Soft computing applications to estimate the quantitative contribution of education on economic growth

First, this article softly categorizes a target system (a country or a region) according to the level of Science and Technology (S & T) progress. We calculate potential human capital stock and actual human capital stock in the same cluster, and set up the internal correlation between them (fuzzy reflection). Second, we conceptualize actual human capital as one production factor, joined with the other two production factors, land and technology, to set up the fuzzy mapping to economic growth. Finally, we obtain the economic contribution rate of education (ECRE) through two marginal rates, namely marginal economic growth to actual human capital stock, and marginal actual human capital to potential human capital. This method greatly reduces the bias in the ECRE that results from the indirect and lagged effects of education. It therefore identifies the effect of education on economic growth more explicitly. Based on the level of science and technology progress, 31 provinces in China could be classified into three clusters. The first cluster (Developed S & T) has an ECRE of 11.60%, and contains two provinces; the second cluster (developing S & T) has an ECRE of 8.84%, and contains 11 provinces; the third cluster (underdeveloped S & T) has an ECRE of 1.49% and contains 18 provinces.