The rise of income inequality in many countries from 1985 onwards, and particularly during the recent crisis, has prompted a current debate on the causes and consequences of higher inequality and its effects on future growth (see, for example, OECD 2011, IMF 2014, or Ostry et al. 2014). As a result, and despite the slight reduction from 1960 to 1985, the average income Gini coefficient for developing countries was almost the same in 1960 (0.42) as it was in 2005 (0.41). As Figure 1 shows, most regions around the world follow this pattern, which has generated a growing interest in the underlying determinants of income inequality dynamics. According to growth theory, human capital inequality is one such determinant.
Figure 1. Income Gini coefficient, 1960–2005
A puzzling fact
In recent decades, governments around the world have made a great effort to improve the education of their citizens. In developing countries, the effort has been concentrated on the eradication of illiteracy in several hundreds of millions of people. As a result, the inequality in the distribution of education has been reduced by more than half. Using an updated dataset on human capital inequality indicators, based on the improved educational variables by Barro and Lee (2013), Figure 2 shows a significant reduction in education inequality around the world from 1950 to 2010.1 In most countries, this large reduction has mainly been due to a sizeable decline in the share of illiterates. For example, in the Sub-Saharan African region, the average human capital Gini coefficient dropped from 0.80 in 1950 to 0.41 in 2010. This fact is not restricted to developing countries alone – the human capital Gini coefficient in the high-income OECD countries was 0.22 in 1950 and decreased to 0.15 in 2010.
Figure 2. Gini coefficient for education, 1950–2010 (population 15 years old and above)
Human capital theory suggests that productivity is based largely on workers’ knowledge and skills, which are the result of an investment process in human capital (Becker 1962). As more productive workers will be rewarded with higher wages, education is thus a key determinant of social mobility and a major factor determining the distribution of income. Therefore, other things equal, we should expect the reduction in education inequality would have translated into a similar reduction in income inequality. However, the comparison of Figure 1 and Figure 2 makes it clear that this has not been the case. Despite the equalising process in the distribution of education, inequality in the distribution of income has hardly changed. A question therefore arises: Why have the reductions in education inequality not resulted in similar reductions in income inequality?
Explaining the puzzle
We test several hypotheses that may explain the lack of correlation between income and education inequality and find empirical support for them.
A first explanation for the puzzle is that the returns to education may be convex, as in Figure 3. The impact of the distribution of education on income inequality will depend not only on the size of the investments in education but also on the rate of return of these investments. If the returns to education are increasing, an extra year of education at the primary level brings a smaller increase in wages than it does at higher levels of education. We test this hypothesis in a sample of 144 countries for the period 1950–2010, and find that, at the aggregate level, the returns to education have increased with the level of schooling, that is, the increase in income per worker for an additional year of schooling is lower in primary education (0.02 per cent) than in secondary (0.07 per cent) and tertiary education (0.23 per cent). Thus, in terms of the distribution of income, the smaller effects of large improvements in education at the bottom end of the distribution (mainly due to the fall in the share of illiterates) have been partially compensated for by the greater effects on wages of smaller improvements in education at the top. This evidence is in line with recent studies for individual countries that find returns to education as an explanation for the evolution of income and wage inequality (see, for example, Goldin and Katz 2007).
Figure 3. Increasing returns to education and human capital
A second alternative explanation, as illustrated in Figure 4, could be that improvements in literacy, which increase the wage of the population at the bottom end of income distribution, have also coincided with an increase of wages in cohorts with higher education, such that all of them maintain their income shares. The latter could be the result of exogenous forces such as globalisation (e.g. Goldberg and Pavcnik 2007) or skill-biased technological progress (e.g. Katz and Murphy 1992) that have increased wages at the top. In fact, there is evidence that much of the rise in income inequality in the last decades has been driven by top wage incomes (Atkinson et al. 2011).
Figure 4. Skill-biased technological change and human capital
For a broad sample of countries, we show that increases in the supply of skills and reductions in education inequality would have reduced income inequality if their effects had not been offset by the increase in the demand for skilled workers and the effects of globalisation. Our results show that in a sample of wealthy economies, skill-biased technological change has increased the wage gap between skilled and unskilled workers by 23% between 2000 and 2011. We corroborate this result in a broader sample that includes 133 countries in a specification in which the dependent variable is the income Gini coefficient. We find that reductions in the inequality in the distribution of education have tended to reduce income inequality. At the same time, however, a higher wage gap has contributed to the increase in income inequality.
On the whole, the evidence indicates that improvements in education and human capital inequality observed in many countries have not resulted in significant reductions in income inequality due to forces in the opposite direction.
All these results are highly relevant for development policies. Many governments have made great efforts to reduce illiteracy rates, but these policies have not been accompanied by a more even distribution of income because other forces have offset the effects of lower education inequality. Our results do not imply that educational policies have not reduced poverty or improved wages and the standards of living of hundreds of millions with better education. On the contrary, better education is crucial to increasing average earnings per worker, and the eradication of illiteracy is a necessary condition to ensure access to higher levels of education for all people. Furthermore, better education is also the best policy to avoid the potential effects of skill-biased technological change and globalisation on greater income inequality.
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