We estimate the effect of minimum wages on low-wage jobs using 138 prominent state-level minimum wage changes between 1979 and 2016 in the United States using a difference-in-differences approach. We first estimate the effect of the minimum wage increase on employment changes by wage bins throughout the hourly wage distribution. We then focus on the bottom part of the wage distribution and compare the number of excess jobs paying at or slightly above the new minimum wage to the missing jobs paying below it to infer the employment effect. We find that the overall number of low-wage jobs remained essentially unchanged over the five years following the increase. At the same time, the direct effect of the minimum wage on average earnings was amplified by modest wage spillovers at the bottom of the wage distribution. Our estimates by detailed demographic groups show that the lack of job loss is not explained by labor-labor substitution at the bottom of the wage distribution. We also find no evidence of disemployment when we consider higher levels of minimum wages. However, we do find some evidence of reduced employment in tradeable sectors. We also show how decomposing the overall employment effect by wage bins allows a transparent way of assessing the plausibility of estimates.
We assess alternative research designs for minimum wage studies. States in the U.S. with larger minimum wage increases di er from others in business cycle severity, increased inequality and polarization, political economy, and regional distribution. The resulting time-varying heterogeneity biases the canonical two-way fixed e ects estimator. We consider alternatives including border discontinuity designs, dynamic panel data models, and the synthetic control estimator. Results from four datasets and six approaches all suggest employment e ects are small. Covariates are more similar in neighboring counties, and the synthetic control estimator assigns greater weights to nearby donors. These findings also support using local area controls.
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