The Effect of Minimum Wages on Low-Wage Jobs*
By Doruk Cengiz, Arindrajit Dubé, A. Lindner, Ben Zipperer. Quarterly Journal of Economics, 2019
1,659 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, General Health Professions
View Paper: https://www.semanticscholar.org/paper/036858c0cc2bcff4082d438fe51882ab8fd10ec7
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In Economics and Econometrics
- Jonathan Roth
- Ellora Derenoncourt
- Pedro H. C. Sant’Anna
- A. Baker
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In General Health Professions
Citations in the corpus
- Selection into Identification in Fixed Effects Models, with Application to Head Start (2019, neutral): “At the same time, the characteristics of switching border counties are likely to be different from interior counties, in terms of the education distribution, population density, or industry composition, which could influence the response to minimumwage increases (Cengiz et al. 2019). Thus, reweighti”
- When Is Parallel Trends Sensitive to Functional Form? (2020, methodology): “We use data from Cengiz et al. (2019), who compile panel data on state-level minimum wages and employment-to-population ratios in 25-cent wage-bins.10 Note that the employment-topopulation ratio in wage bin w corresponds with the mass function of Wi at w.”
- Minimum Wages and Racial Inequality* (2020, background): “A large body of work discusses the efficiency costs of the minimumwage and focuses on its employment effects (see, e.g., Card, 1992; Card et al., 1993; Neumark andWasher, 1992; Card andKrueger, 1995; Neumark andWasher, 2008; Dube et al., 2010; Cengiz et al., 2018).”
- How Much Should We Trust Staggered Difference-In-Differences Estimates? (2021, methodology): “A published example of stacked regression is Cengiz et al. (2019), 22 Electronic copy available at: https://ssrn.com/abstract=3794018 which estimates the impact of minimum wage changes on low-wage jobs across a series of 138 prominent state-level minimum wage changes between 1979 and 2016 in the…”
- Revisiting event study designs: robust and efficient estimation (2021, neutral): “40 Therefore, more (38)Fadlon and Nielsen (2015) propose a similar approach based on matching treated and not-yet-treated units, while Cengiz et al. (2019) develop a related “stacked regressions” approach. (39)Indeed, the equivalent versions of the Sun and Abraham (2021) and Callaway and Sant’Anna (”
- How Do Firms Respond to Minimum Wage Increases? Understanding the Relevance of Non-Employment Margins (2021, background): “In an analysis of 138 state-level minimum wage changes in the United States, for example, Cengiz et al. (2019) find no decline in the number of lower-wage jobs over a time horizon of several years.”
- What’s trending in difference-in-differences? A synthesis of the recent econometrics literature (2022, neutral): “Another related approach is to run a stacked regression where each treated unit is matched to ‘clean’ (i.e. not-yet-treated) controls and there are separate fixed effects for each set of treated units and its control, as in Cengiz et al. (2019) among others.”