摘要
面对新一轮科技革命,如何在人工智能与制造业深度融合过程中推进“稳就业”目标实现是一项重要命题。本文在理论阐释的基础上,基于2011-2019年省级面板数据,实证分析了中国人工智能发展对制造业就业规模和就业技能结构的影响,进而探讨其空间溢出效应。研究结果表明,2011-2019年各省人工智能发展水平逐年提升且呈现“东高西低”的地理格局;非线性模型检验显示,人工智能发展对制造业就业规模和低技能就业的影响呈“正U型”,对高技能就业的影响呈“倒U型”;由空间杜宾模型进一步检验可知,人工智能发展对制造业就业规模和低技能就业产生显著的正向空间溢出效应,且存在空间异质性。因此,应持续提高科研资金投入,大力推行人工智能在生产制造方面的应用;完善制造业高技能人才供给体系,促进人口红利向人才红利转变;搭建区域协作平台,构筑人工智能多中心发展格局,辐射带动周边地区人工智能普及应用。
Faced with the new technological revolution,it is an important question to stabilize employment in the process of integration of artificial intelligence and manufacturing.This paper empirically analyzes the impact of artificial intelligence(AI)development on the scale and skill structure of manufacturing employment together with its spatial spillover effects,based on a theoretical analysis and exploiting China's provincial panel data from 2011-2019.The results show that the development level of AI in various provinces has kept increasing and showing a geographical pattern of“high in the east and low in the west”.According to the nonlinear model test,the impact of AI development on manufacturing employment scale and low-skilled employment presents a U-shape,while the impact on high-skilled employment takes on an inverted U-shape.According to a further test of the spatial Durbin model,the development of AI has significant positive spatial spillover effects on manufacturing employment scale and low-skilled employment,and the effects are characterized with significant spatial heterogeneities.The results imply that the government should raise R&D input,promote the application of AI in manufacturing,strengthen the supply of talents,and improve regional coordination of AI development.
作者
朱嘉蔚
金晓贝
Zhu Jiawei;Jin Xiaobei(Graduate School,East China Jiaotong University;School of Economics and Management,East China Jiaotong University)
出处
《劳动经济研究》
2023年第5期121-143,共23页
Studies in Labor Economics
基金
江西省社会科学“十四五”(2022年)基金项目(项目编号:22GL15)
江西省研究生创新专项资金项目(项目编号:YC2022-s533)的资助
关键词
人工智能
制造业就业
非线性关系
空间溢出效应
artificial intelligence
manufacturing employment
nonlinear relationships
spatial spillovers