While Korean economy has performed outstanding rate of growth since 1960, many researchers attributed the phenomenal success to the human capital advanced via investment on education. Young(1994) also have highlighted several foremost factors for the high rate of growth, which are strong investment, low fertility rate, trade openness, institutional improvement and human capital development. A large part of human capital development, in particular, were a result of increase in level of educational attainment backed by strong motivation for education. Since 2000’s, however, Korean economic growth began to decelerate, which is brought by aging society and saturated rate of human capital growth. 

What could a social planner do for human capital growth in a country with the high rate of tertiary education enrollment such as in Korea? One may want to look into the quality of human capital in tangible numbers and question which quality is the effective policy variable for upgrading human capital, especially for regional policymakers. We built up regional human capital indicators in the 229 municipality(‘si’, ‘gun’, ‘gu’) level in diverse forms, which are human capital stock as a pecuniary measure, regional level skill character, the portion of core group workforce. We analyzed the indicators in terms of regional divergence and general trend of human capital, and found a trend of human capital stock centralization around southern hinterlands of capital area. Moreover, skill character analysis shows that active manufacturing activity and knowledge based industry intensity are correlated rather with skill on technique than the others(cognitive, physical, relative). In particular, analysis on ‘core ratio’ presents significant growth rate of high skilled labor force in science and engineering in southern Gyeonggi-do area. 

Next, we investigated whether regional convergence of human capital stock has taken place for the last 10 years. σ-convergence tests and β-convergence tests show general convergence between municipalities. For the later period(2015-2020), however, shows the weak evidence of convergence than the previous period. This research performed the convergence tests as well on ‘core ratio’, in which the divergence pattern of high skilled human resources especially in the hinterlands of the capital is tested. This result implies that even though the national level human capital convergence dominates, the innovative region’s human capital growth may dominate in the future and drives regional human capital divergence.

We then take advantage of the data-set we built, for estimating growth accounting model with human capital stock. In the estimation, we analyzed the effectiveness of human capital stock in explaining regional per capita industrial growth rate. Applying Sunde & Vischer(2015)’s remedy for potential estimation bias, we successfully evidenced that the human capital stock growth rate explains a large part of the growth rate of per capita regional manufacturing output. Moreover, we hypothesized that human capital growth pattern may also be affected by regional output growth pattern and tested it with simultaneous equation model. The estimation shows that regional total output growth rate gives rise to the human capital stock per capita growth rate as we hypothesized. All in all, the regional human capital stock we built in this research is shown to be effective in explaining regional growth pattern.