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Cited by 2,688Labor market dynamics and wage inequalityGender, Labor, and Family DynamicsHistorical Economic and Social Studies

The Effect of Minimum Wages on Low-Wage Jobs*

Cengiz, Doruk, Dube, Arindrajit, Lindner, Attila, Zipperer, Ben·2019·The Quarterly Journal of Economics
DOI 10.1093/qje/qjz014
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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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Crossref · 10.1093/qje/qjz014

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  • Two-way fixed effects regressions deliver consistent estimates only under strong assumptions about homogeneous treatment effects and may be biased when treatment effects vary over time or by adoption cohort.

    “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

    Section IV reports that a two-way fixed-effects minimum-wage specification can be confounded, supporting the general caution about its estimates. But the paper attributes the problem in its example to employment shocks long before the treatment window; it does not establish that consistent estimates require homogeneous treatment effects or that variation over time or by adoption cohort causes the bias. Page numbers are unavailable in our copy. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Many newer DiD specifications are saturated, estimating an ATT for each post-intervention time period and sometimes for specific units or groups.

    “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]

    Section II.B estimates effects by event time, and Section III.C estimates effects separately for each minimum-wage event. The robustness checks in Section III call one model “highly saturated.” These passages support the paper as an example of time- and event-specific estimation, but they do not establish the broader claim that many new DiD specifications are saturated or describe those estimates as ATTs. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Many saturated models have been developed to address staggered treatment adoption issues.

    “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]

    In III.C, the paper uses stacked event-by-event estimates to check whether negative weighting from staggered treatments drives its results. In III (Robustness Checks), it also describes a highly saturated model, but that model adds controls for time-varying heterogeneity; the paper does not present it as a model developed to address staggered-adoption issues. Page numbers are unavailable in our copy. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Several authors have developed DiD estimators for staggered adoption, where treatment starts at different calendar times for different units.

    “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]

  • Stacked regression allows both time and cohort heterogeneity and requires parallel trends only over the horizon the researcher specifies.

    “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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Cites

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4 of the 53 references couldn’t be matched to a known paper.

Research works printed but not linked: 3

  1. [10]10. Card, David, and Alan B. Krueger, "Minimum Wages and Employment: A Case Study of the New Jersey and Pennsylvania Fast Food Industries," American Economic Review, 84 (1994), 772-793.
  2. [17]17. Dickens, Richard, Stephen Machin, and Alan Manning, "Estimating the Effect of Minimum Wages on Employment from the Distribution of Wages: A Critical View," Labour Economics, 5 (1998), 109-134.
  3. [33]33. Kleven, Henrik Jacobsen, "Bunching," Annual Review of Economics, 8 (2016), 435- 464.

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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)
ORCID
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
DOI
10.1093/qje/qjz014
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 · 2018: 15 · 2017: 7 · 2016: 2 · 2015: 1 · 2013: 1
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

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