Investment Behavior Related to Automated Machines and Biased Technical Change: Based on Evidence From Listed Manufacturing Companies in China - Jiang, Hong; Wang, Xue; Xiao, Qian; Li, Silin (Frontiers in Psychology, 2022)
This paper studies the impact of a recent increase in the ratio of automated machines to ordinary capital (RAMOC) on the bias of technical change in the manufacturing industry, and the mechanism influencing this. Using panel data of A-share listed manufacturing companies on the Shanghai and Shenzhen stock exchanges from 2012 to 2019, and combined with the Xtfrontier model and trans-log production function, we measure the index of the bias of technical change of the manufacturing industry in China. Furthermore, we adopt a fixed effects model to test the impact of an increase of investment in automated machines on the bias of technical change. We also use an intermediary effect model to examine the intermediate mechanism from the perspectives of capital and skill matching. The results show that technical change in the manufacturing industry is biased toward automated machine capital. An incremental increase in RAMOC leads to technical change in the manufacturing industry becoming biased toward automated machine capital, wherein the intermediary mechanism is the labor structure effect. Based on industrial linkage, the investment in automated machines in the upstream (downstream) manufacturing industry increases, the technical change of the downstream (upstream) manufacturing industry is biased toward automated machine capital, and the forward linkage effect is greater than the backward linkage effect. This research enhances understanding of (1) the direction and characteristics of technical change in China, (2) how to improve the output efficiency of automated machines, (3) differences in factor revenue distribution, and (4) how new growth points in the economy can be cultivated. They show that we should encourage and support investment in automated machines, vigorously promote technical change to bias toward automated machine capital, improve the skill level of the labor force, and strengthen the match between automated machines and labor.