İşletme Fakültesi Seminerler Serisi / Doruk Cengiz

Title: Seeing Beyond the Trees: Using machine learning to estimate the impact of minimum wages on affected individuals

Abstract: Much of the minimum wage literature has focused on narrow sub-groups such as teens. We use machine learning tools to construct much more representative demographically-based treatment groups capturing more than 73% of all likely minimum wage workers. We find that there is a very clear increase in average wages of workers in these groups following a minimum wage increase, validating our approach to constructing these treatment groups. At the same time, there is little evidence of employment loss or a decline in fringe benefits in response to the policy change. These results are robust for a variety of methods to construct the counterfactuals including a data-driven interactive fixed effects model. Importantly, when we consider specifications that indicate a disemployment effect for teens similar to some of the literature, we find no adverse employment effect on affected non-teens—suggesting that the current controversy is largely limited to teens, a small and a shrinking share of the minimum wage workers. Finally we propose a falsification test that reveals whether an estimated minimum wage effect is confounded by shocks to unaffected individuals which further reconciles conflicting evidence in the literature

yer : AB2-345 nolu Toplantı Odası