摘要
利用2012~2019年中国省际层面30种投入要素,采用因果森林和随机森林方法实证检验数据要素促进制造业增长的效应、贡献、边际报酬规律以及内在机制。研究发现:数据要素投入显著促进了中国制造业增长;在影响制造业增长的众多要素中,数据要素的重要性和贡献率较高,数据已经成为影响中国制造业增长的关键要素之一,对制造业增长贡献显著;当其他要素投入不变时,制造业产出的增长幅度随着数据要素投入的增加而下降,表明数据要素在制造业增长中遵循边际报酬递减规律;机制分析表明,数据挖掘能力的提升增强了数据要素促进制造业增长的效应。鉴于此,应加快完善数据产权、交易、公开等制度,着力培育高效、有序的数据要素交易市场。同时,优化数据劳动供给、增加数据资本投资、提高数据技术应用水平,从而有效释放数据要素价值。
This paper uses 30 input factors at the provincial level in China from 2012 to 2019,and adopts causal forest and random forest methods to conduct an empirical test on the effect,contribution,marginal return law and intrinsic mechanism of data factors in promoting manufacturing growth.Research has found that the input of data factors significantly promotes the growth of China's manufacturing industry.Among the many factors that affect the growth of the manufacturing industry,the importance and contribution rate of data factors are relatively high,fully indicating that data has become one of the key input factor affecting the growth of China's manufacturing industry and has a significant contribution to the growth of the manufacturing industry.When the input of other factors remains unchanged,the growth rate of manufacturing output decreases with the increase of data factors input,which shows that data factors follow the law of marginal diminishing returns in the growth of manufacturing.Mechanism analysis shows that the improvement of data mining capabilities enhances the effect of data factors on promoting manufacturing growth.In view of this,it is necessary to accelerate the improvement of data property rights,trading,and disclosure systems,and strive to cultivate an efficient and orderly data factors trading market.At the same time,optimize the supply of data labor,increase data capital investment,and improve the application level of data technology,in order to effectively release the value of data factors.
作者
于柳箐
高煜
YU Liu-qing;GAO Yu(School of Economics&Management,Northwest University,Xi'an 710127,China;China Western Economic Development Study Center,Northwest University,Xi'an 710127,China)
出处
《经济体制改革》
CSSCI
北大核心
2024年第3期175-183,共9页
Reform of Economic System
基金
国家社会科学基金后期资助项目“创新驱动价值链升级的理论与实证研究”(21FJLB028)
陕西省社会科学基金项目“现代产业分工推动西安都市圈与关中平原城市群协调发展研究”(2021DA016)。
关键词
数据要素
制造业
随机森林
因果森林
机器学习
data factors
manufacturing industry
random forest
causal forest
machine learning
