Spatio‐temporal dynamics of technical efficiency in China’s specialized markets: A stochastic frontier analysis approach

Abstract

China’s specialized markets as a special form of bottom‐up capital agglomeration have played a key role in fostering regional development. It once exhibited positive externalities with high efficiencies. However, given the rapid proliferation of specialized markets and the penetration of E‐commerce, their advantages may have shifted and the understanding of this shift is limited. The paper explores the spatio‐temporal dynamics of China’s specialized markets in terms of technical efficiency. Based on turnover data from Statistical Yearbooks of China Commodity Exchange Market from 2000 to 2016, technical efficiencies in specialized markets are measured by a Stochastic Frontier Analysis (SFA) approach using panel data. The results show that (a) the technical efficiencies in China’s specialized markets are significantly divergent in space over time; (b) labor input has notable effect on efficiency increase, while capital input has no significant effect; (c) informatization level, cluster size, and degree of market openness are identified to have a positive effect on specialized market’s technical efficiency. This paper argues that specialized markets should be taken seriously in the cluster evolution research. The role of proximity and the bounded links between specialized markets and their local clusters is the key to understanding their changing forms, performances, and trajectories.

University‐industry‐government triple helix relationship and regional innovation efficiency in China

“…The former indicator reflects the output capacity of new knowledge and technology in each region, while the latter one represents the transformation capacity and commercialization level of innovation achievements. Regional innovation output will lag behind innovation input, in order to reduce the simultaneity problem and enhance the robustness of regression results, we follow previous studies (Chen et al, 2020; Zhang et al., 2020), and construct data models with a 1 year lag and 2‐year lag, respectively. The VRS model with variable scale returns can be used to calculate the comprehensive efficiency of regional innovation (TE), which can be divided into pure technical efficiency (PTE) and scale efficiency (SE), the relationship is TE = PTE * SE.…”

Technical efficiency in the Malaysian electric and electronic manufacturing industry: A stochastic frontier analysis approach

“…Battese and Coelli (1992) and Battese and Coelli (1995) continued developing this model specifically to evaluate corporate efficiency performance. Most TE research uses the SFA model as it presents several advantages, such as incorporating random error and uncontrollable factors in the calculation, more statistical parameters, user-friendliness, and simple equations (Abunyuwah et al, 2019;Zhang, Hu, & Xu, 2020). Battese, Rao, and O'donnell (2004) also emphasised that the SFA model can identify irrelevant factors contributing to technical inefficiency.…”

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