead to a higher savings rate, consistent with Kaldor’s hypothesis.

• 2. Channels by which Inequality and Poverty Can Depress Growth

Poverty Traps and Human Capital

Poverty can undermine growth by hindering the accumulation of human capital through both health and education. Poverty is associated with high rates of malnutrition, especially in developing countries ( Cerra et al. 2021 , Chapter 14). Stunting (a low height-to-age ratio)—an indicator of chronic malnutrition—and child survival rates are correlated with income across and within countries. Poor nutrition impairs children’s capacity to learn. Poor children may also be kept out of school in order to support low family incomes through home production or informal work or because families cannot afford school fees. Students from poor households have higher learning gaps even when attending school ( World Bank 2018 ). Empirical evidence shows that inequality of wealth, not just inequality of income, reduces the effectiveness of educational interventions ( Deininger and Olinto 2000 ).

As described in Section II , lower-income countries experience higher poverty rates, partly reflecting the correlation between average country income and the income of the bottom of the distribution. Poor countries have weak capacity to supply public goods such as health and education. Indeed, public spending on health and education is lower for countries with high poverty rates ( Figure 10 , top left and right panels). Higher poverty is associated with lower access to doctors and higher illiteracy rates (bottom left and right panels).

Access to Health and Education

Inequality in education attainment can undermine growth as economies develop ( Galor and Moav 2004 ). In the initial stage of development when physical capital is the prime source of growth, inequality raises growth because it channels resources to individuals with a higher propensity to save. This is reversed later in the development process: as human capital replaces physical capital as the main engine of growth, more equality leads to growth as it alleviates adverse effects of credit constraints on human capital accumulation.

Credit Market Imperfections

Weak credit markets can impede the poor from borrowing to invest in physical or human capital, thereby reducing growth. In the model proposed by Galor and Zeira (1993) , wealthy individuals can invest in human capital using their own resources, while individuals with low levels of wealth can only invest in human capital if they have access to credit markets. However, financial frictions increase the interest cost for borrowers. Below a threshold of initial wealth, poor individuals find the cost of borrowing higher than the return to human capital and choose not to invest. In this economy, higher inequality reduces growth. However, redistribution provides the opportunity for the poor to invest in human capital, stimulating economic growth.

In their analysis of the impact of poverty on growth, López and Servén (2015) develop an endogenous growth model with learning-by-doing externalities and subsistence consumption. Poor consumers have a low endowment of wealth and no access to capital markets. The model predicts that in economies where the share of poor people is high enough, economic growth rates are lower because the poor are unable to invest and accumulate capital, resulting in a reduction of the potential growth rate of the economy. López and Servén (2015) report robust results consistent with this prediction.

Banerjee and Newman (1993) argue that, given credit constraints, wealth inequality can influence the occupational choice of individuals, thereby affecting growth . In their model, poor people decide to become (low-skilled) workers, rich people decide to become entrepreneurs, and the rest become self-employed. The model predicts that highly unequal societies stagnate since wages remain too low. Highly equal societies display a large share of self-employed workers. At an intermediate level of inequality, the society can “take off” and converge to a developed economy with a combination of entrepreneurs and workers receiving high wages.

Aghion and Bolton (1997) examine credit constraints where the accumulation of capital by the rich benefits the poor because more funds become available to the poor for investment purposes. Unlike Milton Friedman, they find that the laissez-faire outcome is not efficient because it does not allow the poor to invest amounts consistent with an optimal allocation of resources. Instead, a permanent redistribution of wealth can achieve the optimal allocation.

Demand and structural transformation

Inequality can shape the composition of demand and thereby impact growth and structural transformation. For goods produced with technologies subject to economies of scale, sales need to be large enough to cover fixed costs. If only high-income individuals can afford the price of the goods, a moderate level of inequality may be required so that there are enough rich people to make adoption of the technology feasible. Income generated by the sectors can spill over into demand for other goods and spur industrialization, but only if income is distributed broadly enough ( Murphy, Shleifer, and Vishny 1989 ). In addition, productivity improvements through learning by doing can reduce the production costs and prices, making the goods affordable to more people. This can trigger mass production and industrialization provided that inequality is not too severe ( Matsuyama 2002 ).

Risk Aversion and Decision-Making Capabilities

Inequality and poverty might also have a long-term impact on growth through the effects on individuals’ decision-making processes. In order for people to overcome poverty, they must save and reinvest continually in order to earn higher wages, which also contributes to higher economic growth rates. However, living in impoverished conditions can prevent individuals from making the best decisions to escape poverty.

This faulty decision making can occur as a result of the particularly burdensome risks and uncertainty imposed by poverty. As noted by Banerjee (2000) , the poor might be more risk averse than the rest of the population because they have more to lose if a bad shock materializes, even risking malnourishment or starvation. In the absence of developed financial and insurance markets, the poor will avoid investing in profitable investment opportunities that are intrinsically risky. That behavior self-perpetuates poverty, as the poor do not engage in risky activities that might boost their income. Dercon (2005) surveys several studies conducted in developing countries that support this hypothesis. He finds that if the poor could insure against risks in the same way as the rich, their income could be higher by at least 25 percent.

An alternative behavioral channel through which poverty is perpetuated and economic growth prospects is curtailed is through the lack of self-control in consumption and saving decisions. Banerjee and Mullainathan (2010) develop a model with “temptation” goods (such as cigarettes or alcohol) that provide utility in the present, but not in the future. Under the assumption that the share of expenditures on temptation goods declines with the level of income, the model can lead to poverty traps, whereby the poor overvalue the present and undervalue the future, and thus decide not to make investments that could yield a higher income later. Their model is consistent with the evidence that the poor spend a large fraction of their income on goods that are not survival necessities such alcohol, tobacco, and festivities ( Banerjee and Duflo, 2007 ).

Shah, Mullainathan, and Shafir (2012) study an alternative mechanism through which poverty affects the decision-making process. Through several experiments, they illustrate how the poor devote a significant fraction of their attention span to satisfying basic needs, such as obtaining food, leaving them with less attention to handle other problems, such as investment decisions that would enable their businesses to expand and grow.

Lower aspirations induced by poverty is another channel through which poverty may affect the decision-making process of the poor, resulting in lower economic growth. La Ferrara (2019) reviews the theoretical literature on aspirations and provides empirical evidence on how they are correlated with poverty rates and income inequality. Data on aspirations are obtained from the tests on academic performance administered through the OECD’s Programme for International Student Assessment (PISA), and are measured as the expectations of students as to what academic degree and job they will achieve in the future. The intuition of this channel is as follows. The poor have lower aspirations than the rich because they anticipate that the lack of resources (including financial buffers to withstand adverse shocks) will impede their success in the future. As result, the poor may lack the incentives to invest in their future income opportunities for their families, such as the education of their children or the adoption of new technologies. This in turn perpetuates their poverty, leading to a vicious cycle in which low growth breeds poverty and poverty promotes stagnation.

All these mechanisms share the common feature that poverty influences the behavior of poor individuals, with negative consequences on the accumulation of capital and long-term growth, hence self-perpetuating poverty. For instance, this behavioral channel is consistent with the empirical evidence that the poor borrow repeatedly at very high rates instead of self-financing through savings ( Banerjee and Duflo 2005 ) or do not invest in profitable small-scale investment such as purchasing fertilizer ( Duflo, Kremer, and Robinson 2011 ), preventing them from escaping poverty.

Political Economy

There are two key channels through which inequality has political economy effects that depress long-term growth. The first, the “redistribution” channel, is when inequality generates political pressures from voters for redistribution, which results in an increase in distortionary taxation, and consequently lower investment and growth. The second, “the institutional” channel, is when inequality leads the rich and powerful to influence institutions in such way that laws benefit them but are not conducive to sustained growth for the population at large.

The redistribution channel is illustrated by Alesina and Rodrik (1994) based on the endogenous growth model of Barro (1990) , where government spending is productive but is financed through distortionary capital taxation. Taxation and the growth rate of the economy exhibit an inverted “U” relationship. For low levels of tax rates, increasing the tax rate raises growth by funding the expansion of productive public infrastructure. After some point, however, further increasing the tax rate reduces growth because it reduces the incentives to accumulate private capital and may also provide declining marginal return to public expenditure. In the electoral process, the median voter prefers to impose a tax higher than the growth-maximizing tax rate, as they benefit from the public good while the tax falls disproportionately on capital owners. The model implies that the more unequal is the distribution of wealth or capital, the higher the tax rate chosen by the median voter, resulting in a lower rate of economic growth. Persson and Tabellini (1994) obtain similar theoretical results in an overlapping generations framework. Milanovic (2000 , 2010) finds empirical support that more unequal countries redistribute more to the poor.

The view that redistribution harms growth was challenged by Gilles Saint-Paul and Thierry Verdier (1993) . When tax revenues are invested in education, the growth rate is higher. The implication is that the growth effects of fiscal policy depend jointly on the tax distortions and expenditure benefits. Moreover, inequality does not necessarily imply demand for more redistribution; it depends on the position of the decisive voter’s income relative to the mean ( Meltzer and Richard 1981 ). In a democracy, redistribution depends on the skewness of the income distribution, which places the median voter below the mean ( Saint-Paul and Verdier 1996 ).

The institutional channel is illustrated by Glaeser, Scheinkman, and Shleifer (2003) . They propose that the wealthy and politically connected can subvert legal, political, and regulatory institutions, damaging growth through two distinctive mechanisms. First, the elite can weaken the protection of property rights of people at large, discouraging the accumulation of capital by the non-elite, with a negative impact on growth. Second, the elite can influence regulations in order to protect incumbents against entrant firms, with detrimental effects on technological innovation, capital accumulation, and growth. This implies that in countries with weak institutions geared toward the interests of the elite, only elite invest and accumulate wealth. The middle class can expand only when institutions are strong enough to protect them from the rich.

The causality between inequality and institutions goes in both directions. High initial inequality facilitates the elite’s ability to subvert institutions toward their interests, but weak institutions can lead to higher inequality to the extent that only the rich and powerful can protect themselves. The authors find empirical support that inequality reduces growth only for countries with poor rule of law. Their results are also consistent with what Acemoglu and Robinson (2019) call “extractive political institutions.” These institutions, where power is concentrated, benefit the elite at the expense of the rest of the society, leading to high inequality and low growth. 16

Sociopolitical Unrest

Under this channel, inequality leads to a polarization of the society, social unrest, and violence, if the demands of the voters cannot be met through the traditional political system. Alesina and Perotti (1996) analyze this channel and find that an increase in inequality (inversely related to the income of the middle class, in their estimation) has a statistically negative effect on political stability. In the empirical analysis, the authors construct an index of political stability based on a dummy variable for democratic regimes, the number of assassinations and deaths, and the number of coups. In addition, they find that political instability negatively affects investment, a key determinant of long-term growth across countries. Their results are broadly consistent with three different mechanisms through which political stability affects investment. First, higher instability tends to shorten the horizon of the government in power; this, in turn, tends to be associated with higher taxation and lower investment, as the reputational costs of taxation are lower for regimes of short duration. Second, social unrest might lead to a disruption of productive activities and therefore a reduction in productivity. Third, political instability increases uncertainty, which can induce investor to postpone projects or to invest abroad.

Rodrik (1999) studies the interaction between social conflict (measured by inequality or ethnic and linguistic fragmentation) and the quality of government institutions in developing countries in response to external shocks (specifically, terms of trade shocks). Rodrik’s analysis is intended to capture the experience in Latin America, the Middle East, and Sub-Saharan Africa, which had a sharp slowdown in growth after the negative shocks in the 1970s. The main channel through which social conflict exacerbates negative shocks is through macroeconomic mismanagement, in particular in the context of weak institutions. As societies become more polarized, the impact of the initial negative shock is exacerbated by the implementation of populist policies that have palliative short-term effects but result in uncertainty, low investment, and, consequently, poor long-term economic growth.

Gender Inequality

Galor and Weil (1996) develop a theory whereby gender inequality, measured as the wage gap between male and female workers, has a long-term impact on growth. In their model, an increase in the stock of capital per capita makes workers more productive, but mores of or female than male workers (because as economies develop, the rewards to “brain relative to brawn” increase). The decline in the wage gap, in turn, increases the opportunity cost of raising children, and hence reduces the fertility rate and increases female labor force participation. Consequently, the reduction in the fertility rate leads to lower population growth and an increase in the stock of capital per capita, which in turns generates a positive feedback loop boosting growth and the relative wage of female workers.

This model accounts for the fact that some countries might experience development traps in which a low stock of capital per capita results in low wages for women, a high fertility rate, and high population growth, which further depresses the stock of capital per capita, generating an equilibrium of self-perpetuating stagnation. Kremer and Chen (2002) and de la Croix and Doepke (2003) corroborate that inequality is associated with higher fertility differentials within countries, with the poor having more children and achieving less education, which in turn leads to lower growth.

Several recent studies find that gender inequality reduces growth. Based on a difference-indifference approach for advanced economies and emerging markets, Bertay, Dordevic, and Sever (2020) find that that gender inequality reduces real economic growth at the industry level for the manufacturing sector. Cuberes, Newiak, and Teignier (2017) find that gender inequality in labor markets leads to income losses of 15.5 percent in OECD countries and 17.5 percent in non-OECD countries. Stotsky (2006) discusses the macroeconomic impacts of gender inequality. Cerra et al. (2021 , Chapter 16) examines gender and inclusive growth more extensively.

• V. Conclusion and Policy Implications

This paper traces the factors and policies that affect the nexus of growth, inequality, and poverty. Figure 11 presents an illustration of the main channels of this nexus. The relationships are complex, and a multitude of papers have been written to elucidate them. Bourguignon (2004) argues that creating development strategies for reducing poverty is challenging not because of its relationship with growth on the one hand and with inequality on the other. Rather, the difficulty lies in the two-way interaction between growth, inequality, and poverty. Following this idea, Figure 12 summarizes the evidence from a large number of empirical papers on the multidirectional links in the nexus between growth, inequality, and poverty.

Key Channels in the Growth–Poverty–Inequality Nexus

Empirical Literature on the Growth-Poverty-Inequality Nexus

Two main conclusions emerge from analyzing the impact of growth on inclusion.

A nearly universal consensus in the empirical literature suggests that growth reduces poverty. Economic growth experienced in emerging and low-income economies has had a first-order effect on poverty reduction. Through various mechanisms, growth increases education, health, and job opportunities for the poor and improves their access to public goods and services, lifting their incomes and prospects for the future.

On the other hand, the impact of growth on inequality (a relative measure of the well-being of the poor) is ambiguous and depends on the sources of growth. For example, growth propelled by skill-biased technological change can disproportionately benefit capital owners and skilled workers to the detriment of unskilled workers, whose earnings are generally low and who tend to be in the lowest quantiles of the income distribution. This type of technological innovation, while usually positive for economic growth, can induce an increase in inequality. Thus, identifying the underlying sectors driving economic growth is crucial for understanding the impact on inclusiveness. Most sources of growth generate unbalanced growth rates across sectors, industries, regions, and factors , so it is not possible to generalize about the distributional effects of growth.

Two conclusions also emerge from analyzing the impact of inclusion on growth: the reverse direction of causation.

Most plausible mechanisms suggest that poverty impedes growth by reducing the ability and incentives of the poor to accumulate physical and human capital and assets. Poverty curtails access to markets and public services and distorts the incentives for entrepreneurship and forward-looking behavior, leading to individual and social stagnation. The empirical evidence amply supports the negative effect of poverty on economic growth.

However, the impact of inequality on growth is less straightforward. A case can be made that inequality can serve as an incentive for effort and investment. However, other theoretical arguments and empirical evidence point to a negative effect of inequality on growth through a variety of channels, such as higher distributional pressures, lower institutional quality, greater social conflict, and higher fertility rates.

What are the implications of this analysis for the policy framework that should be adopted to promote inclusive growth?

First, policies to promote growth are most relevant—crucially, because growth helps reduce poverty. An increase in growth is a necessary condition for lifting incomes; improving nutrition; and expanding access to health, education, and opportunities for high-quality jobs. While there is no single set of policies that will work in all countries, some general recommendations can be made. For instance, The Growth Report ( Commission on Growth and Development 2008 ) describes a set of policies that has been adopted successfully in countries that have experienced large and sustained growth: an average rate of 7 percent per year or more for 25 years or longer. While the list of policies is not intended to be prescriptive, it provides a good benchmark of what has worked for supporting a successful growth strategy. The report explores policies falling into five broad categories: accumulation of human and physical capital; innovation and technology adoption; efficient allocation of resources; macroeconomic stabilization; and social inclusion.

Second, economic growth is not an objective in itself, but a way to achieve human development. This requires that the benefits of growth are widely shared across society. Therefore, policy analysis must determine the distributional consequences as well as the growth consequences of policy interventions. Inevitably, market forces will not guarantee that growth is balanced. Thus, public measures will also be needed to ensure that the (absolute and relative) losers of any economic transformation have opportunities to move to better jobs, and support policies will be needed to provide social protection in the meantime.

Finally, is there a trade-off between inequality and growth? Or more precisely, must society tolerate inequality in order to spur growth? Considering the various channels from inequality to growth, the answer may reside in differentiating between inequality of outcomes and inequality of opportunities .

The possibility of achieving high returns and higher incomes provides incentives to save, invest, acquire skills, innovate, and take risks, all of which can lead to higher growth. So, indeed some inequality of outcomes is necessary to motivate behavior that enhances growth.

However, if the opportunity to save, invest, acquire skills, innovate, and take risks are thwarted by barriers (such as fixed costs) that depend on an individual’s initial income/wealth/place of birth/race/ethnicity/sexual orientation/disabilities, inequality can prevent many poor and marginalized people from contributing to growth. Moreover, if segments of the population do not perceive that growth is benefiting them, it can fuel discontent in the society and if not addressed can lead to political instability and social unrest.

The policy message is straightforward: policies to remove barriers to markets and public goods and services can improve growth and equity at the same time. In other words, equality of opportunity does not pose a trade-off with economic growth. Expanding access to health care, education, safety, justice, social protection, and finance, for example, can simultaneously boost growth and inclusion.

• Appendix A. List of Empirical Literature on Growth–Poverty–Inequality Nexus

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We thank Izzati Ab Razak and Jaime Sarmiento for their superb research assistance. We also thank Barry Eichengreen, Andrew Berg, Piergiorgio Carapella, Reda Cherif, Fuad Hasanov, Maksym Ivanyna, Futoshi Narita, Marco Pani, Martin Schindler, Nikola Spatafora, Xin Tan, Junjie Wei, Younes Zouhar, and participants in the Inclusive Growth book seminar series organized by the IMF Institute for Capacity Development for their comments. This is a draft of a chapter that has been accepted for publication by Oxford University Press in the forthcoming book titled: “How to Achieve Inclusive Growth,” edited by V. Cerra, B. Eichengreen, A. El-Ganainy, and M. Schindler due for publication in 2021.

GDP omits some components of economic production, such as housework and home production, because it measures goods and services traded in market transactions. It also fails to deduct economic “bads” such as environmental degradation or to fully account for other aspects of well-being and happiness. For a full discussion, see the 2020 IMF report, Measuring Economic Welfare: What and How? .

While there are multiple ways of measuring inclusiveness, this paper focuses the analysis on two metrics: the poverty rate and the Gini coefficient of income distribution. The first measure captures the percentage of the population that is unable to meets its needs, based on an estimated threshold defining the cost of consumption basket for satisfying basic needs. To expand the coverage of data, this paper uses the World Bank’s threshold of $3.20 per day in purchasing power parity (PPP) terms, rather than the $1.90 PPP indicator of extreme poverty. The second measure of inclusiveness, the Gini coefficient, captures the degree of dispersion or inequality in the distribution of income, where a value of 1 indicates maximum inequality (whereby one person accrues all income) and 0 indicates perfect equality (whereby everyone in the entire population receives the same income). Additional indicators that might capture different dimensions of inequality, living standards, and inclusiveness are discussed in more detail in Cerra et al. (2021 , Chapter 1), along with their limitations.

This section analyzes trends in poverty and inequality starting in 1980s. Longer time series on wealth and income inequality have been collected by Piketty (2014) and are restricted mostly to advanced economies. Piketty and Saez (2014) report sustained improvements in wealth and income distribution across Europe and the United States from the 1930s to 1970s, followed by a worsening of inequality starting in the 1970s to 1980s. This section captures the rise in inequality in advanced economies starting in the 1980s. Later sections examine several channels that might account for this more recent trend.

Fabrizio et al. (2017) provide an overview of income inequality trends in low-income countries.

Johnson and Papageorgiou (2020) present a literature survey on growth convergence.

Additional studies such as Bluhm, de Crombrugghe, and Szirmai (2018) and Fosu (2017) also find that poverty reduction has been driven primarily by economic growth, with changes in income distribution playing a secondary, albeit important, role.

Note, however, that the original Kuznets formulation is for structural transformation for a country over time, as discussed in section III.B.4 , and does not necessarily apply to the cross-section of countries.

In addition, a slowdown in educational attainment starting in the early 1980s reduced the supply of skilled workers.

Matlack and Vigdor (2008) , using Census data for US cities, show that an increase in income at the top of the income distribution leads to an overall increase in housing rents that disproportionally affect the poor, exacerbating inequality.

Globalization and technological change influence growth and inequality through different components of GDP. Trade globalization and technological change impact the income distribution through labor income and the skill premium, whereas financial flows affect capital income.

More specifically, Furceri and Ostry (2019) estimate the drivers of inequality using weighted-average least square (WALS) techniques, whereby the reported coefficients are a weighted average of the estimated coefficients across all possible models. This technique addresses model uncertainty and endogeneity issues related to omitted variables typically present in empirical studies focused on income inequality.

“Poverty trap” is a common narrative of economic development whereby some countries are stuck in poverty and would need external support (or a “big push”) for them to escape it. Easterly (2006) rejects, however, the claim that “well-governed poor nations” are stuck in a trap just because they are poor. The author cannot statistically discern any effect of initial poverty on subsequent growth once bad governance is controlled for.

The negative relationship between inequality and growth remains robust even when the analysis controls for the initial level of income, as is standard in growth regressions (see Barro 2000 ).

The relationship between intergenerational mobility and growth is complex and may depend on inheritance laws and uncertainty of property rights. Cerra et a l. (2021 , Chapter 18) examines these issues in more detail.

Cerra et al. (2021 , Chapter 10) covers the impact of governance on inclusiveness in a society. Cerra et al. (2021 , Chapter 15) discusses the political economy factors that influence the supply and demand for reform and redistribution in more detail. Ostry, Loungani, and Berg (2019) highlight the impact of political choices in the relationship between inequality and growth.

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• Links Between Growth, Inequality, and Poverty: A Survey
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Poverty, Inequality and Development

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Table of contents (18 chapters)

Front matter, poverty, inequality and development: micro-macro perspectives and linkages.

• Alain de Janvry, Ravi Kanbur

Erik Thorbecke: Growth and Roots

On the consistency of poverty lines.

• Martin Ravallion, Michael Lokshin

Poverty Indices

• James E. Foster

Should Poverty and Inequality Measures be Combined?

• Gary S. Fields

Equality of What? Evidence from India

• David E. Sahn

Household Investments in Education and Income Inequality at the Community Level: Evidence from Indonesia

• Jeffrey B. Nugent, Shailender Swaminathan

Poverty Traps and Safety Nets

• Christopher B. Barrett, John G. McPeak

Progress in the Modeling of Rural Households’ Behavior under Market Failures

• Alain de Janvry, Elisabeth Sadoulet

Labor Laws and Labor Welfare in the Context of the Indian Experience

• Kaushik Basu

Macro Models and Multipliers: Leontief, Stone, Keynes, and CGE Models

• Sherman Robinson

Multiplier Effects and the Reduction of Poverty

• Graham Pyatt, Jeffery I. Round

Developing an Accounting Matrix for the Euro Area: Issues and Applications

• Tjeerd Jellema, Steven Keuning, Peter McAdam, Reimund Mink

Globalization, Economic Reform, and Structural Price Transmission: Sam Decomposition Techniques with an Empirical Application to Vietnam

• David Roland-Holst, Finn Tarp

Institutions, Factor Endowment and Inequality in Ghana, Kenya and Senegal

• Christian Morrisson

Incentives, Inequality and the Allocation of Aid When Conditionality Doesn’t Work: An Optimal Nonlinear Taxation Approach

• Ravi Kanbur, Matti Tuomala

Agricultural Research and Policy to Achieve Nutrition Goals

• Per Pinstrup-Andersen

Is Dualism Worth Revisiting?

• Gustav Ranis
• Economic Development
• Income inequality
• development

Alain Janvry

Ravi Kanbur

Book Title : Poverty, Inequality and Development

Book Subtitle : Essays in Honor of Erik Thorbecke

Editors : Alain Janvry, Ravi Kanbur

Series Title : Economic Studies in Inequality, Social Exclusion and Well-Being

DOI : https://doi.org/10.1007/0-387-29748-0

Publisher : Springer New York, NY

eBook Packages : Business and Economics , Economics and Finance (R0)

Copyright Information : Springer-Verlag US 2006

Hardcover ISBN : 978-1-4020-7850-7 Published: 05 December 2005

Softcover ISBN : 978-1-4419-5445-9 Published: 16 December 2010

eBook ISBN : 978-0-387-29748-4 Published: 09 June 2006

Series ISSN : 2364-107X

Series E-ISSN : 2364-1088

Edition Number : 1

Number of Pages : XIII, 385

Topics : Econometrics , Development Economics

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Income Inequality and Poverty

The first part of this paper argues that income inequality is not a problem in need of remedy. The common practice of interpreting a rise in the gini coefficient measure of inequality as a bad thing violates the Pareto principle and is equivalent to using a social welfare function that puts negative weight on increases in the income of high income individuals. The real distributional problem is not inequality but poverty. The paper considers three sources of poverty and asks what if anything might be done about each of them: unemployment; a low level of earning capacity; and individual choice.

• Acknowledgements and Disclosures

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Five Takeaways From Nikole Hannah-Jones’s Essay on the ‘Colorblindness’ Trap

How a 50-year campaign has undermined the progress of the civil rights movement.

By Nikole Hannah-Jones

Nikole Hannah-Jones is a staff writer at the magazine and the creator of The 1619 Project. She also teaches race and journalism at Howard University.

Last June, the Supreme Court ruled that affirmative action in college admissions was not constitutional. After the decision, much of the discussion was about its impact on the complexions of college campuses. But in an essay in The Times Magazine, I argue that we were missing the much bigger and more frightening story: that the death of affirmative action marks the culmination of a radical 50-year strategy to subvert the goal of colorblindness put forth by civil rights activists, by transforming it into a means of undermining racial justice efforts in a way that will threaten our multiracial democracy.

What do I mean by this? Here are the basic points of my essay:

The affirmative-action ruling could bring about sweeping changes across American society.

Conservatives are interpreting the court’s ruling broadly, and since last summer, they have used it to attack racial-justice programs outside the field of higher education. Since the decision, conservative groups have filed and threatened lawsuits against a range of programs that consider race, from diversity fellowships at law firms to maternal-health programs. One such group has even challenged the medical school of Howard University, one of the nation’s pre-eminent historically Black universities. Founded to educate people who had been enslaved, Howard’s mission has been to serve Black Americans who had for generations been systematically excluded from American higher education. These challenges to racial-justice programs will have a lasting impact on the nation’s ability to address the vast disparities that Black people experience.

Conservatives have co-opted the civil rights language of ‘colorblindness.’

In my essay, I demonstrate that these challenges to racial-justice programs often deploy the logic of “colorblindness,” the idea that the Constitution prohibits the use of race to distinguish citizens and that the goal of a diverse, democratic nation should be a society in which race does not determine outcomes for anyone. Civil rights leaders used the idea of colorblindness to challenge racial apartheid laws and policies, but over the last 50 years, conservatives have successfully co-opted both the rhetoric and the legal legacy of the civil rights era not to advance racial progress, but to stall it. And, I’d argue, reverse it.

Though the civil rights movement is celebrated and commemorated as a proud period in American history, it faced an immediate backlash. The progressive activists who advanced civil rights for Black Americans argued that in a society that used race against Black Americans for most of our history, colorblindness is a goal. They believed that achieving colorblindness requires race-conscious policies, such as affirmative action, that worked specifically to help Black people overcome their disadvantages in order to get to a point where race no longer hindered them. Conservatives, however, invoke the idea of colorblindness to make the case that race-conscious programs, even to help those whose race had been used against them for generations, are antithetical to the Constitution. In the affirmative-action decision, Chief Justice John G. Roberts Jr., writing for the majority, embraced this idea of colorblindness, saying: “Eliminating racial discrimination means eliminating all of it.”

The Supreme Court’s decision undermines attempts to eliminate racial inequality that descendants of slavery suffer.

But mandating colorblindness in this way erases the fact that Black Americans still suffer inequality in every measurable aspect of American life — from poverty to access to quality neighborhoods and schools to health outcomes to wealth — and that this inequality stems from centuries of oppressive race-specific laws and policies. This way of thinking about colorblindness has reached its legal apotheosis on the Roberts court, where through rulings on schools and voting the Supreme Court has helped constitutionalize a colorblindness that leaves racial disparities intact while striking down efforts to ameliorate them.

These past decisions have culminated in Students for Fair Admissions v. Harvard, which can be seen as the Supreme Court clearing the way to eliminate the last legal tools to try to level the playing field for people who descend from slavery.

Affirmative action should not simply be a tool for diversity but should alleviate the particular conditions of descendants of slavery.

Part of the issue, I argue, is that the purpose of affirmative action got muddled in the 1970s. It was originally designed to reduce the suffering and improve the material conditions of people whose ancestors had been enslaved in this country. But the Supreme Court’s decision in the 1978 Bakke case changed the legally permissible goals of affirmative action, turning it into a generalized diversity program. That has opened the door for conservatives to attack the program for focusing on superficial traits like skin color, rather than addressing affirmative action's original purpose, which was to provide redress for the disadvantages descendants of slavery experienced after generations of oppression and subordination.

Working toward racial justice is not just the moral thing to do, but it is also crucial to our democracy.

When this country finally abolished slavery, it was left with a fundamental question: How does a white-majority nation, which wielded race-conscious policies and laws to enslave and oppress Black people, create a society in which race no longer matters? After the short-lived period of Reconstruction, lawmakers intent on helping those who had been enslaved become full citizens passed a slate of race-conscious laws. Even then, right at the end of slavery, the idea that this nation owed something special to those who had suffered under the singular institution of slavery faced strident opposition, and efforts at redress were killed just 12 years later with Reconstruction’s end. Instead, during the nearly 100-year period known as Jim Crow, descendants of slavery were violently subjected to a dragnet of racist laws that kept them from most opportunities and also prevented America from becoming a true democracy. During the civil rights era, when Black Americans were finally assured full legal rights of citizenship, this question once again presented itself: In order to address the disadvantage Black Americans faced, do we ignore race to eliminate its power, or do we consciously use race to undo its harms? Affirmative action and other racial-justice programs were born of that era, but now, once again, we are in a period of retrenchment and backlash that threatens the stability of our nation. My essay argues that if we are to preserve our multiracial democracy, we must find a way to address our original sin.

Nikole Hannah-Jones is a domestic correspondent for The New York Times Magazine focusing on racial injustice. Her extensive reporting in both print and radio has earned a Pulitzer Prize, National Magazine Award, Peabody and a Polk Award. More about Nikole Hannah-Jones

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Northern Ireland Poverty and Income Inequality Report 2022/23

Information on Household Income and poverty rates in Northern Ireland.

Applies to Northern Ireland

https://www.communities-ni.gov.uk/topics/family-resources-survey

The Northern Ireland Poverty and Income Inequality Report presents annual estimates of the proportion of people, children, working-age adults and pensioners in Northern Ireland living in poverty, and other statistics on household income and income inequality.

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