7 Ways to Survive when Your Income is Slashed in 1/2 by a Poor Economy
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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.
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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.
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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.
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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.
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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.
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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.
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.