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Regular version of the site

Book chapter

Capitalization of Professional Knowledge in Economies with Different Level of Innovation Development

P. 628-637.

This paper is focused on the problem of aggregate return to education. We consider this factor as an indicator of the professional knowledge capitalization that can be used as a proxy for human capital stock in extended Cobb-Douglas type equation for modeling the economic growth. Identification of the indicator is based on time-series data on countries and regional economies. To divide the objects into groups with respect to the aggregate return to education, we employ the fuzzy classification methodology instead of the traditional clustering procedure. This approach provides more relevant dividing the whole sample into three homogeneous groups. The first group includes the economies with low level of innovation development. These economies have close to zero or negative rate of return to education. The second group is most numerous. It mostly consists of developed economies with moderate aggregate rate of return to education. The third group includes objects with high return to education. It turned out that they are predominantly emerging economies developing on the base of new technologies. Such a typology provides the opportunities to make some suggestions concerning the relation between innovation development and quality of human capital stock. In particular, high rate of return to education in emerging economies we explain by the excess demand on high-qualified staff on the labor market that cannot be fully satisfied by existing educational system in the economies with higher level of innovation potential. Conversely, in developed economies, educational system meets the demand of the labor market, so the aggregate return to education is not so high. So it is necessary to be more specific about the structure of educational system for preparing high qualified staff for the most promising directions of innovation development. Comprehensive research of the considered problem at the regional level requires more elaborated data sources.

In book

Edited by: C. Loue, S. Ben Slimane. P.: ACPI, 2017.