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Symposium 2013

Proposal - Inequality and Human Capital

The Challenge

Both advanced industrial economies such as the United States and rapidly growing economies such as China are exhibiting increasing levels of inequality and disadvantage. Recent research on the sources ...

Both advanced industrial economies such as the United States and rapidly growing economies such as China are exhibiting increasing levels of inequality and disadvantage. Recent research on the sources of inequality has begun the process of creating a new social science paradigm which integrates economics, psychological sociological and biological factors in order to provide a comprehensive understanding of the determinants of socioeconomic status across the life course as well as intergenerational mobility. This new work embodies a rich conception of the forces that underlie individual decision making that draws upon the insights of many disciplines. These insights are unified by conceptualizing inequality in socio-economic outcomes as derivative from inequalities that emerge in cognitive and noncognitive skills, which include personality traits as well as human capital and intelligence.

Inequality and human capital are two interesting yet seemingly independent concepts. However, if interrelatedness between the two concepts can be found, then policy implications can be drawn accordingly.

The relationship between inequality and human capital can be examined through the correlation between the Gini coefficient[1] (Gini) and various educational indicators that reflect the accumulation of human capital (HK), such as illiteracy rates and other relevant education statistics on the corresponding country.

There are 22 countries, including Hong Kong, who published Gini in different years, and 21 of them had compiled Gini on personal, rather than household, income basis. This formed the largest possible data base for our advance analysis. We calculated the correlation coefficients (CC) between Gini and various indicators of HK. The results were summarized in the Table.

By pooling together data from the developed and developing countries, the Table suggested that the higher the illiteracy rates, the higher the inequality. Meanwhile, the CC was as high as 0.8240[2]. The Table also suggested that if we confined the illiteracy rate statistics to population aged 15 to 24, the CC was still as high as 0.7019.  Upon examination of the CC between Gini and gross enrollment rates (GER) for different levels of education, our findings showed that the CC was 0.5723 for primary education, -0.3023 for secondary education, and -0.6362 for higher education across countries.

The above findings suggested that: first, the higher the illiteracy rates for all ages in a country, the worse the situation of inequality. This was shown in the very high correlation between illiteracy rates and income inequality. However, the results for OECD countries were quiet different, i.e., the CC was actually [-0.1358] for OECD countries, compared to (0.8992) for developing countries. We also saw a similar pattern in the illiteracy rate statistics for population aged 15 to 24.

Secondly, the above results suggested that it would be very beneficial for developing countries to lower illiteracy rates in order to achieve a more equalized income distribution. This may then raise the question regarding the extent of education required to effectively reverse the inequality situation. In the Table, we calculated the CC for different levels of education. Our empirical findings suggested that there existed a strong positive correlation between inequality and the GER for primary education across countries. More specifically, the corresponding CC was as high as 0.5723. Moreover, the CC was as high as (0.9658) for developing countries alone.

Contrary to many arguments that emphasize the importance of primary education, our empirical findings suggested that focusing on primary education alone was not sufficient for improving inequality. In other words, it may require education beyond the primary level to reverse the situation. This was shown in our findings that, for developing countries in particular, the CC between Gini and GER first turned negative at the secondary education level and that the CC was as high as (-0.9972).

Furthermore, the CC was -0.6362 for high education across countries. This was primarily due to the fact that CC was also shown to be strongly negative (-0.9748) for developing countries. However, the situation for the OECD country group was just the opposite as the CC was on the positive side and was as strong as [0.4312].

The above results revealed two contrasting situations, namely, first, in order for developing countries to improve upon the problem of escalating inequality, efforts may be required to improve both secondary school education and high education for effective and consistent results, i.e., primary education alone cannot solve the problem. Secondly, for OECD developed countries, our findings suggested that higher education can actually hinder income distribution.

Based on the above findings, the following two policy suggestions warrant our attention. Namely, first, human capital is relevant in determining the inequality of a society. In particular, only human capital with secondary or higher level education showed real capabilities to reverse the inequality situation in developing countries.  As such, strong commitment may be required for developing countries to improve the inequality situation, i.e., to devote precious resources to education and be persistent in the long run. This undoubtedly presents a big challenge for many developing economies.

Secondly, while higher education could worsen the income distribution problem in developed countries, the case for developing countries was entirely the opposite. Based on our findings, it is advisable for developing countries to take active measures to reduce illiteracy rates through improving both primary and secondary education.

Surely, it’s never too late for a country to strengthen its own human capital.

 

Table: Correlation Coefficients (CC) between Gini Coefficient and Relevant Education Indicators for Human Capital

Relevant Education
Indicators

Illiteracy Rates for Relevant Countries in 2000-2005 (%)

Population 15 years old and above

Population between age 15 to 24

Correlation Coefficient with Gini Coefficient

0.8240
(0.8992) [-0.1358]

0.7019
(0.7742) [-0.2577]

Relevant Education
Indicators

Gross Enrollment Rates for Relevant countries in 2005

Primary Education

Secondary Education

High Education

Correlation Coefficient with Gini Coefficient

0.5723
(0.9685) [0.1885]

-0.3023
(-0.9972) [0.0967]

-0.6362
(-0.9748) [0.4312]

Note: numbers without brackets, with ( ), with [ ], are CC for all samples, for developing countries, and for OECD countries, respectively.

 


[1] A measurement for income inequality and the larger the Gini coefficient, the wider the income distribution gap for a country.

[2] where 1 (-1) is the possible maximum value for a positive (negative) correlation, and 0 stands for no correlation at all for the relevant two factors or variables.

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