THE EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS∗
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. JEL Codes: J23, J38, J88.
∗ We
thank David Autor, David Card, Sebastian Findeisen, Eric French, Hedvig Horvath, Gabor Kezdi, Patrick Kline, Steve Machin, Alan Manning, Sendhil Mullainathan, Suresh Naidu, James Rebitzer, Michael Reich, Janos Vincze,
Daniel Wilhelm, and participants at WEAI 2016 Annual Meetings, CREAM 2016
conference, Boston University Empirical Micro workshop, Colorado State University, IFS-STICERD seminar, Michigan State University, NBER Summer Institute
2018 (Labor Studies), Northeastern University, University of Arizona, University
of Illinois, Urbana-Champaign, University of California Berkeley IRLE, University of Mannheim, and University of Warwick for very helpful comments. We thank
the staff at Minnesota Department of Employment and Economic Development,
Oregon Employment Department, and Washington State Employment Security
Department for generously sharing administrative data on hourly wages. Dube
acknowledges financial support from the Russell Sage Foundation. Dube and Lindner acknowledge financial support from the Arnold Foundation. A previous version of this article was circulated with the title “The Effect of Minimum Wages on
Low-Wage Jobs: Evidence from the United States Using a Bunching Estimator.”
C The Author(s) 2019. Published by Oxford University Press on behalf of President
and Fellows of Harvard College. All rights reserved. For Permissions, please email:
journals.permissions@oup.com
The Quarterly Journal of Economics (2019), 1405–1454. doi:10.1093/qje/qjz014.
Advance Access publication on May 2, 2019.
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DORUK CENGIZ
ARINDRAJIT DUBE
ATTILA LINDNER
BEN ZIPPERER
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I. INTRODUCTION
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Minimum wage policies have featured prominently in recent
policy debates in the United States at the federal, state, and local levels. California, Illinois, Massachusetts, New Jersey, and
New York have passed legislation to eventually increase minimum
wages to $15/hour, and at least five other states are on paths to
raise their minimum wages to $12 or more. Over a dozen cities
have instituted city-wide minimum wages during the past three
years, typically by substantial amounts above state and federal
standards. Underlying much of the policy debate is the central
question: what is the overall effect of minimum wages on lowwage jobs?
Even though nearly three decades have passed since the advent of “new minimum wage research” (see, e.g., Card and Krueger
1995; Neumark and Wascher 2008), there is surprisingly little
research on the effect of the policy on overall employment. This
shortcoming is particularly acute given the importance policy
makers place on understanding overall responses. For example, in
its attempt to arrive at such an estimate, the 2014 Congressional
Budget Office (CBO) report noted the paucity of relevant research
and then used estimates for teen minimum wage elasticities to
extrapolate the total impact on low-wage jobs.
In this article we use a difference-in-differences design to
estimate the impact of minimum wage increases on the entire frequency distribution of wages and subsequently focus on changes
at the bottom of the distribution to estimate the impact on employment and wages of affected workers. Our approach relies on the
idea that the overall employment and wage effects of the policy
can be inferred from the localized employment changes around the
minimum wage. An increase in the minimum wage will directly
affect jobs that were previously paying below the new minimum
wage. The jobs shifted into compliance create a “bunching” and
show up as “excess jobs” at and slightly above the minimum. The
effect of the minimum wage on the wage distribution fades out and
becomes negligible beyond a certain point. Therefore, the overall
employment and wage effects of the policy can be inferred from
the localized employment changes around the minimum wage. For
instance, we can assess the changes in employment from the difference between the number of excess jobs at and slightly above
the minimum wage and the number of missing jobs below the
minimum.
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To identify the effect of the minimum wage on the frequency
distribution of wages, we implement an event study analysis that
exploits 138 prominent state-level minimum wage increases between 1979 and 2016. We estimate employment changes in each
dollar wage bin relative to the new minimum wage for three years
prior to and five years after an event. Our empirical approach
therefore disaggregates the total employment effect of the policy
into constituent wage bins, and we use these bin-by-bin estimates
locally around the minimum wage to assess the effect of the policy.
There are several advantages of our disaggregated approach
relative to the more standard approach that estimates the disemployment effect using aggregate employment or wage changes
(e.g., Meer and West 2016). First, we focus on employment changes
locally around wage levels where minimum wages are likely to
play a role. When only a small fraction of the aggregate workforce
is affected by the minimum wage, such a localized approach is
crucial for uncovering meaningful “first-stage” wage effects of the
minimum wage—something that is not possible with the standard
approach except for subgroups like teens. Second, by decomposing the aggregate employment impact by wage bins, we are able
to assess employment changes in the upper tail of the wage distribution. This can provide an additional falsification test, since
large changes in the upper part of the wage distribution are unlikely to reflect a causal effect of the minimum wage. Third, our
localized focus on jobs around the minimum wage gains precision
by filtering out random shocks to jobs in the upper part of the
wage distribution.
We use hourly wage data from the 1979–2016 Current Population Survey to estimate the effect of the minimum wage by wage
bins. We find that an average minimum wage hike led to a large
and significant decrease in the number of jobs below the new minimum wage in the five years after implementation. At the same
time, there was clear evidence for the emergence of excess jobs at
or slightly above the minimum wage. However, as expected, we
find no indication of any employment changes in the upper part
of the wage distribution—providing further validation to the empirical design. We estimate that the number of excess jobs closely
matched the number of missing jobs: the employment for affected
workers rose by a statistically insignificant 2.8% (std. err. 2.9%).
Our estimates also allow us to calculate the impact of the policy
on the average wages of affected workers, which rose by around
6.8% (std. err. 1.0%). The significant increase in average wages of
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affected workers implies an employment elasticity with respect to
own wage (or the labor demand elasticity in a competitive model)
of 0.41 (std. err. 0.43), which rules out elasticities more negative
than −0.45 at the 95% confidence level.
An additional advantage of estimating the effect of the minimum wage on the frequency distribution of wages is that we can
assess the extent to which the direct wage effects of the minimum
wage are amplified by wage spillovers. We find that spillovers extended up to $3 above the minimum wage and represent around
40% of the overall wage increase from minimum wage changes.
Interestingly, we also find that the benefits of wage spillovers
were not equally shared: workers who had a job before the minimum wage increase (incumbents) experienced significant wage
spillovers, but we do not find any evidence of such spillovers for
new entrants. This asymmetry suggests that spillovers may reflect relative pay concerns within the firm (Dube, Giuliano, and
Leonard 2018) and the value of outside options or reservation
wages of nonemployed workers is unlikely to play a key role in
generating wage spillovers (e.g., Flinn 2006).
Our estimates are highly robust to a wide variety of approaches to controlling for time-varying heterogeneity that has
sometimes produced conflicting results in the existing literature
(e.g., Allegretto et al. 2017; Neumark and Wascher 1992). Moreover, the shifts in the missing and excess jobs are strongly related
to the timing of minimum wage change—providing further support that we are identifying the causal effect of the policy. Both
missing jobs below the new minimum and excess jobs above were
close to zero prior to the minimum wage increase, which suggests that the treatment and the control states were following a
parallel trend. The drop in jobs below the minimum wage is immediate, as is the emergence of excess jobs at or slightly above the
minimum. Over the five-year post-treatment period, the magnitude of the missing jobs below the new minimum wage decreases
only slightly, underscoring the durability of the minimum wage
changes studied here.
To go beyond our overall assessment of the 138 case studies
used for identification, we also produce event-by-event estimates
of the minimum wage changes. Although we find substantial heterogeneity in the bite of the events, the distribution of employment
effects are consistent with a sharp null of zero effect everywhere.
For example, our event-by-event analysis finds that the estimated missing jobs rose substantially in magnitude with the
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minimum-to-median wage (Kaitz) index. At the same time, the
number of excess jobs also rose for these events to a nearly identical extent. As a consequence, there is no relationship between
the employment estimate and the Kaitz index up to around 59%,
confirming that the minimum wage changes in the United States
that we study have yet to reach a level above which significant
disemployment effects emerge.
The lack of responses in overall employment might mask
some heterogeneity in response across types of workers. Our localized approach around the minimum wage can be easily applied
to various subgroups, including those where only a small fraction
of workers are affected by the minimum wage. As a result, we
can provide a more complete picture of how various groups are
affected by the minimum wage.
We examine whether there is a shift from low-skill to highskill workers at the bottom of the wage distribution by partitioning
workers into groups based on education and age. Comparing the
number of excess jobs at or above the new minimum wage and
missing jobs below it across age-by-education groups shows no
evidence that low-skilled workers are replaced with high-skilled
workers following a minimum wage increase. We also use demographics to predict the probability of being exposed to the minimum wage increase, and then assign workers to high, medium,
and low probability groups along the lines of Card and Krueger
(1995). While there is considerable variation in the bite of the policy, the employment effects in these subgroups are mostly close to
zero and not statistically significant. The similar responses across
demographic groups also suggest that the benefit of minimum
wage policies were shared broadly.
Our approach also allows us to provide a more comprehensive picture on responses across various sectors of the economy.
We show that the minimum wage is likely to have a negative effect on employment in the tradeable sector, and in manufacturing
in particular—with an employment elasticity with respect to own
wage of around −1.4—although the estimates are imprecise. At
the same time, the effect of the minimum wage is close to 0 in
nontradeable sectors (such as restaurants or retail), which employ most minimum wage workers in the United States today.
This evidence suggests that the industry composition of the local
economy is likely to play an important role in determining the disemployment effect of the minimum wage (Harasztosi and Lindner
forthcoming).
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This article makes several contributions to the existing literature on minimum wages. First, it relates to the large and controversial literature on the employment effects of the minimum
wage. The debate has often been concentrated on the impact
on teen employment (Card 1992; Neumark and Wascher 1992;
Neumark, Salas, and Waschler 2014; Allegretto et al. 2017), workers in specific sectors (Lester 1964; Katz and Krueger 1992; Card
and Krueger 1994; Dube, Lester, and Reich 2010), or workers earning low wages prior to the minimum wage increase (Currie and
Fallick 1996; Abowd et al. 2000; Clemens and Wither 2019), while
the evidence on the impact on overall employment is scant. By
disaggregating the standard difference-in-differences estimates
by wage bins, we can identify the effects of the minimum wage
on overall employment and obtain meaningful first-stage wage
effects at the same time.
A notable exception studying overall employment changes is
Meer and West (2016), who examine the relationship between
aggregate employment at the state level and minimum wage
changes without assessing the wage effects. Meer and West (2016)
find a large negative employment estimate using variants of the
classic two-way fixed effects regression on log minimum wage.
To highlight the importance of disaggregating the aggregate employment effects into wage bins, we calculate the bin-by-bin employment effects in such a specification. This exercise produces a
striking finding: the specifications that indicate a large negative
effect on aggregate employment tend to be driven by an unrealistically large drop in the number of jobs at the upper tail of the wage
distribution, which is unlikely to be a causal effect of the minimum wage. We also provide an explanation for why the classic
two-way fixed effect and our event study approach produce different results. We show that the large negative effect on employment
is driven entirely by inclusion of the 1980s and the early 1990s
in the sample—a period with very few minimum wage changes.
However, aggregate employment changes in the 1980s turn out to
be correlated with minimum wage changes in the 2000s. Although
inclusion of the 1980s biases the estimation in the two-way fixed
effect approach, it does not affect our event study approach, which
focuses on employment changes locally around the event window.
It is worth noting that the disagreement on the choice of specification for estimating the impact of minimum wages on teen employment is also driven by these early period confounding shocks
(Neumark, Salas, and Wascher 2014; Allegretto et al. 2017). We
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
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II. METHODOLOGY AND DATA
II.A. The Conceptual Framework
We infer the effect of the minimum wage from the employment
changes at the bottom of the wage distribution. We illustrate our
1. In a recent working paper, Brochu et al. (2017) use the hazard rate for
wages to estimate spillover effects in the presence of disemployment effects.
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find that in the post-1992 period, there is little evidence of disemployment for teens across any of the standard specfications.
Our article also contributes to the literature on the effect of
the minimum wage on overall wage inequality (DiNardo, Fortin,
and Lemieux 1996; Lee 1999; Autor, Manning, and Smith 2016).
These papers examine shifts in the wage density and assume away
any possible disemployment effect. In contrast, we focus on the
frequency distribution of wages instead of the wage density, which
allows us to assess the effect on wage inequality and employment
at the same time.1 We show that the measured wage spillovers
are not an artifact of disemployment, which would truncate the
wage distribution. In addition, we provide a wide range of evidence
that these spillovers are unlikely to be an artifact of measurement
error. Our spillover estimates are similar to the findings of Autor,
Manning, and Smith 2016 and Brochu et al. (2017), and more
limited than those in Lee (1999).
Finally, our article is also related to the literature that uses
bunching to elicit behavioral responses to public policies (Kleven
2016). At the same time, while most bunching analyses estimate
the counterfactual distribution from purely cross-sectional variation (Saez 2010; Chetty et al. 2013), here we use a differencein-differences strategy to construct the counterfactual frequency
distribution of wages and the estimated excess and missing jobs.
The rest of the article is structured as follows. Section II explains the conceptual approach and the empirical implementation. Section III presents the main empirical findings on overall
employment effects, wage spillovers, and heterogenous responses
to the minimum wage. Section IV demonstrates the importance of
assessing employment changes far above the minimum wage and
highlights problems with the classic two-way fixed effects estimation. Section V concludes. Finally, all the Appendix materials can
be found in the Online Appendix.
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The figure shows the effect of the minimum wage on the frequency distribution
of hourly wages. The red dashed line (color version available online) shows the
wage distribution before the introduction of the minimum wage, and the blue solid
line shows the distribution afterwards. Because compliance is less than perfect,
some workers are paid below the minimum wage, and the post-treatment distribution starts below the minimum wage. For other workers, the introduction of
the minimum wage produces “missing jobs” (b), as shown by the striped red
shaded area (under the red dashed line) between the origin and MW. These missing jobs may either reflect workers getting a raise, or their jobs being destroyed.
The former group creates the “excess jobs above” (a), as shown by the solid
blue shaded area (under the blue solid line) between MW and W, the upper limit
for any effect of the minimum wage on the earnings distribution. The overall
change in employment due to the minimum wage (e) is the sum of the two areas
(a + b).
approach using Figure I, which summarizes the effect of the minimum wage on the wage distribution. The red dashed line (color
version available online) shows a hypothetical (frequency) distribution of wages in the absence of the minimum wage. The blue
solid line depicts the actual wage distribution with a minimum
wage at MW.
In the presence of a binding minimum wage, there should be
no jobs below MW. In practice, however, some jobs observed in
the data will be sub–minimum wage because of imperfect coverage, imperfect compliance, or measurement error. Therefore, the
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FIGURE I
The Impact of Minimum Wages on the Frequency Distribution of Wages
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
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2. When we refer to the “bite” of the minimum wage, or to the extent to which
the minimum wage is “binding,” we mean how effective the minimum wage is
in raising wages at the bottom. Therefore, the bite is a function of (i) how many
workers are earning below the new minimum wage, (ii) how many of those workers
are legally covered by the policy, and (iii) the extent of compliance.
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number of missing jobs below MW, given by b = Emp1 [w < MW]
− Emp0 [w < MW], reflects the bite of the minimum wage.2 Here
Emp1 [.] and Emp0 [.] are the actual and counterfactual frequency
distributions of wages, respectively.
Not all missing jobs below the minimum wage are destroyed. Some or all of the jobs below the minimum wage may be
preserved—with their hourly pay raised to the minimum wage,
creating a spike at MW. Some jobs may be pushed slightly above
the minimum wage to maintain wage hierarchy within the firm
or because the minimum wage raises the bargaining power of
workers (e.g., Flinn 2011). Moreover, a minimum wage increase
might induce low-wage workers to participate in job search, some
of whom may find a job above the minimum wage. However, the
ripple effects of the minimum wage are likely to fade out at a certain point, which we denote W in Figure I. In models with labor
market frictions, wage spillovers also typically fade out, because
workers and firms in the upper tail of the wage distribution are
operating in different labor market segments (see Van den Berg
and Ridder 1998; Engbom and Moser 2017 for examples of such
models).
The neoclassical model suggests that there may be some positive employment effects in the upper tail of the wage distribution
caused by labor-labor substitution. However, as we discuss in Online Appendix B, because minimum wage workers’ share in overall
production is very small (around 2% in the United States), reasonable calibrations of a neoclassical model would suggest very
small upper-tail effects. For example, if we consider an elasticity
of substitution between high- and low-wage workers of around
1.4 based on Katz and Murphy (1992), and an output demand
elasticity of around 1 based on Aaronson and French (2007), the
implied upper-tail employment elasticity with respect to the minimum wage would be around 0.006. In Online Appendix Table B.1,
we show that reasonable variations in the key parameters uniformly suggest that plausible estimates of minimum wage impact
on upper-tail employment should be very small. Moreover, any
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II.B. Empirical Implementation
The key empirical challenge is to estimate the counterfactual
wage frequency distribution in the absence of a minimum wage
increase. Instead of using either ad hoc functional forms (Meyer
and Wise 1983; Dickens, Machin, and Manning 1998) or the distribution prior to the minimum wage increase (Harasztosi and
Lindner forthcoming), we exploit state-level variation in the minimum wage and identify the counterfactual distribution using a
difference-in-differences event study design. Our event-based approach uses a similar framework as Autor, Donohue, and Schwab
(2006) and examines employment changes within an eight-year
window around 138 prominent state-level minimum wage events,
where states increased their minimum wage by at least $0.25,
and where at least 2% of the workers were directly affected by
the increase.3 By focusing on employment changes around the
event window, we incompletely capture long-run effects of the
minimum wage. Nevertheless, as we show below, we find no evidence of a change in employment up to five years after the minimum wage hike, and so it strikes us as unlikely that our empirical
3. We exclude federal increases from our primary sample of events because
for these events, the change in missing jobs, b, is identified only from timeseries variation—as there are no “control states” with a wage floor lower than the
new minimum wage. However, we show in Online Appendix Table A.4 that our
employment and wage estimates are similar when we include federal events as
well.
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theoretical upper-tail effects would be positive, so ignoring them
will overstate the net job losses.
We assess the employment effect of the minimum wage on
low-wage workers by summing the missing and excess jobs, b
+ a, which is equal to the employment change below a wage
threshold W: b + a = Emp1 [w < W] − Emp0 [w < W ]. Such an
estimator is similar to the “bunching” method developed in the recent public finance literature, which uses bunching around points
that feature discontinuities in incentives to elicit behavioral responses (Kleven 2016). Although the estimation of the overall
effect on low-wage jobs does not require a decomposition by wage
bins relative to the minimum (e.g., into excess and missing jobs),
such a decomposition does help assess the bite of the policy and
exactly how the policy affects jobs and wages at the bottom. For
example, the shape of the excess jobs can tell us about the extent
of wage spillovers.
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
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(1)
4
17
Esjt
τk
=
ατ k Isjt
+ μsj + ρ jt + sjt + usjt ,
Nst
τ =−3 k=−4
where Esjt is the employment in $0.25 wage bins j in state s and at
quarter t, and Nst is the size of the population in state s and quarτk
equals 1 if the minimum wage was
ter t. The treatment dummy Isjt
raised τ years from date t and for the $0.25 wage bins j that fall
between k and k + 1 dollars relative to the new minimum wage.
This definition implies that τ = 0 represents the first year following the minimum wage increase (i.e., the quarter of treatment and
the subsequent three quarters), and τ = −1 is the year (four quarτk
treatment variables are
ters) prior to treatment. Moreover, the Isjt
a function of not only state and time but also the wage bins. For instance, k = 0 represents the four $0.25 bins between MW and MW
+ $0.99 and k = −1 is a “below” bin with wages paying between
MW − $0.01 and MW − $1.00. Our benchmark specification also
controls for state-by-wage-bin and period-by-wage-bin effects, μsj
and ρ jt . This allows us to control for state-specific factors in the
earnings distribution and also the nationwide evolution of wage
inequality. Finally, sjt include controls for small or federal increases, and usjt is the error term.4 We cluster our standard errors
by state, the level at which policy is assigned. Our standard errors
therefore account for the possibility that employment changes at
different parts of the wage distribution may be correlated within
a state.
4. Our primary minimum wage events exclude very small increases. To ensure
they do not confound our main effects, we include controls for these small events.
We also separately control for federal minimum wages. In particular, separately
for small events and federal events, we construct a set of six variables by interacting {BELOW, ABOVE} × {EARLY, PRE, POST}. Here BELOW and ABOVE are
dummies equal to 1 for all wage bins that are within $4 below and above the new
minimum, respectively; EARLY, PRE, and POST are dummies that take on 1 if
−3 τ −2, τ = −1, or 0 τ 4, respectively. These two sets of six variables
are included as controls in the regression (sjt in equation (1)).
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design misses important long-term employment changes. Online
Appendix Table A.6 shows the robustness of estimates to alternative window lengths, including allowing for up to a seven-year
post-treatment period.
We estimate the effect of the minimum wage not just on aggregate employment but also on employment in every $0.25 wage
bin. Our basic regression specification is the following:
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5. This idea is similar to Autor, Manning, and Smith (2016) who use unrealistically large spillover effects to validate the empirical model in use.
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As with any difference-in-differences design our approach
identifies the causal effect of the minimum wage under the
assumption that the entire frequency distribution of wages in
the treated and untreated states would move in parallel in the
absence of the policy change. Although this assumption cannot
be tested directly, we conduct a variety of checks whose results
will be reported below. As is standard, we use the leading terms
to assess preexisting trends. As an added check, when we calculate event-by-event estimates in Section III.C, we test whether
the distribution of leading effects is consistent with a sharp null
of zero effects everywhere.
In addition, since our approach locates the source of the employment effects within the wage distribution, we can use the
upper-tail employment changes as an added falsification test.5
Because large positive or negative changes in jobs paying above,
say, $15 an hour are unlikely to reflect the causal effect of the
minimum wage, reporting such employment changes in the upper tail can be highly informative about model validity. Moreover,
the potential bias from the confounding factors affecting the upper tail can be especially large when only a small fraction of the
workforce is directly affected by the minimum wage (as is true in
the United States). The contribution of these omitted variables
may be sizable compared with the relatively small expected effect
of the minimum wage on aggregate employment. As a result, the
bias arising from shocks to the upper tail can be particularly severe when we are interested in estimating the overall employment
effect of the minimum wage.
There are numerous advantages of decomposing the aggregate employment changes by wage bins. First, such a decomposition allows us to focus on employment changes locally around
the new minimum wage—the part of the wage distribution where
we expect the policy to play a role. This variation is highly informative, yet rarely exploited. Second, and more importantly, our
localized approach allows us to estimate the effects on overall employment as well as for subgroups where the standard approaches
often fail to provide meaningful estimates on employment and
wages. When only a small fraction of workers are directly affected
by the minimum wage, the effect on the average wage of such
subgroups will be very small. Without a clear wage effect, it is
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
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mum wage as aτ =
bτ =
−1
k=−4
4
k=0
ατ k − −1
k=−4 α−1k
.
EP OP −1
ατ k − 4k=0 α−1k
, and the missing jobs below as
EP OP −1
By dividing the employment changes by
EP OP −1 , the sample average employment-to-population ratio in
treated states during the year (four quarters) prior to treatment,
we normalize the excess and missing jobs by the pretreatment
total employment. The aτ and bτ values plot out the evolution
of excess and missing jobs over event time τ . We also report the
excess and missing employment estimates averagedover the five
years following the minimum wage increase, b = 15 4τ =0 bτ and
a = 15 4τ =0 aτ .
Given our normalization, e = a + b represents the estimate for the percentage change in total employment due to the
6. In Online Appendix Table A.1 we demonstrate that the standard approach,
which looks at the wage and employment effects aggregated over the entire wage
distribution, fails to produce positive and statistically significant wage effects in
most cases. This indicates that the standard approach fails to capture the program
effect of the minimum wage for these subgroups. At the same time, our estimates
focused on low-wage jobs always produce sizable and significant wage effects. The
own-wage elasticity of employment estimated using minimum wage variation is
effectively a Wald-IV estimate; hence the lack of a strong “first stage” means
estimates are biased toward the OLS estimate obtained by naively regressing
employment on wages (Bound, Jaeger, and Baker 1995).
7. Online Appendix Table A.2 confirms that the standard errors tend to be
lower when we consider counts of low-wage jobs compared with an approach using
total number of jobs.
8. Online Appendix Table A.5 shows that the results are robust to higher
cutoffs.
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not clear how to interpret the size of any employment effect found
for those groups.6 Third, the localized focus around the minimum
wage often improves the precision of estimates by filtering out
random shocks to jobs in the upper part of the wage distribution.7
We use the estimated α τ k from equation (1) to calculate the
change in employment throughout the wage distribution in response to the policy. The change in the number of jobs (per capita)
paying below the new minimum
wage
event date −1 and
between
−1
τ can be calculated as −1
k=−4 ατ k −
k=−4 α−1k. To be clear, this
is a difference-in-differences estimate, as it nets out the change
in the counterfactual distribution implicitly defined by the regression equation (1). Analogously, the change in the number of
jobs (per capita) paying between the minimum wage and W is
W −MW
W −MW
ατ k − k=0
α−1k. For our baseline estimates, we set
k=0
8
W = MW + 4. We define
the
excess jobs at or above the mini
1418
THE QUARTERLY JOURNAL OF ECONOMICS
%Total Employment
a + b
=
.
%MW
%MW
We define the percentage change in affected employment as the
change in employment divided by the (sample average) share of
the workforce earning below the new minimum wage the year
before treatment, b−1 :9
%Affected Employment = %e =
a + b
b−1
.
We also use the estimated coefficients to compute the percentage change in the average hourly wage for affected workers. We
calculate the average wage by taking the ratio of the total wage bill
collected by workers below the new minimum wage to the number
−1
. Here
of such workers. Prior to treatment, it is equal to w−1 = wb
b
−1
the wage bill, wb−1 , and the number of workers earning below the
new minimum wage just prior to the increase, b−1 , are averages for
the full sample of events. The minimum wage increase causes both
the wage bill and employment to change. The new average wage
in the post-treatment period is equal to w = wbb−1 +wb
.10 Therefore,
+e
−1
9. Notice that we divide by the actual share of the workforce and not by the
change in it. As we pointed out earlier, these two are not the same if there is imperfect compliance, imperfect coverage, or measurement error in wages. Although
both divisions are meaningful, dividing by the actual share is the more policyrelevant elasticity. This is because policy makers can calculate the actual share of
workers at the new minimum wage and use the estimates presented in this article.
However, the change in the jobs below the new minimum wage is only known after
the minimum wage increase, so it cannot be used for a prospective analysis of the
policy’s impact.
10. The change in wage bill can be written as a function of our regression coefficients as follows. Averaging the coefficients over thefive-yearpost-treatment win
dow, αk = 15 4τ =0 ατ,k, we can write wb = 4k=−3 k + MW · (αk − α−1k), where
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minimum wage increase. We refer to this estimate as “event-based
bunching” or EB-bunching estimates to highlight that we are (i)
using an event-based difference-in-differences design and (ii) estimating the excess and missing jobs locally around the bunching
in the distribution at the minimum wage.
If we divide e by the percentage change in the minimum
wage averaged across our events, %MW, we obtain the employment elasticity with respect to the minimum wage:
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
1419
the percentage change in the average wage of affected workers is
given by:
(2)
%w =
w
−1=
w−1
b−1 + e
wb−1
−1=
%wb − %e
.
1 + %e
b−1
The percentage change in the average wage is obtained by taking
the difference in percentage change in wage bill and employment,
and dividing by the retained employment share. This formula
implicitly assumes the average wage change of those workers exiting or entering due to the policy is the same as the average wage
change of affected workers who remain employed.
Finally, armed with the changes in employment and wages
for affected workers, we estimate the employment elasticity with
respect to own-wage (or the “labor demand elasticity” in a competitive market):
1 a + b
%Affected Employment
=
.
%Affected Wage
%w b−1
We calculate the standard errors for this elasticity using the delta
method.
Although our use of wage-bin-by-state-by-quarter data is useful for decomposing the employment changes by bins relative to
the minimum wage, our employment and wage elasticities do
not rely on this binning. To clarify this point, we show results
from a simpler method that estimates a regression using stateby-quarter data, where the outcomes are the (per capita) number
of jobs or total wage bill under, say, $15/hour, and the event indicators are just by state and quarter. We show below that the
resulting employment and wage estimates (and standard errors)
are very similar when using this simpler method.
II.C. Data and Sample Construction
We use the individual-level NBER Merged Outgoing Rotation
Group of the Current Population Survey for 1979–2016 (CPS) to
MW is (approximately) the sample average of the new minimum wage. We say
approximately because k is based on $1 increments, and so MW is calculated as
the sample mean of [MW, MW + 1).
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wb−1 + wb
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THE QUARTERLY JOURNAL OF ECONOMICS
11. The NBER CPS merged ORG data are available at
http://www.nber.org/morg/. Wage imputation status markers in the CPS vary and
are not comparable across time. In general we follow Hirsch and Schumacher
(2004) to define wage imputations. During 1979–1988 and September 1995–2016,
we define wage imputations as records with positive BLS allocation values
for hourly wages (for hourly workers) and weekly earnings or hours (for other
workers). For 1989–1993, we define imputations as observations with missing
or zero “unedited” earnings but positive “edited” earnings (which we also do for
hours worked and hourly wages).
12. In general, there has been an increase in the rate of imputation over
time. However, in Online Appendix Table A.3 and Online Appendix Figure A.2, we
show that minimum wage raises are not systematically related to changes in the
imputation rate. Event study estimates for the effect of minimum wages on the
imputation rate show no substantial or statistically significant change three years
before and five years after the treatment.
13. We assign all wages between $0 and $1 to a single bin and all wages above
$30 to the $30 bin. The resulting 117 wage bins are (0.00, 1.25), [1.25, 1.50), . . . ,
[29.75, 30.00), [30, ∞).
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calculate quarterly, state-level distributions of hourly wages. For
hourly workers, we use the reported hourly wage, and for other
workers we define the hourly wage to be their usual weekly earnings divided by usual weekly hours. We do not use any observations with imputed wage data to minimize the role of measurement error.11 There are no reliable imputation data for January
1994 through August 1995, so we exclude this entire period from
our sample. Our available sample of employment counts therefore
spans 1979q1 through 1993q4 and 1995q4 through 2016q4.12
We deflate wages to 2016 dollars using the CPI-U-RS and
for a given real hourly wage assign its earner a $0.25 wage bin
w running from $0.00 to $30.00.13 For each of these 117 wage
bins we collapse the data into quarterly, state-level employment
counts Eswt using the person-level ORG sampling weights. We
use estimates for state-level population aged 16 and over, Nst ,
from the CPS-MORG (which in turn is based on the census), as
the denominator for constructing per capita counts. Our primary
sample includes all wage earners and the entire state population,
but we also explore the heterogeneity of our results using different
subgroups, where the bite of the policy varies.
The aggregate state-quarter-level employment counts from
the CPS are subject to sampling error, which reduces the precision of our estimates. To address this issue, we benchmark
the CPS aggregate employment-to-population ratio to the implied employment-to-population ratio from the Quarterly Census
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
1421
14. The minimum wage series is available at https://github.com/
benzipperer/historicalminwage/releases.
15. All minimum wage increases including our events are shown in Online
Appendix Figure A.1
16. Overall, we have 847,314 wage-bin-state-period observations, which we
obtained from 4,694,104 individual-level observations, producing a count of 5.5
workers per $0.25 bin. However, the count per bin is higher in the $5-to-$15/hour
range because the upper-tail wage bins are more sparse. The $5-to-$15/hour range
is the relevant one because it contains the [MW − $4, MW + $4] windows for all
of our events.
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of Employment and Wages (QCEW), which is a near universe of
quarterly employment (but lacks information on hourly wages).
Online Appendix F explains the QCEW benchmarking in detail.
As we discuss below, the QCEW benchmarking has little effect on
our point estimates but substantially increases their statistical
precision.
Our estimation of the change in jobs paying below and above a
new minimum wage requires us to specify minimum wage–
increasing events. For state-level minimum wage levels, we
use the quarterly maximum of the state-level daily minimum
wage series described in Vaghul and Zipperer (2016).14 For the
138 minimum wage events, on average, 8.6% of workers were below the new minimum wage in the year before these events and
the mean real minimum wage increase was 10.1%.15
One concern when using $0.25 bins and CPS data is that some
of the bins may be sparse with very few or no workers. However,
we stress that our employment estimate is based on the sum of
employment changes in 36 cells covering a $9 range [MW − $4,
MW + $4], summed over at least 4 quarters (typically 20 quarters). As a result, small or zero employment in particular cells is
not a major concern. In each state, there are, on average, around
seven workers each quarter in each of the $0.25 bins between
$5 and $15/hour in our sample.16 Since the coefficients for our
event dummies are estimated at a $1-bin-year-state level, on average, for these we use around 112 individual-level observations
per event. Moreover, when we assess the total employment effects, we calculate the sum of the $1-bin estimates between $4
below and $4 above the minimum wage, and we consider five-year
averages. This implies that, on average, we use approximately
5,040 individual worker observations per event. This is a wellsized sample that allows a reliable estimate of the true counts of
employment for each event. Consistent with this point, we note
1422
THE QUARTERLY JOURNAL OF ECONOMICS
17. In Online Appendix F, we also structurally estimate a model of measurement error in reported wages proposed by Autor, Manning, and Smith (2016), and
show that the contribution of misreporting error to the overall variance in wages
in the CPS and in administrative data on hourly wages from three states are very
similar. Furthermore, we semiparametrically deconvolve the CPS wage distribution using the estimated measurement error model and show that our estimates
using this measurement error–corrected distribution are very similar to the baseline estimates (Online Appendix Table F.3). In Online Appendix C we implement
our approach using administrative data from Washington and find estimates to
be similar when using the CPS. Although each piece of evidence has limitations,
together they suggest that our employment and wage results are not likely to be
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again that results from our approach are very similar to those
from a simpler method that uses state-by-quarter data and where
the outcomes are the (per capita) number of jobs or total wage bill
under $15/hour.
Another potential concern with the data is that misreporting of wages in the CPS may bias our estimates. If reported wages
contain some measurement error, some workers earning above the
minimum wage will appear to earn below it, which could attenuate
the estimate for b. However, this does not affect the consistency
of the estimate for a + b as long as the minimum wage only
affects reported wages below W. The reason is straightforward.
Assume that 1% of the workforce mistakenly report earning below
the new minimum wage in the post-treatment period. This would
lead our estimate of the missing jobs to be too small in magnitude:
ˆ = b + 0.01. However, this misreporting would also lead to an
b
equal reduction in the number of excess jobs above, producing
ˆ = a − 0.01; this will be true as long as these
the estimate a
misreported workers are coming from the range [MW, W ), which
is likely to be satisfied for a wide variety of classical and nonclassical measurement error processes where the support of the
measurement error is contained in [MW − W, W − MW]. Thereˆ is likely to be unaffected
ˆ + b
fore, the employment estimate a
by measurement error in reported wages. We also directly assess how misreported wages in the CPS may affect our results in
Online Appendix E, where we compare the CPS hourly wage distribution to microaggregated administrative data on hourly wages
from three U.S. states that collect this information. Reassuringly,
the evolution of the number of jobs paying below the minimum
wage, and the number of jobs paying up to $5 above the minimum
wage in the CPS data from these three states match quite well
with their counterparts using administrative data.17
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
1423
The figure shows the main results from our event study analysis (see equation (1)) exploiting 138 state-level minimum wage changes between 1979 and
2016. The blue bars show for each dollar bin (relative to the minimum wage)
the estimated average employment changes in that bin during the five-year posttreatment relative to the total employment in the state one year before the treatment. The error bars show the 95% confidence interval using standard errors that
are clustered at the state level shown using the error bar. The dashed red line
(color version available online) shows the running sum of employment changes up
to the wage bin it corresponds to.
III. RESULTS
We begin our analysis by estimating the effect of the minimum wage on the frequency distribution of hourly wages.
Figure II shows the results from our baseline specification (see
equation (1)). We report employment
changes averaged over the
five-year post-treatment period, 15 4τ =0 ατ k, for each dollar wage
bin (k) relative to the minimum wage. Recall that all employment
biased substantially due to measurement error. At the same time, more precise
wage data could help better discern the exact nature of the wage effects including
the extent of spillovers, the size of the spike, and the extent of noncompliance.
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FIGURE II
Impact of Minimum Wages on the Wage Distribution
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THE QUARTERLY JOURNAL OF ECONOMICS
18. The discrepancy between the actual number of jobs below the new minimum, which is 8.6% of total pretreatment employment on average, and the change
in the number of jobs below it, which is 1.8% on average, can be explained by the
following factors. First, some of the jobs below the minimum wage (e.g., tipped
workers) are exempted from the minimum wage in most states. Second, there are
often multiple changes in the minimum wage in a relatively short period. In these
cases, the cumulative effect of the various treatments should be considered: when
we adjust for this we find the change in the number of jobs below the minimum
rises in magnitude from 1.8% to 2.5%. Third, there is some wage growth even in
the absence of a minimum wage increase, and our event study design controls for
these changes.
19. The $3 above the minimum wage is around the 23rd percentile of the
wage distribution on average. Autor, Manning, and Smith (2016) finds the wage
spillovers are effectively zero at around the 25th percentile.
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changes are normalized to pretreatment total employment in the
state. Several points should be noted.
First, there is a clear and significant drop in the number of
jobs below the new minimum wage, amounting to 1.8% (std. err.
0.4%) of the total pretreatment employment.18 Around 34 of this
reduction occurs in the $1 wage bin just under the new minimum.
Second, there is also a clear and significant increase in jobs just
at the new minimum wage (at the $0 wage bin). Third, there is
also a statistically significant increase in employment in the wage
bin $3 above the new minimum and modest, statistically insignificant increases in the $1 and $2 bins. This pattern of employment
changes is consistent with limited wage spillovers resulting from
the minimum wage increase, as suggested in Autor, Manning,
and Smith (2016).19 Fourth, the excess jobs between the new minimum and $4 above it represents 2.1% (std. err. 0.3%) of the total
pretreatment employment. Fifth, the employment changes in the
upper-tail wage bins, from $5 above the minimum wage to $17 or
more (the final bin), are all small and statistically insignificant—
individually and cumulatively as shown by the red line, which
represents the running sum of employment changes. Finally, it is
worth emphasizing that the drop in employment just below the
new minimum, the equal-sized increase just above it, and the lack
of employment change in the upper tail is exactly what we expect
if employers are complying with the law and adjusting wages but
not employment.
We estimate the employment change by adding the missing
jobs below and excess jobs above the minimum wage: a + b.
We divide this change by the jobs below the new minimum wage
(b−1 = 8.6%) to obtain a change in the affected employment of
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
1425
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2.8% (std. err. 2.9%), which is positive but statistically insignificant. We can also divide the employment change a + b by the
sample-averaged minimum wage increase of 10.1% to calculate
the employment elasticity with respect to the minimum wage
of 0.024 (std. err. 0.025). This estimate is statistically insignificant, and the 95% confidence interval rules out substantial reductions in the aggregate employment, including the baseline
aggregate employment elasticity of −0.074 in Meer and West
(2016) (see their Table 4). The most common minimum wage employment elasticities are from teens; for example, Neumark and
Wascher (2008) argue that this falls between −0.1 and −0.3, while
Allegretto et al. (2017) argue that it is closer to 0. However, the
directly affected share of teens (43.2%) is much larger than the
workforce overall (8.6%). Therefore, to make our estimates on
overall employment comparable to the estimates for teens we can
multiply our estimate and standard errors by the ratio of the
= 5.02. This leads to an affected-share-adjusted 95%
shares 0.432
0.086
confidence interval of [−0.13, 0.37], which rules out most of the
−0.1 to −0.3 range.
Second, using the formula in equation (2), we can also calculate the change in the average wage and the employment elasticity
with respect to own wage (i.e., the labor demand elasticity in the
competitive model). We estimate that the effect of the minimum
wage on average wages is 6.8% (std. err. 1.0%), which is statistically significant. The estimate for the elasticity of employment
with respect to own wage is 0.411 (std. err. 0.430). The confidence
intervals rule out any own-wage elasticities more negative than
−0.450 at the 95% confidence level. Such a lower bound rules out
many estimates in the literature that found a negative employment elasticity (see Online Appendix Figure A.7; also, Neumark
and Wascher (2008) argue that the own-wage employment elasticity can easily be −1 or even −2).
Figure III shows the changes in the missing jobs paying below the new minimum wage (bτ ), and the excess jobs paying up
to $4 above the minimum wage (aτ ) over annualized event time
using our baseline specification. All the estimates are expressed
as changes from event date τ = −1, or the year just prior to treatment, the estimates for which are normalized to 0. There are four
important findings that we would like to highlight. First, we find
a very clear reduction in the jobs paying below the new minimum wage (shown in red dashed line) between the year just prior
to treatment (τ = −1) and the year of treatment (τ = 0)—this
-.04
-.02
0
.02
.04
THE QUARTERLY JOURNAL OF ECONOMICS
-3
-2
-1
0
1
2
Years relative to the minimum wage change
3
4
FIGURE III
Impact of Minimum Wages on the Missing and Excess Jobs over Time
The figure shows the main results from our event study analysis (see equation (1)) exploiting 138 state-level minimum wage changes between 1979 and
2016. The figure shows the effect of a minimum wage increase on the missing jobs
below the new minimum wage (red dashed line; color version available online) and
on the excess jobs at or slightly above it (blue solid line) over time. The red dashed
line shows the evolution of the number of jobs (relative to the total employment
one year before the treatment) between $4 below the new minimum wage and the
new minimum wage (b); the blue solid line shows the number of jobs between
the new minimum wage and $5 above it (a). We also show the 95% confidence
interval based on standard errors that are clustered at the state level.
shows that the minimum wage increases under study are measurably binding. Second, although there is some reduction in the
magnitude of the missing jobs in the post-treatment window, it
continues to be very substantial and statistically significant five
years out, showing that the treatments are fairly durable over
the medium run.20 Third, the response of the excess jobs at or
above the new minimum (a) exhibits a very similar pattern in
magnitudes, with the opposite sign. There is an unmistakable
20. The durability of the treatment can also be seen in Online Appendix
Figure A.4, which plots the progression of the minimum wage using our event
study design.
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Excess and missing jobs relative to the pretreatment total employment
1426
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
1427
1. Robustness Checks. In Table I, we assess the robustness of
the main results to including additional controls for time-varying,
unobserved heterogeneity. This is particularly important because
results in the existing literature are often sensitive to the inclusion of various versions of time-varying heterogeneity (e.g.,
Neumark, Salas, and Wascher 2014; Allegretto et al. 2017).
In column (1) we report the five-year-averaged post-treatment
estimates for the baseline specification shown in Figures II
and III. Columns (2) and (3) add wage-bin-by-state specific linear
and quadratic time trends, respectively. Note that in the presence of three pretreatment and five post-treatment dummies, the
trends are estimated using variation outside of the eight-year
window around the treatment, and thereby are unlikely to be affected by either lagged or anticipation effects. Columns (4)–(6)
allow the wage-bin-period effects to vary by the nine census divisions. Column (6) represents a highly saturated model allowing
for state-specific quadratic time trends and division-period effects
for each $0.25 wage bin.
Overall, the estimates from the additional specifications are
fairly similar to the baseline estimate. In all cases, there is a
clear bite of the policy as measured by the reduction in jobs paying below the minimum, b. Consistent with the presence of a
substantial bite, there is a statistically significant increase in
real wages of affected workers in all specifications: these range
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jump in excess employment at τ = 0, and a substantial portion
of it persists and is statistically significant even five years out.
Fourth, for both the changes in the excess and missing jobs there
is only a slight indication of a preexisting trend prior to treatment.
The τ = −2 leads are statistically indistinguishable from 0 and
although there is some evidence of changes three years prior to
treatment, the leading effects are very small relative to the posttreatment effect estimates. Moreover, the slight downward trend
in excess jobs, and the slight upward trend in missing jobs is consistent with a falling value of the real minimum wage prior to
treatment. The sharp jump in both the excess and missing jobs at
τ = 0, the lack of substantial pretreatment trends, and the persistent post-treatment gap between the two shares all provide
strong validation of the research design. Online Appendix Figure
A.5 shows analogous time paths for wages and employment showing a sharp and persistent wage effect at τ = 0 coupled with little
change in employment over the event window—either before or
after treatment.
0.057∗∗∗
(0.010)
0.000
(0.023)
0.068∗∗∗
(0.010)
0.028
(0.029)
Excess jobs above new MW (a)
0.086
0.101
138
847,314
4,694,104
0.086
0.101
138
847,314
4,694,104
0.086
0.101
138
847,314
4,694,104
0.019
(0.018)
0.326
(0.313)
0.068∗∗∗
(0.012)
0.022
(0.021)
(0.004)
0.020∗∗∗
(0.003)
− 0.018∗∗∗
(3)
0.086
0.101
138
847,314
4,694,104
− 0.016
(0.018)
− 0.449
(0.574)
− 0.001
(0.018)
− 0.032
(0.439)
0.086
0.101
138
847,314
4,694,104
0.043∗∗∗
(0.010)
− 0.019
(0.021)
(0.002)
0.014∗∗∗
(0.003)
− 0.016∗∗∗
(5)
0.049∗∗∗
(0.010)
− 0.002
(0.021)
(0.002)
0.016∗∗∗
(0.002)
− 0.016∗∗∗
(4)
0.086
0.101
138
847,314
4,694,104
− 0.000
(0.019)
− 0.003
(0.455)
0.050∗∗∗
(0.011)
− 0.000
(0.023)
(0.002)
0.015∗∗∗
(0.003)
− 0.015∗∗∗
(6)
(7)
0.086
0.101
138
14,484
4,694,104
0.023
(0.024)
0.410
(0.421)
0.065∗∗∗
(0.010)
0.027
(0.028)
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Jobs below new MW (b−1 )
% MW
Number of events
Number of observations
Number of workers in the sample
Emp. elasticity w.r.t. affected wage
Employment elasticity w.r.t. MW
% affected employment
0.000
(0.020)
0.006
(0.402)
(0.004)
0.018∗∗∗
(0.003)
(0.004)
0.021∗∗∗
(0.003)
Missing jobs below new MW (b)
0.024
(0.025)
0.411
(0.430)
− 0.018∗∗∗
− 0.018∗∗∗
% affected wages
(2)
(1)
TABLE I
IMPACT OF MINIMUM WAGES ON EMPLOYMENT AND WAGES
1428
THE QUARTERLY JOURNAL OF ECONOMICS
Y
Y
Y
Y
Y
(2)
Y
Y
Y
Y
(3)
Y
Y
b̄−1
Y
Y
Y
(5)
Y
Y
(4)
Y
Y
Y
Y
Y
(6)
Y
Y
(7)
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a+b whereas the sixth row, employment elasticity with respect to the wage, reports
1
a+b . The line on the number of observations
to the minimum wage, is calculated as %MW
%W b̄−1
shows the number of quarter-bin cells used for estimation, while the number of workers refers to the underlying CPS sample used to calculate job counts in these cells.
Notes. The table reports the effects of a minimum wage increase based on the event study analysis (see equation (1)) exploiting 138 state-level minimum wage changes between
1979 and 2016. The table reports five-year averaged post-treatment estimates on missing jobs up to $4 below the new minimum wage, excess jobs at and up to $5 above it, employment,
and wages. Column (1) shows the benchmark specification while columns (2)–(6) explore robustness to bin-state time trends and bin-division-period fixed effects. Column (7) reports
the simpler methodology estimates where we calculate changes in affected wage and employment by using state-by-quarter data, where the outcomes are the number of jobs or the
total wage bill under $15 per hour. Regressions are weighted by state-quarter aggregated population. Robust standard errors in parentheses are clustered by state. Significance
levels are ∗ 0.10, ∗∗ 0.05, ∗∗∗ 0.01.
The first two rows report the change in number of missing jobs below the new minimum wage (b), and excess jobs above the new minimum wage (a) relative to the pretreatment
total employment. The third row, the percentage change in average wages in the affected bins, (%W), is calculated using equation (2). The fourth row, percentage change in
employment in the affected bins, is calculated by dividing change in employment by jobs below the new minimum wage ( a+b ). The fifth row, employment elasticity with respect
Controls
Bin-state FE
Bin-period FE
Bin-state linear trends
Bin-state quadratic trends
Bin-division-period FE
State FE
Year FE
(1)
TABLE I
CONTINUED
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
1429
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THE QUARTERLY JOURNAL OF ECONOMICS
21. The threshold is W = 15, which is at least $4 above the new minimum
wage in all of our events but one.
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between 5.7% and 6.9% with common wage-bin-period effects
(columns (1)–(3)), and between 4.3% and 5.0% with divisionspecific wage-bin-period effects (columns (4)–(6)). In contrast, the
proportionate change in employment for affected workers is never
statistically significant, and is numerically smaller than the wage
change, ranging between −1.9% and 3.6% across the eight specifications. For the most part, the employment estimates are small
or positive; the only exception is column (5) with state-specific
linear trends and bin-division-specific period effects. The employment elasticity with respect to the wage is −0.449 (std. err.
0.574). However, adding quadratic trends to the former specification (column (6)) substantially reduces the magnitude of the employment elasticity with respect to the wage to −0.003 (std. err.
0.455).
Finally, column (7) provides employment and wage estimates
using a state-by-period panel, where we regress either the per
capita wage bill or employment under an absolute wage threshold (W), and then estimate the change in affected wage and employment using the same formulae as our baseline.21 The estimates and standard errors for affected employment (0.025, std.
err. 0.029) and wage (0.063, std. err. 0.011) are virtually identical
to column (1), clarifying that use of wage bins or choices around
those have no impact on our key estimates. At the same time,
unlike our baseline specification, this simpler method using an
absolute wage threshold cannot provide separate estimates for
excess and missing jobs.
Online Appendix Table A.4 shows that our results are robust
to focusing only on the events occurring in the states that do not
allow tip credits; dropping occupations that allows tipping; using
full-time equivalent job counts; restricting the sample to hourly
workers; additionally using federal-level minimum wage changes
for identification; using the raw CPS data instead of the QCEW
benchmarked CPS; without using population weights; or focusing
on the post-1992 period. We also show robustness to alternative
event window lengths (Online Appendix Table A.6), and alternative values of the upper endpoint of the wage window, W (Online
Appendix Table A.5).
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
1431
III.A. Heterogenous Responses to the Minimum Wage
1. By Demographic Groups. We assess the presence of laborlabor substitution at the bottom of the wage distribution by
examining employment responses across various demographic
groups.22 In Table II we report estimates for workers without
a high school degree, those with high school or less schooling,
women, black or Hispanic individuals, and teens using our baseline specification (see equation (1)).
As expected, restricting the sample by education and age produces a larger bite. For example, for those without a high school
degree, the missing jobs estimate, b, is −6.5% while for those
with high school or less schooling it is −3.2%. These estimates
for the missing jobs are, respectively, 261% and 78% larger than
the baseline estimate for the overall population (−1.8%, from
Table I, column (1)). Nevertheless, the large variation in the missing jobs across various demographic groups is matched closely by
excess jobs above the new minimum wage.23 In all cases, except
for the black or Hispanic group, the excess jobs are larger than
the missing jobs, indicating a positive albeit statistically insignificant employment effect. For black or Hispanic individuals, the
difference between excess and missing jobs is negligible. As a result, the employment elasticities with respect to own wage range
between −0.086 and 0.570 for the first five demographic groups of
the table. In all cases but one, the elasticities are statistically indistinguishable from 0. The sole exception is those without a high
school degree, for whom the employment elasticity with respect to
the wage is 0.475 (std. err. 0.268) which is marginally significant
at the ten percent level. The minimum wage elasticity for teens
is 0.125, which is more positive than some of the estimates in the
22. Existing evidence on labor-labor substitution has typically focused on specific groups like teens (Giuliano 2013), individual case studies (Fairris and Bujanda
2008), or specific segments like online labor platforms (Horton 2018).
23. In Online Appendix Figure A.8 we also show that the close match between
excess jobs and missing jobs holds also if we fully partition the workforce into 23
age-education cells.
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We can use our approach focused on low-wage jobs to estimate
the effect of the minimum wage on specific subgroups.
0.076∗∗∗
(0.014)
0.043
(0.030)
0.080∗∗∗
(0.014)
0.038
(0.024)
Excess jobs above new MW (a)
0.264
0.103
0.145
0.103
0.432
0.102
0.125
(0.134)
0.356
(0.317)
0.083∗∗∗
(0.018)
0.030
(0.032)
(0.010)
0.127∗∗∗
(0.020)
− 0.114∗∗∗
(3)
0.102
0.101
0.025
(0.027)
0.343
(0.362)
0.072∗∗∗
(0.011)
0.025
(0.027)
(0.005)
0.026∗∗∗
(0.004)
− 0.023∗∗∗
(4)
0.358
0.103
0.052
(0.062)
0.206
(0.233)
− 0.005
(0.058)
− 0.086
(1.005)
0.133
0.100
0.073∗∗∗
(0.011)
0.015
(0.018)
(0.010)
0.100∗∗∗
(0.012)
− 0.094∗∗∗
(6)
0.044∗∗∗
(0.012)
− 0.004
(0.044)
(0.008)
0.028∗∗∗
(0.006)
− 0.028∗∗∗
(5)
0.104
0.103
0.016
(0.049)
0.304
(0.904)
0.051∗∗∗
(0.013)
0.015
(0.048)
(0.005)
0.021∗∗∗
(0.003)
− 0.020∗∗∗
(7)
(8)
0.027
0.103
0.003
(0.014)
0.184
(0.841)
0.060∗
(0.032)
0.011
(0.055)
− 0.004∗∗∗
(0.001)
0.004∗∗∗
(0.001)
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Jobs below new MW (b−1 )
% MW
Emp. elasticity w.r.t. affected wage
Employment elasticity w.r.t. MW
% affected employment
0.061
(0.042)
0.570
(0.386)
(0.007)
0.038∗∗∗
(0.006)
(0.010)
0.075∗∗∗
(0.011)
Missing jobs below new MW (b)
0.097
(0.061)
0.475∗
(0.268)
− 0.032∗∗∗
− 0.065∗∗∗
% affected wages
(2)
(1)
TABLE II
IMPACT OF MINIMUM WAGES ON EMPLOYMENT AND WAGES BY DEMOGRAPHIC GROUPS
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THE QUARTERLY JOURNAL OF ECONOMICS
Less than
high school
138
847,314
660,771
High school
or less
138
847,314
2,248,711
(2)
Teen
138
847,314
287,484
(3)
Women
138
847,314
2,277,624
(4)
Black or
Hispanic
138
846,729
781,003
(5)
High
probability
138
847,314
469,226
(6)
Medium
probability
138
847,314
1,830,393
(7)
Low
probability
138
847,314
2,349,485
(8)
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a+b , whereas the sixth row, employment elasticity with respect to the wage, reports
1
a+b . The line on the number of
with respect to the minimum wage, is calculated as %MW
%W b̄−1
observations shows the number of quarter-bin cells used for estimation, while the number of workers refers to the underlying CPS sample used to calculate job counts in these cells.
b̄−1
Notes. The table reports effects of a minimum wage increase by demographic groups based on the event study analysis (see equation (1)) exploiting 138 state-level minimum wage
changes between 1979 and 2016. The table reports five-year averaged post-treatment estimates on missing jobs up to $4 below the new minimum wage, excess jobs at and up to $5
above it, employment, and wages for individuals without a high school degree (column (1)), for individuals with high school degree or less schooling (column (2)), for teens (column
(3)), for women (column (4)), for black or Hispanic workers (column (5)). Columns (6)–(8) report the results for groups of workers with differential probability of being exposed to the
minimum wage changes. We use the Card and Krueger (1995) demographic predictors to estimate the probability of being exposed (see the text for details). Column 6 shows the
results for the workers who have a high probability of being exposed to the minimum wage increase, column (7) for the middle-probability group, and column (8) for the low-probability
group. All specifications include wage bin-by-state and wage bin-by-period fixed effects. Regressions are weighted by state-quarter aggregated population of the demographic groups.
Robust standard errors in parentheses are clustered by state; significance levels are ∗ 0.10, ∗∗ 0.5, ∗∗∗ 0.01.
The first two rows report the change in number of missing jobs below the new minimum wage (b), and excess jobs above the new minimum wage (a) relative to the pretreatment
total employment. The third row, the percentage change in average wages in the affected bins, (%W), is calculated using equation (2) in Section 2.2. The fourth row, percentage
change in employment in the affected bins, is calculated by dividing change in employment by jobs below the new minimum wage ( a+b ). The fifth row, employment elasticity
Sample
Number of events
Number of observations
Number of workers in the sample
(1)
TABLE II
CONTINUED
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
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THE QUARTERLY JOURNAL OF ECONOMICS
24. We note that the teen estimates are unrelated to a focus on low-wage jobs,
since the benefits of focusing on employment changes around the minimum wage
is small for groups where most workers are low-wage ones. In Online Appendix
Table A.10 we show that our event study estimates are close to 0 for teens even
if we use overall teen employment. At the same time, the classic two-way fixed
effect specification with log minimum wage (TWFE-logMW) generates a sizable
negative estimate for teens and for overall employment as well. In Section IV
we discuss this discrepancy and argue that the difference between our approach
and TWFE-logMW are driven by how the two empirical models are affected by
employment shocks in the 1980s and early 1990s. Online Appendix Table G.7
shows that in the post-1992 period, there is little divergence in teen minimum wage
elasticities across standard specifications (including the TWFE-logMW); none of
the specifications suggest noticeable losses to teen employment, and the elasticities
are no more negative than −0.03.
25. We use the same predictors as in Card and Krueger (1995): all three-way
interactions of nonwhite, gender, and teen indicators; all three-way interactions
of nonwhite, gender, and age 20–25 indicators; an indicator for having less than
high school education; continuous highest grade completed variable; a third-order
polynomial in labor market experience; Hispanic ethnicity indicator; interactions
of the education and experience variables with gender. Cengiz (2018) shows the
predictions using this Card and Krueger model compare favorably with those from
more sophisticated machine learning–based methods.
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literature, though we note that it is not statistically significant
given a standard error of 0.134.24
In addition, we examine the effects on groups of workers with
differential probability of being exposed to the minimum wage
changes. To determine the likelihood of exposure, we construct a
prediction model analogous to Card and Krueger (1995). We use
observations from three years prior to the 138 events that also lie
outside any of the five-year post-treatment windows and estimate
a linear probability model of having a wage less than 125% of
the statutory minimum wage on a rich set of demographic predictors.25 We use the estimated model to obtain predicted probabilities of being exposed to minimum wage increases for all individuals in the sample regardless of their actual employment status.
We then use the predicted probabilities to sort individuals into
three groups: a “high-probability” group that contains individuals
in the top 10% of the predicted probability distribution; a “lowprobability” group that contains workers in the bottom 50% of the
predicted probabilities; and a middle group containing the rest.
As expected, the high-probability group shows a considerably larger bite (b = −9.4%) than the middle group (b =
−2.0%) and the low-probability group (b = −0.4%). At the same
time, the employment elasticities are very similar across the
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
1435
2. By Industrial Sectors. Much of the literature has focused
on specific sectors like restaurants, where the minimum wage is
particularly binding—therefore making it easier to detect a clear
effect on the sectoral wage. In contrast, by focusing on changes
at the bottom of the distribution, our approach can recover employment and wage responses even in industries where only a
small fraction of workers are directly affected by the minimum
wage increase. This allows us to provide a more comprehensive
assessment of the effect of the policy across a range of industries.
In Table III we report estimates for various sectors in the
economy. We assign workers to tradeable and nontradeable sectors
following Mian and Sufi (2014).27 The table shows that the bite of
26. In Online Appendix A we show that if we estimate the impact of the events
on the aggregate wage and employment outcomes for each of the three probability
groups, we can obtain a clear wage effect only for the high-probability group—
capturing only around 36% of all minimum wage workers. This highlights the
value of focusing at the bottom of the wage distribution which allows us to get an
overall estimate for all low-wage workers.
27. Mian and Sufi (2014) define “tradeable” industries as having either the
sum of imports and exports exceeding $10,000 per worker or $500 million total;
their “nontradeable” sector consists of a subset of restaurant and retail industries; “construction” consists of construction, real estate, or land development–
related industries. We use the list in Mian and Sufi (2014) of four-digit NAICS
industries and census industry crosswalks to categorize all the industries in the
CPS for 1992–2016. In our sample the shares of employment are 13%, 14%, 10%,
for tradeable, nontradeable, and construction, respectively. See more details in
Online Appendix E. Since consistent industrial classifications limit our sample to
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probability groups. It is worth mentioning that the most precise
estimate of the own-wage employment elasticity reported in this
article appears in Table II, column (6), where we look only locally
around the minimum wage and also focus on the high-probability
group: the confidence interval rejects any value smaller than
−0.251 and larger than 0.663. Such a confidence interval is
quite narrow and rejects many estimates in the literature—
highlighting the gains from combining the demographic profiling
approach of Card and Krueger with the approach based on lowwage jobs advanced herein. The employment elasticities for the
other groups are similar in magnitude, though less precise.26
Overall, these findings provide little evidence of heterogeneity in the employment effect by skill level; the lack of a reduction
in overall low-wage jobs does not appear to mask a shift in employment from low-skill to high-skill workers.
Jobs below new MW (b−1 )
% MW
Emp. elasticity w.r.t. affected wage
0.087
0.098
0.270
0.098
(0.007)
0.072∗∗∗
(0.011)
0.036
0.098
0.019
(0.059)
0.530
(1.311)
0.097
(0.086)
0.051
(0.163)
− 0.003
(0.002)
0.005
(0.006)
(4)
0.057
0.098
0.005
(0.026)
0.166
(0.763)
0.056∗∗∗
(0.013)
0.009
(0.044)
(0.003)
0.011∗∗∗
(0.002)
− 0.011∗∗∗
(5)
0.434
0.098
− 0.002
(0.117)
− 0.011
(0.542)
0.049∗∗∗
(0.012)
− 0.001
(0.026)
(0.015)
0.101∗∗∗
(0.015)
− 0.101∗∗∗
(6)
0.136
0.098
0.086
(0.111)
1.040
(1.058)
0.060∗∗∗
(0.021)
0.062
(0.080)
(0.003)
0.041∗∗∗
(0.010)
− 0.033∗∗∗
(7)
(8)
0.050
0.098
− 0.052
(0.074)
− 1.385
(2.956)
0.073
(0.078)
− 0.101
(0.145)
− 0.017∗∗
(0.008)
0.011
(0.009)
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0.050
0.098
0.060
(0.103)
0.387
(0.597)
− 0.056
(0.069)
− 1.910
(3.922)
Employment elasticity w.r.t. MW
% affected employment
0.007
(0.027)
0.140
(0.523)
0.056∗∗∗
(0.014)
0.022
(0.037)
0.058
(0.073)
− 0.111
(0.136)
0.058∗∗∗
(0.011)
0.008
(0.031)
Excess jobs above new MW (a)
% affected wages
(0.008)
0.011
(0.008)
(0.004)
0.020∗∗∗
(0.003)
Missing jobs below new MW (b)
− 0.066∗∗∗
− 0.016∗
− 0.019∗∗∗
(3)
(2)
(1)
TABLE III
IMPACT OF MINIMUM WAGES ON EMPLOYMENT AND WAGES BY SECTORS (1992–2016)
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THE QUARTERLY JOURNAL OF ECONOMICS
Overall
118
554,931
384,498
(3)
118
554,931
274,812
(4)
Tradeable Nontradeable Construction
118
554,931
358,086
(2)
Other
118
554,931
1,504,643
(5)
Restaurants
118
554,931
156,634
(6)
Retail
118
554,931
315,397
(7)
Manufacturing
118
554,931
349,749
(8)
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a+b , whereas the sixth row, employment elasticity with respect to the wage, reports
1
a+b . The line on the number of observations
to the minimum wage, is calculated as %MW
%W b̄−1
shows the number of quarter-bin cells used for estimation, while the number of workers refers to the underlying CPS sample used to calculate job counts in these cells.
b̄−1
Notes. The table reports the effects of a minimum wage increase by industries based on the event study analysis (see equation (1)) exploiting 138 state-level minimum wage
changes between 1992 and 2016. The table reports five-year averaged post-treatment estimates on missing jobs up to $4 below the new minimum wage, excess jobs at and up to $5
above it, employment, and wages for all sectors (column (1)), tradable sectors (column (2)), nontradeable sectors (column (3)), construction (column (4)), other sectors (column (5)),
restaurants (column (6)), retail (column (7)), and manufacturing industries (column (8)). Our classification of tradable, nontradeable, construction, and other sectors follows Mian
and Sufi (2014) (see Online Appendix D for the details). Regressions are weighted by state-quarter aggregated population. Robust standard errors in parentheses are clustered by
state; significance levels are ∗ 0.10, ∗∗ 0.05, ∗∗∗ 0.01.
The first two rows report the change in number of missing jobs below the new minimum wage (b), and excess jobs above the new minimum wage (a) relative to the pretreatment
total employment. The third row, the percentage change in average wages in the affected bins, (%W), is calculated using equation (2). The fourth row, percentage change in
employment in the affected bins, is calculated by dividing change in employment by jobs below the new minimum wage ( a+b ). The fifth row, employment elasticity with respect
Sector
Number of events
118
Number of observations
554,931
Number of workers in the sample 2,652,792
(1)
TABLE III
CONTINUED
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
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THE QUARTERLY JOURNAL OF ECONOMICS
3. By Pretreatment Employment Status. We consider the effect of the minimum wage separately on workers who were employed prior to the minimum wage increase (incumbent workers)
and for new entrants into the labor market. We partition our sample of wage earners into incumbent workers and new entrants
by exploiting the fact that the CPS interviews each respondent
twice, exactly one year apart.28 The partition limits our sample to
the 1992–2016 period, we first replicate our benchmark analysis using all industries for this restricted sample in Table III, column (1). The estimated employment
and wage effects on this restricted sample are similar to the full 1979–2016 sample.
28. All CPS respondents are interviewed for four months in the first interview
period, then rotated out of the survey for eight months, and then rotated back
into the survey for a final four months of interviews. In the fourth month of each
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the minimum wage varies a lot across industries. The minimum
wage is highly binding in the restaurant sector with a missing
jobs estimate of 10.1%, whereas it does not appear to be binding
in the construction sector. The minimum wage is more binding in
the nontradeable sector (6.6%) than in the tradeable sector (1.6%)
or in the manufacturing sector (1.7%).
The effect of the minimum wage on employment also varies
by sector. We find that the number of excess jobs at or above the
minimum wage is smaller than the missing jobs in the tradeable
sector, and so the employment effect is negative (−11.1%, std.
err. 13.6%), but not statistically significant. Similarly, the point
estimate in the manufacturing sector suggests that around 10.1%
(std. err. 14.5%) of the jobs directly affected by the minimum wage
are destroyed. The implied employment elasticity with respect to
own wage is quite large in magnitude in both sectors (−1.910 in
the tradeable sector and −1.385 in manufacturing), although the
estimates are imprecise and statistically insignificant.
At the same time, we find no indication for negative disemployment effects in the nontradeable, restaurant, and retail sectors where most minimum wage workers are employed in the
United States. The employment elasticity with respect to own
wage in the nontradeable sector is positive (0.387, std. err. 0.597),
which is in stark contrast to the tradeable sector, where we find
a large negative elasticity. Harasztosi and Lindner (forthcoming)
find similar sectoral patterns in Hungary and argue, using revenue data, that the larger job losses for tradeables reflect a more
elastic consumer demand in that sector.
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
1439
III.B. Wage Spillovers
So far we have focused on the employment effects of the
minimum wage. However, an equally important question is understanding the nature of the wage effects. In this section, we
quantify the direct effect of the minimum wage and the indirect
effect that comes from wage spillovers.
interview period (the “outgoing rotation group”), respondents are asked questions
about wages. Online Appendix E explains how we match workers across rotation
groups.
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the 1980–2016 time period covering 137 eligible minimum wageraising events and also restricts our time window to one year
around the minimum wage increase rather than the five years in
our baseline sample.
Figure IV shows the event study estimates for new entrants
(Panel A) and incumbents (Panel B) for each k-dollar wage bin
relative to the new minimum wage. For both subgroups, new minimum wages clearly bind, with significantly fewer jobs just below
and significant more at the new minimum. This highlights that
studies that restricts their sample to incumbent workers (e.g.,
Currie and Fallick 1996; Abowd et al. 2000; Clemens and Wither
2019) can only provide a partial characterization of the full effects of the minimum wage increase, since new entrants are also
affected by the policy.
For both groups the excess jobs closely match the missing
jobs (for incumbents a = 1.3% and b = −1.2% and for new
entrants a = 0.6% and b = −0.5%), so the net employment
changes are approximately 0. The green and blue dashed lines
(color version available online) show the running sums of employment changes up to the corresponding wage bin for each group.
The lines show that in both cases there is little change in uppertail employment. We note that if employers are replacing lowerskilled workers with higher-skilled ones, we should expect to see
some reduction in jobs for previously employed workers, perhaps
offset by high-skilled entrants; the lack of job loss for incumbents
provides additional evidence against such labor-labor substitution. The affected wage increase for incumbents (9.5%, std. err.
2.0%) is significantly larger than it is for new entrants (1.9%, std.
err. 1.3%) and some of these differences can be explained by the
lack of spillover effects for the new entrants. In the next section
we return to this issue.
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THE QUARTERLY JOURNAL OF ECONOMICS
(A) New entrants
FIGURE IV
Impact of Minimum Wages on the Wage Distribution by Pretreatment
Employment Status: New Entrants and Incumbents
The figure shows the main results for new entrants (Panel A) and for incumbents
(Panel B) from our event study analysis (see equation (1)) exploiting 138 state-level
minimum wage changes between 1979 and 2016. The blue bars in Panel A (color
version available online) show for each dollar bin the estimated change in the
number of new entrants in that bin one year post-treatment relative to the total
employment of the new entrants one year before the treatment. The green bars in
Panel B show the equivalent for incumbents. Incumbent workers were employed
a year prior to the minimum wage increase, whereas new entrants were not.
The error bars show the 95% confidence interval calculated using standard errors
that are clustered at the state level. The dashed green and blue lines show the
running sum of employment changes up to the wage bin they correspond to for
new entrants and incumbents, respectively. The figures highlight that the ripple
effect of the minimum wage mainly comes from incumbent workers.
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(B) Incumbents
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
1441
−1
(3)
%wno spillover =
k=−4
k (αk − α−1k)
wb−1
.
The total wage increase of affected workers, %w, in equation (2)
incorporates this direct effect and the add-on effect from wage
spillovers. Therefore, the difference between the two measures,
%w − %wno spillover , provides an estimate of the size of the wage
spillovers.
Note that our spillover estimates use the frequency distribution of wages, which contrasts with the earlier literature relying on the density of wages (see, e.g., Card and Krueger 1995;
DiNardo, Fortin, and Lemieux 1996; Lee 1999; Autor, Manning,
and Smith 2016). As a result, changes in employment—which
could create an artificial spillover effect when using the wage
density—do not affect our estimates.
We report our estimates of wage spillovers in Table IV, where
the columns show estimates of the total wage effect %w, the “no
spillover” wage effect %wno spillover , and the spillover share of the
%w−%wno spillover
total wage increase calculated as
. The first row
%w
shows the estimated effects for the entire workforce. Column (1)
repeats the estimated total wage effect from Table I, column (1),
which is 6.8% (std. err. 1.0%). Column (2) shows that in the absence of spillovers, wages would increase by 4.1% (std. err. 0.9%).
Column (3) shows that 39.7% (std. err. 11.9%) of the total wage
effect is caused by the ripple effect of the minimum wage.
In Table IV we also report estimates for several subgroups.
The share of spillovers in the total wage increase is relatively similar for several key demographic groups, such as those without a
high school degree (37.0%), teens (34.7%), those without a college
degree (40.2%), and women (35.9%). In most cases, the spillover
share is statistically significantly different from 0 at the 5% level.
One exception is black or Hispanic individuals, for whom the estimated share of wage spillover is much smaller at 17.9% (std.
err. 26.5%), which is less than half of the 39.7% (std. err. 11.9%)
spillover share for all workers. Although the difference is not
statistically significant, this finding nonetheless suggests that the
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We calculate the direct (or “no spillover”) wage increase by
moving each missing job under the new minimum wage exactly to
the new minimum wage:
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TABLE IV
THE SIZE OF THE WAGE SPILLOVERS
Spillover share of
wage increase
%w −%wNo spillover
%w
%w
%wNo spillover
Overall
0.068∗∗∗
(0.010)
0.041∗∗∗
(0.009)
0.397∗∗∗
(0.119)
Less than high school
0.077∗∗∗
(0.013)
0.081∗∗∗
(0.015)
0.073∗∗∗
(0.013)
0.070∗∗∗
(0.011)
0.045∗∗∗
(0.012)
0.048∗∗∗
(0.009)
0.053∗∗∗
(0.007)
0.043∗∗∗
(0.011)
0.045∗∗∗
(0.010)
0.037∗∗∗
(0.010)
0.370∗∗∗
(0.078)
0.347∗∗∗
(0.059)
0.402∗∗∗
(0.100)
0.359∗∗∗
(0.120)
0.179
(0.265)
0.058
(0.073)
0.056∗∗∗
(0.014)
0.065∗∗
(0.028)
0.043∗∗∗
(0.006)
− 0.114
(1.157)
0.237
(0.191)
0.095∗∗∗
(0.020)
0.019
(0.013)
0.055∗∗∗
(0.011)
0.023∗∗∗
(0.006)
0.422∗∗
(0.181)
− 0.178
(0.748)
Teen
High school or less
Women
Black or Hispanic
Tradeable
Nontradeable
Incumbent
New entrant
Notes. The table reports the effects of a minimum wage increase on wages based on the event study analysis
(see equation (1)) exploiting 138 state-level minimum wage changes between 1979 and 2016. The table reports
the percentage change in affected wages with (column (1)) and without (column (2)) taking spillovers into
account for all workers, workers without a high school degree, teens, individuals with high school or less
schooling, women, black or Hispanic workers, in tradeable industries, in nontradeable industries, those who
were employed one year before the minimum wage increase (incumbents); and those who did not have a job
one year before (new entrants). The first column is the estimated change in the affected wages calculated
according to equation (2), and the second column assumes no spillovers (see equation (3)). In the last column,
the spillover share of the wage effect is calculated by subtracting 1 from the ratio of the estimates in the
second to the first column. Robust standard errors in parentheses are clustered by state; significance levels
are ∗ 0.10, ∗∗ 0.05, ∗∗∗ 0.01.
wage gains at the bottom may be more muted for some disadvantaged groups.29
We also find a substantially smaller change in wages due
to spillovers in the tradeable sector, though the estimates here
are a bit imprecise. This highlights that wage effects are small
29. The smaller spillover for black/Hispanic workers is not due to sectoral
or incumbency composition, which are very similar to other workers (results not
reported).
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% Affected wage
EFFECT OF MINIMUM WAGES ON LOW-WAGE JOBS
1443
III.C. Event-Specific Estimates
So far, most of our evidence has come from averaging the
effects across all 138 events. In this section, we estimate treatment
30. In Online Appendix C we implement our approach using administrative
data from Washington. In that data we find similar spillover effects which provides
additional evidence that the spillovers are not primarily caused by CPS-specific
misreporting by survey respondents. In addition, as shown in Online Appendix
Table F.3, our wage estimates are similar using a deconvolved distribution that
purges the type of measurement error proposed in Autor, Manning, and Smith
(2016).
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in the tradeable sector. The combination of this evidence and the
disemployment effects suggest that there may be more unintended
consequences of minimum wages when the tradeable sector constitutes a more sizable share of the affected workforce.
We also find a stark difference in the spillover shares of wage
increases for incumbents versus new entrants. Incumbents receive a larger total wage increase (9.5%) than the overall workforce (6.8%), but the spillover share for incumbents and all
workers is relatively similar (42.2% and 39.7%, respectively). In
contrast, the spillover share for entrants is −17.8%, suggesting
that essentially all of the wage increase received by new entrants
is by the creation of jobs at or very close to the new minimum.
Larger spillovers for incumbents relative to entrants can also be
seen in Figure IV. Two points should be noted.
First, the stark differences in the size and scope of spillovers
for the incumbent and for the new entrants are inconsistent with
a simple measurement error process common to both groups. This
suggests that spillover effects found are likely to reflect real responses and not measurement error in CPS-based wages, a possibility that is raised by Autor, Manning, and Smith (2016).30
Second, because we find that essentially none of the wage
spillovers accrue to workers who were not employed prior to
the minimum wage increase, it is unlikely that our estimates of
spillovers primarily reflect an increase in the value of the outside
options or reservation wages of nonemployed workers (e.g., Flinn
2006). In contrast, the spillovers may reflect some “optimization
friction” that firms face when they set incumbent workers’ wages.
Kleven (2016) discusses a range of optimization frictions in the
context of bunching at kink points. Moreover, our results are also
consistent with Dube, Giuliano, and Leonard (2018) who argue
that firms are constrained by relative-pay norms inside the firm.
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THE QUARTERLY JOURNAL OF ECONOMICS
(4)
Ysjth =
4
4
τk
ατ kh Isjth
+ μsjh + ρ jth + sjth + usjth
τ =−3 k=−4
where j indicates the jth dollar bin relative to the minimum wage.
Then, Ysjth is the per capita number of jobs in state s time t, and jth
bin relative to the minimum wage in data set h. The calculation
of the event-specific change in excess jobs above (ah ), change in
missing jobs below (bh ), and employment change (eh = ah +
bh ) are similar to the ones described in Section II.B. sjth controls
for other primary, federal, and small events whose five-year posttreatment periods take place within the data set h. It takes the
value of 1 for all post-treatment periods of these events.31
Figure V, Panel (A) shows the nonparametric bin-scattered relationship between the event-by-event estimates on missing jobs
31. Online Appendix Figure D.1 reports event-specific estimates for excess
and missing jobs, and employment effects, along with (Ferman and Pinto, forthcoming) confidence intervals that are appropriate for a single treated unit and
heteroskedasticity. Although there is considerable heterogeneity in the bite of the
policy, the distribution of employment estimates is consistent with the sharp null
of zero effect everywhere: only 5.3% of estimates are statistically significant at
the 5% level. In addition, the stacked event-by-event estimates can be also used
to estimate the average effect of the minimum wage across events. In Online
Appendix Table D.1 we report estimates using that approach and show that estimates are very similar to our panel regression-based event study. This shows that
issues about negative weighting using staggered treatments (e.g., Abraham and
Sun 2018) are unlikely to be driving our results. Finally, the event-by-event estimates in Online Appendix Figure D.2 confirm that the lack of leading effects and
upper-tail employment changes hold event by event, and not just on average: only
5.4% of the events experience statistically significant upper-tail effects at the 5%
level, while 7.7% of the events experience statistically significant leading effects.
For additional details, see Online Appendix D.
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