人工智能、财政支出结构偏向与高质量就业

何勤¹　邱玥¹　许干²

（1. 首都经济贸易大学劳动经济学院；2. 首都经济贸易大学金融学院）

 

内容提要：本文选取2009—2018年省级层面数据，运用面板固定效应模型分析人工智能应用对高质量就业的影响及其传导机制。研究结果表明，人工智能应用总体能够促进高质量就业。调节效应检验结果显示，财政支出能够正向调节人工智能应用对高质量就业的积极影响，并由于财政支出呈现结构偏向的特征，不同性质的财政支出会使人工智能应用程度对高质量就业的影响产生差异。作用机制结果显示，人工智能应用程度会通过产业结构升级间接促进高质量就业。异质性分析结果表明，在地区分布上，人工智能应用程度对高质量就业的积极影响主要体现在东部地区；在时期分布上，在自动化赋能阶段（2009—2014年），人工智能应用程度呈现出对高质量就业的促进作用，而在智能化创新阶段（2015—2018年）则不明显。本文的研究结论为挖掘人工智能应用在中国如何实现更高质量就业，以及探究财政政策导向在其中的作用提供参考。　　

关键词：人工智能；高质量就业；财政支出；结构偏向；产业结构升级

作者简介：何勤，首都经济贸易大学劳动经济学院教授、博士生导师，北京，100070；邱玥，首都经济贸易大学劳动经济学院博士研究生；许干，首都经济贸易大学金融学院博士研究生，通信作者。

基金项目：国家社会科学基金重点项目“人工智能对劳动力市场的冲击及劳动者知识技能转换应对研究”（19AGL025）；首都经济贸易大学博士生学术新人项目“数字经济下的劳动争议预警及协调机制研究”（2022XSXR11）

引用格式：何勤,邱玥,许干.人工智能、财政支出结构偏向与高质量就业[J].经济与管理研究,2024,45(2):70-86.DOI:10.13502/j.cnki.issn1000-7636.2024.02.004.

　　

　　

Artificial Intelligence, Structural Bias in Fiscal Expenditure, and High-quality Employment

HE Qin, QIU Yue, XU Gan

(Capital University of Economics and Business, Beijing 100070)

　　

　　Abstract: The current new technology characterized by artificial intelligence (AI) is an important driving force for economic development and social progress. AI applications are crucial for achieving high-quality and full employment. Based on the provincial-level panel data from 2009 to 2018 in China, this paper analyzes how AI applications affect high-quality employment using a panel fixed effects model. Then, it investigates the influencing mechanism of AI applications from the perspective of industrial structure upgrading and the external environment from the perspective of fiscal expenditure. In addition, the paper assesses the heterogeneity of the impact of AI applications on high-quality employment across provincial-level regions and periods.

　　The findings indicate that AI applications contribute to high-quality employment, which remains valid after robustness and endogeneity tests. This suggests that the current development of AI in China is in line with the goal of high-quality employment, which has a positive impact on employment in all provincial-level regions. The moderating effect test reveals that fiscal expenditure can positively moderate the impact of AI applications on high-quality employment, with human capital expenditure playing a more significant positive role than physical capital expenditure. Mechanism analysis shows that AI applications indirectly promote high-quality employment through industrial structure upgrading. This suggests that industrial structure upgrading plays an important role in the transmission between AI and high-quality employment. The heterogeneity study finds that the positive impact of AI applications on high-quality employment is mainly reflected in the eastern region. Moreover, AI applications make a more significant contribution to high-quality employment in the automation-enabling stage (2009-2014) than in the intelligence innovation stage (2015-2018).

　　The contributions of this paper are as follows. First, it broadens the existing research on the impact of AI on employment. Based on the relationship between AI and high-quality employment at the macro level, this paper constructs a theoretical analysis framework and empirically analyzes the overall impact of AI applications on high-quality employment. Second, it analyzes the influencing mechanism of AI applications on high-quality employment from the perspectives of fiscal policy bias and industrial structure upgrading. Third, it focuses on the differentiated impact of AI applications on high-quality employment across provincial-level regions and periods, providing empirical evidence for realizing high-quality employment according to the economic strength of each region and the characteristics of technological development stages. Meanwhile, this paper provides a new perspective for AI development enhancing high-quality employment levels by adjusting structural bias in fiscal expenditure under different techno-economic constraints.

　　Keywords: artificial intelligence; high-quality employment; fiscal expenditure; structural bias; industrial structure upgrading

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