Minimum Wages and Employment Summary

On April 1, 1992, New Jersey’s minimum wage rose from $4. 25 to $5. 05 per hour. To evaluate the impact of the law we surveyed 410 fast-food restaurants in 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 minimum wage? The prediction from conventional 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 comparisons of employment at affected and unaffected establishments often did not (e. g. , Richard A. Lester, 1960, 1964).

Several re-cent studies that rely on a similar comparative methodology have failed to detect a negative employment effect of higher minimum wages. Analyses of the 1990-1991 increases 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 establishmentlevel 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 empirical methodology, several other features of *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, ~niversitJ of Chicago, and the NBER for comments and suggestions. We also acknowledge the expert research assistance 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. 772 VOL. 84 NO. 4 CARD AND KRUEGER: MINIMUM WAGE AND EMPLOYMENT the New Jersey law and our data set are also significant. First, the rise in the minimum wage occurred during a recession. The increase had been legislated two years earlier 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 Pennsylvania 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 employment are similar in New Jersey and eastern Pennsylvania, as well as across high- and low-wage stores within New Jersey, our comparative methodology effectively “differences out” any. seasonal employment effects. Third, we successfully followed nearly 100 percent of stores from a first wave of interviews 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 employment 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 773 and 1991 to measures of the relative minimum 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 scheduled 1992 increase gave New Jersey the highest state minimum wage in the country and was strongly opposed by business leaders 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 recession. Concerned with the potentially adverse 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 required 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 minimumwage earners, the legislature dropped the issue.

Despite a strong last-minute challenge, 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 minimum wage by surveying fast-food restaurants in New Jersey and eastern Pennsylvaniae2 Our 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 Stores in: A1l NJ PA 473 63 410 86. 7 364 33 331 90. 9 109 30 79 72. 5 410 6 2 2 1 399 331 Waue I, February 15-March 4, 1992: Number of stores in sample frame:a Number of refusals: Number interviewed: Response rate (percentage): Wace 2, Nocember 5 – December 31, 1992: Number of stores in sample frame: Number closed: Number under rennovation: Number temporarily closed:’ Number of refusals: Number i n t e r v i e ~ e d : ~ 5 2 2 1 321 79 1 0 0 0 78 aStores with working phone numbers only; 29 stores in original sample frame had 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 regulations 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 relatively homogeneous, making it easier to obtain 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 franchised 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 constructed a sample frame of fast-food restau- rants in New Jersey and eastern Pennsylvania 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 characteristic~. ~ Table 1 shows that 473 stores in our sample frame had working telephone numbers when we tried to reach them in FebruaryMarch 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 restaurants, for an overall response rate of 87 percent. The response rate was higher in New Jersey (91 percent) than in Pennsylva- 3 ~ an pilot 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. 4 ~ h seample was derived from white-pages telephone 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. VOL. 84 NO. 4 C A m AND KRUEGER: MINIiiMUM WAGE AND EMPLOYMENT nia (72. 5 percent) because our interviewer made fewer call-backs to nonrespondents in In the analysis below we inPenn~ylvania. ~ vestigate possible biases associated with the degree of difficulty in obtaining the firstwave interview.

The second wave of the survey was conducted in November and December 1992, about eight months after the minimum-wage increase. Only the 410 stores that responded in the first wave were contacted in the second round of interviews. We successfully 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 result, we have second-wave interview data for 99. 8 percent of the restaurants that responded in the first wave of the survey, and information on closure status for 100 percent 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, employment in wave 2 is set to 0 for the perma-6 ~ e s p o n s erates 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 Pennsylvania stores 42. 2 percent responded on the first call, and 71. 6 percent responded after at most two callbacks. 7 ~ ofs April 1993 the store closed because of road construction and one of the stores closed for renovation 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. 775 nently closed stores but is treated as missing for the temporarily closed stores.

(Fulltime-equivalent [FTE] employment was calculated 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 Jersey 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 (companyowned 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 distributions of starting wages in the two states before and after the rise. In wave 1, the distributions in New Jersey and Pennsylvania were very similar.

By wave 2 virtually all ‘ w e discuss the sensitivity of our results to alternative 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” designation or other noncash bonuses are excluded from our tabulations. THE AMERICAN ECONOMIC REVIEW SEPTEMBER 1994 Stores in: Variable NJ PA ta 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.

Distribution of Store Types (percentages): a. b. c. d. e. Burger King KFC Roy Rogers Wendy’s Company-owned 2. Means in Wave I: a. FTE employment b. Percentage full-time employees c. Starting wage d. Wage = $4. 25 (percentage) 20. 4 (0. 51) 32. 8 (1. 3) 4. 61 (0. 02) 30. 5 (2. 5) e. Price of full meal f. Hours open (weekday) g. Recruiting bonus 3. Means in Ware 2: a. FTE employment b. Percentage full-time employees c. Starting wage d. Wage = $4. 25 (percentage) e. Wage = $5. 05 (percentage) f. Price of full meal g. Hours open (weekday) h.

Recruiting bonus 21. 0 (0. 52) 35. 9 (1. 4) 5. 08 (0. 01) 0. 0 85. 2 (2. 0) 3. 41 (0. 04) 14. 4 (0. 2) 20. 3 (2. 3) 1. 8 10. 8 36. 1 5. 0 – 0. 8 – 0. 6 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. Interestingly, the minimum-wage increase had no apparent “spillover” on higher-wage restaurants in the state: the mean percentage wage change for these stores was – 3. 1 percent.

Despite the increase in wages, full-timeequivalent 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 VOL. 84 NO. 4 CARD AND KRUEGER: MINIMUM WAGE AND EMPLOYMENT February 1 9 9 2 Wage Range November 1 9 9 2 Wage Range New Jersey Pennsylvania FIGURE 1. DISTRIBUTION OF STARTING WAGERATES 778 THE AMERICAN ECONOMIC REVIEW difference in wave 2.

Only two other variables 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 Pennsylvania. We can assess the reliability of our survey questionnaire by comparing the responses of 11 stores that were inadvertently interviewed 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 combined 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 different from restaurants with complete responses.

We find that stores with missing data on employment, wages, or prices are similar in other respects to stores with complete data. There is a significant size differential 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 coefficient in the probit model. SEPTEMBER I994 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 differences between the averages for the two waves (i. e. , row 2 minus row 1).

An alternative 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 presents 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 Pennsylvania counterparts but grew relative to Pennsylvania stores after the rise in the minimum wage. The relative gain (the “difference in differences” of the changes in employment) is 2. 76 FTE employees (or 13 percent), with a t statistic of 2. 03.

Inspection of the averages in rows 4 and 5 shows that the relative change between New Jersey and Pennsylvania stores is virtually identical 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 expanded 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 V O L . 84 NO. 4 CARD AND KRUEGER: MINIMUM WAGE AND EMPLOYMENT largely unaffected by the new minimum wage, this comparison provides a specification 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 estimated employment effect of the minimum wage is similar.

The results in Table 3 suggest that employment 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 worsening 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 procyclical, the rise in unemployment would be expected to lower fast-food employment in the absence of other factors.

14 B. Regression-Adjusted Models 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 restaurant 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 unemployment 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+~, + ( l b ) 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 minimum rate. Variation in GAP, reflects both the New Jersey-Pennsylvania contrast and differences within New Jersey based on reported 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.

T h e discrepancy between the two estimates is due to the restricted sample in Table 4. In Table 4 and the remaining tables in this section we restrict our analysis to the set of stores with available employment and wage data in both waves of the 1 5 regression ~ of the proportional wage change between waves 1 and 2 on GAP, has a coefficient of 1. 03.

THE AMERICAN ECONOMIC REVlEW TABLE3-AVERAGE EMPLOYMENT PER STOREBEFOREAND IN NEW JERSEYMINIMUM WAGE PA Variable (i) Stores by state Difference, NJ-PA NJ (iii) (ii) SEPTEMBER 1994 I ~ E THE R RISE Stores in New Jersey a Differences within N J ~ Wage = Wage = Wage r LowMidrangehigh high $4. 25 $4. 26-$4. 99 $5. 00 (vii) (viii) (iv) (v) (vi) 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 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 Independent variable (i) (ii) (iii) (iv) (v) 1. New Jersey dummy 2. 33 (1. 19) 2. 30 (1. 20) – – – 15. 65 (6. 08) no 14. 92 (6. 21) yes 11. 91 (7. 39) yes 2. Initial wage gapa – 3.

Controls for chain and ownershipb 4. Controls for regionC 5. Standard error of regression 6. Probability value for controlsd no – yes Notes: Standard errors a r e given in parentheses. T h e sample consists of 357 stores with available data o n employment and starting wages in waves 1 and 2. T h e dependent variable in all models is change in F T E employment. T h e mean and standard deviation of the dependent variable are -0. 237 and 8. 825, respectively. All models include a n unrestricted constant (not reported). aProportional increase in starting wage necessary to raise starting wage t o new minimum rate.

For stores in Pennsylvania the wage gap is 0. b ~ h r e de ummy variables for chain type and whether or not the store is companyowned are included. ‘Dummy variables for two regions of New Jersey and two regions of eastern Pennsylvania are included. d ~ r o b a b i l i t yv alue of joint F test for exclusion of all control variables. VOL. 84 NO. 4 CARD AND KRUEGER: MINIMUM WAGE AND EMPLOYMENT 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 Jersey 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 coefficient of the New Jersey dummy then provides a test of the Pennsylvania control group. When we estimate these models, the coefficient of the New Jersey dummy is insignificant (with t ratios of 0. 3-0. 7), implying 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 column (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 regions~ecificdemand shocks and identifv the effeet of the minimum wage by employment changes at higher- and lowerwithin the same region of New wage Jersey. The probability value in row 6 shows no evidence of regional components in employment growth.

The addition of the region dummies attenuates the GAP coefficient and raises its standard error, however, making it no longer possible to reject the 781 null hypothesis of a zero employment effect of the minimum wage. One explanation for this attenuation is the presence of measurement error in the starting wage.

Even if employment growth has no regional component, the addition of region dummies will lead to some attenuation of the estimated GAP coefficient if some of the true variation in GAP is explained by region. Indeed, calculations based on the estimated reliability of the GAP variable (from the set of 11 double interviews) suggest that the fall in the estimated GAP coefficient from column (iv) to column (v) is just equal to the expected 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 GAP variable are uniformly positive in these models but insignificantly different from 0 at conventional levels. The implied employment effects of the minimum wage are also smaller when the dependent variable is expressed in proportional terms. For example, the GAP 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 percent. 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 proportional-change models (using initial employment as a weight) give rise to wage elastici- 16 In 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 dum