数字技术、融合创新对地区全要素生产率影响机理研究

Effects of Digital Technology,Integrated Digital Innovation on Regional Total Factor Productivity

提高全要素生产率是高质量发展的关键。数字技术创新则是新一轮科技革命的焦点,如何优化数字技术创新资源配置,提升数字技术对全要素生产率的积极作用至关重要。在系统梳理现有研究成果的基础上,使用专利数据构建“省份—地区”层面的三大类创新指数:数字技术创新指数、数字技术与其他技术的融合创新指数以及一般大类技术创新指数,进一步识别以数字技术为驱动的生产力提升路径。基准回归结果表明:数字技术对地区全要素生产率提升具有显著的积极效应,在考虑内生性并经一系列稳健性测试后依然成立。一般大类技术创新在2012年之前呈现显著的正效应,但在2004—2018年的全样本范围内显著性减弱,存在创新资源配置低效现象。在此基础上,使用门槛效应的实证检验发现,在前期全要素生产率高区的地区,融合创新更能发挥对全要素生产率的提升效应。对比企业与高校科研院所的创新,后者仅在全要素生产率高区对于技术进步能够产生显著的提升效应,需要进一步挖掘和释放。

Currently,the new generation of digital technology represented by artificial intelligence and big data is driving a new round of scientific and technological revolution,which not only directly promotes the development of new economies and new business formats,but also integrates with other industrial technologies to promote the improvement of total factor productivity from multiple angles.On March 12,2021,the"14th Five-Year Plan"and the outline of long-term goals for 2035 were released,clearly proposing to"create new advantages in the digital economy".By 2035,the core industries of the digital economy will account for 10%of GDP.It is foreseeable that investment in the development of digital economy-related technology industries in various regions will be further strengthened in the future in China.Therefore,how to further improve total factor productivity with the help of digital technology innovation is a key question that needs to be answered in the new development stage.This article continues the theoretical framework of productivity growth in the literature and conducts an empirical analysis of how digital technology affects economic growth quality.This article selects 29 mainland provinces and regions except Hong Kong,Macao,Taiwan,Tibet,and Hainan as the research objects,and uses the Non-Parametric Malmquist Index method to measure the core explained variable,the regional total factor productivity change index(TFP),Technological Progress Change Index(TECH)and Technical Efficiency Change Index(EFF)for estimation;use patent application data and technology classification information in patent documents to conduct digital technology innovation(lnDTS),digital technology integration innovation(lnITS),as well as the measurement of the development level of general technological innovation(lnGTS)at the"province and city-year"level,explore the impact of the three types of innovation on total factor productivity,and use the threshold panel model to further explore the impact of integrated innovation,digital technology innovation and general category innovation on the overall regional The interaction of factor productivity and the heterogeneity analysis of enterprise and university innovation.The results of the empirical analysis include the following three aspects:Firstly,between 2004 and 2018,the productivity effect of general technological innovation was significant from 2004 to 2012,and then gradually weakened,and even had a suppressive effect.Secondly,when digital technology innovation(lnDTS)is less than 8.97,the impact of the convergence innovation index(lnIDS)on productivity is not significant;when digital technology innovation reaches above the critical point,the convergence innovation index begins to have a significant impact:productivity-enhancing effect.Finally,the productivity effect of integrated innovation is weak,and the integrated innovation of university research institutes shows a significant improvement effect in areas with higher initial productivity levels.Therefore,when formulates digital technology innovation policies in the future,it should further increase investment in digital technology innovation and strengthen the integration and innovation of digital technology and other technologies in advantageous areas;scientifically understand the role conditions of digital technology in improving regional economic growth,and pay attention to the digital construction process regional differences,implement differentiated policy formulation based on the characteristics of regional economic development stages;continue to optimize the resource allocation capabilities of the innovation system,formulate digital transformation plans and roadmaps,and clarify the boundaries of enterprise technological innovation,university and scientific research institute knowledge innovation,and government system innovation division of labor and positioning,optimizing the corresponding organizational model and institutional support,and opening up collaborative innovation channels for the integration of government,industry,academia,and research.