A Study On The Effect Of China's Industrial Intelligence On Total Factor Productivity

According to economic growth theory,capital accumulation,labor input and total factor productivity are the core driving forces of economic development.Since the reform and opening up,China's economy has achieved growth miracle relying on the cost advantage and policy dividend of traditional production factors.With the disappearance of demographic dividend and the decline of marginal return of capital,China's economic growth begins to slow down,and the traditional mode of economic development driven by factor input is difficult to continue.The key to achieve highquality economic development is to change the mode of economic growth based on total factor productivity.At present,a new round of industrial technology revolution is gradually rising.Western industrialized countries are trying to promote technological change and industrial upgrading through industrial intelligence.In order to seize the opportunity of the new round of industrial revolution,China has successively issued "China Manufacturing 2025","Intelligent Manufacturing Development Plan(2016-2020)" and "A New Generation Artificial Intelligence Development Plan".The report of the 19 th National Congress of the Communist Party of China also emphasizes the integration of Internet,big data,artificial intelligence and real economy.Industrial intelligence has risen to the national strategy.In the future,the development of industrial intelligence will be more rapid,which will lead to major changes in technological progress and have a profound impact on China's economy.Based on the above background,it is of great significance to study the impact of China's industrial intelligence on total factor productivity and its transmission mechanism,so as to promote the process of China's industrial intelligence and achieve high-quality and sustainable economic development.Under the framework of Acemoglu and Restrepo(2018),this dissertation constructs a general equilibrium model composed of final product sector,intermediate product sector and task sector,theoretically analyzes the impact of industrial intelligence on total factor productivity and its mechanism,calculates the level of industrial intelligence and total factor productivity in China,explores its characteristics and evolution rules,empirically tests the effect of industrial intelligence on total factor productivity and its transmission mechanism,and examines the heterogeneous effect of different industries and enterprises.The main research contents and conclusions are as follows:(1)This dissertation extends the task model of Acemoglu and Restrepo(2018),constructs a multi sectors general equilibrium model,describes the industrial production process from the perspective of final product,intermediate product and industrial task,introduces three factors of intelligent machinery,traditional capital and labor,deduces the balanced total factor productivity,theoretically investigates the impact of industrial intelligence on total factor productivity,compares the effects of industrial intelligence under different conditions,and deeply analyzes the internal mechanism of the impact of industrial intelligence on total factor productivity.The theoretical research finds that: Firstly,the change of total factor productivity is mainly affected by the factor-augmenting technical change,man-machine mismatched production loss,task scale and industrial intelligence,among which industrial intelligence promotes total factor productivity.Secondly,there are significant differences in the promotion effect of industrial intelligence on total factor productivity under different levels of intelligence,human capital,enterprise scale and subsidy.Moderate level of intelligence,higher level of human capital,larger enterprise scale and stronger degree of subsidies can strengthen the positive role of industrial intelligence in promoting total factor productivity.Thirdly,industrial intelligence affects total factor productivity through the internal mechanism of knowledge spillover effect,cost saving effect and factor allocation optimization effect.The knowledge spillover effect shows that the industrial intelligence of the sector can promote the total factor productivity of other sectors through knowledge spillover.The cost saving effect shows that industrial intelligence can reduce cost by replacing traditional production mode with intelligent production mode,and then positively affect total factor productivity.The factors allocation optimization effect shows that with the development of industrial intelligence,it can optimize factor allocation by alleviating the mismatch between capital and labor,and then promote total factor productivity.(2)Combined with the multi index system,this dissertation constructs the measurement index of industrial intelligence,covering the three dimensions of intelligent foundation,intelligent production application and intelligent benefit,explores the evolution characteristics of industrial intelligence in China,further measures the total factor productivity at different levels,and explores the evolution trend of total factor productivity.The results show that: Firstly,the level of China's industrial intelligence is rising steadily.From the results of different dimensions of intelligence,it shows an upward trend,but there are obvious differences in the increase.The increase of intelligent foundation and intelligent production application is obvious,and the growth of intelligent benefit is slow.The level of industrial intelligence shows obvious regional differences,with the eastern region taking the lead,the central region taking the second place,and the western and northeast regions relatively lagging behind.Among them,Guangdong,Jiangsu,Beijing,Shanghai,Shandong and Zhejiang are in the leading position,and these regions are in the top of the intelligent foundation,intelligent production application and intelligent benefit dimensions.The intelligence level of the industrial industry is gradually rising,with obvious industry differences.The electronic and electrical equipment manufacturing industry,transportation equipment manufacturing industry and general and special equipment manufacturing industry rank in the top three.Secondly,the average annual growth rate of China's industrial total factor productivity is 6.41%,with obvious regional differences.Guangdong,Shandong,Jiangsu,Shanghai,Zhejiang and Beijing are in the leading position.The average annual growth rate of industrial industry total factor productivity is 6.82%,showing significant industry heterogeneity.Electronic and electrical equipment manufacturing industry,transportation equipment manufacturing industry and general and special equipment manufacturing industry are in the forefront.The average annual growth rate of industrial enterprises total factor productivity is 3.16%,showing significant enterprise heterogeneity.The total factor productivity of large-scale enterprises,private and foreign-funded enterprises and enterprises with high subsidies is higher.(3)This dissertation empirically tests the impact of industrial intelligence on total factor productivity.According to the characteristics of industries and enterprises,the samples are divided into industries with different factor intensive,intelligent degree and human capital level,as well as enterprises with different scale,ownership and subsidy income.The results show that: Firstly,industrial intelligence has a significant promoting effect on total factor productivity.After a series of robustness tests and regression of instrumental variables,the conclusion is still valid.Secondly,the impact of industrial intelligence on total factor productivity is different among different industries.From the perspective of different factor intensive industries,technology intensive industries have a promoting role,while capital and labor-intensive industries have no significant role;for industries with different levels of intelligence,the role of low intelligence industries is not obvious,while the promotion role of medium and high intelligence industries decreases;for industries with different levels of human capital,the role of low human capital level industries is not significant,and the promotion of medium and high human capital level industries is gradually strengthened.Thirdly,the impact of industrial intelligence on total factor productivity has significant enterprises heterogeneity.For enterprises with different sizes,the promotion strength gradually increases with the expansion of enterprise scale;for enterprises with different ownership systems,the role of state-owned and collective enterprises is not obvious,while private and foreign-funded enterprises show a significant promotion role,and the role of foreign-funded enterprises is higher;for enterprises with different subsidy income,the role of low subsidy income enterprises is not significant,and the promotion degree of medium and high subsidy income enterprises is gradually enhanced.(4)This dissertation uses the mediating effect model to test the transmission mechanism of the impact of industrial intelligence on total factor productivity,to investigate the differences of mediating effect,and to refine the effect of transmission mechanism in different industries and enterprises.The results show that: Firstly,industrial intelligence improves total factor productivity through knowledge spillover effect,cost saving effect and factor allocation optimization effect,in which the intermediary effects of knowledge spillover,cost saving and factor allocation optimization account for 22.35%,19.27% and 18.34% of the total effects.Secondly,the transmission mechanism of the impact of industrial intelligence on total factor productivity shows industries heterogeneity.Technology intensive industries improve total factor productivity mainly through knowledge spillover and cost saving effect.The medium and high intelligent industries can improve total factor productivity through three transmission mechanisms,and the cost saving effect is dominant.With the improvement of the level of intelligence,the mediating effect is declining.The three transmission mechanisms of industries with medium and high human capital levels all play a significant role,and the knowledge spillover effect plays a major role.With the improvement of human capital level,the mediating effect shows an increasing trend.Thirdly,the transmission mechanism of the impact of industrial intelligence on total factor productivity shows enterprises heterogeneity.For different scale enterprises,the factor allocation optimization effect plays a major role.With the expansion of enterprise scale,the intermediary effect shows an upward trend.From the perspective of private enterprises and foreign-funded enterprises,the cost saving effect is in the first place,and the intermediary effect of foreign-funded enterprises is more obvious.The three transmission mechanisms of middle subsidy income enterprises are not significant,while the knowledge spillover effect of high subsidy income enterprises plays a primary role.