Source: Borjas (1996)

PERSPECTIVES ON LABOUR AND INCOME 20 Statistics Canada

Fact sheet on minimum wage

The proportion of employees earning minimum wage edged up in 2004 after falling steadily since 1997.

Minimum wageTotal

employees Total Incidence

’000 ’000 %Both Sexes15 and over 13,497.9 621.1 4.6

15 to 24 2,358.6 408.6 17.315 to 19 881.8 302.0 34.220 to 24 1,476.8 106.6 7.2

25 and over 11,139.3 212.4 1.925 to 34 3,105.8 64.2 2.135 to 44 3,460.0 61.0 1.845 to 54 3,100.2 47.8 1.555 and over 1,473.3 39.4 2.7

Men15 and over 6,867.1 226.3 3.3

15 to 24 1,190.8 153.1 12.915 to 19 439.3 112.5 25.620 to 24 751.5 40.6 5.4

25 and over 5,676.3 73.1 1.325 to 34 1,608.6 22.7 1.435 to 44 1,751.5 19.2 1.145 to 54 1,532.8 15.2 1.055 and over 783.4 16.0 2.0

Women15 and over 6,630.8 394.8 6.0

15 to 24 1,167.8 255.5 21.915 to 19 442.5 189.5 42.820 to 24 725.3 66.0 9.1

25 and over 5,462.9 139.3 2.525 to 34 1,497.2 41.5 2.835 to 44 1,708.5 41.8 2.445 to 54 1,567.3 32.6 2.155 and over 689.9 23.4 3.4

Source: Labour Force Survey, 2004

Most minimum wage workers are women and young

Women accounted for almost two-thirds of minimumwage workers, but less than half of all employees. Thistranslated into a higher proportion of women work-ing for minimum wage—1 in 17 compared with 1 in30 men. This overrepresentation held across all agegroups, with rates for women being mostly doublethose for men.

One in three teenagers aged 15 to 19 worked for mini-mum wage. This age group had by far the highest rateand accounted for nearly half of all minimum wageworkers. A large majority were attending schooleither full or part time. Another 17% of minimumwage workers were aged 20 to 24, almost half of themstudents.2 In total, two-thirds of minimum wageworkers were under 25, compared with only 17% ofall employees. This translates into an incidence rate ninetimes that of those 25 years and older—1 in 6 versus1 in 53.

A sizeable proportion (28%) of minimum wage work-ers were aged 25 to 54, many of them women. Forthese individuals in their core working and peak earn-ing years, minimum wage work is likely not a transi-tory phase.

The incidence of working for minimum wage declinedsharply with age but rose slightly among those 55 andolder. The latter is a reflection of some of the low-wage occupations in which working seniors tend tobe concentrated: retail salespersons and sales clerks;general office clerks; janitors, caretakers and buildingsuperintendents; babysitters, nannies and parents’helpers; and light duty cleaners.

From 1997 to 2003, the proportion of employeesearning minimum wage or less fell steadily, from 5.7%to 4.1%. In 2004, the rate edged up to 4.6%.

3.0

4.0

5.0

6.0

1997 1998 1999 2000 2001 2002 2003 2004

%

Source: Labour Force Survey

Source: Factsheet on Minimum Wage, Statistics Canada, 2005.

PERSPECTIVES ON LABOUR AND INCOME 21 Statistics Canada

Fact sheet on minimum wage

Education makes a difference

Where do they work?

Minimum wageTotal

employees Total Incidence

’000 ’000 %

Education 13,497.9 621.1 4.6Less than high school 1,897.4 249.6 13.2

Less than grade 9 379.8 29.2 7.7Some high school 1,517.6 220.4 14.5

High school graduate 2,782.8 128.6 4.6At least some

postsecondary 8,817.6 243.0 2.8Some postsecondary 1,404.3 112.6 8.0Postsecondary

certificate or diploma 4,623.4 94.9 2.1University degree 2,789.9 35.5 1.3

Source: Labour Force Survey, 2004

Those with less than a high school diploma werealmost five times as likely to be working for mini-mum wage or less as those with at least somepostsecondary training—1 in 8 compared with 1 in35. Four in 10 minimum wage workers did not have ahigh school diploma, compared with 1 in 7 for allemployees. This corresponds with the high rates ofminimum wage work among young people, many ofwhom have not yet completed their studies.

Minimum wage work is concentrated in the servicesector. Accommodation and food services industrieshad by far the highest incidence, with 1 in 5 workers ator below minimum wage. Trade also had high rates—1 in 11. These industries are characterized by high con-centrations of youth and part-time workers, who tendto have less work experience and weaker attachmentto the labour force. These industries often do notrequire specialized skills or a postsecondary education,and have low levels of unionization. The many part-time jobs tend to favour a greater presence of women.

Agriculture also had a relatively high incidence of mini-mum wage workers—1 in 10. Farm labour has tradi-tionally been excluded from minimum wageprovisions. Workers in agriculture are often notunionized, but may be compensated for lower wagesthrough non-wage benefits such as free room andboard.

Highly unionized industries such as construction,public administration, and manufacturing were amongthose with the lowest rates of minimum wageworkers.

Minimum wageTotal

employees Total Incidence

’000 ’000 %

Industry 13,497.9 621.1 4.6Goods-producing 3,331.4 50.9 1.5Agriculture 116.8 12.2 10.4Forestry, fishing, mining,

oil and gas 236.6 3.2 1.4Utilities 132.8 F FConstruction 642.1 5.9 0.9Manufacturing 2,203.1 29.2 1.3

Service-producing 10,166.5 570.2 5.6Trade 2,201.5 206.7 9.4Transportation and

warehousing 667.8 13.0 1.9Finance, insurance, real

estate and leasing 807.9 23.4 2.9Professional, scientific

and technical 651.4 9.9 1.5Management, administrative

and other support 484.1 18.6 3.8Education 990.9 16.9 1.7Health care and social

assistance 1,521.3 25.1 1.6Information, culture

and recreation 614.0 35.5 5.8Accommodation and food 921.3 180.2 19.6Public administration 829.1 7.8 0.9Other services 477.2 33.1 6.9

Source: Labour Force Survey, 2004

Source: Factsheet on Minimum Wage, Statistics Canada, 2005.

Figure 3: The realized wage distributions for hired workers in ALL, by experimental group

$4/hour minimum wage

$3/hour minimum wage

$2/hour minimum wage

Control − No Minimum

0.5 1.0 2.0 3.0 4.0 10.0 25.0 100.0

0.0

0.5

1.0

1.5

2.0

0.0

0.5

1.0

1.5

2.0

0.0

0.5

1.0

1.5

2.0

0.0

0.5

1.0

1.5

2.0

Mean wage over contract ($/hour)

Density

Notes: This figure shows a density histogram of log observed hourly wages in each of the experimental cells. The

x-axis is on a log scale. The bars in the histogram are each $1 wide, with intervals of [a, a+1), where a is an integer.

13

Source: Horton (2017)

Table 12: Effects of the minimum wage on log hours-worked, conditional upon a hire

Dependent variable:

log hours-worked, conditional upon a hire

ALL ADMIN LPW

(1) (2) (3)Panel A: Group indicators

MW4 −0.063∗ −0.208∗∗∗ −0.283∗∗∗

(0.027) (0.056) (0.061)MW3 −0.089∗∗∗ −0.257∗∗∗ −0.263∗∗∗

(0.027) (0.055) (0.058)MW2 −0.046 −0.168∗∗ −0.116∗

(0.027) (0.055) (0.058)Constant 2.783∗∗∗ 3.270∗∗∗ 3.322∗∗∗

(0.008) (0.016) (0.017)

Observations 47,434 12,894 11,685R2 0.0004 0.003 0.003Panel B: Min wage as a regressor

Minimum Wage, w −0.021∗∗∗ −0.066∗∗∗ −0.074∗∗∗

(0.005) (0.011) (0.012)Constant 2.782∗∗∗ 3.267∗∗∗ 3.322∗∗∗

(0.008) (0.016) (0.017)

Observations 47,434 12,894 11,685R2 0.0003 0.003 0.003

Notes: The dependent variable is the log hours worked, conditional upon a hire.In Panel A, the independentvariables are indicators for each experimental group, with the control group excluded; in Panel B, the inde-pendent variable is the imposed minimum wage (with 0 for the control). ADMIN are job openings posted inthe administrative category. LPW are job openings predicted to have hourly wages less than $5/hour basedon a model fit with historical data. A plot of the data in this table can be found in Figure 5. Significanceindicators: p ≤ 0.05 : ∗, .p ≤ 0.01 : ∗∗, and p ≤ .001 : ∗∗∗.

63

Source: Horton (2017)

Source: Borjas (1996)

Source: Borjas (1996)

Source: Borjas (1996)

Source: Hammermesh (1993)

1400 THE AMERICAN ECONOMIC REVIEW DECEMBER 2000

{_ Original 7 Countes

Additional 7 Counties

Nuriber of Restaurants in Original Survey

.2

.3

.6

70 0 70 140 Niles

FIGURE 1. AREAS OF NEW JERSEY AND PENNSYLVANIA COVERED BY ORIGINAL SURVEY AND BLS DATA

restaurants that were not linked to subsequent months' data were assumed closed and assigned zero employment for these months, even though some of these restaurants may not have closed. This is probably a more common occurrence for New Jersey than Pennsylvania: 0.4 percent of the Pennsylvania restaurants had zero or miss- ing employment at the end of 1992, as com- pared to 3.4 percent of New Jersey restaurants. In our original survey, 1.3 percent of Pennsyl- vania restaurants and 2.7 percent of New Jersey restaurants were temporarily or permanently closed at the end of 1992.7

Also note that because firms are allowed to report on more than one unit in a county in the BLS data, some of the records reflect an aggre- gation of data for multiple establishments. We address both of these issues in the analysis below. Importantly, however, these problems do not affect the repeated cross-sectional files that we also analyze.

To draw the repeated cross-sectional file, the final name-search algorithm described above was applied each quarter between 1991:Q4 and 1997:Q3. Again, data were selected for the same chains in New Jersey and the 14 counties in eastern Pennsylvania. Every month's data from the sampled quarters was selected. The cross-sectional sample probably provides the cleanest estimates of the effect of the minimum- wage increase because it incorporates births as well as deaths of restaurants, and because pos- sible problems caused by changes in reporting units over time are minimized.

B. Summary Statistics and Differences-in- Differences

Table 1 reports basic employment summary statistics for New Jersey and for the Pennsylva- nia counties, before and after the April 1992 increase in New Jersey's minimum wage. Panel A is based on the longitudinal BLS sample of fast-food restaurants. In the first row, the "be- fore" period pertains to average employment in February and March of 1992, and the "after" pertains to average employment in November

7An interviewer visited all of the nonresponding stores in both states to determine if they were closed in our original survey.

This content downloaded from 137.82.185.87 on Wed, 4 Feb 2015 17:14:15 PMAll use subject to JSTOR Terms and Conditions

Nicole

Rectangle

Nicole

Rectangle

Nicole

Rectangle

We merge information on the state (or local) and federalminimum wage in effect in each quarter from 1990q1 to2006q2 into our quarterly panel of county-level employ-ment and earnings. During the sample period, the federalminimum wage changed in 1991–1992 and again in 1996–1997. The number of states with a minimum wage abovethe federal level ranged from 3 in 1990 to 32 in 2006.

C. Sample Construction

Our analysis uses two distinct samples: a sample of allcounties and a sample of contiguous border county-pairs. Insection IVB, where we present our empirical specificationcomparing contiguous border counties, we explain the needfor the latter sample in greater detail. Our replication ofmore traditional specifications uses the full set of countieswith balanced panels. This all counties (AC) sample con-sists of 1,381 out of the 3,081 counties in the United States.The number of counties with a balanced panel of reporteddata yields a national sample of 91,080 observations.

The second sample consists of all the contiguous county-pairs that straddle a state boundary and have continuousdata available for all 66 quarters.11 We refer to this sampleas the contiguous border county-pair (CBCP) sample. TheQCEW provides data by detailed industry only for countieswith enough establishments in that industry to protect confi-dentiality. Among the 3,108 counties in the mainlandUnited States, 1,139 lie along a state border. We have a full

(66 quarters) set of restaurant data for 504 border counties.This yields 316 distinct county-pairs, although we keepunpaired border counties with full information in our bordersample as well. Among these, 337 counties and 288 county-pairs had a minimum wage differential at some point in oursample period.12 Figure 2 displays the location of thesecounties on a map of the United States. Since we considerall contiguous county-pairs, an individual county will havep replicates in our data set if it is part of p cross-statepairs.13

Table 1 provides descriptive statistics for the two sam-ples. Comparing the AC sample (column 1) to the CBCPsample (column 2), we find that they are quite similar interms of population, density, employment levels, and aver-age earnings.

D. Contiguous Border Counties as Controls

Contiguous border counties represent good control groupsfor estimating minimum wage effects if there are substan-tial differences in treatment intensity within cross-statecounty-pairs, and a county is more similar to its cross-statecounterpart than to a randomly chosen county. In contrast,panel and period fixed-effects models used in the national-

11 As we report below, this exclusion has virtually no impact on ourresults.

12 We also use variation in minimum wage levels within metropolitanstatistical areas, which occur when the official boundaries of a metropoli-tan area span two or more states. We use the OMB’s 2003 definitionof metropolitan areas. Of the 361 core-based statistical areas defined asmetropolitan, 24 cross state lines. See note 16 for a full list of cross-statemetropolitan areas.

13 The issue of multiple observations per county is addressed by theway we construct our standard errors. See section IVC.

FIGURE 2.—CONTIGUOUS BORDER COUNTY-PAIRS IN THE UNITED STATES WITH A MINIMUM WAGE DIFFERENTIAL, 1990–2006Q2

949MINIMUM WAGE EFFECTS ACROSS STATE BOUNDARIES

Source: Dube, Lester, and Reich (2010)

with and without the inclusion of a state-level time trend(specification 3 is just specification 2 with such a trend andhas been reported in table 2). We find that the traditionalspecifications with common time effects (1 and 5) are parti-cularly sensitive to the inclusion of such a linear trend. Thesensitivity of the estimates from the traditional specification(1) to the inclusion of a linear time trend does not necessar-ily imply that it is biased. Inclusion of parametric trendsmay ‘‘overcontrol’’ if minimum wages themselves reducethe employment trends of minimum wage workers, as thetwo coefficients are estimated jointly under functional formassumptions. However, the estimates from including suchlinear time trends in our local specification (6) are virtually

identical with respect to both the point estimate and thestandard error. This combination of evidence provides fur-ther internal validity to our local specification using discon-tinuity at the policy borders.

One limitation of the QCEW data is that we do notobserve hours of work. Therefore, although the effect ofminimum wages on head count employment is around 0 inour local specification, it is possible that there is somereduction in hours. Here we provide some rough calcula-tions that place bounds on the hours effect. To begin, notethat the minimum wage elasticity of weekly earnings is0.188. This elasticity reflects the combined effect on hourlywages and weekly hours. If we can use auxiliary estimates

FIGURE 4.—TIME PATHS OF MINIMUM WAGE EFFECTS, BY SAMPLE AND SPECIFICATION

954 THE REVIEW OF ECONOMICS AND STATISTICS

Source: Dube, Lester, and Reich (2010)

on how much earnings ‘‘should’’ rise absent an hours effect,we can approximate the effect on hours.

Using the 2006 CPS, we find that 23.0% of restaurantworkers (at the three-digit NAICS level) earn no more thanthe minimum wage. The difference between our earningselasticity of 0.188 and this 0.230 figure suggests a �0.042elasticity for hours. It is likely, however, that some workersbelow the minimum wage do not get a full increasebecause of tip credits in some states, that some additionalworkers above the old minimum wage but below the newminimum get a raise, and that some workers even abovethe new minimum wage get a raise because of wage spill-overs.

While a full accounting of these effects is beyond thescope of this paper, we can provide a very approximatebound for a 10% increase in the minimum wage. About32.5% of restaurant workers nationally are paid no morethan 10% above the minimum wage.23 Assuming a uniformdistribution of wages between the new and old minimumsuggests a minimum wage elasticity for hours of �0.090.However, this estimate is likely to be an upper bound, asnot all of those below the minimum will get a full increase.We conclude that the elasticity of weekly earnings is relatively

FIGURE 4.—(CONTINUED)

The cumulative response of minimum wage increases using a distributed lag specification of four leads and sixteen lags based on quarterly observations. All specifications include county fixed effects and control forthe log of annual county-level population. Specifications 1 and 4 (panels 1 and 4) include period fixed effects. Specification 3 includes state-level linear trends. Specification 2 includes census division–specific periodfixed effects, and specification 5 includes county-pair–specific period fixed effects. For all specifications, we display the 90% confidence interval around the estimates in dotted lines. The confidence intervals were calcu-lated using robust standard errors clustered at the state level for specifications 1, 2, and 4 (panels 1, 2, and 4) and at both the state level and the border segment level for our local estimators (panels 3, 5, and 6).

23 Authors’ calculations based on the current population survey.

955MINIMUM WAGE EFFECTS ACROSS STATE BOUNDARIES

Source: Dube, Lester, and Reich (2010)

estimated employment effects, and the results for the general sector donot provide statistically significant evidence of a minimum-wage effect.For reasons discussed in Section 3, our preference is to directly modelcounty-specific trends, and with this approach the estimates are quiteprecise. For example, a 95% confidence interval for the minimum-wageeffect on employment ranges from −0.043 to 0.021, clearly excludingthe elasticities close to −0.2 often alluded to in the literature.17 Thesecond row of Table 2 allows for potential lagged effects of theminimumwage by using afive-quartermoving average of theminimumwage,withlittle change in the results.

Separate estimates for the full-service and limited-service restaurantsectors are reported in the third and fourth rows of Table 2. Our expec-tation is that as the use of minimum wage labor is presumably moreprevalent in the limited-service sector, the minimum wage impact onearnings should also be higher. This expectation is consistent with ourresults for the country-trend specification (but not for the border-county results). Perhaps contrary to expectations, however, theonly (marginally) statistically significant minimum-wage employmenteffect is found for the full-service sector (and only after including countytrends). This estimate is nonetheless quite small – an elasticity ofaround −0.04 – and the 95% confidence interval of [−0.079, −0.003]again excludes effects close to −0.2. This small estimate is significantdue to the very small standard error provided by the county-trend anal-ysis. Indeed,most of the standard errors are smaller in the current set ofresults thanwith the parallel findings in Addison et al. (2012) and Dubeet al. (2010), despite the shorter time period. This outcome likely stemsfrom the greater variation in the minimum wage variable in the laterperiod compared to the earlier data.18

4.2. Results from the CPS and ACS

For analyses at the industry level, the QCEWwould seem preferableto the CPS given its larger and more complete sample of employment,with the possibility of controlling for county-level factors (rather thanthe state-level factors with the CPS). Perhaps the only advantage of

the CPS is that employment can be measured at the monthly level, sothat the correlation with minimum-wage changes is more accurate.For analyses at the demographic-level, however, alternative data setsare instrumental as information on the demographics of employees isnot available with the QCEW. We report estimates from the CPS forthe food services and drinking places sector, allowing comparisonwith results from the QCEW. But our primary use of the CPS is to esti-mate models for teenagers, the main focus of earlier research with theCPS.19

The estimated employment elasticities for the CPS are reported inthe first two columns of Table 3, with the first row showing the foodservices and drinking places results. The CPS analysis of this sectorprovides considerably less precise estimates than the county-levelanalysis with the QCEW. Both specifications provide negative estimatedeffects, but it is not possible to reject the null that the coefficients equalzero. However, the standard errors are almost four times as large withthe CPS, and an elasticity of−0.2 would be in the 95% confidence inter-val even with the state-trends specification. The results for teenagers(in the second row) are also imprecise, but in this casewe do havemar-ginal significance of the negative estimated elasticity when state trendsare incorporated.20 The estimate of−0.18 is also in the range typicallysuggested by Neumark and Wascher as to be expected for teenagers.The bottom two rows of Table 3 provide estimates using a 13-monthmoving average of the minimum wage to allow for lagged effects.These estimates are naturally less precise (given the lower variation inthe moving average variable). The teenager elasticity estimate is nowinsignificant, although the actual coefficient estimate is not that muchsmaller than when the contemporaneous minimum-wage variablewas used.21 Conclusions are unaffected for the other two groupswhen using themoving-averagedminimum-wage variable.We also es-timated equations forwhich the employment of young adults (aged20–

17 Neumark and Wascher (2007) identify the “consensus range” of the earliestminimum-wage literature as suggesting elasticities between −0.1 and −0.3, and arguethat the preponderance of studies in the newminimum-wage research supports a findingof negative employment effects.18 To explore the sensitivity of the results in the full-service sector to the treatment ofminimum wages for tipped employees (see footnote 10), we constructed a new mini-mum wage variable that holds the nominal minimum wage constant in states wherechanges in the regular minimum wage occurred but where the cash minimum wagewas not changed. Although the employment coefficient estimate for the baselinemethod is now larger in magnitude and statistically significant when we use this alter-native measure of the state minimum wage (an estimate of −0.09, with a p-value of0.001), conclusions from the county-trend and border-county results are not affected.

19 Only minimum-wage estimates for the employment equations are reported. Thefull specifications for the employment equations are reported in Appendix Table B.We have also estimated earnings equations with the CPS, generally finding increasingimpacts on earnings from higher minimum wages. Earnings estimates are availablefrom the authors upon request.20 The result from our basic specification without state-level trends may seem at oddswith that of Even and Macpherson (2010), who report a statistically significant nega-tive employment elasticity for teenagers using the CPS over a similar time period whenusing a somewhat similar specification. If we replicate the Even and Macpherson spec-ification, we too find a negative and marginally significant coefficient estimate. Howev-er, this result is no longer significant when we either (a) change the national-trendcontrols from annual dummies (which they use) to monthly dummies (which we use),or (b) weight by state population. (Results are available from the authors upon request.)21 We also estimated employment equations for young adults (aged 20–24 years),and for junior-high-school dropouts (25 and older). In neither case were the coefficientestimates significant (other than a positive estimate for dropouts when state trends arenot included).

Table 2Minimum wage estimates of employment and earnings equations, QCEW, 2005–2009.

Earnings Employment

Basic County trends Border county Basic County trends Border county

(1) Food services and drinking places 0.134***(0.017)

0.131***(0.014)

0.163***(0.031)

−0.015(0.019)

−0.011(0.016)

−0.076(0.051)

(2) Food service and drinking places:MW moving average

0.129***(0.025)

0.153***(0.018)

0.153***(0.051)

−0.037(0.028)

−0.023(0.027)

−0.090(0.072)

(3) Full-service restaurants 0.134***(0.021)

0.118***(0.017)

0.181***(0.050)

−0.031(0.028)

−0.038*(0.021)

0.035(0.041)

(4) Limited-service restaurants 0.152***(0.016)

0.149***(0.019)

0.147***(0.042)

0.002(0.021)

−0.003(0.022)

−0.017(0.065)

Notes: Onlyminimum-wage coefficient estimates are reported, although each regression contains all covariates discussed in Eq. (1). Clustered standard errors are in parentheses. See textfor details on the estimation procedure for coefficients and clustered standard errors for each specification. All regressions include fixed-effects for county and quarter-year, and areweighted by the average county population over the 2005–2009 period. Estimates in row 2 use an equally weighted 5-quarter moving average of the current minimum wage with 4lags of the minimumwage.***,**,* denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

35J.T. Addison et al. / Labour Economics 23 (2013) 30–39

estimated with the QCEW would not be significant given the stan-dard error of the CPS estimate). On the other hand, there is strongevidence of a larger minimum-wage impact among teenagerswhen the unemployment rate is high. In this case, the observed in-crease in the unemployment rate of 2.8 percentage points wouldsuggest a minimum-wage elasticity of −0.34 at the average unem-ployment level during 2008–2009.25 In this case, it does appear thatthe recession did make teenagers particularly susceptible to in-creases in the minimum wage. (We also estimated an interactioneffect for young adults, but it was statistically insignificant oncestate trends were incorporated.) Part of the response may havebeen a substitution away from teenagers and towards older,more-skilled workers made available by general job losses duringthe recession. As a result, sector-specific results fail to show a par-ticularly large increase in employment losses, even though changesmay have been occurring in the demographic-group composition ofthose sectors.26

5. Conclusions

Much of the recent research on the employment effects ofminimumwages has failed to provide convincing evidence that increases in theminimum wage are associated with material reductions in employ-ment. One possible explanation for negative findings of this typemight be that the typical increases in wage minima over 1980–2005have simply been too small, taking wages to levels that are still quitelow in both real and relative terms. Combined with potentially lowown-wage elasticities for low-skilled labor in many service-typesectors, such modest increases may have not provided sufficient

identifying information or traction for the researcher to detect sta-tistically any negative effects of minimum wages on low-wageemployment.

In the present paper, we have focused on contemporary economicdevelopments in the U.S., motivated by the perception that recentminimum wage increases have been altogether more substantialthan in the past. This perception, although not strongly borne out inpractice, does receive some support in the data. More importantly,our time frame encompasses a period of major recession that mightalso be expected to provide a more severe response of employers tominimum-wage hikes. By focusing on this recent period, we areable to evaluate the possibility that more recent effects of minimumwages have been larger than the typically small elasticities reportedin the literature, especially as the unemployment rate increases. Tothis end, we have used three different data sets (including one thathas never been used before for this purpose) to estimate the employ-ment effects of minimum wages for low wage groups during the2005–2010 period, applying three principal estimation strategiesemployed in the literature.

Our findings produce limited evidence of a negativeminimumwageimpact on employment. The results support a small negativeminimum-wage elasticity among full-service restaurants, and a larger estimatedelasticity among teenagers. When focusing on elasticities in stateswith particularly high unemployment rates, we report a negative esti-mated minimum-wage elasticity within the general food services anddrinking places sector.More importantly,wefindevidence of a substan-tially higher minimum-wage elasticity for teenagers in states with thehighest unemployment rates during our sample period. The suggestionis that the large increase in teenage unemployment during the reces-sion is at least partly explained by the accompanying increases in theminimum wage.

The bottom line of this investigation is that evenwhen implementedduring a significant economic downturn, minimum-wage increases donot appear to have particularly strong effects in reducing employmentwithin the sector of the economymost likely to be affected by the min-imum wage. However, minimum-wage elasticities for teenagers doseem to have increased substantially in states where the unemploy-ment rate was particularly high. If the minimum-wage impact waslargely concentrated among teenagers, this outcome may be less of aconcern than if it had been felt more generally (on the reasoning thatcomparatively few minimum-wage teenagers are to be found in poorfamilies).

25 The increase in the unemployment rate is different between the two data sets asthe QCEW analysis is based on county-level unemployment rates for all workers, whilethe CPS uses state-level unemployment rates just for prime-age men.26 We have attempted to test this possibility directly by estimating models in whichemployment of teenagers in the food services and drinking places sector is the depen-dent variable. Unfortunately, the small number problem with the CPS when focusingon such specific groups renders the estimates very imprecise. The estimatedminimum-wage elasticity for employment was negative (and larger in magnitude thanthe overall food services and drinking places elasticity), but it was not statisticallysignificant.

Table 4Minimum wage estimates of employment equations with recessionary interaction.

QCEW (2005–2009): Interaction with county unemployment rate

Food services and drinking places Full-service restaurants Limited-service restaurants

Basic County trends Basic County trends Basic County trends

Minimum wage −0.025 −0.018 −0.040 −0.042** −0.006 −0.011(0.018) (0.016) (0.030) (0.020) (0.020) (0.023)

Minimum wage ∗ unemployment rate −0.017*** −0.012*** −0.017 −0.008 −0.015** −0.014**(0.005) (0.005) (0.011) (0.008) (0.006) (0.006)

p-Value: joint test for no minimum-wage impact 0.00 0.01 0.14 0.29 0.05 0.02

CPS (2005–2010): Interaction with state unemployment rate

Food services and drinking places Teenage workers

Basic State trends Basic State trends

Minimum wage −0.095(0.083)

−0.043(0.082)

−0.033(0.085)

−0.174*(0.095)

Minimum wage ∗ unemployment rate −0.011(0.021)

0.002(0.024)

−0.066***(0.022)

−0.059***(0.017)

p-Value: joint test for no minimum-wage impact 0.52 0.85 0.01 0.00

***,**,* denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

37J.T. Addison et al. / Labour Economics 23 (2013) 30–39

CEPR Why Does the Minimum Wage Have No Discernible Effect on Employment? 5

their view: “Two scenarios are consistent with this empirical research record. First, minimum wages may simply have no effect on employment... Second, minimum-wage effects might exist, but they may be too difficult to detect and/or are very small.”13

FIGURE 1

Trimmed Funnel Graph of Estimated Minimum-Wage Effects (n = 1,492)

Source: Doucouliagos and Stanley (2009).

Paul Wolfson and Dale Belman have carried out their own meta-analysis of the minimum wage, focusing on studies published only since 2000. They identified 27 minimum wage studies that produced the necessary elasticity estimates and corresponding standard errors, yielding 201 employment estimates in total. They then produced a range of meta-estimates, controlling for many features of the underlying studies, including the type of worker analyzed (teens or fast food workers), whether the study focused on the supply or the demand side of the labor market, who the authors of the study were, and other characteristics. The resulting estimates varied, but revealed no statistically significant negative employment effects of the minimum wage: "The largest in magnitude

13 Doucouliagos and Stanley (2009), p. 422. Doucouliagos and Stanley also "find strong evidence of publication

selection for significantly negative employment elasticities" (2009, p. 422) They conclude: "Even under generous assumptions about what might constitute 'best practice' in this area of research, little or no evidence of an adverse employment effect remains in the empirical research record, once the effects of publication selection are removed." (p. 423)

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Elasticity

Credible Research Designs for Minimum Wage Studies: A Response

to Neumark, Salas and Wascher

Sylvia Allegretto, Arindrajit Dube, Michael Reich and Ben Zipperer

ú

September 29, 2015

Abstract

We assess the Neumark, Salas and Wascher (NSW) critique of our minimum wage findings. Recentstudies, including one by NSW, obtain small employment elasticities for restaurants, -0.06 or less inmagnitude. The substantive critique in NSW thus centers primarily upon teens. Using a longer (1979-2014) sample than used by NSW and in our own previous work, we find clear evidence that teen minimumwage employment elasticities from a two-way fixed-e�ects panel model are contaminated by negative pre-existing trends. Simply including state-specific linear trends produces small and statistically insignificantestimates (around -0.07); including division-period e�ects further reduces the estimated magnitudes to-ward zero. A LASSO-based selection procedure indicates these controls for time-varying heterogeneityare warranted. Including higher order state trends does not alter these findings, contrary to NSW. Con-sistent with bias in the fixed-e�ects estimates from time-varying heterogeneity, first di�erence estimatesare small or positive. Small, statistically insignificant, teen employment elasticities (around -0.06) obtainfrom border discontinuity design with contiguous counties. Contrary to NSW, such counties are moresimilar to each other than to other counties. Synthetic control studies also indicate small minimum wageelasticities (around -0.04). Nearby states receive significantly more weight in creating synthetic controls,providing further support for using regional controls. Finally, NSW’s preferred new matching estimatesare plagued by a problematic sample that mixes treatment and control units, obtains poor matches, andshows the largest employment drops in areas with relative minimum wage declines.

1 Introduction

Recent controversies in minimum wage research have centered on how to credibly estimate employment

e�ects. Since the inception of the “new minimum wage” literature in the early 1990s, the source of identifying

variation in the United States has largely come from state-level di�erences in minimum wage policy—either

directly, or in interaction with federal policy. As shown in panel A of Figure 1, state minimum wages

proliferated substantially over the past three decades. Between the years 1979 and 1985, only one stateúAllegretto: Institute for Research on Labor and Employment, University of California, Berkeley; Dube: Department

of Economics, University of Massachusetts Amherst and IZA; Reich: Department of Economics and Institute for Researchon Labor and Employment, University of California, Berkeley; Zipperer: Washington Center for Equitable Growth. We aregrateful to Doruk Cengiz, Zachary Goldman, Carl Nadler, Thomas Peake and Luke Reidenbach for excellent research assistance.Financial support for this paper came entirely from the University of California, Berkeley and the University of MassachusettsAmherst.

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