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Seeing Beyond the Trees: Using machine learning to estimate the impact of minimum wages on affected individuals
September 18, 2018•12:00 pm – 1:30 pm
The majority of teens, the commonly studied group in the minimum wage literature, are minimum wage workers; yet most minimum wage workers are not teens. To overcome this discrepancy, Cengiz uses machine learning tools to construct two demographically-based groups according to the size of the bite of the minimum wage: a high impact group and a baseline group that contain 39.1% and 73.4% of all minimum wage workers. In his presentation, Cengiz will show that while there is a very clear increase in average wages of the groups when the minimum wage rises, there is no evidence of job destruction or a decline in own-employment-sponsored health insurance rates. These results are robust for a variety of methods to construct the counterfactuals, and they hold for demographic subgroups as well. He will also show that the canonical model estimates that are indicating a disemployment effect for teens cannot be generalized to affected non-teens, suggesting that the current controversy is largely limited to teens, a small and a shrinking share of the minimum wage workers. Lastly, Cengiz will propose a falsification test that reveals whether argued minimum wage effects are mainly due to the affected workers or they are spurious results driven by shocks on unaffected individuals.
Doruk Cengiz currently works at the Department of Economics, University of Massachusetts Amherst. His research interests include research in Labor Economics and Econometrics. His research has appeared in Bloomberg, been published by the London School of Economics and Politcal Science’s Centre for Economic Performance, and been referenced by the Washington Center for Equitable Growth.