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| Continuous Dynamic Measurement and Decomposition of Temporal and Spatial Differences of Innovation Total Factor Productivity in China |
| Yu Aishui1,Zhang Jiru2,Yu Deshui3 |
| (1.School of Government,Beijing Normal University,Beijing 100875, China;2.School of Public Management, Inner Mongolia University,Hohhot 010021,China;3.Wuhai Energy Co., Ltd, National Energy Investment Group Co., Ltd.Wuhai 016000,China) |
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Abstract Innovation total factor productivity is an essential factor to enhance international competitiveness, and also the driving force for China′s economic growth.Confronted with the reality that the traditional production factors of labor and capital have a declining contribution to the national economic growth, China needs to rely more on the rise of innovation total factor productivity to promote industrial digitization and digital industrialization, and to help traditional energy-intensive industries upgrade in an innovative direction.However, prior research has little to say with regard to China′s innovation total factor productivity, and the in-depth discussion on the continuous dynamic measurement of total factor productivity of innovation and the decomposition of temporal and spatial differences are scarce.#br#The absence of relevant research is at odds with the importance attributed to China′s innovation total factor productivity.Then what are the continuous dynamic evolution process and time characteristics of China′s innovation total factor productivity？ Is there regional heterogeneity？ In order to answer the above questions, it is necessary to sort out relevant data and conduct an empirical test based on the development status of China′s innovation total factor productivity.Therefore, drawing on the panel data of 30 provinces from 2011 to 2020, this study uses the secondary weighted factor analysis method to conduct a sub-item and comprehensive evaluation of China′s innovation total factor productivity, and introduces the OWGA operator and OWA operator to implement the transformation and upgrading of mixed factors, with the aim to clarify its dynamic evolution process.In order to further reveal the vertical changes of the regional differences of China′s innovation total factor productivity in the temporal dimension, the LMDI factor decomposition method is used to explore the driving effect of temporal and spatial differences on innovation resources, knowledge innovation, collaborative innovation, innovation policy and innovation environment.#br#In terms of the dynamic measurement, the innovation total factor productivity of most provinces in China has shown a steady growth trend over time, while only a few provinces have experienced a decline in innovation total factor productivity due to a lack of funds, outdated production equipment and inconvenient transportation.In terms of the decomposition of temporal and spatial differences, the first is the temporal decomposition.The effect of innovation resources on innovation total factor productivity shows volatility, and the impact of knowledge innovation on the total factor productivity of innovation presents a promoting effect, and the effect intensity is far greater than that of innovation resources.Collaborative innovation shows the effect of promotion first and then inhibition, and there is a continuous upward trend of the impact of innovation policy and innovation environment .The second is spatial decomposition.The strongest and most significant spatial concentration is the effect of knowledge innovation, while the spatial concentration of innovation resources, collaborative innovation, innovation policy and innovation environment effects all present staggered distribution trends.In the eastern region, except for the decline of collaborative innovation effect, the other four driving effects have maintained a steady upward trend.The five driving effects in the central and western regions have a weak impact on regional innovation total factor productivity.#br#Compared with the existing literature, this paper makes a reasonable explanation of innovation total factor productivity, and then establishes an indicator system for scientific measurement based on a comprehensive consideration of innovation resources, knowledge innovation, collaborative innovation, innovation policy and innovation environment.Second, from the perspective of temporal and spatial evolution, this paper systematically analyzes the evolution of temporal and spatial patterns and temporal characteristics of innovation total factor productivity, and reveals the regional differences in China′s innovation total factor productivity.The paper sheds light on the continuous dynamic measurement and decomposition of temporal and spatial differences in innovation total factor productivity in China.The findings are conducive to further improving China′s innovation capability and levels, and provide a reference for relevant government departments to formulate development strategies to improve innovation total factor productivity.#br#
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Received: 19 September 2022
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