On A New Measure of Human Capital and Its Impact on Gross Domestic Product

Debasis Bandyopadhyay, P Lahiri and Feng Yu

No 164, Working Papers from Department of Economics, The University of Auckland

Abstract: The general goal of this paper is first to develop an operationally simple measure of human v capital using the relative frequency histogram of the highest educational attainment and then to analyze the cross-country variations of the proposed measure. Visual inspection and the matrix of rank correlation coefficients show that relative frequency distributions of the highest educational attainment are similar for countries with similar Gross Domestic Products (GDP) level, but they are very different for countries whose GDP levels are quite different. Guided by intuition, we define a simple descriptive statistic, EER measured by the relative proportion of labor force with education beyond the secondary level to those with no formal education. This simple statistic turns out to extract the most essential information contained in the relative frequency histogram of the highest educational attainment to forecast future economic growth of a country. Consequently, we propose this statistic EER as a new measure of human capital. Non-parametric tests show that both the means and variances of the distribution of log (EER) for the high GDP countries are significantly higher than the corresponding means and variances for the low GDP countries. A chi-square test reveals that for the two groups of low and high GDP countries, the distributions of EER can be characterized by a unified class of gamma distributions with the same shape parameter but with very different scale parameters. Based on the data created by Barro and Lee (1993), we note that our new measure of human capital (i.e., EER) alone can explain cross-country variations in per capita GDP much better than the other growth models such as Solow (1956) and Mankiw, Romer, and Weil(1992). Those models include population growth rate and investment rate as covariates and the latter model use an additional covariate SCHOOL measured by the average secondary school enrollment rate or in addition to those two covariates. We explain the better performance of our model by noting that the statistic EER is significantly negatively correlated with population growth rate and positively correlated with investment rate and SCHOOL.

Keywords: Chi-square test; Economics (search for similar items in EconPapers)
Date: 1999
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Persistent link: https://EconPapers.repec.org/RePEc:auc:wpaper:164

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