Gini coefficient

The Great Leveler: Violence and the History of Inequality From the Stone Age to the Twenty-First Century

by Walter Scheidel  · 17 Jan 2017  · 775pp  · 208,604 words

100–160s and 190s–260s CE in Roman Egypt 11.4Wealth inequality in Augsburg: number of taxpayers, average tax payments, and Gini coefficients of tax payments, 1498–1702 13.1Gross National Income and Gini coefficients in different countries, 2010 13.2Estimated and conjectured income Gini coefficients for Latin America, 1870–1990 (population-weighted averages for four,

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in the twentieth century 15.1Top 1 percent income shares in twenty OECD countries, 1980–2013 A.1Inequality possibility frontier A.2Estimated income Gini coefficients and the inequality possibility frontier in preindustrial societies A.3Extraction rates for preindustrial societies and their counterpart modern societies A.4Inequality possibility frontier for different values of

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larger project.8 HOW IS IT DONE? There are many ways of measuring inequality. In the following chapters, I generally use only the two most basic metrics, the Gini coefficient and percentage shares of total income or wealth. The Gini coefficient measures the extent to which the distribution of income or material assets deviates from

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000 versus $10,000. National income subsequently doubles while the distribution of income remains unchanged. The Gini coefficient and income shares remain the same as before. From this perspective, incomes have gone up without raising inequality in the process. Yet at the same time, the income gap between the top and bottom

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in favor of smaller and multidirectional changes in the distribution of material resources. In this book, I follow convention in prioritizing standard measures of relative inequality such as the Gini coefficient and top income shares but draw attention to their limitations where appropriate.10 A different problem stems from the

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end of this book.11 This brings me to the second category: problems related to the quality of the evidence. The Gini coefficient and top income shares are broadly congruent measures of inequality: they generally (though not invariably) move in the same direction as they change over time. Both are sensitive to the

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top income shares in the “World Wealth and Income Database” has put our understanding of income inequality on a more solid footing and redirected attention from somewhat opaque single-value metrics such as the Gini coefficient to more articulated indices of resource concentration.12 All these problems pale in comparison to those we

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income taxes rarely predate the twentieth century. In the absence of household surveys, we have to rely on proxy data to calculate Gini coefficients. Prior to about 1800, income inequality across entire societies can be estimated only with the help of social tables, rough approximations of the incomes obtained by different parts of

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herein, chapter 2, p. 78 (fortunes), chapter 9, p. 266 (handouts), and Scheidel and Friesen 2009: 73–74, 86–87 (GDP and income Gini coefficient). For overall levels of inequality, see herein, appendix, p. 455. For the Black Death, see herein, chapter 10, pp. 300–306. 6 Revelation 6:4, 8. 7 Milanovic

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Roman empire, but it was chiefly located outside Europe proper. For what it is worth, an isolated estimate of the income Gini coefficient for England and Wales around 1290 puts inequality at a comparable level of per capita output at a slightly lower level than in the Roman empire of the second century

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better diet and grew taller bodies. As I show in chapter 10, tax records from Italian cities show a dramatic drop in wealth inequality as local or regional Gini coefficients fell by more than 10 points and top wealth shares by a third or more. Hundreds of years of disequalization had been undone

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(with Ginis of 0.8 to 0.9), whereas smaller towns lagged far behind (0.5–0.65). Urban income inequality was also very high in Amsterdam, where the relevant Gini coefficient reached 0.69 in 1742. English tax records from 1524 to 1525 reveal urban wealth Ginis that were generally above 0

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to the early 1910s. Yet at the same time, real wages recovered and skill premiums declined. The Gini coefficient of national income distribution appears to have been similar in 1800 and 1914, which suggests that inequality had largely stabilized at a (high) plateau.29 Scandinavian countries provide relatively rich but sometimes puzzling information

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Similar reservations apply to records concerning a one-time tax in 1789 that have been taken to suggest an income Gini coefficient of 0.6 to 0.7, values that would have put inequality close to or even right at the theoretically possible maximum for that economy. These concerns make it difficult to

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and 1860, the rapid growth of the labor force, technological progress that favored industry and cities, and improved financial institutions drove up inequality to unprecedented levels. By 1860, the income Gini coefficient for the entire country reached 0.51, up from 0.44 in 1774 and 0.49 in 1850, and the “1

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Southern states, significantly more than the total value of all farmland and associated buildings. Slaveownership had driven up inequality in the South to higher levels than elsewhere in the country: by 1860 the Gini coefficient of household income reached 0.61 in the Southern Atlantic states, 0.55 in the Eastern Southern Central

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very little attention. A pioneering study of 128 countries from 1960 to 2004 found that civil war increased inequality, particularly during the first five years after a conflict. On average, the income Gini coefficient rose 1.6 percentage points in countries during civil war and 2.1 percentage points during the recovery phase

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(which depressed top income shares), wage compression from reruralization under Franco (with reduced overall wage inequality), and rising returns to property, especially land, under autarky (which offset these effects to produce the overall Gini coefficient of income inequality). All this took place in the context of zero net real per capita GDP growth from

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be available from the late fifteenth century onward. Throughout that latter period, we observe a persistent trend of rising inequality. In most cases, eighteenth-century entries from each town yield higher Gini coefficients than the corresponding records from the end of the medieval period. This holds true for urban as well as rural

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communities—and regardless of whether inequality is measured through Gini coefficients or the wealth shares of the richest decile, both of which are used in Fig. 10.4. This general trend toward property concentration

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and which is thought to have killed as much as a third of the population of northern Italy, failed to have any comparable effect on inequality: Gini coefficients and top wealth shares in 1650 or 1700 were consistently higher than they had been in 1600, even after the preceding 150 years of recovery

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overall trends (Fig. 10.6). The only observed period of significant decline is associated with the plague; in rural areas, inequality generally grew from about 1450 onward; after around 1600, observed Gini coefficients were almost always higher than they had been in previous centuries, invariably peaking during the eighteenth century. Moreover, in several

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trends thus seem fairly representative. The data document a striking degree of change over time. Thanks to the accumulation and concentration of capital, the Gini coefficient of inequality in wealth taxes rose from 0.66 in 1498 to 0.89 in 1604 (Fig. 11.4).31 Economic stratification in 1618 was intense:

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falling real wages, just as in many of the other urban populations surveyed in the previous chapter.32 Figure 11.4Wealth inequality in Augsburg: number of taxpayers, average tax payments, and Gini coefficients of tax payments, 1498–1702 Such was the situation right at the beginning of the Thirty Years War, a complex

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(whichever is later) until 2010, Daron Acemoglu and his associates find no consistent effect of democracy on market or even disposable income inequality. An observed negative effect on the Gini coefficient of disposable income distribution does not reach statistical significance. It is true that the lack of precision of many of the underlying

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not yet been available to Kuznets himself.3 Across-country data aggregations that relate per capita GDP in different places to a measure of inequality, usually the Gini coefficient of the income distribution, ostensibly provide a striking illustration of Kuznets’ prediction. When applied to global data sets and plotted in a chart,

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replaced by regional dummy variables, the inverted U-shape simply disappears from across-country charts. This holds true regardless of whether Gini coefficients or top income deciles are used to measure inequality. In most of the world, countries having dramatically different per capita incomes, from low-income countries in Sub-Saharan Africa and

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per capita GDP are fairly heterogeneous in general, and especially at the high end, which the high-inequality United States shares with low-inequality Japan and parts of Europe.5 Figure 13.1Gross National Income and Gini coefficients in different countries, 2010 EAP: East Asia and Pacific, ECA: Eastern Europe and Central Asia, LAC

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solid evidence for leveling by peaceful means. Since the 1980s, Western countries generally have not registered more than highly temporary declines in income inequality. Drops in the Gini coefficient of market income in Portugal and Switzerland in the 1990s conflict with information regarding top income shares. Post-Soviet countries have partly recovered from

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per capita GDP ranging from $1,500 to $4,800 in 1990 International Dollars that are associated with national inequality peaks (expressed by Gini coefficients), but his survey remains problematic for several reasons. The suggested inequality peaks for the Netherlands in 1732, Italy in 1861, and the United Kingdom in 1867 may not be

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have not risen at least minimally since around 1990. If we follow the same method to track Gini coefficients, we find that disposable income inequality increased everywhere except in Austria, Ireland, and Switzerland—and that market inequality grew without any exception at all. And in most cases, the concentration of income has been much

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01 percent of households for the first time exceeded the high-water mark of 1929. Moreover, it is very likely that published Gini coefficients of income distribution understate actual levels of inequality, because they are derived from surveys that have trouble capturing information about the most affluent households. For the United States, various

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As a result, between 1990 and 2010, the extraction rate—the proportion of the theoretically possible maximum degree of inequality that was actually reached—remained largely flat in China as per capita GDP and Gini coefficients climbed in tandem but doubled in Russia, where output failed to outgrow Soviet levels. More generally, income

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thirteenfold from 2.1 percent in 1977 to 27.3 percent in 2012.21 These changes have helped drive up wealth inequality across American society: between 2001 and 2010, the Gini coefficient of the distribution of net worth rose from 0.81 to 0.85 and that for financial assets from 0.85

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practice in focusing on relative indices of income and wealth distribution. However, in terms of absolute inequality—the width of the gap between high and low incomes—even the fairly constant or only gently rising Gini coefficients and top income shares observed in some western European countries translate to growing imbalances in actual

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disposable household incomes is very much the result of massive redistribution that offsets generally high levels of market income inequality. In 2011, the Gini coefficient for market incomes—before taxes and transfers—in five famously redistributive societies—Denmark, Finland, France, Germany, and Sweden—averaged 0.474, a figure virtually indistinguishable

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levels, substantial taxable benefits paid out for each child, and a minimum income for all citizens—the Gini coefficient of equivalized disposable income would fall by 5.5 percentage points, thereby narrowing the current inequality gap between Britain and Sweden by a little more than half. More limited changes would translate to correspondingly

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as a whole not until 1985—and in India a decade later.2 Dividing an observed income Gini coefficient by the maximum possible value (IPF) yields the “extraction rate,” which measures the proportion of theoretically possible inequality that was actually extracted by earners of incomes above subsistence. The extraction rate may range from

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by colonial powers or a foreign conquest elite, conditions that might have raised predatory extraction to exceptionally high levels.4 Figure A.2Estimated income Gini coefficients and the inequality possibility frontier in preindustrial societies Calculation of the IPF and extraction rates offers two important insights. It highlights the fact that early societies tended

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specific observations. It is the principle that counts: extraction rates give us a better sense of real inequality than Gini coefficients do alone. Does this mean that conventional inequality measures overstate the extent of real income inequality in modern societies relative to those found in the more distant past or in the poorest developing countries

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subsistence floors—was generally high not only across premodern history but also during the early stages of industrialization. Measures of nominal inequality, as expressed in Gini coefficients or top income shares and real inequality adjusted for social minima, thus converge in supporting the impression of massive income disparities prior to the Great Compression.7

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extraction rate of 63 percent. In a different context, Milanovic has argued that even under fairly extreme assumptions about feasible labor and capital income inequality, the Gini coefficient for the overall American income distribution could not rise above 0.6. But even 0.6 might be too high for a U.S.-style

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constrained in the first instance by the volume of output beyond what is needed to ensure bare physiological subsistence. A Gini coefficient of 0.4—middling by contemporary standards—points to extremely high effective inequality in a society in which average per capita GDP is only twice minimum subsistence and the potential for

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greatly vary among different stages of economic development, then it is legitimate directly to compare Gini coefficients from antiquity to the present.11 Figure A.5Different types of inequality possibility frontiers Whether the real extraction rate of inequality in the United States or in the United Kingdom today is as high as it was

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to suggest. Although the current effective extraction rate in America is almost certainly lower than it was in 1929, inequality has been remarkably persistent—or resurgent—in real terms. But not everywhere: Gini coefficients for disposable income in the mid-0.2s such as those found in Scandinavian countries today are necessarily much

Inequality and the 1%

by Danny Dorling  · 6 Oct 2014  · 317pp  · 71,776 words

part of this new austerity norm. As the economists at the IFS explained in 2013, ‘Over the past two decades … inequality among the bottom 99 per cent has fallen: the Gini coefficient for the bottom 99 per cent was 5 per cent lower in 2011/12, at 0.30, than in 1991.’9

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children into account complicates the picture further. Finally, calculating entire distribution measures of inequality, such as the Gini coefficient, tends to cause many more readers’ eyes to glaze over. Fortunately there is a strong correlation between the complex Gini coefficient of income inequality (measured after tax and benefits and adjusting for household size) and the simple

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Figure 6.5).45 Such claims for falling worldwide inequality are, of course, disputed; and measures of inequality that are more sensitive to the 1 per cent taking an ever greater share may not be as forgiving of extreme greed as the Gini coefficient; but inequalities within the middle of the distribution can nonetheless fall

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. Source: Branko Milanovic, 2012 Figure 6.5 Global and selected countries’ income inequality Gini coefficients 1966–2006 Shortly after the release of the 2012 World Bank report suggesting that

Termites of the State: Why Complexity Leads to Inequality

by Vito Tanzi  · 28 Dec 2017

time, in 1912 an Italian statistician, Corrado Gini, had proposed a way to measure income inequality in countries. The use of the so-called Gini coefficient, which soon became a popular statistic, may indicate that income inequality had become a serious social concern by that time. Workers’ associations and labor unions were being

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those at the top, the aristocrats, nobles, or major landowners, or in some countries toward those who owned slaves. Thus, the concept of a Gini coefficient– inspired redistribution had had almost no meaning historically. By the 1950s the entitlements had started acquiring aspects of “property claims” by some categories of citizens

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often in free goods and services). This kind of assistance has often existed in organized communities. That redistribution was not influenced by statistics, such as Gini coefficients, but by the poor conditions of some individuals. However, historically, much of the formal redistribution that had actually taken place in countries had been upward

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and the more uneven the initial income distribution, the greater would be the improvement in the distribution of income as measured, for example, by the Gini coefficient. A broad-based and flat-rate value-added tax would be an especially efficient instrument for pursuing this objective in addition to a broad-based

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seems to 218 Termites of the State have happened in recent decades in the United States and in many other countries. For changes in the Gini coefficients in recent decades for a large number of countries, see IMF (2012b, appendix, table 1). As mentioned earlier, in the distant past there were

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There would not be a clear reference point, such as a given level of, or change of the Gini coefficient, that would guide redistribution policy. No level of 220 Termites of the State the Gini coefficient could receive universal endorsement as being the optimal level. And no clear economic variable, such as annual income

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had already recognized in his seminal 1959 book, redistribution would inevitably remain a political issue. There is no objective way to determine what an ideal Gini coefficient, or another ideal measure of distribution of income or wealth, should be for a country. Nor, for example, is there an objective way to

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several programs that were more favorable to those at the lower end of the income distribution and that were financed by highly progressive taxes, the Gini coefficients stopped falling and started rising in several countries, even though the share of public spending into GDP and the welfare programs had not been

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significantly changed in the later decades. In recent years the Gini coefficients and other measures of unevenness have reached politically worrisome levels in several countries, especially in Anglo-Saxon countries, but also in countries such as

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by, the top 1 percent, or even the top 0.1 percent, in the income distribution. They have lamented the increases in the estimated Gini coefficients observed in the United States and in several other countries, in spite of the still high level of social spending. Some have used the statistical

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, regardless of whether it is financed with more or less progressive taxes, the lower are the countries’ poverty rates, and the lower are the Gini coefficients. Denmark has one of the highest ratios of public spending to GDP in the world and the lowest poverty rate. Generally, countries that have high

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least as measured by consumption rather than by income) than they would have had without the War on Poverty programs. Some statistics suggest that the Gini coefficients may understate the redistributive impact of American social policies, because they understate the value of real transfers that increase the consumption by (but not

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The implications of that evidence have been discussed in many recent books, some listed in the references to this book. The Gini coefficient, a statistic that is less sensitive to inequality at the lower end of the income distributions, has become larger in most countries in the last four decades, and in some

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recent years popular support for redistribution policies has increased especially in countries, such as China, Finland, Germany, and several Eastern European countries, where the Gini coefficient has increased the most, while that support has declined in countries, such as Bulgaria, Mexico, Peru, and Ukraine, where the

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Gini coefficient has decreased. The evidence for the United States is more ambiguous, at least as indicated by surveys and, perhaps, by the results of the 2016 elections. If governmental intervention is called for to deal with the growing income inequality, at what costs should that intervention

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governments of many countries. This loss of public revenue has contributed to swelling the incomes of the rich and the super rich, thus increasing the Gini coefficients in many countries. Dependent workers are not the ones who benefit from these tax avoidance schemes. Many exchanges in the modern economies involve the

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. Once again it must be remarked that those who benefit from these interpretations are not the average workers, and that these strategies end up affecting Gini coefficients and equity. Because of their number, their complexity, and the way in which they are now written (often requiring thousands of pages), many laws

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receiving were increasing significantly, contributing to higher Gini coefficients. Because income taxes are one of the most important instruments available to governments for changing the market-generated income distribution, the changes in the tax systems inevitably reduced the governments’ ability to deal with inequality and intensified the changes that were occurring in

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in taxes, the gross incomes of those who benefit the most are the HNWIs. This makes the real (and not just the officially measured) Gini coefficients go up. The Panama Papers, in April 2016, also showed that many rich individuals evaded taxes directly and not just through the tax-evading actions

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the corporations in which they owned shares. Tax Rates, Tax Structures, and Tax Avoidance 371 The issues that link tax developments to changes in Gini coefficients are complex and are likely to be of varying importance among countries. They are likely to differ significantly between, for example, the United States and

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decades must have played a significant role in making the after-tax distribution of income less equitable, and in raising the observed values of the Gini coefficients. Those changes were especially damaging to those workers who did not experience wage increases but that still continued to pay significant taxes on their

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between income inequality and the fraction of the labour force that is constituted by guard labor.” For a group of 18 countries, for which they provide data on guard labor – table 3.2 on p. 82 – there seem to be a strong direct correlation between guard labor and Gini coefficients. The higher

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and 2014 real incomes for workers in advanced countries were flat and they fell. They fell for 65 to 70 percent of the households. Gini coefficients became widely known statistics in recent years, and so did the shares of total income received by those at the top of the income distributions

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of government, 85, 313–14 on “political market,” 334 in School of Public Choice, 5, 6 on sovereign debt, 64 Buffet, Warren, 379 Bulgaria, Gini coefficient in, 317 Bureaucracy legal rules and, 253–54 public institutions and, 297–98 Bureaucratic state, 22 Burke, Edmund generally, 7–8 on constitutions, 270 Index

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, 69, 79, 110, 313–14 China allocation of resources in, 188 “fake goods” in, 149 Food Safety Law, 149–50 Gini coefficient in, 317 growth in, 311, 386–87 Index income inequality in, 221, 227, 306, 322 intellectual property in, 347, 358 mislabeling of fish in, 149–50 Cicero, 71 Cisco Systems,

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, 165 Drucker, Peter, 82 Duesenberry, James R., 3, 319 Du Pont, Pierre, 307 Dutch Republic, welfare policies in, 50 Dynamic scoring, 76 Eastern Europe Gini coefficient in, 317 laissez faire in, 35 taxation in, 381–82 Easy credit, 107–9 Eckstein, Otto, 2–5 Economic aristocracy, 117–18 Economic freedom deregulation

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sector,” 243 transaction activity in, 330 The Financial Times, 217, 352 Financial versus real investment, 106–7 432 Fine, Sidney, 18 Fines, 142 Finland Gini coefficient in, 317 marginal tax rates in, 376 Fiscal councils, 72–73, 272 Fiscal drag, 61 Fiscal policy, stabilization policies and, 237 Fiscal rules, 71–72

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, 377 General Theory (Keynes), 46–47 Genetically modified food, 182–83 Germany authoritarian government in, 23 economic planning in, 27 “fake goods” in, 149 Gini coefficient in, 317 laissez faire in, 18 marginal tax rates in, 376 reforms in, 22 “revolving door policies” in, 335–36 unions in, 231 welfare policies

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in, 218, 219 welfare states in, 20 Gini, Corrado, 19 Gini coefficient. See also specific country generally, 19, 209 changes in, 316 income redistribution and, 219–21, 224, 317 public spending and, 228–29 taxation and, 368

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, 319–21 in executive compensation, 82–83, 85–86, 105–6 ex post income distribution and, 118, 119 externalities and, 321 in France, 306 Gini coefficient. See Gini coefficient as government failure, 225 Hayek on, 227 importance of, 393 increase in, 316 in India, 221, 227 intellectual property, effect of, 343–45 justification

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193 effects on, 191–92 explicit government actions, 192, 200–1. See also Public spending; Taxation ex post income distribution, 118, 119 general policies, 191 Gini coefficient and, 219–21, 224, 317 globalization and, 205, 385–86 growth and, 227, 228 historical background, 209, 218–19 human capital and, 193 incentives

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Meckling, William, 363 Memoire sur le paupérisme (Tocqueville), 210 Mercantilism, 70, 123 Messi, Lionel, 352 Mexico economic planning in, 27 Gini coefficient in, 317 regulations in, 282 Microsoft, 347 Middle class, income inequality and, 206–7 Milanovic, Branko, 227 Mill, John Stuart, 147, 212, 312, 342 Minimal role of state. See Limited

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313–14 Pensions resistance to changing policies, 222–23 in US, 222–23 Performance-based compensation, 363–64 Performers, 204–5 Persson, Torsten, 286 Peru, Gini coefficient in, 317 Pharmaceutical industry, 115–17, 158, 365–66 Pharmaceutical Research and Manufacturers of America, 171 Phillips curve, 61 Pigou, A.C., 184, 185

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compensation, 83, 129–30 Public spending. See also specific country apparatus responsible for, 188, 233 as disincentive, 74–75 Gini coefficient and, 228–29 historical overview, 23–24, 35–36 income inequality and, 228–29 increases in, 384 law of public expenditure growth, 51, 87 limited role of government and, 96 market

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, 333–34 fees and fines, 368 flat taxes, 381–82 Friedman on, 367–68 Gini coefficient and, 368–69, 371, 382 globalization and, 378 Haig-Simons principle, 377–79 of high net worth individuals (HNWIs), 368 income inequality and, 368–69, 382 income redistribution and, 223–24 income tax. See Income tax

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, 53 revenue growth and, 385 stabilization policies and, 240 voluntariness of, 90–91 wealth tax, 341–42 welfare policies and, 45, 48–49 Tax avoidance Gini coefficient and, 331 globalization and, 369–70 by high net worth individuals (HNWIs), 370, 382 rise in, 379 tax competition, 370, 378 tax evasion versus,

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, 156 Southeast Asian financial crisis (1997–1998) compared, 243–44 Ukraine, Gini coefficient in, 317 Unemployment compensation, 237–38 Unions decline in, 398 in Germany, 231 government control of, 23 Hayek on, 231 historical background, 19 importance of, 23 income inequality and decline in, 342 Index in Italy, 23, 231 Keynes on,

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Mac, 138, 140–41 Full Employment Act of 1948, 49 Gini coefficient in, 317, 391–92 Government Accountability Office (GAO), 290 guns in, 165 Head Start, 213 Health and Human Services Department, 127, 213 health care in, 208, 222–23, 298 income inequality in, 161–62, 208–9, 221, 224, 227, 315

The Haves and the Have-Nots: A Brief and Idiosyncratic History of Global Inequality

by Branko Milanovic  · 15 Dec 2010  · 251pp  · 69,245 words

to the top income of 34,564 should all be taken into account. One such, and by far the most popular, measure of inequality is called the Gini coefficient, named after Corrado Gini, the Italian statistician and economist who defined it in 1914 and whose life partly overlapped with Pareto’s.26 The

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Gini coefficient compares the income of each person with the incomes of all other people individually, and the sum of all such bilateral income differences is

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included in this calculation and the average income of the group. The ultimate result is such that the Gini coefficient ranges from 0 (where all individuals have the same income and there is no inequality) to 1 (where the entire income of a community is received by one individual). This is a

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a society; now, since most people’s incomes were not very different from each other, such inequality statistics cannot be very high, either. In terms of our favorite measure of inequality, the Gini coefficient (see Essay I), inequality in the early Roman Empire was estimated to have been around 41-42 points.12 This is

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How much was socialism more equal than a corresponding capitalist society? How was this achieved? Was it worth it? And what type of inequalities existed under socialism? The value of the Gini coefficient for socialist countries was in the upper 20s or lower 30s (see Essay I for the empirical values of the

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Gini coefficient). These are some of the lowest values registered after the end of World War II when measurement of inequality became standard in most countries in the world. Approximately speaking, socialism was some 6-7 Gini

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points more equal than capitalism, with European capitalist countries such as West Germany, France, Italy, and Denmark having at the time (1970s and 1980s) Gini coefficients in the low- to mid

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isolated dissident in the early 1970s posed a then unthinkable question. The single most serious threat to Chinese unity is increasing inequality. Not only has Chinese inequality almost doubled, as the Gini coefficient increased from less than 30 in the early 1980s at the start of the reforms to 45 in 2005, but the

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of papers have been written on it. It is in almost equal measure supported and rejected. When, at a point in time, we plot countries’ Gini coefficients against countries’ income levels, we find something that looks like an inverted U shape, but, it is true, only when we work very, very

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from 1988 to 2005.2 The results show, not surprisingly, that global inequality is extremely high. Its Gini coefficient reaches the value of 70, which is higher than inequality in any single country in the world, even in such paragons of inequality like South Africa and Brazil that exhibit Ginis of “only” around 60.

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in the willingness of the rich to participate. Our Gini estimates thus come with rather large standard errors. Since 1988, when such global inequality calculations were first conducted, the Gini coefficient has remained at about the same level of 70; each “reading” has been within one standard error of every other. But this

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round of the EU enlargement (as Bulgaria and Romania became members), the overall inequality in the European Union, composed of twenty-seven member countries, and in the United States, composed of fifty states, is about the same. The Gini coefficient in both is just above 40. The United States is, as commonly perceived

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in South Dakota and Wisconsin (the two most equal states) and end with Texas and Tennessee, whose Gini coefficients are almost at Latin American levels, around 45 Gini points. This is to be contrasted with inequality in European countries that ranges from the most equal, Hungary and Denmark, with Ginis of around 24

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-two Latin American countries. Let us now change the point of view and consider internal inequalities in countries of the two continents. The lowest inequality in Latin America is in Uruguay, with a Gini coefficient of 45; the highest inequality is in Brazil and Bolivia, with Ginis just below 60. Now, in Asia, the

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States and the world an unnecessary crisis. Vignette 3.7 Did Colonizers Exploit as Much as They Could? The main measure of inequality, as we have seen before, is the Gini coefficient.1 Its value runs from a theoretical value of 0, when the entire income (of a country, community, continent, world—whatever

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a zero income (or consumption) for any period of time, much less for a year, which is the conventional unit of time over which inequality (and the Gini coefficient) are measured. Let us thus introduce the constraint that all members of a society have at least the physiological subsistence minimum, for otherwise people

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find the average extraction ratio to have been around 75 percent. This is about twice as high as in today’s United States, where the Gini coefficient is 40 and the frontier Gini is almost 100. But what is interesting is that, in this sample of thirty societies, there are six

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Problemi di formazione, di ripartizione e di misurazione, edited by M. Zenga [Milan: Vita e Pensiero, 1987], 307-328), the concentration ratio, later dubbed the Gini coefficient, was first introduced in Gini’s article “Sulla misura della concentrazione e della variabilità dei caratteri,” published in 1914 in Atti del Reale Istituto Veneto

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Malden, MA: Blackwell, 2006. Index Abramovich, Roman AC Milan soccer club Africa direct foreign investment in economy in Gini coefficient in global inequality and global middle class and household surveys in income divergence in intercountry inequality and migration and African Americans Agrarian reform Agriculture Albania Algeria Amalrik, Andrei Andalusia Angola Anna Karenina (Tolstoy) Anonymity

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principle Arabs Argentina Aristocracy Arkansas Armenia Aron, Raymond Arsenal Asceticism Asia Gini coefficient in global inequality and heterogeneity of Latin America as mirror image of Asia Minor Assimilation Atkinson, Anthony Aurelius, Marcus Austen, Jane Australia Austria Balkans Banerjee, Abhijit Bangladesh

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soccer club Chiang Kai-shek Chile China direct foreign investment in economic growth in GDP per capita in Gini coefficient for global inequality and growth rate in household surveys in income distribution in intercountry inequality and interpersonal inequality and population in price level in Chongqing, China Christianization Citizenship Clark, Gregory Class income distribution and interpersonal

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Ethics Ethiopia EU. See European Union Europe communism, end of and global inequality and income divergence in intercountry inequality and soccer clubs in European Court of Justice European Union (EU) GDP per capita in Gini coefficient for global inequality and income inequality in interpersonal inequality and soccer clubs and United States vs. Fanon, Frantz Fascism Feudalism, feudalists

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product Geopolitics George III, King German Social Democrats Germany intercountry inequality and redistribution in soccer clubs in Ghana Gibbon, Edward Gilded Age Gillet, George Gini, Corrado Gini coefficient Global financial crisis Global inequality Asia and evolution of GDP per capita in geopolitics and Gini coefficient and global income distribution and global middle class and globalization and

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income level of and taxation and theory of Investment foreign interpersonal inequality and poor and taxation and wages and workers and IPF. See Inequality Possibility Frontier Ireland IRS. See Internal Revenue Service Italy Ivory Coast Japan GDP per capita in Gini coefficient for intercountry inequality and price level in Jerusalem, University of Jiangsu, China Julian

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Khodorovsky, Mikhail Kirchner, Nestor Korea Kosovo Kuznets, Simon Kuznets’ hypothesis Kynaston, David Labor government employment and migration of Lampedusa detention camp Landlords Laos Latin America Asia as mirror image of GDP per capita in Gini coefficient

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club Mao Zedong Marginalist revolution Marshall, Alfred Marx, Karl Measurement between-component of inequality and consumption and fiscal data and GDP and Gini coefficient and of global inequality household surveys and of intercountry inequality of interpersonal inequality tax data and within-component of inequality and Mechanization Mercantilism Messi, Lionel Mexican flu Mexico GDP per capita in migration

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Redistribution beneficiaries of fiscal interpersonal inequality and justice and middle class and poor and voting and Rentiers Republican Party, Republicans Ricardo, David Rich investment and savings and social arrangements and Rich countries, migration to Rockefeller, John D. Rodrik, Dani Roman Empire Christianization of GDP per capita in Gini coefficient in middle class and

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senators of social structure of Romania Russia Gini coefficient in intercountry inequality and interpersonal inequality and wealth, history of in See also Soviet Union Russian revolution (1917) Saez, Emmanuel Sardinia Saudi Arabia

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function Socialism capitalism vs. Communist Party and economic cycles and education and employment and Gini coefficient for income distribution in innovation and interpersonal inequality and proletariat revolution and wages and workers and Society capitalism and class and intercountry inequality and Socrates Solidarity Somalia South Africa South Dakota South Korea Soviet Union collapse of communism

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See Great Britain United States direct foreign investment in EU vs. GDP per capita in Gini coefficient for global financial crisis and global inequality and global middle class and income distribution in income inequality in intercountry inequality and interpersonal inequality and middle class in migration and price level in redistribution in sports in Uruguay U.S

The Globalization of Inequality

by François Bourguignon  · 1 Aug 2012  · 221pp  · 55,901 words

I will use basically four measures of inequality: the share that goes to the richest (1%, 5%, or 10%), the relative gap between standards of living in the extreme deciles (the richest 10% and the poorest 10%), the Gini coefficient, and the Theil coefficient. The Gini coefficient is probably the most frequently employed measure of

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a whole. For example, in a society where the average standard of living is $40,000, a Gini coefficient of 0.4 would mean that the average gap between two individuals chosen at random Global Inequality19 in the population would be $32,000.9 The Theil coefficient also takes into account the

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approximately $45,000 per capita per year, more than twice the national average, while that of the bottom 10% was only $6,600. France’s Gini coefficient, as defined earlier, was 0.29. This means that on average, the gap between the standard of living of two people This difference is due

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highest inequality. In the United States, GDP per capita was $43,000 in 2008, the average standard of living as recorded by the Current Population Survey was around $25,000 per person, that of the richest 10% was $70,000, while that of the poorest 10% was $4,500. The Gini coefficient for

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twentieth that of the poorest French. As a result, the gap between the richest 10% and the poorest 10% was higher than 50, and the Gini coefficient was 0.58. Ethiopia is a poor country in Africa. GDP per capita in 2008 was only $850, whereas the standard of living recorded in

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50:1! Even if we expand the extreme percentiles that we examine, global inequality remains considerable; the richest 20% have a standard of living that is still forty times higher than the poorest 20%. As for the global Gini coefficient, it was 0.70 for standard of living in 2008 when using household

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poorest 10%; by 1980, this number would be three times larger. The Gini coefficient in 1820 was around 0.5, similar to a relatively unequal country today. By 1980 it was 0.66, higher than any existing level of national inequality. The second striking point that this graph shows is a sharp d

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the discontinuity in the series shown in figure 1. Nonetheless, in relation to the historical series, the drop in inequality is both undeniable and sizable. In the last twenty years, the Gini coefficient and even the relative gap between the two extreme deciles decreased almost as much as they had increased since 1900

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their growth rate remained quite positive, whereas growth disappeared or even became negative in the majority of developed countries. Without any change in national inequalities, the Gini coefficient would thus have kept going down since 2008. Of course, it remains to be seen whether slower GDP growth has been accompanied by a worsening

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2008, just before the recent crisis, the level of income inequality, as measured by the share of the top 10% tax units in total household market income, had returned to levels that had not been seen in a century. The Gini coefficient of gross income per person shows a similar evolution. After forty

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same phenomenon taking place in other countries? The fact is, a large majority of high-­income OECD countries have experienced rising income inequality over the last two decades. The Gini coefficient of disposable income per adult equivalent has risen by at least 2 percentage points in more than three-­quarters of OECD countries

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evident in several of the countries that are on the vanguard of the global South catching up with the North. The rise in inequality in China, where the Gini coefficient for this period increased from 0.28 to 0.42, is frequently cited as one of the black marks on its record of

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receive 40% of total primary household income, but possess 71% of total wealth. For the richest 1%, the numbers are 15% and 35%, respectively. The Gini coefficient, which is around 0.38 for standard of living, rises to 0.83 for wealth. The same numbers are just as striking, although at a

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whereas they account for only 25% of total household income. Similarly, the Gini coefficient is 0.64 for wealth, while it is only slightly above or below 0.30 for income in the recent years. The evolution of wealth inequality over the course of the past several decades varies between countries. It has

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economies, we have periodic estimates of wealth distribution for only some of them. The numbers that we have show very high levels of inequality, comparable to developed countries. The Gini coefficient for wealth was estimated to be 0.55 in China, 0.65 in India, and 0.78 in Brazil and Mexico in

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time. At the very least, we can think that, due to intra-­and intergenerational accumulation, wealth inequality will also rise in countries in which income inequality has risen significantly. Thus, a recent study found that the Gini coefficient for wealth increased by almost 10 percentage points in China between 1995 and 2002,9 but

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Handbook of Income Distribution, volume 2 (Amsterdam: Elsevier, 2014), chapter 15. 9 Since then, and quite remarkably, it would seem that wealth inequality has drastically increased, with the Gini coefficient raising from 0.55 in 2002 to 0.76 in 2010, an order of magnitude comparable to the United States. See Shi Li

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crisis completely autonomously. After three years that were especially hard on the population, growth returned and remained at high levels. However, inequality had shot up in the adjustment process. The Gini coefficient, which was 0.50 in 1999, had risen to 0.54 in 2003 at the moment when the crisis was on

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that, under the overly optimistic assumption that it were distributed equally among the inhabitants of recipient countries, it could contribute to a drop in the Gini coefficient for global standard of living of around 0.25 percentage points. This effect is minute in view of the fig- Policies for a Fairer Globalization

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among children under age five. On the other hand, in both Brazil and Mexico, it is estimated that the conditional cash transfer programs reduced the Gini coefficient by one percentage point at a cost of less than 1% of GDP. To come back to the overall levels of redistribution achieved in developing

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split and, 55–58, 60; efficiency and, 142–45; evolution of inequality and, 41, 42t, 44t, 45, 46t, 48–59, 64, 71–72; fairer globalization and, 148, 153, 156–73, 175, 178; geographical disequilibria and, 83; Gini coefficient and, 18 (see also Gini coefficient); global, 18–19, 25, 29, 39, 41, 46t, 121, 124–

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economies and, 57; exceptions and, 52–53; France and, 46t, 51f, 52–53, 55, 58, 59n8, 62–63, 66, 70–71; ghettos and, 66–67; Gini coefficient and, 39, 42t, 44t, 48, 50, 51f, 53, 58–59; Great Depression and, 48; growth and, 33, 49–50, 54; India and, 54, 57,

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78, 182; Everything But Arms (EBA) initiative and, 155; exports and, 147, 154–55, 176, 178; France and, 147, 159–61, 164, 169, 175, 177; Gini coefficient and, 156, 166; goods and services sector and, 180; growth and, 147–52, 155, 162, 167–68, 171, 177, 180, 183; health issues and, 152

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and, 162, 164, 167, 170–73 Fitoussi, Jean-­Paul, 14 France: evolution of inequality and, 46t, 51f, 52–53, 55, 58, 59n8, 62–63, 66, 70–71; fairer globalization and, 147, 159–61, 164, 169, 175, 177; Gini coefficient of, 20; global inequality and, 2, 9, 11, 20–21; offshoring and, 81; rise in

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150 Germany, 2, 21, 46t, 50, 51f, 80, 88, 92 Ghana, 46t, 54 ghettos, 66–67 Giertz, Seth, 160–61 Gini coefficient: Brazil and, 22; Current Population Survey and, 21; evolution of inequality and, Index197 39, 42t, 44t, 48, 50, 51f, 53, 58– 59; fairer globalization and, 156, 166; France and, 20

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94, 96, 105, 108, 111, 116, 118– 19, 129–35, 140–45, 157–58, 164, 167, 170–71, 175, 180–81, 188; Gini coefficient and, 18 (see Index also Gini coefficient); income, 2, 4, 41, 48–50, 56–64, 68, 70, 72–73, 83, 98, 102–3, 107–8, 114, 125, 132–34, 137

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monopolies, 94, 111, 127, 136 Morocco, 173 Morrisson, Christian, 28 movies, 87 Murtin, Fabrice, 28 national inequality, 2–4; correcting, 158–80; education and, 167–73; fairer globalization and, 147, 158; Gini coefficient and, 27 (see also Gini coefficient); globalization and, 119; market regulation and, 173–75; protectionism and, 147, 157, 176–79; redistribution and

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GDP measurement and, 29, 41, 43– 45; global inequality and, 13, 15, 22–23, 26, 29 Occupy Wall Street movement, 6, 135 OECD countries, 27t; evolution of inequality and, 42t, 43, 44t, 50– 52, 64, 65n13; fairer globalization and, 149, 159, 162, 164– 65; Gini coefficient and, 51; income distribution and, 51; relaxation of

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of, 41–73; executives and, 73, 88– 89, 97, 174; exports and, 76, 82–84; France and, 80, 88, 92– 93, 95, 97, 99, 103; Gini coefficient and, 110; globalization and, 117–18; goods and services sector and, 80, 85, 91, 102; growth and, 75, 79, 82, 84, 109– 12; imports and

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Population Survey and, 21; deregulation and, 94–95, 97– 98, 102–8; evolution of inequality and, 47–50, 51f, 58, 59n9, 66–70, 73; fairer globalization and, 155, 159–61, 163–64, 169, 174–75, 182; Gini coefficient of, 21; globalization and, 135–39; manufacturing and, 80; Occupy Wall Street movement and,

Visions of Inequality: From the French Revolution to the End of the Cold War

by Branko Milanovic  · 9 Oct 2023

, they must have been aware of the main trends—their work testifies to that. Even if Quesnay did not know empirically the level of inequality in prerevolutionary France, and could not calculate its Gini coefficient (a measure invented some 150 years later), he was quite conscious of the main types of French

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France, as we can gauge based on tax data and social tables, was very high. French inequality was considered to be greater than inequality in England. The Gini coefficient for France, calculated from contemporary sources including data provided by Quesnay himself, ranges between 49 and 55, compared to an English Gini estimated to

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population) grows increasingly wealthy. It could be said that, in Smith’s view, development results in less income inequality (as we would measure it today using common synthetic measures of inequality like the Gini coefficient), but possibly also results in more polarization, and an even greater share for the top one percent, who

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of 1.2 implies (as will become clear in the next chapter) a very thick right-end tail of income distribution, and a very high Gini coefficient (another methodological innovation for which we shall have to wait until the 1920s) of seventy-one among the English and Welsh taxpayers. Figure 4.

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very recently. It has to do with the very sharp, or gradual, way the guillotine operates, and with synthetic measures of inequality like the Gini coefficient. The relationship between the Pareto and Gini coefficients is straightforward: the higher the absolute value of the constant, the lower the Gini. d This is not fully intuitive,

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1949, and a much more hypothetical distribution for 1894–1895. For the advanced part of the world, Kuznets’s calculations show an increase in the Gini coefficient from 28 points in 1894–1895 to 36–37 points in 1938—thus, an income divergence among the rich. For the world in 1949,

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term. What is noticeable in the period of concern here is the sharp increase in inequality during the Great Depression, principally on account of high unemployment, followed by the long slide extending to 1957. A Gini coefficient that in 1933 exceeded 50 fell to only 34 in 1957. Such huge declines are

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chart such as Figure 6.2 ), and the underlying curves that Brazil and Sweden were “traveling” on might be quite different in their heights (Gini coefficients, plotted on the vertical axis) even if both looked like an inverted U. Therefore, putting all the Gini numbers together might result in something that

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hypothesis. 31 Figure 6.3. Relationship between GDP per capita and inequality, 1970–2014 (pooled cross-sectional and time-series data) Note: Each dot represents a country / year with its GDP per capita in 2011 international dollars (in logs) and Gini coefficient of disposable income (expressed in percentages). The thick line is

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The two horizontal dotted lines show the approximate lower and upper bounds of the Gini coefficient. Data sources: “All the Ginis” data set, World Bank, World Development Indicators, Washington, DC. It was also argued that the peak of inequality found in cross-sectional studies around the middling level of income was an artifact

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merely as measured by an overall indicator like the Gini coefficient, but also regarding the individuals receiving high, middle, or low incomes. Workers’ wages for a given type of labor should be more unequally distributed in market socialism than in capitalism, while overall inequality, thanks to nonprivate appropriation of capital income, may

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is, indeed, where a student of economics would have found out that there was such a topic as inequality, and that there were tools to study it. (The Lorenz curve and the Gini coefficient were typically introduced only in textbooks focused on development.) This is also where Kuznets’s theory was taught.

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unequal asset holdings, remained so highly concentrated that the Gini coefficient of capital income was in most Western countries around 0.9 in the early 1980s, the earliest period for which we have consistent and country-comparable data. This is about twice the inequality of labor incomes (see Figure 7.4 ). These

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But the 90–10 approximation is not far off the mark—and this extreme inequality in wealth (and income from wealth) is clearly incompatible with the neoclassical economists’ vision of an almost classless society. Figure 7.4. Gini coefficients of capital and labor incomes in Western countries, around 1980 Data source: Calculated

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its relative income is higher. The Egypt example is just an extreme case of such regularity: its Gini coefficient, even without taking into account the intra-class inequality, is extraordinarily high at almost 77 points. This excessive inequality, presided over by a tiny and rich “comprador” bourgeoisie, was, according to the dependency doctrine,

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some seventy years after Kuznets articulated his original hypothesis. Lindert and Williamson produced the first estimates of inequality for the entire United States, beginning with the thirteen colonies. For 1774, they find a Gini coefficient of 44.1 and a share of 8.5 percent of total income for the top one

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97 , 209 , 310n14 , 313n17 ; aristocracy, 22 , 56 , 59 , 88 , 265 ; economy, 47–48 , 49 ; GDP, 33 , 87 , 302n13 ; Gini coefficient, 32 , 87 , 87–88 ; income distribution, 158–159 , 160 ; income inequality, 32 , 86–89 , 90 ; inequality in Scotland and, 56–58 ; mean income, 33–34 ; peasantry, 59 , 60 ; population, 56 , 57 , 89 , 310n15 ; real

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US growth-incidence curve, 1986 and 2007, 288 , 289 ; wages and, 38 ; western countries and Gini coefficients of capital and labor, 272 , 273 ; workers, 57 , 58 , 60 , 88 , 90 , 104 , 311n30 . See also mean income Income, Inequality and Poverty (Milanovic, B.), 330n49 income distribution: under capitalism, 3 ; with class-based analysis eliminated, 220

Global Inequality: A New Approach for the Age of Globalization

by Branko Milanovic  · 10 Apr 2016  · 312pp  · 91,835 words

U-shaped rather than inverted-U-shaped as Kuznets thought. But can Piketty’s approach explain changes in inequality in the preindustrial period? Consider Figure 2.1, which plots inequality levels (measured by Gini coefficients)3 for the past two to three centuries for the United States and the United Kingdom, the two

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calamities, or political action, a statement which is manifestly at odds with reality: periods of decreasing inequality driven by economic forces have occurred under capitalism. Even technically, inequality (whether estimated by top income shares or by Gini coefficients), is, unlike GDP per capita, bounded from above and cannot keep on rising forever. More realistically

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(not simply because the Gini coefficient ranges in value from 0 to 1), it is bounded from above

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income. But as the mean income rises, the surplus above the subsistence level increases as well, and the possible, or feasible, inequality becomes greater. The inequality possibility frontier is a locus of maximum feasible inequality levels (measured by the Gini coefficient) that obtain for different values of mean income. The frontier is concave: maximum feasible

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inequality increases with mean income but at a decreasing rate. Figure 2.2 shows the relationship: for a mean income level equal to subsistence, the maximum Gini coefficient is 0. It then gradually increases as mean income exceeds subsistence, and

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when it exceeds it by 15–20 times, the maximum Gini coefficient is close to 1 (or to 100 if expressed in percent).6 FIGURE 2.2. Inequality possibility frontier: the locus of maximum feasible Gini coefficients as a function of mean income level This graph shows the maximum feasible

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inequality (measured by the Gini coefficient) for various levels of average per capita income. Maximum feasible inequality is defined as maximum inequality under the condition that no person has

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low, there was simply, as suggested by the inequality possibility frontier, very little “space” for inequality to exist; that is, there were fewer people who could command higher incomes without driving others to starvation. When the mean income is equal to subsistence, the only Gini coefficient that is compatible with the survival of all

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Escosura (2007; based on the use of the Williamson ratio to estimate Gini coefficients)29 and another from Bértola et al. (2009) (based on social tables). Although the two data sets do not show exactly the same pattern of increasing inequality in the mid-nineteenth and early twentieth centuries, they both show that

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Gini points (in the United States) and 30 Gini points (in the United Kingdom). In all countries, there were indeed massive declines in inequality, cutting its level, measured by the Gini coefficient, almost in half and in the Netherlands and the United Kingdom by more than half. The fact that the downswing in

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of data, which results in a higher estimate of the global Gini coefficient by Lakner and Milanovic than by Bourguignon and Morrisson. It is important to keep in mind, however, that this difference in estimates of the overall level of global inequality does not affect, in any substantial way, conclusions about changes in

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approximately 2000, there have been unmistakable signs of a decrease in global inequality: each successive year for which we have the data—broadly the same household surveys from the same set of countries—exhibits a slight decline in the Gini coefficient (as shown in Figure 3.1). This slight downward tendency is present

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use (Gini or Theil, another popular measure of inequality, or yet a third one), and it will always be a weighted sum, where weights can be the shares of each country in total world population or in total world income or both. The Gini coefficient is special because it does not decompose exactly

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as Figure 4.1 shows, this was not the case until at least the year 2000. The dashed line in Figure 4.1 shows the Gini coefficient calculated across mean GDPs per capita for practically all countries in the world, with each country’s weight being the same.8 When this line

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on the evidence we do have, it looks as though income inequality did not rise in the five to six years before 2013 and may in fact have declined a little. The data from household surveys show that the all-China Gini coefficient has stayed relatively stable since 2000 (Figure 4.4). The

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Economic models that combine low inequality of market incomes and a relatively small state are not unheard of; indeed, they exist in several Asian countries. Figure 5.1 shows a comparison of selected Western countries and three rich Asian countries (South Korea, Taiwan, and Japan). The Gini coefficient for disposable income (after taxes

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elasticity of carbon emissions is unitary (i.e., a 10 percent increase in real income entails a 10 percent increase in carbon emissions), then the Gini coefficient of global carbon emissions is around 70 points, which would mean that more than one-half of all emissions are made by the global top

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run] supply and demand forces, the rents that have been earned by university-educated workers will come to an end.” 3. The Gini coefficient is the most popular measure of income inequality. It takes into account the entire distribution (that is, the incomes of everyone), unlike, for example, measures based on top income

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shares, which ignore all of the distribution except the top. The Gini coefficient ranges from a value of 0, for the theoretical case where

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has the same income, to a value of 1, for the equally theoretical case where one individual possesses the entire income of a country. The Gini coefficient is often expressed as a percentage (e.g., as 41 rather than 0.41) and referred to simply as the Gini. When the Gini has

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with the current concentration coefficients of capital and labor income, respectively about 0.8 and 0.4, the Gini coefficient would be 60 (0.5 × 80 + 0.5 × 40). This is the level of inequality existing today in Brazil and South Africa. 5. The same is true in modern societies (as we shall

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main features of an economy: it provides the plot of the second moment of the distribution of personal incomes (if incomes are distributed lognormally, the Gini coefficient is uniquely determined by the variance) against the first moment of the distribution (mean per capita income). 13. In this chapter, where we deal

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before, that each person has the GDP per capita of her country). 16. In the case of the Gini coefficient (with which we work here), the point at which a unit begins to add to inequality depends on its rank (let’s call it the “turning point rank”), that is, the number of

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to i > ½ (G + 1)n, where i = the turning point rank (the rank i runs from 1 to n), n = total number of units, G = Gini coefficient. Note that the turning point is n/2 (i.e., the median) only when the Gini is zero. For the derivation of the formula, see

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capita (mean income). See income gender, 111–112, 227–228, 228–229, 263n4 Gibbon, Edward, 243n14 GIC (growth incidence curves), 23 Gilens, Martin, 189, 261n28 Gini coefficients, 53, 121, 126, 128, 177, 245n3, 246n6, 247n12, 253n5, 259n16 global governance, 1–2, 9, 230–231. See also democracy globalization: labor and, 18,

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82, 109, 255n19. See also market income; taxes; welfare regimes transfers between countries, 255n19 Turchin, Peter, 247n17 turning point rank (Gini coefficient), 259n16 twentieth century: capitalism and, 48–50; forces limiting inequality, 80–81, 93–103; short, 100; early, 241n2. See also Great Leveling; World War I twentieth and twenty-first centuries, 158

Capitalism, Alone: The Future of the System That Rules the World

by Branko Milanovic  · 23 Sep 2019

the world and thus in global income inequality among all citizens of the world, which we can estimate with relative precision from 1820 onward, as illustrated in Figure 1.1. In this graph, and throughout the book, inequality is measured using an index called the Gini coefficient, which ranges in value from 0 (

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capitalism that capital income is extremely concentrated and is received mostly by the rich.24 Note too that inequality in labor income (before taxes) in these countries has increased during this period, from a Gini coefficient of under 0.5 to about 0.6. Looking at a snapshot of capital and labor income

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have extremely concentrated income from capital, with Gini coefficients above 0.86 (Figure 2.2). Labor income Ginis are much lower

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per capita, Switzerland is 53 times better off than India, but it has almost 100 times more wealth per adult than India. FIGURE 2.1. Gini coefficients of capital income and labor income in the United States, the United Kingdom, Germany, and Norway, 1970s and 1980s to 2010s Both capital and labor

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real GDP variable used for cross-country comparisons at a given point in time). The importance of reduced global inequality does not reside in a decrease in a single number (the Gini coefficient of inequality), but rather in the convergence in real incomes across vast groups of people. We can thus, for perhaps

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is methodologically not different from income inequality within, say, the United States. The only difference is that the area over which we calculate global inequality is larger. But the methodology and the tools of measurement (e.g., the use of the Gini coefficient, the most popular measure of inequality) are the same.1 The data

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stock of schooling (years of education) will become smaller and smaller. This is already the case in rich countries. For example, around year 2000, the Gini coefficient for years of education was 0.6 in India, 0.43 in Brazil (which is undergoing the transition from low to medium level of schooling

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and social gains” (1971, 55). 35. Based on Leijonhufvud (1985) and Bowles and Gintis (1986). 36. This drop in inequality is evident not only when we use synthetic inequality indicators like the Gini coefficient, which look at income levels across the entire global income distribution (as shown in Figure 1.1), but even when

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Nicholas. 2018. Skin in the Game: Hidden Asymmetries in Daily Life. New York: Random House. Thomas, Vinod, Yan Wang, and Xibo Fan. 2001. “Measuring Education Inequality—Gini Coefficients of Education.” Policy Research Working Paper No. WPS 2525, World Bank, Washington, DC, January. http://documents.worldbank.org/curated/en/361761468761690314/pdf/multi-page.pdf

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123–124 Economy and Society (Weber), 91 Education: bifurcated system of in United States, 61; elite, 59–62, 65–66; Gini coefficient for years of, 241–242n40; marriage education premium, 39; wage inequality and, 50. See also Public education Educational ceiling, 44 Egalitarian capitalism, 46, 216 Einstein, Albert, 207 Elite: characteristics of ruling

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inequality in income from capital and labor in, 26–27, 29; limits of tax-and-transfer redistribution in, 44–45; migration and, 137, 242n47; share of global GDP, 9, 10; subcitizenship in, 136 Gernet, Jacques, 105–106, 115 Ghettoization, of migrants, 146–147 Gig economy, 190, 192, 194 Gilens, Martin, 56 Gini coefficients

The Meritocracy Trap: How America's Foundational Myth Feeds Inequality, Dismantles the Middle Class, and Devours the Elite

by Daniel Markovits  · 14 Sep 2019  · 976pp  · 235,576 words

Time (Five-Year Moving Averages) FIGURE 4 shows the Gini coefficients of the United States, calculated in three ways. The upward-sloping dark gray line displays the Gini for the entire U.S. economy. Its steep rise reflects the commonplace sense that inequality has shown a stark increase, from levels that resembled

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Norway: See Table 5 of Carola Grün and Stephan Klasen, “Growth, Inequality, and Well-Being: Intertemporal and Global Comparisons,” Discussion Paper no. 95, Ibero-America Institute for Economic Research, Ibero-Amerika Inst. Für Wirtschaftsforschung, Göttingen (2003), 21–23 (listing Gini coefficients for over 150 countries from 1960 to 1998). India, Morocco, Indonesia,

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org/indicator/SI.POV.GINI. Bangkok, Thailand: The U.S. Census Bureau’s American Community Survey estimates that Fairfield County had a Gini coefficient of 53.52 in 2011. Bangkok’s Gini coefficient that year, as reported by the UN, was 40.0. See U.S. Census Bureau, “2007–2011 American Community Survey,”

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usa/. The calculation uses post-tax and -transfer incomes, in order to capture the true circumstances of the various segments of the economy that the Gini coefficients describe. In addition, the calculation uses one hundred data points for each year, corresponding to income levels at each percentile in the distribution. This

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increases accuracy and, more important for present purposes, makes it possible to calculate Gini coefficients for parts of the distribution, as the main text does. Many alternative U.S. Gini series, by contrast, calculate the Gini Index using only

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after the state—with both its taxes and its social welfare programs—has intervened in their lives. U.S. Top-End, Bottom-End, and Full Gini Coefficients over Time: Data from the World Top Incomes Database, Post-tax national income / equal-split adults / Average / Adults / constant 2015 local currency, https://wid

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.world/country/usa/. calculated in three ways: The figures again calculate the Gini coefficients using post-tax-and-transfer incomes, in order to capture the true circumstances of the various segments of the economy that the coefficients describe. bottom

Unsustainable Inequalities: Social Justice and the Environment

by Lucas Chancel  · 15 Jan 2020  · 191pp  · 51,242 words

public health that deserves to be taken very seriously. Figure 1.1. Income inequality and social well-being. Disposable income inequality measured in terms of the Gini coefficient. In Japan, where the level of inequality is low, health and social well-being scores are the highest among OECD countries. Data for 2005. Sources and

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or some other group?) and on the quality of the data that can be marshaled for this purpose. The Historical Evolution of Inequality The Gini coefficient is a composite measure of inequality having a value of zero when there is perfect equality and a value of one (or 100 percent) when there is perfect

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inequality, that is, when only one person owns all available resources. Let us begin by considering Gini coefficients of income inequality for different countries, which since the beginning of the twentieth century have never fallen below 20 percent

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level of inequality at the beginning of the 1980s. A few countries have managed to resist this trend: Belgium, France, and the Netherlands have done better than most of their European neighbors—even if income and wealth differentials have widened in these same countries over the past three decades. The Gini coefficient has

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one advantage by comparison with other measures: its panoramic view. The Gini coefficient tells us about changes in inequality in society as a whole—or, more precisely, it yields a composite picture of

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of the social pyramid. Moreover, it can mask significant developments in the middle, notably the inability of middle-class incomes in recent decades to keep pace with the gains registered by other segments of society. Consider the following example. The Gini coefficient of world

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the lower half are catching up with this same middle class. The Gini coefficient gives no insight into this state of affairs. The limitations of the Gini coefficient are compounded by limitations inherent in the data that international bodies currently use to measure inequalities of personal income and accumulated wealth. In trying to piece together

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and wealth employed vary according to period and place. Figure 2.1. Global income inequality dynamics, 1980–2016. Distribution of per adult pretax national income, measured at purchasing power parity. In 2016, the global pretax income Gini coefficient was equal to its 1980 value. The gap between the incomes of the top 10

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is being analyzed. THE EXPLOSION OF TOP INCOMES Any measure of inequality rests on a certain view of justice in a given society, represented by a social welfare function. In order to be able to compare different countries using the Gini coefficient, one must make a series of normative choices associated with the

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Anthony Atkinson, who almost fifty years ago argued that the social welfare function implied by the Gini coefficient does not reflect commonly accepted criteria of justice.3 A simple and persuasive way of following the evolution of inequalities is to observe the share of incomes (or of wealth) allocated to different income groups

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studying changes in the share of the pie enjoyed by these different groups, the right sort of social welfare function is more illuminating than the Gini coefficient. It is generally agreed that a society in which the share of income captured by the richest (the top 10 percent, or the top

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51, 146 generational effect on CO2e emissions, 98–101 Germany: education in, 143; energy management in, 114–115, 123; income inequality in, 19, 24, 42, 44, 46; tax rates in, 55 Gini coefficient, 19, 36–40, 159n28 global coordination of environmental solutions, 142–148. See also environmental responsibility; names of specific initiatives Global

The Price of Inequality: How Today's Divided Society Endangers Our Future

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