Seeing beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes

• 成果类型: Article
• 署名作者: Cengiz, Doruk; Dube, Arindrajit; Lindner, Attila; Zentler-Munro, David
• 署名单位: University of Massachusetts System; University of Massachusetts Amherst; National Bureau of Economic Research; IZA Institute Labor Economics; University of London; London School Economics & Political Science; University College London; University of London; University College London; University of Essex
• 刊物名称: JOURNAL OF LABOR ECONOMICS
• ISSN/ISSBN: 0734-306X
• DOI: 10.1086/718497
• 发表日期: 2022
• 页码: S203-S247
• 关键词: employment; increases; search; youth; MODEL

摘要

We assess the effect of the minimum wage on labor market outcomes. First, we apply modern machine learning tools to predict who is affected by the policy. Second, we implement an event study using 172 prominent minimum wage increases between 1979 and 2019. We find a clear increase in wages of affected workers and no change in employment. Furthermore, minimum wage increases have no effect on the unemployment rate, labor force participation, or labor market transitions. Overall, these findings provide little evidence of changing search effort in response to a minimum wage increase.