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Using post-regularization distribution regression to measure the effects of a minimum wage on hourly wages, hours worked, and monthly earnings
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DOI：10.1093/ectj/utaf014.png)
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Abstract
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We evaluate the distributional effects of a minimum wage introduction based on a dataset with a moderate sample size, but a large number of potential covariates. In this context, the selection of relevant control variables at each distributional threshold is crucial to test hypotheses about the impact of the continuous treatment variable. To this end, we use a post-double-selection logistic distribution regression approach, which allows for uniformly valid inference about the target coefficients of our low-dimensional treatment variables across the entire outcome distribution. Our empirical results show that the minimum wage replaced hourly wages below the minimum threshold, increased monthly earnings in the lower-middle segment, but not at the very bottom of the distribution, and did not significantly affect the distribution of working hours.
Keywords:
wage structure
automatic specification search
double machine learning
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