Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania
David Card; Alan B. Krueger
The American Economic Review, Vol. 84, No. 4. (Sep., 1994), pp. 772-793. Stable URL:
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Minimum Wages and Employment:
A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania
On April 1, 1992, New Jersey’s minimum wage rose from $4.25 to $5.05 per hour.Toevaluatetheimpactofthelawwesurveyed410fast-foodrestaurantsin New Jersey and eastern Pennsylvania before and after the rise. Comparisons of employment growth at stores in New Jersey and Pennsylvania (where the minimum wage was constant)provide simple estimates of the effect of the higher minimum wage. We also compare employment changes at stores in New Jersey that were initially paying high wages (above $5) to the changes at lower-wage stores. We find no indication that the rise in the minimum wage reduced employment. (JEL 530, 523)
How do employers in a low-wage labor market respond to an increase in the mini- mum wage? The prediction from conven- tional economic theory is unambiguous: a rise in the minimum wage leads perfectly competitive employers to cut employment (George J. Stigler, 1946). Although studies in the 1970’s based on aggregate teenage employment rates usually confirmed this prediction,’ earlier studies based on com- parisons of employment at affected and un- affected establishments often did not (e.g., Richard A. Lester, 1960, 1964). Several re-
*Department of Economics, Princeton University, Princeton, NJ 08544. We are grateful to the Institute for Research on Poverty, University of Wisconsin, for partial financial support. Thanks to Orley Ashenfelter, Charles Brown, Richard Lester, Gary Solon, two anonymous referees, and seminar participants at Princeton, Michigan State, Texas A&M, University of Michigan, university of Pennsylvania, ~niversitJof Chicago, and the NBER for comments and sugges- tions. We also acknowledge the expert research assis- tance of Susan Belden, Chris Burris, Geraldine Harris, and Jonathan Orszag.
‘see Charles Brown et al. (1982,1983) for surveys of this literature. A recent update (Allison J. Wellington, 1991) concludes that the employment effects of the minimum wage are negative but small: a 10-percent increase in the minimum is estimated to lower teenage employment rates by 0.06 percentage points.
cent studies that rely on a similar compara- tive methodology have failed to detect a negative employment effect of higher mini- mum wages. Analyses of the 1990-1991 in- creases in the federal minimum wage (Lawrence F. Katz and Krueger, 1992; Card, 1992a) and of an earlier increase in the minimum wage in California (Card, 1992b) find no adverse employment impact. A study of minimum-wage floors in Britain (Stephen Machin and Alan Manning, 1994) reaches a similar conclusion.
This paper presents new evidence on the effect of minimum wages on establishment- level employment outcomes. We analyze the experiences of 410 fast-food restaurants in New Jersey and Pennsylvania following the increase in New Jersey’s minimum wage from $4.25 to $5.05 per hour. Comparisons of employment, wages, and prices at stores in New Jersey and Pennsylvania before and after the rise offer a simple method for evaluating the effects of the-minimum wage. ~~~~~~i~~~~within N~~ jerseybetween
high-wage paying
than the new minimum rate prior to its effective date) and other stores provide an alternative estimate of the impact of the new lawe
In addition to the simplicity of our empir- ical methodology, several other features of
772
VOL. 84 NO. 4 CARDAND KRUEGER: MINIMUM WAGEAND EMPLOYMENT 773
the New Jersey law and our data set are also significant. First, the rise in the mini- mum wage occurred during a recession. The increase had been legislated two years ear- lier when the state economy was relatively healthy. By the time of the actual increase, the unemployment rate in New Jersey had risen substantially and last-minute political action almost succeeded in reducing the minimum-wage increase. It is unlikely that the effects of the higher minimum wage were obscured by a rising tide of general economic conditions.
Second, New Jersey is a relatively small state with an economy that is closely linked to nearby states. We believe that a control group of fast-food stores in eastern Pennsyl- vania forms a natural basis for comparison with the experiences of restaurants in New Jersey. Wage variation across stores in New Jersey, however, allows us to compare the experiences of high-wage and low-wage stores within New Jersey and to test the validity of the Pennsylvania control group. Moreover, since seasonal patterns of em- ployment are similar in New Jersey and eastern Pennsylvania, as well as across high- and low-wage stores within New Jer- sey, our comparative methodology effec- tively “differences out” any. seasonal em- ployment effects.
Third, we successfully followed nearly 100 percent of stores from a first wave of inter- views conducted just before the rise in the minimum wage (in February and March 1992) to a second wave conducted 7-8 months after (in November and December 1992). We have complete information on store closings and take account of employ- ment changes at the closed stores in our analyses. We therefore measure the overall effect of the minimum wage on average employment, and not simply its effect on surviving establishments.
-Our analysis of employment trends at stores that were open for business before the increase in the minimum wage ignores any potential effect of minimum wages on the rate of new store openings. To assess the likely magnitude of this effect we relate state-specific growth rates in the number of McDonald’s fast-food outlets between 1986
and 1991 to measures of the relative mini- mum wage in each state.
I. The New Jersey Law
A bill signed into law in November 1989 raised the federal minimum wage from $3.35 per hour to $3.80 effective April 1, 1990, with a further increase to $4.25 per hour on April 1, 1991. In early 1990 the New Jersey legislature went one step further, enacting parallel increases in the state minimum wage for 1990 and 1991 and an increase to $5.05 per hour effective April 1, 1992. The sched- uled 1992 increase gave New Jersey the highest state minimum wage in the country and was strongly opposed by business lead- ers in the state (see Bureau of National Affairs, Daily Labor Report, 5 May 1990).
In the two years between passage of the $5.05 minimum wage and its effective date, New Jersey’s economy slipped into reces- sion. Concerned with the potentially ad- verse impact of a higher minimum wage, the state legislature voted in March 1992 to phase in the 80-cent increase over two years. The vote fell just short of the margin re- quired to override a gubernatorial veto, and the Governor allowed the $5.05 rate to go into effect on April 1 before vetoing the two-step legislation. Faced with the prospect of having to roll back wages for minimum- wage earners, the legislature dropped the issue. Despite a strong last-minute chal- lenge, the $5.05 minimum rate took effect as originally planned.
11. Sample Design and Evaluation
Early in 1992 we decided to evaluate the impending increase in the New Jersey mini- mum wage by surveying fast-food restau- rants in New Jersey and eastern Pennsylva- niae2Our choice of the fast-food industry was driven by several factors. First, fast-food stores are a leading employer of low-wage workers: in 1987,franchised restaurants em-
2At the time we were uncertain whether the $5.05 rate would go into effect or be overridden.
THE AMERICAN ECONOMIC REVIEW
SEPTEMBER 1994
WaueI, February 15-March 4, 1992:
Number of stores in sample frame:a 473 Number of refusals: 63 Number interviewed: 410 Response rate (percentage): 86.7
Wace 2, Nocember 5 – December 31, 1992:
Number of stores in sample frame: 410 Number closed: 6 Number under rennovation: 2 Number temporarily closed:’ 2 Number of refusals: 1 Number intervie~ed:~ 399
aStores with working phone numbers only; 29 stores disconnected phone numbers.
‘~ncludes one store closed because of highway construction and one store closed
because of a fire.
‘Includes 371 phone interviews and 28 personal interviews of stores that refused an
initial request for a phone interview.
ployed 25 percent of all workers in the restaurant industry (see U.S. Department of Commerce, 1990 table 13). Second, fast-food restaurants comply with minimum-wage reg- ulations and would be expected to raise wages in response to a rise in the minimum wage. Third, the job requirements and products of fast-food restaurants are rela- tively homogeneous, making it easier to ob- tain reliable measures of employment, wages, and product prices. The absence of tips greatly simplifies the measurement of wages in the industry. Fourth, it is relatively easy to construct a sample frame of fran- chised restaurants. Finally, past experience (Katz and Krueger, 1992) suggested that fast-food restaurants have high response rates to telephone survey^.^
Based on these considerations we con- structed a sample frame of fast-food restau-
3 ~ anpilot survey Katz and Krueger (1992) obtained very low response rates from McDonald’s restaurants. For this reason, McDonald’s restaurants were excluded from Katz and Krueger’s and our sample frames.
rants in New Jersey and eastern Pennsylva- nia from the Burger King, KFC, Wendy’s, and Roy Rogers chain^.^ The first wave of the survey was conducted by telephone in late February and early March 1992, a little over a month before the scheduled increase in New Jersey’s minimum wage. The survey included questions on employment, starting wages, prices, and other store characteris- tic~.~
Table 1 shows that 473 stores in our sam- ple frame had working telephone numbers when we tried to reach them in February- March 1992. Restaurants were called as many as nine times to elicit a response. We obtained completed interviews (with some item nonresponse) from 410 of the restau- rants, for an overall response rate of 87 percent. The response rate was higher in New Jersey (91 percent) than in Pennsylva-
4 ~ h seample was derived from white-pages tele- phone listings for New Jersey and Pennsylvania as of February 1992.
‘copies of the questionnaires used in both waves of the survey are available from the authors upon request.
Stores in: A1l NJ PA
364 109 33 30 331 79
90.9 72.5
331 79 5 1 2 0 2 0 1 0
321 78
in original sample frame had
VOL.84 NO. 4 C A mAND KRUEGER: MINIiiMUM WAGEAND EMPLOYMENT 775
nia (72.5 percent) because our interviewer made fewer call-backs to nonrespondents in Penn~ylvania.I~n the analysis below we in- vestigate possible biases associated with the degree of difficulty in obtaining the first- wave interview.
The second wave of the survey was con- ducted in November and December 1992, about eight months after the minimum-wage increase. Only the 410 stores that re- sponded in the first wave were contacted in the second round of interviews. We success- fully interviewed 371 (90 percent) of these stores by phone in November 1992. Because of a concern that nonresponding restaurants might have closed, we hired an interviewer to drive to each of the 39 nonrespondents and determine whether the store was still open, and to conduct a personal interview if possible. The interviewer discovered that six restaurants were permanently closed, two were temporarily closed (one because of a fire, one because of road construction), and two were under renovation.’ Of the 29 stores open for business, all but one granted a request for a personal interview. As a re- sult, we have second-wave interview data for 99.8 percent of the restaurants that re- sponded in the first wave of the survey, and information on closure status for 100 per- cent of the sample.
Table 2 presents the means for several key variables in our data set, averaged over the subset of nonmissing responses for each variable. In constructing the means, employ- ment in wave 2 is set to 0 for the perma-
6~esponserates per call-back were almost identical in the two states. Among New Jersey stores, 44.5 percent responded on the first call, and 72.0 percent responded after at most two call-backs. Among Penn- sylvania stores 42.2 percent responded on the first call, and 71.6 percent responded after at most two call- backs.
7 ~osf April 1993 the store closed because of road construction and one of the stores closed for renova- tion had reopened. The store closed by fire was open when our telephone interviewer called in November 1992 but refused the interview. By the time of the follow-up personal interview a mall fire had closed the store.
nently closed stores but is treated as missing for the temporarily closed stores. (Full- time-equivalent [FTE] employment was cal- culated as the number of full-time workers [including managers] plus 0.5 times the number of part-time workers.)’ Means are presented separately for stores in New Jer- sey and Pennsylvania, along with t statistics for the null hypothesis that the means are equal in the two states.
Rows la-e show the distribution of stores by chain and ownership status (company- owned versus franchisee-owned). The Burger King, Roy Rogers, and Wendy’s stores in our sample have similar average food prices, store hours, and employment levels. The KFC stores are smaller and are open for fewer hours. They also offer a more expensive main course than stores in the other chains (chicken vs, hamburgers).
In wave 1, average employment was 23.3 full-time equivalent workers per store in Pennsylvania, compared with an average of 20.4 in New Jersey. Starting wages were very similar among stores in the two states, although the average price of a “full meal” (medium soda, small fries, and an entree) was significantly higher in New Jersey. There were no significant cross-state differences in average hours of operation, the fraction of full-time workers, or the prevalence of bonus programs to recruit new worker^.^
The average starting wage at fast-food restaurants in New Jersey increased by 10 percent following the rise in the minimum wage. Further insight into this change is provided in Figure 1, which shows the dis- tributions of starting wages in the two states before and after the rise. In wave 1, the distributions in New Jersey and Pennsylva- nia were very similar. By wave 2 virtually all
‘ w e discuss the sensitivity of our results to alterna- tive assumptions on the measurement of employment in Section 111-C.
‘ ~ h e s e programs offer current employees a cash “bounty” for recruiting any new employee who stays on the job for a minimum period of time. Typical bounties are $50-$75. Recruiting programs that award the recruiter with an “employee of the month” desig- nation or other noncash bonuses are excluded from our tabulations.
THE AMERICAN ECONOMIC REVIEW
SEPTEMBER 1994
V ariable NJ 1. Distribution of Store Types (percentages):
a. Burger King b. KFC
c. Roy Rogers d. Wendy’s
e. Company-owned 2. Means in Wave I:
a. FTE employment 20.4 (0.51)
b. Percentage full-time employees 32.8 (1.3) c. Starting wage 4.61
(0.02) d. Wage = $4.25 (percentage) 30.5
e. Price of full meal
f. Hours open (weekday) g. Recruiting bonus
3. Means in Ware2:
a. FTE employment 21.0
(0.52) b. Percentage full-time employees 35.9
(1.4) c. Starting wage 5.08
(0.01) d. Wage = $4.25 (percentage) 0.0
e. Wage = $5.05 (percentage) 85.2 (2.0) f. Price of full meal 3.41
(0.04) g. Hours open (weekday) 14.4 (0.2) h. Recruiting bonus 20.3
(2.3)
P A
t a
Notes: See text for definitions. Standard errors are given in parentheses. aTest of equality of means in New Jersey and Pennsylvania.
restaurants in New Jersey that had been paying less than $5.05 per hour reported a starting wage equal to the new rate. Inter- estingly, the minimum-wage increase had no apparent “spillover” on higher-wage restau- rants in the state: the mean percentage wage change for these stores was – 3.1 percent.
Despite the increase in wages, full-time- equivalent employment increased in New Jersey relative to Pennsylvania. Whereas New Jersey stores were initially smaller, employment gains in New Jersey coupled with losses in Pennsylvania led to a small and statistically insignificant interstate
(2.5)
Stores in:
21.2 (0.94)
30.4 (2.8) 4.62
(0.04) 25.3
(4.9) 1.3 (1.3)
3.03 (0.07) 14.7 (0.3) 23.4 (4.9)
– 0.2 1.8 10.8
–
36.1 5.0 – 0.8 – 0.6
VOL. 84 NO. 4
CARDAND KRUEGER: MINIMUM WAGEAND EMPLOYMENT
February 1992
Wage Range
November 1 9 9 2
Wage Range
New Jersey Pennsylvania
FIGURE1. DISTRIBUTIOFNSTARTINWGAGERATES
778 THE AMERICAN ECONOMIC REVIEW SEPTEMBER I994
difference in wave 2. Only two other vari- ables show a relative change between waves 1 and 2: the fraction of full-time employees and the price of a meal. Both variables increased in New Jersey relative to Pennsyl- vania.
We can assess the reliability of our survey questionnaire by comparing the responses of 11 stores that were inadvertently inter- viewed twice in the first wave of the survey.10 Assuming that measurement errors in the two interviews are independent of each other and independent of the true variable, the correlation between responses gives an estimate of the “reliability ratio” (the ratio of the variance of the signal to the com- bined variance of the signal and noise). The estimated reliability ratios are fairly high, ranging from 0.70 for full-time equivalent employment to 0.98 for the price of a meal.”
We have also checked whether stores with missing data for any key variables are dif- ferent from restaurants with complete re- sponses. We find that stores with missing data on employment, wages, or prices are similar in other respects to stores with com- plete data. There is a significant size differ- ential associated with the likelihood of the store closing after wave 1. The six stores that closed were smaller than other stores (with an average employment of only 12.4 full-time-equivalent employees in wave 1).12
111. Employment Effects of the Minimum-Wage Increase
A. Differences in Differences
Table 3 summarizes the levels and
changes in average employment per store in
10
These restaurants were interviewed twice because their phone numbers appeared in more than one phone book, and neither the interviewer nor the respondent
noticed that they were previously interviewed.
11
Similar reliability ratios for very similar questions were obtained by Katz and Krueger (1992).
”A probit analysis of the probability of closure shows that the initial size of the store is a significant predictor of closure. The level of starting wages has a numerically small and statistically insignificant coeffi- cient in the probit model.
our survey. We present data by state in columns (i) and (ii), and for stores in New Jersey classified by whether the starting wage in wave 1 was exactly $4.25 per hour [column (iv)] between $4.26 and $4.99 per hour [column (v)] or $5.00 or more per hour [column (vi)]. We also show the differences in average employment between New Jersey and Pennsylvania stores [column (iii)] and between stores in the various wage ranges in New Jersey [columns (viil-(viii)].
Row 3 of the table presents the changes in average employment between waves 1 and 2. These entries are simply the differ- ences between the averages for the two waves (i.e., row 2 minus row 1). A n alterna- tive estimate of the change is presented in row 4: here we have computed the change in employment over the subsample of stores that reported valid employment data in both waves. We refer to this group of stores as the balanced subsample. Finally, row 5 pre- sents the average change in employment in the balanced subsample, treating wave-2 employment at the four temporarily closed stores as zero, rather than as missing.
As noted in Table 2, New Jersey stores were initially smaller than their Pennsylva- nia counterparts but grew relative to Penn- sylvania stores after the rise in the mini- mum wage. The relative gain (the “dif- ference in differences” of the changes in employment) is 2.76 FTE employees (or 13 percent), with a t statistic of 2.03. Inspec- tion of the averages in rows 4 and 5 shows that the relative change between New Jer- sey and Pennsylvania stores is virtually iden- tical when the analysis is restricted to the balanced subsample, and it is only slightly smaller when wave-2 employment at the temporarily closed stores is treated as zero.
Within New Jersey, employment ex- panded at the low-wage stores (those paying $4.25 per hour in wave 1) and contracted at the high-wage stores (those paying $5.00 or more per hour). Indeed, the average change in employment at the high-wage stores (- 2.16 FTE employees) is almost identical to the change among Pennsylvania stores (- 2.28 FTE employees). Since high-wage stores in New Jersey should have been
VOL. 84 NO. 4 CARDAND KRUEGER: MINIMUM WAGEAND EMPLOYMENT
largely unaffected by the new minimum wage, this comparison provides a specifica- tion test of the validity of the Pennsylvania control group. The test is clearly passed. Regardless of whether the affected stores are compared to stores in Pennsylvania or high-wage stores in New Jersey, the esti- mated employment effect of the minimum wage is similar.
The results in Table 3 suggest that em- ployment contracted between February and November of 1992 at fast-food stores that were unaffected by the rise in the minimum wage (stores in Pennsylvania and stores in New Jersey paying $5.00 per hour or more in wave 1). We suspect that the reason for this contraction was the continued worsen- ing of the economies of the middle-Atlantic states during 1992.13 Unemployment rates in New Jersey, Pennsylvania, and New York all trended upward between 1991 and 1993, with a larger increase in New Jersey than Pennsylvania during 1992. Since sales of franchised fast-food restaurants are pro- cyclical, the rise in unemployment would be expected to lower fast-food employment in the absence of other factors.14
B. Regression-AdjustedModels
The comparisons in Table 3 make no allowance for other sources of variation in employment growth, such as differences across chains. These are incorporated in the estimates in Table 4. The entries in this table are regression coefficients from mod-
13
An alternative possibility is that seasonal factors
produce higher employment at fast-food restaurants in
February and March than in November and December.
An analysis of national employment data for food
preparation and service workers, however, shows higher
average employment in the fourth quarter than in the
first quarter.
14
To investigate the cyclicality of fast-food restau- rant sales we regressed the year-to-year change in U.S. sales of the McDonald’s restaurant chain from 1976-1991 on the corresponding change in the unem- ployment rate. The regression results show that a 1-percentage-point increase in the unemployment rate reduces sales by $257 million, with a t statistic of 3.0.
els of the form:
(la) AE,=a+bXi+cNJi+~,
(lb)
AE, = a’ +blXi+clGAPi+E{
where AE, is the change in employment from wave 1 to wave 2 at store i, Xi is a set of characteristics of store i, and NJ, is a dummy variable that equals 1 for stores in New Jersey. GAP, is an alternative measure of the impact of the minimum wage at store i based on the initial wage at that store (W,,):
GAP, = 0 = 0
for stores in Pennsylvania for stores in New Jersey with
for other stores in New Jersey.
GAP, is the proportional increase in wages at store i necessary to meet the new mini- mum rate. Variation in GAP, reflects both the New Jersey-Pennsylvania contrast and differences within New Jersey based on re- ported starting wages in wave 1. Indeed, the value of GAP, is a strong predictor of the actual proportional wage change between waves 1 and 2 (R*= 0.75), and conditional on GAP, there is no difference in wage behavior between stores in New Jersey and Pennsylvania.l5
The estimate in column (i) of Table 4 is directly comparable to the simple difference-in-differences of employment changes in column (iv), row 4 of Table 3. The discrepancy between the two estimates is due to the restricted sample in Table 4. In Table 4 and the remaining ta- bles in this section we restrict our analysis to the set of stores with available employ- ment and wage data in both waves of the
1 5 re~gression of the proportional wage change be- tween waves 1 and 2 on GAP, has a coefficient of 1.03.
V ariable
1. FTE employment before, all available observations
2. FTE employment after, all available observations
3. Change in mean FTE employment
4. Change in mean FTE employment, balanced sample of storesC
5. Change in mean FTE employment, setting FTE at temporarily closed stores to Od
(i) (ii)
(iii) (iv)
(v) (vi) (vii)
(viii)
Independent variable
1. New Jersey dummy
2. Initial wage gapa
3. Controls for chain and ownershipb
4. Controls for regionC
5. Standard error of regression
6. Probability value for controlsd
(i) (ii)
2.33 2.30 (1.19) (1.20) —
no yes
(iii) (iv) (v)
– – –
THE AMERICAN ECONOMIC REVlEW SEPTEMBER 1994
TABLE3-AVERAGE EMPLOYMENPTER STOREBEFOREAND I ~ E TRHE RISE IN NEWJERSEYMINIMUMWAGE
Stores by state Stores in New Jersey a Differences within N J ~
Difference, Wage = Wage = Wage r Low- Midrange- PA NJ NJ-PA $4.25 $4.26-$4.99 $5.00 high high
Notes: Standard errors are shown in parentheses. The sample consists of all stores with available data on employment. FTE (full-time-equivalent) employment counts each part-time worker as half a full-time worker. Employment at six closed stores is set to zero. Employment at four temporarily closed stores is treated as missing.
astares in New Jersey were classified by whether starting wage in wave 1 equals $4.25 per hour ( N = 101), is between $4.26 and $4.99 per hour ( N = 140), or is $5.00 per hour or higher ( N = 73).
b ~ i f f e r e n c ein employment between low-wage ($4.25 per hour) and high-wage ( 2$5.00 per hour) stores; and difference in employment between midrange ($4.26-$4.99 per hour) and high-wage stores.
‘Subset of stores with available employment data in wave 1 and wave 2.
this row only, wave-2 employment at four temporarily closed stores is set to 0. Employment changes are based on the
subset of stores with available employment data in wave 1 and wave 2.
TABLE4-REDUCED-FORM MODELSFOR CHANGEIN EMPLOYMENT Model
Notes: Standard errors are given in parentheses. The sample consists of 357 stores with available data on employment and starting wages in waves 1 and 2. The dependent variable in all models is change in FTE employment. The mean and standard deviation of the dependent variable are -0.237 and 8.825, respectively. All models include an unrestricted constant (not reported).
aProportional increase in starting wage necessary to raise starting wage to new minimum rate. For stores in Pennsylvania the wage gap is 0.
b ~ h r e deummy variables for chain type and whether or not the store is company- owned are included.
‘Dummy variables for two regions of New Jersey and two regions of eastern Pennsylvania are included.
d~robabilityvalue of joint F test for exclusion of all control variables.
15.65 14.92 (6.08) (6.21)
11.91
(7.39) no yes yes
VOL. 84 NO. 4 CARDAND KRUEGER: MINIMUM WAGEAND EMPLOYMENT 781
survey. This restriction results in a slightly smaller estimate of the relative increase in employment in New Jersey.
The model in column (ii) introduces a set of four control variables: dummies for three of the chains and another dummy for company-owned stores. As shown by the probability values in row 6, these covariates add little to the model and have no effect on the size of the estimated New Jersey dummy.
The specifications in columns (iiil-(v) use the GAP variable to measure the effect of the minimum wage. This variable gives a slightly better fit than the simple New Jer- sey dummy, although its implications for the New Jersey-Pennsylvania comparison are similar. The mean value of GAPi among New Jersey stores is 0.11. Thus the estimate in column (iii) implies a 1.72 increase in FTE employment in New Jersey relative to Pennsylvania.
Since GAP, varies within New Jersey, it is possible to add both GAP, and NJ, to the employment model. The estimated coeffi- cient of the New Jersey dummy then pro- vides a test of the Pennsylvania control group. When we estimate these models, the coefficient of the New Jersey dummy is in- significant (with t ratios of 0.3-0.7), imply- ing that inferences about the effect of the minimum wage are similar whether the comparison is made across states or across stores in New Jersey with higher and lower initial wages.
An even stronger test is provided in col- umn (v), where we have added dummies representing three regions of New Jersey (North, Central, and South) and two regions of eastern Pennsylvania (Allentown-Easton and the northern suburbs of Philadelphia). These dummies control for any region- s~ecificdemand shocks and identifv the ef- feet of the minimum wage by employment changes at higher- and lower- wage within the same region of New Jersey. The probability value in row 6 shows no evidence of regional components in em- ployment growth. The addition of the re- gion dummies attenuates the GAP coeffi- cient and raises its standard error, however, making it no longer possible to reject the
null hypothesis of a zero employment effect of the minimum wage. One explanation for this attenuation is the presence of measure- ment error in the starting wage. Even if employment growth has no regional compo- nent, the addition of region dummies will lead to some attenuation of the estimated GAP coefficient if some of the true varia- tion in GAP is explained by region. Indeed, calculations based on the estimated reliabil- ity of the GAP variable (from the set of 11 double interviews) suggest that the fall in the estimated G A P coefficient from column (iv) to column (v) is just equal to the ex- pected change attributable to measurement error.16
We have also estimated the models in Table 4 using as a dependent variable the proportional change in employment at each store.17 The estimated coefficients of the New Jersey dummy and the G A P variable are uniformly positive in these models but insignificantly different from 0 at conven- tional levels. The implied employment ef- fects of the minimum wage are also smaller when the dependent variable is expressed in proportional terms. For example, the G A P coefficient in column (iii) of Table 4 implies that the increase in minimum wages raised employment at New Jersey stores that were initially paying $4.25 per hour by 14 per- cent. The estimated GAP coefficient from a corresponding proportional model implies an effect of only 7 percent. The difference is attributable to heterogeneity in the effect of the minimum wage at larger and smaller stores. Weighted versions of the propor- tional-change models (using initial employ- ment as a weight) give rise to wage elastici-
16In a regression model without other controls the expected attenuation of the GAP coefficient due to measurement error is the reliability ratio of GAP (yo), which we estimate at 0.70. The expected attenuation factor when region dummies are added to the model is yl=(Yo-~2)/(1-~2)w,here ~2 is the R-square statistic of a regression of GAP on region effects (equal to 0.30). Thus, we expect the estimated GAP coeffi- cient to fall by a factor of YI /YO = 0.8 when region dummies are added to a regression model.
” ~ h e s e specifications are reported in table 4 of Card and Krueger (1993).
782 THEAMERICAN ECONOMIC REVIEW SEPTEMBER I994
ties similar to the elasticities implied by the estimates in Table 4 (see below).
C. Specification Tests
The results in Tables 3 and 4 seem to contradict the standard prediction that a rise in the minimum wage will reduce em- ployment. Table 5 presents some alternative specifications that probe the robustness of this conclusion. For completeness, we re- port estimates of models for the change in employment [columns (i) and (ii)] and esti- mates of models for the proportional change in employment [columns (iii) and (iv)].18 The first row of the table reproduces the “base specification” from columns (ii) and (iv) of Table 4. (Note that these models include chain dummies and a dummy for company- owned stores). Row 2 presents an alterna- tive set of estimates when we set wave-2 employment at the temporarily closed stores to 0 (expanding our sample size by 4). This change has a small attenuating effect on the coefficient of the New Jersey dummy (since all four stores are in New Jersey) but less effect on the G A P coefficient (since the size of G A P is uncorrelated with the probability of a temporary closure within New Jersey).
Rows 3-5 present estimation results us- ing alternative measures of full-time-equiv- alent employment. In row 3, employment is redefined to exclude management employ- ees. This change has no effect relative to the base specification. In rows 4 and 5, we include managers in FTE employment but reweight part-time workers as either 40 per- cent or 60 percent of full-time workers (in- stead of 50 percent).19 These changes have
18
The proportional change in employment is de- fined as the change in employment divided by the average level of employment in waves 1 and 2. This results in very similar coefficients but smaller standard errors than the alternative of dividing by wave-1 em- ployment. For closed stores we set the proportional
change in employment to – 1. 19
Analysis of the 1991 Current Population Survey reveals that part-time workers in the restaurant indus- try work about 46 percent as many hours as full-time workers. Katz and Krueger (1992) report that the ratio of part-time workers’ hours to full-time workers’ hours in the fast-food industry is 0.57.
little effect on the models for the level of employment but yield slightly smaller point estimates in the proportional-employment- change models.
In row 6 we present estimates obtained from a subsample that excludes 35 stores in towns along the New Jersey shore. The ex- clusion of these stores, which may have a different seasonal pattern than other stores in our sample, leads to slightly larger mini- mum-wage effects. A similar finding emerges in row 7when we add a set of dummy variables that indicate the week of the wave-2 inter vie^.^’
As noted earlier, we made an extra effort to obtain responses from New Jersey stores in the first wave of our survey. The fraction of stores called three or more times to ob- tain an interview was higher in New Jersey than in Pennsylvania. To check the sensitiv- ity of our results to this sampling feature, we reestimated our models on a subsample that excludes any stores that were called back more than twice. The results, in row 8, are very similar to the base specification.
Row 9 presents weighted estimation re- sults for the proportional-employment- change models, using as weights the initial levels of employment in each store. Since the proportional change in average employ- ment is an employment-weighted average of the proportional changes at each store, a weighted version of the proportional-change model should give rise to elasticities that are similar to the implied elasticities arising from the levels models. Consistent with this expectation, the weighted estimates are larger than the unweighted estimates, and significantly different from 0 at conventional levels. The weighted estimate of the New Jersey dummy (0.13) implies a 13-percent relative increase in New Jersey employment -the same proportional employment effect implied by the simple difference-in-dif- ferences in Table 3. Similarly, the weighted estimate of the G A P coefficient in the proportional-change model (0.81) is close to
20
We also added dummies for the interview dates for the wave-1 survey, but these were insignificant and did not change the estimated minimum-wage effects.
VOL. 84 NO. 4
CARDAND KRUEGER: MINIMUM WAGEAND EMPLOYMENT 783
Specification
1. Base specification
2. Treat four temporarily closed stores as permanently closeda
3. Exclude managers in employment countb
4. Weight part-time as 0.4x full-timec
5. Weight part-time as 0.6X full-timed
6. Exclude stores in NJ shore areae
7. Add controls for wave-2 interview dateE
8. Exclude stores called more than twice in wave lg
9. Weight by initial employmenth
10. Stores in towns around Newark’
11. Stores in towns around CamdenJ
12. Pennsylvania stores only
NJ dummy (i)
2.30 (1.19)
Gap measure (ii)
14.92 (6.21)
NJ dummy (iii)
Gap measure (iv)
.
Subsample of 51 stores in towns around Newark.
Subsample of 54 stores in town around Camden.
Subsample of Pennsylvania stores only. Wage gap is defined as percentage increase in starting wage necessary
to raise starting wage to $5.05.
Change in employment
Proportional change in employment
– – –
33.75 (16.75)
10.91 (14.09)
– 0.30 (22.00)
Notes: Standard errors are given in parentheses. Entries represent estimated coefficient of New Jersey dummy [columns (i) and (iii)] or initial wage gap [columns (ii) and (iv)] in regression models for the change in employment or the percentage change in employment. All models also include chain dummies and an indicator for company- owned stores.
aWave-2 employment at four temporarily closed stores is set to 0 (rather than missing).
b~ull-timequivalent employment excludes managers and assistant managers.
CFull-timeequivalent employment equals number of managers, assistant managers, and full-time nonmanage-
ment workers, plus 0.4 times the number of part-time nonmanagement workers.
d~ull-timequivalent employment equals number of managers, assistant managers, and full-time nonmanage-
ment workers, plus 0.6 times the number of part-time nonmanagement workers.
eSample excludes 35 stores located in towns along the New Jersey shore.
‘ ~ o d e l sinclude three dummy variables identifying week of wave-2 interview in November-December 1992. gSample excludes 70 stores (69 in New Jersey) that were contacted three or more times before obtaining the
wave-1 interview.
h~egressionmodel is estimated by weighted least squares, using employment in wave 1 as a weight.
i
784 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 1994
the implied elasticity of employment with respect to wages from the basic levels speci- fication in row 1, column (iiI2l These find- ings suggest that the proportional effect of the rise in the minimum wage was concen- trated among larger stores.
One explanation for our finding that a rise in the minimum wage has a positive employment effect is that unobserved de- mand shocks within New Jersey outweighed the negative employment effect of the mini- mum wage. To address this possibility, rows 10 and 11 present estimation results based on subsamples of stores in two narrowly defined areas: towns around Newark (row 10) and towns around Camden (row 11). In each case the sample area is identified by the first three digits of the store’s zip code.22 Within both areas the change in employ- ment is positively correlated with the GAP variable, although in neither case is the effect statistically significant. T o the extent that fast-food product market conditions are constant within local areas, these results suggest that our findings are not driven by unobserved demand shocks. Our analysis of price changes (reported below) also sup- ports this conclusion.
A final specification check is presented in row 12 of Table 5. In this row we exclude stores in New Jersey and (incorrectly) de- fine the GAP variable for Pennsylvania stores as the proportional increase in wages necessary to raise the wage to $5.05 per hour. In principle the size of the wage gap for stores in Pennsylvania should have no systematic relation with employment growth. In practice, this is the case. There is no indication that the wage gap is spuriously related to employment growth.
21~ssumingaverage employment of 20.4 in New Jersey, the 14.92 GAP coefficient in row 1, column (ii) im lies an employment elasticity of 0.73.
“The “070” three-digit zip-code area (around Newark) and the “080” three-digit zip-code area (around Camden) have by far the largest numbers of stores among three-digit zip-code areas in New Jersey, and together they account for 36 percent of New Jersey stores in our sample.
We have also investigated whether the first-differenced specification used in our employment models is appropriate. A first-differenced model implies that the level of employment in period t is related to the lagged level of employment with a coeffi- cient of 1. If short-run employment fluctua- tions are smoothed, however, the true co- efficient of lagged employment may be less than 1. Imposing the assumption of a unit coefficient may then lead to biases. To test the first-differenced specification we reesti- mated models for the change in employ- ment including wave-1 employment as an additional explanatory variable. T o over- come any mechanical correlation between base-period employment and the change in employment (attributable to measurement error) we instrumented wave-1 employment with the number of cash registers in the store in wave 1 and the number of registers in the store that were open at 11:OO A.M. In all of the specifications the coefficient of wave-1 employment is close to zero. For example, in a specification including the G A P variable and ownership and chain dummies, the coefficient of wave-1 employ- ment is 0.04, with a standard error of 0.24. We conclude that the first-differenced spec- ification is appropriate.
D. Full-Time and Part-Time Substitution
Our analysis so far has concentrated on full-time-equivalent employment and ig- nored possible changes in the distribution of full- and part-time workers. An increase in the minimum wage could lead to an in- crease in full-time employment relative to part-time employment for at least two rea- sons. First, in a conventional model one would expect a minimum-wage increase to induce employers to substitute skilled work- ers and capital for minimum-wage workers. Full-time workers in fast-food restaurants are typically older and may well possess higher skills than part-time workers. Thus, a conventional model predicts that stores may respond to an increase in the minimum wage by increasing the proportion of full- time workers. Nevertheless, 81 percent of restaurants paid full-time and part-time
VOL. 84 NO. 4
CARD AND KRUEGER: MINIMUM WAGEAND EMPLOYMENT 785
Outcome measure
Store Characteristics:
1. Fraction full-time workersc (percentage)
2. Number of hours open per weekday
3. Number of cash registers
4. Number of cash registers open at 11:OOA.M.
Employee Meal Programs:
5. Low-price meal program (percentage)
6. Free meal program (percentage)
7. Combination of low-price and free meals (percentage)
Wage Profile:
8. Time to first raise (weeks)
9. Usual amount of first raise (cents)
10. Slope of wage profile (percent per week)
NJ PA (i) (ii)
NJ-PA (iii)
NJ dummy (iv)
Wagegapa Wagegapb (v) (vi)
Mean change in outcome
Regression of change in outcome variable on:
Notes: Entries in columns (i) and (ii) represent mean changes in the outcome variable indicated by the row heading for stores with available data on the outcome in waves 1 and 2. Entries in columns (iv)-(vi) represent estimated regression coefficients of indicated variable (NJ dummy or initial wage gap) in models for the change in the outcome variable. Regression models include chain dummies and an indicator for company-owned stores.
aThe wage gap is the proportional increase in starting wage necessary to raise the wage to the new minimum rate. For stores in Pennsylvania, the wage gap is zero.
b ~ o d e lisn column (vi) include dummies for two regions of New Jersey and two regions of eastern Pennsylvania. ‘Fraction of part-time employees in total full-time-equivalent employment.
workers exactly the same starting wage in wave 1 of our survey.23This suggests either that full-time workers have the same skills as part-time workers or that equity concerns lead restaurants to pay equal wages for un- equally productive workers. If full-time
231nthe other 19 percent of stores, full-time workers are paid more, typically 10 percent more.
workers are more productive (but equally paid), there may be a second reason for stores to substitute full-time workers for part-time workers; namely, a minimum-wage increase enables the industry to attract more full-time workers, and stores would natu- rally want to hire a greater proportion of full-time workers if they are more produc- tive.
Row 1 of Table 6 presents the mean changes in the proportion of full-time work-
786 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 1994
ers in New Jersey and Pennsylvania be- tween waves 1 and 2 of our survey, and coefficient estimates from regressions of the change in the proportion of full-time work- ers on the wage-gap variable, chain dum- mies, a company-ownership dummy, and re- gion dummies [in column ( 4 1 . The results are ambiguous. The fraction of full-time workers increased in New Jersey relative to Pennsylvania by 7.3 percent (t ratio = 1.841, but regressions on the wage-gap variable show no significant shift in the fraction of full-time workers.24
E. OtherEmployment-RelatedMeasures
Rows 2-4 of Table 6 present results for other outcome variables that we expect to be related to the level of restaurant employ- ment. In particular, we examine whether the rise in the minimum wage is associated with a change in the number of hours a restaurant is open on a weekday, the num- ber of cash registers in the restaurant, and the number of cash registers typically in operation in the restaurant at 11:OO A.M. Consistent with our employment results, none of these variables shows a statistically significant decline in New Jersey relative to Pennsylvania. Similarly, regressions includ- ing the gap variable provide no evidence that the minimum-wage increase led to a systematic change in any of these variables [see columns (v) and (vi)].
IV. Nonwage Offsets
One explanation of our finding that a rise in the minimum wage does not lower em- ployment is that restaurants can offset the effect of the minimum wage by reducing nonwage compensation. For example, if workers value fringe benefits and wages equally, employers can simply reduce the level of fringe benefits by the amount of the minimum-wage increase, leaving their em-
24~ithinNew Jersey, the fraction of full-time em- ployees increased about as quickly at stores with higher and lower wages in wave 1.
ployment costs unchanged. The main fringe benefits for fast-food employees are free and reduced-price meals. In the first wave of our survey about 19 percent of fast-food restaurants offered workers free meals. 72 percent offered reduced-price meals, a i d 9 percent offered a combination of both free and reduced-price meals. Low-price meals are an obvious fringe benefit to cut if the minimum-wage increase forces restaurants to pay higher wages.
Rows 5 and 6 of Table 6 present esti- mates of the effect of the minimum-wage increase on the incidence of free meals and reduced-price meals. The proportion of res- taurants offering reduced-price meals fell in both New Jersey and Pennsylvania after the minimum wage increased, with a some- what greater decline in New Jersey. Con- trary to an offset story, however, the reduc- tion in reduced-price meal programs was accompanied by an increase in the fraction of stores offering free meals. Relative to stores in Pennsylvania, New Jersey employ- ers actually shifted toward more generous fringe benefits (i.e., free meals rather than reduced-price meals). However, the relative shift is not statistically significant.
We continue to find a statistically in- significant effect of the minimum-wage in- crease on the likelihood of receiving free or reduced-price meals in columns (v) and (vi), where we report coefficient estimates of the G A P variable from regression models for the change in the incidence of these pro- grams. The results provide no evidence that employers offset the minimum-wage in- crease by reducing free or reduced-price meals.
Another possibility is that employers re- sponded to the increase in the minimum wage by reducing on-the-job training and flattening the tenure-wage profile (see Jacob Mincer and Linda Leighton, 1981). Indeed, one manager told our interviewer in wave 1 that her workers were forgoing ordi- nary scheduled raises because the minimum wage was about to rise, and this would provide a raise for all her workers. To de- termine whether this phenomenon occurred more generally, we analyzed store man- agers’ responses to questions on the amount
VOL. 84 NO. 4 CARDAND KRUEGER: MINIMUM WAGEAND EMPLOYMENT 787
of time before a normal wage increase and the usual amount of such raises. In rows 8 and 9 we report the average changes be- tween waves 1 and 2 for these two variables, as well as regression coefficients from mod- els that include the wage-gap variable.25Al- though the average time to the first pay raise increased by 2.5 weeks in New Jersey relative to Pennsylvania, the increase is not statistically significant. Furthermore, there is only a trivial difference in the relative change in the amount of the first pay incre- ment between New Jersey and Pennsylvania stores.
Finally, we examined a related variable: the “slope” of the wage profile, which we measure by the ratio of the typical first raise to the amount of time until the first raise is given. As shown in row 10, the slope of the wage profile flattened in both New Jersey and Pennsylvania, with no significant rela- tive difference between states. The change in the slope is also uncorrelated with the G A P variable. In summary, we can find no indication that New Jersey employers changed either their fringe benefits or their wage profiles to offset the rise in the mini- mum wage.26
V. PriceEffectsoftheMinimum-Wage Increase
A final issue we examine is the effect of the minimum wage on the prices of meals at fast-food restaurants. A competitive model of the fast-food industry implies that an increase in the minimum wage will lead to an increase in product prices. If we assume constant returns to scale in the industry, the increase in price should be proportional to the share of minimum-wage labor in total
2 5 ~ nwave 1, the average time to a first wage in- crease was 18.9 weeks, and the average amount of the first increase was $0.21 per hour.
2 6 ~ a t aznd Krueger (1992) report that a significant fraction of fast-food stores in Texas responded to an increase in the minimum wage by raising wages for workers who were initially earning more than the new minimum rate. Our results on the slope of the tenure profile are consistent with their findings.
factor cost. The average restaurant in New Jersey initially paid about half its workers less than the new minimum wage. If wages rose by roughly 15 percent for these work- ers, and if labor’s share of total costs is 30 percent, we would expect prices to rise by about 2.2 percent ( = 0.15 X 0.5 X 0.3) due to the minimum-wage rise.27
In each wave of our survey we asked managers for the prices of three standard items: a medium soda, a small order of french fries, and a main course. The main course was a basic hamburger at Burger King, Roy Rogers, and Wendy’s restaurants, and two pieces of chicken at KFC stores. We define “full meal” price as the after-tax price of a medium soda, a small order of french fries, and a main course.
Table 7 presents reduced-form estimates of the effect of the minimum-wage increase on prices. The dependent variable in these models is the change in the logarithm of the price of a full meal at each store. The key independent variable is either a dummy in- dicating whether the store is located in New Jersey or the proportional wage increase required to meet the minimum wage (the GAP variable defined above).
The estimated New Jersey dummy in col- umn (i) shows that after-tax meal prices rose 3.2-percent faster in New Jersey than in Pennsylvania between February and November 1 9 9 2 . ~T~he effect is slightly larger controlling for chain and company- ownership [see column ($1. Since the New Jersey sales tax rate fell by 1 percent- age point between the waves of our survey, these estimates suggest that pretax prices rose 4-percent faster as a result of the
” ~ c c o r d i n ~to the McDonald’s Corporation 1991 Annual Report. payroll and benefits are 31.3 percent of operating costs at company-owned stores. This calcula-
tion is only approximate because minimum-wage work- ers make up less than half of payroll even though they are about half of workers, and because a rise in the minimum wage causes some employers to increase the pay of other higher-wage workers in order to maintain relative pay differentials.
he effect is attributable to a 2.0-percent increase in prices in New Jersey and a 1.0-percent decrease in prices in Pennsylvania.
THE AMERICAN ECONOMIC REVIEW SEPTEMBER 1994
TABLE7-REDUCED-FORMMODELSFOR CHANGIEN THE PRICEOF A FULLMEAL
————— –
Independent variable 1. New Jersey dummy
2. Initial wage gapa
3. Controls for chain andb ownership
4. Controls for regionC
5. Standard error of regression
Dependent variable: change in the log price of a full meal
(i) (ii) (iii) (iv) (v)
0.033 0.037 – – – (0.014) (0.014)
– – 0.077 0.146 (0.075) (0.074)
0.063 (0.089)
no yes no yes Yes no no no no yes
0.101 0.097 0.102 0.098
0.097
Notes: Standard errors are given in parentheses. Entries are estimated regression coefficients for models fit to the change in the log price of a full meal (entrCe, medium soda, small fries). The sample contains 315 stores with valid data on prices, wages, and employment for waves 1 and 2. The mean and standard deviation of the dependent variable are 0.0173 and 0.1017, respectively.
aProportional increase in starting wage necessary to raise the wage to the new minimum-wage rate. For stores in Pennsylvania the wage gap is 0.
bThree dummy variables for chain type and whether or not the store is company- owned are included.
‘Dummy variables for two regions of New Jersey and two regions of eastern Pennsylvania are included.
minimum-wage increase in New Jersey- slightly more than the increase needed to pass through the cost increase caused by the minimum-wage hike.
The pattern of price changes within New Jersey is less consistent with a simple “pass-through” view of minimum-wage cost increases. In fact, meal prices rose at approximately the same rate at stores in New Jersey with differing levels of initial wages. Inspection of the estimated GAP coefficients in column (v) of Table 7 con- firms that within regions of New Jersey, the GAP variable is statistically insignificant.
In sum, these results provide mixed evi- dence that higher minimum wages result in higher fast-food prices. The strongest evi- dence emerges from a comparison of New Jersey and Pennsylvania stores. The magni- tude of the price increase is consistent with predictions from a conventional model of a competitive industry. On the other hand, we find no evidence that prices rose faster among stores in New Jersey that were most affected by the rise in the minimum wage.
One potential explanation for the latter finding is that stores in New Jersey compete in the same product market. As a result, restaurants that are most affected by the minimum wage are unable to increase their product prices faster than their competitors. In contrast, stores in New Jersey and Penn- sylvania are in separate product markets, enabling prices to rise in New Jersey rela- tive to Pennsylvania when overall costs rise in New Jersey. Note that this explanation seems to rule out the possibility that store- specific demand shocks can account for the anomalous rise in employment at stores in New Jersey with lower initial wages.
VI. Store Openings
An important potential effect of higher minimum wages is to discourage the open- ing of new businesses. Although our sample design allows us to estimate the effect of the minimum wage on existing restaurants in New Jersey, we cannot address the effect of the higher minimum wage on potential
VOL. 84 NO. 4 CARDAND KRUEGER: MINIMUM WAGEAND EMPLOYMENT 789
entrants.29To assess the likely size of such an effect, we used national restaurant direc- tories for the McDonald’s restaurant chain to compare the numbers of operating restaurants and the numbers of newly opened restaurants in different states over the 1986-1991 period. Many states raised their minimum wages during this period. In addition, the federal minimum wage in- creased in the early 1990’s from $3.35 to $4.25, with differing effects in different states depending on the level of wages in the state. These policies create an opportunity to measure the impact of minimum-wage laws on store opening rates across states.
The results of our analysis are presented in Table 8. We regressed the growth rate in the number of McDonald’s stores in each state on two alternative measures of the minimum wage in the state and a set of other control variables (population growth and the change in the state unemployment rate). The first minimum-wage measure is the fraction of workers in the state’s retail trade industry in 1986 whose wages fell be- tween the existing federal minimum wage in 1986 ($3.35 per hour) and the effective min- imum wage in the state in April 1990 (the maximum of the federal minimum wage and the state minimum wages as of April 1990).” The second is the ratio of the state’s effec- tive minimum wage in 1990 to the average hourly wage of retail trade workers in the state in 1986. Both of these measures are designed to gauge the degree of upward wage pressure exerted by state or federal minimum-wage changes between 1986 and 1990.
The results provide no evidence that higher minimum-wage rates (relative to the retail-trade wages in a state) exert a nega-
tive effect on either the net number of restaurants or the rate of new openings. To the contrary, all the estimates show positice effects of higher minimum wages on the number of operating or newly opened stores, although many of the point estimates are insignificantly different from zero. While this evidence is limited, we conclude that the effects of minimum wages on fast-food store opening rates are probably small.
VII. Broader Evidence on Employment Changes in New Jersey
Our establishment-level analysis suggests that the rise in the minimum wage in New Jersey may have increased employment in the fast-food industry. Is this just an anomaly associated with our particular sample, or a phenomenon unique to the fast-food indus- try? Data from the monthly Current Popu- lation Survey (CPS) allow us to compare state-wide employment trends in New Jer- sey and the surrounding states, providing a check on the interpretation of our findings. Using monthly CPS files for 1991 and 1992, we computed employment-population rates for teenagers and adults (age 25 and older) for New Jersey, Pennsylvania, New York, and the entire United States. Since the New Jersey minimum wage rose on April 1, 1992, we computed the employment rates for April-December of both 1991 and 1992. The relative changes in employment in New Jersey and the surrounding states then give an indication of the effect of the new law.
A comparison of changes in adult em- ployment rates show that the New Jersey labor market fared slightly worse over the 1991-1992 period than either the U.S. labor market as a whole or labor markets in Pennsylvania or New York (see Card and Krueger, 1993 table 9l3’ Among teenagers, however, the situation was reversed. In New Jersey, teenage employment rates fell by 0.7 percent from 1991 to 1992. In New York,
31
The employment rate of individuals age 25 and older fell by 2.6 percent in New Jersey between 1991 and 1992, while it rose by 0.3 percent in Pennsylvania, and fell by 0.2 percent in the United States as a whole.
29
vealed that Wendy’s opened two stores in New Jersey
in 1992 and one store in Pennsylvania. The other
chains were unwilling to provide information on new
openings.
30
We used the 1986 Current Population Survey (merged monthly file) to construct the minimum-wage variables. State minimum-wage rates in 1990 were ob- tained from the Bureau of National Affairs Labor Relations Reporter Wages and Hours Manual (undated).
Direct inquiries to the chains in our sample re-
790
THE AMERICAN ECONOMIC REVIEW
SEPTEMBER 1994
Independent variable
Minimum-Wage Variable:
1. Fraction of retail workers
in affected wage range 1986″
2. (State minimum wage in 1991)+ (average retail wage in 1986Ib
Other Control Variables:
3. Proportional growth in population, 1986-1991
4. Change in unemployment rates, 1986-1991
5. Standard error of regression
(i) (ii) (iii) (iv) (v) (vi)
0.33 – 0.13 – 0.37 – (0.20) (0.19) (0.22)
– 0.38 – 0.47 – 0.47 (0.22) (0.22) (0.23)
– – 0.88 1.03 – –
(vii)
0.16 (0.21)
–
0.86 (0.25)
– 1.85 (0.68) 0.077
(viii)
–
0.56 (0.24)
1.04 (0.25)
– 1.40 (0.65) 0.073
Dependent variable: proportional increase in number of stores
Dependent variable: (number of newly opened stores)+ (number in 1986)
(0.23) (0.23) – – -1.78 -1.40
– –
(0.62) (0.61)
0.083 0.083 0.071 0.068 0.088 0.088
Notes: Standard errors are shown in parentheses. The sample contains 51 state-level observations (including the District of Columbia) on the number of McDonald’s restaurants open in 1986 and 1991. The dependent variable in columns (i)-(iv) is the proportional increase in the number of restaurants open. The mean and standard deviation are 0.246 and 0.085, respectively. The dependent variable in columns (v)-(viii) is the ratio of the number of new stores opened between 1986and 1991to the number open in 1986.The mean and standard deviation are 0.293 and 0.091, respectively. All regressions are weighted by the state population in 1986.
aFraction of all workers in retail trade in the state in 1986 earning an hourly wage between $3.35 per hour and the “effective” state minimum wage in 1990 (i.e., the maximum of the federal minimum wage in 1990 ($3.80) and the state minimum wage as of April 1, 1990).
b~aximumof state and federal minimum wage as of April 1, 1990, divided by the average hourly wage of workers in retail trade in the state in 1986.
Pennsylvania, and the United States as a whole, teenage employment rates dropped faster. Relative to teenagers in Pennsylva- nia, for example, the teenage employment rate in New Jersey rose by 2.0 percentage points. While this point estimate is consis- tent with our findings for the fast-food in- dustry, the standard error is too large (3.2 percent) to allow any confident assessment.
VIII. Interpretation
As in the earlier study by Katz and Krueger (1992), our empirical findings on the effects of the New Jersey minimum wage are inconsistent with the predictions of a conventional competitive model of the fast- food industry. Our employment results are consistent with several alternative models, although none of these models can also explain the apparent rise in fast-food prices in New Jersey. In this section we briefly
summarize the predictions of the standard model and some simple alternatives, and we highlight the difficulties posed by our find- ings.
A. Standard CompetitiveModel
A standard competitive model predicts that establishment-level employment will fall if the wage is exogenously raised. For an entire industry, total employment is pre- dicted to fall, and product price is predicted to rise in response to an increase in a bind- ing minimum wage. Estimates from the time-series literature on minimum-wage ef- fects can be used to get a rough idea of the elasticity of low-wage employment to the minimum wage. The surveys by Brown et al. (1982. 1983) conclude that a 10-~ercenitn- crease in the coverage-adjusted minimum wage will reduce teenage employment rates by 1-3 percent. Since this effect is for all
VOL. 84 NO. 4 CARD AND KRUEGER: MINIMUM WAGEAND EMPLOYMENT 791
teenagers, and not just those employed in low-wage industries, it is surely a lower bound on the magnitude of the effect for fast-food workers. The 18-percent increase in the New Jersey minimum wage is there- fore predicted to reduce employment at fast-food stores by 0.4-1.0 employees per store. Our empirical results clearly reject the upper range of these estimates, al- though we cannot reject a small negative effect in some of our specifications.
A possible defense of the competitive model is that unobserved demand shocks affected certain stores in New Jersey- specifically, those stores that were initially paying wages less than $5.00 per hour. How- ever, such localized demand shocks should also affect product prices. (In fact, in a competitive model, product demand shocks work through a rise in prices.) Although lower-wage stores in New Jersey had rela- tive employment gains, they did not have relative price increases. Furthermore, our analysis of employment changes in two ma- jor suburban areas (around Newark and Camden) reveals that, even within local areas, employment rose faster at the stores that had to increase wages the most because of the new minimum wage.
B. Alternative Models
An alternative to the conventional com- petitive model is one in which firms are price-takers in the product market but have some degree of market power in the labor market. If fast-food stores face an upward- sloping labor-supply schedule, a rise in the minimum wage can potentially increase em- ployment at affected firms and in the indus- try as a whole.32
This same basic insight emerges from an equilibrium search model in which firms post wages and employees search among posted offers (see Dale T. Mortensen, 1988). Kenneth Burdett and Mortensen (1989) de-
” ~ a n i e l G. Sullivan (1989) and Michael R Ransom (1993) present empirical results for nurses and univer- sity teachers that suggest monopsony-like behavior of employers.
rive the equilibrium wage distribution for a noncooperative wage-search/wage-posting model and show that the imposition of a binding minimum wage can increase both wages and employment relative to the initial equilibrium. Furthermore, their model pre- dicts that the minimum wage will increase employment the most at firms that initially paid the lowest wages.
Although monopsonistic and job-search models provide a potential explanation for the observed employment effects of the New Jersey minimum wage, they cannot explain the observed price effects. In these models, industry prices should have fallen in New Jersey relative to Pennsylvania, and at low- wage stores in New Jersey relative to high- wage stores in New Jersey. Neither predic- tion is confirmed: indeed, prices rose faster in New Jersey than in Pennsylvania, al- though at about the same rate at high- and low-wage stores in New Jersey. Another puzzle for equilibrium search models is the absence of wage increases at firms that were initially paying $5.05 or more per hour.
The strict link between the employment and price effects of a rise in the minimum wage may be broken if fast-food stores can vary the quality of service (e.g., the length of the queue at peak hours, or the cleanliness of stores). Another possibility is that stores altered the relative prices of their various menu items. Comparisons of price changes for the three items in our survey show slight declines (-1.5 percent) in the price of french fries and soda in New Jersey relative to Pennsylvania, coupled with a relative in- crease (8 percent) in entrCe prices. These limited data suggest a possible role for rela- tive price changes within the fast-food in- dustry following the rise in the minimum wage.
One way to test a monopsony model is to identify stores that were initially “supply- constrained” in the labor market and test for employment gains at these stores rela- tive to other stores. A potential indicator of market power is the use of recruitment bonuses. As we noted in Table 2, about 25 percent of stores in wave 1 were offering cash bonuses to employees who helped find a new worker. We compared employment
792 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 1994
changes at New Jersey stores that were of- fering recruitment bonuses in wave 1, and also interacted the GAP variable with a dummy for recruitment bonuses in several employment-change models. We do not find faster (or slower) employment growth at the New Jersey stores that were initially using recruitment bonuses, or any evidence that the GAP variable had a larger effect for stores that were using bonuses.
IX. Conclusions
Contrary to the central prediction of the textbook model of the minimum wage, but consistent with a number of recent studies based on cross-sectional time-series com- parisons of affected and unaffected markets or employers, we find no evidence that the rise in New Jersey’s minimum wage reduced employment at fast-food restaurants in the state. Regardless of whether we compare stores in New Jersey that were affected by the $5.05 minimum to stores in eastern Pennsylvania (where the minimum wage was constant at $4.25 per hour) or to stores in New Jersey that were initially paying $5.00 per hour or more (and were largely unaf- fected by the new law), we find that the increase in the minimum wage increased employment. We present a wide variety of alternative specifications to probe the ro- bustness of this conclusion. None of the alternatives shows a negative employment effect. We also check our findings for the fast-food industry by comparing changes in teenage employment rates in New Jersey, Pennsylvania, and New York in the year following the increase in the minimum wage. Again, these results point toward a relative increase in employment of low-wage work- ers in New Jersey. We also find no evidence that minimum-wage increases negatively affect the number of McDonald’s outlets opened in a state.
Finally, we find that prices of fast-food meals increased in New Jersey relative to Pennsylvania, suggesting that much of the burden of the minimum-wage rise was passed on to consumers. Within New Jer- sey, however, we find no evidence that prices increased more in stores that were most
affected by the minimum-wage rise. Taken as a whole, these findings are difficult to explain with the standard competitive model or with models in which employers face supply constraints (e.g., monopsony or equi- librium search models).
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Charles Brown; Curtis Gilroy; Andrew Kohen
Journal of Economic Literature, Vol. 20, No. 2. (Jun., 1982), pp. 487-528.
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http://links.jstor.org/sici?sici=0022-0515%28198206%2920%3A2%3C487%3ATEOTMW%3E2.0.CO%3B2-C
1
Time-Series Evidence of the Effect of the Minimum Wage on Youth Employment and Unemployment
Charles Brown; Curtis Gilroy; Andrew Kohen
The Journal of Human Resources, Vol. 18, No. 1. (Winter, 1983), pp. 3-31.
Stable URL: http://links.jstor.org/sici?sici=0022-166X%28198324%2918%3A1%3C3%3ATEOTEO%3E2.0.CO%3B2-Q
1
Alison J. Wellington
The Journal of Human Resources, Vol. 26, No. 1. (Winter, 1991), pp. 27-46.
Stable URL:
http://links.jstor.org/sici?sici=0022-166X%28199124%2926%3A1%3C27%3AEOTMWO%3E2.0.CO%3B2-5
The Effect of The Minimum Wage on Employment and Unemployment
Effects of the Minimum Wage on the Employment Status of Youths: An Update
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Lawrence F. Katz; Alan B. Krueger
Industrial and Labor Relations Review, Vol. 46, No. 1. (Oct., 1992), pp. 6-21.
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Lawrence F. Katz; Alan B. Krueger
Industrial and Labor Relations Review, Vol. 46, No. 1. (Oct., 1992), pp. 6-21.
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19
Lawrence F. Katz; Alan B. Krueger
Industrial and Labor Relations Review, Vol. 46, No. 1. (Oct., 1992), pp. 6-21.
Stable URL: http://links.jstor.org/sici?sici=0019-7939%28199210%2946%3A1%3C6%3ATEOTMW%3E2.0.CO%3B2-S
26
Lawrence F. Katz; Alan B. Krueger
Industrial and Labor Relations Review, Vol. 46, No. 1. (Oct., 1992), pp. 6-21.
Stable URL: http://links.jstor.org/sici?sici=0019-7939%28199210%2946%3A1%3C6%3ATEOTMW%3E2.0.CO%3B2-S
32
Daniel Sullivan
Journal of Law and Economics, Vol. 32, No. 2, Part 2, Empirical Approaches to Market Power: A
Conference Sponsored by the University of Illinois and the Federal Trade Commission. (Oct., 1989), pp. S135-S178.
Stable URL:
http://links.jstor.org/sici?sici=0022-2186%28198910%2932%3A2%3CS135%3AMPITMF%3E2.0.CO%3B2-1
32
Michael R. Ransom
The American Economic Review, Vol. 83, No. 1. (Mar., 1993), pp. 221-233.
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The Effect of the Minimum Wage on the Fast-Food Industry
The Effect of the Minimum Wage on the Fast-Food Industry
The Effect of the Minimum Wage on the Fast-Food Industry
The Effect of the Minimum Wage on the Fast-Food Industry
Monopsony Power in the Market for Nurses
Seniority and Monopsony in the Academic Labor Market
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The Effect of The Minimum Wage on Employment and Unemployment
Charles Brown; Curtis Gilroy; Andrew Kohen
Journal of Economic Literature, Vol. 20, No. 2. (Jun., 1982), pp. 487-528.
Stable URL: http://links.jstor.org/sici?sici=0022-0515%28198206%2920%3A2%3C487%3ATEOTMW%3E2.0.CO%3B2-C
Time-Series Evidence of the Effect of the Minimum Wage on Youth Employment and Unemployment
Charles Brown; Curtis Gilroy; Andrew Kohen
The Journal of Human Resources, Vol. 18, No. 1. (Winter, 1983), pp. 3-31.
Stable URL: http://links.jstor.org/sici?sici=0022-166X%28198324%2918%3A1%3C3%3ATEOTEO%3E2.0.CO%3B2-Q
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David Card
Industrial and Labor Relations Review, Vol. 46, No. 1. (Oct., 1992), pp. 22-37.
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David Card
Industrial and Labor Relations Review, Vol. 46, No. 1. (Oct., 1992), pp. 38-54.
Stable URL: http://links.jstor.org/sici?sici=0019-7939%28199210%2946%3A1%3C38%3ADMWREA%3E2.0.CO%3B2-%23
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Lawrence F. Katz; Alan B. Krueger
Industrial and Labor Relations Review, Vol. 46, No. 1. (Oct., 1992), pp. 6-21.
Stable URL: http://links.jstor.org/sici?sici=0019-7939%28199210%2946%3A1%3C6%3ATEOTMW%3E2.0.CO%3B2-S
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Industrial and Labor Relations Review, Vol. 13, No. 2. (Jan., 1960), pp. 254-264.
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Stephen Machin; Alan Manning
Industrial and Labor Relations Review, Vol. 47, No. 2. (Jan., 1994), pp. 319-329. Stable URL: http://links.jstor.org/sici?sici=0019-7939%28199401%2947%3A2%3C319%3ATEOMWO%3E2.0.CO%3B2-7
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George J. Stigler
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Daniel Sullivan
Journal of Law and Economics, Vol. 32, No. 2, Part 2, Empirical Approaches to Market Power: A Conference Sponsored by the University of Illinois and the Federal Trade Commission. (Oct., 1989), pp. S135-S178.
Stable URL:
http://links.jstor.org/sici?sici=0022-2186%28198910%2932%3A2%3CS135%3AMPITMF%3E2.0.CO%3B2-1
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