when is a good time to raise the minimum wage? · when is a good time to raise the minimum wage?...

WHEN IS A GOOD TIME TO RAISE THE MINIMUM WAGE?

SAMUEL M. LUNDSTROM∗

I analyze changes in the target efficiency of the federal minimum wage over the past25 years. Using static simulation methods I find that minimum wage target efficiency iscurrently close to its 25-year peak—of the total monetary benefits generated by a 12%increase in the federal minimum wage, 16.8% would flow to workers in poverty. Thisexceeds the least target efficient year over this period by 4.7 percentage points and isonly 0.6 percentage points below the peak. Furthermore, I find a very strong positiverelationship between minimum wage target efficiency and the real federal minimumwage. The implication is that, from an efficiency standpoint, a good time to raise theminimum wage is when it is already high. This discovery raises the possibility that theminimum wage increases the employment of low-skilled poor individuals relative to theemployment of low-skilled non-poor individuals. Moreover, this discovery may bolsterthe rationale for an indexed minimum wage whereby it is prevented from falling to lessefficient levels. (JEL J21, J31, J38)

I. INTRODUCTION

The minimum wage is widely viewed—bycritics and advocates alike—to be an inefficientform of income redistribution.1 Regardless ofwhether or not the minimum wage is efficient,it is, nevertheless, an enduring policy at both thestate and federal level. Given that it is almost cer-tainly here to stay, it is important for policy mak-ers to understand how to make the most effectiveuse of minimum wage policy. Among the manyquestions faced by those who are tasked withminimum wage policy is the question of timing.Several economic and labor market changes have

∗I am grateful to David Neumark, Marianne Bitler,Damon Clark, and Greg Duncan for helpful comments onthis project. I am also grateful to two anonymous refereesfor their helpful comments and suggestions. Finally, I amgrateful to the University of California, Irvine for supportingme in my research.Lundstrom: Department of Economics, University of

California-Irvine, Irvine, CA 92697-5100. Phone 801-656-5421, Fax 949-824-2182, E-mail [email protected]

1. Even Card and Krueger, whose work on the employ-ment effects of the minimum wage is frequently cited by min-imum wage advocates, acknowledge that “the minimum wageis evidently a ‘blunt instrument’ for redistributing income tothe poorest families” (Card and Krueger 1995). A primary rea-son for the inefficiency of the minimum wage is that the vastmajority of low-wage workers are not poor, a fact first doc-umented by Gramlich (1976) and confirmed in a number oflater studies (e.g., Burkhauser and Sabia 2007, 2010; Con-gressional Budget Office 2014).

occurred in recent years which suggest—from anefficiency standpoint, at least—that now mightbe a good time to raise the minimum wage.First, since the late 1990s the teen employmentrate has fallen precipitously. In 1999, 42% ofteens aged 16–19 were employed. By 2014, thisfigure had fallen to 26%.2 Since teens make upa significant share of low-wage workers, anda larger share of non-poor low-wage workers,a reduction in teen employment could improveminimum wage target efficiency.3 Second, since2001 the poverty rate among low-skilled individ-uals has increased substantially. In 2001, 19.1%of low-skilled individuals (individuals with lessthan a 12th grade education) lived in householdswith incomes below the poverty line. By 2014this figure had risen to 23.1%.4 If the house-hold income of minimum wage workers near the

2. Estimated using data from the March CurrentPopulation Survey.

3. In 1999, 28.6% of low-wage workers (those earningless than half the median wage) were teens aged 16–19. Only8% of these workers were in households with income belowthe poverty line. Based on March 1999 CPS.

4. These calculations are derived from the March CPS forthe respective years.

ABBREVIATIONS

CBO: Congressional Budget OfficeCPS: Current Population Survey

29Contemporary Economic Policy (ISSN 1465-7287)Vol. 35, No. 1, January 2017, 29–52Online Early publication February 22, 2016

doi:10.1111/coep.12169© 2016 Western Economic Association International

30 CONTEMPORARY ECONOMIC POLICY

poverty line falls, these workers might be pushedinto poverty, thereby improving target efficiency.

In this study, my objectives are to: (1) deter-mine how the target efficiency of the federalminimum wage has changed during this periodof declining teen employment and increasingpoverty and (2) determine whether, in general, theteen employment rate or the poverty rate are goodpredictors of minimum wage target efficiency.This is done by examining how closely changesin target efficiency correlate with changes in teenemployment or with changes in the poverty rate.I then consider other possible correlates of mini-mum wage target efficiency.

Using data from the March Current Popula-tion Survey (CPS) for the years 1990 through2014, I find that the target efficiency of the fed-eral minimum wage is currently near a 25-yearhigh. Evidence from a static simulation impliesthat of the total monetary benefits generated by a12% increase in the real federal minimum wage,16.8% would flow to workers in poverty. Thisexceeds the least target efficient year over thisperiod by 4.7 percentage points and is only 0.6percentage points below the peak. I find fairlyweak relationships between the teen employmentrate and minimum wage target efficiency andbetween the poverty rate and minimum wage tar-get efficiency. However, I find a very strong pos-itive relationship between minimum wage targetefficiency and the level of the real federal min-imum wage. This is surprising because, giventhe positive relationship between the skill andincome distributions, we expect the minimumwage to better target poor workers when it is low.The implication is that a good time to raise theminimum wage—from an efficiency standpoint,at least—is when it is already high. This dis-covery raises the possibility that the minimumwage increases the employment of low-skilledpoor individuals relative to the employment oflow-skilled non-poor individuals. Furthermore,this finding could bolster the rationale for index-ing the minimum wage, whereby it is preventedfrom falling to less efficient levels.

II. LITERATURE REVIEW

Neumark and Wascher (2008) provide athorough review of the literature concernedwith minimum wage target efficiency. The firstof these was conducted by Gramlich (1976).Gramlich simulated an increase in the 1973federal minimum wage, assuming no behav-ioral or general equilibrium effects. He found

that only about half of the total benefits wouldflow to workers with family incomes belowthe median. Since the Gramlich (1976) study, anumber of other papers have conducted similarsimulation-type analyses of the target efficiencyof the minimum wage (e.g., Burkhauser andSabia 2007, 2010; Congressional Budget Office2014; Horrigan and Mincy 1993; Johnson andBrowning 1983). Many of these newer studiesattempt to improve upon the Gramlich approachby accounting for employment effects and otherrelevant parameters in their simulation models.However, in general, these studies do not allowmodel parameters to vary across the incomedistribution. As an analysis of minimum wagetarget efficiency relies on identifying differentialimpacts across the income distribution, it is notclear that these more complicated models provideadditional insight on target efficiency. A notableexception is the recent report issued by theCongressional Budget Office (CBO 2014). Theyinclude a disemployment effect in their modelthat varies by worker age, and they account forchanges in prices and business profits, all ofwhich have distributional consequences. How-ever, in many cases the parameter values imposedon their model are crudely approximated, duelargely to the lack of reliable estimates inthe literature.5

In the simulation conducted here, I opt to fol-low the more simple Gramlich approach, ignor-ing any behavioral or general equilibrium effects.I do this for two reasons. First, since there is noconsensus with regards to the employment effectsof the minimum wage, it is not clear how themodel should be adjusted to account for behav-ioral responses.6 Second, while more complexsimulations, as in the CBO study, are extremelyvaluable, there is also value in a static analysisas it provides a type of “best-case” scenario forminimum wage changes.

III. SIMULATION METHODS

The simulation methodology employedhere loosely follows Burkhauser and Sabia

5. An example is the imposition of a minimumwage–employment elasticity for adults that is one-thirdthe size of the teen elasticity. Since there are few estimatesof the minimum wage employment elasticity for low-skilledadults, the chosen value appears to largely be based on atheoretical prediction rather than an empirical estimate.

6. To see the current state of the minimum wage debatesee, for example, Dube, Lester, and Reich (2010), and Neu-mark, Salas, and Wascher (2014).

LUNDSTROM: WHEN TO RAISE THE MINIMUM WAGE? 31

(2007, 2010; hereafter BS). Instances where myapproach differs from BS’s approach are madeexplicit throughout this section (in the footnotes).These differences are also summarized in theAppendix Table A1. In addition, in the AppendixTable A2, I replicate BS’s results, then illustratehow the simulation results are changed usingmy approach.

For the simulations, I postulate that in eachyear from 1990 through 2014 the federal mini-mum wage was higher by a fixed percent thanit actually was. The sizes of the minimum wageincreases used in these simulations are based onhistorical means. Since 1990, the federal mini-mum wage has increased seven times. The meanincrease was 11.7% and the range of increaseswas 8.4%–13.6%. With this in mind, the staticcalculations are based on simulated increases inthe federal minimum wage of 8%, 12%, and 16%in each year from 1990 to 2014, thereby cover-ing a range that exceeds somewhat the historicalnorms.7 Furthermore, the 12% increase roughlymatches the first phase of a proposal by HouseDemocrats to raise the federal minimum wagefrom its current level of $7.25 to $10.10. The FairMinimum Wage Act (H.R. 1010) proposes thatthis increase take place in three steps of $0.95each (the first $0.95 increase represents a 13.1%increase in the federal minimum wage from its2014 level).8

The simulation conducted here is static,assuming no behavioral or general equilibriumeffects. Furthermore, it is assumed that the sim-ulated minimum wage increase only affects thewages of workers who are “directly affected”by the increase. A directly affected worker isdefined as one whose hourly wage falls between$0.05 less than the prevailing minimum wage(i.e., the higher of the state or federal minimumwage) and the new federal minimum wage.9 In

7. Burkhauser and Sabia do not simulate the same per-centage increase in each year. Rather, they simulate minimumwage increases that match proposed increases. In 1996 theysimulate an increase from $4.25 to $5.15 (21%); in 2004 theysimulate an increase from $5.15 to $7.25 (41%); and in 2008they simulate an increase from $7.25 to $9.50 (31%). Whilethere is merit in simulating minimum wage hikes that matchproposed increases, it makes cross-year comparisons difficultsince efficiency calculations are sensitive to the size of thesimulated increase.

8. The full text of H.R. 1010 can be accessed at thefollowing website: http://democrats.edworkforce.house.gov/sites/democrats.edworkforce.house.gov/files/documents/FairMinimumWageAct-BillText.pdf.

9. Burkhauser and Sabia define a directly affected workeras a worker whose wage falls between some amount (theamount varies from simulation to simulation) less than the

other words, if the prevailing minimum wageis $7.25 and the new federal minimum wageis $8.20, only those workers earning between$7.20 and $8.20 are assumed to experience awage increase.

During the time period covered in this study,many states adopted minimum wages thatexceeded the contemporaneous federal level.Observations from those states where the pre-vailing minimum wage exceeds the federallevel will experience a simulated increase in theminimum wage that is less than the simulatedfederal increase. In some instances the prevailingstate minimum wage exceeds even the simulatedfederal level, meaning that no observations fromthose states will be included in the sample forthose time periods. I test whether my conclusionsare sensitive to this restriction by performingthe analysis again using observations only fromstates that are fully bound by the federal mini-mum wage over this time period. The advantageof this latter approach is that the same set ofstates is represented in each year. The disadvan-tage is that it fails to accurately represent the setof states that would be impacted by an increasein the federal minimum wage in each year.

The simulation is performed in two steps.First, the annual benefit received by each directlyaffected worker is calculated as the product ofhis wage increase (due to the simulated mini-mum wage hike), his usual weekly hours, andhis annual weeks worked. Second, minimumwage target efficiency is determined by identify-ing the total share of annual benefits that flowsto poor workers. A worker is defined as poorif his income-to-needs ratio—the ratio of totalhousehold income to the household size adjustedpoverty level—is less than one.

IV. DATA

Data are drawn from the March CPS, outgo-ing rotation group files for the years 1990–2014.These data are ideally suited to the presentstudy since they provide detailed informationon individual wages and household income.Observations are included for individuals aged16–64 from all 50 states plus the District ofColumbia who are determined to be directlyaffected by an increase in the federal minimum

federal minimum wage and the simulated federal minimumwage. This approach does not account for the fact that in manycases the state minimum wage is binding.

http://democrats.edworkforce.house.gov/sites/democrats.edworkforce.house.gov/files/documents/FairMinimumWageAct-BillText.pdf




FIGURE 1Time-Series Plots of the Share of Benefits Accruing to Poor Directly Affected Workers

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Notes: Share of annual benefits accruing to poor workers who are directly affected by the respective simulated increases inthe federal minimum wage. Directly affected workers are defined as those workers earning between $0.05 less than the prevailingminimum wage (the higher of the state or federal minimum wage) and the simulated federal minimum wage. “Poor” workers aredefined as those workers with household income below the poverty line. Data are drawn from the outgoing rotation group MarchCPS files. Observations are weighted using the CPS earnings weight. The table of values used to construct this figure is shownin the Appendix Table A3.

wage. Observations are weighted using the CPSearnings weights.10

As described in the previous section, an annualbenefit is simulated for each directly affectedworker by computing the product of his wageincrease, his usual weekly hours, and the weeksworked per year. For hourly workers the CPSreports the hourly wage; for non-hourly workers,hourly wages are estimated from usual weeklyearnings and usual weekly hours worked. TheCPS reports the number of weeks worked forthe previous year and it is assumed that thisnumber does not change in the current year. Inconstructing a worker’s income-to-needs ratio,total annual household income is divided by ahousehold-size adjusted poverty threshold that ispublished by the U.S. Census Bureau.

V. SIMULATION RESULTS

Estimates from the 8%, 12%, and 16% sim-ulations are shown in Figure 1. This figure dis-plays a time-series plot of the share of benefits

10. Burkhauser and Sabia use workers aged 16–64 in onestudy and 17–64 in a second study. Burkhauser and Sabia are,apparently, inconsistent in their use of sample weights—apoint that is made clear in the replication that is includedin the Appendix. Moreover, Burkhauser and Sabia restrictthe sample to only include individuals who work at least15 hours/week and who worked at least 14 weeks in the pastyear. I do not include these hours and weeks restrictionssince doing so would likely be particularly restrictive for teenworkers who are, on average, less attached to the labor force.

accruing to poor workers for each simulation.As presented here, the results are quite noisy. Inorder to reduce noise in the estimates, the resultsare shown again in Figure 2, but this time using 5-year moving averages. All remaining results arepresented using 5-year moving averages. (Note:the x-axis on figures using 5-year moving aver-ages only shows the midpoint year for each5-year span.) Several things stand out when look-ing at Figure 2. First, for each simulation, tar-get efficiency peaked near the 2007–2011 timeperiod (labeled 2009 on Figure 2), and target effi-ciency remains close to the peak in 2010–2014(labeled 2012 on Figure 2). This suggests that,from an efficiency standpoint, now is probably agood time to increase the federal minimum wage(although it would have been slightly better afew years ago). Second, target efficiency, in gen-eral, is better when the minimum wage hike issmaller.

A. What Is Driving Changes in Target Efficiencyover this Time Period?

The share of total minimum wage benefitsaccruing to poor workers will increase for tworeasons: (1) the mean annual benefit receivedby poor workers increases relative to the meanannual benefit received by non-poor workers,and/or (2) the fraction of directly affected work-ers in poverty increases. The objective in thissection is to determine which of these factors ismost closely associated with changes in target


FIGURE 2Time-Series Plots of the Share of Benefits Accruing to Poor Directly Affected Workers, Based on

5-Year Moving Averages

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Notes: Results are computed using 5-year moving averages where the listed year represents the midpoint of the 5-year average.Directly affected workers are defined as those workers earning between $0.05 less than the prevailing minimum wage (the higherof the state or federal minimum wage) and the simulated federal minimum wage. “Poor” workers are defined as those workerswith household income below the poverty line. Observations are weighted using the CPS earnings weight. The table of valuesused to construct this figure is shown in the Appendix Table A4.

efficiency over this time period. In this portion ofthe analysis I focus exclusively on the 12% min-imum wage hike simulation. I do this in order tosimplify the discussion and because the conclu-sion that is drawn is the same regardless of whichsimulation is used.

Mean Benefit Received by Poor Workers Relativeto Non-Poor Workers. A time-series of the ratioof the mean annual benefit for poor workers to themean annual benefit for non-poor workers (usingthe 12% simulation) is shown in Figure 3. Thistime-series is then overlaid on a time-series ofthe simulated benefits accruing to poor workers.This plot allows us to visualize whether changesin this ratio are potentially driving changes in theshare of benefits accruing to poor workers overthis time period. Looking at Figure 3, the relation-ship appears quite weak (correlation coefficientof −0.179).

Fraction of Directly Affected Workers in Poverty.A time-series plot of the fraction of directlyaffected workers in poverty overlaid on a time-series plot of the simulated benefits accruingto poor workers (using the 12% simulation) isshown in Figure 4. As before, this plot allowsus to visualize the strength of this relationship.Looking at Figure 4, it is clear that changes in thefraction of directly affected workers in povertyare driving changes in target efficiency (correla-tion coefficient of 0.952).

VI. WHAT IS DRIVING CHANGES IN THE FRACTIONOF DIRECTLY AFFECTED WORKERS IN POVERTY?

Having established that changes in minimumwage target efficiency from 1990 through 2014were driven by changes in the fraction of directlyaffected workers in poverty, I now try to iden-tify the variables that are most highly correlatedwith the fraction of directly affected workersin poverty. In general, the fraction of directlyaffected workers in poverty will increase forthree reasons: (1) the mean household incomeof directly affected workers falls, (2) the wagesof non-poor directly affected workers rise rela-tive to the wages of poor directly affected work-ers (thereby pushing relatively more non-poorworkers beyond the directly affected wage rangethan poor workers), or (3) the employment oflow-skilled poor individuals rises relative to theemployment of low-skilled non-poor individuals(the decline in teen employment is an instance ofthis). In this section my objective is to identifywhich of these factors is most closely associatedwith changes in the fraction of directly affectedworkers in poverty.

A. Examining the Relationship betweenHousehold Income and the Fraction of DirectlyAffected Workers in Poverty

Changes in household income among low-skilled individuals might drive changes in thefraction of directly affected workers in poverty.


FIGURE 3Time-Series of the Mean Annual Benefit Received by Poor Workers Relative to the Mean AnnualBenefit Received by Non-Poor Workers, Overlaid on a Time-Series of the Share of Total Benefits

Accruing to Poor Workers. Based on 12% Simulation

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Notes: Results are computed using 5-year moving averages where the listed year represents the midpoint of the 5-year average.“Poor” workers are defined as those workers with household income below the poverty line. Observations are weighted using theCPS earnings weight. The values used to create this figure are shown in the Appendix Table A5.

FIGURE 4Time-Series of the Fraction of Directly Affected Workers in Poverty, Overlaid on a Time-Series of

the Share of Total Benefits Accruing to Poor Workers. Based on 12% Simulation

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Notes: Results are computed using 5-year moving averages where the listed year represents the midpoint of the 5-year average.Directly affected workers are defined as those workers earning between $0.05 less than the prevailing minimum wage (the higherof the state or federal minimum wage) and the simulated federal minimum wage. “Poor” workers are defined as those workerswith household income below the poverty line. Observations are weighted using the CPS earnings weight. The values used tocreate this figure are shown in the Appendix Table A6.

In order to explore this relationship, a time-seriesplot of the poverty rate of low-skilled individualsis overlaid on a time-series plot of the fractionof directly affected workers in poverty (wherean individual is considered low-skilled if he hasless than a 12th grade education). A separateplot is created for each simulation and shownin Figure 5. As expected, the correlations are

all positive, but they are not particularly strong(the correlation coefficients are 0.381, 0.442, and0.372 for the 8%, 12%, and 16% simulations,respectively). This finding suggests that whilechanges in household income likely explain someof the variation in the fraction of directly affectedworkers in poverty over this time period, it isprobably not the whole story.


FIGURE 5Time-Series of the Poverty Rate of Low-Skilled Individuals Overlaid on a Time-Series of the Fractionof Directly Affected Workers in Poverty. (a) 8% Simulation, (b) 12% Simulation, (c) 16% Simulation

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Notes: Observations are limited to states where the simulated federal minimum wage is binding. An individual is “low-skilled” if they have education <12 years. Results are computed using 5-year moving averages where the listed year representsthe midpoint of the 5-year average. Directly affected workers are defined as those workers earning between $0.05 less thanthe prevailing minimum wage (the higher of the state or federal minimum wage) and the simulated federal minimum wage. Aworker “in poverty” is defined as one whose household income falls below the poverty line. When constructing the 5-year movingaverages, annual poverty rates are weighted by the annual population of working age individuals. The values used to create thesefigures are shown in the Appendix Table A7.


B. Examining the Relationship between theWages of Non-Poor Workers Relative to theWages of Poor Workers, and the Fraction ofDirectly Affected Workers in Poverty

If the wages of non-poor directly affectedworkers rise relative to the wages of poordirectly affected workers, then relatively morenon-poor workers will be pushed beyond thedirectly affected wage range than poor workers.This will lead to an increase in the fraction ofdirectly affected workers in poverty. To explorethis relationship, a time-series plot of the ratioof the mean wage of non-poor directly affectedworkers to the mean wage of poor directlyaffected workers is overlaid on a time-series plotof the fraction of directly affected workers inpoverty. As before, a separate plot is producedfor each simulation. These plots are shown inFigure 6. The correlations are all positive—asexpected—but they are also quite weak (cor-relation coefficients of 0.159, 0.083, and 0.096for the 8%, 12%, and 16% simulations, respec-tively). The implication is that an increase inthe wages of non-poor workers relative to thewages of poor workers does a bad job of predict-ing changes in the fraction of directly affectedworkers in poverty over this time period.

C. Examining the Relationship between theEmployment of Non-Poor Individuals Relative tothe Employment of Poor Individuals, and theFraction of Directly Affected Workers in Poverty

If the employment of low-skilled poor indi-viduals rises relative to the employment oflow-skilled non-poor individuals, the fraction ofdirectly affected workers in poverty may rise. Asdiscussed in the Introduction, the large reduc-tion in teen employment since the late 1990scould lead to such an outcome. To explore therelationship between teen employment and thefraction of directly affected workers in poverty,a time-series plot of teen employment is overlaidon a time-series plot of the fraction of directlyaffected workers in poverty. A separate plotis created for each simulation and shown inFigure 7. (Note: the right-hand-side y-axis—forteen employment—is reversed in order to makethe relationship between the two variables moreapparent.) The correlation is quite weak for eachsimulation (correlation coefficients of −0.174,−0.311, and −0.323 for the 8%, 12%, and16% simulations, respectively). This suggeststhat, while changes in teen employment mayplay some role in changes in target efficiency

over this time period, the role is probablyminor.

Of course, the relationship between teenemployment and target efficiency is just one ofmany that could be explored in this section. Moregenerally, I would like to create a time-seriesshowing the employment of poor, low-skilledindividuals relative to the employment ofsimilarly-skilled non-poor individuals. Thistime-series could then be compared to a time-series of the fraction of directly affected workersin poverty. Unfortunately, the CPS does not con-tain a skill variable that can be used to identifysimilarly-skilled individuals across the incomedistribution.11 However, it might be possible todetect this relationship in another way. Recallthat one of the assumptions underlying the staticsimulation methodology employed here is thatchanges in the level of the federal minimumwage do not affect employment (or, if they do,the effects are similar across the income distribu-tion). If this is not true—if, for example, as thereal minimum wage falls more poor workers arepriced back into the labor market than non-poorworkers—the level of the federal minimumwage might itself be strongly correlated withchanges in target efficiency.

In order to explore this possibility, a time-series plot of the real federal minimum wage(in 2014 dollars) is overlaid on a time-seriesplot of the fraction of directly affected work-ers in poverty for each simulation. These plotsare shown in Figure 8. The correlations are allvery strong (correlation coefficients of 0.828,0.892, and 0.865 for the 8%, 12%, and 16%simulations, respectively). Surprisingly, thecorrelations are all positive. If the relationshipis causal, this implies that as the real federalminimum wage increases, the employment ofpoor minimum wage workers rises relative tothe employment of non-poor minimum wageworkers. And, conversely, as the real minimumwage falls the employment of poor minimumwage workers falls relative to the employmentof non-poor minimum wage workers. Whetherthe relationship is causal or not, these resultsclearly show that—over the last 25 years,

11. I attempted to use low-education as a proxy for low-skill, but this is very imperfect. The wages of poor high-school dropouts are, on average, $9.79 (in 2014 dollars)over this time period. The wages of non-poor high-schooldropouts are $12.19 (in 2014 dollars) over this same timeperiod. This wage discrepancy indicates that the non-poorhigh-school dropouts are likely higher skilled than poor high-school dropouts.


FIGURE 6Time-Series of the Mean Wage of Non-Poor Directly Affected Workers Relative to the Mean Wage of

Poor Directly Affected Workers Overlaid on a Time-Series of the Fraction of Directly AffectedWorkers in Poverty. (a) 8% Simulation, (b) 12% Simulation, (c) 16% Simulation

(b)

(c)

Rat

io o

f n

on

-po

or

wag

esto

po

or

wag

esR

atio

of

no

n-p

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ages

to p

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ages

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r =0.096

r = 0.083

(a)

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es

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r = 0.159

Notes: A time-series plot of the ratio of wages for non-poor directly affected workers to the wages of poor directly affectedworkers is overlaid on a time-series plot of the fraction of directly affected workers in poverty for each figure. Results arecomputed using 5-year moving averages where the listed year represents the midpoint of the 5-year average. Directly affectedworkers are defined as those workers earning between $0.05 less than the prevailing minimum wage (the higher of the state orfederal minimum wage) and the simulated federal minimum wage. “Poor” workers are defined as those workers with householdincome below the poverty line. The values used to create these figures are shown in the Appendix Table A8.

at least—the real level of the federal minimumwage is a very strong predictor of minimum wagetarget efficiency.

VII. SENSITIVITY ANALYSIS

It is possible that the results of the preced-ing analysis are sensitive to certain restrictions

placed on the sample. In particular, in any givenyear the sample consists of observations fromall states that are bound by the simulated fed-eral minimum wage. Many observations in eachyear come from states where the state minimumwage rate exceeds the contemporaneous federallevel, meaning that the simulated wage hike issomewhat less than intended (e.g., if the state


FIGURE 7Time-Series of Teen Employment to Population Ratio Overlaid on a Time-Series of the Fraction ofDirectly Affected Workers in Poverty. (a) 8% Simulation, (b) 12% Simulation, (c) 16% Simulation

(a)

(b)

(c)

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Teen Employment to Population Ratio



0.240

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een

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r = -0.174

r = -0.311

r = -0.323

Notes: Observations are limited to states where the simulated federal minimum wage is binding. Results are computed using5-year moving averages where the listed year represents the midpoint of the 5-year average. Directly affected workers are definedas those workers earning between $0.05 less than the prevailing minimum wage (the higher of the state or federal minimum wage)and the simulated federal minimum wage. “Poor” workers are defined as those workers with household income below the povertyline. When constructing 5-year moving averages the annual teen employment rates are weighted by teen population. The valuesused to create these figures are shown in the Appendix Table A9.


FIGURE 8Time-Series of Real Federal Minimum Wage Rate (in 2014 Dollars) Overlaid on a Time-Series of theFraction of Directly Affected Workers in Poverty. (a) 8% Simulation, (b) 12% Simulation, (c) 16%

Simulation

(a)

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5.750

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Real federal minimum wage





5.75

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r =0.865

r = 0.892

r = 0.828

Notes: Results are computed using 5-year moving averages where the listed year represents the midpoint of the 5-year average.Directly affected workers are defined as those workers earning between $0.05 less than the prevailing minimum wage (the higherof the state or federal minimum wage) and the simulated federal minimum wage. A worker “in poverty” is defined as one whosehousehold income falls below the poverty line. The real federal minimum wage may vary somewhat across simulations sincedifferent states are included in each year for the different simulations (because of the constraint that observations are only drawnfrom states where the simulated federal minimum wage binds)—this affects the population in a given year and moving averagesare population weighted. The values used to create these figures are shown in the Appendix Table A10.

minimum wage rate is $8.00 and the federalminimum wage rate is $7.25, a 12% simulatedincrease in the federal minimum wage will effec-tively raise the minimum wage in that state byonly 1.5%). In other cases, the state minimumwage rate exceeds the simulated federal mini-mum wage rate, meaning that no observations are

drawn from that state in those years. It is unclearhow these year-to-year changes in both the size ofthe effective simulated minimum wage change,as well as which states are represented, may beinfluencing the results.

In this section I redo the analysis, but thistime the sample is restricted to only include


observations from states that are fully bound bythe federal minimum wage in all sample years(i.e., the sample is restricted to states that neverincrease the minimum wage beyond the federallevel). There are 17 states that meet this crite-ria.12 The results from this sensitivity analysisare shown in Figure 9. For the sake of brevity,only results from the 12% simulation are pre-sented, though the conclusions are the same forall simulations. In Figure 9(a), a time-series plotof the share of benefits accruing to poor work-ers using the restricted sample is overlaid on atime-series plot of the share of benefits accruingto poor workers using the original sample. Thegeneral pattern of changes in target efficiency isthe same for both samples over this time period,though the estimates are generally somewhathigher for the restricted sample. Nevertheless,using the restricted sample, we would still con-clude that—from an efficiency standpoint—nowis a good time to increase the minimum wage:18.8% of the total annual benefits would accrueto workers in the 2010–2014 time period (labeled2012 on Figure 9(a)), 1.8 percentage points belowthe peak, and 5.3 percentage points above thetrough over this time period.

In Figure 9(b), a time-series plot of the fractionof directly affected workers in poverty is over-laid on a time-series plot of the share of bene-fits accruing to poor workers using the restrictedsample. As with the original sample, the corre-lation is very strong (correlation coefficient of0.882). The relationship between the share ofbenefits accruing to poor workers and the meanannual benefit for poor workers relative to themean annual benefit for non-poor workers iscomparatively weak—the correlation coefficientis 0.589 (for the sake of brevity this figure isnot displayed). As with the original sample, theimplication is that changes in target efficiencyare primarily driven by changes in the fraction ofdirectly affected workers in poverty.

In Figure 9(c), a time-series plot of the realfederal minimum wage is overlaid on a time-series plot of the fraction of directly affectedworkers in poverty. As with the original sam-ple, the relationship is quite strong—the cor-relation coefficient is 0.714. For the sake ofbrevity, the other plots are not displayed, butthe relationships are all comparatively weak (the

12. The 17 states that are fully bound by the federal min-imum wage in every year from 1990 to 2014 are: Alabama,Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Mis-sissippi, Nebraska, Oklahoma, South Carolina, South Dakota,Tennessee, Texas, Utah, Virginia, and Wyoming.

correlation coefficient for the fraction of directlyaffected workers in poverty and teen employ-ment is 0.094; the correlation coefficient for thefraction of directly affected workers in povertyand the poverty rate of low-skilled individualsis 0.485).

In summary, the conclusions reached using therestricted sample are identical to the conclusionsthat are reached using the original sample.

VIII. DISCUSSION OF RESULTS

The preceding analysis reveals three things.First that the target efficiency of the federal min-imum wage is currently near a 25-year high.Second, that changes in target efficiency overthe past 25 years are overwhelmingly a functionof changes in the fraction of directly affectedworkers in poverty, rather than changes in themean annual benefit received by poor workersrelative to non-poor workers. And third, whilethe decline in teen employment and the gen-eral increase in poverty may have contributedto the increase in minimum wage target effi-ciency over the past decade, of the factors consid-ered here, the real level of the federal minimumwage is the strongest predictor of minimum wagetarget efficiency.

The discovery of a positive relationshipbetween the real federal minimum wage andminimum wage target efficiency is surprising.Given the positive relationship between the skilland income distributions, we would expect thisrelationship to be negative. That is, if the realminimum wage level falls, we expect individ-uals to be priced into the market who are, onaverage, lower skilled than the lowest skilledworkers in the existing labor pool. Furthermore,given the positive relationship between skill andincome, we would expect that these individualsare, on average, poorer than are workers in theexisting labor pool. These findings suggest thatthe opposite might be taking place. Though apositive relationship between the real minimumwage and target efficiency is counterintuitive,this result can easily be explained if the supplyof poor low-skilled labor is more elastic than isthe supply of non-poor low skilled labor. A highlabor supply elasticity for poor low-skilled labormight occur, for example, if these individualsare on the margin of eligibility for means-testedincome support or welfare programs.

While the techniques employed in this studyprovide a weak basis for causal inference, thefact that such a strong positive relationship


FIGURE 9Sensitivity Analysis: The Sample Is Restricted to States That Are Fully Bound by the Federal

Minimum Wage in All Years. (a) Time-Series of the Share of Benefits Accruing to Poor WorkersUsing the Restricted Sample Overlaid on a Time-Series of the Share of Benefits Accruing to Poor

Worker Using the Original Sample. (b) Time-Series of the Share of Benefits Accruing to Poor WorkersOverlaid on a Time-Series of the Fraction of Directly Affected Workers in Poverty (Restricted

Sample). (c) Time-Series of the Real Federal Minimum Wage Rate (in 2014 Dollars) Overlaid on aTime-Series of the Fraction of Directly Affected Workers in Poverty (Restricted Sample)

(b)

(c)

0.100

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r =0.714

r = 0.882

r = 0.789

Notes: Results are computed using 5-year moving averages where the listed year represents the midpoint of the 5-year average.Directly affected workers are defined as those workers earning between $0.05 less than the federal minimum wage and thesimulated federal minimum wage. “Poor” workers are defined as those workers with household income below the poverty line.The values used to create these figures are shown in the Appendix Table A11.

between the minimum wage and target efficiencyexists at least raises the possibility that a bind-ing minimum wage causes the employment oflow-skilled poor individuals to increase relativeto the employment of low-skilled non-poor indi-viduals. If future research reveals that this is thecase, there are several important implications.

First, this implies that a static simulation of thesort employed in this study tends to understatetarget efficiency since it fails to account forthe increased employment of poor individualsrelative to non-poor individuals that is caused bya minimum wage hike. Indeed, such a discoverywould raise questions concerning the finding


presented in Section IV that “target efficiency,in general, is better when the minimum wagehike is smaller” since this finding is derived froma static simulation. If a higher minimum wagelevel is more efficient than a lower minimumwage level, then it might also be the case that alarger minimum wage change is no less efficientthan a smaller one. Of course, it may still be thecase that the optimal path to a higher, more effi-cient minimum wage is through a series of smallchanges. This is a subject for future research.

A second, and related, implication of a posi-tive relationship between the minimum wage andtarget efficiency is the potential desirability ofindexing the minimum wage, whereby it is pre-vented from falling to less efficient levels. This isespecially true in light of the possibility that largeminimum wage changes are less efficient thansmall ones. Indexing the minimum wage ensuresthat changes to the minimum wage occur in small,incremental steps.

Finally, there are a number of cautions thatmust be exercised in interpreting these results.First, it might be tempting for minimum wageadvocates to use these results as a justificationfor dramatic increases in the minimum wage, onthe grounds of improved efficiency. But it mustbe remembered that over the 25-year span con-sidered in this study, the federal minimum wagestayed within a narrow range of $6.23 and $7.55(in 2014 dollars, based on 5-year moving aver-ages). It is not at all certain that the positiverelationship between the minimum wage and tar-get efficiency would persist at levels outside ofthis range. Second—and at the risk of beingrepetitive—it must be remembered that the meth-ods employed in this study provide a weak basisfor causal inference. While the strong relation-ship between the real federal minimum wageand target efficiency is compelling, it is possiblethat this relationship is being driven by factorsthat have not been considered. Lastly, the policyobjective of optimal target efficiency cannot, inthe end, be entirely divorced from considerationsof employment and general equilibrium effects. Ifefficiency gains come at the expense of an over-all reduction in employment or an increase in theprice of goods, then these costs must be weighedin the balance.

IX. CONCLUSION

In this study I use static simulation techniquesto analyze changes in the target efficiency of thefederal minimum wage over the past 25 years.

In so doing I discover that the target efficiencyof the federal minimum wage is currently nearits 25-year peak. In particular, I find that ofthe total monetary benefits generated by a 12%increase in the federal minimum wage, 16.8%would accrue to poor households. This exceedsthe least target efficient year over this time periodby 4.7 percentage points and is only 0.6 percent-age points below the peak. Furthermore, I finda very strong positive relationship between thelevel of the federal minimum wage (in 2014 dol-lars) and minimum wage target efficiency. Theimplication is that a good time to raise the min-imum wage—from an efficiency standpoint, atleast—is when it is already high. This discov-ery raises the possibility that the minimum wageincreases the employment of low-skilled poorindividuals relative to the employment of low-skilled non-poor individuals.

Those who favor the minimum wage as ameans to combat poverty might view the resultsof this study as an indication that the mini-mum wage is better positioned now than at mostyears in the past few decades to do so (froman efficiency standpoint, at least). They mightalso view the results of this study as being sup-portive of an indexed federal minimum wage,whereby the minimum wage would be preventedfrom falling to less efficient levels. At the sametime, minimum wage critics might argue that,even with so many forces working in favor ofimproved target efficiency—including decreasedteen employment, and a high poverty rate—theminimum wage continues to do a bad job of tar-geting the poor. And both groups would be cor-rect. The minimum wage is a blunt instrumentfor addressing poverty, and there is no evidencesuggesting that it will cease to be so in the fore-seeable future. Nevertheless, so long as policymakers continue to rely on the minimum wage,the results of this study may be useful as theyconsider ways to make the most efficient use ofan imperfect tool.

APPENDIX

BURKHAUSER AND SABIA REPLICATION

Burkhauser and Sabia (2007, 2010; hereafter, BS) per-form simulations for the years 1996, 2004, and 2008. Theapproach taken by BS is similar to mine, with a few differ-ences: (1) they restrict the sample to workers who work atleast 15 hours/week and 14 weeks in the past year; (2) theyinclude workers aged 17–64 in the 2007 study, and 16–64 in2010 study; (3) they are (apparently) inconsistent in the use ofweights—I can replicate the 1996 and 2004 results if I do notweight the observations, but I must use weights to replicate the


2008 result; (4) they define directly affected workers as havingwages between the contemporaneous federal minimum wageand the simulated minimum wage (versus the prevailing min-imum wage—i.e., higher of state or federal—and simulatedminimum wage); (5) they do not use the same percentageincrease in the minimum wage in each year—they simulateminimum wage changes that are consistent with proposed oractual increases for the given years. While there is certainlymerit in simulating proposed minimum wage increases, itmakes cross-year comparisons difficult since target efficiencycalculations are sensitive to the size of the simulated increase.

In this Appendix I replicate BS’s results, then show howthe results are changed as my approach is implemented, stepby step. All results are shown in Table A2. Column 1 showsBS’s published results for the share of benefits accruing to

poor (i.e., ITN< 1) workers. Column 2 shows the results frommy replication. I am able to replicate BS’s results almostperfectly. Column 3 shows results from a simulation whereone change is made to the BS approach: all observations areweighted (using the CPS earnings weight). The 1996 esti-mate is reduced somewhat, and the 2004 estimate increasesslightly, though the changes are not significant. Column 4shows results from a simulation that includes the column 3change, plus two additional changes: workers aged 16–64are included in all years and the work requirement of atleast 15 hours/week and 14 weeks/year is dropped. I am con-cerned that the hours and weeks restriction might be partic-ularly restrictive for teens given their low attachment to thelabor market. In fact, these changes have very little impacton the estimates. Column 5 shows results from a simulation

TABLE A1

Differences in Simulation Methodologies Between Burkhauser and Sabia (2007, 2010) and Lundstrom (this article)

Burkhauser and Sabia Lundstrom(1) (2)

(1) Use of sample weights: Sample weights appear to be used in the2010 paper, but not the 2007 paper.

Sample weights are used for allsimulations.

(2) Working-age populationdefinition:

Workers ages 16–64 in the 2010 paper,and 17–64 in the 2007 paper

Workers ages 16–64 are used for allsimulations.

(3) Other sample restrictions: To be included a worker must work atleast 15 hours/week and must haveworked 14 weeks in the past year.

No additional sample restrictions otherthan the age restriction.

(4) Directly affected workerdefinition:

A worker whose wage is between thefederal minimum wage and thesimulated federal minimum wage.

A worker whose wage is between theprevailing minimum wage (i.e., thehigher of the state or federal level) andthe simulated minimum wage.

(5) Size of the simulatedminimum wage change:

Varies from one simulation to the next. The same percent increase for each year.

TABLE A2

Replication of Burkhauser and Sabia

Burkhauser and Sabia Results Replication Step No. 1 Step No. 2 Step No. 3 Step No. 4(1) (2) (3) (4) (5) (6)

Share of benefits accruing to workers in poor households:Year: 1996 0.142 0.141 0.131 0.134 0.127 0.136

Standard error: — (0.019) (0.019) (0.018) (0.019) (0.029)Sample size: — 689 689 900 832 363

Year: 2004 0.127 0.122 0.128 0.129 0.124 0.101Standard error: — (0.012) (0.015) (0.014) (0.014) (0.028)Sample size: — 1,351 1,351 1,737 1,559 300

Year: 2008 0.109 0.106 0.106 0.106 0.109 0.158Standard error: — (0.011) (0.011) (0.011) (0.011) (0.049)Sample size: — 1,733 1,733 1,994 1,783 123

The share of minimum wage benefits accruing to poor workers. Standard errors are presented in parentheses. Burkhauser andSabia results (column 1): the 1996 and 2004 results are from Burkhauser and Sabia (2007), Table 10. The 2008 result is fromBurkhauser and Sabia (2010), Table 7. Replication (column 2): these estimates are based on a replication following the Burkhauserand Sabia approach exactly. Step no. 1 (column 3): one change from the Burkhauser and Sabia approach: all observations areweighted (versus just weighting the 2008 observations). Step no. 2 (column 4): all changes from column 3 are included plus thesample is restricted to workers ages 16–64 (as opposed to 17–64 for 2004 and 2008), and the sample restriction requiring 15hours of work/week and 14 weeks of work/year is dropped. Step no. 3 (column 5): all changes from column 4 are included, plusdirectly affected workers are defined as having wages between the prevailing minimum wage (i.e., higher of state or federal) andnew minimum wage, as opposed to workers with wages between the federal minimum wage and new minimum wage. Step no. 4(column 6): all changes from column 5 are included, plus the same 12% increase in the minimum wage is implemented in eachyear (12% is roughly the historical average increase).


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30.

098

0.18

10.

058

0.25

00.

169

0.13

10.

180

0.20

50.

166

0.17

50.

112

Stan

dard

erro

r:(0

.033

)(0

.034

)(0

.016

)(0

.022

)(0

.026

)(0

.026

)(0

.033

)(0

.023

)(0

.046

)(0

.037

)(0

.029

)(0

.028

)(0

.041

)(0

.031

)(0

.034

)(0

.066

)(0

.026

)(0

.117

)(0

.061

)(0

.042

)(0

.027

)(0

.035

)(0

.033

)(0

.034

)(0

.034

)Sa

mpl

esi

ze:

323

504

779

675

467

413

276

565

141

381

297

342

278

277

229

115

7349

8616

040

936

732

726

915

2Pa

nelB

:12%

sim

ulat

ion

Shar

eof

bene

fits

topo

orw

orke

rs:

0.20

40.

170

0.12

30.

154

0.14

40.

133

0.13

60.

151

0.21

80.

180

0.14

90.

146

0.18

60.

109

0.10

10.

175

0.07

00.

240

0.15

80.

130

0.17

00.

188

0.17

30.

152

0.11

8St

anda

rder

ror:

(0.0

33)

(0.0

27)

(0.0

14)

(0.0

20)

(0.0

23)

(0.0

23)

(0.0

29)

(0.0

20)

(0.0

39)

(0.0

30)

(0.0

26)

(0.0

26)

(0.0

35)

(0.0

27)

(0.0

28)

(0.0

57)

(0.0

27)

(0.1

01)

(0.0

49)

(0.0

32)

(0.0

23)

(0.0

27)

(0.0

25)

(0.0

26)

(0.0

28)

Sam

ple

size

:43

372

796

883

657

452

936

374

018

954

144

847

137

935

630

016

010

359

123

248

708

737

631

563

324

Pane

lC:1

6%si

mul

atio

nSh

are

ofbe

nefit

sto

poor

wor

kers

:0.

199

0.15

60.

122

0.14

60.

140

0.13

30.

133

0.14

30.

219

0.16

20.

150

0.15

10.

173

0.10

60.

102

0.16

30.

073

0.21

40.

160

0.13

40.

164

0.17

50.

174

0.13

90.

118

Stan

dard

erro

r:(0

.029

)(0

.022

)(0

.013

)(0

.018

)(0

.021

)(0

.022

)(0

.025

)(0

.018

)(0

.034

)(0

.024

)(0

.021

)(0

.024

)(0

.030

)(0

.024

)(0

.025

)(0

.047

)(0

.027

)(0

.084

)(0

.043

)(0

.027

)(0

.019

)(0

.021

)(0

.021

)(0

.020

)(0

.024

)Sa

mpl

esi

ze:

500

827

1,03

687

962

957

539

993

120

759

049

151

642

538

733

217

411

770

154

391

822

884

757

671

394

Not

es:

Thi

sta

ble

was

used

toco

nstr

uctF

igur

e1.

Shar

eof

annu

albe

nefit

sac

crui

ngto

poor

wor

kers

who

are

dire

ctly

affe

cted

byth

ere

spec

tive

sim

ulat

edin

crea

ses

inth

efe

dera

lmin

imum

wag

e.D

irec

tlyaf

fect

edw

orke

rsar

ede

fined

asth

ose

wor

kers

earn

ing

betw

een

$0.0

5le

ssth

anth

epr

evai

ling

min

imum

wag

e(t

hehi

gher

ofth

est

ate

orfe

dera

lmin

imum

wag

e)an

dth

esi

mul

ated

fede

ralm

inim

umw

age.

“Poo

r”w

orke

rsar

ede

fined

asth

ose

wor

kers

with

hous

ehol

din

com

ebe

low

the

pove

rty

line.

Dat

aar

edr

awn

from

the

outg

oing

rota

tion

grou

pM

arch

CPS

files

.Obs

erva

tions

are

wei

ghte

dus

ing

the

CPS

earn

ings

wei

ght..

Num

bers

inpa

rent

hese

sre

pres

ents

tand

ard

erro

rs.


TA

BL

EA

4Sh

are

ofA

nnua

lBen

efits

Acc

ruin

gto

Wor

kers

inPo

orH

ouse

hold

s(U

sing

5-Y

ear

Mov

ing

Ave

rage

s)

1990

–19

9419

91–

1995

1992

–19

9619

93–

1997

1994

–19

9819

95–

1999

1996

–20

0019

97–

2001

1998

–20

0219

99–

2003

2000

–20

0420

01–

2005

2002

–20

0620

03–

2007

2004

–20

0820

05–

2009

2006

–20

1020

07–

2011

2008

–20

1220

09–

2013

2010

–20

14

Pane

lA:8

%si

mul

atio

nSh

are

ofbe

nefit

sto

poor

wor

kers

:0.

157

0.14

90.

142

0.14

90.

152

0.16

30.

169

0.16

90.

179

0.16

10.

143

0.14

30.

138

0.12

00.

135

0.14

80.

166

0.18

60.

180

0.18

00.

177

Stan

dard

erro

r:(0

.011

)(0

.010

)(0

.010

)(0

.011

)(0

.013

)(0

.014

)(0

.014

)(0

.014

)(0

.016

)(0

.016

)(0

.015

)(0

.017

)(0

.019

)(0

.021

)(0

.025

)(0

.027

)(0

.020

)(0

.019

)(0

.017

)(0

.015

)(0

.015

)Sa

mpl

esi

ze:

2,74

82,

838

2,61

02,

396

1,86

21,

776

1,66

01,

726

1,43

91,

575

1,42

31,

241

972

743

552

483

777

1,07

11,

349

1,53

21,

524

Pane

lB:1

2%si

mul

atio

nSh

are

ofbe

nefit

sto

poor

wor

kers

:0.

149

0.14

20.

137

0.14

50.

150

0.15

80.

162

0.16

30.

173

0.15

70.

142

0.14

30.

136

0.12

10.

133

0.14

30.

158

0.17

40.

172

0.16

90.

168

Stan

dard

erro

r:(0

.009

)(0

.009

)(0

.009

)(0

.010

)(0

.011

)(0

.012

)(0

.012

)(0

.012

)(0

.014

)(0

.013

)(0

.013

)(0

.015

)(0

.016

)(0

.017

)(0

.021

)(0

.021

)(0

.017

)(0

.015

)(0

.013

)(0

.012

)(0

.012

)Sa

mpl

esi

ze:

3,53

83,

634

3,27

03,

042

2,39

52,

362

2,28

12,

389

2,02

82,

195

1,95

41,

666

1,29

897

874

569

31,

241

1,87

52,

447

2,88

72,

963

Pane

lC:1

6%si

mul

atio

nSh

are

ofbe

nefit

sto

poor

wor

kers

:0.

144

0.13

80.

134

0.14

00.

147

0.15

20.

155

0.15

80.

166

0.15

00.

140

0.13

90.

130

0.11

70.

129

0.14

10.

153

0.16

60.

167

0.16

20.

160

Stan

dard

erro

r:(0

.008

)(0

.008

)(0

.008

)(0

.009

)(0

.010

)(0

.010

)(0

.010

)(0

.010

)(0

.012

)(0

.011

)(0

.011

)(0

.013

)(0

.014

)(0

.015

)(0

.018

)(0

.018

)(0

.014

)(0

.012

)(0

.011

)(0

.010

)(0

.009

)Sa

mpl

esi

ze:

3,87

13,

946

3,51

83,

413

2,74

12,

702

2,61

82,

735

2,22

92,

409

2,45

11,

834

1,43

51,

080

847

906

1,55

42,

321

3,00

83,

525

3,52

8

Not

es:

Thi

sta

ble

was

used

toco

nstr

uctF

igur

e2.

Shar

eof

annu

albe

nefit

sac

crui

ngto

poor

wor

kers

who

are

dire

ctly

affe

cted

byth

ere

spec

tive

sim

ulat

edin

crea

ses

inth

efe

dera

lmin

imum

wag

e.D

irec

tlyaf

fect

edw

orke

rsar

ede

fined

asth

ose

wor

kers

earn

ing

betw

een

$0.0

5le

ssth

anth

epr

evai

ling

min

imum

wag

e(t

hehi

gher

ofth

est

ate

orfe

dera

lmin

imum

wag

e)an

dth

esi

mul

ated

fede

ralm

inim

umw

age.

“Poo

r”w

orke

rsar

ede

fined

asth

ose

wor

kers

with

hous

ehol

din

com

ebe

low

the

pove

rty

line.

Dat

aar

edr

awn

from

the

outg

oing

rota

tion

grou

pM

arch

CPS

files

.Obs

erva

tions

are

wei

ghte

dus

ing

the

CPS

earn

ings

wei

ght.

Num

bers

inpa

rent

hese

sre

pres

ents

tand

ard

erro

rs.


TA

BL

EA

5T

ime-

Seri

esof

the

Mea

nA

nnua

lBen

efitR

ecei

ved

byPo

orW

orke

rsR

elat

ive

toth

eM

ean

Ann

ualB

enefi

tRec

eive

dby

Non

-Poo

rW

orke

rs,a

nda

Tim

e-Se

ries

ofth

eSh

are

ofTo

talB

enefi

tsA

ccru

ing

toPo

orW

orke

rs.B

ased

on12

%Si

mul

atio

n

1990

–19

9419

91–

1995

1992

–19

9619

93–

1997

1994

–19

9819

95–

1999

1996

–20

0019

97–

2001

1998

–20

0219

99–

2003

2000

–20

0420

01–

2005

2002

–20

0620

03–

2007

2004

–20

0820

05–

2009

2006

–20

1020

07–

2011

2008

–20

1220

09–

2013

2010

–20

14(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)(1

1)(1

2)(1

3)(1

4)(1

5)(1

6)(1

7)(1

8)(1

9)(2

0)(2

1)

Rat

ioof

poor

tono

n-po

orav

erag

ean

nual

bene

fits:

0.96

20.

955

0.93

80.

989

0.93

70.

981

0.98

50.

992

1.00

71.

049

1.01

81.

056

1.09

01.

048

1.01

60.

935

0.99

41.

021

0.97

30.

988

0.98

6Sh

are

ofto

tal

bene

fits

topo

orw

orke

rs:

0.14

90.

142

0.13

70.

145

0.15

00.

158

0.16

20.

163

0.17

30.

157

0.14

20.

143

0.13

60.

121

0.13

30.

143

0.15

80.

174

0.17

20.

169

0.16

8Sa

mpl

esi

ze:

3,53

83,

634

3,27

03,

042

2,39

52,

362

2,28

12,

389

2,02

82,

195

1,95

41,

666

1,29

897

874

569

31,

241

1,87

52,

447

2,88

72,

963

Not

es:

Thi

sta

ble

was

used

toco

nstr

uct

Figu

re3.

Res

ults

are

com

pute

dus

ing

5-ye

arm

ovin

gav

erag

esw

here

the

liste

dye

arre

pres

ents

the

mid

poin

tof

the

5-ye

arav

erag

e.“P

oor”

wor

kers

are

defin

edas

thos

ew

orke

rsw

ithho

useh

old

inco

me

belo

wth

epo

vert

ylin

e.O

bser

vatio

nsar

ew

eigh

ted

usin

gth

eC

PSea

rnin

gsw

eigh

t.

TA

BL

EA

6T

ime-

Seri

esof

the

Frac

tion

ofD

irec

tlyA

ffec

ted

Wor

kers

inPo

vert

y,an

da

Tim

e-Se

ries

ofth

eSh

are

ofTo

talB

enefi

tsA

ccru

ing

toPo

orW

orke

rs.B

ased

on12

%Si

mul

atio

n

1990

–19

9419

91–

1995

1992

–19

9619

93–

1997

1994

–19

9819

95–

1999

1996

–20

0019

97–

2001

1998

–20

0219

99–

2003

2000

–20

0420

01–

2005

2002

–20

0620

03–

2007

2004

–20

0820

05–

2009

2006

–20

1020

07–

2011

2008

–20

1220

09–

2013

2010

–20

14

Frac

tion

ofdi

rect

lyaf

fect

edw

orke

rsin

pove

rty:

0.15

40.

147

0.14

50.

147

0.15

90.

160

0.16

40.

164

0.17

20.

150

0.14

00.

137

0.12

60.

116

0.13

10.

152

0.15

90.

171

0.17

60.

171

0.17

0Sh

are

ofto

tal

bene

fits

topo

orw

orke

rs:

0.14

90.

142

0.13

70.

145

0.15

00.

158

0.16

20.

163

0.17

30.

157

0.14

20.

143

0.13

60.

121

0.13

30.

143

0.15

80.

174

0.17

20.

169

0.16

8Sa

mpl

esi

ze:

3,53

83,

634

3,27

03,

042

2,39

52,

362

2,28

12,

389

2,02

82,

195

1,95

41,

666

1,29

897

874

569

31,

241

1,87

52,

447

2,88

72,

963

Not

es:

Thi

sta

ble

was

used

toco

nstr

uctF

igur

e4.

Res

ults

are

com

pute

dus

ing

5-ye

arm

ovin

gav

erag

esw

here

the

liste

dye

arre

pres

ents

the

mid

poin

tof

the

5-ye

arav

erag

e.D

irec

tlyaf

fect

edw

orke

rsar

ede

fined

asth

ose

wor

kers

earn

ing

betw

een

$0.0

5le

ssth

anth

epr

evai

ling

min

imum

wag

e(t

hehi

gher

ofth

est

ate

orfe

dera

lmin

imum

wag

e)an

dth

esi

mul

ated

fede

ralm

inim

umw

age.

“Poo

r”w

orke

rsar

ede

fined

asth

ose

wor

kers

with

hous

ehol

din

com

ebe

low

the

pove

rty

line.

Obs

erva

tions

are

wei

ghte

dus

ing

the

CPS

earn

ings

wei

ght.


TA

BL

EA

7T

ime-

Seri

esof

the

Pove

rty

Rat

eof

Low

-Ski

lled

Indi

vidu

als

and

aT

ime-

Seri

esof

the

Frac

tion

ofD

irec

tlyA

ffec

ted

Wor

kers

inPo

vert

y

1990

–19

9419

91–

1995

1992

–19

9619

93–

1997

1994

–19

9819

95–

1999

1996

–20

0019

97–

2001

1998

–20

0219

99–

2003

2000

–20

0420

01–

2005

2002

–20

0620

03–

2007

2004

–20

0820

05–

2009

2006

–20

1020

07–

2011

2008

–20

1220

09–

2013

2010

–20

14

(a)

8%si

mul

atio

nPo

vert

yra

teof

low

-ski

lled

indi

vidu

als:

0.25

00.

254

0.25

70.

256

0.25

00.

239

0.22

70.

215

0.20

20.

198

0.19

70.

198

0.20

30.

209

0.21

60.

220

0.22

80.

243

0.24

60.

250

0.25

2Fr

actio

nof

dire

ctly

affe

cted

wor

kers

inpo

vert

y:0.

160

0.15

30.

151

0.15

60.

167

0.17

10.

177

0.17

80.

183

0.16

00.

144

0.14

20.

133

0.12

20.

144

0.15

80.

168

0.17

90.

177

0.17

60.

178

(b)

12%

sim

ulat

ion

Pove

rty

rate

oflo

w-s

kille

din

divi

dual

s:0.

246

0.25

10.

255

0.25

40.

248

0.23

70.

227

0.21

60.

205

0.19

90.

198

0.19

80.

203

0.20

90.

216

0.21

90.

226

0.23

90.

242

0.24

70.

249

Frac

tion

ofdi

rect

lyaf

fect

edw

orke

rsin

pove

rty:

0.15

40.

147

0.14

50.

147

0.15

90.

160

0.16

40.

164

0.17

20.

150

0.14

00.

137

0.12

60.

116

0.13

10.

152

0.15

90.

171

0.17

60.

171

0.17

0(c

)16

%si

mul

atio

nPo

vert

yra

teof

low

-ski

lled

indi

vidu

als:

0.24

60.

251

0.25

40.

253

0.24

80.

237

0.22

70.

216

0.20

50.

199

0.19

80.

198

0.20

30.

209

0.21

60.

220

0.22

60.

239

0.24

20.

246

0.24

7Fr

actio

nof

dire

ctly

affe

cted

wor

kers

inpo

vert

y:0.

152

0.14

50.

143

0.14

10.

152

0.15

20.

158

0.15

90.

170

0.14

90.

140

0.13

50.

125

0.11

50.

132

0.15

20.

158

0.16

50.

169

0.16

50.

163

Not

es:

Thi

sta

ble

was

used

toco

nstr

uctF

igur

e5.

Obs

erva

tions

are

limite

dto

stat

esw

here

the

sim

ulat

edfe

dera

lmin

imum

wag

eis

bind

ing.

An

indi

vidu

alis

“low

-ski

lled”

ifth

eyha

veed

ucat

ion<

12ye

ars.

Res

ults

are

com

pute

dus

ing

5-ye

arm

ovin

gav

erag

esw

here

the

liste

dye

arre

pres

ents

the

mid

poin

tof

the

5-ye

arav

erag

e.D

irec

tlyaf

fect

edw

orke

rsar

ede

fined

asth

ose

wor

kers

earn

ing

betw

een

$0.0

5le

ssth

anth

epr

evai

ling

min

imum

wag

e(t

hehi

gher

ofth

est

ate

orfe

dera

lm

inim

umw

age)

and

the

sim

ulat

edfe

dera

lm

inim

umw

age.

Aw

orke

r“i

npo

vert

y”is

defin

edas

one

who

seho

useh

old

inco

me

falls

belo

wth

epo

vert

ylin

e.W

hen

cons

truc

ting

the

5-ye

arm

ovin

gav

erag

es,a

nnua

lpov

erty

rate

sar

ew

eigh

ted

byth

ean

nual

popu

latio

nof

wor

king

age

indi

vidu

als.


TA

BL

EA

8T

ime-

Seri

esof

the

Mea

nW

age

ofN

on-P

oor

Dir

ectly

Aff

ecte

dW

orke

rsR

elat

ive

toth

eM

ean

Wag

eof

Poor

Dir

ectly

Aff

ecte

dW

orke

rsan

da

Tim

e-Se

ries

ofth

eFr

actio

nof

Dir

ectly

Aff

ecte

dW

orke

rsin

Pove

rty

1990

–19

9419

91–

1995

1992

–19

9619

93–

1997

1994

–19

9819

95–

1999

1996

–20

0019

97–

2001

1998

–20

0219

99–

2003

2000

–20

0420

01–

2005

2002

–20

0620

03–

2007

2004

–20

0820

05–

2009

2006

–20

1020

07–

2011

2008

–20

1220

09–

2013

2010

–20

14

(a)

8%si

mul

atio

nR

atio

ofno

n-po

orw

ages

topo

orw

ages

:1.

009

1.00

51.

004

1.00

21.

005

1.00

51.

006

1.00

41.

002

0.99

60.

992

0.99

70.

998

1.00

81.

004

1.01

60.

997

1.00

51.

004

1.00

41.

004

Frac

tion

ofdi

rect

lyaf

fect

edw

orke

rsin

pove

rty:

0.16

00.

153

0.15

10.

156

0.16

70.

171

0.17

70.

178

0.18

30.

160

0.14

40.

142

0.13

30.

122

0.14

40.

158

0.16

80.

179

0.17

70.

176

0.17

8(b

)12

%si

mul

atio

nR

atio

ofno

n-po

orw

ages

topo

orw

ages

:1.

011

1.00

71.

008

1.00

71.

008

1.00

81.

009

1.00

51.

003

0.99

90.

994

0.99

91.

000

1.00

81.

012

1.01

11.

001

1.00

71.

004

1.00

61.

005

Frac

tion

ofdi

rect

lyaf

fect

edw

orke

rsin

pove

rty:

0.15

40.

147

0.14

50.

147

0.15

90.

160

0.16

40.

164

0.17

20.

150

0.14

00.

137

0.12

60.

116

0.13

10.

152

0.15

90.

171

0.17

60.

171

0.17

0(c

)16

%si

mul

atio

nR

atio

ofno

n-po

orw

ages

topo

orw

ages

:1.

011

1.00

71.

008

1.01

01.

010

1.01

11.

010

1.00

71.

004

1.00

00.

994

1.00

01.

001

1.00

81.

008

1.00

40.

998

1.00

71.

005

1.00

61.

006

Frac

tion

ofdi

rect

lyaf

fect

edw

orke

rsin

pove

rty:

0.15

20.

145

0.14

30.

141

0.15

20.

152

0.15

80.

159

0.17

00.

149

0.14

00.

135

0.12

50.

115

0.13

20.

152

0.15

80.

165

0.16

90.

165

0.16

3

Not

es:

Thi

sta

ble

was

used

toco

nstr

uctF

igur

e6.

Atim

e-se

ries

plot

ofth

era

tioof

wag

esfo

rno

n-po

ordi

rect

lyaf

fect

edw

orke

rsto

the

wag

esof

poor

dire

ctly

affe

cted

wor

kers

isov

erla

idon

atim

e-se

ries

plot

ofth

efr

actio

nof

dire

ctly

affe

cted

wor

kers

inpo

vert

yfo

rea

chfig

ure.

Res

ults

are

com

pute

dus

ing

5-ye

arm

ovin

gav

erag

esw

here

the

liste

dye

arre

pres

ents

the

mid

poin

tof

the

5-ye

arav

erag

e.D

irec

tlyaf

fect

edw

orke

rsar

ede

fined

asth

ose

wor

kers

earn

ing

betw

een

$0.0

5le

ssth

anth

epr

evai

ling

min

imum

wag

e(t

hehi

gher

ofth

est

ate

orfe

dera

lmin

imum

wag

e)an

dth

esi

mul

ated

fede

ralm

inim

umw

age.

“Poo

r”w

orke

rsar

ede

fined

asth

ose

wor

kers

with

hous

ehol

din

com

ebe

low

the

pove

rty

line.


TA

BL

EA

9T

ime-

Seri

esof

Teen

Em

ploy

men

tto

Popu

latio

nR

atio

and

aT

ime-

Seri

esof

the

Frac

tion

ofD

irec

tlyA

ffec

ted

Wor

kers

inPo

vert

y

1990

–19

9419

91–

1995

1992

–19

9619

93–

1997

1994

–19

9819

95–

1999

1996

–20

0019

97–

2001

1998

–20

0219

99–

2003

2000

–20

0420

01–

2005

2002

–20

0620

03–

2007

2004

–20

0820

05–

2009

2006

–20

1020

07–

2011

2008

–20

1220

09–

2013

2010

-20

14

(a)

8%si

mul

atio

nTe

enem

ploy

men

tra

te:

0.39

70.

396

0.39

60.

404

0.41

80.

418

0.42

40.

420

0.41

40.

394

0.37

90.

366

0.36

00.

354

0.35

20.

350

0.32

30.

289

0.26

90.

260

0.25

6Fr

actio

nof

dire

ctly

affe

cted

wor

kers

inpo

vert

y:0.

160

0.15

30.

151

0.15

60.

167

0.17

10.

177

0.17

80.

183

0.16

00.

144

0.14

20.

133

0.12

20.

144

0.15

80.

168

0.17

90.

177

0.17

60.

178

(b)

12%

sim

ulat

ion

Teen

empl

oym

ent

rate

:0.

400

0.39

70.

399

0.40

60.

416

0.41

40.

416

0.41

30.

407

0.39

10.

377

0.36

60.

361

0.35

40.

354

0.34

30.

313

0.28

20.

262

0.25

10.

243

Frac

tion

ofdi

rect

lyaf

fect

edw

orke

rsin

pove

rty:

0.15

40.

147

0.14

50.

147

0.15

90.

160

0.16

40.

164

0.17

20.

150

0.14

00.

137

0.12

60.

116

0.13

10.

152

0.15

90.

171

0.17

60.

171

0.17

0(c

)16

%si

mul

atio

nTe

enem

ploy

men

tra

te:

0.40

00.

397

0.39

90.

407

0.41

60.

414

0.41

60.

413

0.40

70.

391

0.37

70.

366

0.36

10.

354

0.35

40.

342

0.31

20.

281

0.26

30.

251

0.24

3Fr

actio

nof

dire

ctly

affe

cted

wor

kers

inpo

vert

y:0.

152

0.14

50.

143

0.14

10.

152

0.15

20.

158

0.15

90.

170

0.14

90.

140

0.13

50.

125

0.11

50.

132

0.15

20.

158

0.16

50.

169

0.16

50.

163

Not

es:

Thi

sta

ble

was

used

toco

nstr

uctF

igur

e7.

Obs

erva

tions

are

limite

dto

stat

esw

here

the

sim

ulat

edfe

dera

lmin

imum

wag

eis

bind

ing.

Res

ults

are

com

pute

dus

ing

5-ye

arm

ovin

gav

erag

esw

here

the

liste

dye

arre

pres

ents

the

mid

poin

tof

the

5-ye

arav

erag

e.D

irec

tlyaf

fect

edw

orke

rsar

ede

fined

asth

ose

wor

kers

earn

ing

betw

een

$0.0

5le

ssth

anth

epr

evai

ling

min

imum

wag

e(t

hehi

gher

ofth

est

ate

orfe

dera

lmin

imum

wag

e)an

dth

esi

mul

ated

fede

ralm

inim

umw

age.

“Poo

r”w

orke

rsar

ede

fined

asth

ose

wor

kers

with

hous

ehol

din

com

ebe

low

the

pove

rty

line.

Whe

nco

nstr

uctin

g5-

year

mov

ing

aver

ages

the

annu

alte

enem

ploy

men

trat

esar

ew

eigh

ted

byte

enpo

pula

tion.


TA

BL

EA

10T

ime-

Seri

esof

Rea

lFed

eral

Min

imum

Wag

eR

ate

(in

2014

Dol

lars

)O

verl

aid

ona

Tim

e-Se

ries

ofth

eFr

actio

nof

Dir

ectly

Aff

ecte

dW

orke

rsin

Pove

rty

1990

–19

9419

91–

1995

1992

–19

9619

93–

1997

1994

–19

9819

95–

1999

1996

–20

0019

97–

2001

1998

–20

0219

99–

2003

2000

–20

0420

01–

2005

2002

–20

0620

03–

2007

2004

–20

0820

05–

2009

2006

–20

1020

07–

2011

2008

–20

1220

09–

2013

2010

–20

14

(a)

8%si

mul

atio

nR

ealf

eder

alm

inim

umw

age:

6.77

6.84

6.79

6.76

6.85

6.95

7.04

7.14

7.11

6.94

6.76

6.61

6.45

6.31

6.23

6.34

6.87

7.26

7.45

7.55

7.54

Frac

tion

ofdi

rect

lyaf

fect

edw

orke

rsin

pove

rty:

0.16

00.

153

0.15

10.

156

0.16

70.

171

0.17

70.

178

0.18

30.

160

0.14

40.

142

0.13

30.

122

0.14

40.

158

0.16

80.

179

0.17

70.

176

0.17

8(b

)12

%si

mul

atio

nR

ealf

eder

alm

inim

umw

age:

6.76

6.83

6.79

6.75

6.86

6.97

7.06

7.15

7.12

6.96

6.77

6.61

6.45

6.31

6.24

6.44

6.96

7.30

7.45

7.53

7.53

Frac

tion

ofdi

rect

lyaf

fect

edw

orke

rsin

pove

rty:

0.15

40.

147

0.14

50.

147

0.15

90.

160

0.16

40.

164

0.17

20.

150

0.14

00.

137

0.12

60.

116

0.13

10.

152

0.15

90.

171

0.17

60.

171

0.17

0(c

)16

%si

mul

atio

nR

ealf

eder

alm

inim

umw

age:

6.75

6.83

6.79

6.75

6.86

6.96

7.05

7.15

7.12

6.96

6.77

6.61

6.45

6.31

6.24

6.45

6.97

7.30

7.45

7.53

7.53

Frac

tion

ofdi

rect

lyaf

fect

edw

orke

rsin

pove

rty:

0.15

20.

145

0.14

30.

141

0.15

20.

152

0.15

80.

159

0.17

00.

149

0.14

00.

135

0.12

50.

115

0.13

20.

152

0.15

80.

165

0.16

90.

165

0.16

3

Not

es:

Thi

sta

ble

was

used

toco

nstr

uctF

igur

e8.

The

real

fede

ralm

inim

umw

age

may

vary

som

ewha

tacr

oss

sim

ulat

ions

sinc

edi

ffer

ents

tate

sar

ein

clud

edin

each

year

for

the

diff

eren

tsim

ulat

ions

(bec

ause

ofth

eco

nstr

aint

that

obse

rvat

ions

are

only

draw

nfr

omst

ates

whe

reth

esi

mul

ated

fede

ralm

inim

umw

age

bind

s)—

this

affe

cts

the

popu

latio

nin

agi

ven

year

and

mov

ing

aver

ages

are

popu

latio

nw

eigh

ted.


TA

BL

EA

11Se

nsiti

vity

Ana

lysi

s:T

heSa

mpl

eis

Res

tric

ted

toSt

ates

Tha

tAre

Fully

Bou

ndby

the

Fede

ralM

inim

umW

age

inA

llY

ears

1990

–19

9419

91–

1995

1992

–19

9619

93–

1997

1994

–19

9819

95–

1999

1996

–20

0019

97–

2001

1998

–20

0219

99–

2003

2000

–20

0420

01–

2005

2002

–20

0620

03–

2007

2004

–20

0820

05–

2009

2006

–20

1020

07–

2011

2008

–20

1220

09–

2013

2010

–20

14

(a)

Targ

etef

ficie

ncy

usin

gth

eor

igin

alan

dre

stri

cted

sam

ple

Shar

eof

bene

fits

topo

orw

orke

rs(o

rigi

nals

ampl

e):

0.14

90.

142

0.13

70.

145

0.15

00.

158

0.16

20.

163

0.17

30.

157

0.14

20.

143

0.13

60.

121

0.13

30.

143

0.15

80.

174

0.17

20.

169

0.16

8Sh

are

ofbe

nefit

sto

poor

wor

kers

(res

tric

ted

sam

ple)

: 0.17

90.

166

0.15

80.

178

0.19

90.

202

0.19

70.

196

0.20

60.

180

0.16

30.

174

0.16

40.

135

0.14

60.

137

0.16

10.

189

0.18

70.

186

0.18

8(b

)T

ime-

seri

esof

the

shar

eof

bene

fits

accr

uing

topo

orw

orke

rsan

da

time-

seri

esof

the

frac

tion

ofdi

rect

lyaf

fect

edw

orke

rsin

pove

rty

(Res

tric

ted

Sam

ple)

Shar

eof

bene

fits

topo

orw

orke

rs:

0.17

90.

166

0.15

80.

178

0.19

90.

202

0.19

70.

196

0.20

60.

180

0.16

30.

174

0.16

40.

135

0.14

60.

137

0.16

10.

189

0.18

70.

186

0.18

8Fr

actio

nof

dire

ctly

affe

cted

wor

kers

inpo

vert

y:0.

187

0.17

10.

166

0.18

40.

199

0.20

30.

197

0.18

60.

187

0.16

30.

152

0.16

70.

162

0.14

30.

161

0.15

70.

169

0.18

50.

193

0.18

90.

189

(c)

Tim

e-se

ries

ofth

ere

alfe

dera

lmin

imum

wag

era

te(i

n20

14do

llars

)an

da

time-

seri

esof

the

frac

tion

ofdi

rect

lyaf

fect

edw

orke

rsin

pove

rty

(Res

tric

ted

Sam

ple)

Rea

lfed

eral

min

imum

wag

e(2

014

dolla

rs):

6.73

6.83

6.78

6.75

6.86

6.96

7.05

7.14

7.10

6.94

6.76

6.60

6.42

6.24

6.20

6.36

6.70

7.02

7.33

7.52

7.52

Frac

tion

ofdi

rect

lyaf

fect

edw

orke

rsin

pove

rty:

0.18

70.

171

0.16

60.

184

0.19

90.

203

0.19

70.

186

0.18

70.

163

0.15

20.

167

0.16

20.

143

0.16

10.

157

0.16

90.

185

0.19

30.

189

0.18

9

Not

es:

Thi

sta

ble

was

used

toco

nstr

uctF

igur

e9.

Res

ults

are

com

pute

dus

ing

5-ye

arm

ovin

gav

erag

esw

here

the

liste

dye

arre

pres

ents

the

mid

poin

tof

the

5-ye

arav

erag

e.D

irec

tlyaf

fect

edw

orke

rsar

ede

fined

asth

ose

wor

kers

earn

ing

betw

een

$0.0

5le

ssth

anth

efe

dera

lmin

imum

wag

ean

dth

esi

mul

ated

fede

ralm

inim

umw

age.

“Poo

r”w

orke

rsar

ede

fined

asth

ose

wor

kers

with

hous

ehol

din

com

ebe

low

the

pove

rty

line.


that includes all of the column 4 changes, plus directlyaffected workers are defined as individuals whose hourlywage is between $0.05 less than the prevailing minimumwage (i.e., higher of state or federal level) and the simulatedminimum wage. This change affects the estimates somewhat,though the differences are not significant. Finally, column 6shows results from a simulation that includes all of the column5 changes, plus the same percentage increase (12%) is simu-lated in each year. This leads to a slightly increased estimatefor 1996, a slightly reduced estimate for 2004, and a muchlarger estimate for 2008 (though the standard errors are largeenough that the changes are not significant).

The takeaway from this exercise is that the estimates aresomewhat sensitive to the simulation design—particularlyto the size of the simulated wage increase—but not dra-matically so. Using my simulation methodology doeschange the interpretation of the time-trend though: whileBurkhauser and Sabia find a small reduction in target effi-ciency from 1996 through 2008, I find a small increase intarget efficiency.

REFERENCES

Burkhauser, R. V., and J. J. Sabia. “The Effectiveness ofMinimum Wage Increases in Reducing Poverty: Past,Present, and Future.” Contemporary Economic Policy,25(2), 2007, 262–81.

. “Minimum Wages and Poverty: Will a $9.50Federal Minimum Wage Really Help the Working

Poor?” Southern Economic Journal, 76(3), 2010,592–623.

Card, D., and A. Krueger. Myth and Measurement. Princeton,NJ: Princeton University Press, 1995.

Congressional Budget Office.“The Effects of a Minimum-Wage Increase on Employment and Family Income.”2014. Accessed October 1, 2014. http://www.cbo.gov/publication/44995.

Dube, A., T. W. Lester, and M. Reich. “Minimum WageEffects across State Borders: Estimates Using Con-tiguous Counties.” Review of Economics and Statistics,92(4), 2010, 945–64.

Gramlich, E. M. “Impact of Minimum Wages on OtherWages, Employment, and Family Incomes.” BrookingsPapers on Economic Activity, 2, 1976, 409–51.

Horrigan, M. W., and R. B. Mincy. “The Minimum Wageand Earnings and Income Inequality,” in Uneven Tides:Rising Inequality in America, edited by S. Danzigerand P. Gottschalk. New York: Russell Sage Foundation,1993, 151–275.

Johnson, W. R., and E. K. Browning. “The Distributional andEfficiency Effects of Increasing the Minimum Wage: ASimulation.” American Economic Review, 73(1), 1983,204–11.

Neumark, D., and W. Wascher. Minimum Wages. Cambridge,MA: The MIT Press, 2008.

Neumark, D., J. M. I. Salas, and W. Wascher. “Revisiting theMinimum Wage-Employment Debate: Throwing Outthe Baby with the Bathwater?” Industrial and LaborRelations Review, 67, 2014, 608–48.

http://www.cbo.gov/publication/44995

http://www.cbo.gov/publication/44995

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