Minimum wages and poverty: will a $9.50 federal minimum wage really help the working poor?
Sabia, Joseph J. ; Burkhauser, Richard V.
1. Introduction
Proposals to increase the minimum wage are politically popular
because they are widely seen as an effective way to help the working
poor (AP-AOL 2006). Former President Bill Clinton captured this majority
view in his statement of support for an increase in the federal minimum
wage when he said: "It's time to honor and reward people who
work hard and play by the rules .... No one who works full time and has
children should be poor anymore" (Clinton and Gore 1992). The goal
of helping the working poor was also an important motivation behind the
most recent legislation to increase the federal minimum wage from $5.15
to $7.25 per hour in 2007, and it remains a key rationale for Senate
Bill 2514, the Standing with Minimum Wage Earners Act of 2007, which
would increase the federal minimum wage yet again from $7.25 to $9.50
per hour. (1)
While reducing poverty among the working poor is a laudable policy
goal, the evidence suggests that minimum wage increases have thus far
provided little more than symbolic support to this population (Card and
Krueger 1995; Neumark and Wascher 2002; Gundersen and Ziliak 2004;
Burkhauser and Sabia 2007; Leigh 2007; Sabia 2008). Several explanations
have been offered for this finding. Card and Krueger (1995) emphasize
that minimum wages fail to reduce poverty because many poor Americans do
not work. Others have argued that even among the working poor, the
relationship between earning a low hourly wage rate and living in
poverty is weak and has become weaker over time (Stigler 1946;
Burkhauser, Couch, and Glenn 1996; Burkhauser and Sabia 2007). Moreover,
even among affected workers, there is strong evidence that increases in
the minimum wage reduce the employment of low-skilled workers (Neumark
and Wascher 2008). While an increase in the minimum wage will lift out
of poverty the families of some low-skilled workers who remain employed,
other low-skilled workers will lose their jobs or have their hours
significantly cut, reducing their income and dropping their families
into poverty (Neumark and Wascher 2002; Neumark, Schweitzer, and Wascher
2004, 2005; Sabia 2008).
Despite evidence on the ineffectiveness of past increases, a new
set of large state and federal minimum wage increases was initiated
between 2003 and 2007, all with the promise of helping the working poor.
(2) The newly proposed federal minimum wage increase to $9.50 per hour
is also being justified as an important anti-poverty tool. Our article
provides a first look at the effectiveness of these twenty-first century
state and federal minimum wage increases in reducing poverty and
compares the target efficiency of raising the federal minimum wage to
$9.50 per hour with that of prior increases. Moreover, our work augments
the static analysis of Burkhauser and Sabia (2007) by accounting for the
likely behavioral effects of a new federal minimum wage increase in our
simulations of its distributional consequences. Further, because there
continues to be controversy over the size of employment effects of
minimum wage increases, we estimate a "break-even" elasticity
value where the proposed minimum wage hike will produce no net benefits
for workers.
Using data drawn from the March Current Population Survey (CPS), we
find no evidence that minimum wage increases between 2003 and 2007
lowered state poverty rates. Moreover, we find that the newly proposed
federal minimum wage increase from $7.25 to $9.50 per hour, like the
last increase from $5.15 to $7.25 per hour, is not well targeted to the
working poor. Only 11.3% of workers who will gain from an increase in
the federal minimum wage to $9.50 per hour live in poor households, an
even smaller share than was the case with the last federal minimum wage
increase (15.8%). Of those who will gain, 63.2% are second or third
earners living in households with incomes twice the poverty line, and
42.3% live in households with incomes three times the poverty line, well
above $50,233, the income of the median household in 2007. (3)
With an average employment elasticity of -0.6 for minimum wage
workers aged 16-29 without a high school diploma and an elasticity of
-0.2 for other minimum wage workers, we estimate that nearly 1.3 million
jobs will be lost if the federal minimum wage is increased to $9.50 per
hour, including 168,000 jobs currently held by the working poor. We
estimate that average employment elasticities greater (in absolute
value) than -0.86 will cause net monthly earnings losses to the set of
low-skilled workers who are affected by this proposed minimum wage
legislation. We conclude that further increases in the minimum wage will
do little to reduce poverty and are a poor substitute for further
expansions in the federal Earned Income Tax Credit (EITC) program as a
mechanism for reducing poverty.
2. Literature Review
Poverty Effects of Minimum Wage Increases
Several recent studies have examined the income and poverty effects
of minimum wage increases (see, for example, Card and Krueger 1995;
Addison and Blackburn 1999; Neumark and Wascher 2002; Gundersen and
Ziliak 2004; Neumark, Schweitzer, and Wascher 2004, 2005; Burkhauser and
Sabia 2007; Sabia 2008), and all but one have found that past minimum
wage hikes had no effect on poverty. (4) These studies have generally
taken one of two approaches. The first approach uses matched CPS data
and examines family income changes caused by minimum wage increases
(Neumark and Wascher 2002; Neumark, Schweitzer, and Wascher 2004, 2005).
These studies find that some low-skilled workers living in poor families
who remain employed see their incomes rise and move out of poverty when
the minimum wage increases. However, other low-skilled workers lose
their jobs or have their hours substantially reduced as a result of
minimum wage hikes, causing income losses and increased poverty. On net,
Neumark and Wascher (2002) find that the families of low-skilled workers
are no better off and may be made worse off by minimum wage hikes. Sabia
(2008) finds a similar result for less-educated single mothers.
A second approach, taken by Card and Krueger (1995) and Burkhauser
and Sabia (2007), estimates the effect of state minimum wage increases
on state poverty rates. These studies also find no evidence that past
minimum wage increases have significantly reduced poverty either among
the families of all individuals or among the families of workers.
Employment and Hours Worked Effects of Minimum Wage Increases
Another explanation for the ineffectiveness of past minimum wage
increases in reducing poverty is theory based and focuses on their
adverse labor demand effects. Neoclassical economic theory suggests that
minimum wage increases reduce the demand for low-skilled labor, thus
reducing employment and hours worked (see Stigler 1946). Much of the
literature examining the employment effects of minimum wage increases
has focused on low-skilled workers, usually teenagers and high school
dropouts, or on workers in low-skilled industries because these
populations are more likely to be affected by such increases.
Neumark and Wascher (2007) review over 90 studies published since
the iconoclastic Card and Krueger (1994, 1995) studies of the mid-1990s
and conclude that there is overwhelming evidence that the least-skilled
workers experience the strongest disemployment effects from minimum wage
increases (see, for example, Neumark and Wascher 1992; Williams 1993;
Deere, Murphy, and Welch 1995; Currie and Fallick 1996; Abowd et al.
1999; Partridge and Partridge 1999; Burkhauser, Couch, and Wittenburg
2000a, b; Couch and Wittenburg 2001; Neumark 2001; Neumark and Wascher
2002, 2004; Campolieti, Fang, and Gunderson 2005; Campolieti, Gunderson,
and Riddell 2006; Sabia 2008, 2009a, b). Median employment elasticities
range from -0.1 to -0.3, though a few studies have found employment
elasticities that are larger (between -0.6 and -0.9) for less-educated
single mothers (Sabia 2008) and younger high school dropouts
(Burkhauser, Couch, and Wittenberg 2000b).
Recently, however, articles by Dube, Lester, and Reich (2008) and
Addison, Blackburn, and Cotti (2008) have renewed this debate. These
authors argue that the identification strategy used in many national
panel studies is flawed due to unmeasured low-skilled employment trends
across states. To better ensure common underlying trends across
treatment and comparison states, they use variation in minimum wages in
contiguous counties across borders for identification, finding no
evidence of adverse employment effects across low-skilled sectors. But
this finding is far from definitive. Other studies that have examined
low-skilled workers across sectors have found evidence of adverse
employment and welfare take-up effects even after controlling for
unmeasured state trends (Page, Spetz, and Millar 2005; Sabia 2008; Sabia
and Burkhauser 2008).
Examining only employment effects, however, may mask full labor
demand effects. Firms may respond to minimum wage hikes by (i) reducing
both employment and average hours worked by employed workers or (ii)
increasing hours of retained workers to compensate for reduced
employment (Couch and Wittenburg 2001; Neumark and Wascher 2007). The
evidence on hours worked effects is mixed. Couch and Wittenburg (2001)
and Sabia (2009b) find some evidence that employment effects alone
understate full labor demand effects, but Zavodny (2000), Sabia (2008),
and Sabia and Burkhauser (2008) find little evidence of conditional
hours worked effects.
Simulations of Who Gains from Minimum Wage Increases
While lower labor force participation rates among the poor (Card
and Krueger 1995) and adverse labor demand effects of minimum wages
(Neumark and Wascher 2002; Neumark, Schweitzer, and Wascher 2004, 2005;
Sabia 2008) may help to explain the ineffectiveness of past minimum wage
increases in reducing poverty, another explanation may be the poor
target efficiency of the minimum wage. A series of studies by Burkhauser
and Finegan (1989); Burkhauser, Couch, and Glenn (1996); Burkhauser and
Harrison (1999); and Burkhauser and Sabia (2007) have avoided the
controversies surrounding the magnitude of employment and hours worked
effects of past minimum wage increases and have instead focused on the
target efficiency of proposed increases. These studies assume no
behavioral effects of the minimum wage, giving proposed minimum wage
increases their best chance to benefit affected workers. But even under
the optimistic assumption of no employment or hours worked effects, the
authors find that workers living in poor households received few of the
benefits of past minimum wage increases because their hourly wages were
already greater than the proposed state or federal minimum wages.
Instead, most of the benefits went to second or third earners living in
households well above the poverty line.
One important critique of these simulations is that they overstate
the benefits of minimum wages to the working poor because they ignore
employment effects. As the authors note, because they assume zero
employment elasticities, their simulations are likely to be upper-bound
estimates of the benefits to workers (Burkhauser and Sabia 2007). And,
in a recent case study of New York State, Sabia and Burkhauser (2008)
find that when they account for the adverse labor demand effects of the
minimum wage, workers in poor households receive an even smaller share
of a shrinking pie of additional net wage earnings.
This article integrates and contributes to previous studies in the
literature in several ways. First, we extend the work of Burkhauser and
Sabia (2007) by estimating the effects of minimum wage increases from
2003 to 2007 on state poverty rates. No studies in the literature of
which we are aware have estimated the effect of minimum wages on state
poverty rates in the mid- to late 2000s, a period providing a rich new
source of state-level identifying variation: 28 states increased their
minimum wages above the federal level, and the federal minimum wage rose
from $5.15 to $5.85 per hour. Second, we are the first to examine the
target efficiency of the Standing with Minimum Wage Earners Act of 2007,
which would raise the federal minimum wage from $7.25 to $9.50 per hour,
and compare its target efficiency to the last federal minimum wage
increase from $5.15 to $7.25 per hour. Finally, unlike previous
studies' simulations of federal minimum wage increases that have
assumed no behavioral effects of the minimum wage, we simulate the
distribution of benefits from the proposed minimum wage increase using a
range of employment elasticities estimated in the literature. We use
these elasticities and workers' wage rates to estimate
individual-specific probabilities of job loss and expected net benefits
from the newly proposed minimum wage increase.
3. Data and Estimation Strategy
Our analysis uses data drawn from the outgoing rotation groups of
the March CPS. We use the March CPS because it contains information not
only on current employment and wage rates but also on household income
and household size, which we use, together with household size-specific
poverty thresholds, to calculate an income-to-needs ratio for each
worker. (5) For example, in 2007, the poverty threshold for a household
size of four was $20,650. Thus, a household of four with total household
income of $41,300 would have an income-to-needs ratio of 2.0. Workers in
households with income-to-needs ratios less than 1.0 are classified as
"poor," and those with income-to-needs ratios between 1.0 and
1.5 are defined as "near poor."
Information on workers' individual wage rates and hours worked
comes from the outgoing rotation group and are measured in the last
week. For workers who report being paid hourly, their wage rate is
directly reported from their current job. For those who are not paid
hourly, wage rates are calculated as the ratio of weekly earnings to
weekly hours in the past week. Information on household income comes
from the previous calendar year, so mapping individual wages to the
poverty status of the household requires the assumption that the
income-to-needs ratio of the household was the same in 2007 as it was in
March 2008 (see Burkhauser, Couch, and Glenn [1996] and Burkhauser and
Sabia [2007] for a discussion of this issue).
Poverty Effects of Minimum Wage Increases
To examine the effect of past minimum wage increases on state
poverty rates, we pool data from the March 2004 through March 2008 CPS
and estimate a fixed effects model similar to Card and Krueger (1995)
and Burkhauser and Sabia (2007). To be consistent with this poverty
literature, we follow these authors and use the family unit to calculate
poverty status and estimate the following model: (6)
[P.sub.st] = [alpha] + [beta][MW.sub.st] + [X'.sub.st][delta]
+ [[theta].sub.s] + [[tau].sub.t] + [[epsilon].sub.ist], (1)
where [P.sub.st] is the natural log of the poverty rate in state s
at time t; [MW.sub.st] is the natural log of the higher of the state or
federal minimum wage; (7) and Xs, is a vector of state-specific,
time-varying socioeconomic controls, including the unemployment rate for
prime-age males aged 25-54, the average adult wage for working
individuals aged 25-54, the share of older (aged 55-64) and younger
(aged 16-24) individuals in the state population, a time-invariant state
effect ([[theta].sub.s]), and a state-invariant time effect
([[tau].sub.t]). Because family income is measured in the previous year,
the sample used in the regression corresponds to calendar years
2003-2007. The key parameter of interest in this model is
[[beta].sub.1]. Thus, much of the identifying variation is coming from
state minimum wage increases. (8)
Simulations of Minimum Wage Increases
To simulate the employment and distributional consequences of the
newly proposed federal minimum wage increase as well as the last federal
minimum wage hike from $5.15 to $7.25 per hour, (9) we follow Baicker
and Levy (2008), Burkhauser and Simon (2008), and Yelowitz (2008), who
use estimates of employment elasticities from the minimum wage
literature to simulate the effect of pay-or-play health insurance
reforms. We use the household unit to link workers to the poverty status
of their households, consistent with the income distribution literature
and Burkhauser and Sabia (2007). This simulation approach uses the March
CPS to identify the set of workers who are affected by a policy change.
For the last federal minimum wage increase, we define these workers as
those earning hourly wages between $5.00 and $7.24 per hour in the March
2007 CPS, and for the new federal minimum wage increase, these are
workers earning between $5.70 and $9.49 per hour in the March 2008 CPS.
(10)
For each simulation, we calculate an individual-specific
probability of job loss:
[p.sub.i] = (FMW-[w.sub.i])/[w.sub.i][absolute value of [e.sub.i]],
(2)
where FMW is the federal minimum wage, [w.sub.i] is worker i's
current hourly wage rate, and e is the estimated employment elasticity
that applies to worker i. The true employment elasticity that should be
applied to each minimum wage worker is unknown. We use a range of
elasticities for minimum wage workers from zero (Card and Krueger 1995;
Addison, Blackburn, and Cotti 2008; Dube, Lester, and Reich 2008) to
"consensus" elasticities of -0.1 to -0.3 (Neumark and Wascher
2007) to upper-bound estimates of -0.6 to -0.9 (Burkhauser, Couch, and
Glenn 2000b; Sabia 2008; Sabia and Burkhauser 2008). Thus, the
distribution of job loss by incometo-needs ratio of households will
depend on (i) the share of minimum wage workers in each income-to-needs
category, (ii) the magnitude of the gap between the worker's
current wage and the new federal minimum wage, and (iii) the elasticity
that should be applied to each worker. Total job loss is calculated by
summing the product of the individual probabilities of job loss and the
population weights attached to each worker.
To simulate the expected net benefits of the minimum wage increase
to each minimum wage worker, we calculate expected monthly net benefits
for each worker as follows:
[[EB].sub.i] = (1-(FMW - [w.sub.i])/[w.sub.i][absolute value of
[e.sub.i]])(FMW - [w.sub.i])[H.sub.i] - (FMW -
[w.sub.i])/[w.sub.i][absolute value of
[e.sub.i]])([w.sub.i][H.sub.i]-[EUI.sub.i], (3)
where [H.sub.i] is the usual monthly hours worked by worker i and
[EUI.sub.i] is the expected unemployment insurance benefits received by
worker i. The first term on the right-hand side of Equation 3 is the
expected monthly earnings gains from a federal minimum wage hike from a
retained job. The second term on the right-hand side is the expected
earnings losses from a job loss due to the minimum wage increase. Thus,
three types of minimum wage workers are described in Equation 3: (i)
those who keep their jobs, retain their hours, and get a wage boost from
a minimum wage increase; (ii) those who become unemployed due to a
minimum wage increase and lose their entire monthly earnings; and (iii)
those who become unemployed due to a minimum wage increase and lose
their monthly earnings but have some share of their earnings replaced by
unemployment insurance for a portion of the month. We calculate total
net benefits for workers in each income-to-needs category by aggregating
individual net benefits using earnings weights.
A number of simplifying assumptions are needed to interpret the
expression in Equation 3 as the expected net benefit to minimum wage
workers. First, we assume that there are no wage spillovers to workers
earning more than the federal minimum wage. This assumption is
reasonable given that we find no evidence that minimum wage increases
have important spillover effects (Burkhauser and Sabia 2007; Sabia 2008;
Sabia and Burkhauser 2008). Second, as in the simulation of job loss, we
must make assumptions about the employment elasticities that are applied
to minimum wage workers. We apply a broad range of employment
elasticities from the literature to estimate employment and
distributional effects, and in our preferred models we assign different
elasticities to different types of minimum wage workers. Third, we
assume that minimum wages have no effect on hours worked by retained
workers. Existing estimates in the literature tend to point to either no
effects or only small negative effects (see, for example, Zavodny 2000;
Sabia and Burkhauser 2008; Sabia 2009b); thus, we conservatively assume
no adverse hours worked effects. Finally, we assume that if a worker is
laid off, his monthly earnings are zero, but he may receive unemployment
benefits. We calculate expected monthly unemployment insurance payments
as follows:
E [UI.sub.is]=[[theta]r.sub.s][w.sub.i][alpha][H.sub.i], (4)
where [theta] is the probability of unemployment insurance uptake,
[r.sub.s] is a state-specific measure of earnings replacement rates for
workers, and [alpha] is the share of the month during which the
unemployed worker receives benefits.
First, because the majority of unemployed workers do not apply for
unemployment insurance (see Vroman 1991 for a discussion), we include
the parameter [theta] and assume that it takes on a value less than 1.
We experimented with a number of estimates of [theta] but use the
national average in 2000, 0.35 (Wenger 2001). (11) Second, we generated
state-specific estimates of earnings replacement rates ([r.sub.s]).
Wenger (2001) reports average unemployment insurance (UI) benefits
received by unemployed minimum wage workers. Given that there is a fair
amount of heterogeneity in earnings replacements across states, we use
this information, along with state minimum wage levels, to calculate the
implicit earnings replacement rate for each state. The most generous
state in terms of replacing minimum wage earnings in our sample is
Kentucky (0.68), and the least generous is North Dakota (0.41). Finally,
unemployed workers do not receive unemployment insurance benefits
immediately following a layoff; there is generally, at minimum, a one-
to two-week waiting period (Wenger 2001). We assume that unemployed
workers receive benefits for three weeks in their first unemployed
month, which allows a oneand-a-half week delay until benefits. (12)
There are, of course, limitations to these simplifying assumptions.
For instance, if consumers face higher prices as a result of higher
costs of producing goods and services (Aaronson and French 2006, 2007)
or if our employment estimates are underestimated due to a failure to
capture full lagged effects of minimum wage increases (Baker, Benjamin,
and Stranger 1999; Burkhauser, Couch, and Wittenburg 2000a; Neumark and
Wascher 2004; Page, Spetz, and Millar 2005; Campolieti, Gunderson, and
Riddell 2006), our estimates will overstate the true benefits of the
minimum wage. Moreover, if there are heterogeneous effects of the
minimum wage by poverty status or if unemployment insurance uptake rates
differ by poverty status, our simulations may mask other distributional
effects. Finally, while we assume that some unemployed workers will have
a share of their earnings losses replaced by government-mandated
unemployment insurance benefits, increased UI payments caused by minimum
wage-induced job losses are not costless from a federal budget
perspective. In sum, while our assumptions are imperfect, incorporating
estimates of the behavioral consequences of past minimum wage increases
will be an important improvement over past simulations.
4. Results
Poverty Effects of Minimum Wage Increases
Table 1 presents fixed effects estimates of the effect of recent
minimum wage increases on state poverty rates among 16-64-year-olds. In
column 1, we find no evidence that minimum wage increases between 2003
and 2007 affected overall state poverty rates. While the sign on the
estimate of [[beta].sub.1] is negative, the effect is not statistically
different from zero and is, in fact, smaller than the estimate obtained
by Burkhauser and Sabia (2007) in their examination of the 1988-2003
period (-0.052 in column 1 of Table 1 versus -0.082 in column 4 of table
7 of their article). When the sample is restricted to workers (column
2), which gives the minimum wage its best chance to reduce poverty by
raising incomes of low-skilled workers, we still find no effect on
poverty rates. In fact, the magnitude of the poverty elasticity (-0.020)
is even smaller. Therefore, the absence of poverty-alleviating effects
is not solely attributable to the fact that many individuals in poor
families do not work, as suggested by Card and Krueger (1995).
The above findings are quite robust across definitions of poverty.
When we define poverty more broadly--encompassing those with incomes
falling below 125% of the poverty line--estimates remain statistically
insignificant and small across all individuals (column 3) and workers
(column 4). And finally, when we estimate poverty as those with family
incomes below 150% of the poverty line (columns 5-6), the estimate of
[[beta].sub.1] actually becomes positive, though still statistically
indistinguishable from zero.
As noted previously, the models estimated in Table 1 include
controls for the average private sector wage, the prime-age male
unemployment rate, and the share of older and younger individuals in the
state. We examined the robustness of the results in Table 1 along
several lines. First, we redefine the minimum wage variable as a
Kaitz-type index, the ratio of the state minimum wage to the average
state private sector wage (see Table A1 in the Appendix). This allows us
to measure the effect of the minimum wage relative to its position in
the state wage distribution. In these specifications, we continue to
find no effect of the minimum wage on poverty rates of all individuals
or of workers.
We also experiment with additional state-specific, time-varying
controls: the prime-age female unemployment rate, the youth (aged 16-24)
unemployment rate, the high school graduation rate, and the college
graduation rate. Models including these controls produce results that
are substantively similar (see Table A2 in the Appendix). (13)
Taken together, the estimates in Table 1 suggest that recent
minimum wage increases enacted between 2003 and 2007 had no effect on
state poverty rates. While lower labor force participation rates among
poor, as compared to non-poor, workers is one explanation for the lack
of poverty effects among all individuals (Card and Krueger 1995), the
fact that the minimum wage has no effect on poverty rates of working
individuals suggests that this is not the only explanation. Alternative
explanations include the adverse labor demand effects of the minimum
wage and its poor target efficiency. Keeping these explanations in mind,
we now focus on who will gain from the newly proposed federal minimum
wage increase to $9.50 per hour; how this population compares to those
who gained from the last increase; and whether they are, in the main,
poor.
Who Will Benefit?
Table 2 shows cross-tabulations of the wage distribution of
non-self-employed 16-64-year-olds by the income-to-needs ratio of their
households using the March 2008 CPS. Each column shows a different wage
category, and each row shows the income-to-needs ratio of workers'
households. Workers who are expected to be directly affected by the
proposed increase are those who earn between $7.25 and $9.49 per hour.
However, in March 2008, when wage rates of workers are measured, the
federal minimum wage was $5.85 per hour. The federal minimum wage was
increased to $6.55 on July 24, 2008, and increased again to $7.25 on
July 24, 2009. We take a conservative approach and assume that workers
earning between $5.70 and $9.49 in March 2008 will be affected by the
newly proposed federal minimum wage increase. (14) We treat those who
earned less than $5.70 per hour as uncovered by the federal minimum
wage. (15)
We see from Table 2 that a minority of workers will be affected by
the newly proposed federal minimum wage increase. Approximately 17.7% of
all workers in the United States earn hourly wages between $5.70 and
$9.49 per hour and stand to be directly affected by the increase, while
80.3% of all workers earn hourly wages of $9.50 per hour or more.
To assess how well the proposed federal minimum wage hike will
target the working poor, we first examine the share of workers living in
poor households who will be affected by the new federal minimum wage
increase. While 4.4% of all workers live in poor households, not all of
them will be affected by this minimum wage increase because 48.9%
already earn wages greater than $9.50 per hour.
In the final column of Table 2, we show the distribution of workers
who earn between $5.70 per hour and $9.50 per hour by the
income-to-needs ratios of their households. We find that 11.3% of these
minimum wage workers live in poor households. When workers living in
near-poor households are also included (households with income-to-needs
ratios between 1.0 and 1.5), this number rises to 23.4%. However, 63.2%
of minimum wage workers live in households with incomes over twice the
poverty line, and 42.3% live in households with incomes over three times
the poverty line ($61,950 for a four-person household).
One concern with the sample examined in Table 2 is that it consists
of both hourly and non-hourly workers. Recent work by Bollinger and
Chandra (2005) suggests that imputing hourly wages from reported
earnings may introduce substantial measurement error. Thus, it may be
that some workers we assume are in the uncovered sector (those reporting
hourly wages less than $5.70 per hour) are, in fact, covered, and other
workers we assume are unaffected by the minimum wage increase (those
reporting hourly wages greater than $9.49 per hour) are affected.
To explore whether measurement error in wages is affecting our
results, we take the approach of Bollinger and Chandra (2005) and
present separate results for hourly workers and non-hourly workers.
These findings are presented in Tables A5 and A6, respectively, in the
Appendix. While hourly workers are more likely to be poor than are
non-hourly workers, the final column of Appendix Table A5 shows that
just 11.6% of hourly paid minimum wage workers live in poor households
(compared to 11.3% of minimum wage workers in the full worker sample),
while 42.6% live in households with incomes over three times the poverty
line (compared to 42.3% of minimum wage workers in the full worker
sample). We find a similar pattern of results for non-hourly minimum
wage workers: The vast majority do not live in poor households, but
instead live in households with incomes two or three times the poverty
line.
In summary, the descriptive evidence in Table 2 suggests that
raising the federal minimum wage to $9.50 per hour will not be a
target-efficient anti-poverty tool because (i) many poor and near-poor
workers already earn hourly wages greater than $9.50 per hour and (ii)
most workers who will benefit are not poor.
How does the target efficiency of the new federal minimum wage
proposal compare to that of the last increase from $5.15 to $7.25? Table
3 replicates Appendix Table A3 from Burkhauser and Sabia (2007) using
the March 2007 Current Population Survey. (16) As we saw in Table 2, not
all of the working poor would gain from an increase in the federal
minimum wage to $9.50 per hour because 48.9% already have an hourly wage
that is greater than $9.50. This was an even bigger problem with respect
to the last federal minimum wage increase from $5.15 to $7.25 per hour
because an even larger percentage (71%) of the working poor already
earned more than $7.25 per hour. Nonetheless, the percentage of workers
who will gain from an increase in the minimum wage to $9.50 (11.3%--see
the last column of Table 3) is still less than the percentage who gained
from the previous increase in the minimum wage to $7.25 per hour
(15.8%--see the next-to-last column of Table 3). Like the last increase,
the current proposal will largely affect workers living in non-poor
households with incomes that are over two or three times the poverty
line. (17)
But how do these facts square with the image of a minimum wage
worker often invoked by advocates of minimum wage increases--a single
mother struggling to support her children? (18) As Table 4 shows, only
11.1% of those who will gain from the proposed increase in the minimum
wage to $9.50 per hour are single mothers, down from 12.0% from the last
federal increase, but even the stereotype that the minimum wage earner
is the primary earner in the household is misleading. Only about
one-half of those who would gain from the minimum wage increase to $9.50
are the primary earners in their household, up from 43.4% from the last
federal increase, but this difference is mainly because more of the
gainers are living in one-person households or in households without
children. (19)
Taken together, the results in Tables 2 and 4 suggest that, like
past state and federal minimum wage hikes (Tables 1 and 3), the current
proposal to raise the federal minimum wage to $9.50 per hour will not be
well targeted to poor workers and, in fact, may be even less target
efficient than the last federal increase. This finding is consistent
with Stigler's (1946) claim that the relationship between earning a
low wage and living in poverty is "fuzzy" and has become
fuzzier over time.
Simulations
Poor target efficiency is one important reason why minimum wage
increases are ineffective at reducing poverty among workers; adverse
labor demand effects are another. In Table 5, we simulate expected job
losses from the proposed federal minimum wage increase. We estimate that
the proposed hike to $9.50 per hour will affect over 21 million workers
(final row, column 2), including 2.41 million workers living in poor
households and 2.56 million living in near-poor households. To estimate
job losses, we calculate individual probabilities of job loss as
described in Equation 2 using a range of employment elasticities from
the literature. Columns 3 and 4 present estimates of job losses by
income-to-needs ratios of households using the range of
"consensus" estimates in the literature (Neumark and Wascher
2007), while columns 5 and 6 present simulations using upper-bound
estimates of -0.6 and -0.86 (Burkhauser, Couch, and Wittenberg 2000b;
Sabia 2008; Sabia and Burkhauser 2008). Lower-bound elasticity estimates
imply job losses of 467,000 to 1.40 million, while upper-bound estimates
imply job losses of approximately 3 million to 4 million.
In our preferred estimates, we allow employment elasticities to
differ by characteristics of the minimum wage worker. Because larger
employment elasticities have been found for younger high school
dropouts, we assign an employment elasticity of -0.6 to minimum wage
workers aged 16-29 without a high school diploma (representing over
one-quarter of the sample) and an elasticity of -0.2 to other minimum
wage workers. In this simulation, we estimate 1.3 million jobs lost.
Importantly, the share of job losses experienced by workers in poor
households (12.8%; column 9, row 1) is larger than that experienced by
the share of minimum wage workers who are poor (11.3%). This is because
their hourly wage rates were on average lower than were those of
affected workers living in many non-poor households, thus leading to a
higher probability of job loss. But our estimate of job losses borne by
poor workers is likely to understate the actual difference between
workers living in poor and non-poor households, since the demand for
these workers may be more elastic than that of non-poor workers as a
group (see, for example, Sabia 2008).
The magnitude of simulated job losses from the current proposal is
much larger than that from the last increase because the last increase
affected far fewer workers (see Table 6). Using our preferred employment
elasticities, our simulation indicates that the last federal minimum
wage hike from $5.15 to $7.25 will, when fully implemented, reduce
employment by approximately 374,900 jobs. However, in contrast to the
current proposal, the last increase did not yield higher percentage job
losses among the working poor.
While job losses are certainly possible, and even probable given
the consensus of existing empirical evidence (Neumark and Wascher 2008),
net income gains are still possible if adverse employment effects are
sufficiently small. But are the gains from minimum wage increases
received, in the main, by working poor, as proponents expect? In Table
7, we simulate the expected monthly benefits from the proposed federal
minimum wage hike to $9.50 per hour. Column 1 shows the distribution of
monthly benefits assuming no behavioral effects of the minimum wage, as
was assumed by Burkhauser and Finegan (1989); Burkhauser, Couch, and
Glenn (1996); and Burkhauser and Sabia (2007). If no minimum wage
workers are laid off or have their hours reduced, the minimum wage
increase is simulated to yield $4.0 billion in monthly benefits. This
estimate can be considered an upper-bound estimate of benefits, given
our optimistic behavioral assumptions. However, even under these
assumptions, just 10.9% ($439 million) of these benefits will be
received by the working poor (column 2), and 24.6% of the benefits will
be received by workers living in poor or near-poor households. Nearly
62% of the benefits will be received by workers in households with
incomes over twice the poverty line, and 40.7% will be received by
workers in households with incomes over three times the poverty line.
Thus, even under optimistic assumptions of zero employment elasticities
(Card 1992; Card and Krueger 1994, 1995; Addison, Blackburn, and Cotti
2008; Dube, Lester, and Reich 2008), only a small share of the benefits
will be received by the working poor.
In columns 3-8, we improve on the previous literature's
simulations by allowing for behavioral effects of the federal minimum
wage increase. At a conservative employment elasticity of -0.1, the
total net benefits from the minimum wage fall by 11.7%, to $3.56
billion, but the distribution of benefits remains similar to that when
no employment effects were assumed: Approximately 10.9% of benefits are
received by workers living in poor households.
At higher employment elasticities, net benefits fall substantially.
An employment elasticity of -0.3 reduces net benefits by 34.7%, to $2.63
billion (column 4), and an elasticity of -0.6 reduces net benefits by
69.5%, to $1.23 billion (column 5). We estimate the break-even
employment elasticity, where Equation 4 equals zero, to be -.086 (column
6). While an employment elasticity of -0.86 is large relative to the
consensus estimates in the literature, a few studies have found
estimates as large for less-educated single mothers (Sabia 2009b) and
young high school dropouts (Burkhauser, Couch, and Wittenberg 2000b;
Sabia and Burkhauser 2008). Thus, it is not implausible to imagine that
the benefits of a minimum wage increase to $9.50 to the working poor
would be quite small, or even negative. Using our preferred estimates,
which assume a -0.6 employment elasticity for younger dropouts and a
-0.2 elasticity for other workers, we find that the net benefits are
$2.84 billion, with just 10.5% of these benefits received by poor
workers.
When we compare the distribution of benefits from the current
proposal at our preferred employment elasticities (Table 8, columns 1-2)
to the distribution of benefits of the last increase (Table 8, columns
3-4), we find that the benefits from the new proposal are even less well
targeted than are those from the last increase. Approximately 15.5% of
the simulated monthly net benefits of the last increase went to workers
living in poor households, compared to 10.5% of the benefits from an
increase to $9.50 per hour. The break-even elasticity of the last
federal minimum wage increase is -0.91 (column 5), somewhat higher than
for the current proposal.
Again, our estimates of benefits to workers from the minimum wage
increase include unemployment insurance benefits, which are, in fact,
costly to the federal government and are only a partial short-run remedy
for unemployed workers. Moreover, the vast majority of these
unemployment insurance benefits are received by non-poor workers, who
comprise 87.2% of minimum wage workers who lose their jobs. If we
exclude unemployment insurance benefits from the above benefit
simulations, the break-even employment elasticity of the current minimum
wage proposal falls to -0.77.
5. Conclusions
This study first examines the effect of recent minimum wage
increases on state poverty rates and then compares the target efficiency
of the last federal minimum wage increase from $5.15 to $7.25 per hour
to the target efficiency of a newly proposed hike from $7.25 to $9.50
per hour. Our results show that recent minimum wage increases between
2003 and 2007 had no effect on state poverty rates. Moreover, the
proposal to raise the federal minimum wage to $9.50 per hour is unlikely
to be any better at reducing poverty because (i) most workers (89.0%)
who are affected are not poor, (ii) many poor workers (48.9%) already
earn hourly wages greater than $9.50 per hour, and (iii) the minimum
wage increase is likely to cause adverse employment effects for the
working poor. Our evidence also suggests that the target efficiency of
federal minimum wage increases is not improving, and it may actually be
worsening. When compared to the last federal increase, the current
proposal appears even less target efficient; 15.5% of the benefits of
the last increase were received by the working poor, compared to 10.5%
from the current proposal. At an employment elasticity of -0.6 for
minimum wage workers who are young dropouts and -0.2 for others, we
forecast that approximately 1.3 million low-skilled workers will lose
their jobs if the federal minimum wage is raised to $9.50 per hour,
including 168,000 jobs held by the working poor. And at employment
elasticities greater than -0.86, we estimate that net monthly benefits
from the minimum wage increase will actually become negative.
While raising the federal minimum wage is an increasingly
ineffective anti-poverty strategy, expansions in the EITC program may be
a promising alternative for several reasons. First, because eligibility
is based on family income rather than a wage rate, the benefits are much
more likely to be received by workers living in poor families
(Burkhauser, Couch, and Glenn 1996; Neumark and Wascher 2001; Burkhauser
and Sabia 2007; Congressional Budget Office 2007). Thus, most of the
48.9% of poor workers who earned hourly wages greater than $9.50 per
hour in March 2008 and would not gain from the proposed increase in the
federal minimum wage could gain from expansions in the EITC. Second,
because the costs of the EITC are not directly borne by employers,
expansions in this wage subsidy do not cause adverse labor demand
effects. In fact, a large body of empirical literature finds that
expansions in the EITC increase employment among low-skilled single
mothers (Eissa and Liebman 1996; Ellwood 2000; Meyer and Rosenbaum 2000,
2001; Hotz, Mullin, and Scholz 2002; Grogger 2003; Hotz and Scholz 2003;
Eissa and Hoynes 2005). Given that employment is an important
anti-poverty mechanism and wage subsidies can increase income to the
working poor, expansions in the EITC may be a more effective means of
aiding the working poor than would be increasing the federal minimum
wage.
We conclude that further increases in the minimum wage will do
little to reduce poverty and are a poor substitute for further
expansions in the federal Earned Income Tax Credit program as a
mechanism for reducing poverty.
Table A1. Estimates of Relationship between the Minimum
Wage and Log of State Poverty Rates, 2003-2007
Poverty Rate (INR < 1.0)
Overall Workers
(1) (2)
Log (ratio of minimum wage
to average state wage) 0.046 (0.080) 0.008 (0.124)
Prime-age male unemployment
rate 1.76 (0.762) ** 1.54 (0.915) *
Percentage of individuals
aged 54-64 0.712 (1.02) 0.104 (1.09)
Percentage of individuals
aged 16-24 2.08 (0.657) *** 3.46 (1.23) ***
State effects? Yes Yes
Year effects? Yes Yes
Mean poverty rate 0.108 0.059
N 225 255
Poverty Rate (INR < 1.25)
Overall Workers
(3) (4)
Log (ratio of minimum wage
to average state wage) 0.039 (0.071) 0.001 (0.104)
Prime-age male unemployment
rate 1.55 (0.668) ** 1.60 (0.807) *
Percentage of individuals
aged 54-64 0.101 (0.772) -0.91 (1.02)
Percentage of individuals
aged 16-24 1.18 (0.655) * 2.19 (1.02) **
State effects? Yes Yes
Year effects? Yes Yes
Mean poverty rate 0.144 0.067
N 255 255
Poverty Rate (INR < 1.5)
Overall Workers
(5) (6)
Log (ratio of minimum wage
to average state wage) 0.015 (0.066) 0.008 (0.100)
Prime-age male unemployment
rate 0.754 (0.619) 0.538 (0.699)
Percentage of individuals
aged 54-64 0.465 (0.638) -0.545 (0.735)
Percentage of individuals
aged 16-24 0.518 (0.560) 1.03 (0.735)
State effects? Yes Yes
Year effects? Yes Yes
Mean poverty rate 0.183 0.093
N 255 255
Source: Computed by the authors.
The poverty rate is calculated using family income and the
family size-adjusted poverty line. Adult wage measures and
unemployment rates are calculated for those aged 25-54. All
regressions are weighted by the relevant population of workers,
and standard errors are corrected for clustering on the state.
INR = income-to-needs ratio.
***, **, * indicate significance at the 1%, 5%, and
10% levels, respectively.
Table A2. Estimates of Relationship between the Minimum Wage
and Log of State Poverty Rates, 2003-2007
Poverty Rate (INR <1.0)
Overall Workers
(1) (2)
Log (minimum wage) -0.033 (0.164) -0.012 (0.225)
Log (average adult wage
rate) -0.002 (0.005) 0.002 (0.005)
Prime-age male
unemployment rate 1.66 (0.770) ** 1.26 (0.926)
Prime-age female
unemployment rate 0.271 (0.889) 1.47 (1.03)
Youth (16-24)
unemployment rate -0.054 (0.419) -0.393 (0.573)
High school graduation
rate -0.038 (1.22) -1.51 (1.47)
College graduation rate -0.241 (1.06) 0.308 (1.34)
Percentage of individuals
aged 54-64 0.695 (1.07) 0.318 (1.15)
Percentage of individuals
aged 16-24 2.13 (0.663) *** 3.32 (1.28) **
State effects? Yes Yes
Year effects? Yes Yes
Mean poverty rate 0.108 0.059
N 225 255
Poverty Rate (INR <1.25)
Overall Workers
(3) (4)
Log (minimum wage) 0.024 (0.156) 0.014 (0.200)
Log (average adult wage
rate) -0.001 (0.004) 0.002 (0.004)
Prime-age male
unemployment rate 1.34 (0.670) * 1.24 (0.837)
Prime-age female
unemployment rate 1.09 (0.768) 2.27 (1.09) **
Youth (16-24)
unemployment rate -0.081 (0.342) -0.434 (0.468)
High school graduation
rate 0.207 (0.983) -0.897 (1.17)
College graduation rate -0.378 (0.826) 0.532 (0.921)
Percentage of individuals
aged 54-64 0.132 (0.822) -0.745 (1.05)
Percentage of individuals
aged 16-24 1.10 (0.645) * 1.94 (1.07) *
State effects? Yes Yes
Year effects? Yes Yes
Mean poverty rate 0.144 0.067
N 255 255
Poverty Rate (INR < 1.5)
Overall Workers
(5) (6)
Log (minimum wage) 0.021 (0.132) 0.028 (0.183)
Log (average adult wage
rate) 0.001 (0.003) 0.002 (0.003)
Prime-age male
unemployment rate 0.583 (0.683) 0.281 (0.664)
Prime-age female
unemployment rate 0.717 (0.626) 1.30 (0.929)
Youth (16-24)
unemployment rate 0.087 (0.319) -0.078 (0.415)
High school graduation
rate -0.152 (0.874) -1.63 (1.02)
College graduation rate -0.408 (0.637) 0.408 (0.609)
Percentage of individuals
aged 54-64 0.533 (0.683) -0.338 (0.802)
Percentage of individuals
aged 16-24 0.467 (0.524) 0.91 (0.740)
State effects? Yes Yes
Year effects? Yes Yes
Mean poverty rate 0.183 0.093
N 255 255
Source: Computed by the authors.
The poverty rate is calculated using family income and the
family size-adjusted poverty line. Adult wage measures and
unemployment rates are calculated for those aged 25-54. All
regressions are weighted by the relevant population of workers,
and standard errors are corrected for clustering on the state.
INR = income-to-needs ratio.
***, **, * indicate significance at the 1%, 5%, and
10% levels, respectively.
Table A3. Estimates of Relationship between the Minimum Wage
and Log of State Poverty Rates, 2003-2007
Poverty Rate (INR <1.0)
Overall Workers
(1) (2)
Log (minimum wage) -0.024 (0.129) -0.030 (0.181)
Log (average adult
wage rate) -0.003 (0.005) 0.000 (0.005)
Prime-age male employment
ratio -2.30 (0.607) *** -1.06 (0.876)
Prime-age female
employment ratio -1.10 (0.544) ** -0.896 (0.662)
Youth (16-24) employment
ratio -0.305 (0.324) 0.634 (0.511)
High school
graduation rate -0.447 (1.00) -1.89 (1.47)
College graduation rate 0.066 (0.701) 0.408 (1.18)
Percentage of
individuals aged 54-64 1.12 (0.982) 0.210 (1.09)
Percentage of
individuals aged 16-24 2.07 (0.690) *** 3.41 (1.32) **
State effects? Yes Yes
Year effects? Yes Yes
Mean poverty rate 0.108 0.059
N 225 255
Poverty Rate (INR < 1.25)
Overall Workers
(3) (4)
Log (minimum wage) 0.002 (0.130) -0.060 (0.164)
Log (average adult
wage rate) -0.002 (0.004) 0.001 (0.005)
Prime-age male employment
ratio -1.57 (0.588) *** -0.045 (0.780)
Prime-age female
employment ratio -0.648 (0.480) 0.093 (0.534)
Youth (16-24) employment
ratio -0.181 (0.292) 0.760 (0.451) *
High school
graduation rate -0.262 (0.874) -1.39 (1.24)
College graduation rate -0.114 (0.610) 0.532 (0.946)
Percentage of
individuals aged 54-64 0.476 (0.753) -0.834 (0.987)
Percentage of
individuals aged 16-24 1.13 (0.676) * 2.10 (1.08) *
State effects? Yes Yes
Year effects? Yes Yes
Mean poverty rate 0.144 0.067
N 255 255
Poverty Rate (INR < 1.5)
Overall Workers
(5) (6)
Log (minimum wage) 0.031 (0.125) 0.007 (0.169)
Log (average adult
wage rate) -0.000 (0.004) 0.001 (0.003)
Prime-age male employment
ratio -1.33 (0.477) *** 0.049 (0.593)
Prime-age female
employment ratio -0.645 (0.377) * 0.202 (0.457)
Youth (16-24) employment
ratio -0.011 (0.237) 0.803 (0.355) **
High school
graduation rate -0.367 (0.731) -1.73 (0.989) *
College graduation rate -0.208 (0.434) 0.338 (0.597)
Percentage of
individuals aged 54-64 0.669 (0.610) -0.59 (0.735)
Percentage of
individuals aged 16-24 0.457 (0.526) 0.934 (0.667)
State effects? Yes Yes
Year effects? Yes Yes
Mean poverty rate 0.183 0.093
N 255 255
Source: Computed by the authors.
The poverty rate is calculated using family income and the
family size-adjusted poverty line. Adult wage measures and
unemployment rates are calculated for those aged 25-54. All
regressions are weighted by the relevant population of
workers, and standard errors are corrected for clustering
on the state. INR = income-to-needs ratio.
***, **, * indicate significance at the 1%, 5%, and 10%
levels, respectively.
Table A4. Estimates of Relationship between the Minimum
Wage and Log of State Poverty Rates, 2003-2007
Poverty Rate (INR < 1.0)
Overall Workers
(1) (2)
Log (ratio of minimum
wage to average
state wage) 0.071 (0.083) 0.036 (0.112)
Prime-age male
employment ratio -2.31 (0.624) *** -1.07 (0.884)
Prime-age female
employment ratio -1.15 (0.539) ** -0.949 (0.671)
Youth (16-24)
employment ratio -0.279 (0.317) 0.663 (0.502)
High school
graduation rate -0.289 (1.05) -1.81 (1.49)
College graduation rate -0.023 (0.699) 0.368 (1.14)
Percentage of
individuals aged 54-64 1.13 (1.00) 0.117 (1.07)
Percentage of
individuals aged 16-24 1.97 (0.705) *** 3.36 (1.27) **
State effects? Yes Yes
Year effects? Yes Yes
Mean poverty rate 0.108 0.059
N 225 255
Poverty Rate (INR < 1.25)
Overall Workers
(3) (4)
Log (ratio of minimum
wage to average
state wage) 0.054 (0.076) 0.006 (0.094)
Prime-age male
employment ratio -1.57 (0.591) ** -0.054 (0.784)
Prime-age female
employment ratio -0.687 (0.476) 0.053 (0.536)
Youth (16-24)
employment ratio -0.163 (0.287) 0.784 (0.446) *
High school
graduation rate -0.176 (0.892) -1.31 (1.25)
College graduation rate -0.157 (0.607) 0.482 (0.937)
Percentage of
individuals aged 54-64 0.449 (0.744) -0.879 (0.958)
Percentage of
individuals aged 16-24 1.08 (0.681) 2.05 (1.04) **
State effects? Yes Yes
Year effects? Yes Yes
Mean poverty rate 0.144 0.067
N 255 255
Poverty Rate (INR < 1.5)
Overall Workers
(5) (6)
Log (ratio of minimum
wage to average
state wage) 0.032 (0.070) 0.009 (0.085)
Prime-age male
employment ratio -1.33 (0.473) *** 0.046 (0.589)
Prime-age female
employment ratio -0.663 (0.380) * 0.184 (0.463)
Youth (16-24)
employment ratio -0.002 (0.235) 0.813 (0.352) **
High school
graduation rate -0.369 (0.795) -1.75 (1.05)
College graduation rate -0.195 (0.439) 0.356 (0.618)
Percentage of
individuals aged 54-64 0.600 (0.597) -0.678 (0.745)
Percentage of
individuals aged 16-24 0.463 (0.565) 0.949 (0.700)
State effects? Yes Yes
Year effects? Yes Yes
Mean poverty rate 0.183 0.093
N 255 255
Source: Computed by the authors.
The poverty rate is calculated using family income and the
family size-adjusted poverty line. Adult wage measures and
unemployment rates are calculated for those aged 25-54. All
regressions are weighted by the relevant population of workers,
and standard errors are corrected for clustering on the state.
INR = income-to-needs ratio.
***, **, * indicate significance at the 1%, 5%, and
10% levels, respectively.
Table A5. Wage Distribution of Hourly Workers in 2008 by
Income-to-Needs Ration of Their Household
Hourly Wage Categories (a)
Income-to-Needs $0.01 to $5.70 to $7.25 to $9.50 to
Ratio $5.49 $7.24 $9.49 $11.99
Less than 1.00 2.5 12.3 39.0 19.8
1.00 to 1.24 0.9 10.1 35.4 21.0
1.25 to 1.49 3.4 10.2 33.9 22.3
1.50 to 1.99 3.3 6.7 35.5 22.8
2.00 to 2.99 2.3 6.1 20.2 22.4
3.00 or above 1.5 4.6 13.8 13.8
Whole category
share (b) 1.9 5.9 19.8 17.2
Hourly Wage Categories (a)
Percentage
Income-to-Needs $12.00 to $16.00 and of All
Ratio $15.99 Over Total Workers
Less than 1.00 15.9 10.5 100.0 5.8
1.00 to 1.24 20.2 12,4 100.0 3.6
1.25 to 1.49 20.9 9.3 100.0 3.5
1.50 to 1.99 21.3 10.5 100.0 8.1
2.00 to 2.99 29.1 19.9 100.0 19.8
3.00 or above 23.1 43.2 100.0 59.3
Whole category
share (b) 23.5 31.8 100.0 100.0
Percentage of Workers
Income-to-Needs Earning More Than $5.70
Ratio and Less Than $9.50
Less than 1.00 11.6
1.00 to 1.24 6.4
1.25 to 1.49 5.9
1.50 to 1.99 13.3
2.00 to 2.99 20.3
3.00 or above 42.6
Whole category
share (b) 100.0
Source: Estimated from the outgoing rotation group of the
Current Population Survey, March 2008.
(a) Hourly wage rates are based on a direct question concerning
earnings per hour on workers' current primary job. All
household income data used to calculate income-to-needs ratios
come from retrospective information from the previous year
because that is the period for which it is reported. Wages
are in 2008 dollars.
(b) Share of all workers with wage earnings in each category.
Table A6. Wage Distribution of Non-Hourly Workers in 2008 by
Income-to-Needs Ratio of Their Household
Hourly Wage Categories (a)
Income-to-Needs $0.01 to $5.70 to $7.25 to $9.50 to
Ratio $5.69 $7.24 $9.49 $11.99
Less than 1.00 16.6 14.4 12.6 18.2
1.00 to 1.24 8.8 10.4 17.9 26.3
1.25 to 1.49 16.5 10.9 18.4 23.1
1.50 to 1.99 4.5 6.8 15.1 13.0
2.00 to 2.99 4.0 4.0 9.8 13.0
3.00 or above 1.3 1.0 2.3 3.6
Whole category 2.4 2.1 4.3 5.9
share (b)
Hourly Wage Categories (a)
Percentage
Income-to-Needs $12.00 to $16.00 and of All
Ratio $15.99 Over Total Workers
Less than 1.00 12.0 26.2 100.0 2.4
1.00 to 1.24 18.3 18.3 100.0 1.1
1.25 to 1.49 12.7 18.4 100.0 1.3
1.50 to 1.99 22.5 38.2 100.0 4.1
2.00 to 2.99 26.1 43.1 100.0 11.4
3.00 or above 11.9 79.9 100.0 79.8
Whole category 14.1 71.3 100.0 100.0
share (b)
Percentage of Workers+
Earning More Than
Income-to-Needs $5.70 and Less
Ratio Than $9.50
Less than 1.00 10.1
1.00 to 1.24 4.7
1.25 to 1.49 5.7
1.50 to 1.99 13.9
2.00 to 2.99 24.5
3.00 or above 41.0
Whole category 100.0
share (b)
(a) Hourly wage rates are based on a calculated ratio of
weekly earnings to weekly hours. All household income data
used to calculate income-to-needs ratios come from
retrospective information from the previous year because
that is the period for which it is reported. Wages are in
2008 dollars.
(b) Share of all workers with wage earnings in each category.
Table A7. Wage Distribution of Hourly Workers in 2007 by
Income-to-Needs Ratio in Their Household
Hourly Wage Categories (a)
Income-to-Needs $0.01 to $5.00 to $5.15 to $7.25 to
Ratio $4.99 $5.14 $7.24 $8.99
Less than 1.00 2.9 0.5 25.5 28.0
1.00 to 1.24 2.0 1.4 15.6 25.9
1.25 to 1.49 1.8 1.0 16.8 23.4
1.50 to 1.99 2.6 0.0 10.4 18.4
2.00 to 2.99 1.2 0.5 9.7 14.5
3.00 or above 1.1 0.2 6.4 10.1
Whole category 1.4 0.3 9.2 13.8
share (b)
Hourly Wage Categories (a)
Percentage
Income-to-Needs $9.00 to $15.00 and of All
Ratio $14.99 Over Total Workers
Less than 1.00 37.1 6.1 100.0 6.3
1.00 to 1.24 47.6 7.4 100.0 3.2
1.25 to 1.49 42.5 14.5 100.0 3.4
1.50 to 1.99 48.7 20.0 100.0 7.0
2.00 to 2.99 47.7 26.6 100.0 19.9
3.00 or above 34.7 47.6 100.0 58.2
Whole category 39.4 35.9 100.0 100.0
share (b)
Percentage of
Percentage of Hourly Workers
Workers Earning Earning More
More Than $4.99 Than $5.70 and
Income-to-Needs and Less Than Less Than $9.49
Ratio $7.25 in 2008
Less than 1.00 17.0 1.6
1.00 to 1.24 5.6 6.4
1.25 to 1.49 6.3 5.9
1.50 to 1.99 10.0 13.3
2.00 to 2.99 21.0 20.3
3.00 or above 40.0 42.6
Whole category 100.0 100.0
share (b)
Source: Estimated from the outgoing rotation group of
the Current Population Survey, March 2007.
(a) Hourly wage rates are based on a direct question
concerning earnings per hour on workers' current primary
job. All household income data used to calculate
income-to-needs ratios come from retrospective information
from the previous year because that is the period for which it
is reported. Wages are in 2007 dollars.
(b) Share of all workers with wage earnings in each category.
Table A8. Wage Distribution of Non-Hourly Workers in 2007
by Income-to-Needs Ratio of Their Household
Hourly Wage Categories (a)
Income-to-Needs $0.01 to $5.00 to $5.15 to $7.25 to
Ratio $4.99 $5.14 $7.24 $8.99
Less than 1.00 17.5 2.8 9.5 8.2
1.00 to 1.24 8.5 0.0 9.6 19.7
1.25 to 1.49 1.4 0.7 14.0 12.9
1.50 to 1.99 4.4 2.1 9.5 6.9
2.00 to 2.99 0.7 0.5 4.8 5.5
3.00 or above 0.7 0.1 1.2 1.9
Whole category 1.3 0.3 2.5 3.1
share (b)
Hourly Wage Categories (a)
Percentage
Income-to-Needs $9.00 to $15.00 and of All
Ratio $14.99 over Total Workers
Less than 1.00 36.8 25.2 100.0 2.3
1.00 to 1.24 50.8 11.4 100.0 1.2
1.25 to 1.49 50.3 20.8 100.0 1.8
1.50 to 1.99 37.8 39.4 100.0 4.0
2.00 to 2.99 35.0 53.6 100.0 12.1
3.00 or above 14.8 81.4 100.0 78.6
Whole category 19.7 73.1 100.0 100.0
share (b)
Percentage of Non-
Percentage of Hourly Workers
Workers Earning Earning More Than
Income-to-Needs More Than $4.99 $5.70 and Less
Ratio and Less Than $7.25 Than $9.49 in 2008
Less than 1.00 10.2 10.1
1.00 to 1.24 4.1 4.7
1.25 to 1.49 9.7 5.7
1.50 to 1.99 16.6 13.9
2.00 to 2.99 22.8 24.5
3.00 or above 36.6 41.0
Whole category 100.0 100.0
share (b)
Source: Estimated from the outgoing rotation group of
the Current Population Survey, March 2007.
(a) Hourly wage rates are based on a calculated ratio of
weekly earnings to weekly hours. All household income
data used to calculate income-to-needs ratios come from
retrospective information from the previous year because
that is the period for which it is reported. Wages are in
2007 dollars.
(b) Share of all workers with wage earnings in each category.
Table A9. Demographic Characteristics of Workers Affected
by Past and Future Increases in the Federal Minimum Wage:
By Hourly versus Non-Hourly Status (a)
Hourly Non-Hourly
Family Type New Proposal
Not highest earner in family 51.2 44.7
Highest earner, unmarried female,
children under 18 years old in family 11.3 10.0
Highest earner, unmarried male,
children under 18 years old in family 5.8 6.2
Highest earner, married with children
under 18 years old in family 8.6 13.5
Highest earner, family size greater
under no children 10.4 12.0
Highest earner, family size equal to 1 12.7 13.6
Whole category share 100 100
Hourly Non-Hourly
Family Type Last Federal Increase
Not highest earner in family 57.3 53.3
Highest earner, unmarried female,
children under 18 years old in family 12.5 9.8
Highest earner, unmarried male,
children under 18 years old in family 5.5 7.4
Highest earner, married with children
under 18 years old in family 6.3 8.9
Highest earner, family size greater
under no children 7.5 13.2
Highest earner, family size equal to 1 11.0 7.3
Whole category share 100 100
(a) The first three columns ("New Proposal") consist of a
weighted sample of workers that includes all non-military,
non- self-employed workers who earned between $5.70
and $9.49 per hour in March 2008, based on the March
2008 Current Population Survey (CPS) outgoing rotation
group. The final three columns ("Last Federal Increase")
consist of weighted sample of workers that includes all
non-military, non-self-employed workers who earned
between $5.00 and $7.24 per hour in March 2007, based
on the March 2007 CPS outgoing rotation group.
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Joseph J. Sabia * and Richard V. Burkhauser ([dagger])
* American University, Department of Public Administration &
Policy, School of Public Affairs, 4400 Massachusetts Avenue, NW, 336
Ward Circle Building, Washington, DC 20016, USA; E-mail
sabia@american.edu.
([dagger]) Cornell University, Department of Policy Analysis &
Management, College of Human Ecology, 125 MVR Hall, Ithaca, NY
14853-4401, USA; E-mail rvbl@cornell.edu; corresponding author.
We thank Andres Araoz for excellent research assistance and Melody
Reinecke for excellent editing assistance. This research was funded, in
part, by the Employment Policies Institute. This article was completed
while Burkhauser was the R. I. Downing Fellow in Social Economics in the
Faculty of Economics and Commerce at the University of Melbourne. The
authors take responsibility for all remaining errors.
Received October 2008; accepted February 2009.
(1) Raising the federal minimum wage to $9.50 per hour has support
among leading Democrats, including President Barack Obama
(BarackObama.com 2008); the late Senator Edward Kennedy (Zappone 2007);
former Senator John Edwards (Montanaro 2007); and Secretary of State
Hillary Clinton (Zappone 2007), who as a senator introduced S.2514 in
December 2007.
(2) Between 2003 and 2007, 28 states raised their minimum wage
above the federal level, and in 2007, the federal minimum wage rose from
$5.15 to $5.85 per hour. For examples of proponents of these hikes, see
Bernstein (2004), Hindery (2004), Kennedy (2005), Clinton (2006), Fiscal
Policies Institute (2006), Wolfson (2006), and Bernstein (2007).
(3) In 2007, the poverty line for a family of four was $20,650.
Three times the poverty threshold for a family this size is $61,950,
well above the median household income of $50,233 in 2007 (DeNavas-Walt,
Proctor, and Smith 2008).
(4) The one exception is Addison and Blackburn (1999), who find
that minimum wage increases reduce poverty among junior high school
dropouts. However, as Neumark and Wascher (2008) note, junior high
school dropouts are older and unlikely to have small children; whereas,
most anti-poverty efforts focus on families with younger children.
(5) These data also contain information on family income and family
size, which can be used to construct poverty measures using the family
unit, as has been done in the previous literature (Card and Krueger
1995; Burkhauser and Sabia 2007).
(6) The results are not sensitive to using the household unit to
calculate poverty.
(7) If multiple minimum wages prevailed during the year, this
variable is coded as the average minimum wage that prevailed during the
year, weighted by the share of the year each wage was in effect.
(8) During this period, the following 28 states raised their
minimum wage: Arizona, Arkansas, California, Colorado, Connecticut,
Delaware, District of Columbia, Florida, Hawaii, Illinois, Maine,
Maryland, Massachusetts, Michigan, Minnesota, Missouri, Nevada, New
Hampshire, New Jersey, New York, North Carolina, Ohio, Oregon,
Pennsylvania, Rhode Island, Vermont, Washington, and Wisconsin. The
federal minimum wage rose from $5.15 to $5.85 per hour on July 24, 2007.
(9) The federal minimum wage rose again from $5.85 to $6.55 per
hour on July 24, 2008, and increased again to $7.25 per hour in July
2009.
(10) As discussed below, the federal minimum wage in March 2008 was
$5.85 per hour. Thus, we are taking a conservative approach by assuming
that workers earning hourly wages between $5.70 and $7.24 will be
earning $7.25 at the time the new minimum wage plan is considered. As in
past simulations (see Burkhauser and Finegan 1989; Burkhauser, Couch,
and Glenn 1996; Burkhauser and Sabia 2007), we assume that workers
earning hourly wages less than $0.15 below the current federal minimum
wage are in the "uncovered" sector. Theoretically, workers
earning wages greater than $9.50 per hour could benefit from minimum
wage increases if there are wage spillovers. But there is little
empirical evidence that such spillovers exist (see, for example, Sabia
and Burkhauser 2008).
(11) We experimented with a number of values from 0.3 to 0.6 for
[theta], and the distributional results were substantively unchanged.
(12) Note that if we extended our period of analysis beyond one
month, laid-off minimum wage workers who applied for and received
unemployment insurance benefits would be eligible for such benefits in
each week of subsequent months. However, if we extended the time horizon
of our analysis beyond six months, we would have to account for the fact
that UI benefits are generally limited to 26 weeks unless the federal
government enacts an extension.
(13) We also experiment with controlling for the share of
individuals who were employed rather than the unemployment rate. In
Appendix Table A3, we include as controls employment ratios rather than
unemployment rates, defined as the share of all individuals in a
particular age group who are working. The results are unchanged. In the
specifications in Appendix Table A4, we include the same set of controls
as in Appendix Table A3 but use the ratio of the state minimum wage to
the average state wage rate as our minimum wage measure. Again, we find
no evidence that state minimum wage hikes reduce poverty among all
individuals or workers.
(14) Following Burkhauser and Finegan (1989); Burkhauser, Couch,
and Glenn (1996); and Burkhauser and Sabia (2007), we assume that
workers earning $0.15 below the federal minimum wage--in this case,
those earning hourly wages between $5.70 and $5.84 per hour in March
2008--are working in jobs covered by the federal minimum wage and their
wages simply reflect reporting error.
(15) The reported occupations of these workers suggest that many
are tipped workers or those working in the informal sector, and thus
they will be uncovered by the $9.50 federal minimum wage. For the full
worker sample, we find that 34% of these workers were food service
workers, 12% were home health care or other personal service workers,
12% were retail or other service workers, and 7% were in education
services. In the sample of workers who report being paid hourly, 56%
were food service workers, 11% were home health care or other personal
service workers, 4% were in retail, and 3.5% were in education services.
(16) Burkhauser and Sabia (2007) use the March 2003 CPS. The March
2007 CPS is the latest annual March CPS available when all workers faced
a federal minimum wage of $5.15 per hour.
(17) These results for the last federal minimum wage increase are
robust across the samples of hourly and non-hourly workers (see Appendix
Tables A7 and A8, respectively).
(18) See, for example, Hindery (2004), Kennedy (2005), and Clinton
(2006).
(19) Appendix Table A9 shows that these demographic characteristics
are generally similar across hourly and non-hourly workers.
Table 1. Estimates of Relationship between the Minimum
Wage and Log of State Poverty Rates, 2003-2007
Poverty Rate (INR < 1.0)
Overall Workers
(1) (2)
Log (minimum wage) -0.052 (0.146) -0.020 (0.203)
Prime-age male 1.71 (0.754) ** 1.52 (0.901) *
unemployment rate
Log (average adult wage -0.103 (0.121) -0.025 (0.155)
rate)
Percentage of individuals 0.558 (1.00) 0.059 (1.11)
aged 54-64
Percentage of individuals 2.18 (0.681) *** 3.49 (1.26) ***
aged 16-24
State Effects? Yes Yes
Year Effects? Yes Yes
Mean of dependent variable 0.108 0.059
N 225 255
Poverty Rate (INR < 1.25)
Overall Workers
(3) (4)
Log (minimum wage) -0.016 (0.104) -0.013 (0.186)
Prime-age male 1.52 (0.025) ** 1.59 (0.779) **
unemployment rate
Log (average adult wage -0.072 (0.101) -0.010 (0.136)
rate)
Percentage of individuals 0.013 (0.780) -0.933 (1.06)
aged 54-64
Percentage of individuals 1.23 (0.672) * 2.20 (1.03) **
aged 16-24
State Effects? Yes Yes
Year Effects? Yes Yes
Mean of dependent variable 0.144 0.067
N 255 255
Poverty Rate (INR < 1.5)
Overall Workers
(5) (6)
Log (minimum wage) 0.004 (0.132) 0.045 (0.196)
Prime-age male 0.748 (0.599) 0.560 (0.658)
unemployment rate
Log (average adult wage -0.21 (0.090) 0.013 (0.107)
rate)
Percentage of individuals 0.447 (0.645) -0.487 (0.836)
aged 54-64
Percentage of individuals 0.529 (0.540) 0.989 (0.695)
aged 16-24
State Effects? Yes Yes
Year Effects? Yes Yes
Mean of dependent variable 0.183 0.093
N 255 255
Source: Computed by the authors.
The poverty rate is calculated using family income and the
family size-adjusted poverty line. Adult wage measures and
unemployment rates are calculated for those aged 25-54. All
regressions are weighted by the relevant population of
workers, and standard errors are corrected for clustering
on the state.
***, **, * indicate significance at the 1%, 5%, and
10% levels, respectively.
Table 2. Wage Distribution of All Workers in 2008 by
Income-to-Needs Ratio of Their Household
Hourly Wage Categories (a)
Income-to-Needs $0.01 to $5.70 to $7.25 to $9.50 to
Ratio $5.69 $7.24 $9.49 $11.99
Less than 1.00 5.7 12.7 32.7 19.5
1.00 to 1.24 2.3 10.1 32.1 22.1
1.25 to 1.49 6.1 10.4 30.7 22.5
1.50 to 1.99 3.6 6.7 30.0 20.2
2.00 to 2.99 2.8 5.4 17.2 19.6
3.00 or above 1.4 2.8 8.2 8.9
Whole category 2.1 4.3 13.3 12.5
share (b)
Hourly Wage Categories (a)
Percentage
Income-to-Needs $12.00 to $16.00 and of All
Ratio $15.99 Over Total Workers
Less than 1.00 15.5 13.9 100.0 4.4
1.00 to 1.24 19.7 13.8 100.0 2.6
1.25 to 1.49 19.2 11.2 100.0 2.5
1.50 to 1.99 21.7 17.8 100.0 6.4
2.00 to 2.99 28.2 26.7 100.0 16.3
3.00 or above 17.6 61.1 100.0 67.8
Whole category 19.6 48.2 100.0 100.0
share (b)
Percentage of Workers
Income-to-Needs Earning More than $5.70
Ratio and Less than $9.49
Less than 1.00 11.3
1.00 to 1.24 6.2
1.25 to 1.49 5.9
1.50 to 1.99 13.4
2.00 to 2.99 20.9
3.00 or above 42.3
Whole category 100.0
share (b)
Source: Estimated from the outgoing rotation group of the
Current Population Survey, March 2008.
(a) For hourly workers, wage rates are based on a direct
question concerning earnings per hour on their current
primary job; for non-hourly workers, wages are calculated
as the ratio of reported weekly earnings to weekly hours
worked. All household income data used to calculate
income-to-needs ratios come from retrospective information
from the previous year because that is the period for which
it is reported. Wages are in 2008 dollars.
(b) Share of all workers with wage earnings in each category.
Table 3. Wage Distribution of All Workers in 2007 by
Income-to-Needs Ratio of Their Household
Hourly Wage Categories (a)
Income-to-Needs $0.01 to $5.00 to $5.15 to $7.25 to
Ratio $4.99 $5.14 $7.24 $8.99
Less than 1.00 6.0 1.2 21.9 23.6
1.00 to 1.24 3.4 1.1 14.3 24.6
1.25 to 1.49 1.7 0.9 16.0 20.3
1.50 to 1.99 3.0 0.5 10.2 15.5
2.00 to 2.99 1.0 0.5 8.1 11.8
3.00 or above 0.9 0.2 3.8 6.0
Whole category 1.4 0.3 6.4 9.3
share (b)
Hourly Wage Categories (a)
Percentage
Income-to-Needs $9.00 to $15.00 of All
Ratio $14.99 and Over Total Workers
Less than 1.00 37.1 10.3 100.0 4.6
1.00 to 1.24 48.3 8.3 100.0 2.3
1.25 to 1.49 44.5 16.6 100.0 2.7
1.50 to 1.99 46.0 24.8 100.0 7.0
2.00 to 2.99 43.6 35.0 100.0 16.6
3.00 or above 24.8 64.4 100.0 66.8
Whole category 31.1 51.6 100.0 100.0
share (b)
Percentage of Workers
Percentage of Workers Earning More Than $5.70
Income-to-Needs Earning More Than $4.99 and Less Than $9.49
Ratio and Less Than $7.25 in 2008
Less than 1.00 15.8 11.3
1.00 to 1.24 5.4 6.2
1.25 to 1.49 6.9 5.9
1.50 to 1.99 11.2 13.4
2.00 to 2.99 21.4 20.9
3.00 or above 39.4 42.3
Whole category 100.0 100.0
share (b)
Source: Estimated from the outgoing rotation group of
the Current Population Survey, March 2007.
(a) For hourly workers, wage rates are based on a direct
question concerning earnings per hour on their current
primary job; for non-hourly workers, wages are calculated
as the ratio of reported weekly earnings to weekly hours
worked. All household income data used to calculate
income-to-needs ratios come from retrospective information
from the previous year because that is the period for which
it is reported. Wages are in 2007 dollars.
(b) Share of all workers with wage earnings in
each category.
Table 4. Demographic Characteristics of Workers Affected by
Past and Future Increases in the Federal Minimum Wage:
Family Type and Gender (a)
New Proposal
Total Male Female
Family Type (%) (%) (%)
Not highest earner in family 50.2 20.0 30.2
Highest earner, unmarried female,
children under 18 years old in family 11.1 -- 11.1
Highest earner, unmarried male, children
under 18 years old in family 5.8 5.8 --
Highest earner, married with children
under 18 years old in family 9.3 5.1 4.2
Highest earner, family size greater
than 1, no children 10.5 4.7 5.9
Highest earner, family size equal to 1 12.9 6.4 6.5
Whole category share 100.0 42.1 57.9
Last Federal Increase
Total Male Female
Family Type (%) (%) (%)
Not highest earner in family 56.6 23.9 32.7
Highest earner, unmarried female,
children under 18 years old in family 12.0 -- 12.0
Highest earner, unmarried male, children
under 18 years old in family 5.8 5.8 --
Highest earner, married with children
under 18 years old in family 6.7 2.8 3.9
Highest earner, family size greater
than 1, no children 7.5 3.4 5.1
Highest earner, family size equal to 1 10.3 5.5 4.8
Whole category share 100.0 41.5 58.5
(a) The first three columns ("New Proposal") consists of a
weighted sample of workers that includes all non-military,
non-self-employed workers who earned between $5.70 and $9.49
per hour in March 2008, based on the March 2008 Current
Population Survey (CPS) outgoing rotation group. The final
three columns ("Last Federal Increase") consists of a weighted
sample of workers that includes all non-military,
non-self-employed workers who earned between $5.00 and $7.24
per hour in March 2007, based on the March 2007 CPS outgoing
rotation group.
Table 5. Simulated Employment Losses of Proposed Federal
Minimum Wage Increase to $9.50 per Hour, by Household
Income-to-Needs Ratio (ab)
Percentage of
Workers Earning
More Than Job Losses
$5.70 and Less Number of (000s)
Income-to-Needs Than $9.49 (ab) Workers (000s) (e = -0.1) (c)
Ratio (1) (2) (3)
Less than 1.00 11.3 2,413 57.5
1.00 to 1.24 6.2 1,316 28.7
1.25 to 1.49 5.9 1,255 28.4
1.50 to 1.99 13.4 2,851 57.5
2.00 to 2.99 20.9 4,453 96.5
3.00 or above 42.3 9,015 198.8
Total 100.0 21,303 467.5
Job Losses Job Losses Job Losses
(000s) (000s) (000s) (e =
Income-to-Needs (e = -0.3) (c) (e = -0.6)d -0.86) (d)
Ratio (4) (5) (6)
Less than 1.00 172.5 344.9 496.5
1.00 to 1.24 86.2 172.4 247.5
1.25 to 1.49 85.3 170.7 245.5
1.50 to 1.99 172.5 345.0 496.2
2.00 to 2.99 289.5 579.0 833.8
3.00 or above 596.5 1,193 1,716
Total 1,402.0 2,805.0 4,034.0
Job Losses
(000s)
Percentage (e = -0.6 Young Percentage of
of Total Job Dropouts; e = Total Job Loss
Income-to-Needs Loss -0.2 Others) (Column 8)
Ratio (7) (8) (9)
Less than 1.00 12.3 168.4 12.8
1.00 to 1.24 6.1 78.8 6.0
1.25 to 1.49 6.1 70.0 5.4
1.50 to 1.99 12.3 147.1 11.2
2.00 to 2.99 20.6 282.5 21.5
3.00 or above 42.5 566.0 43.1
Total 100.0 1,313.0 100.0
(a) For hourly workers, wage rates are based on a direct
question concerning earnings per hour on their current primary
job; for non-hourly workers, wages are calculated as the ratio
of reported weekly earnings to weekly hours worked. All
household income data used to calculate income-to-needs ratios
come from retrospective information from the previous year
because that is the period for which it is reported. Wages are
in nominal dollars. Sample restricted to 16-4-year-olds who
report positive weeks and weekly hours worked in the previous
year.
(b) This wage category corresponds to March 2008.
(c) Consensus estimates in minimum wage literature
(see Neumark and Wascher 2007).
(d) Upper-bound estimates found in new minimum wage literature
(see Burkhauser, Couch, and Wittenberg 2000b; Sabia 2008;
Sabia and Burkhauser 2008).
Table 6. Simulated Employment Losses from the Last Federal
Minimum Wage Increase to $7.25 per Hour, by Household
Income-to-Needs Ratio (ab)
Percentage of Workers
Earning More Than
$5.00 and Less Than Number of
Income-to-Needs $7.25 in 2007 (ab) Workers (000s)
Ratio (1) (2)
Less than 1.00 15.8 1274
1.00 to 1.24 5.4 431.2
1.25 to 1.49 6.9 552.7
1.50 to 1.99 11.2 897.7
2.00 to 2.99 21.4 1718
3.00 or above 39.4 3169
Total 100.0 8042.0
Job Losses (000s)
(e = -0.6 Young
Dropouts; Percentage of
e = -0.2 Others) Total Job Loss
(3) (4)
Less than 1.00 51.5 13.7
1.00 to 1.24 25.4 6.8
1.25 to 1.49 18.7 5.0
1.50 to 1.99 44.6 14.8
2.00 to 2.99 79.4 21.2
3.00 or above 155.3 40.8
Total 374.9 100.0
(a) For hourly workers, wage rates are based on a direct
question concerning earnings per hour on their current primary
job; for non-hourly workers, wages are calculated as the ratio
of reported weekly earnings to weekly hours worked. All
household income data used to calculate income-to-needs
ratios come from retrospective information from the previous
year because that is the period for which it is reported.
Wages are in nominal dollars. Sample restricted to
16-64-year-olds who report positive weeks and weekly
hours worked in the previous year.
(b) This wage category corresponds to March 2007.
Table 7. Simulated Monthly Net Benefits from Proposed Federal
Minimum Wage Increase to $9.50, by Household Income-to-Needs
Ratio (ab)
Net Benefits Net Benefits
in Billions $ % Net Benefits in Billions $
Income-to-Needs (e = 0) (e = 0) (e = -0.1)
Ratio ( 1 ) (2) (3)
Less than 1.00 0.439 10.9 0.389
1.00 to 1.24 0.282 7.0 0.249
1.25 to 1.49 0.270 6.7 0.239
1.50 to 1.99 0.566 14.0 0.502
2.00 to 2.99 0.832 20.6 0.734
3.00 or above 1.64 40.7 1.45
Total 4.03 100.0 3.56
Net Benefits Net Benefits Net Benefits
in Billions $ in Billions $ in Billions $
Income-to-Needs (e = -0.3) (e = -0.6) (e = -0.86) (c)
Ratio (4) (5) (6)
Less than 1.00 0.287 0.135 0.001
1.00 to 1.24 0.184 0.086 0.000
1.25 to 1.49 0.177 0.084 0.003
1.50 to 1.99 0.374 0.183 0.014
2.00 to 2.99 0.539 0.245 -0.012
3.00 or above 1.07 0.495 -0.006
Total 2.63 1.23 0.000
Net Benefits
in Billions $
(e = -0.6 for
16- 29-Year-Old
Dropouts;
e = -0.2 for % Net Benefits
Income-to-Needs % Net Benefits Others) (Column 6)
Ratio (7) (8) (9)
Less than 1.00 10.9 0.298 10.5
1.00 to 1.24 7.0 0.201 7.1
1.25 to 1.49 6.8 0.195 6.9
1.50 to 1.99 14.9 0.413 14.5
2.00 to 2.99 19.9 0.565 19.9
3.00 or above 40.2 1.17 41.2
Total 100.0 2.84 100.0
(a) Expected benefits are calculated as the weighted sum of
(1 - p)($9.50 - w)H - pwH + pUI for each minimum wage worker,
where p is the probability of job loss from the minimum wage
hike, [($9.50 - w)/w]e; w is the worker's hourly wage rate; H
is monthly hours worked; UI is the expected unemployment
insurance benefit; and e is the employment elasticity.
(b) The analysis uses data from the outgoing rotation groups
of the March 2008 CPS. A minimum wage worker is defined as
earning between $5.70 and $9.49 per hour in March 2008. Sample
restricted to 16-64-year-olds who report positive weeks and
weekly hours worked in previous year.
(c) The break-even elasticity is -0.863.
Table 8. Comparison of Simulated Monthly Net Benefits from
Proposed Federal Minimum Wage Increase to the Last Federal
Minimum Wage Increase, by Household Income-to-Needs Ratio (ab)
Net Benefits in
Billions $ from New
Proposal (e = -0.6 for
16-29-Year-Old % Net Benefits from
Dropouts; New Proposal
Income-to-Needs e = -0.2 for Others) (Column 1)
Ratio (1) (2)
Less than 1.00 0.298 10.5
1.00 to 1.24 0.201 7.1
1.25 to 1.49 0.195 6.9
1.50 to 1.99 0.413 14.5
2.00 to 2.99 0.565 19.9
3.00 or above 1.170 41.2
Total 2.840 100.0
Net Benefits in
Billions $ from Last
Federal Increase
(e = -0.6 for
16-29-Year-Old % Net Benefits from
Dropouts; e = -0.2 Last Federal Increase
Income-to-Needs for Others) (Column 3)
Ratio (3) (4)
Less than 1.00 0.086 15.5
1.00 to 1.24 0.026 4.7
1.25 to 1.49 0.038 6.8
1.50 to 1.99 0.075 13.5
2.00 to 2.99 0.122 21.9
3.00 or above 0.211 37.9
Total 0.556 100.0
Net Benefits in
Billions $ from Last
Federal Increase
Income-to-Needs (e = -0.91) (c)
Ratio (5)
Less than 1.00 0.002
1.00 to 1.24 0.000
1.25 to 1.49 0.000
1.50 to 1.99 -0.001
2.00 to 2.99 0.002
3.00 or above -0.004
Total 0.000
(a) Expected benefits from last federal minimum wage increase
are calculated as the weighted sum of (1 - p)($7.25 - w)H -
pwH + pUI for each minimum wage worker, where p is the
probability of job loss from the minimum wage hike, [($7.25 - w)/w]e;
w is the worker's hourly wage rate; H is monthly hours
worked; UI is expected unemployment insurance benefits; and e
is the employment elasticity.
(b) The analysis uses data from the outgoing rotation groups
of the March 2007 CPS. A minimum wage worker is defined as
earning between $5.00 and $7.24 per hour in March 2007. Sample
restricted to 16-64-year-olds who report positive weeks and
weekly hours worked in previous year.
(c) The break-even elasticity is -0.912.