world income inequality in the global era: new estimates, 1990-2008

World Income Inequality in the Global Era: New Estimates, 1990-2008Author(s): Rob ClarkSource: Social Problems, Vol. 58, No. 4 (November 2011), pp. 565-592Published by: University of California Press on behalf of the Society for the Study of Social ProblemsStable URL: http://www.jstor.org/stable/10.1525/sp.2011.58.4.565 .

Accessed: 12/10/2013 18:59

Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .http://www.jstor.org/page/info/about/policies/terms.jsp

.JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact [email protected].

.

University of California Press and Society for the Study of Social Problems are collaborating with JSTOR todigitize, preserve and extend access to Social Problems.

http://www.jstor.org

This content downloaded from 128.143.23.241 on Sat, 12 Oct 2013 18:59:45 PMAll use subject to JSTOR Terms and Conditions

http://www.jstor.org/action/showPublisher?publisherCode=ucal

http://www.jstor.org/action/showPublisher?publisherCode=sssp

http://www.jstor.org/stable/10.1525/sp.2011.58.4.565?origin=JSTOR-pdf

http://www.jstor.org/page/info/about/policies/terms.jsp


World Income Inequality in the Global Era:New Estimates, 1990–2008

Rob Clark, University of Oklahoma

Several studies have recently found that world income inequality declined during the closing years of thetwentieth century. However, these studies feature a number of shortcomings, including the use of outdatednational income estimates to measure inequality between countries, as well as sparse data to capture the smaller(but growing) component found within countries. The current study addresses these concerns and offers newestimates of world income inequality based on 151 countries covering 95 percent of the world’s population duringthe 1990–2008 period. Overall, the results are fairly compatible with prior efforts, lending greater confidence toearlier findings. Nevertheless, the results suggest that prior studies covering the 1990s overestimate the declinein between-country inequality, but underestimate the rise in within-country inequality. Consequently, totalinequality did not begin to decline substantially until the post-2000 era. After presenting these estimates, I thenexamine factors associated with income mobility among the 15,100 subnational percentile groups in my data set.The results suggest that (a) the negative effect of inequality is larger than the positive effect of economic growthamong the poorest 25 percent of the world’s population, and (b) late industrialization has contributed to incomeconvergence between countries, while economic globalization has primarily served to stretch income distributionswithin nations. Keywords: economic growth; globalization; mobility; poverty; world income inequality.

Few issues in the field of macro comparative research have generated as much debate asthe empirics surrounding world income inequality. Recently, a number of studies have pur-sued various descriptions of global inequality, taking into account the disparities that exist bothwithin and between countries (Bhalla 2002; Bourguignon and Morrisson 2002; Dowrick andAkmal 2005; Firebaugh 1999, 2003; Firebaugh and Goesling 2004; Goesling 2001; Korzeniewiczand Moran 1997; Milanovic 2005, 2009; Sala-i-Martin 2006; Schultz 1998). As the evidenceaccumulates, a number of these studies have concluded that inequality has declined slightly inrecent years, with its larger between-country component now shrinking and its smallerwithin-country component now growing. However, existing research features several limita-tions that lead some to question the conclusions drawn from these efforts (Anand and Segal2008; Dowrick and Ackmal 2005; Milanovic 2005, 2009).

In this study, I address a number of these concerns, updating previous estimates of worldincome inequality with improved, more recent, and more comprehensive data across 151countries representing over 95 percent of the world’s population during the 1990–2008 pe-riod. In doing so, I am able to overcome concerns associated with prior studies that relied ondata of lesser quality and quantity. After presenting these estimates, I then examine factorsassociated with income mobility during the 1990–2008 period among the 15,100 subnationalpercentile groups in my data set. In doing so, I first compare the impact of economic growthand inequality on income mobility. I then consider the role of industrialization and economicglobalization in reducing (or intensifying) inequality both between and within countries.

Direct correspondence to: Rob Clark, Department of Sociology, University of Oklahoma, Kaufman Hall 331, 780Van Vleet Oval, Norman, OK 73019. E-mail: [email protected].

Social Problems, Vol. 58, Issue 4, pp. 565–592, ISSN 0037-7791, electronic ISSN 1533-8533. © 2011 by Society for the Study ofSocial Problems, Inc. All rights reserved. Please direct all requests for permission to photocopy or reproduce article contentthrough the University of California Press’s Rights and Permissions website at www.ucpressjournals.com/reprintinfo/asp.DOI: 10.1525/sp.2011.58.4.565.



Existing Descriptions

Branko Milanovic’s (2005) review of world inequality research neatly categorizes thiswork into three ideal types. “Concept One” (C1) refers to the study of population-unweightedbetween-country inequality, where economic disparities within countries are ignored, and allcountries are given the same weight (i.e., countries serve as the unit of analysis). “ConceptTwo” (C2) refers to the study of population-weighted between-country inequality, where dis-parities within countries continue to be ignored, but countries are weighted based on theirpopulation size (i.e., individuals serve as the unit of analysis). Given that the majority of globalinequality consists of the between-country component, both study designs (C1 and C2) havebeen used to generate proxy measures of world income inequality. C1 is more useful analyti-cally for determining what set of national policies or characteristics are effective in producingconvergence across societies, while C2 is more useful descriptively for generating actual esti-mates of global income inequality. Nevertheless, both concepts fall short of the ultimate goalof actually determining the level of global income inequality because, as Milanovic explains,they both ignore the smaller (but growing) within-country component. “Concept Three”(C3) inequality addresses this problem by combining information about between-countryand within-country inequality from multiple sources.1

As the evidence accumulates, a number of stylized facts have emerged from this collectionof studies. First, between the early 1800s and the mid-1900s, Western industrialization pro-duced massive income disparities between countries, leading to the now-familiar “North-South gap,” such that between-country inequality has come to explain a large majority(approximately 75 percent) of global income inequality. This period is generally associatedwith a substantial rise in all three forms of world income inequality (C1, C2, and C3). Second,during the post-WWII era, C1 inequality has continued to rise because wealthier countrieshave tended to grow faster than poor countries. Third, during this same time, C2 inequalitybegan to fall because a small number of poor, but highly populated, countries in Asia haveoutgrown the rest of the world, moving large segments of the global population economicallycloser to the wealthy end of the welfare continuum. Fourth, C3 inequality has essentially sta-bilized, or even decreased slightly, as the decline in (population-weighted) inequality betweennations has been offset by a simultaneous rise in inequality within nations. And, finally,because of the above trends, within-country inequality now explains a larger proportion ofworld inequality than it did several decades ago.

Existing Explanations

A number of scholars see convergence between advanced and developing economies asthe result of economic globalization and the spread of industrialization (Bhalla 2002; Firebaugh2003; Firebaugh and Goesling 2004). From this perspective, if the steep rise in between-nationinequality during the nineteenth and twentieth centuries can be correctly attributed to Westernindustrialization, then it follows that the spread of industrial production to the developing worldexplains why inequality between nations has started to reverse course. A related idea has beento implicate globalization in the cross-national convergence of incomes. First, economicglobalization has played a role in the shifting of industrial production away from the advancedworld (Firebaugh and Goesling 2004) via capital mobility and the liberalization of exchangepolicies. In addition, globalization may have played a direct role in reducing inequality betweennations. Theoretically, integration in the world economy serves as a catalyst for growth, given

1. While there are several ways to produce C3 estimates, one common approach (and the one employed in thisstudy) is to use national income means to calculate the between-country component of global income inequality andthen estimate the dispersion around these means using national levels of income inequality.

566 CLARK



that open economies have a greater capacity to absorb new ideas and technological innovationsthat are generated elsewhere. Indeed, research shows that trade integration has helped poorercountries catch up to affluent nations (Sachs and Warner 1995).

However, while the spread of industrial production and cross-national economic integra-tion may help countries converge with one another, they may also stretch income distribu-tions internally (Firebaugh 2003). In particular, the North-South industrial shift has thecapacity to increase economic stratification within a large number of countries, in accordancewith (a) the rising portion of the Kuznets curve among emerging economies, and (b) the“Great U-Turn” among deindustrializing nations. Late industrialization in the developingworld corresponds to the rising portion of the Kuznets curve for two reasons: (1) industrialwages are higher than agricultural wages, and (2) there is greater economic inequality withinthe industrial sector than the agricultural sector (Kuznets 1955). Thus, the initial shift to indus-trial production is accompanied by rising inequality, as a small (but growing) number of peo-ple begin entering a higher-paying sector whose wages show greater dispersion around themean. It is only until advanced industrialization that income inequality peaks and begins todissipate, where agricultural workers constitute a small (and shrinking) portion of the work-force, thereby corresponding to the falling portion of the Kuznets curve. However, in recentdecades, a number of deindustrializing economies in the advanced world have experiencedan inequality uptick, now widely known as the “Great U-Turn” (Harrison and Bluestone1988), whereby the transition to a service economy implies the loss of middle-income indus-trial occupations, as well as a decline in union density. Collectively, then, deindustrializationand the spread of industrial production to the developing world both portend rising within-nation inequality across a large number of countries across the globe. Accordingly, duringthe 1980s and 1990s, the average level of within-nation income inequality rose in everyregion in the world, except for Africa (Firebaugh 2003:161, Table 9.2).

Moreover, globalization may pose similar consequences. In his analysis of inequality amongWestern European nations, Jason Beckfield (2006, 2009) attributes the decline in between-country inequality and the rise in within-country inequality to political and economic integrationwithin the European Union. As he explains, regional integration draws countries closer togethervia economic mechanisms (e.g., the diffusion of technology) and political mechanisms (e.g., thehomogenization of national policies that affect growth, thereby placing countries on similar eco-nomic trajectories). Conversely, integration also has the capacity to weaken labor by expandingthe pool of competition across national borders, thereby widening disparities within countries.Applying this model to the world stage, global integration may similarly narrow economicdisparities across nations, but at the expense of expanding income distributions within them.For example, openness may widen disparities within countries to the extent that trade andinvestment liberalization increase demand for skilled labor (Rodrik 1999:13–14). Using largecross-national samples, several studies find that foreign investment tends to increase within-country inequality, but that the positive effect attenuates or reverses at higher levels (Aldersonand Nielsen 1999) or when the size of the host government increases beyond a certain threshold(Lee, Nielsen, and Alderson 2007). Among affluent nations, foreign investment outflows andSouthern imports are linked to growing within-nation inequality (Alderson and Nielsen 2002).And in the transition economies of Eastern Europe, inward foreign investment has been shownto positively affect inequality (Bandelj and Mahutga 2010; Mahutga and Bandelj 2008). In sum,there is reason to suspect that globalization may exert countervailing effects on the between-country and within-country components of world income inequality.

The Limits of Industrialization and Globalization

Despite the widespread appeal of the above narrative, others speculate that late industri-alization and/or economic globalization have been ineffective in producing cross-national

World Income Inequality in the Global Era 567



convergence. A number of scholars remain skeptical that late industrialization has been able tonarrow the North-South gap, noting that industrialization has only produced substantial growthin some parts of the developing world (e.g., East Asia), but not others, suggesting that industri-alization per se is not a sufficient condition for catching up with the West (Arrighi, Silver, andBrewer 2003). Moreover, despite the diffusion of industrial production, countries continue tovary by their level of industrial sophistication. While core trading powers are more likely tospecialize in high technology and heavy manufacturing, peripheral countries tend to specializein the extractive, low-wage, light manufacturing, and food industries, thereby indicatingasymmetrical levels of processing (Mahutga 2006). Accordingly, some scholars suggest that lateindustrialization has merely reproduced core/periphery relations, with “little or no reduction inthe overall magnitude of world-system inequality” (Chase-Dunn 1998:120).

In addition, claims that globalization has contributed to income convergence belie otherresearch showing that foreign investment may not be optimal in the developing world (Dixonand Boswell 1996a, 1996b; Firebaugh 1992, 1996), that investment dependence may actuallystunt economic growth among developing countries (Dixon and Boswell 1996a, 1996b; Kentor1998; Kentor and Boswell 2003), and that trade and foreign investment may be causing wealthyand poorer nations to diverge from one another in the long term (Rasler and Thompson 2009).World-system scholars have explained that reliance on trade and foreign investment can sloweconomic growth in the developing world, thereby hindering the ability for developing nationsto keep pace with advanced economies. Traditionally, world-system theory emphasized theharmful impact of trade specialization (e.g., exporting raw materials) or commodity/partner con-centration in trade. More recently, though, the impact of foreign investment in developingnations has captured the attention of world-system researchers. From this perspective, foreigninvestment is thought to be “linkageweak” in the developingworld because profits are repatriated(rather than re-invested), transforming the local economy into an outward-oriented node ofextraction for the world market. Alternatively, foreign investment may destroy local competition,thereby reducing domestic investment. In short, there is reason to suspect that economic globali-zation has not produced enough between-country convergence to offset the rise in inequality ithas created within countries.

Methodological Critiques of Inequality Estimates

More fundamentally, previous estimates of world income inequality warrant skepticismgiven a number of shortcomings featured in these studies (Anand and Segal 2008; Dowrickand Ackmal 2005; Milanovic 2005, 2009). First, the World Bank’s International ComparisonProgram has recently revised its estimates of purchasing power parities, substantially reducingthe economic size of the developing world (International Comparison Program 2007, 2008).Most notably, China’s share of the world’s gross domestic product is now estimated to be nota-bly smaller (from 14 percent to 10 percent), as is India’s (6 percent to 4 percent). In fact, therelative size of developing countries, in general, has been downgraded by more than 15 per-cent. Table 1 compares the World Bank’s old and revised estimates of gross domestic productper capita (GDP PC) for the year 2005 based on purchasing power parities (PPP) for selectedcountries in East Asia (top panel) and the West (bottom panel) (International Bank for Recon-struction and Development 2007, 2010). Among East Asian countries, the estimates for China(–32.19 percent) and India (–27.27 percent) have been downgraded. More generally, 13 of the15 countries in East Asia with a GDP PC of less than $10,000 have revised estimates that arelower. By contrast, the four East Asian countries with a GDP PC of $10,000 or greater (HongKong, Japan, Singapore, and South Korea), as well as every Western nation, have revised GDPPC estimates that are larger. In short, these revised figures suggest that the level of worldincome inequality may be significantly greater than previously estimated. Accordingly, in hisreestimation of world income inequality using the World Bank’s more recent PPPs, Milanovic

568 CLARK



(2009) reports a Gini coefficient that is 5 to 10 percent higher than originally estimated, as wellas a Theil coefficient that is 15 to 20 percent higher.

How does underestimating the level of inequality between countries impact the estimatedreduction in between-country inequality over time? Ultimately, if growth rates remain con-stant, deflating the level of inequality will result in overestimating its reduction. As an example,assume a world with three people: one with 250 marbles, one with 10 marbles, and one with asingle marble. Assume then that the wealthiest person annually doubles his/her marble sup-ply, the middle class individual annually triples his/her supply, and the poorest person

Table 1 • Comparing the World Bank’s Old and Revised Estimates of GDP PC (PPP), 2005

OldEstimate ($)

RevisedEstimate ($)

Change(percent)

Bangladesh 1,826.82 1,069.27 −41.47Cambodia 2,426.34 1,452.71 −40.13China 6,011.65 4,076.31 −32.19Fiji 5,381.73 4,231.81 −21.37Hong Kong 30,989.40 35,677.90 15.13India 3,071.54 2,233.90 −27.27Indonesia 3,418.97 3,216.81 −5.91Japan 27,816.70 30,310.30 8.96Laos 1,814.09 1,651.30 −8.97Malaysia 9,681.23 11,754.50 21.42Mongolia 1,874.79 2,612.82 39.37Nepal 1,379.37 955.93 −30.70Papua New Guinea 2,280.11 1,859.10 −18.46Philippines 4,570.57 2,926.97 −35.96Singapore 26,389.50 43,755.10 65.80South Korea 19,598.10 22,783.30 16.25Sri Lanka 4,087.64 3,545.88 −13.25Thailand 7,719.97 6,750.94 −12.55Vietnam 2,732.16 2,142.74 −21.57

Australia 28,285.90 34,167.30 20.79Austria 29,981.10 33,495.80 11.72Belgium 28,574.90 32,048.90 12.16Canada 29,692.70 35,002.30 17.88Denmark 30,224.00 33,214.40 9.89Finland 28,604.80 30,637.90 7.11France 27,032.80 29,808.70 10.27Germany 26,210.30 31,377.50 19.71Iceland 32,481.50 34,922.60 7.52Ireland 34,255.90 38,596.10 12.67Italy 25,381.10 28,144.00 10.89Luxembourg 53,582.60 68,217.20 27.31Malta 17,071.60 19,558.70 14.57Netherlands 29,077.90 35,104.50 20.73New Zealand 22,238.20 24,876.50 11.86Norway 36,849.50 47,305.50 28.37Portugal 18,158.30 20,656.20 13.76Spain 24,171.20 27,376.80 13.26Sweden 28,936.50 32,319.40 11.69Switzerland 31,701.30 35,733.10 12.72United Kingdom 29,570.60 32,690.10 10.55United States 37,267.30 41,832.70 12.25

Sources: International Bank for Reconstruction and Development (2007, 2010)




annually quadruples his/her supply. As Figure 1 illustrates, the distribution would be highlyunequal at first, yielding a Gini coefficient of .636. By the ninth year, however, the Gini wouldfall to .006, and global marble inequality will have mostly disappeared. Interestingly, though,most of the reduction in the Gini occurs towards the end of this period, even though growthrates remain constant for all three actors throughout, because the wealthiest individual’s shareof the total falls at an increasing rate. In short, the level of the Gini plays an independent rolein affecting how much it decreases. Thus, correctly estimating the level of inequality is criticalfor accurately measuring its change over time.

Complicating matters is the fact that previous studies have used a variety of conversiontechniques to make GDP PC data comparable across countries. Of the various options, PPPexchange rates are now widely favored over market exchange rates (Anand and Segal 2008;Firebaugh 2003). However, among the different techniques used to construct PPP rates, theGeary-Khamic (GK) method used by the Penn World Tables (PWT) is considered flawed,unlike the Elteto-Koves-Szulc (EKS) method adopted more recently by the World Bank(Anand and Segal 2008; Dowrick and Ackmal 2005; International Comparison Program2007). Unfortunately, Surjit Bhalla (2002), Milanovic (2005), and Xavier Sala-i-Martin(2006) all rely on PWT PPP rates, either exclusively or in conjunction with World Bank PPPrates (Anand and Segal 2008).

A related, and more fundamental, issue is whether national accounts, such as GDP PC,should even be used at all. While the use of national accounts is the predominant practice in exist-ing research, it has been critiqued on the grounds that measures such as GDP PC include compo-nents other than household income (although this constitutes the majority) (Anand and Segal2008). One alternative is a per capita measure of each country’s household final consumptionexpenditure (HFCE). While imperfect, HFCE estimates more closely approximate each country’sincome mean. Unfortunately, HFCE estimates are not available for nearly as many country-yearobservations as GDP PC (International Bank for Reconstruction and Development 2010). More-over, GDP PC and HFCE PC are almost perfectly correlated with one another among the countriesin my sample during the 1990–2008 period (r = .971 − .982). This suggests that, while GDP mea-sures contain information beyond household income, the additional information is tantamount toadding a constant value to each country’s income mean.

0.1

.2.3

.4.5

.6

Gin

i Coe

ffic

ient

1 2 3 4 5 6 7 8 9

Year

Figure 1 • Hypothetical Reduction in Gini Coefficient Based on Constant Growth Rates

570 CLARK



A more radical alternative is to measure income means directly using nationallyrepresentative household surveys, as Milanovic (2005) does in generating his estimates ofworld income inequality. Unfortunately, only 86 countries (covering 85 percent of the world’spopulation) are included in his common sample appearing in all three benchmark years(1988, 1993, and 1998). When allowing the sample to float, 102 to 122 countries (covering87 to 93 percent of the world’s population) are included. However, the study loses compar-ability across time. And, in either case, annual estimates are not possible, as the study islimited to three data points during the 1988–1998 period. Moreover, the household surveysused in this study are not strictly comparable to one another given the use of both income-based and expenditure-based surveys (approximately half of the surveys had becomeexpenditure-based by 1998). Similar limitations apply to Milanovic’s (2009) re-estimationof world income inequality using the World Bank’s more recent PPPs, which remains basedon a floating sample (103 to 124 countries comprising 87 to 94 percent of the world’s popu-lation) and covers only four years during the 1988–2002 period. In sum, the minor lossesin accuracy incurred by using GDP PC to represent each country’s income mean seem tobe outweighed by the losses in data coverage incurred by using more accurate measures,such as household survey means or HFCE PC. This is confirmed by Milanovic (2005:118),who finds that “the upscaling of survey incomes to GDP per capita still leaves the level ofworld inequality about the same as before,” and that the differences between both sets ofGini scores are not statistically significant.

In addition to the above challenges surrounding national income estimates, the largerproblem has been to find comparable and reliable estimates of inequality within countries.To summarize the issue, past estimates of within-country inequality have relied on a relativelysmall number of data points. For example, while Brian Goesling (2001) uses a panel of 125nations (representing 93 percent of the world’s population) to calculate between-nationinequality for six years during the 1980–1995 period, he uses a floating sample of countriesto calculate the corresponding within-nation component, ranging from 28 countries (in1983) to 60 countries (in 1992). Steve Dowrick and Muhammad Akmal (2005) calculate thewithin-country portion of world inequality based on only 67 countries, and do so for onlytwo points in time (c.1980 and c.1993). Likewise, Roberto Korzeniewicz and Timothy Moran(1997) and T. Paul Schultz (1998) relied on a relatively small number of countries to calculatewithin-country inequality (46 and 56 countries, respectively).

Sala-i-Martin (2006) generates estimates of world income inequality for the entire1970–2000 period using GDP PC data for 138 countries (93 percent of the world’s population)to calculate the between-country component. However, his data on within-country inequalityare only available for a few years during the sample period (5.5 years on average) (Milanovic2005:124). For the 81 countries (covering 84 percent of theworld’s population) contributingmul-tiple observations to the within-country component, Sala-i-Martin (2006:358) uses a “lineartime-trend forecast” to estimate the missing values. However, for 29 countries (covering 5 percentof the world’s population), within-country inequality is only available for one year in the sample.To fill in these missing data, he imputes the trend in within-country inequality from neighboringcountries in the same region. Finally, for 28 countries (covering 4 percent of the world’s popula-tion), data on within-country inequality are not available at all. Thus, he imputes both the leveland the trend in within-country inequality from neighboring countries.

Similarly, Bhalla (2002) imputes regional averages when survey data are not availableacross the 130 countries (representing 94 percent of the world’s population) in his sample.Bhalla (2002) calculates world income inequality for the entire 1950–2000 period. However,according to Milanovic (2005:124), data on within-country inequality come from one ofthree years (1960, 1980, and 2000), and within-country inequality during the interveningyears are imputed based on these benchmark values. In sum, prior studies estimating thewithin-country component have had to make due with a relatively small amount ofinformation.




Moreover, the practice of imputing regional averages (e.g., Bhalla 2002; Sala-i-Martin2006) is problematic. For some regions (e.g., Eastern Europe, the West), a positive trendin recent years can probably be inferred. For others, however, there is considerable diversitywithin regions. Half of Latin America (14 of 28 countries) and the Middle East (6 of 11countries) experienced a decline in inequality during the 1990–2008 period, while the otherhalf experienced inequality increases or no net change. Moreover, the magnitude of inequal-ity change ranges considerably within regions. While 26 of 28 countries in Eastern Europeexperienced an inequality increase during my sample period, the magnitude ranges from3.5 to 92.6 percent. In East Asia, 14 countries experienced an increase between .7 and 42.6percent, while 6 other countries in East Asia experienced a decrease between .6 and 18.1 per-cent. How does one accurately forecast a time-trend for this region? In short, world regions arefar too heterogeneous to meaningfully use an average value as the basis for assigning longitu-dinal trends. In effect, the use of regional averages to impute missing data most likely flattensthe reported trend in within-country inequality because positive and negative cases will tendto cancel each other out, making each region appear trendless (rather than diverse, as is actu-ally the case).

One of the more popular sources for cross-national inequality data has been the worldincome inequality database created by the United Nations University-World Institute forDevelopment Economics Research (UNU-WIDER) (2008). The UNU-WIDER data set (version2.0c) provides Gini coefficients for 5,314 country-year observations across 160 countries (Solt2009). However, given that these Gini scores are pulled together from a variety of data collec-tion efforts, most of the observations are not comparable due to differences in definition (e.g.,income vs. expenditure), unit of analysis (e.g., individual vs. household), area coverage (e.g.,urban vs. total), population coverage (e.g., employed vs. total), age coverage (e.g., adult vs.total), and the overall quality of the data. Thus, achieving comparability across these measure-ment differences typically results in a dramatic loss of data. “The most common combinationincludes just 508 different country-years in only 71 countries and so discards the vast majorityof the information in the data set” (Solt 2009:234).

In sum, the use of outdated GDP PC (PPP) estimates to construct the between-country com-ponent, as well as the use of a small number of observations to construct the within-countrycomponent, suggests that existing estimates of world income inequality may be inaccurate.Previous studies have probably underestimated the level of between-country inequality, andtherefore overestimated its reduction over time. Prior studies may also have misestimated thelevel of within-country inequality and/or the amount that it has increased over time. In address-ing these limitations, the selected time period for the present study (1990–2008) allows me tocompare my estimates with prior efforts for the pre-2000 period, while covering new groundin presenting estimates for the post-2000 period.

Methods

Data

The three pieces of data I use to calculate world income inequality are (a) incomemeans, (b) income distributions, and (c) population sizes, all at the country level. I mea-sure each country’s mean income using the World Bank’s revised estimates of GDP PC(PPP), found in their World Development Indicators database (International Bank for Recon-struction and Development 2010). The GDP PC data are in constant 2005 internationaldollars, where an international dollar has the same purchasing power over GDP as theU.S. dollar has in the United States. There are 151 countries in my data set, representing95.3 percent of the world’s population (as of 2008), covering the 1990–2008 period,thereby producing a total of 2,869 country-year observations (151 countries × 19 years).

572 CLARK



The World Bank provides complete population data for all 151 countries across all 19years. However, it only provides GDP PC (PPP) estimates for 2,857 of these observations(99.6 percent of the total), requiring that I impute the remaining 12 observations basedon the surrounding GDP PC values. These 12 observations are Cambodia (1990–1992),Haiti (1990), Iran (2008), Malta (2008), Mauritania (2008), Moldova (1990–1991), andZimbabwe (2006–2008).

To measure income distributions within countries, I rely on a new data set designed toenhance the comparability of the observations found in the UNU-WIDER data set (version2.0c). The Standardized World Income Inequality Database (SWIID) reports comparable Ginicoefficients for a large cross-national sample during the 1960–2009 period (Solt 2009). Thedatabase maximizes the comparability of UNU-WIDER observations that are based on fullpopulation coverage by using Gini ratios generated through the pairings of observations cate-gorized by reference unit code (household per capita, household adult equivalent, householdwithout adjustment, employee, and person) and income definition (net income, gross income,expenditures, and unidentified), using inequality data from the Luxembourg Income Study toserve as a baseline.2

In the present study, I use the SWIID’s Gini estimates (based on net income) to calculatethe within-country component of world income inequality for the 151 countries in my dataset. In total, SWIID provides 2,195 country-year observations during the 1990–2008 period.On average, each country contributes about 14.5 observations during the 19-year sample pe-riod, and each country contributes at least one observation. In fact, only three countries supplyexactly one estimate (Dominica, Grenada, and Saint Vincent-Grenadines). Overall, 106 coun-tries contribute 15 to 19 observations across the sample period. This represents 70.2 percent ofthe countries in my data set and covers 89.4 percent of the world’s population. In total, the2,195 country-year observations for which SWIID provides estimates represent 76.5 percentof all possible observations in my data set (151 countries × 19 years = 2,869 possible country-year observations). Compared to previous efforts, where a large majority of countries areeither excluded or assigned regional averages, this marks a dramatic improvement in datacoverage.

When observations are not available for a specific country, I assign that country its near-est Gini estimate, rather than attempt to simulate a trend. The decision to assign nearbyvalues is quite reasonable, especially given the relative stability of income distributions withincountries over long historical periods (Korzeniewicz and Moran 2009). Indeed, among all 151countries in my sample, the average annual rate of change in their Gini score is less than halfa percent (.47 percent). Moreover, forecasting trends for each country would be problematicgiven that trends within countries frequently change. Of the 151 countries in my sample, 126of them (83.4 percent) experience at least one change in the direction of their inequalitytrend (i.e., a positive trend becoming negative or vice-versa). In fact, over half of these coun-tries (78 of 151) experience at least three changes in the direction of their trend, and almostone-third (45 of 151) experience at least five changes. In sum, Gini scores are fairly stable, butpredicting trends to fill missing cases would be quite challenging, suggesting that the use ofnearby estimates is the most prudent strategy. Moreover, in adopting this approach, we cansafely conclude that any observable rise or fall in within-country inequality during the sampleperiod is not a function of a forecasting procedure, but rather, an accurate reflection ofreported trends.3

2. See Solt (2009) for a more detailed description of the methodology.3. Of the 674 missing Gini values in the data set, 96.1 percent appear either at the beginning or at the end of the

sample period. Thus, replacing these missing cases with the nearest Gini estimate simply involved extending the earliestreported value back to 1990 or extending the most recent reported value up to 2008. In those few remaining instanceswhere a missing case interrupted reported values on either side, I used the earlier value.




Constructing Income Shares

In this section, I describe the construction of country-specific income shares. The first stepin this process is to determine the level at which to group income shares within countries.Most previous estimates construct income shares at the quintile level (Bhalla 2002; Dowrickand Akmal 2005; Goesling 2001; Korzeniewicz and Moran 1997; Sala-i-Martin 2006; Schultz1998). A notable exception is Milanovic (2005), whose household surveys specify incomegroups closer to the decile level. Similarly, I construct income shares at the decile level, whichis likely to produce more accurate estimates of within-country inequality. How much of a dif-ference in accuracy will this make? Dowrick and Akmal (2005) report that quintile-levelgroupings explain about 90 percent of the actual variance, but that decile-level groupingsexplain more than 95 percent of the actual variance. Thus, while both versions are likely togenerate accurate estimates, the decile-level groupings are noticeably preferable.

Next, I examine a set of Gini coefficients in the UNU-WIDER data set (version 2.0c) thatalso feature income shares specified at the decile level (UNU-WIDER 2008). In this data set,there are 487 “high quality” observations across 42 countries during the 1990–2005 periodthat feature both a Gini coefficient and a set of income shares reported at the decile level.Table 2 presents descriptive statistics for each decile and the Gini. The first column indicatesthe mean income share for each decile. For example, the average income share for Decile 1(the poorest decile) is 2.95 percent, while the average income share for Decile 10 (the richestdecile) is 25.54 percent. Importantly, note the standard deviation in column two. Nine of the10 deciles feature a standard deviation of about one or less, indicating that the average dis-tance from the mean income share is about one. Thus, there is not a great amount of variabil-ity in these scores. Decile 10, due to its much higher mean, is a notable exception, with astandard deviation of more than five. Moreover, the inter-quartile ranges indicate that themiddle 50 percent of values for each decile tend to be within one or two percentage pointsof each other (again, Decile 10 is the exception). The final row provides descriptive character-istics for the Gini coefficients. Notice, in particular, that these Gini scores range from a low of16.95 to a high of 70.84, indicating that the Gini scores used here capture a wide range ofincome distributions.4 Most importantly, the final column indicates the degree of associationbetween the Gini and each decile. All of the coefficients are statistically significant at the high-est level (p < .001), six of the ten deciles feature correlation coefficients whose absolute valueis .9 or higher, and three of the ten deciles feature correlations whose absolute value rangesfrom .752 to .873. Only Decile 8 features a noticeably weaker correlation (r = -.218) becausethis is where the association between the Gini and the income share transitions from negativeto positive.

I then impute income shares for the SWIID Gini coefficients based on the relationshipbetween the UNU-WIDER Gini coefficients and their corresponding income shares. To accom-plish this, I performed ten sets of imputations (one for each decile), with the independent vari-able for each imputation being the Gini coefficient (a variable comprised of both the SWIIDand UNU-WIDER Gini coefficients), and the dependent variable being the income share foreach decile (with the SWIID income shares representing the missing cases to be imputed).To examine the success of this imputation, I compare my estimated income shares for theSWIID Gini scores to the actual income shares reported in UNU-WIDER that were used forthe imputation. For this comparison, I restrict attention to the 268 country-year observations(across 41 countries) that feature a Gini score in both UNU-WIDER and SWIID during the1990–2005 period. To draw my comparisons, I regress the actual income share on the

4. Gini coefficients are oftentimes multiplied by 100 and expressed as a percentage between zero and 100, as is donein the UNU-WIDER data set.

574 CLARK



estimated income share for each decile (all models include a clustered sandwich estimator toallow for intra-country correlation). I also control for the distance between the UNU-WIDERand SWIID Gini scores. As expected, the UNU-WIDER and SWIID Gini coefficients are highlycorrelated with one another (r = .896). Nevertheless, the farther apart the UNU-WIDER andSWIID scores are from one another, the farther apart the estimated and actual income shareswill be. Thus, I include a control for the distance between the two Gini scores, which I con-structed by residualizing the UNU-WIDER Gini by the SWIID Gini. I present these results inTable 3.

Each model reports the unstandardized coefficient with the robust standard error inparentheses. As these models indicate, the estimated income share corresponds highly withthe actual income share. In seven of the ten models, the unstandardized coefficient rangesfrom .9 to 1.1 (p < .001), indicating that, for every percentage point increase in the estimatedincome share, the actual income share rises by approximately one point. For Deciles 1 and 9,the coefficients are slightly smaller (b = .870 and .856, respectively), but the correspondence isstill quite strong and statistically significant at the highest level (p < .001). For Decile 8, thecoefficient is noticeably larger (b = 1.791), indicating that the actual income share is not asresponsive to changes in the estimated income share. However, the association is nonethelessstatistically significant (p < .05). Overall, these models indicate that, while the estimated andactual income shares are not perfectly correlated with one another, they are quite close acrossmost deciles.

To get a better sense of how close the estimated and actual income shares are to one another,it is perhaps useful to examine specific cases. Thus, in Table 4, I report both estimated and actualincome shares for three cases where the UNU-WIDER and SWIID Gini coefficients are within .01points of one another: Czech Republic (1996), Estonia (2001), and Slovenia (1997). For eachdecile, the UNU-WIDER income share is reported, followed by the estimated income share, andthen the difference between the two. In 16 of the 30 comparisons across all three countries, thedifference between the two income shares is less than one-fifth of a percentage point (.2 or less).In 25 of the 30 comparisons, the difference is less than half of a percentage point (.5 or less).

Table 2 • Descriptive Statistics of Decile Income Shares and the Gini Coefficient from 42 Countries(UNU-WIDER)

MeanStandardDeviation

Inter-QuartileRange Minimum Maximum

Correlationwith Gini

Decile 1 2.95 .97 1.54 .40 5.11 −.873***Decile 2 4.74 1.04 1.33 1.52 6.70 −.972***Decile 3 5.82 1.03 1.17 2.30 7.60 −.983***Decile 4 6.82 .99 .96 3.15 8.30 −.980***Decile 5 7.83 .92 .69 4.08 9.10 −.957***Decile 6 8.96 .82 .52 5.25 10.00 −.904***Decile 7 10.35 .71 .62 6.82 11.70 −.752***Decile 8 12.11 .58 .51 9.35 13.70 −.218***Decile 9 14.90 .87 1.29 13.00 17.84 .764***Decile 10 25.54 5.57 4.57 18.00 52.41 .973***Gini Coefficient 38.35 9.62 13.10 16.95 70.84 ———

Source: UNU-WIDER (2008)Note: The 42 countries contributing observations to these descriptive statistics are Austria, Belgium, Bulgaria, Cameroon,Canada, Croatia, Czech Republic, Denmark, Ecuador, Estonia, Finland, France, Germany, Greece, Hungary, Ireland,Israel, Italy, Jordan, Latvia, Lithuania, Luxembourg, Madagascar, Malaysia, Mexico, Moldova, Netherlands, New Zealand,Norway, Poland, Portugal, Romania, Slovakia, Slovenia, South Korea, Spain, Sweden, Switzerland, Taiwan, Thailand,United Kingdom, and United States. N = 487*p < .05 **p < .01 ***p < .001 (two-tailed tests)




Tab

le3

•RegressingActualIncomeShare

onEstim

atedIncomeShare

forAll10Deciles

Across41Cou

ntries

Actual

IncomeSh

are

Decile1

Decile2

Decile3

Decile4

Decile5

Decile6

Decile7

Decile8

Decile9

Decile10

Estim

ated

incomeshare

.870***

(.094)

.908***

(.034)

.907***

(.021)

.920***

(.027)

.940***

(.044)

.990***

(.070)

1.107***

(.132)

1.791*

(.804)

.856***

(.165)

.979***

(.043)

UNU-W

IDERGini

(Residualized

bySWIIDGini)

−.143***

(.013)

−.142***

(.007)

−.144***

(.005)

−.130***

(.005)

−.108***

(.008)

−.078***

(.012)

−.028

(.018)

.039

(.028)

.113***

(.026)

.588***

(.050)

R2

.772

.947

.964

.954

.909

.832

.656

.202

.595

.956

Sources:Solt(2009)an

dUNU-W

IDER

(2008)

Notes:Allmodelsincludeaclustered

sandwichestimatorto

allow

forintra-countrycorrelation.Eachcellreportstheunstan

dardized

coefficien

twiththerobu

ststan

darderrorin

par-

entheses.The41countriescontributingobserva

tionsto

thesean

alyses

areAustria,

Belgium,Bulgaria,Cam

eroon,Can

ada,

Croatia,Czech

Rep

ublic,Den

mark,Ecu

ador,

Estonia,Fin-

land,France,German

y,Greece,

Hungary,Irelan

d,Israel,Italy,

Jordan

,Latvia,

Lithuan

ia,Luxem

bourg,Mad

agascar,

Malay

sia,

Mex

ico,Moldova

,Netherlands,

New

Zealand,

Norw

ay,Poland,Portugal,Roman

ia,Slova

kia,Slove

nia,South

Korea,

Spain,Swed

en,Switzerlan

d,Thailand,United

Kingd

om,an

dUnited

States.N

=268

*p<.05**p<.01***p

<.001(two-tailedtests)

576 CLARK



And in 29 of the 30 comparisons, the difference is less than one percentage point (1 or less). Insum, Tables 2 through 4 suggest that (a) Gini coefficients can be used to generate income sharesat the decile level, and (b) the estimated income shares for the SWIID Gini coefficients are quiteclose to the actual income shares reported by UNU-WIDER.

Assigning income shares at the decile level for all 151 countries in my sample produces aset of 1,510 income groups across the world for each year during the sample period. Unfortu-nately, this still assumes that the distribution of income within each decile group is perfectlyequal, which becomes problematic when considering the more than 130 million people occu-pying a single decile in China. Thus, following previous work (Bhalla 2002; Sala-i-Martin2006), I “smoothed” the data by estimating Lorenz curve ordinates at the percentile level.Thus, each country is subdivided into 100 income groups (rather than 10), thereby dramati-cally reducing (but, of course, not eliminating) the amount by which within-countryinequality is underestimated. To perform this routine, I used the Distributive Analysis StataPackage (DASP), version 2.1, a software package that interacts with Stata 11.1 (Stata Cor-poration 2009). Among the different functional forms from which to choose, I opted forthe Beta Lorenz curve, which produces a “reliable measurement of inequality” (Anandand Segal 2008:88). The creation of percentile groups for all 151 countries thereby produces

Table 4 • Comparing Actual and Estimated Income Shares Across Specific Cases

Czech Republic (1996) Estonia (2001) Slovenia (1997)

Gini coefficient: UNU-WIDER 25.90 35.42 25.01Gini coefficient: SWIID 25.90 35.42 25.00Gini coefficient: difference .00 .00 .01Decile 1: UNU-WIDER 4.00 2.52 3.81Decile 1: estimated 3.68 2.63 3.78Decile 1: difference .32 −.11 .03Decile 2: UNU-WIDER 5.78 4.63 5.75Decile 2: estimated 5.60 4.35 5.72Decile 2: difference .18 .28 .03Decile 3: UNU-WIDER 6.83 5.78 6.88Decile 3: estimated 6.69 5.43 6.81Decile 3: difference .14 .35 .07Decile 4: UNU-WIDER 7.71 6.52 7.78Decile 4: estimated 7.65 6.45 7.77Decile 4: difference .06 .07 .01Decile 5: UNU-WIDER 8.46 7.23 8.67Decile 5: estimated 8.58 7.49 8.69Decile 5: difference −.12 −.26 −.02Decile 6: UNU-WIDER 9.25 8.19 9.59Decile 6: estimated 9.60 8.68 9.69Decile 6: difference −.35 −.49 −.10Decile 7: UNU-WIDER 10.20 9.60 10.71Decile 7: estimated 10.80 10.15 10.87Decile 7: difference −.60 −.55 −.16Decile 8: UNU-WIDER 11.63 11.88 12.06Decile 8: estimated 12.22 12.06 12.23Decile 8: difference −.59 −.18 −.17Decile 9: UNU-WIDER 13.88 15.81 14.15Decile 9: estimated 14.33 15.15 14.25Decile 9: difference −.45 .66 −.10Decile 10: UNU-WIDER 22.28 27.84 20.61Decile 10: estimated 20.91 27.62 20.27Decile 10: difference 1.37 .22 .34




a total of 15,100 income groups across the world for each year during the 1990–2008 pe-riod. I then assigned income values to each group by multiplying each percentile’s incomeshare by that country’s GDP PC.5 For example, in 2000, Yemen’s poorest percentile groupreceived approximately 9.6 percent of its mean income ($2,064.39), thereby producingan income value of $197.80. Conversely, Yemen’s wealthiest percentile group receivedapproximately 522.3 percent of its mean income, thereby producing a much higher incomevalue of $10,782.25.

Estimating World Income Inequality

The final step is to estimate the level of population-weighted inequality across the 15,100income groups for each year during the 1990–2008 period. To do so, I use two popular sum-mary measures, the Gini coefficient and the Theil entropy measure. Unlike other measures,both the Gini and Theil are scale invariant (i.e., inequality is not affected by the mean) andthey obey the “principle of transfers” at all points on the income distribution (i.e., inequalityis reduced when income is transferred from a richer person to a poorer person). While thetwo measures tend to be highly correlated with one another, including the estimates reportedbelow (r = .982), they are sensitive to transfers occurring at different points along the incomedistribution. In particular, the Gini is most sensitive to transfers occurring near the middle ofthe income distribution, while the Theil is most sensitive to transfers occurring at the wealthyend of the distribution.6 In addition, the Theil is a decomposable index, allowing me to alsoestimate changes in the between-country and within-country components. All estimationswere performed in Stata 11.1 (Stata Corporation 2009) using the “ineqerr” command to gen-erate the initial Gini and Theil estimates, and using the “dentropyg” command via DASP todecompose the Theil into its component parts.

Results

World Income Inequality, 1990–2008

Table 5 presents world income inequality estimates for the 1990–2008 period, with theleft panel reporting the full sample results based on the Gini coefficient, the Theil entropymeasure, and the decomposition of the Theil into its between-country and within-countrycomponents, and the right panel replicating these estimates when China is excluded fromthe sample. The bottom three rows indicate the rate of inequality change across the entiresample period (1990–2008), the first half of the period (1990–1999), and the second half(1999–2008).7

Turning first to the full sample results for the 1990–1999 period, world inequality wasfairly stable during this time. The Gini shows a 1.3 percent decline (.697 to .688), while theTheil shows a .1 percent increase (.930 to .931). The results from the Theil decompositionhelp explain the general stability in these trends. While between-country inequalitydeclined by 5.3 percent (.734 to .695) during the 1990s, within-country inequality increasedby a much larger 20.4 percent (.196 to .236). The overall trend is flat, though, because thebetween-country component is weighted more heavily, comprising about three-fourths of

5. Of the 286,900 observations in my data set (15,100 observations × 19 years), 788 of these observations (.27 per-cent of the total, representing .11 percent of the world’s population) featured negative income values (this is due to theoriginal imputing in which a few deciles were assigned small negative income shares). In order to retain these cases, Irecoded these incomes to a positive value of $1, which is the minimum value in the data set. In separate analyses, I findthat excluding these observations produces estimations that are almost identical to those presented below.

6. See Firebaugh (2003:76–84) for a more detailed discussion of these inequality measures.7. I also report the main estimates with 95 percent confidence intervals in Appendices A and B.

578 CLARK



Tab

le5

•World

IncomeInequality,1990–2008

FullSa

mple

Exclude

China

Year

Gini

Theil

Between-Country

Within-Country

Gini

Theil

Between-Country

Within-Country

1990

.697

.930

.734

78.9

%.196

21.1

%.664

.809

.613

75.7

%.197

24.3

%1991

.698

.936

.730

78.0

%.206

22.0

%.668

.823

.617

74.9

%.207

25.1

%1992

.698

.942

.726

77.0

%.216

23.0

%.674

.842

.625

74.3

%.217

25.7

%1993

.696

.940

.716

76.2

%.224

23.8

%.676

.853

.630

73.8

%.224

26.2

%1994

.695

.943

.714

75.7

%.229

24.3

%.680

.868

.639

73.7

%.229

26.3

%1995

.693

.938

.704

75.1

%.234

24.9

%.681

.873

.639

73.2

%.234

26.8

%1996

.689

.930

.695

74.7

%.235

25.3

%.681

.874

.638

73.0

%.236

27.0

%1997

.688

.926

.693

74.8

%.234

25.2

%.682

.878

.643

73.3

%.235

26.7

%1998

.689

.932

.696

74.6

%.236

25.4

%.685

.892

.655

73.4

%.237

26.6

%1999

.688

.931

.695

74.6

%.236

25.4

%.686

.897

.661

73.7

%.236

26.3

%2000

.687

.932

.691

74.1

%.241

25.9

%.688

.902

.662

73.5

%.239

26.5

%2001

.684

.920

.678

73.7

%.242

26.3

%.686

.897

.659

73.4

%.238

26.6

%2002

.682

.911

.666

73.1

%.246

26.9

%.685

.895

.656

73.3

%.239

26.7

%2003

.677

.895

.648

72.4

%.247

27.6

%.682

.887

.647

73.0

%.240

27.0

%2004

.674

.884

.631

71.4

%.253

28.6

%.680

.880

.638

72.5

%.242

27.5

%2005

.669

.868

.613

70.5

%.256

29.5

%.677

.873

.628

71.9

%.245

28.1

%2006

.663

.849

.593

69.8

%.257

30.2

%.674

.862

.617

71.5

%.246

28.5

%2007

.656

.827

.569

68.8

%.258

31.2

%.670

.849

.604

71.1

%.246

28.9

%2008

.649

.806

.548

68.1

%.257

31.9

%.666

.835

.591

70.7

%.244

29.3

%

Δ19

90–20

08−6.9%

−13

.3%

−25

.3%

31.1

%.3

%3.2%

−3.6%

23.9

%Δ1990–1999

−1.3

%.1

%−5.3

%20.4

%3.3

%10.9

%7.8

%19.8

%Δ1999–2008

−5.7

%−13.4

%−21.2

%8.9

%−2.9

%−6.9

%−10.6

%3.4

%




the total during this time (74.6 percent in 1999). These results are broadly compatible withprevious estimates covering the 1990s. First, similar to previous studies, it appears thatworld income inequality stopped growing in the final years of the twentieth century. Sec-ond, between-country inequality declined in the 1990s, while within-country inequalitygrew, which replicates trends reported in earlier studies. And, third, despite the previoustrends, between-country inequality continues to account for a majority of total inequality,a belief now widely held.

Nevertheless, when making more detailed comparisons between my estimates and thosereported in previous studies, the findings reported here depart from prior efforts in severalimportant ways. For the year 2000, Sala-i-Martin’s (2006) Theil estimate for between-countryinequality is .499, while my estimate of .691 is substantially higher. For the year 1998,Milanovic’s (2005) Theil estimate for between-country inequality is .557, while Glenn Firebaughand Goesling’s (2004) Theil estimate is .503, both of which are much smaller than my estimateof .696. And, for 1995, Goesling’s (2001) Theil estimate for between-country inequality is .53,while my estimate is .704. Consequently, if the size of between-country inequality has beenunderestimated in prior studies, then the decline in between-country inequality has probablybeen overestimated. We can see this by comparing the trends in my estimates to those foundin these other studies focusing on the portion of our sample periods that overlap. During the1990–2000 period, between-country inequality shows a larger decrease in Sala-i-Martin’sstudy (–10.4 percent) than in mine (-5.9 percent). Similarly, in Firebaugh and Goesling’sstudy, between-country inequality declines by 11.6 percent during the 1990–1998 period,but it declines by only 5.2 percent during this same time in my study. During the 1993–1998 period, between-country inequality declines by 5.4 percent in Milanovic’s study, butonly by 2.8 percent in my study. And, during the 1989–1995 period, between-countryinequality decreases by 11.7 percent in Goesling’s study, whereas it only decreases by 4.1 per-cent during the 1990–1995 period in my study.8

By contrast, the estimates for within-country inequality levels appear to be more compa-rable. For the year 2000, Sala-i-Martin’s within-country estimate is .284, while my estimate is.241. For 1998, Milanovic’s within-country estimate is .232, while my estimate is .236. And,for 1995, Goesling’s within-country estimate is .25, while my estimate is .234. Not surpris-ingly, using these same years for comparison, I find that the relative contribution ofbetween-country inequality in my study is higher (74.1 percent, 74.6 percent, and 75.1 per-cent, respectively) than that reported by Sala-i-Martin (63.8 percent), Milanovic (70.6 per-cent), and Goesling (67.9 percent).

However, when I compare trends in my within-country estimates to those found in theseother studies, an interesting pattern emerges. During the 1990–2000 period, within-countryinequality shows a much smaller increase in Sala-i-Martin’s study (8.8 percent) than in mine(23.0 percent). Similarly, during the 1993–1998 period, within-country inequality increasesby 1.8 percent in Milanovic’s study, but it increases by 5.4 percent in mine. And, duringthe 1989–1995 period, within-country inequality increases by 19.0 percent in Goesling’sstudy, whereas it increases by a slightly larger 19.4 percent during the 1990–1995 period inmy study.

In sum, the key patterns that emerge from these more detailed comparisons are (1) theestimated decline in between-country inequality during the 1990s is lower in my study thanthat reported in previous studies, and (2) the estimated increase in within-country inequalityduring the 1990s is larger in my study than that reported in previous studies. Thus, in contrastto previous work, I find that the overall change in inequality during the 1990–1999 periodwas minimal. I attribute these differences to methodological improvements regarding boththe quality and quantity of data used to generate my estimates. In contrast to previous studies,

8. I do not extend these comparisons to Milanovic’s (2009) more recent study using the World Bank’s revised PPPsas he does not include a decomposition of total inequality into its between-country and within-country components.

580 CLARK



the GDP PC data used here are based on revised purchasing power parities that downgradethe relative economic size of developing countries and that were constructed using a prefer-able (EKS) method. Moreover, the dispersion of income within countries is more preciselyestimated in this study due to more comprehensive data coverage and the construction ofincome shares at the decile level.

Turning to the second half of the sample period (1999–2008), the results indicate notablydifferent trends. Both the Gini (-5.7 percent) and Theil (-13.4 percent) indicate substantialdeclines in world income inequality, a trend which departs from the stability exhibited inthe first half of the sample period. Between-country inequality continues to decline, andwithin-country inequality continues to rise. However, the decline in between-countryinequality is now larger (-21.2 percent) than the rise in within-country inequality (8.9 per-cent). And because inequality between countries still comprises more than two-thirds of thetotal (68.1 percent in 2008), the overall trend is one of decline during the post-2000 era.

Why do the between-country and within-country trends noticeably change between thetwo periods? Between-country inequality features an accelerated decline during the secondhalf of the sample period due to the improved economic performance of several countries inAfrica and Eastern Europe. During the 1990–1999 period, 52 of the 151 states in my sampleexperienced negative growth, including 25 from Africa and 21 from Eastern Europe. In thisperiod, only China experienced a growth rate of 100 percent or greater. By contrast, duringthe 1999–2008 period, only 12 states experienced negative growth, and a number of poorerstates experienced growth rates of 100 percent or greater, including China, Angola, and sixcountries from Eastern Europe.

Regarding the decelerated increase in within-country inequality, 22 countries in my sam-ple experienced an inequality increase of 25 percent or greater during the 1990–1999 period,including 17 countries from Eastern Europe. By contrast, only two countries (Bulgaria andTurkmenistan) experienced a 25 percent or more increase in inequality during the 1999–2008 period. Alternatively, only 45 countries reduced their Gini score during the 1990–1999period, including 20 from Africa. However, during the 1999–2008 period, 75 countriesreduced their inequality, including 30 from Africa and 16 from Latin America. Thus, risinginequality (especially large increases in inequality) became less common during the secondhalf of the sample period, thereby producing the smaller rise in within-country inequality dur-ing the 1990–2008 period.

Overall, when considering the entire 1990–2008 period, world income inequalitydeclined according to both the Gini (-6.9 percent) and Theil (-13.3 percent) scores reportedin Table 5. Interestingly, the overall 25.3 percent decline in between-country inequality(from .734 to .548) is overshadowed by the larger 31.1 percent increase in within-countryinequality (from .196 to .257). Accordingly, the relative contribution of between-countryinequality fell from 78.9 percent in 1990 to 68.1 percent in 2008. Nevertheless, becausebetween-country inequality still accounts for more than two-thirds of the world’s totalinequality, its decline during the sample period more than offsets the continued rise inwithin-country inequality.

Finally, in the right panel of Table 5, I replicate my inequality estimates when excludingChina in order to examine what effect its presence has had on world income inequality duringthe sample period. Overall, the results show that China continues to be an egalitarian force,preventing inequality from rising during the 1990s, and magnifying the decline in inequalityduring the 2000s. Without China, world income inequality would have risen slightly (.3 and3.2 percent, respectively) during the sample period, and the decline in between-countryinequality would have been much smaller (-3.6 percent). However, it is important to pointout that China’s role as a global equalizer is close to changing. China is now a middle-incomecountry, whose continued economic growth will do little to promote further convergencebetween countries. In 1990, China’s GDP PC stood at $1,099, which ranked 124th amongmy sample of 151 countries (the 19th percentile). By 2008, however, China’s GDP PC had




grown to $5,515, which ranks 84th among the 151 countries (the 45th percentile) and clearlyplaces them into the middle class.

Moreover, this tells only half the story. Notice that when China is excluded, the increasein within-country inequality (23.9 percent) is somewhat smaller. A closer look at the numbersreveals that most of the difference occurs during the latter half of the sample period. Between1990 and 1999, within-country inequality increases from .197 to .236 without China, which iscomparable to what occurs when China is included in the sample (.196 to .236). As notedabove, much of the increase in within-country inequality between 1990 and 1999 was drivenby Eastern Europe, whose countries experienced an average inequality growth rate of 30.7percent, which was about twice as high as China’s inequality growth rate (15.6 percent). How-ever, between 1999 and 2008, further increases in inequality were driven more by China(19.5 percent), as Eastern Europe (4.9 percent) returned to relative stability. In fact, China’sgrowth rate of 19.5 percent during this time was the sixth highest in the entire sample. Thus,notice that when I exclude China, within-country inequality increases more slowly between1999 and 2008 (.236 to .244) than when it is included (.236 to .257). In fact, China’s relativecontribution to the within-country component of world inequality grew from 3.3 percent in1990 to 16.2 percent in 2008, which is second only to the United States (18.8 percent).

Income Mobility, 1990–2008

In the next stage of analysis, I examine income mobility for all 15,100 percentile groups.I calculate income mobility as a difference-of-logs measure (interpreted as a growth rate)between 1990 and 2008. Table 6 provides a regional summary. To present these data, I dividemy sample into six regional groups: Europe and the West (22 countries), Latin America andthe Caribbean (28 countries), Central and Sub-Saharan Africa (42 countries), North Africaand the Middle East (11 countries), East Asia and the Pacific (20 countries), and EasternEurope and Central Asia (28 countries). I report each region’s average income mobility amongall percentiles and across different percentile groups (wealthier percentile groups are numberedhigher), followed by each region’s average change in GDP PC and the Gini (both constructed asdifference-of-logs scores between 1990 and 2008), and then each region’s average change inindustry and globalization.

I measure each country’s industrial production as value added in mining, manufacturing,construction, electricity, water, and gas, corresponding with International Standard IndustrialClassification divisions 10 through 45, calculated as a percentage of GDP. Data come from theWorld Development Indicators database (International Bank for Reconstruction and Development2010). I measure economic globalization with each country’s (a) inward stocks of foreigndirect investment (FDI), and (b) merchandise exports, both calculated as a percentage ofGDP. Investment and trade data come from UNCTAD’s online Handbook of Statistics (UNCTAD2010). Similar to GDP PC and the Gini, these measures are calculated as difference-of-logsscores [ln(T2)-ln(T1)], where T1 refers to each measure’s average value during the 1990–1994 period, and T2 refers to each measure’s average value during the 2004–2008 period.Due to missing data, the summary statistics for the industry and globalization measures onlycover 132 countries.

At first glance, Table 6 reveals impressive income mobility across all percentile groupsacross all six world regions. During the 1990–2008 period, all 24 percentile groups experiencedpositive income growth (ranging from .037 to .677). In fact, when I group percentile groupsinto deciles rather than quartiles (not shown), 59 of the world’s 60 groups experienced posi-tive growth (the poorest decile in Eastern Europe is the only exception). However, thesegroupings do mask substantial income declines experienced by individual percentiles. In fact,16.5 percent of the world’s 15,100 percentiles experienced an income loss, with over half com-ing from Africa and another third coming from Eastern Europe. From a different perspective,33.1 percent of all percentiles in Africa experienced an income decline, as did 27.0 percent of

582 CLARK



all percentiles in Eastern Europe. In contrast, negative income growth was far less common inLatin America (8.4 percent) and East Asia (2.2 percent), and income losses were almost non-existent in the West (.0 percent) and the Middle East (.1 percent).

On average, countries in East Asia (.575) experienced greater income growth than anyother region. In fact, every percentile group in East Asia experienced greater income growth(.467 through .677) than any percentile group outside of East Asia. This robust growth, ledby China, is generally considered the primary reason why between-country inequality hasdeclined in recent years. Among the 151 countries in my sample, seven of the top ten coun-tries in GDP PC growth during the sample period were from East Asia. Six of these countries,China ($5,515), Vietnam ($2,574), Bhutan ($4,395), India ($2,721), Laos ($1,962), and SriLanka ($4,215), all featured GDP PCs that were below the median ($6,835) in 2008, whileSouth Korea’s GDP PC ($25,498) was far above the median. Overall, then, East Asia’s growth,especially by China and India, has had an inequality-reducing effect.

However, if one examines the income mobility figures by different percentile groups, thedivergence of incomes within countries becomes readily apparent. In five of the six regions,

Table 6 • Regional Summary of Income Mobility, 1990–2008

Europe& theWest

Latin America& the

Caribbean

Central &Sub-Saharan

Africa

North Africa& the Middle

East

East Asia& thePacific

EasternEurope &

Central Asia

Number of countries 22 28 42 11 20 28Average income mobility[ln(2008)-ln(1990)]All percentiles .327 .380 .241 .414 .575 .203Percentiles 1-25 .291 .364 .435 .409 .467 .037Percentiles 26-50 .314 .373 .235 .412 .554 .147Percentiles 51-75 .331 .384 .176 .414 .601 .228Percentiles 76-100 .371 .400 .117 .420 .677 .400

Average GDP PC change[ln(2008)-ln(1990)]

.345 .391 .146 .416 .634 .290

Average Gini change[ln(2008)-ln(1990)]

.068 .021 -.071 .009 .111 .289

Average industry changea

[ln(T2)-ln(T1)] × 10−.015 .005 .034 .017 .072 −.031

Average FDI stock changea

[ln(T2)-ln(T1)] × 10.650 .703 .684 .786 .758 1.478

Average export changea

[ln(T2)-ln(T1)] × 10.325 .376 .383 .474 .465 .642

Notes: Europe and the West = Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Iceland,Ireland, Italy, Luxembourg, Malta, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, UnitedKingdom, andUnited States;LatinAmerica and the Caribbean = Argentina, Belize, Bolivia, Brazil, Chile, Colombia, CostaRica, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico,Nicaragua, Panama, Paraguay, Peru, Saint Lucia, Saint Vincent-Grenadines, Suriname, Trinidad-Tobago, Uruguay, andVenezuela; Central and Sub-SaharanAfrica = Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde,Central African Republic, Chad, Comoros, Congo (DR), Congo (R), Cote d’Ivoire, Djibouti, Ethiopia, Gabon, Gambia, Ghana,Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia,Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Swaziland, Tanzania, Togo, Uganda, Zambia, and Zimbabwe;North Africa and the Middle East = Algeria, Egypt, Iran, Israel, Jordan, Lebanon, Morocco, Pakistan, Tunisia, Turkey,and Yemen; East Asia and the Pacific = Bangladesh, Bhutan, Cambodia, China, Fiji, Hong Kong, India, Indonesia, Japan,Laos, Malaysia, Mongolia, Nepal, Papua NewGuinea, Philippines, Singapore, South Korea, Sri Lanka, Thailand, and Vietnam;Eastern Europe and Central Asia = Albania, Armenia, Azerbaijan, Belarus, Bulgaria, Croatia, Cyprus, Czech Republic,Estonia, Georgia, Greece, Hungary, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Macedonia, Moldova, Poland, Romania,Russia, Serbia-Montenegro, Slovakia, Slovenia, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan.a Due to missing data, the summary statistics for the industry and globalization measures cover 132 countries.




the wealthiest percentiles experienced the most upward mobility, while the poorest percentilesexperienced the least upward mobility. For example, percentiles 1 through 25 in the Westexperienced lower income growth (.291) than all other percentiles in the West. Likewise, inEastern Europe, percentiles 1 through 25 experienced almost no income growth at all (.037)compared to wealthier percentiles. Moreover, while countries in Latin America (.380), theMiddle East (.414), and East Asia (.575) all experienced higher average income growth thanthe West (.327), it is the wealthiest percentile groups in these developing regions that experi-enced the greatest upward mobility. It is only in Africa where the poorest percentiles fared bet-ter. In Africa, percentiles 1 through 25 experienced higher income growth (.435) than all otherpercentile groups. The experience of this region notwithstanding, though, the global era ischaracterized by the substantial divergence occurring within countries as much as it is markedby convergence between them.

Among all the income groups across the world, the wealthiest percentiles (76 through100) in East Asia experienced the greatest upward mobility (.677), while the poorest percen-tiles (1 through 25) in Eastern Europe experienced the least upward mobility (.037). Noticealso that East Asia experienced the largest GDP PC growth (.634) among all regions, whileEastern Europe experienced the greatest inequality increase (.289). To the extent that thesepatterns are connected, it is reasonable to hypothesize that economic growth yields greaterreturns for the wealthiest, while inequality changes are more consequential for the poor. Touse a different example, every region except for Africa experienced positive inequality changeduring the sample period, and Africa experienced the lowest economic growth (.146) amongall six regions. This may explain why upward mobility within Africa was dominated by thepoorest percentiles.

Finally, cross-national trends in industry and globalization may also be related to incomemobility. Countries in East Asia experienced the greatest industrial growth (.072) during thesample period, which is where the greatest upward mobility occurs. Conversely, countries inEastern Europe experienced the largest negative industrial growth (-.031), which is whereincome growth is lowest. Notice also that countries in Eastern Europe experienced the greatestgrowth in inequality, while also experiencing the greatest growth in FDI stock (1.478) andexports (.642). Conversely, Africa is the only region where inequality fell and, among devel-oping countries, globalized little during the sample period, featuring the lowest growth inFDI stock (.684), and second lowest growth in exports (.383). These patterns suggest thatindustrialization may have helped developing countries converge with the West, while global-ization may have contributed to rising inequality within nations.

Finally, I examine the impact of economic growth, inequality change, industrialization,and globalization on income mobility through a set of difference models. I first regress incomemobility for all 15,100 percentile groups on its country’s economic growth and inequalitychange, controlling for each group’s lagged 1990 income and its percentile (1–100). I thenexamine the impact of industrialization and globalization with measures of each country’schange in (a) industrial production, (b) FDI inward stock, and (c) exports, along with regionalcontrols (West is the excluded reference category).

Table 7 presents the results. All models include a clustered sandwich estimator to allowfor intra-country correlation. Each cell reports the t-ratio with the standardized coefficient(mean = 0; standard deviation = 1) in bold. I also report the R2 and Bayesian InformationCriterion (BIC) at the bottom of each model (smaller BIC values indicate better fit), alongwith the maximum variance inflation factor (VIF) score. The maximum VIF score across everymodel is always well below 10, suggesting that collinearity is not problematic in these models(Chatterjee, Hadi, and Price 2000:240).

In Model 1, I estimate the lagged dependent variable, the percentile indicator, and the dif-ference-of-logs terms for GDP PC and the Gini. The lagged income measure is negativelysigned and statistically significant (B = −.16; p < .01), indicating that poorer income groupsexperienced greater income growth than wealthier groups. This is consistent with the decline

584 CLARK



in between-country inequality reported in Table 5. Conversely, percentile is positively signedand significant (B = .09; p < .001), indicating that wealthier percentiles experienced greaterincome growth than poorer percentiles. This is consistent with the reported rise in within-country inequality. As expected, the two component parts of income growth (GDP PC andthe Gini) are statistically significant. Economic growth is a significant, positive predictor ofmobility (p < .001), while change in inequality negatively affects mobility (p < .001). Also,the effect of GDP PC growth is more than three times as large (B = .67) as inequality change(B = −.22), indicating that, on average, growth is more effective in triggering upward mobilitythan redistribution.

Table 7 • Difference Models of Income Mobility, 1990–2008

(1) (2) (3) (4) (5) (6) (7)

Lagged income(Log)

−2.87**−.16

−2.87**−.16

−2.18*−.10

−2.05*−.32

−2.04*−.32

−1.85−.29

−1.98−.31

Percentile 4.15***.09

4.06***.09

4.00***.07

3.05**.17

3.04**.17

2.72**.15

2.99**.17

GDP PC (Δ) 38.42***.67

38.49***.67

46.55***.66

Gini (Δ) −9.06***−.22

−9.03***−.22

−8.80***−.24

Percentile ×GDP PC (Δ)

1.26.04

Percentile ×Gini (Δ)

9.56***.36

Latin America −.82−.06

−.82−.06

−.63−.05

−.75−.06

Africa −2.80**−.38

−2.80**−.38

−2.58*−.35

−2.72**−.37

Middle East −.74−.04

−.74−.04

−.57−.03

−.68−.04

East Asia −.23−.02

−.22−.02

−.04−.00

−.16−.01

EasternEurope

−2.27*−.17

−2.27*−.17

−2.18*−.17

−2.23*−.17

Industry (Δ) 2.14*.15

2.15*.15

2.19*.16

2.16*.16

FDI stock (Δ) −1.05−.08

−1.05−.08

−1.03−.08

−1.04−.08

Exports (Δ) 1.94.15

1.94.15

1.94.15

1.94.15

Percentile ×industry (Δ)

−.88−.02

Percentile ×FDI stock (Δ)

3.87***.12

Percentile ×exports (Δ)

2.18*.08

Observations 15,100 15,100 15,100 13,200 13,200 13,200 13,200Max VIF 1.45 1.45 1.48 5.21 5.21 5.30 5.23R2 .483 .484 .608 .152 .153 .164 .157BIC 32,940 32,911 28,754 35,455 35,456 35,270 35,382

Notes: All models include a clustered sandwich estimator to allow for intra-country correlation. Each cell reports thet-ratio, with the standardized coefficient in bold.*p < .05 **p < .01 ***p < .001 (two-tailed tests)




However, as the following two models reveal, the impact of inequality varies significantlyacross the population. In Models 2 and 3, I include interaction terms between the percentileindicator and the GDP PC and Gini change scores. In Model 2, the percentile × GDP PC inter-action is positive, indicating that economic growth enhances upward mobility by wealthierpercentiles. However, the effect is small and statistically nonsignificant. In Model 3, the per-centile × Gini interaction is positive and highly significant, indicating that the negative effectof inequality change on income mobility is significantly reduced among wealthier percentiles.This is quite intuitive, as increases in inequality would only serve to benefit the wealthiest per-centiles. However, the significant interaction also implies that the negative effect of inequalitychange is significantly enhanced among poorer percentiles.

Pursuing this result further, I replicated Model 1 across different population groups andpresent the results in Figure 2. For presentation purposes, the absolute value of each standard-ized coefficient is reported in order to facilitate comparisons between the positive effect ofGDP PC and the negative effect of the Gini. As Figure 2 shows, the negative effect of inequalitychange grows considerably stronger as the sample is restricted to poorer segments of the worldpopulation (consistent with Model 3), while the positive effect of GDP PC growth becomesslightly weaker (consistent with Model 2). In fact, the negative effect of inequality changebecomes larger (B = −.678; p < .001) than the positive effect of economic growth (B = .662;p < .001) when examining the poorest 25 percent of the world’s percentile groups, and the dis-parity grows larger as I continue to exclude the wealthiest remaining groups. Overall, theseresults indicate that reducing inequality is more effective than economic growth in producingupward mobility among the poorest one-quarter of the world’s population.

Models 4 through 7 estimate the impact of industrialization and globalization on incomemobility net of the lagged dependent variable, the percentile indicator, and regional controls.Consistent with the regional summaries reported in Table 6, African and Eastern Europeancountries experienced significantly less income growth than the West, while the remainingregions are not significantly different net of the other predictors. In Models 4 through 7, thechange score for industry positively affects income mobility (p < .05). Thus, income groupsin industrializing countries experience greater upward mobility than percentile groups

.2.3

.4.5

.6.7

.8.9

1.0

1.1

Stan

dard

ized

Coe

ffic

ient

(Abs

olut

e V

alue

)

95 85 75 65 55 45 35 25 15 5Population Coverage (Poorest % of World Population)

Standardized Coefficient (GDP PC)Standardized Coefficient (Gini)

Figure 2 • Model 1 Replications Across Different Population Groups

Note: The absolute value of each standardized coefficient is reported.

586 CLARK



elsewhere. Conversely, neither globalization measure performs well. Exports are marginallysignificant as a positive predictor in these models (p < .10), while FDI stock is nonsignificantand negatively signed.

In Models 5 through 7, I introduce interaction terms to investigate the differential effect ofthe industrialization and globalization measures across percentile groups. In Model 5, the per-centile × industry interaction is nonsignificant, indicating that the positive effect of industriali-zation is widespread. That is, the benefits of industrialization do not seem to be concentratedamong wealthy or poor segments of the population. In Model 6, however, the percentile × FDIstock interaction is highly significant (B = .12; p < .001) as a positive predictor of incomemobility,indicating that the positive effect of the percentile indicator is enhanced among those countrieswith high growth in FDI stock. In fact, the effect of FDI stock at one standard deviation belowthe mean percentile group is negative and significant (B = −.19; p < .05), while it is nonsignificantat one standard deviation above the mean percentile group. In other words, FDI stock negativelyaffects income mobility among poorer percentile groups, while having no significant effectamong wealthier groups. In Model 7, the percentile × exports interaction is positively signedand significant (B = .08; p < .05), indicating that the positive effect of the percentile indicatoris likewise enhanced among countries with high export growth. Moreover, the effect of exportsat one standard deviation above the mean percentile group is positive and highly significant(B = .23; p < .01), while it is nonsignificant at one standard deviation below the mean percentilegroup. Thus, exports positively affect incomemobility among thewealthiest percentile groups, butexert no significant impact among poorer groups.

In sum, global integration has the effect of stretching income distributions within countriesby (a) reducing income mobility among the poorest percentile groups via foreign investment,and (b) enhancing income mobility among the wealthiest percentile groups via trade. Thus,the spread of industrialization to the developing world may explain some of the between-country convergence observed in my estimates, while globalization better explains theincrease in within-country inequality.9 Nevertheless, to the extent that late industrializationin developing countries is a function of capital mobility and the removal of trade barriers, eco-nomic globalization may be said to have indirectly contributed to between-country convergence,while simultaneously widening income gaps within nations.

Discussion

This study builds upon previous work to provide new estimates of world incomeinequality during the 1990–2008 period. Improvements in the source data now allow formore accurate and comprehensive estimates of world income inequality to be generated.The results are broadly compatible with previous estimates, lending greater confidence toearlier studies. World income inequality is no longer rising, with declines in between-countryinequality continuing to offset the rise in within-country inequality. However, the resultsalso suggest that past studies misestimated the magnitude of these trends during the 1990s,overestimating the between-country decline and underestimating the increase in within-country inequality, such that overall inequality did not begin to substantially decline until thepost-2000 era.

Moreover, it is important not to overstate the magnitude of this recent overall decline.Rather, world income inequality should perhaps be seen as obeying a “high-inequalityequilibrium” (Korzeniewicz and Moran 2009), whose short-term trends are overshadowedby persistently high levels. In 1990, world inequality was quite high (Gini = .697), featuringan income distribution similar to Namibia (Gini = .708), the most unequal country in the

9. In separate analyses, I find that (a) adding a control for FDI flows, or (b) replacing exports with imports, produceresults that are substantively identical to those presented here.




world at that time. The top 10 percent of the world owned 52.5 percent of the world’s totalincome in 1990, while the bottom half owned 6.1 percent of all income. Compare this toNamibia, where the top 10 percent owned 51.2 percent of its income, while the bottom halfowned 6.1 percent. How had the situation changed by the end of the sample period? In2008, the wealthiest 10 percent of the world owned 50.0 percent of the world’s income, whilethe poorest half owned 9.1 percent. This level of inequality (Gini = .649) is comparable toComoros (Gini = 642), the third most unequal country in the world (as of 2008), in whichthe top 10 percent receives 47.5 percent of all the income, and the bottom half receives 9.2percent of all the income. In sum, between 1990 and 2008, the world has moved from featur-ing higher inequality than all but one country (Namibia) to featuring higher inequality than allbut two countries (Namibia and South Africa).

It is against this backdrop of high global inequality that Korzeniewicz and Moran (2009)outline several paths leading to social mobility for impoverished groups across the world.One path highlights the upward mobility of individual people who must face the relative dis-parities that exist within countries, while a second path highlights the upward mobility ofentire nations that attempt to address absolute disparities that exist across societies. To thisend, my analyses provide insight as to the relative utility of economic policies that emphasizegrowth (addressing between-country disparities) versus distribution (addressing within-countrydisparities) for impoverished groups who may benefit from either form of advancement.Previous studies have examined the welfare impact of economic growth in the developingworld (Brady, Kaya, and Beckfield 2007; Clark 2011; Firebaugh and Beck 1994). The findingsreported here extend this work by assessing the capacity for economic growth to lift the globalpoor out of poverty. While the positive effect of economic growth remains fairly stable acrossdifferent population groups (thereby revealing the benefits of growth for the masses), the find-ings here actually highlight the much greater importance of reducing inequality for promotingincome mobility among the very poor. Specifically, the results indicate that the bottom quarterof the world benefits more when their countries reduce inequality than when they enhancegrowth. Thus, in regions where there is a large concentration of the world’s poorest percentilegroups (e.g., Africa), policies aimed at redistribution may be more effective in stimulatingupward mobility. These findings thus build on prior work that has begun to draw greaterattention to the role that distribution plays in shaping poverty (Fosu 2010).

Nevertheless, Korzeniewicz and Moran (2009) emphasize a third path to social mobility:cross-national migration. In particular, they argue that migration represents “the single mostimmediate and effective means of global social mobility for populations in most countries of theworld” (Korzeniewicz and Moran 2009:107). Interestingly, globalization scholars routinelyemphasize the importance of capital mobility (rather than labor mobility) for addressing incomegaps between countries.With this inmind, I examine the spread of industrial production and eco-nomic globalization as factors that promote upward mobility. I find that late industrialization inthe developing world has strong positive effects on income mobility. Moreover, these effects donot vary by percentile group, but promote widespread benefits across the class structure withinsocieties. Thus, the North-South shift in industrial production may be responsible for much ofthe continuing convergence between countries without contributing, simultaneously, to thestretching of income distributions within societies. Conversely, I find that measures of global inte-gration, FDI stock and exports, have no significant effect on income mobility. Rather, through aseries of interactionmodels, I find that globalization enhances the relativemobility of wealthy per-centile groups.While foreign investment negatively affectsmobility among the poor, exports posi-tively affect mobility among the affluent. In this way, globalization has played a larger role inraising inequality within nations than in reducing inequality between them.

Accordingly, it is important to consider the rising significance of within-country inequal-ity. In 1990, only 28 of the 151 countries in my sample (18.5 percent) placed at least one of itspercentile groups into all ten world income deciles. By contrast, in 2008, 46 countries (30.5percent) did so. One of those countries is China, which now places more than 13 million

588 CLARK



people across every decile group in the world. In 1990, 84 percent of China lived in the bottomhalf of the world. But by 2008, only 45 percent of China lived in the bottom half. And whilemany have correctly attributed between-country convergence to this dramatic upward mobil-ity, it is important to recognize that China’s role in reducing inequality between countries hascome at the expense of increasing inequality within its own borders in a similarly dramaticfashion.

Wang Feng (2008) describes China’s post-socialist transformation during the late twenti-eth century, transitioning from one of the most egalitarian countries in the world to one thatfeatures one of the more unequal income distributions. Already, in 1990, China had the 53rd

lowest Gini coefficient among the 151 countries in my sample. Still, only about 35 percent ofthe world’s countries featured lower inequality at that time. However, China’s 38.1 percentgrowth rate in inequality during the sample period is ranked 20th highest in my sample.And by 2008, China had the 108th lowest Gini in my sample. Thus, now over 70 percent ofthe world’s countries feature lower inequality than China. In fact, by 2002, Feng (2008) notesthat income inequality had become the nation’s most serious social problem according tohigh-level officials.

Not surprisingly, existing research suggests that a “new geography” of world incomeinequality has emerged (Firebaugh 2003; Goesling 2001), such that nationality is not as gooda predictor of income today as in the past. Rather, the distribution of income within societiesnow plays a greater role than it used to in shaping one’s life chances. The present study buildsupon this work and offers partial support for this narrative. It is true that inequality within coun-tries is rising and now comprises a greater share (almost one-third) of total inequality. However,two qualifications need to be made. First, as Korzeniewicz and Moran (2009) remind us, thecountry where one is born remains the best predictor of that individual’s life chances. It isimportant to note that 92.8 percent of percentile groups in Africa live in the bottom 70 percentof the world, while 97.9 percent of percentile groups in the West live in the top 30 percent.Essentially, the two regions remain economically segregated from one another. In this way,geography remains quite salient. And, second, although countries have been slowly convergingwith one another for several decades now, the prospects for this continuing is in doubt giventhat the most significant force producing convergence during this time (i.e., China) will soonbecome a force that contributes to between-country divergence.

Conclusion

In critiquing popular depictions of global inequality, Firebaugh’s (2003) study seeks todebunk two claims commonly advanced by the media: (1) global inequality is increasing,and (2) globalization is the cause of this increase. In general, the findings from this study areconsistent with Firebaugh’s argument that global inequality is not rising, and that the effectsof globalization are countervailing, drawing national incomes closer together, while stretchingtheir distributions. Thus, in conjunction with existing research, the results from this study sug-gest the need to revise claims commonly made in public discourse regarding world incomeinequality trends and their suspected causes.

In this vein, I close with several important conclusions to be drawn from this study:(1) world income inequality has stopped growing and has now started to decline during thepost-2000 era, but that (2) the high level of world income inequality continues to overshadowshort-term trends; (3) between-country inequality has continued to decline (perhaps inresponse to the North-South industrial shift), and is now falling at an accelerated pace duringthe post-2000 era, but that (4) China’s economic trajectory as a middle-income nation suggeststhat convergence between countries may begin to slow down in the near future; (5) within-country inequality has continued to grow, although now at a slower pace during the post-2000 era, but that nevertheless (6) rising inequality within countries is not likely to relent ifglobal integration continues apace.




References

Alderson, Arthur and Francois Nielsen. 1999. “Income Inequality, Development, and Dependence:A Reconsideration.” American Sociological Review 64:606–31.

———. 2002. “Globalization and the Great U-Turn: Income Inequality Trends in 16 OECD Countries.”American Journal of Sociology 107:1244–99.

Anand, Sudhir and Paul Segal. 2008. “What Do We Know about Global Income Inequality?” Journal ofEconomic Literature 46:57–94.

Arrighi, Giovanni, Beverly Silver, and Benjamin Brewer. 2003. “Industrial Convergence, Globalization, andthe Persistence of the North-South Divide.” Studies in Comparative International Development 38:3–31.

.64

.66

.68

.70

.72

Gin

i Coe

ffici

ent

1990 1993 1996 1999 2002 2005 2008

Year

Appendix A • World Income Inequality (Gini Coefficient with 95% Confidence Intervals)

.75

.80

.85

.90

.95

1.00

The

il En

trop

y

1990 1993 1996 1999 2002 2005 2008

Year

Appendix B • World Income Inequality (Theil Entropy with 95% Confidence Intervals)

590 CLARK



Bandelj, Nina and Matthew Mahutga. 2010. “How Socio-Economic Change Shapes Income Inequality inPost-Socialist Europe.” Social Forces 88:2133–62.

Beckfield, Jason. 2006. “European Integration and Income Inequality.” American Sociological Review71:964–85.

———. 2009. “Remapping Inequality in Europe: The Net Effect of Regional Integration on Total IncomeInequality in the European Union.” International Journal of Comparative Sociology 50:486–509.

Bhalla, Surjit. 2002. Imagine There’s No Country: Poverty, Inequality, and Growth in the Era of Globalization.Washington, DC: Institute for International Economics.

Bourguignon, Francois and Christian Morrisson. 2002. “Inequality among World Citizens: 1820–1992.”American Economic Review 92:727–44.

Brady, David, Yunus Kaya, and Jason Beckfield. 2007. “Reassessing the Effect of Economic Growth onWell-Being in Less-Developed Countries, 1980–2003.” Studies in Comparative International Development42:1–35.

Chase-Dunn, Christopher. 1998. Global Formation: Structures of the World-Economy, Updated Edition. Lanham,MD: Rowman and Littlefield Publishers.

Chatterjee, Samprit, Ali Hadi, and Bertram Price. 2000. Regression Analysis by Example. 3d ed. New York:Wiley.

Clark, Rob. 2011. “World Health Inequality: Convergence, Divergence, and Development.” Social Scienceand Medicine 72:617–24.

Dixon, William and Terry Boswell. 1996a. “Dependency, Disarticulation, and Denominator Effects:Another Look at Foreign Capital Penetration.” American Journal of Sociology 102:543–62.

———. 1996b. “Differential Productivity, Negative Externalities, and Foreign Capital Dependency: Replyto Firebaugh.” American Journal of Sociology 102:576–84.

Dowrick, Steve and Muhammad Akmal. 2005. “Contradictory Trends in Global Income Inequality: A Taleof Two Biases.” Review of Income and Wealth 51:201–29.

Feng, Wang. 2008. Boundaries and Categories: Rising Inequality in Post-Socialist Urban China. Stanford, CA:Stanford University Press.

Firebaugh, Glenn. 1992. “Growth Effects of Foreign and Domestic Investment.” American Journal of Sociology98:105–30.

———. 1996. “Does Foreign Capital Harm Poor Nations? New Estimates Based on Dixon and Boswell’sMeasures of Capital Penetration.” American Journal of Sociology 102:563–75.

———. 1999. “Empirics of World Income Inequality.” American Journal of Sociology 104:1597–1630.———. 2003. The New Geography of Global Income Inequality. Cambridge, MA: Harvard University Press.Firebaugh, Glenn and Brian Goesling. 2004. “Accounting for the Recent Decline in Global Income Inequal-

ity.” American Journal of Sociology 110:283–312.Firebaugh, Glenn and Frank Beck. 1994. “Does Economic Growth Benefit the Masses? Growth, Depen-

dence, and Welfare in the Third World.” American Sociological Review 59:631–53.Fosu, Augustin. 2010. “Inequality, Income, and Poverty: Comparative Global Evidence.” Social Science

Quarterly 91:1432–46.Goesling, Brian. 2001. “Changing Income Inequalities Within and Between Nations: New Evidence.”

American Sociological Review 66:745–61.Harrison, Bennett and Barry Bluestone. 1988. The Great U-Turn: Corporate Restructuring and the Polarizing of

America. New York: Basic Books.International Bank for Reconstruction and Development. 2007. World Development Indicators CD-ROM: 2007.

Washington, DC: International Bank for Reconstruction and Development.———. 2010. World Development Indicators. Washington, DC: International Bank for Reconstruction and

Development. Retrieved May 2010 (http://data.worldbank.org/data-catalog).International Comparison Program. 2007. 2005 International Comparison Program: Preliminary Results.

Washington, DC: International Bank for Reconstruction and Development.———. 2008. 2005 International Comparison Program: Tables of Final Results. Washington, DC: International

Bank for Reconstruction and Development.Kentor, Jeffrey. 1998. “The Long-Term Effects of Foreign Investment Dependence on Economic Growth,

1940–1990.” American Journal of Sociology 103:1024–46.Kentor, Jeffrey and Terry Boswell. 2003. “Foreign Capital Dependence and Development: A New Direction.”

American Sociological Review 68:301–13.Korzeniewicz, Roberto and Timothy Moran. 1997. “World-Economic Trends in the Distribution of Income,

1965–1992.” American Journal of Sociology 102:1000–39.




———. 2009. Unveiling Inequality: A World-Historical Perspective. New York: Russell Sage Foundation.Kuznets, Simon. 1955. “Economic Growth and Income Inequality.” American Economic Review 45:1–28.Lee, Cheol-Sung, Francois Nielsen, and Arthur Alderson. 2007. “Income Inequality, Global Economy, and

the State.” Social Forces 86:77–111.Mahutga, Matthew. 2006. “The Persistence of Structural Inequality? A Network Analysis of International

Trade, 1965–2000.” Social Forces 84:1863–89.Mahutga, Matthew and Nina Bandelj. 2008. “Foreign Investment and Income Inequality: The Natural

Experiment of Central and Eastern Europe.” International Journal of Comparative Sociology 49:429–54.Milanovic, Branko. 2005. Worlds Apart: Measuring International and Global Inequality. Princeton, NJ. Princeton

University Press.———. 2009. “Global Inequality Recalculated: The Effect of New 2005 PPP Estimates on Global Inequality.”

Policy Research Working Paper 5061. Washington, DC: World Bank.Rasler, Karen andWilliam Thompson. 2009. “Globalization and North-South Inequality, 1870–2000: A Factor

for Convergence, Divergence or Both?” International Journal of Comparative Sociology 50:425–51.Rodrik, Dani. 1999. The New Global Economy and Developing Countries: Making Openness Work. Washington,

DC: Overseas Development Council.Sachs, Jeffrey and Andrew Warner. 1995. “Economic Reform and the Process of Global Integration.”

Brookings Papers on Economic Activity 1995:1–95.Sala-i-Martin, Xavier. 2006. “TheWorld Distribution of Income: Falling Poverty and . . . Convergence, Period.”

Quarterly Journal of Economics 121:351–97.Schultz, T. Paul. 1998. “Inequality in the Distribution of Personal Income in the World: How it is Changing

and Why.” Journal of Population Economics 11:307–44.Solt, Frederick. 2009. “Standardizing the World Income Inequality Database.” Social Science Quarterly

90:231–42.Stata Corporation. 2009. Stata 11.1. College Station, TX. Stata Press.United Nations Conference on Trade and Development (UNCTAD). 2010. UNCTAD Handbook of Statistics

Online. Geneva, Switzerland: United Nations Conference on Trade and Development. Retrieved October2010 (www.unctad.org/Templates/Page.asp?intItemID=1890&lang=1).

United Nations University-World Institute for Development Economics Research (UNU-WIDER). 2008.World Income Inequality Database, Version 2.0c. Helsinki, Finland: United Nations University-World Insti-tute for Development Economics Research.

592 CLARK



world income inequality in the global era: new estimates, 1990-2008

Documents