TRAINING REQUIREMENTS, AUTOMATION, AND JOB POLARISATION

• 成果类型: Article
• 署名作者: Feng, Andy; Graetz, Georg
• 署名单位: Uppsala University
• 刊物名称: ECONOMIC JOURNAL
• ISSN/ISSBN: 0013-0133
• DOI: 10.1093/ej/ueaa044
• 发表日期: 2020
• 页码: 2249-2271
• 关键词: biased technological-change; skill content; GROWTH; EMPLOYMENT; task

摘要

We analyse how job training requirements interact with engineering complexity in shaping firms' automation decisions. A model that distinguishes between a task's engineering complexity and its training requirements predicts that when two tasks are equally complex, firms automate the task that requires more training. Under plausible conditions this leads to job polarisation, and in particular to polarisation of employment by initial training requirements. US data provide empirical support for the model's implications. Training requirements and a measure of engineering complexity account for much of US job polarisation from 1980 to 2008.