数据要素、数据挖掘与中国服务业生产率提升——来自双重机器学习的因果推断

Data Factors,Data Mining,and Productivity Improvement of Chinese Service Industry--Causal Inference from Double Machine Learning

数字经济时代下数据要素的出现为实现服务业生产率进一步提升提供了新的可能。本文基于2012—2019年中国省际面板数据,使用双重机器学习方法探究了数据要素影响服务业生产率提升的效应、作用机制与异质性表现。研究发现,数据要素显著促进中国服务业生产率提升;数据挖掘能力的提高增强了数据要素对服务业生产率提升的效应;数据要素更有助于生活性服务业以及中国东部和南方地区服务业的生产率提升。

China’s economy has entered a development stage dominated by the service industry,but the momentum of traditional factors to improve the productivity of the service industry is insufficient.In the era of digital economy,the emergence of data factors provides new possibilities for further improving service industry productivity.Based on China’s inter provincial panel data from 2012 to 2019,this paper uses the double machine learning method to explore the effects,internal mechanisms and heterogeneous effects of data factors on the improvement of service industry productivity.The results show that data factors significantly promote the productivity improvement of China’s service industry;The improvement of data mining ability significantly enhances the effects of data factors on the productivity improvement of the service industry;Data factors are more conducive to the improvement of productivity in the life service industry,and their impact on the improvement of productivity in the service industry in eastern and southern regions of China is more significant.