摘要
推进数据要素市场化配置,是实现数据要素价值转化、驱动数字经济高质量发展的关键。文章基于文献研究和实践经验阐释了数据要素市场化配置的理论过程,将数据要素市场化配置过程划分为市场化建设和价值化配置2个阶段。文章以理论为基础,应用追加中间投入的网络DEA模型,测算2019—2020年我国30个省(区、市)的数据要素市场化配置效率;进一步,通过Malmquist指数分析,研究我国数据要素市场化配置效率和全要素生产率的动态变化情况。研究发现:(1)2019—2020年我国数据要素市场化配置效率整体呈上升趋势,价值化配置阶段效率较高,市场化建设阶段效率低于价值化配置阶段。因此,未来进一步提高公共数据开放水平、完善数据流通和交易基础设施仍是提高数据要素市场化配置效率的工作重点。(2)研究期内,我国全要素生产率提升明显,提高技术进步率和数据要素市场化配置效率均对提升全要素生产率有正向作用,且提高数据要素市场化配置效率对推动全要素生产率提升更为关键。
Promoting the market allocation of data elements is the key to realize the value of data resources,which drives the high-quality development of digital economy.Based on the literature review,this study first analyses the theoretical process of market allocation of data elements,and divides the whole process into two stages:market-based construction and value-based allocation.According to the proposed theoretical model,this study designs the network DEA model with additional intermediate inputs to calculate the market allocation efficiency of data elements of 30 provincial administrative regions in China from 2019 to 2020.Moreover,by Malmquist index analysis,we further investigate the dynamic changes of both market allocation efficiency of data elements and total factor productivity in China.The study finds that during the period of 2019 to 2020,the market allocation efficiency of data elements in China is on an upward trend.The efficiency of value-based allocation stage reached a higher level than that of the market-based allocation stage.This finding suggests that the orderly opening of public data and the construction of data trading platform will still be the focus in improving the market allocation efficiency of data elements in the future.The results of the study further indicates that China’s total factor productivity increased significantly—both the technology progress and the improvement of market allocation efficiency of data elements had a positive effect on improving total factor productivity,and the latter driving force is more critical to promoting the improvement of total factor productivity.
作者
乔晗
李卓伦
QIAO Han;LI Zhuolun(School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190,China;MOE Philosophy and Social Science Laboratory of Digital Economic Monitoring,Forecasting,Early Warning,and Policy Simulation(Cultivation),University of Chinese Academy of Sciences,Beijing 100190,China)
出处
《中国科学院院刊》
CSSCI
CSCD
北大核心
2022年第10期1444-1456,共13页
Bulletin of Chinese Academy of Sciences
基金
国家自然科学基金重大项目(72192843),国家自然科学基金面上项目(71872171)
中央高校基本科研业务费专项资金(E0E48933)。
