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An empirical analysis of the impact of higher education on economic growth: The case of China.

Authors :
Di Qi
Ali, Arshad
Tao Li
Yuan-Chun Chen
Jiachao Tan
Source :
Frontiers in Psychology; 8/18/2022, Vol. 13, p1-11, 11p
Publication Year :
2022

Abstract

China’s domestic labor market has limited demand for tertiary graduates due to an unbalanced industrial structure, with a weak contribution to economic performance over the past decade. This study estimates the asymmetric eects of higher education progress (highly educated employed workforce), higher education utilization (highly educated unemployed workforce), and the separate eects of higher education utilization interactions with high-tech industries on economic growth in China from 1980 to 2020. Using a Nonlinear Autoregressive Distributed Lag (NARDL) model, this study finds that the expansion of higher education progress (the employed workforce with higher education) promotes economic growth, while contraction of higher education progress (employed workforce with higher education) reduces economic growth. Likewise, an increase in higher education utilization (the unemployed labor force with higher education) suppresses economic growth, while a decline in the higher education utilization (the unemployed labor force with higher education) promotes economic growth. The study also found that the expansion of high-tech industries and government spending on education significantly stimulate economic growth. The moderating role of higher education utilization (unemployed labor force with higher education) in the impact of high-tech industries on economic growth is significantly positive. This study strategically proposes that China’s higher-educated unemployed labor force can be adjusted to high-tech industries, which need to be developed equally in all regions. Moreover, the country is required to invest more in higher education and the development of high technological industries across all regions, thus may lead to higher economic growt [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
16641078
Volume :
13
Database :
Complementary Index
Journal :
Frontiers in Psychology
Publication Type :
Academic Journal
Accession number :
158894811
Full Text :
https://doi.org/10.3389/fpsyg.2022.959026