dataesr/scientific-paragraphs-categorization huggingface dataset:
This chapter draws heavily on statistics computed by the PovcalNet project. Some regional initiatives aimed at computing social statistics from household survey microdata in a standardized way are useful to study distributive issues in specific 4 Statistics are derived from the estimation of a general quadratic and a beta Lorenz curves from grouped data. Shorrocks and Wan (2008)  propose an algorithm that reproduces individual data from grouped statistics with a higher degree of accuracy. geographic areas, and as sources of information for world databases. For instance, the Socioeconomic Database for Latin America and the Caribbean (SEDLAC), jointly developed by CEDLAS at the Universidad Nacional de La Plata (Argentina) and the World Bank's LAC poverty group (LCSPP), includes distributive and labor statistics for LAC constructed using consistent criteria across countries and years.
BADEINSO, developed by the United Nations´ ECLAC, is also a large and good-quality database on economic and social variables in LAC. In Eastern and Central Europe the World Bank ECA database includes statistics for 28 countries since 1990 computed from direct access to household surveys. The Household Expenditure and Income Data for Transitional Economies developed by Branko Milanovic in the World Bank is the predecessor of that database. Milanovic has also built the World Income Distribution (WYD) database, which includes data for five benchmark years  (1988, 1993, 1998, 2002 and 2005)  for 146 countries, 75% obtained from direct access to household surveys. The dataset has been used in several studies to compute global inequality  (Milanovic, 2002 (Milanovic, , 2005 (Milanovic, , 2012)).
The Luxembourg Income Study (LIS), described in chapter 9 of this volume, includes distributive information computed from household survey microdata for developed countries. LIS also reports statistics for several transitional economies in Eastern Europe and recently has added some developing countries in Latin America (Brazil, Colombia, Guatemala, Mexico, Peru and Uruguay). The growth in the availability of distributive statistics stimulated efforts to gather and organize them. Deininger and Squire (1996)  put together a large dataset of quintile shares and Gini coefficients for most countries since World War II taken from different studies and national reports. This panel database, which greatly promoted the empirical study of the links between inequality and other economic variables, was updated and extended by the UNU/WIDER-UNDP World Income Inequality Database (WIID)  (WIDER, 2008).
5 The WIID database includes Gini coefficients, quintile and decile shares, and the income shares of the top 5% and bottom 5%. The information is drawn from very different sources, which raises comparability concerns. 6 To provide guidance in the use of the database, ratings are given to the observations, based on the survey quality, the coverage, and the quality of the information provided by the original source. The SWIID database is an effort to identify reasonably comparable information in WIID  (Solt, 2009). 7 5 WIID was initially compiled over 1997-1999 for the UNU/WIDER-UNDP project "Rising Income Inequality and Poverty Reduction: Are They Compatible?" directed by Giovanni Andrea  Cornia. Tabatabai (1996)  at International Labour Organization also made an independent effort to put together distributive statistics for many countries in the world.
6 Analyzing the Deininger and Squire (DS) dataset,  Atkinson and Brandolini (2001)  conclude that "users could be seriously misled if they simply download the accept series (i. e. , the "high quality" subset)". Although WIID implies a significant improvement from the original DS dataset, a similar word of caution applies. 7 SWIID should also be reviewed critically: in many cases it requires a case-by-case analysis, which is simply a sign that much effort is still needed in putting together comparable statistics. As it is based on secondary datasets, external problems are inadvertently incorporated. The All the Ginis database, assembled also by Branko Milanovic, is a compilation and adaptation of Gini coefficients retrieved from five datasets: LIS, SEDLAC, WYD, the World Bank ECA database, and WIID.
Besides gathering all the information in a single file, the All the Ginis database is useful as it provides information on the welfare concept and recipient unit to which the reported Gini refers, facilitating the comparisons. The Chartbook of Economic Inequality, assembled by  Atkinson and Morelli (2012) , presents a summary of evidence about changes in economic inequality (income/consumption, earnings and wealth) in the period from 1911 to 2010 for 25 countries. The information drawn from household surveys for the seven countries in the developing world included in the database (Argentina, Brazil, India, Indonesia, Malaysia, Mauritius and South Africa) starts in the 1950s. All the datasets mentioned above are based on data from national household surveys. 8 ven when they are the best available source of information for distributive analysis, household surveys are plagued with problems for international comparative studies, because, among other reasons, the questionnaires and the procedures to compute income/consumption variables differ among countries, and frequently also within a country over time.
Inequality in the developing countries We start by comparing inequality levels across developing countries based on the Gini coefficient for the distribution of household consumption per capita for year 2010, computed in PovcalNet mostly from household survey microdata. Other inequality measures are highly correlated with the Gini coefficient. For instance, in PovcalNet and WIID datasets the Pearson and Spearman correlations of the Gini and several extreme inequality measures (e. g. the 90/10 and 80/20 income-share ratios) exceed 0. 9. PovcalNet includes information for the distribution of per capita consumption expenditures, except in almost all Latin American and a few Caribbean countries, for which income inequality statistics are reported. In the analysis that follows we adjust the income Gini coefficients in that region to reflect the gap between income and consumption inequality estimates.
Specifically, we selected seven Latin American countries with household surveys that include reasonably good consumption and income data in several years: 14 on average the ratio of the consumption/income Ginis is 0. 861 (standard deviation of 0. 046). We apply that coefficient to the 22 Latin American and Caribbean countries with income data to approximate their consumption Ginis. 15   In most cases the observations correspond to year 2010, or adjacent years. However, some countries are lacking a recent household survey (or it was dropped due to quality concerns). In fact, in 24 countries the survey used to estimate inequality in 2010 was carried out between 2000 and 2005, while in 6 cases (5 of them in the Caribbean) the observation corresponds to the 1990s. With that caveat in mind, the PovcalNet dataset has relatively recent distributive information for 82% of the countries in the developing world, representing 97% of its total population (see Table  A.
WDR (2006)  reports consumption and income Ginis in four Latin American countries; the mean ratio of the Ginis is 0. 81. The value is somewhat lower (0. 77) for the eight non-LA countries in the sample. main missing country in terms of population is Cuba). The dataset in Middle East and North Africa does not contain information only for Lebanon and Libya, which represent 3% of the MENA population. In South Asia the only country missing is Afghanistan, while in Sub-Saharan Africa there is information for 42 out of the 47 countries, representing 95% of the population. Figure  3. 1 displays the range of Gini coefficients for 122 countries around year 2010, ranking from the least unequal (Ukraine, 25. 6) to the most unequal economy (South Africa, 63. 1). 16 The mean value is 39. 8, while the median is 39.
Russia Squire dataset that 90% of the total variance in the Gini coefficient is explained by variation across countries, while only a small percentage is accounted by variation over time. From this observation  Li et al. (1998)  conclude that inequality should be mainly determined by factors which differ substantially across countries, but tend to be relatively stable within countries over time. We find a similar result in a panel of developing countries from 1981 to 2010 (PovcalNet data): 88. 5% of the variance in that panel is accounted by variation across countries. The inequality rankings are relatively stable over time. The Spearman-rank correlation coefficient for the Ginis in 1981 and 2010 is 0. 68, while it rises to 0. 74 for 1990 and 2010, both significant at 1%. The last decades witnessed enormous economic, social and political changes in the developing world, but, although the income distributions have been affected with various intensities, the world inequality ranking has not changed much, a fact that suggests the existence of some underlying factors that are stronger determinants of the level of inequality.
In Figure  3. 2 developing countries are grouped in regions. Sub-Saharan Africa is the geographic area that includes countries with the highest inequality levels, but it is also the region with the highest dispersion, possibly in part due to measurement errors (Table  3. 1). Although eight out of the ten highest Gini coefficients belong to Sub-Saharan African countries, and the arithmetic mean of the Gini coefficient is the highest in the world, the median is lower than in Latin America. 17 17 The harmonic mean is similar in SSA than in LAC. Latin America and the Caribbean has been typically pointed out as the most unequal region in the world. Deininger and Squire (1996) , for instance, state that their dataset confirm the "familiar fact that inequality in Latin America is considerably higher than in the rest of the world".
18 See also Lopez Calva and  Lustig (2010)  and  Chen and Ravallion (2012). The All the Ginis dataset (ATG) includes Gini coefficients from LIS, SEDLAC, WYD, the World Bank ECA database and WIID. We selected consumption Ginis from ATG for year 2005 or close, and applied a similar adjustment as described above for those countries in LAC with only income Ginis. The basic results are similar to the ones obtained with PovcalNet data. The linear correlation coefficient for the Gini between both data sources is 0. 763, while the Spearman rank correlation is 0. 771, both significant at 1%. The Gini coefficients in ATG go from 23. 1 (Czech Republic) to 62. 9 (Comoros). The mean and median coincide in 40. 1. Again, more than half of the observations are in the range  [35, 45]. Only several Eastern European countries have Ginis below 30, while only four Sub-Saharan African countries have Ginis higher than 55.
The evidence on inequality levels in the developing world drawn from WIID is similar. For instance, based on a sample of income Ginis for around 2005,  Gasparini et al. (2013)  find that the mean Gini for the six Sub-Saharan African countries in the dataset is 56. 5, followed by Latin America (52. 9), Asia (44. 7) and Eastern Europe and Central Asia (34. 7). 20 We find that the linear correlation coefficient for year 2005 for the Gini coefficient in PovcalNet and WIID is 0. 871, and the Spearman coefficient is 0. 820. The Luxembourg Income Study database (see chapter 9 of this volume) covers 36 countries, including 6 in Latin America, which occupy the top places in all the income inequality rankings. The LA Ginis go from 50. 6 in Colombia to 43. 9 in Uruguay; the most unequal non-LA country is Russia with a value of 40.
8, while the rest of the countries in LIS go from 37 (USA) to 22. 8 (Denmark). The mean Gini for the Eastern European countries in LIS is slightly higher than the mean for the high-income economies. Data from the World Development Indicators also suggest that inequality in the developing world is significantly higher than in the OECD high-income countries. The mean income Gini for the latter group is 32. 2, which is lower than in any other region in the world. 20 The OECD high-income countries rank as the least unequal in the world with a mean income Gini of 32. 8. The EHII database confirms the high inequality levels of Sub-Saharan Africa and Latin America, but perhaps surprisingly, it records similar levels in South Asia and Middle East and North Africa. 21 The mean Gini for the period  1998-2002 is 47.
3 in SSA, 46. 4 in LAC, 46. 4 in MENA and 47. 2 in SA. According to this dataset inequality is relatively lower in East Asia and Pacific (44. 9) and Eastern Europe and Central Asia (44. 0). The estimated level of the Gini coefficient is substantially lower in the developed economies; the mean is equal to 36. 5. 22 The Pearson (Spearman) correlation coefficient between EHII and PovcalNet Ginis is 0. 642 (0. 603), lower than the resulting value when comparing PovcalNet with WIID or ATG, but still significant at 1%. Most international databases do not provide confidence intervals for the point estimates of the distributive measures, making impossible the assessment of the statistical significance of the differences among countries. However, given that the indicators are calculated from large national household surveys, the confidence intervals are typically relatively narrow.
Inequality beyond the Gini coefficient The international databases usually allow a closer look at the distributions in the world beyond a single parameter, such as the Gini coefficient. Table  3. 3 reports some basic statistics of the decile shares in 120 countries around 2010. 23 On average (unweighted) the poorest 10% of the population in a country accrues 2. 6% of total consumption reported in the survey: that share climbs to 31. 5% for the top 10%. In a typical developing country the aggregate consumption of the poorest 60% of the population is similar than the consumption of the top 10%. It is interesting to notice that the coefficient of variation of the decile consumption shares across countries is decreasing up to the top decile, when it strongly rises: countries in the world seem substantially different in the consumption share of the poor and the rich, but not in the share of the middle strata, in particular the uppermiddle strata.
Inequality in the Gallup World Poll The Gallup World Poll provides new evidence on the international comparisons of income inequality, as it includes identical income and household structure questions applied to national samples in 132 countries. Of course, the reliability of the national inequality estimates in Gallup is lower than those obtained with household surveys, since only one income question is used to approximate well-being, and the sample sizes are considerably smaller. However,  Gluzmann (2012)  finds that the correlation coefficient between the Gini coefficients computed with Gallup microdata and those reported in the World Development Indicators (WDI) that are based on per capita income is high (0. 85). Interestingly, the relationship with the consumption Ginis in WDI is much weaker; the linear correlation coefficient is 0.
Typically, incomes reported to the surveys are checked against the registers, or incomes are directly taken from administrative sources for the individuals in the sample. Even if the combination of survey and administrative data can be seen as an improvement, there remains the issue of the sampling framework for the top of the distribution. 25 In any case, statistics offices in the developing world are not exploiting register data to complement surveys yet. The use of tax statistics is not without drawbacks. First, since only a fraction of the population files a tax return, studies using tax data are restricted to measuring top shares, which are silent about changes in the lower and middle part of the distribution. Second, tax data are collected as part of an administrative process and do not seek to address research needs; both income and tax units are defined by the tax laws and vary considerably across time and countries.
In all regions the share of countries with falling inequality rose in the 2000s, as compared to the 1990s. The two most remarkable changes in the pattern occurred in Latin America, and in Eastern Europe and Central Asia. While the Gini went down in 26% of the LA economies in the 1990s, that share increased to 95% in the 2000s. In ECA while the growth in inequality was generalized in the 1990s, more than half of the countries experienced reductions in the 2000s. 36 We prefer not to report the regional patterns before 1990 since the number of observations is small in several regions. The numbers of countries by region in the sample we use to assess inequality trends are: 8 in EAP, 20 in ECA, 19 in LAC (all in Latin America, none from the Caribbean), 5 in MENA, 4 in SA and 20 in SSA. The picture of national inequalities in the developing world is similar when using other databases.
For instance, the unweighted mean Gini in the All the Ginis database assembled by Milanovic grew from 36. 2 in 1990 to 40. 7 in 1999 and then dropped to 39. 7 by 2005. While in the 1990s inequality rose in 63% of the economies in the ATG database, that share dropped to 35% in the 2000s. The recorded increase in the 1990s was generalized across regions, but especially intense in Eastern Europe and Central Asia (9 Gini points), while the fall in the 2000s was larger in MENA and Latin America. Cornia and Kiiski (2001) ,  Cornia (2011)  and  Dhongde and Miao (2013)  document similar results using WIID data. We find that the linear (rank) correlation coefficient for the change in the Gini coefficient between 1990 and 2005 recorded in PovcalNet and WIID is 0. 776 (0. 868), significant at 1%. The corresponding values for the comparison between PovcalNet and ATG are 0.
721 and 0. 765. The evidence drawn from the EHII database is also roughly consistent with the patterns discussed above. The mean Gini for the developing world remained almost unchanged in the 1980s, increased in the 1990s from 42. 5 in 1990 to 47. 0 in 1999, and dropped to 46. 5 in 2002 (the latest available date). 37 While in 62% of the countries inequality increased in the early 1990s, that share dropped to 55% between 1993 and 1999, and to 49% between 1999 and 2002. The regional patterns are roughly consistent with those described above. The main difference is that EHII reveals a dramatic increase in inequality in the Middle East and North Africa (7 Gini points) that is not present in the evidence drawn from household surveys. In the rest of this section we briefly review the literature on inequality changes in each geographic region of the developing world, while we take a closer look to the story of some particular cases: Brazil, China, India, Indonesia and South Africa.
Eastern Europe and Central Asia The transition from central planning to market system in the countries of Eastern Europe (EE) and those belonging to the former Soviet Union (FSU) had profound socioeconomic impacts, which could be better documented (compared to the situation in previous decades) due to the improvements in the production and release of information by the new administrations  (Milanovic, 1998). 38 The fall of the communist regimes was followed by a substantial increase in inequality in almost all countries. 39  According to PovcalNet data the mean Gini for the distribution of per capita consumption expenditures grew from 26. 4 in 1990 to 31. 9 in 1996. The increase in the first half of the 1990s was particularly strong in those countries belonging to the FSU and in Southeast Europe, and somewhat milder in those economies that joined the European Union.
Latin America and the Caribbean All Latin American countries carry out household surveys that provide information to obtain estimates of poverty and inequality. All regular surveys in the region include income questions; still in a few of them consumption expenditures are reported, as well. The increasing availability of surveys in Latin America allowed the creation of databases that make efforts to standardize the generation of poverty and inequality statistics, favoring a close monitoring of the social and labor situation in the region (SEDLAC by CEDLAS and the World Bank, and BADEINSO by UN´s ECLAC). In contrast, the situation in the Caribbean is much less favorable, as surveys are isolated and information is not easy to access. In fact, trends shown in the literature and in this section are restricted to Latin America, which represents 94% of total LAC population.
The strong expansion of public transfers accounts to a large share of the fall in income inequality in  Brazil (Azevedo et al. , 2011; Barros et al. , 2010; Bérgolo et al. , 2011; Lustig et al. , 2012). The main force was the rapid expansion in the coverage of government cash transfers targeted to the poor, mainly a transfer to the elderly and disabled Despite this constraint, several studies shed some light about the trends in inequality in this region. 47 Authors coincide in dividing the last four decades into three periods. The first one, spanning until 1985, was characterized by rapid economic growth. Page (2007)  reports a substantial reduction in income inequality between the mid-1970s and the early 1990s. 48 Data from PovcalNet confirms that fall, although the magnitude is more modest.
Sub-Saharan Africa Although recently there have been many improvements, the lack of a consistent body of household surveys undermine the assessment of income inequality in Sub-Saharan Africa. Time series data on inequality is severely lacking in most SSA countries, hindering the inferences about trends in the region. For example, in PovcalNet some SSA countries are missing (Equatorial Guinea, Eritrea, Mauritius, Somalia, Zimbabwe), while for thirteen of them there is only one observation in the database for the whole period 1981-2010. In fact, very few countries have reliable surveys in the 1980s, and it is not until the mid-1990s when inequality could be really traced with some confidence in the region. Regional studies typically report a mixed picture, with both increases and decreases in inequality (e.
Data from PovcalNet and other sources suggest a slow downward pattern; but in any case the evidence is mixed and weak. Third, the heterogeneity among countries in terms of inequality levels and patterns is large, partly possibly due to various measurement errors. It is hard to identify a prototype of an inequality pattern in SSA, as in other regions such as LAC or ECA. The scarce literature on inequality in SSA is consistent with these observations. Go, Nikitin, Wang and Zou (2007) report that high income inequality levels in SSA have remained more or less constant over the last four decades. Okojie and Shimeles (2006)  underline the fact that SSA is one of the most unequal regions in the world, and that disparities have remained persistent over time. In contrast, Sala-i-Martin and Pinkovskiy (2010) picture a more optimistic scenario, reporting a significant downward pattern for inequality during the period of growth  (1995 to 2006).
Inequality convergence As suggested above, there are signs of inequality convergence among countries in the developing world. As an example, the mean Gini coefficient for the 20 most unequal countries in our PovcalNet sample in 1981 fell 11% in the following three decades, while it increased 58% for the 20 most egalitarian economies. Bénabou  (1996)  was the first to present empirical evidence for cross-country convergence in income inequality with data from 1970 to 1990 drawn from the Deininger and Squire dataset. He found evidence consistent with the predictions of a neoclassical growth model that yields convergence of the entire income distribution and not just the first moment. Evidence on inequality convergence was also found in studies that used improved data:  Ravallion (2003) it i i it G G G t e 1 1 - = ( + )( -1) + for t = 2, …, T ; i = 1, …, N, where G it is the Gini coefficient for country i in year t and e it is an heteroscedastic error term.