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The Effect of Minimum Wages on Low-Wage Jobs*
Abstract
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.
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“A rapidly growing literature has shown that two-way fixed effects regressions deliver consistent estimates only with strong assumptions about the homogeneity of treatment effects, and may be biased when treatment effects vary over time or by adoption cohort (Cengiz et al., 2019; de Chaisemartin and D'Haultfœuille, 2020; Sun and Abraham, 2021;Goodman-Bacon, 2021; Callaway and Sant'Anna, 2021;Borusy”
From Generative AI at Work (2023) · cites this paper as Cengiz et al., 2019
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“Many new specifications are “saturated,” estimating an ATT specific to each post-intervention time period and sometimes to specific units or groups of units.”
From Difference-in-Differences for Health Policy and Practice: A Review of Modern Methods (2025) · cites this paper as [48]
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“Many saturated models have been developed to address issues related to staggered treatment adoption [34, 35, 45-48].”
From Difference-in-Differences for Health Policy and Practice: A Review of Modern Methods (2025) · cites this paper as [48]
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“For example, several authors have developed estimators for cases when treatment started at different calendar times for different units (“staggered adoption”) [34, 35, 45-48].”
From Difference-in-Differences for Health Policy and Practice: A Review of Modern Methods (2025) · cites this paper as [48]
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“This approach allows both time and cohort heterogeneity, requiring parallel trends over only the horizon of time that the researcher specifies.”
From Difference-in-Differences for Health Policy and Practice: A Review of Modern Methods (2025) · cites this paper as [48]
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“An alternative approach developed by applied researchers for circumventing the issues with TWFE DiD estimators is a “stacked regression” (see, e.g., Gormley and Matsa, 2011; Cengiz et al., 2019; Deshpande and Li, 2019 ).”
From How Much Should We Trust Staggered Difference-in-Differences Estimates? (2022) · cites this paper as Cengiz et al., 2019
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“An important limitation of these robust estimators is that their efficiency properties are not known.”
From Revisiting Event-Study Designs: Robust and Efficient Estimation (2024) · cites this paper as Cengiz et al., 2019
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Metadata
- Authors
- Cengiz, D., Dube, A., Lindner, A., & Zipperer, B.
- Affiliations
- University of Massachusetts Amherst (US)
- University of Massachusetts, Amherst, National Bureau of Economic Research, Institute of Labor Economics
- Institute for Fiscal Studies (GB)
- Economic Policy Institute (US)
- Venue
- The Quarterly Journal of Economics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-05-02
- Volume
- 134
- Issue
- 3
- Pages
- 1405-1454
- ISSN
- 0033-5533 (ISSN-L)
- 1531-4650
- OpenAlex
- W2942870997
- Cited by
- 2,688
- References
- 53
- Citations by year
- 2026: 675 · 2025: 660 · 2024: 502 · 2023: 303 · 2022: 247 · 2021: 169 · 2020: 76 · 2019: 30
- FWCI
- 164.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- The Quarterly Journal of Economics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Russell Sage Foundation
- Sage Foundation
- Washington State University
- Laura and John Arnold Foundation
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Social Sciences › Gender Studies › Gender, Labor, and Family Dynamics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Historical Economic and Social Studies
- Keywords
- minimum wage, low-wage employment, employment effects, wage spillovers, wage distribution, demographic groups
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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