再培训
失业
劳动经济学
工资
自动化
匹配(统计)
经济
劳动力需求
不平等
计算机科学
业务
工程类
国际贸易
数学分析
统计
机械工程
经济增长
数学
作者
ARVIND UPRETI,Varadharajan Sridhar
标识
DOI:10.1142/s0219525922500059
摘要
Artificial Intelligence and associated technologies are rapidly automating routine and non-routine tasks across industries and can severely disrupt labor markets. This paper presents an agent-based, evolutionary model of labor market dynamics where workers adjust to technology shocks induced by automation. Firms produce a homogeneous service by combining the outputs of tasks performed by workers while stochastically adapting to automation of tasks causing displacement of workers. We develop a model that includes: (i) the description of occupation mobility as a directed graph where nodes represent occupations, and the directed edges represent the mobility pathways along which displaced workers can get retrained and redeployed (ii) explicit microfoundations of the processes of job matching and wage setting between firms and heterogeneously skilled workers. The model focuses on the influence of workers’ retraining choices on the employment levels and wage inequality in the labor market. Simulation results indicate distinct tipping points for unemployment and wage inequality with changes in mobility pathways along which workers retrain and redeploy across occupations. An increase in the density of mobility pathways induces a reinforcement effect on employment. Retraining displaced workers without building dense and well-distributed mobility pathways across occupations could widen wage inequality due to excessive crowding of workers around specific tasks. Our work focuses on the finance and insurance industry dataset, where we observe that the reskilling of displaced workers along occupation mobility pathways assisted by a lower retraining cost improves the unemployment levels. Also, if the firms aggressively automate their tasks, an increase in the cost of retraining increases inequality of wages in the labor market.
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