Dynamic spillover capacity of R&D and digital investments in China's manufacturing industry under long-term technological progress based on the industry chain perspective

Wei Zhang, Ting Zhang, Hangyu Li and Han Zhang

Technology in Society, 2022, vol. 71, issue C

Abstract: During the new era of digital-technology economics, R&D and digital investments have emerged as critical factors influencing technological change in the long term. At the same time, technological progress in industries is the fundamental cause of the R&D and digital investments spillover between industries. In order to study the dynamic spillover capacity evolutionary process generated by R&D and digital investments caused by the long-term technological change in China's manufacturing industry, this paper firstly integrates R&D and digital investments as the critical influencing factors affecting technological progress. It establishes a dynamic input-output model including endogenous technological progress, which is simulated by historical data from 2000 to 2014 to obtain the input-output coefficient matrix from 2015 to 2050. On this basis, this paper makes a rare attempt to focus the inter-industry spillover weights on the industry chain perspective to revisit and simulate the dynamic changes of inter-industry R&D and digital investments spillover capacity under the influence of long-term technological progress. As shown in the simulation results, there are significant differences in inter-industry R&D and digital investments spillover capacities among 18 manufacturing industries in China in both industry and time dimensions. The results of this study are presented in the following sections: (1) A horizontal comparison of the spillover capacity of R&D and digital investments in manufacturing industries in the same period from the industry dimension shows that the spillover capacity of R&D and digital investments between industries is dynamic because the development strategies and technological structures of industries may change over time. Hence, the position of each industry in the industry chain is not constant, and the R&D and digital investments spillover capacity between industries are also in a dynamic process. The manner and ability of an industry to generate R&D and digital investments spillover to its upstream and downstream industries may differ depending on its production location in the chain. Generally, for most upstream and near-upstream industries in China's manufacturing industry chain, the spillover capacity of R&D and digital investments of an industry to its downstream industries are greater than that to its upstream industries, and vice versa. Therefore, in order to maximize the technology spillover capacity, the corresponding technology guidance policy should be adjusted by the industry's technological progress characteristics and changes in the production location of the industry chain. (2) When comparing the spillover capacity of R&D and digital investments of each manufacturing industry longitudinally from the time dimension, the demand-pull effect of technological progress caused by R&D and digital investments of industries for their downstream industries is not as profound as the technology-push effect for their upstream industries. Hence, the fluctuations of R&D and digital investments spillover capacity generated by industries located in the upstream, near-upstream of China's manufacturing industry chain are greater than those generated by downstream, near-downstream industries. Therefore, it can be inspected that industries close to the upstream of the industry chain are more efficient in driving economic growth with their inputs in general-purpose technologies.

Keywords: R&D; Digital investment; Long-term technological progress; Industry chain; Spillover capacity; Dynamic input-output (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:71:y:2022:i:c:s0160791x22002706

DOI: 10.1016/j.techsoc.2022.102129

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