文献类型：期刊文献

中文题名：人工智能上市公司全要素生产率测度及其对就业的影响研究

英文题名：Research on Artificial Intelligence Companies’Total Factor Productivity Measurement and the Impact on Employment

作者：李雅宁[1];何勤[2];王琦[1];钟青青[1]

第一作者：李雅宁

机构：[1]北京联合大学管理学院,北京100101;[2]首都经贸大学劳动经济学院,北京100070

第一机构：北京联合大学管理学院

年份：2020

卷号：37

期号：11

起止页码：62-74

中文期刊名：中国人力资源开发

外文期刊名：Human Resources Development of China

收录：CSTPCD;;国家哲学社会科学学术期刊数据库;北大核心:【北大核心2017】；CSSCI:【CSSCI_E2019_2020】；

基金：北京社科基金重大项目“动态匹配视角下人工智能对北京市就业的影响与应对研究”(18ZDA09);教育部人文社会科学研究青年基金项目“普惠金融视角下微型金融机构的可持续发展及制度优化路径研究”(17YJC790082);北京联合大学科研项目“大数据驱动下的科技金融政策精准治理研究”(XP202012)。

语种：中文

中文关键词：人工智能全要素生产率;Malmquist指数;就业;广义最小二乘法

外文关键词：Artificial Intelligence Total Factor Productivity;Malmquist Index;Employment;Generalized Least Square Method(FGLS)

摘要：人工智能是社会发展和技术创新的产物,是促进人类进步的重要技术形态。人工智能技术发展至今,已经成为新一轮科技革命和产业变革的核心驱动力,对生产率、经济增长和劳动力就业等方面产生了深刻的影响。本研究基于103家中国人工智能上市公司2013年-2017年的数据,采用Malmquist指数法测度了人工智能上市公司全要素生产率及其分解,并进一步构建计量经济学模型运用广义最小二乘法(FGLS)研究了人工智能上市公司全要素生产效率对公司就业人数的影响。结果显示:(1)样本考察期的中国人工智能上市公司全要素生产率(TFP)平均增长率为2.9%,超过50%的人工智能上市公司全要素生产效率是提高的,根据全要素生产效率分解特征,人工智能公司全要素生产率变动存在着技术效率和技术进步效率相背离的负向关联现象。技术进步效率是提高全要素生产率的主要动力,从技术效率的构成来看,纯技术效率是影响技术效率增长缓慢的主要原因。(2)人工智能上市公司全要素生产率对公司就业人数有显著的正向影响,全要素生产率提高1%,公司就业人数增加0.142%。与综合类行业的人工智能上市公司相比,制造业、信息传输技术业、传播与文化产业、批发与零售贸易业类的人工智能上市公司对其就业量有显著的促进作用,且相较于民营企业,国有控股人工智能上市公司的就业量更高。本研究丰富了人工智能公司生产效率与就业问题的实证研究,为人工智能上市公司提升生产效率和促进就业提供了改进的建议。
Artificial intelligence is the product of social development and technological innovation,and it is an important technological form to promote human progress.So far,artificial intelligence has become the core driving force of a new round of scientific and technological revolution and industrial transformation,which has exerted a profound impact on productivity,economic growth and labor employment.Based on the data of 103 Chinese artificial intelligence companies from 2013 to 2017,this study uses Malmquist index method to measure the total factor productivity,builds an econometric model,and applies generalized least square method(FGLS)to study the in?uence of total factor production efficiency on the number of employees.The results are shown as following:(1)The average growth rate of total factor productivity(TFP)of artificial intelligence companies is 2.9%,more than 50%of companies’total factor productivity are increasing.According to the decomposition characteristics of TFP,there is a negative correlation between the technical efficiency and the technical progress efficiency.The efficiency of technological progress is the main power to improve total factor productivity.From the composition of technical efficiency,pure technical efficiency is the main reason for the slow growth of technical efficiency.(2)Total factor productivity of artificial intelligence companies has a significant positive impact on the number of employees.Total factor productivity increases by 1%,and the number of employees increases by 0.142%.Compared with comprehensive industries,listed AI companies in manufacturing industry,information transmission technology industry,communication and cultural industry,wholesale and retail trade industry have a significant promotion effect on their employment volume.Moreover,compared with private enterprises,state-owned holding listed AI companies have a higher employment volume.This study enriches the empirical research on total factor production efficiency and employment of artificial intelligence companies,and provides some guidance and suggestions for artificial intelligence companies to improve production efficiency and promote employment.