This chart shows the distribution of annual income among all world citizens. The above visualization is based on estimates of inflation-adjusted average incomes per country GDP per capita and single-point estimates of within-country income inequality. While this gives us a rough idea of how the distribution of incomes changed, it is neither very detailed nor very precise. The visualization below shows the distribution of incomes between and using a different, more precise source of data.
The estimates come from Milanovic and Lakner The downside of this approach is that we can only go as far back in time as household surveys were conducted.
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If you want to use this visualisation for a presentation or for teaching purposes etc. The hypothesis supporting the negative effect of globalization on income inequality can be easily explained in terms of wage differences between high-skilled and low-skilled individuals: if globalization means that a country can import basic manufactured goods more cheaply, paid for by exporting more valuable high-tech services, then wages for high-skilled workers are likely to rise relative to unskilled wages in that country.
The available empirical evidence on the causal link between globalization and inequality is not definitive, but does suggest that we might want to take this hypothesis seriously. Autor, Dorn and Hanson 17 , for example, study the consequences of rising Chinese imports for the US in the period The visualization below shows a scatter plot of cross-regional exposure to rising imports, against changes in employment.
As we can see, there is a negative correlation. In fact, the authors go further and suggest that rising Chinese imports in the period caused higher unemployment, lower labor force participation, and reduced wages in local labor markets that house import-competing manufacturing industries see the paper for details on the empirical strategy used to determine causality.
Economists often argue that changes in productive technologies increase inequality. The intuition behind this claim is that technical change favors more skilled workers, replacing tasks previously performed by the unskilled. Atkinson 19 provides a simple discussion of the economic theory supporting this hypothesis. The view that productive technologies increase inequality is supported by descriptive evidence from the past decades, when high-income countries witnessed both major changes in technology—including the rapid spread of computers in workplaces—and a sharp increase in wage inequality.
The following graph from Acemoglu 21 shows the evolution of the relative supply of college skills, as well as the returns to those skills the college wage premium. This graph shows that in the US there was a large increase in the supply of more educated workers during the second half of the 20th century. Since the returns to education increased while supply was also increasing, we can interpret this as evidence in support of the hypothesis that technological change was biased in favour of skilled workers.
The above remark implies a positive correlation between skill-biased technological change and wage inequality. As always, a correlation does not imply causation—we do not know if it was skill-biased technology that specifically caused more inequality. However, the fact that this correlation has been observed in other countries suggests that technology is likely part—although only part—of the explanation for growing inequality in high-income countries.
In the textbook case of employment in efficient markets, wages are determined exclusively by productivity—so income inequality follows from differences in productivity. Economists usually agree on the fact that the supply and demand forces from the textbook case are important in the real world. Indeed, in the preceding section we argued that trade and technology may increase income inequality precisely by making the skills of some individuals less valuable relative to others.
However, economists also tend to agree that, while relevant, differences in productivity are not sufficient to explain differences in incomes. Social conventions, for example, also play a crucial role. The implication is that market forces provide only bounds on outcomes, and there is scope for notions of fairness to affect inequality.
As the below charts show, inequality is not universally viewed as inherently undesirable. The top panel here represents the frequency of responses at each point in the scale explained above. As a global average, we see substantial polarization: most people picked one of the extremes either a high preference for equal incomes, or strong opposition to a reduction in inequality.
Notice that this polarization is even more pronounced in particular regions. Latin Americans tended to be much more supportive of more equal incomes, whereas the opposite was true in the Middle East.
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In the bottom panel we see how these responses correlate with income. To be specific, we see average responses by income deciles where 1 on the x-axis is the lowest income decile and 10 the highest. Across all regions, we see that richer individuals tend to be less inclined to favour a reduction in inequality i. Regional differences, however, are still significant. We have already pointed out that differences in productivity are not generally sufficient to explain differences in incomes. This is partly reflected in the fact that worker salaries are often the result of bargaining between unions and firms.
Card et al. The following scatter plot shows their results. The estimates correspond to data on the hourly earnings of males. By construction, if union and nonunion workers in a given skill group have the same average wages, the points in this graph will lie on the degree line.
Moreover, this union wage gap appears to be larger for low-wage workers: the points are further above the degree line for low-wage skill groups those on the left. As usual, we have to be careful in interpreting these results. As Card et al. One way to gauge the extent to which taxation and public spending contribute to redistributing resources among individuals in a country is by looking at how the distributions of incomes change before and after taxes and transfers. The following two visualizations show this, by comparing the degree of income inequality Gini coefficients , before and after taxes and transfers.
The chart at the top provides a static overview across all OECD countries using the latest available estimates depending on the country. The interactive chart at the bottom plots time-series for individual countries, using the same definitions and data sources.
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In both charts income before redistribution refers to market earnings before taxes and transfers wages and salaries, self-employment income, capital and property income. Income after redistribution, on the other hand, corresponds to disposable income after taxes and transfers market income, plus social security, cash transfers and private transfers, minus income taxes.
As we can see, taxes and transfers do reduce inequality significantly: in all countries there is less inequality after redistribution takes place via taxes and transfers. Interestingly, however, the achieved reductions in inequality vary considerably between countries, and substantial cross-country heterogeneity in inequality remains after redistribution. In Northern Europe, for example, within-country Gini coefficients after taxes and transfers are below 0. As a benchmark, 0. The data shown in the chart above is also shown in the following interactive visualization. The horizontal and vertical axis shows, respectively, Gini coefficients before and after redistribution.
Along the diagonal line, incomes do not change after redistribution. Hence, the countries further below the diagonal line are those where taxes and transfers have the largest effect on incomes. As we can see, European countries shown in yellow tend to achieve more redistribution than other OECD countries. And this is true across different levels of staring inequality in market incomes. We noted above that taxes and transfers reduce inequality in all OECD countries. Here we focus on the proportional magnitude of such reductions. The following visualization shows the percentage point reduction in Gini coefficients that OECD countries achieve through redistribution.
Here, the definitions of incomes before and after taxes and transfers is the same as in the previous two graphs. These estimates show that across the 24 countries covered, taxes and transfers lower income inequality by around one-third on average equivalent to around 0. Generally speaking, countries that achieve the largest inequality reductions through taxes and transfers tend to be those with the lowest after-tax inequality. While informative for the purpose of cross-country comparisons, these results have to be interpreted carefully, since the before-tax distribution of incomes is already the result of choices made by individuals who take taxes and transfers into consideration.
Put simply, the before-tax distributions of incomes are likely to be different to the actual distributions of incomes that would be in place if there were no taxes or transfers. This can be clearly explained in the context of pensions: individuals receiving state pensions appear in the data as poor before transfers; but many of them would of course have private pensions if they lived in a country without state transfers. Another point to keep in mind when studying these estimates is that inequality is not only reduced by redistribution between individuals at a given point in time, but also by achieving redistribution over the course of life.
Indeed, pensions have scope for reducing within-country inequality by allowing redistribution of incomes between generations. The estimates in the chart below reflect this. Over the last decades, a large body of theoretical and empirical research has attempted to determine whether inequality is good or bad for economic growth. From a theoretical point of view there are arguments in both directions.
It is for example possible that inequality leads to less economic growth via political instability and social unrest. But it is also possible that it leads to more economic growth via higher incentives for people to make productive investments.
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Available OECD data shows that there is a negative correlation between inequality and economic growth across different subnational regions, within Europe, and also within OECD countries in the Americas. The below visualization shows this as well, focusing on Europe. This chart is a scatter plot, where each dot represents a different sub-national region.
France, for example, is divided here in 22 different regions. For each of these sub-national regions, the vertical axis measures the average annual growth rate of GDP per capita in the period , and the horizontal axis measures inequality in Gini coefficients. As we can see, there is a clear negative correlation: regions with more inequality in experienced less average growth in the subsequent years.
The above correlation does not imply causation. Indeed, the empirical literature on the causal effect of inequality on economic growth is largely inconclusive. The Gini coefficient, or Gini index, is a measure of the income distribution of a population. It was developed by Italian statistician Corrado Gini and is named after him. A value of 1 means maximal inequality one person has all income and all others receive no income.
For a simpler visual explanation of the Gini coefficient, you can see the right-hand side panel of the above chart only here:. It is the global distribution of incomes in and in as estimated by Hellebrandt and Mauro. The metaphor of a parade works well. The idea is that you order the people in a population by the level of their income. The first people in the parade are those with the lowest incomes, and the people with higher incomes make their appearance successively in the parade.
In a typical distribution, it is only far into the second half of the parade that we see the person with the mean income appear. At the very end of the parade we see the individuals with the highest incomes. The methods built into PovcalNet are considered reliable for that purpose. However, we cannot be confident that the methods work well for other purposes, including tracing out the entire distribution of income. This data is published as the Incomes Across the Distribution Database. In addition to these extensive data sets there are a number of more specialized panel data sets that contain only information for certain countries continents.
The Transmonee statistics include other socio-economic indicators. The data have micro statistical surveys in each country as a basis. Data are available here. This data set provides measurements of economic inequality and poverty for many countries in Africa, Asia, Europe and Latin America. The measures do not refer to incomes but to expenditures and measure the inequality of consumption.
The data set contains various inequality measures: The measures of poverty by Foster, Greer and Thorbecke and inequality measures, such as the Atkinson index and Gini coefficient. In addition to the above-mentioned panel data sets there are also country specific data sets — mostly based on survey data. Country-specific data are available for some industrial countries but usually do not go far back into the past.
Panel published at Ann Arbor Michigan since the s. From the early s onwards, we see that the UK experiences a divergence between what the Gini and the top income shares tell us about inequality. The Gini remained flat over these two decades and, if anything, fell somewhat during this period. This tells us that inequality across the bulk of the distribution has not increased further in the UK. At the very top, however, the evidence shows a different story. Top income inequality is measured as the share of total income that goes to the income earners at the very top of the distribution.
Historical top income inequality estimates are reconstructed from income tax records, and for many countries these estimates give us insights into the evolution of inequality over more than years. This is much longer than other estimates of income inequality allow as is the case with estimates that rely on income survey data.
The fact that income shares are measured through tax records implies that these estimates measure inequality before redistribution through taxes and transfers. After the s inequality in the USA started increasing, and eventually returned to the level of the pre-war period. We see that this U-shaped long-term trend of top income shares is not unique to the USA. In fact the development in other English-speaking countries, also shown in the left panel, follows the same pattern. However, it would be wrong to think that increasing top income inequality is a universal phenomenon.
The income share of the rich has decreased over many decades, and just like in the English-speaking countries, it reached a low point in the s. Income inequality in Europe and Japan is much lower today than it was at the beginning of the 20th century. A lesson that that we can take away from this empirical research is that political forces at work on the national level are likely important for how incomes are distributed.
A universal trend of increasing inequality would be in line with the notion that inequality is determined by global market forces and technological progress. The reality of different inequality trends within countries suggests that the institutional and political frameworks in different countries also play a role in shaping inequality of incomes. This means that rising inequality is most likely not inevitable. It is important to emphasize that the top income measures of inequality that we discuss above refer to inequality in the distribution of market incomes.
And market incomes are not the same as disposable incomes, because most people pay taxes and receive transfers from the government. In many countries governments have progressive tax systems.
The visualization below shows the difference in Gini coefficients before and after redistribution in the USA. Below we discuss this data in more detail. Bear in mind that in this chart inequality is measured with the Gini index, an inequality measure that not only looks at the top of the income distribution, but captures the whole distribution as explained below.
It is important to note, however, that these estimates are not fully comparable between countries. Absolute poverty is measured with respect to an income level that is fixed in time and across countries. The concept of relative poverty, on the other hand, is defined with respect to an income level that may change over time and across countries. Most often, relative poverty in a country is measured with respect to the median income in the same country i.
Because it is defined in relative terms, it is a measure of economic inequality. The visualization below shows relative childhood poverty. That is, the share of children living in relative poverty. Income inequality in a country is affected by the relative growth of incomes at different points in the income distribution.
This is intuitive: inequality will shrink if the incomes of the poor tend to grow faster than the incomes of the rich. Studying how income levels evolve across the entire distribution is crucial to understanding how the benefits of economic growth are shared in the population. Are some people getting richer while others are getting poorer? The visualization below tracks income levels in the UK at different points in the income distributions.
Each line shows the cutoff-incomes for the 10 deciles of the income distribution i. The UK experienced a large increase in inequality during the s—the incomes of the highest deciles increase while everyone else was left behind. Uneven growth in the years leading up to meant further increases in inequality. Throughout the s and s, more even growth across the distribution has meant little changes in inequality, with rising incomes for everybody. The experience of the USA is worth discussing. This makes the USA an extreme case in terms of inequality, and really an outlier in what is happening to incomes across the distribution over time.
You can find more empirical data and research in our entry dedicated to incomes across the distribution. The visualization below provides a comparison of inequality in consumption and inequality in incomes for a number of middle-income countries. As we can see, consumption inequality in almost all countries is lower than income inequality. This is intuitive, since consumption can be smoothed over time, for example, by saving earned income.
In principle, saving and borrowing allows agrarian societies to have consumption levels that are less volatile—and less reliant on seasonal variation—than incomes. Saving and borrowing is usually harder at low income levels; so consumption and income measures of inequality tend to be closer for poor populations. But as opportunities for saving and borrowing increase, important differences emerge. The following visualization shows that in middle-income countries differences are indeed substantial. Income inequality estimates are usually not fully comparable across countries in different world regions.
But even for this source, global coverage comes at the cost of comparability. Crucially, the PovcalNet data relies on consumption surveys for some countries and income surveys for other countries. As we point out above , this is problematic. Notwithstanding these limitations, it is interesting to consider the world map of economic inequality below.
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The visualization below shows a comparison of income inequality across different world regions. Shown is the simple cross-country average of Gini coefficients—as per the estimates presented in the world map above —without weighting countries by population. In other words, the series in this plot show the evolution of regional averages of inequality levels Gini coefficients. As we can see, Latin America is by far the region with the highest cross-country average inequality levels. And this has been the case for decades: inequality in this part of the world is remarkably persistent.
Another important point to notice in this chart is that variations across world regions are much larger than variations across time. Keeping this in mind is important to contextualize the debate on increasing inequality in high-income countries. Although average inequality in Latin America is going down—and in high-income countries it is going up—the differences in levels remain substantial. This is different to the experience of other OECD countries.
The US is an exception when it comes to income inequality. This is shown in the following chart. Each dot along the horizontal axis represents a different percentile in the income distribution, with the height marking the corresponding average level of income growth in the period after adjusting for inflation. Red and blue, respectively, show changes in incomes before and after taxes. The chart comes from Piketty, Saez and Zucman and it has received substantial media coverage. Without taxes and transfers, those at the bottom have actually seen their incomes shrinking.
Another striking fact is that the relationship is monotonically increasing: independently of where you are in the US income distribution, those who are richer have seen larger income growth. In fact, as Piketty and co-authors point out, in the US the relationship used to be monotonically decreasing : independently of where you were in the income distribution, those who were poorer used to enjoy larger income growth.
We have already noted that Latin America is the world region with the highest income inequality. Here we focus on how different countries in this region have reduced inequality over the last couple of decades. The following visualization shows recent trends in Gini coefficients across different Latin American countries. As we can see, there has been a generalized downward trend although levels remain very high. The fact that inequality reductions have been widespread is remarkable given the underlying differences between countries.
As Lopez-Calva and Lustig 13 point out, inequality declined in countries with high baseline levels of inequality e. Brazil as well as in countries with regionally low baseline levels of inequality e. It declined in fast-growing countries e. Chile and Peru and slow-growing countries e. Brazil and Mexico. It declined in macro-economically stable countries e. Chile and Peru and countries recovering from economic crisis e.
It declined in countries governed by what analysts often consider to be left-leaning political regimes e. Mexico and Peru. Lopez-Calva and Lustig suggest that the main factors contributing to declining inequality in these countries are i a decrease in the earnings gap between skilled and low-skilled workers and ii an increase in government transfers to the poor.
Below we explore in more detail these and other commonly cited drivers of within-country inequality. This chart shows the distribution of annual income among all world citizens. The above visualization is based on estimates of inflation-adjusted average incomes per country GDP per capita and single-point estimates of within-country income inequality. While this gives us a rough idea of how the distribution of incomes changed, it is neither very detailed nor very precise.
The visualization below shows the distribution of incomes between and using a different, more precise source of data. The estimates come from Milanovic and Lakner The downside of this approach is that we can only go as far back in time as household surveys were conducted. If you want to use this visualisation for a presentation or for teaching purposes etc. The hypothesis supporting the negative effect of globalization on income inequality can be easily explained in terms of wage differences between high-skilled and low-skilled individuals: if globalization means that a country can import basic manufactured goods more cheaply, paid for by exporting more valuable high-tech services, then wages for high-skilled workers are likely to rise relative to unskilled wages in that country.
The available empirical evidence on the causal link between globalization and inequality is not definitive, but does suggest that we might want to take this hypothesis seriously. Autor, Dorn and Hanson 17 , for example, study the consequences of rising Chinese imports for the US in the period The visualization below shows a scatter plot of cross-regional exposure to rising imports, against changes in employment. As we can see, there is a negative correlation. In fact, the authors go further and suggest that rising Chinese imports in the period caused higher unemployment, lower labor force participation, and reduced wages in local labor markets that house import-competing manufacturing industries see the paper for details on the empirical strategy used to determine causality.
Economists often argue that changes in productive technologies increase inequality. The intuition behind this claim is that technical change favors more skilled workers, replacing tasks previously performed by the unskilled.
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Atkinson 19 provides a simple discussion of the economic theory supporting this hypothesis. The view that productive technologies increase inequality is supported by descriptive evidence from the past decades, when high-income countries witnessed both major changes in technology—including the rapid spread of computers in workplaces—and a sharp increase in wage inequality. The following graph from Acemoglu 21 shows the evolution of the relative supply of college skills, as well as the returns to those skills the college wage premium.
This graph shows that in the US there was a large increase in the supply of more educated workers during the second half of the 20th century. Since the returns to education increased while supply was also increasing, we can interpret this as evidence in support of the hypothesis that technological change was biased in favour of skilled workers. The above remark implies a positive correlation between skill-biased technological change and wage inequality.
As always, a correlation does not imply causation—we do not know if it was skill-biased technology that specifically caused more inequality.
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