人工智能、技术进步与低技能就业——基于中国制造业企业的实证研究

Artificial Intelligence,Technological Change and Low-skill Employment.Empirical Evidence from Chinese Manufacturing Firms

人工智能重塑中国制造业核心竞争力的同时,也加剧了行业内低技能型劳动力被取代的危机。本文以技术进步影响劳动力技能结构的理论机制为分析框架,聚焦人工智能与制造业的融合对低技能就业比重的因果效应。基于2011-2017年制造业企业的面板数据,首先将人工智能与制造业的融合定义为"AI产业化"和"产业AI化",基于定义、逐家判断人工智能企业名单并定位人工智能的初始时间,再采用双重差分法围绕人工智能对低技能就业比重的因果效应及效应的传导机制展开实证分析。实证结果表明:(1)制造业企业融合人工智能显著降低了低技能的就业比重,即在一定程度上制造业内低技能潜在的就业机会被人工智能所挤出;(2)人工智能对低技能就业比重的负向效应具有动态异质性,即企业融合人工智能的时间越长,低技能就业比重下降的越多;(3)人工智能虽然通过刺激当期资本积累和收入扩张补偿了部分低技能就业比重的减少,但同时降低了低技能劳动力的相对边际产出,促使企业最终减少低技能员工的就业比重。

As Artificial Intelligence(AI)is remodelling the core competitiveness for Chinese manufacturing industry,there is an increasing concern about a substitution for low-skilled labor.This study provides evidence on how AI affects the patterns of skill demands,measured by the share of low-skilled staff in Chinese manufacturing firms.Employing panel data on Chinese firms listed on the SSE,SZSE,and NEEQ over 2011-2017,the introduction of AI is treated as a quasi-natural experiment and use a robust DID model to estimate the effects of AI on patterns in the relative demand for low-skilled labor.AI firms are defined by investigating the AI production and application scenarios in manufacturing and select AI firms based on policy support,industry application,R&D output,and market recognition.The timing of the initial introduction of AI is defined based on firm-level information.In addition,using a PSM model,the control group whose skill patterns follow the parallel trend assumption is developed.The empirical analysis results demonstrate that AI has significant influences on skill demands.First,AI significantly decreases the share of employment of low-skilled labor,which means a small portion of the low-skilled potential employment has been substituted by AI.Second,the negative effect of AI on the share of employment of low-skilled labor has dynamic heterogeneity,and the effect tends to rise over time.Third,AI creates the employment of low-skilled labor through capital accumulation and income expansion while finally urges to decrease the low-skilled share through cutting down marginal output of low-skilled labor.Finally,several possible directions for future studies can be extended in two aspects:First,due to the limitations of the data sources,the range of sample observations is only seven years,and data from a longer period can be used to verify the long-term casual effects in further research.Second,the mechanisms related to different forms of AI introduction and firm-level skill premium can be analyzed.