WHO BENEFITS FROM A MINIMUM WAGE INCREASE?
JOHN W. LOPRESTI AND KEVIN J. MUMFORD*
The authors address the question of how a minimum wage increase
affects the wages of low-wage workers relative to the wage the
worker would have if there had been no minimum wage increase.
The authors’ method allows for the effect to depend not only on
the initial wage of the worker but also nonlinearly on the size of the
minimum wage increase. Results indicate that low-wage workers
who experience a small increase in the minimum wage tend to have
lower wage growth than if there had been no minimum wage
increase. A large increase to the minimum wage not only increases
the wages of those workers who previously earned less than the new
minimum wage but also spills over to workers with moderately
higher wages. Finally, the authors find little evidence of heterogeneity in the effect by age, gender, income, and race.
The minimum wage literature has primarily focused on evaluating the
employment effects of a minimum wage increase.1 In this article, we
address the far less studied question of documenting the wage effects of a
minimum wage increase. We focus our attention on estimating how the
wage effects of a minimum wage increase differ across the wage distribution
and by the size of the increase. Most studies assume that a minimum wage
increase causes those workers with an initial wage between the old and the
new minimum wage to have their wage bumped up to the new minimum.
Some studies allow for minimum wage spillovers to a predefined group of
workers with slightly higher wages.2 When benefits are calculated, however,
1
See Card (1992a, 1992b); Katz and Krueger (1992); Neumark and Wascher (1992, 1995); Card and
Krueger (1994, 1995); Spriggs and Klein (1994); Deere, Murphy, and Welch (1995); Currie and Fallick
(1996); Lang and Kahn (1998); and Baker, Benjamin, and Stanger (1999). Neumark and Wascher
(2007) provide a comprehensive review. 2
The observation that a minimum wage increase affects the wages of workers earning more than the
new minimum wage originated with Gramlich (1976) and has been confirmed in many subsequent
studies.
*JOHN W. LOPRESTI is Assistant Professor of Economics at the College of William and Mary. KEVIN J.
MUMFORD is Associate Professor of Economics at Purdue University. We thank David Hummels, Steve
Martin, Justin Tobias, Stephen Woodbury, and Chong Xiang for helpful discussion and comments. We
acknowledge financial support from the Upjohn Institute for Employment Research. Additional results
and copies of computer programs used to generate the results presented in the article are available from
the authors at [email protected] or [email protected]
KEYWORDs: wage effects, minimum wage
ILR Review, 69(5), October 2016, pp. 1171–1190
DOI: 10.1177/0019793916653595. The Author(s) 2016
Journal website: ilr.sagepub.com
Reprints and permissions: sagepub.com/journalsPermissions.nav
the implicit assumption is that wages for low-wage workers would have
remained constant had it not been for the minimum wage increase.
In contrast, we start with the assumption that low-wage workers would
have experienced wage changes in the absence of a minimum wage
increase. In our approach, the benefit of a minimum wage increase to a particular low-wage worker is the difference between the hourly wage after the
minimum wage increase and the hourly wage the worker would have experienced had there been no increase. It is possible for this difference to be
negative for some workers if the wage increase they would have experienced
is larger than what they actually experienced with a small minimum wage
increase. This approach is most similar to that of Neumark, Schweitzer, and
Wascher (2004) in that we estimate the effect of a minimum wage increase
on the wages of current low-wage workers, allowing the effect to differ for
workers with different initial wage rates. Our analysis is different from that
of Neumark et al. (2004), however, in that we also allow for the effect to
depend on the size of the minimum wage increase without imposing linearity. Allowing for this additional flexibility in the estimation allows us to better understand how a minimum wage increase affects wages.
An alternative approach would be to analyze how a minimum wage
increase affects the wage distribution, as in DiNardo, Fortin, and Lemieux
(1996). This approach, however, is better suited to understanding how the
minimum wage affects income inequality and is not applicable to analyzing
how a minimum wage increase affects the wages of current low-wage workers. Because we estimate the effect for current workers, we can subsequently
analyze how the effect differs by the magnitude of the minimum wage
increase, by the initial wage, and for various demographic groups. For
example, it is well documented that workers earning the minimum wage
are predominantly women, adults (rather than teenagers), and members
of low-income households (bottom 40% of the household income
distribution). This does not necessarily imply, however, that these groups
experience larger wage gains from a minimum wage increase than other
groups do.
Our approach does not address employment effects, nor does it address
the wage effects for new entrants into low-wage positions who were not
working before the minimum wage increase, some of whom benefit from
the law change. These limitations are notable, but our question of how a
minimum wage increase affects the wages of current low-wage workers is
important to crafting minimum wage policy and has not been fully
answered. Our analysis provides a more complete picture of the wage effects
than has been previously available.
Our analysis shows that the wage impact of a minimum wage increase
depends on the size of the increase as well as the characteristics of the individual. Surprisingly, we find that a small increase may actually cause lowwage workers to experience less wage growth than they otherwise would
have without the increase. We do a great deal of sensitivity analysis and show
1172 ILR REVIEW
that this finding is quite robust. We hypothesize that employers may use a
minimum wage increase as a focal point in setting wages, and thus a small
minimum wage increase may limit wage increases.
Data
We use the public-use Current Population Survey (CPS) outgoing rotation
group data between August 2005 and June 2008. CPS respondent households are interviewed for four consecutive months, followed by an eightmonth hiatus, followed by a final four consecutive months of interviews. A
household initially interviewed in January 2006 would thus be interviewed
through April of that year, as well as January through April of 2007. We
include only the fourth and eighth interview months—outgoing months
spaced one year apart—which contain more detailed employment and wage
data. Employing the methodology of Madrian and Lefgren (1999), we
match respondent interviews year to year on the basis of state, month interviewed, household identifiers, sex, race, and age.
Because of both the mobility of respondents between interview years and
reporting error, we are unable to match everyone interviewed in the fourth
interview month to a corresponding interview one year later. We match
72.8% of individuals in the CPS from August 2005 to June 2007 across sample years. This match rate is similar to that found in other time periods.
The less than perfect match rate raises the concern that our sample will not
be representative of the population if the observed attrition is not random.3
Specifically, if attrition is correlated with either wage growth or the size of
the minimum wage change, our results will be biased. We do not observe
wage growth for those individuals who are not matched, so we cannot
directly address this concern. However, we find no significant correlation
between state-level match rates and state average wage growth, state percapita GDP growth rate, or the magnitude of the state minimum wage
change in our time period.4 We view this as evidence against the concern
that the minimum wage increase itself may reduce the number of individuals we observe in the second period.
Our sample includes individuals age 16 and older who are employed at
the time of both interviews. To focus on workers in sectors covered by the
minimum wage, we impose a threshold $ 0.10 below the minimum wage
and exclude individuals reporting a wage below this threshold at the time
3
Our match rate is higher for older workers and those with higher wages. It is lowest for individuals
aged 20 to 24. These young workers are most likely to move from the household, and because the CPS
follows households rather than individuals, we are unable to match those who move between the fourth
and eighth interviews. It is thus not surprising that young, mobile workers are the least likely to be
matched. 4
State-level match rates for workers aged 16 to 65 range from 65.6% in Nevada to 80.4% in West
Virginia. There is a slightly negative, though not statistically significant, correlation between average wage
growth and the match rate. There is a slightly positive, though not statistically significant, correlation
between the magnitude of the minimum wage increase and the match rate.
WHO BENEFITS FROM A MINIMUM WAGE INCREASE? 1173
of either interview.5 We also exclude individuals reporting wage growth
greater than 1,000%. Finally, self-employed workers and those in the agricultural sector have been removed. This leaves us with a final sample of
101,299 observations. We report variable means from the matched full sample in column (1) of Table 1.
The period 2005 to 2008 is notable for a large number of U.S. state-level
minimum wage changes in addition to the federal minimum wage increase
of 2007. From 2005 to 2008, 28 states and the District of Columbia increased
the minimum wage. An additional 20 states were affected by the federal
increase.6 At the level of the individual observation, we define the minimum
wage increase as the change in the applicable minimum wage that occurs in
the year between interviews. For example, the Arkansas minimum wage rose
from $5.15 to $6.25 on October 1, 2006, and there was no minimum wage
change in 2007. An individual living in Arkansas whose first outgoing interview occurred in September 2006 is thus defined as having experienced a
$1.10 minimum wage increase, while an individual first interviewed in
October 2006 is defined as experiencing no increase.
Slightly fewer than 64% of the respondents in our sample experienced a
minimum wage increase between interviews. This includes individuals in 48
states and the District of Columbia. The remaining 36% of individuals who
did not experience a minimum wage increase between interviews span 44
states and the District of Columbia. These minimum wage changes differed
not only in their timing and location but also in their magnitude. Changes
during this period were as small as $ 0.10 and as large as $2.10.7 This dispersion in magnitude across states and time is the primary identifying variation
in our analysis.
This leads to an important question: Why did some states raise their minimum wage by only a small magnitude while others enacted a large increase?
There is a great deal of randomness inherent in the political process, and
this may be the main source of variation in the timing of increases to the
minimum wage. For our estimates to be unbiased, however, the size of the
minimum wage increases must also be ‘‘as good as’’ randomly assigned, conditional on the controls. We argue that this is the case. Some initial
5
For individuals who do not report an hourly wage, we use the reported weekly earnings divided by
the usual hours of work per week. Though this imputation likely introduces some measurement error
and potentially causes us to drop some individuals from the sample who reported either too high or too
low usual hours of work per week, we do not believe the imputation causes bias in the results.
Importantly, there is no evidence that the need to impute wages is in any way correlated with the minimum wage change. In our full sample, the correlation between the minimum wage change in a state-year
and the fraction of workers whose hourly wage is imputed is statistically indistinguishable from zero
(p value = 0.853).
6
Alaska, which had a minimum wage of $7.15 throughout the entire sample, and Minnesota, which
had a minimum wage of $6.15 throughout the entire sample, were not affected by a minimum wage
change in any year. 7
Montana increased its minimum wage from $6.15 to $6.25 on January 1, 2008. Iowa increased its minimum wage from $5.15 to $6.20 on April 1, 2007, and again to $7.25 on January 1, 2008, so individuals
first interviewed between January and March of 2007 experienced an increase of $2.10.
1174 ILR REVIEW
supportive evidence is that those states that enacted a small increase in the
minimum wage come from all regions of the country, with substantial variation in the timing.8 In addition, as reported in columns (1) through (6) of
Table 1, no clear pattern emerges in the characteristics of state-year observations across the different groups defined by size of the minimum wage
increase. Furthermore, we find no statistically significant correlation
between the size of the minimum wage increase and the prior year’s state
Table 1. Summary Statistics
Minimum wage change
(1) (2) (3) (4) (5) (6)
Variables Full sample No change 5% 5%–10% 10%–20% . 20%
Observations 101,299 36,837 16,406 8,244 29,453 10,359
Mean wage 19.33 18.94 19.49 22.17 19.41 18.14
Percentage employed 100 100 100 100 100 100
Sex
Male 51.83 51.67 51.18 51.84 52.21 52.13
Female 48.17 48.33 48.82 48.16 47.79 47.87
Race
White 83.58 83.27 86.89 77.32 81.67 90.08
Black 10.2 10.95 8.18 8.84 11.76 6.87
Hispanic 11.85 10.55 11.1 18.58 13.58 7.13
Education
Less than high school 8.76 9.15 7.23 8.89 9.55 7.17
High school only 47.62 47.54 48.18 41.58 47.44 51.94
Associate’s degree or more 43.61 43.31 44.59 49.53 43.01 40.88
Age
16–19 2.68 2.87 2.23 2.46 2.59 3.16
20–24 6.73 6.73 6.23 6.97 6.76 7.15
25–34 20.05 20.14 19.13 19.91 20.47 19.95
35–44 26.16 26.13 26.36 27.46 26.2 24.94
45–54 27.43 27.54 28.07 26.7 27.13 27.62
65+ 16.95 16.6 17.06 16.6 16.85 17.18
Family income
Low 6.88 7.49 6.71 5.08 6.87 6.45
Low–mid 18.92 18.86 19.07 15.66 19.19 20.35
Mid 20.78 21.23 21.46 18.63 20.15 21.75
Mid–high 30.85 30.71 31.82 28.94 30.38 32.68
High 22.57 21.71 20.95 31.7 23.42 18.77
Notes: The following individuals have been removed: those earning a wage more than $ 0.10 below the
minimum wage, those earning an hourly wage greater than $100, those experiencing a wage change
greater than 1,000%, those listed as self-employed and agricultural workers, and individuals younger
than 16. Low-income families are defined as those with an annual family income of less than $20,000.
Low–mid income includes families earning between $20,000 and $40,000 annually. Mid includes
families earning between $40,000 and $60,000 annually. Mid-high includes families earning between
$60,000 and $100,000 annually, and high includes families earning at least $100,000 annually.
Individuals are weighted by sample weights included in the CPS.
8
In our 2005 to 2008 time period, Arizona, Colorado, Connecticut, Florida, Maine, Michigan,
Missouri, Montana, Nevada, Oregon, Ohio, Rhode Island, Vermont, and Washington all enacted a minimum wage increase that set the new minimum wage no more than 5% higher than the old.
WHO BENEFITS FROM A MINIMUM WAGE INCREASE? 1175
GDP growth rate, unemployment rate, union membership rate, price level,
or poverty rate. We have identified no factors that appear to drive the size
of minimum wage increases, and we view this as support for our assertion.
Before proceeding to the empirical analysis, we pause to note an important aspect of the data. We observe considerable upward wage mobility
among low-wage workers even in the absence of a minimum wage law
change. Table 2, which examines the wage mobility of workers who did not
experience a minimum wage change between interviews, illustrates this
point. Workers are divided into five categories based on their wage relative
to the applicable minimum wage at the time of the first interview. We
report the movement of workers among these groups between their first
and second interviews. Specifically, the table reports the percentage of
workers in a particular group at time t that belong to a given group at time
t+ 1. As shown in the table, most low-wage workers experience considerable wage growth in our sample, even in the absence of a minimum wage
increase. Approximately one-quarter of the workers earning no more than
10% above the minimum wage at the time of their first interview still earn
within 10% of that minimum a year later. Furthermore, more than half of
these individuals earn more than 25% above the minimum wage at the time
of their second interview. For an individual in a state with a minimum wage
of $5.15, this implies that only 25% would still have a wage of no more than
$5.65, and more than half would have a wage greater than $6.40.9
We observe similar patterns higher in the wage distribution. Of those
individuals earning between 25 and 50% above the minimum wage at the
time of their first interview, over 60% earn more than 50% above the minimum wage the following year, with more than 29% earning more than double the minimum. These simple averages reveal that minimum wage
Table 2. Wage Mobility
Second interview wage
First interview wage
Minimum
wage*1.1
MW *1.1–
MW *1.25
MW *1.25–
MW *1.5
MW *1.5–
MW *2 MW *2 \
Minimum wage*1.1 25.01 21.05 19.76 19.01 15.17
MW *1.1–MW *1.25 7.08 19.84 26.55 23.40 23.13
MW *1.25–MW *1.5 3.18 6.47 27.71 32.99 29.65
MW *1.5–MW *2 1.12 2.94 7.12 43.23 45.59
MW *2 \ 0.42 0.82 2.17 7.74 88.85
Notes: The above table includes 36,837 individuals from 44 states and the District of Columbia who did
not experience a minimum wage increase between interviews. Percentages represent the percentage of
a given wage bin at the time of the first interview that belong to a given bin at the time of the second
interview, so that percentages sum horizontally to 100. Individuals are weighted by sample weights
included in the CPS.
9
The federal minimum wage prior to the 2007 increase, $5.15, is the applicable minimum wage for
more than half the individuals who do not experience a minimum wage increase in our sample.
1176 ILR REVIEW
changes are occurring not in a static environment but rather in one in
which there is already a large degree of upward mobility among low-wage
earners.
Estimation
The large number and staggered timing of state-level minimum wage
changes creates a rich environment in which to analyze the effects of minimum wage law changes. We abstract from any employment effects and
focus solely on the wage effects of a minimum wage change conditional on
continued employment. We hypothesize that such effects may differ along
two dimensions. First, following Neumark et al. (2004), we allow the effect
of a minimum wage increase to vary throughout the wage distribution, with
individuals at or near the initial minimum wage level experiencing wage
changes that are different from those experienced by individuals at the
upper end of the wage distribution. The wage effect at or near the initial
minimum wage is primarily mechanical, while those effects higher in the
wage distribution are often called minimum wage spillovers. Second, we
examine effects that vary according to the size of the change in the minimum wage itself.
Nearly 64% of the individuals in our sample experienced a minimum
wage increase, but there is substantial heterogeneity in the size of this
increase. More than 16% experienced a small minimum wage increase of
less than 5% of the initial minimum wage, while more than one-tenth experienced a very large increase of more than 20% of the initial minimum
wage. Figure 1 shows this heterogeneity in a histogram of the size of the
minimum wage increases experienced by the individuals in our sample.
In order for our model to allow for different effects by the initial-wage
group and by the size of the minimum wage increase, we employ the following specification:
%DWimys =b0 + X
7
j =1
bj1(WageGroupimys = j)+ X
5
k =1
gk1(DMinWagemys = k)+
X
7
j =1
X
5
k =1
djk1(WageGroupimys = j) 3 1(DMinWagemys = k)+
hXimys + ls + mm + vy + eimys:
ð1Þ
The dependent variable %DWimys is defined as the fractional wage change
(the percentage wage change divided by 100) between interviews experienced by individual i first interviewed in month m of year y in state s.
The variable 1(WageGroupimys =j) is an indicator variable equal to 1 if
individual i has a wage in the range of wage group j at the time of the first
interview. It is included to account for differences in the rate of wage
growth across the wage distribution. We define seven wage groups, with the
WHO BENEFITS FROM A MINIMUM WAGE INCREASE? 1177
first three groups corresponding to initial hourly wages less than 10% above
the minimum wage at the time of the first interview, between 10 and 20%
above the minimum wage, and between 20 and 30% above the minimum
wage, respectively. The fourth group corresponds to an initial hourly wage
at least 30% above the minimum wage but less than $11 (approximately the
25th percentile of the wage distribution). The final three wage groups
include initial hourly wages within approximately the second, third, and
fourth quartiles of the wage distribution at the time of their first interview.
The wage ranges for the seven wage groups, along with the number of individuals with an initial wage within each wage group, are given in Table 3.
Similarly, 1(DMinWagemys = k) is an indicator variable equal to 1 if the
minimum wage increase in state s in month m of year y falls within
minimum-wage-change group k, where the groups are defined as in
Table 4. More than one-third of the individuals in our sample are included
in the first minimum-wage-change group, indicating no minimum wage
change. The remainder of the sample is divided between groups experiencing a minimum wage change of less than 5%, between 5% and 10%,
between 10% and 20%, and greater than 20%. For those in the sample who
experienced a minimum wage increase, about 45% experienced a minimum wage change of between 10% and 20%, which includes the federal
minimum wage change of approximately 13.6%. Table 4 also indicates the
number of states that experienced a minimum wage change within each
Figure 1. Percentage Change in the Minimum Wage
Notes: The above figure depicts the percentage change in minimum wage laws affecting 64,462 individuals in 48 states and the District of Columbia who experienced a minimum wage change between interviews. The spike at 13.6 is the 2007 federal minimum wage increase.
1178 ILR REVIEW
bin. Note that within our sample period the same state may have experienced both a year with a minimum wage change and a year without a minimum wage change.
To allow for differential effects of a minimum wage increase throughout
the wage distribution, we include the interaction of these two indicator variables. With the no-change group excluded, this leaves 28 djkparameters indicating the effect of an increase in the minimum wage of a given size
(indicated by group k) for initial-wage group j relative to the baseline initialwage groups that experienced no minimum wage change. Not only does
this allow for a differential effect of a minimum wage increase by initial-wage
group, as in Neumark et al. (2004), it also allows for a nonlinear response to
an increase in the minimum wage that differs by the magnitude of the
change. This allows for the possibility not only that minimum wage changes
affect low- and high-wage individuals differently but also that the difference
between the low- and high-wage responses depends on the magnitude of the
minimum wage increase. The flexibility of this model allows for a more complete understanding of the wage effects of a minimum wage increase. The
model also includes a vector of controls, Xisy, including gender, race, ethnicity, education level, family income, and a quadratic term in age.
A primary concern is that state-level minimum wage changes might occur
in response to changes in state-level economic conditions. Our results could
be biased if states with a low rate of wage growth are more (or less) inclined
to increase the minimum wage. In an attempt to control for this, we include
Table 3. Minimum Wage Groups
Wage group Observations
Wage Minimum wage*1.1 2,346
MW*1.1 \ Wage MW*1.2 2,388
MW*1.2 \ Wage MW*1.3 2,089
MW*1.3 \ Wage $11 19,431
$11 \ Wage $16 25,303
$16 \ Wage $24 24,641
$24 \ Wage 25,101
Notes: The above table includes 101,299 observations. The final three rows correspond approximately to
the upper three quartiles of the wage distribution.
Table 4. Minimum Wage Changes
Wage change Observations States
No minimum wage law change 36,837 45
0 \ Minimum wage law change 5% 16,406 14
5% \ Minimum wage law change 10% 8,244 8
10% \ Minimum wage law change 20% 29,453 33
20% \ Minimum wage law change 10,359 11
WHO BENEFITS FROM A MINIMUM WAGE INCREASE? 1179
several variables measuring state-level economic conditions, including the
state poverty rate, the percentage of workers earning a wage that is below
the federal minimum wage, the percentage of workers in the state who
belong to a union, the growth rate in state per capita GDP in the year prior
to the individuals’ first interview, and state price level.10 To control for the
possibility that governments are able to respond within years to changes in
economic conditions, we have included the monthly state unemployment
rate. Finally, to control for broader macroeconomic and geographical
trends as well as seasonality, we include month, year, and state fixed effects.
Results
We report the estimated impact of a minimum wage change of size k for
wage group j, which is given by ^gk +^djk from Equation (1), in Table 5 along
with corresponding standard errors. For ease of exposition, we report only
this estimated effect and corresponding standard errors; complete tables
with all suppressed covariates are available upon request. The columns of
Table 5. Ordinary Least Squares
Minimum wage change (%)
Wage group 5 5–10 10–20 . 20 Observations
Wage Minimum wage*1.1 20.108** 0.036 0.080 0.521*** 2,346
(0.050) (0.051) (0.053) (0.127)
MW*1.1 \ Wage MW*1.2 20.076 20.064 0.06 0.166* 2,388
(0.054) (0.045) (0.049) (0.087)
MW*1.2 \ Wage MW*1.3 20.219*** 20.006 20.033 0.085 2,089
(0.038) (0.083) (0.049) (0.066)
MW*1.3 \ Wage $11 20.056** 0.002 0.024** 0.028 19,431
(0.025) (0.015) (0.012) (0.019)
$11 \ Wage $16 0.012 0.036*** 0.008 20.014 25,303
(0.014) (0.013) (0.012) (0.011)
$16 \ Wage $24 0.008 0.031 0.002 0.015 24,641
(0.013) (0.024) (0.007) (0.011)
$24 \ Wage 20.0004 20.020 20.007 0.008 25,101
(0.018) (0.013) (0.008) (0.013)
Observations 16,406 8,244 29,453 10,359
Notes: The above table reports results from a single ordinary least squares regression that includes all
101,299 observations. Additional covariates not reported above include race, ethnicity, gender,
education level, household income, age and age squared, the state monthly unemployment rate, the
lagged growth in state per capita GDP, the annual state union membership rate, the annual state
poverty rate, the percentage of workers in the state earning below the federal minimum wage, and the
state price level. Fixed effects are included for the state of residence and the month and year of the first
interview. The final three rows correspond approximately to the upper three quartiles of the wage
distribution. Standard errors are clustered at the state level.
Significance levels: *** 1%; **5%; * 10%.
10The annual state price level was calculated following Aten and Figueroa (2014) using the Bureau of
Economic Analysis ‘‘regional price parities’’ in combination with the consumer price index.
1180 ILR REVIEW
Table 5 do not indicate separate specifications, as is common in the literature; the coefficient estimates are from a single regression presented in
matrix form. Each reported coefficient estimate represents the effect of a
given minimum wage change for individuals within a given wage group
relative to individuals within the same wage group who experienced no increase in the
minimum wage. Thus, an individual initially earning within 10% of the minimum wage who experienced an increase in the minimum wage of less than
5% saw her wage increase by 10.8% less than an individual who saw no minimum wage increase. An individual in the same wage group who experienced a
minimum wage increase of greater than 20% (up to 41% in our sample) experienced 50% greater wage growth than did an individual experiencing no minimum wage law change.11
The results are striking. Within the first quartile of the wage distribution,
individuals experiencing minimum wage increases of less than 5% have
lower wage growth than similar individuals who experience no change in the
minimum wage law, with the magnitude of the estimated effect ranging
from 25.6 to 221.9%. Moderate minimum wage changes of 5 to 20% lead
to small, often statistically insignificant wage effects. It is only for minimum
wage increases in excess of 20% that we observe strong positive wage effects
of a minimum wage increase, with these effects concentrated among workers with an initial wage no more than 10% above the minimum wage. Of
the individuals experiencing a minimum wage increase in our sample,
nearly 25% experienced an increase of less than 5%. The possibility that
such changes might yield lower wage growth for low-wage individuals, even
ignoring potential disemployment effects, is surprising.
Though we have limited our sample to individuals whose wage increases
by less than a factor of 10, more than 6% of the individuals in the remaining sample report at least a doubling of their hourly wage between interviews. Furthermore, nearly 5% reported a decline in hourly wages of more
than one-half. It is unlikely that such outcomes are driven primarily by
changes in minimum wage laws. In an effort to mitigate the effect of such
extreme wage changes on our estimates, we repeat the above specification
in a median regression framework as proposed by Koenker and Bassett
(1978). The median regression estimates of the djk parameters from
Equation (1) are the effects of the minimum wage increase at the median
percentage wage change rather than on average. Skewness in the conditional percentage wage change distribution causes the OLS results to be different from the median regression results. To the extent that the results
differ, we prefer the median regression results as they ignore extreme wage
changes.
11An effect this large may not be plausible. Note, however, that the 95% confidence interval ranges
from 26 to 77%. Right skewness in the wage change distribution may be driving these OLS parameter
estimates up.
WHO BENEFITS FROM A MINIMUM WAGE INCREASE? 1181
Table 6 reports results for the median regression specification. Again, the
parameter estimates are from a single median regression; the columns do
not indicate separate regression specifications. The median regression point
estimates are generally smaller than those from the OLS specification, but
the qualitative results are similar. Individuals earning an initial hourly wage
in the bottom quarter of the wage distribution (below $11) experience
lower wage growth following a minimum wage increase of less than 5% than
do similar individuals experiencing no minimum wage change. The magnitude of this effect varies from 22.8 to 29.3%, with results significant at the
5% level for all wage levels except those within 10% of the minimum wage.
Individuals initially earning a wage within 20% of the minimum wage
experience increased wage growth after a minimum wage increase of 10%
or larger, while individuals with an initial wage no more than 30% larger
than the minimum wage experience increased wage growth for minimum
wage increases of 20% or more. The wage effects of a minimum wage
increase of any magnitude disappear for individuals earning an initial wage
in the upper half of the wage distribution (wages above $16).
Thus the story is broadly consistent. Small increases in the minimum
wage have negative effects on wage growth for low-wage individuals. Larger
increases have positive effects on low-wage individuals, with the effects being
Table 6. Median Regression
Minimum wage change (%)
Wage group 5 5–10 10–20 . 20 Observations
Wage Minimum wage*1.1 20.036 0.055 0.081*** 0.286*** 2,346
(0.032) (0.079) (0.029) (0.103)
MW*1.1 \ Wage MW*1.2 20.075** 0.003 0.046** 0.131*** 2,388
(0.032) (0.048) (0.022) (0.029)
MW*1.2 \ Wage MW*1.3 20.093*** 0.070** 20.001 0.107** 2,089
(0.027) (0.030) (0.020) (0.053)
MW*1.3 \ Wage $11 20.028** 20.0080 0.003 0.018* 19,431
(0.011) (0.013) (0.006) (0.010)
$11 \ Wage $16 0.000 0.006 0.001 20.010** 25,303
(0.007) (0.007) (0.005) (0.005)
$16 \ Wage $24 0.009 0.008 20.001 0.002 24,641
(0.007) (0.009) (0.004) (0.007)
$24 \ Wage 0.009 20.009 20.004 0.008 25,101
(0.014) (0.011) (0.008) (0.010)
Observations 16,406 8,244 29,453 10,359
Notes: The above table reports results from a single median regression that includes all 101,299
observations. Additional covariates not reported above include race, ethnicity, gender, education level,
household income, age and age squared, the state monthly unemployment rate, the lagged growth in
state per capita GDP, the annual state union membership rate, the annual state poverty rate, the
percentage of workers in the state earning below the federal minimum wage, and the state price level.
Fixed effects are included for the state of residence and the month and year of the first interview. The
final three rows correspond approximately to the upper three quartiles of the wage distribution.
Standard errors are clustered at the state level.
Significance levels: *** 1%; **5%; * 10%.
1182 ILR REVIEW
felt most strongly by those at or very near the initial minimum wage level.
Minimum wage increases have little to no effect on individuals in the upper
three quartiles of the wage distribution. These results suggest that small
minimum wage increases dampen wage growth for those at the bottom of
the wage distribution. The median low-wage worker experiences higher
wage growth without a minimum wage increase than with a small increase.
Are the estimated effects for low-wage workers experiencing a small minimum wage increase reasonable? The results suggest that wages for low-wage
workers in states with a minimum wage change of less than 5% would have
grown by 2.8 to 9.3 percentage points more had there been no increase in
the minimum wage. The median wage growth for low-wage workers in stateyear combinations with no minimum wage increase is about 20%, so estimates suggesting that a small increase in the minimum wage reduces
expected wage growth by 5 or even 10% are plausible.
One possible explanation for this finding is that the minimum wage
increase acts as a focal point for employers in determining wages. When the
increase is small, employers react by increasing low-wage workers’ wages
only by the required amount. Without a minimum wage increase, however,
there is no low-wage-growth focal point, and the resulting wage growth is
higher for low-wage workers who experience no minimum wage increase
than for those who experience a small increase.
This focal-point explanation for this finding is consistent with a model
proposed by Shelkova (2008) in which low-wage employers tacitly collude in
setting wages. In the model, there is no wage bargaining; employers post a
wage and then wait for vacancies to fill. This creates an incentive for
employers to coordinate on a wage below the marginal product of labor. In
an infinitely repeated game, the equilibrium wage can be anywhere between
the wage that a monopsonist would set and the marginal product of labor
(the competitive equilibrium). The minimum wage may be a focal point
that makes it easier to sustain coordination, as in Schelling (1960).
For workers with a wage above the minimum wage, the change in the
minimum wage could act as a focal point in determining raises. Our results
show that the strongest estimated negative wage effects are for those with
an initial wage between 10% and 30% above the minimum. These individuals would not directly benefit from a small minimum wage increase and
therefore may experience only the low-wage-growth focal point effect from
the increase.
Robustness Tests
Another explanation for this result is that it is simply caused by some
omitted variable bias. It could be that states that increase the minimum
wage by a small amount every year happen to have lower wage growth for
unrelated reasons. Perhaps states that never increase the minimum wage in
our sample period happen to have higher wage growth for unrelated
WHO BENEFITS FROM A MINIMUM WAGE INCREASE? 1183
reasons. As a robustness test, we repeat the median regression specified
above, excluding states that raise the minimum wage each year in our sample, as well as those that did not change the minimum wage at all.12 This
reduces our sample to 85,624 observations, with results reported in Table 7.
The point estimates in Table 7 for low-wage individuals experiencing small
minimum wage increases remain negative, and the level of statistical significance declines only slightly. It seems clear that our main finding is not driven by these ‘‘constant’’ states.
One might worry that even with the inclusion of state fixed effects and
state-year-level controls, there may be omitted variables related to state-level
economic conditions that simultaneously affect percentage wage growth
and the likelihood of a minimum wage change. If these unmeasured stateyear economic conditions are correlated with the state minimum wage
changes, this may bias our results. To control for such potential omitted
variables, we repeat the specification from Table 6 and include state-by-year
fixed effects. Note that with this inclusion, identification no longer comes
12These include Alaska, Florida, Maine, Minnesota, Oregon, Vermont, Washington, and West Virginia.
Table 7. Median Regression with Constant States Removed
Minimum wage change (%)
Wage group 5 5–10 10–20 . 20 Observations
Wage Minimum wage*1.1 20.043 0.038 0.070** 0.277*** 1,834
(0.041) (0.060) (0.029) (0.075)
MW*1.1 \ Wage MW*1.2 20.057** 20.016 0.037 0.119*** 1,893
(0.025) (0.082) (0.023) (0.032)
MW*1.2 \ Wage MW*1.3 20.095** 0.068* 20.0040 0.105* 1,636
(0.038) (0.039) (0.025) (0.058)
MW*1.3 \ Wage $11 20.048*** 20.011 20.0002 0.013 17,182
(0.014) (0.009) (0.006) (0.009)
$11 \ Wage $16 20.002 0.006 20.001 20.013** 21,227
(0.007) (0.007) (0.005) (0.005)
$16 \ Wage $24 0.007 0.013 20.002 0.002 20,578
(0.008) (0.009) (0.004) (0.008)
$24 \ Wage 0.012 20.004 0.003 0.014* 21,274
(0.011) (0.012) (0.006) (0.009)
Observations 6,485 7,825 28,261 10,359
Notes: The above table reports results from a median regression that includes 85,624 observations.
Observations from states with a minimum wage change that falls into the same change bin in each year,
and observations from states that never change the minimum wage have been removed. Additional
covariates not reported above include race, ethnicity, gender, education level, household income, age
and age squared, the state monthly unemployment rate, the lagged growth in state per capita GDP, the
annual state union membership rate, the annual state poverty rate, the percentage of workers in the
state earning below the federal minimum wage, and the state price level. Fixed effects are included for
the state of residence and the month and year of the first interview. The final three rows correspond
approximately to the upper three quartiles of the wage distribution. Standard errors are clustered at
the state level.
Significance levels: *** 1%; **5%; * 10%.
1184 ILR REVIEW
from variation in minimum wage changes that occur on January 1, as such
variation will be collinear with the state-by-year effects. However, a large
number of minimum wage changes—most notably the federal minimum
wage change in 2007—took place during, as opposed to at the beginning
of, the calendar year. Results for this specification are reported in Table 8.
As before, the broad story remains unchanged.
The specifications described thus far have not allowed covariates other
than those pertaining to the magnitude of the minimum wage change to
vary throughout the wage distribution. For instance, the effect of a bachelor’s degree on wage growth is constrained to be the same for an individual
earning a wage near the minimum wage as for an individual earning many
times more than this. This is perhaps a set of overly strict restrictions on the
parameters. Thus, Table 9 reports results for a specification in which the
covariates for race, ethnicity, education, gender, and age, as well as the
state-level economic controls, are each allowed to vary by wage group. This
additional flexibility again leaves the qualitative results largely unchanged.
Finally, from a policy perspective, these results are of most relevance if
they hold for individuals for whom low-wage jobs represent their primary
source of income. We thus repeat the specification from Table 6, including
only workers aged 23 to 65. Results, reported in Table 10, reveal a similar,
albeit slightly noisier pattern. The estimated coefficients for minimum wage
increases of less than 5% are negative for all wage groups in the first
Table 8. Median Regression, State-Year Fixed Effects
Minimum wage change (%)
Wage group 5 5–10 10–20 . 20 Observations
Wage Minimum wage*1.1 20.060* 20.005 0.072** 0.277*** 2,346
(0.031) (0.087) (0.029) (0.106)
MW*1.1 \ Wage MW*1.2 20.093*** 20.059 0.037 0.121*** 2,388
(0.031) (0.042) (0.026) (0.031)
MW*1.2 \ Wage MW*1.3 20.117*** 0.012 20.0140 0.094* 2,089
(0.024) (0.032) (0.019) (0.055)
MW*1.3 \ Wage $11 20.050*** 20.065*** 20.004 0.009 19,431
(0.017) (0.014) (0.006) (0.013)
$11 \ Wage $16 20.023** 20.053*** 20.007 20.019** 25,303
(0.011) (0.015) (0.005) (0.009)
$16 \ Wage $24 20.010 20.047** 20.010** 20.005 24,641
(0.012) (0.018) (0.005) (0.010)
$24 \ Wage 20.012 20.062*** 20.013 0.0002 25,101
(0.018) (0.018) (0.009) (0.013)
Observations 16,406 8,244 29,453 10,359
Notes: The above table reports results from a single median regression that includes 101,299
observations. Additional covariates not reported above include race, ethnicity, gender, education level,
household income, age and age squared, and the state monthly unemployment rate. The final three
rows correspond approximately to the upper three quartiles of the wage distribution. Standard errors
are clustered at the state level.
Significance levels: *** 1%; **5%; * 10%.
WHO BENEFITS FROM A MINIMUM WAGE INCREASE? 1185
quartile of the wage distribution. The estimated negative effects are even
larger than those reported in Table 6. The estimates are less precise, however, because the age restriction reduces the number of observations in the
first quartile of the wage distribution by over 20%.
Tables 7 through 10 indicate that the results hold across a broad range of
specifications and are not likely driven by the alternative explanations given
above. We argue that the results seem most consistent with the minimum
wage’s acting as a focal point in the employer wage-setting decision, which
causes percentage wage growth to be lower for low-wage workers if there is
a small minimum wage increase.
Heterogeneous Effects
The prior specifications allow the effect of a minimum wage increase to differ by the size of the increase and by the initial wage of the worker but not
by the worker’s characteristics. The minimum wage may have different wage
effects by gender, race, age, education, and income. In an attempt to see if
there are heterogeneous effects, we consider the effect of minimum wage
changes of varying sizes across subsamples of male, female, white, black,
Table 9. Median Regression, Flexible Covariates
Minimum wage change (%)
Wage group 5 5–10 10–20 . 20 Observations
Wage Minimum wage*1.1 20.020 0.087 0.089*** 0.274*** 2,346
(0.030) (0.077) (0.024) (0.042)
MW*1.1 \ Wage MW*1.2 20.068** 0.025 0.046 0.117*** 2,388
(0.027) (0.037) (0.029) (0.039)
MW*1.2 \ Wage MW*1.3 20.117*** 0.020 20.0004 0.055 2,089
(0.028) (0.055) (0.030) (0.049)
MW*1.3 \ Wage $11 20.020* 20.018 20.002 0.018** 19,431
(0.011) (0.013) (0.006) (0.009)
$11 \ Wage $16 0.0004 0.005 20.001 20.008 25,303
(0.007) (0.008) (0.005) (0.005)
$16 \ Wage $24 0.012* 0.007 20.001 0.003 24,641
(0.006) (0.009) (0.004) (0.006)
$24 \ Wage 0.001 20.001 0.002 0.002 25,101
(0.009) (0.008) (0.007) (0.007)
Observations 16,406 8,244 29,453 10,359
Notes: The above table reports results from a single median regression that includes 101,299
observations. Additional covariates not reported above include race, ethnicity, gender, education level,
household income, age and age squared, the state monthly unemployment rate, the lagged growth in
state per capita GDP, the annual state union membership rate, the annual state poverty rate, and the
percentage of workers in the state earning below the federal minimum wage, and the state price level,
as well as wage group interactions with the race, ethnicity, education, gender, and age variables. Fixed
effects are included for the state of residence and the month and year of the first interview. The final
three rows correspond approximately to the upper three quartiles of the wage distribution. Standard
errors are clustered at the state level.
Significance levels: *** 1%; **5%; * 10%.
1186 ILR REVIEW
Hispanic, young (age 22 and under), low education (no high school
diploma), and low income (annual household income under $40,000).
Because of the reduction in sample size, we limit the number of parameters
we need to estimate by repeating specification (1) with only two wage
groups: individuals with an initial wage within 30% of the minimum wage
and those with an initial wage more than 30% above it. Table 11 reports the
effects of the various minimum wage changes on the wage growth of individuals with an initial wage within 30% of the minimum wage relative to
low-wage individuals who experienced no minimum wage change. As
before, the results are from median regressions with the additional control
variables and state, month, and year fixed effects included. Each row represents a separate regression.
The qualitative story in this table is much the same as the one discussed
above: for nearly all groups, a minimum wage increase of less than 5% has a
negative effect on wage growth. The estimated effect for Hispanic individuals is slightly positive but is very small and not statistically significant. For all
other groups, the sign of the coefficient is negative. While the coefficient
for men is not statistically significant, the point estimate is negative and similar in magnitude to the estimate for women. We thus view Table 11 as evidence that our findings are not driven by any particular demographic
Table 10. Median Regression, Ages 23–65
Minimum wage change (%)
Wage group 5 5–10 10–20 . 20 Observations
Wage Minimum wage*1.1 20.043 0.154*** 0.157 0.594*** 1,346
(0.087) (0.054) (0.208) (0.098)
MW*1.1 \ Wage MW*1.2 20.113 0.003 0.078 0.299*** 1,543
(0.072) (0.065) (0.053) (0.074)
MW*1.2 \ Wage MW*1.3 20.149 0.115 20.038 0.125 1,492
(0.097) (0.139) (0.091) (0.112)
MW*1.3 \ Wage $11 20.028** 0.0020 0.004 0.015 16,035
(0.012) (0.011) (0.007) (0.011)
$11 \ Wage $16 20.001 0.006 0.001 20.012** 23,939
(0.006) (0.007) (0.005) (0.005)
$16 \ Wage $24 0.010* 0.009 20.002 0.002 24,113
(0.005) (0.010) (0.004) (0.008)
$24 \ Wage 0.005 20.013 20.006 0.007 24,659
(0.013) (0.013) (0.008) (0.010)
Observations 15,185 7,559 27,134 9,450
Notes: The above table reports results from a single median regression that includes 93,127 observations.
Additional covariates not reported above include race, ethnicity, gender, education level, household
income, age and age squared, the state monthly unemployment rate, the lagged growth in state per
capita GDP, the annual state union membership rate, the annual state poverty rate, the percentage of
workers in the state earning below the federal minimum wage, and the state price level. Fixed effects
are included for the state of residence and the month and year of the first interview. The final three
rows correspond approximately to the upper three quartiles of the wage distribution. Standard errors
are clustered at the state level.
Significance levels: *** 1%; **5%; * 10%.
WHO BENEFITS FROM A MINIMUM WAGE INCREASE? 1187
group. The negative wage effect of a small minimum wage increase seems
to hold across nearly all demographic groups.
Conclusion
Strong evidence supports that a small minimum wage increase actually
reduces the annual wage growth for many low-wage workers. This result is
important to labor policy and was previously absent from the minimum
wage literature. Larger minimum wage increases have positive wage effects
that spill over to workers with wages higher than the new minimum wage.
Workers with wages in the top three quartiles of the wage distribution do
not seem to experience any wage impact from a minimum wage increase
regardless of the size. These findings are robust to a variety of alternative
specifications and are generally consistent by income, gender, race, and
age.
We suggest that the negative effects of a minimum wage increase work by
setting a low-wage-growth focal point for employers of low-wage workers.
Had the minimum wage increase not occurred, employers would have
Table 11. Median Regression, Low-Wage Earners
Minimum wage change (%)
Variables 5 5–10 10–20 . 20 Observations
Men 20.047 0.065 0.044 0.154*** 51,023
(0.044) (0.053) (0.034) (0.042)
Women 20.059** 0.040* 0.037** 0.140*** 50,276
(0.027) (0.021) (0.019) (0.038)
White 20.050** 0.036* 0.048*** 0.173*** 86,547
(0.024) (0.021) (0.014) (0.040)
Black 20.141** 0.121 20.002 20.026 8,194
(0.067) (0.111) (0.068) (0.063)
Hispanic 0.023 0.004 0.042** 0.591*** 9,656
(0.037) (0.020) (0.017) (0.052)
22 and under 20.040 0.007 0.044** 0.134*** 5,986
(0.039) (0.024) 20.019 (0.025)
No diploma 20.060*** 0.021 0.040*** 0.134*** 8,263
(0.023) (0.018) (0.016) (0.036)
Low income 20.045 0.024 0.044** 0.210* 22,970
(0.029) (0.033) (0.022) (0.109)
Notes: Each row reported in the table above represents a separate median regression including only the
specified demographic group. Reported coefficients are for individuals earning a wage at the time of
the first interview within 30% of the applicable minimum wage. Additional covariates not reported
above include race, ethnicity, gender, education level, household income, age and age squared, the
‘‘wage bin’’ to which an individual belongs at the time of the first interview, the state monthly
unemployment rate, the lagged growth in state per capita GDP, the annual state union membership
rate, the annual state poverty rate, the percentage of workers in the state earning below the federal
minimum wage, and the state price level. The low-income group includes individuals with a reported
household income of no more than $40,000 annually. Standard errors are clustered at the state level.
Significance levels: *** 1%; **5%; * 10%.
1188 ILR REVIEW
provided larger wage increases to their employees. Though this focal-point
story is consistent with the results, we provide no evidence to substantiate
that this is the mechanism.
The data come from a period, 2005 to 2008, that is ideal for studying the
effect of a minimum wage increase because of the large number of statelevel minimum wage increases of various sizes. The short time period helps
us to address other methodological and interpretation issues common in
minimum wage studies. Our use of median regression methods increases
confidence that the results are being driven by the minimum wage increases
and not by skewness in the annual wage growth distribution.
It should be recognized that new entrants to low-wage jobs would likely
benefit from a minimum wage increase, and these individuals have been
excluded from the analysis. Similarly, workers who experience large wage
losses would also benefit from a minimum wage increase. Because there are
few of these workers their impact on the OLS regression result is small.
Finally, our study does not consider any increased unemployment risk for a
low-wage worker as a result of a minimum wage increase. Even with these
limitations, the results indicate that a small minimum wage increase likely
reduces wage growth for low-wage workers.
References
Aten, Bettina H., and Eric B. Figueroa. 2014. Regional price parities and real regional
income for the United States: 2008-2012. Paper presented at the 33rd General Conference of the International Association for Research in Income and Wealth, Rotterdam,
The Netherlands, August 24–30.
Baker, Michael, Dwayne Benjamin, and Shuchita Stanger. 1999. The highs and lows of the
minimum wage effect: A time-series cross-section study of the Canadian law. Journal of
Labor Economics 17(2): 318–50.
Card, David E. 1992a. Do minimum wages reduce employment? A case study of California,
1987–1989. Industrial and Labor Relations Review 46(1): 38–54.
———. 1992b. Using regional variation in wages to measure the effects of the federal minimum wage. Industrial and Labor Relations Review 46(1): 22–37.
Card, David E., and Alan B. Krueger. 1994. Minimum wages and employment: A case study
of the fast-food industry in New Jersey and Pennsylvania. American Economic Review 84(4):
772–93.
———. 1995. Myth and Measurement. Princeton, NJ: Princeton University Press.
Currie, Janet, and Bruce C. Fallick. 1996. The minimum wage and the employment of youth:
Evidence from the NLSY. Journal of Human Resources 31(2): 404–28.
Deere, Donald, Kevin M. Murphy, and Finis Welch. 1995. Employment and the 1990–1991
minimum-wage hike. American Economic Review Papers and Proceedings 85(2): 232–37.
DiNardo, John, Nicole Fortin, and Thomas Lemieux. 1996. Labor market institutions and
the distribution of wages: 1973–1992: A semi-parametric approach. Econometrica 65(5):
1001–44.
Gramlich, Edward M. 1976. Impact of minimum wages on other wages, employment, and
family incomes. Brookings Papers on Economic Activity 2: 409–51.
Katz, Lawrence F., and Alan B. Krueger. 1992. The effect of the minimum wage on the fast
food industry. Industrial and Labor Relations Review 46(1): 6–21.
Koenker, Roger, and Gilbert Bassett. 1978. Regression quantiles. Econometrica 46(1): 33–50.
Lang, Kevin, and Shulamit Kahn. 1998. The effect of minimum-wage laws on the distribution
of employment: Theory and evidence. Journal of Public Economics 69: 67–82.
WHO BENEFITS FROM A MINIMUM WAGE INCREASE? 1189
Madrian, Brigitte C., and Lars J. Lefgren. 1999. A note on longitudinally matching Current
Population Survey (CPS) respondents. NBER Working Paper No. T0247. Cambridge, MA:
National Bureau of Economic Research.
Neumark, David, and William Wascher. 1992. Employment effects of minimum and subminimum wages: Panel data on state minimum wage laws. Industrial and Labor Relations Review
46(1): 55–81.
———. 1995. Reconciling the evidence on employment effects of minimum wages: A review
of our research findings. Federal Reserve Board Finance and Economics Discussion Series
no. 95-53.
———. 2007. Minimum wages and employment. Foundations and Trends in Microeconomics
3(1–2): 1–182.
Neumark, David, Mark Schweitzer, and William Wascher. 2004. Minimum wage effects
throughout the wage distribution. Journal of Human Resources 39(2): 425–50.
Schelling, Thomas 1960. The Strategy of Conflict. Cambridge, MA: Harvard University Press.
Shelkova, Natalya Y. 2008. Low-wage labor markets and the power of suggestion. UConn Economics Working Paper No. 200833.
Spriggs, William E., and Bruce W. Klein. 1994. Raising the Floor: The Effects of the Minimum
Wage on Low-Wage Workers. Washington, DC: Economic Policy Institute.
1190 ILR REVIEW
Copyright of Industrial & Labor Relations Review is the property of Cornell University and
its content may not be copied or emailed to multiple sites or posted to a listserv without the
copyright holder’s express written permission. However, users may print, download, or email
articles for individual use.
Are you busy and do not have time to handle your assignment? Are you scared that your paper will not make the grade? Do you have responsibilities that may hinder you from turning in your assignment on time? Are you tired and can barely handle your assignment? Are your grades inconsistent?
Whichever your reason is, it is valid! You can get professional academic help from our service at affordable rates. We have a team of professional academic writers who can handle all your assignments.
Students barely have time to read. We got you! Have your literature essay or book review written without having the hassle of reading the book. You can get your literature paper custom-written for you by our literature specialists.
Do you struggle with finance? No need to torture yourself if finance is not your cup of tea. You can order your finance paper from our academic writing service and get 100% original work from competent finance experts.
Computer science is a tough subject. Fortunately, our computer science experts are up to the match. No need to stress and have sleepless nights. Our academic writers will tackle all your computer science assignments and deliver them on time. Let us handle all your python, java, ruby, JavaScript, php , C+ assignments!
While psychology may be an interesting subject, you may lack sufficient time to handle your assignments. Don’t despair; by using our academic writing service, you can be assured of perfect grades. Moreover, your grades will be consistent.
Engineering is quite a demanding subject. Students face a lot of pressure and barely have enough time to do what they love to do. Our academic writing service got you covered! Our engineering specialists follow the paper instructions and ensure timely delivery of the paper.
In the nursing course, you may have difficulties with literature reviews, annotated bibliographies, critical essays, and other assignments. Our nursing assignment writers will offer you professional nursing paper help at low prices.
Truth be told, sociology papers can be quite exhausting. Our academic writing service relieves you of fatigue, pressure, and stress. You can relax and have peace of mind as our academic writers handle your sociology assignment.
We take pride in having some of the best business writers in the industry. Our business writers have a lot of experience in the field. They are reliable, and you can be assured of a high-grade paper. They are able to handle business papers of any subject, length, deadline, and difficulty!
We boast of having some of the most experienced statistics experts in the industry. Our statistics experts have diverse skills, expertise, and knowledge to handle any kind of assignment. They have access to all kinds of software to get your assignment done.
Writing a law essay may prove to be an insurmountable obstacle, especially when you need to know the peculiarities of the legislative framework. Take advantage of our top-notch law specialists and get superb grades and 100% satisfaction.
We have highlighted some of the most popular subjects we handle above. Those are just a tip of the iceberg. We deal in all academic disciplines since our writers are as diverse. They have been drawn from across all disciplines, and orders are assigned to those writers believed to be the best in the field. In a nutshell, there is no task we cannot handle; all you need to do is place your order with us. As long as your instructions are clear, just trust we shall deliver irrespective of the discipline.
Our essay writers are graduates with bachelor's, masters, Ph.D., and doctorate degrees in various subjects. The minimum requirement to be an essay writer with our essay writing service is to have a college degree. All our academic writers have a minimum of two years of academic writing. We have a stringent recruitment process to ensure that we get only the most competent essay writers in the industry. We also ensure that the writers are handsomely compensated for their value. The majority of our writers are native English speakers. As such, the fluency of language and grammar is impeccable.
There is a very low likelihood that you won’t like the paper.
Not at all. All papers are written from scratch. There is no way your tutor or instructor will realize that you did not write the paper yourself. In fact, we recommend using our assignment help services for consistent results.
We check all papers for plagiarism before we submit them. We use powerful plagiarism checking software such as SafeAssign, LopesWrite, and Turnitin. We also upload the plagiarism report so that you can review it. We understand that plagiarism is academic suicide. We would not take the risk of submitting plagiarized work and jeopardize your academic journey. Furthermore, we do not sell or use prewritten papers, and each paper is written from scratch.
You determine when you get the paper by setting the deadline when placing the order. All papers are delivered within the deadline. We are well aware that we operate in a time-sensitive industry. As such, we have laid out strategies to ensure that the client receives the paper on time and they never miss the deadline. We understand that papers that are submitted late have some points deducted. We do not want you to miss any points due to late submission. We work on beating deadlines by huge margins in order to ensure that you have ample time to review the paper before you submit it.
We have a privacy and confidentiality policy that guides our work. We NEVER share any customer information with third parties. Noone will ever know that you used our assignment help services. It’s only between you and us. We are bound by our policies to protect the customer’s identity and information. All your information, such as your names, phone number, email, order information, and so on, are protected. We have robust security systems that ensure that your data is protected. Hacking our systems is close to impossible, and it has never happened.
You fill all the paper instructions in the order form. Make sure you include all the helpful materials so that our academic writers can deliver the perfect paper. It will also help to eliminate unnecessary revisions.
Proceed to pay for the paper so that it can be assigned to one of our expert academic writers. The paper subject is matched with the writer’s area of specialization.
You communicate with the writer and know about the progress of the paper. The client can ask the writer for drafts of the paper. The client can upload extra material and include additional instructions from the lecturer. Receive a paper.
The paper is sent to your email and uploaded to your personal account. You also get a plagiarism report attached to your paper.
GET A PERFECT SCORE!!! 
Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.
You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.
Read moreEach paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.
Read moreThanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.
Read moreYour email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.
Read moreBy sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.
Read more