Have S&T Innovation and Educational Development in China’s Coastal Provinces and Regions Achieved Synchronization? A threshold approach

Haiying Liu, Xianzhe Cai () and Yajing Hui
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Haiying Liu: Dalian Maritime University
Xianzhe Cai: Dalian Maritime University
Yajing Hui: Dalian Maritime University

Journal of the Knowledge Economy, 2024, vol. 15, issue 1, No 116, 2808-2835

Abstract: Abstract This article aims to assess the S&T innovation-education integration (STIEI) mechanism’s efficacy in the fast-evolving knowledge economy and to provide strategies for boosting the role of technology and knowledge in economic growth. Using data envelopment analysis (DEA) methods to process STIEI input-output panel data from 2006 to 2015, this paper first uses the coastal regions as an example, which are the frontier of China’s development, to measure and compare the efficiency of the three systems, including STIEI, S&T innovation, and education. Then, using the panel threshold regression method, we examine the effects of the innovation environment and the foundation of educational activities on economic performance in the coastal, central, and western regions individually, taking into account the reality of regional disparities in China. The results show that first, the STIEI mechanism, which considers S&T innovation and education as a whole, makes significantly more efficient use of human and financial resources. Second, the STIEI mechanism is unable to function at its best and leads to noticeable regional disparities in coastal regions due to the lack of synchronization between S&T innovation and education. Third, the effects of the innovation environment and the basis of educational activities on economic performance vary significantly across various Chinese areas. Our empirical evidence is valuable for local governments and policymakers to create more effective STIEI strategies and planning in developing countries.

Keywords: S&T innovation–education integration; Innovation; Education; Data envelopment analysis; Threshold regression; Heterogeneity (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13132-023-01164-5

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