Seeing Beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes
We assess the effect of the minimum wage on labor market outcomes such as employment, unemployment, and labor force participation for most workers affected by the policy. We apply modern machine learning tools to construct demographically-based treatment groups capturing around 75% of all minimum wage workers—a major improvement over the literature which has focused on fairly narrow subgroups where the policy has a large bite (e.g., teens). By exploiting 172 prominent minimum wages between 1979 and 2019 we find that there is a very clear increase in average wages of workers in these groups following a minimum wage increase, while there is little evidence of employment loss. Furthermore, we find no indication that minimum wage has a negative effect on the unemployment rate, on the labor force participation, or on the labor market transitions. Furthermore, we detect no employment or participation responses even for sub-groups that are likely to have a high extensive margin labor supply elasticity—such as teens, older workers, or single mothers. Overall, these findings provide little evidence for changing search effort in response to a minimum wage increase.
Doruk Cengiz & Arindrajit Dube & Attila Lindner & David Zentler-Munro, 2022. "Seeing beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes," Journal of Labor Economics, vol 40(S1), pages S203-S247.