Does the Minimum Wage Bite into Fast-Food Prices?∗

Emek Basker†

University of MissouriMuhammad Taimur KhanIslamic Development Bank

September 2013

Abstract

We study the effect of increases in state minimum wages on the prices of severalfast-food items using quarterly city-level data from 1993–2012, a period duringmuch of which the federal minimum wage declined in real value while state-levellegislation flourished. For two products, burgers and pizza, we find robust elas-ticities of 0.09 with respect to the minimum wage, consistent with a competitivelabor market. Our estimates for a third product, fried chicken, are very impre-cise. We show that the price effect is driven by increases in restaurant wages andfast-food outlets’ payroll outlays.

JEL Codes: J31, J42, L81

Keywords: Minimum wage, fast food, pass-through, prices

∗Preliminary and incomplete. Comments very welcome. We thank C.Y. Choi, Dean Frutiger, Wen Sun,and Erol Yildirim for help with the C2ER data; and Peter Klein, Cory Koedel, Mark Lewis, Jeff Milyo,Peter Mueser, and David Neumark for helpful comments and conversations.†Corresponding author; emek@missouri.edu

1 Introduction

This paper revisits earlier work by Card and Krueger (1994), Katz and Krueger (1992), and,

particularly, Aaronson (2001), which examines the pass-through of minimum-wage increases

onto fast-food prices. The first two papers found mixed results, while Aaronson (2001) found

an elasticity of fast-food prices with respect to the minimum wage ranging from 0.07 to 0.16,

depending on the data set and specification. In the current analysis, we find elasticities of

0.09 for two fast-food items, burgers and pizza, and a very imprecise estimate for the third

(fried chicken).

We are motivated by two facts to reexamine past evidence. First, the federal minimum

wage eroded quite significantly during the 2000s, falling more than 25% in real terms between

September 1997 and July 2007. Did this erosion reduce the “bite” of the minimum wage?

If so, we would expect it to have had a much smaller effect on prices. The federal minimum

wage is not binding in all states, but it did bind in 20 states in June 2007 and in 36 states

just two years earlier, well into its real-value slide.

The second motivation is statistical. Earlier studies were limited by relatively few state-

level changes in minimum-wage legislation, but between 1993 and 2012, the period on which

the present study focuses, there was robust activity in this realm, especially during the

decade during which the (nominal) federal rate was left unchanged. Consequently, we have

significantly more variation with which to identify the effect of the minimum wage using a

full difference-in-difference framework.

Before we turn to estimating the price effects, we first test the degree to which the

minimum wage was binding for fast-food outlets during this time period using two outcome

variables: the wages of restaurant workers and the payroll outlays of restaurants, in par-

ticular limited-service restaurants. The first variable comes from the Current Population

Survey outgoing-rotation group survey; the second comes from state-level County Business

Patterns files. We find that restaurant workers’ wages increase with the minimum wage, with

an elasticity of approximately 0.17; the effect is much larger for part-time workers. Con-

sistent with these finding, restaurant payrolls’ elasticity with respect to the minimum wage

is approximately 0.14 on average, and ranges from 0.12 for full-service restaurants to 0.17

for limited-service restaurants such as fast-food outlets. These results are all statistically

significant at conventional levels.

These results confirm that the minimum wage did bite fast-food establishments over the

period of our study, and also that our statistical power is sufficient to identify these effects.

Both are critical to interpreting our price regressions. If the minimum wage is not binding for

fast-food outlets, we would not expect any effect on prices, and could not learn anything from

price regressions about the underlying market structure, a key motivation to understanding

the effect of minimum wages on prices (Aaronson, 2001; Aaronson, French, and MacDonald,

2008; Aaronson and French, 2007). Second, these exercises provide a “proof of concept”

for our identification strategy, demonstrating that state-month and even state-year variation

over the period of our study, 1993–2012, is sufficient to identify the effect of the minimum

wage even with full time and location fixed effects, state-specific linear time trends, and

state-level clustering of standard errors. This, too, gives us context in which to interpret

our price results; strong statistical power in these regressions increases our confidence in the

price estimates.

We estimate the price effects of the minimum wage for three fast-food products using

quarterly city-level price data from the Council for Community and Economic Research:

McDonald’s burgers, Pizza Hut pizzas, and Kentucky Fried Chicken fried chicken. We find

large point estimates for the first two: elasticity estimates around 0.09, larger than those

found in Aaronson’s (2001) original study. The estimate of the elasticity of the burger price

is fairly precise, with a standard error of about 0.02; the estimate for the price of pizza is

much noisier, with a standard error of 0.05, just barely significant at the 10% level. For

the third product, KFC fried chicken, we find large standard errors around a negative point

estimate.

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The rest of the paper is organized as follows. Section 2 provides some industry back-

ground and summarizes previous research. Section 3 describes the datasets we use in our

study. Sections 4, 5, and 6 discuss the estimation and results for the effect of the minimum

wage on restaurant workers’ wages, restaurant payrolls, and fast-food prices, respectively,

and their implications. Section 7 concludes.

2 Background on the Fast-Food Sector

The fast-food sector constitutes roughly a third of the restaurant sector. The Census Bureau

includes fast-food restaurants in NAICS 722211, “limited-service restaurants.” (This cate-

gory excludes bars, cafeterias, ice-cream parlors, coffee shops, and food trucks, all of which

are classified elsewhere.) In 2011, there were more than 200,000 limited-service restaurants

in the U.S., accounting for 37% of all restaurants and bars, more than 35% of the sector’s

employees, and approximately 30% of its combined payroll.

Franchising plays an important role in this sector. The 2002 Census of Accommodation

and Food Services (CAF) reports that half of all limited-service restaurants used “a trade

name authorized by a franchisor,” two-thirds of which are franchised outlets (the remaining

third are corporate-owned).

Franchised establishments have more wage-setting and pricing flexibility than corporate-

owned establishments, a fact used by Ater and Rigbi (2012) to explain McDonald’s intro-

duction of the “Dollar Menu” in 2002. Krueger (1991) finds evidence that corporate-owned

outlets pay higher wages than franchised outlets for the same jobs; at the same time, fran-

chisees’ prices are higher on average and more variable than corporate-owned establishments’

(Lafontaine, 1995, 1999; Ater and Rigbi, 2012). Nevertheless, the flexibility of franchise own-

ers is not complete, as the franchising firms specify contract terms that protect their brand

names from franchisee free riding (Lafontaine and Shaw, 2005). For this reason, franchise

3

owners may not have the contractual flexibility in the short run to reduce shift coverage in

response to cost increases (Wimmer, 1996).

Lemos (2008) provides an extensive survey of the literature on the effect of the minimum

wage on prices; we summarize the main papers using U.S. data here.

Katz and Krueger (1992) and Card and Krueger (1994) use data on restaurants in

two states (New Jersey and Pennsylvania) and use the 1992 New Jersey minimum-wage

increase as a basis for a difference-in-difference regression estimating the effect of a minimum-

wage hike on fast-food prices. Card and Krueger (1994) find an increase in the prices of

some fast-food items when the New Jersey minimum wage increased; Katz and Krueger

(1992) do not find statistically significant evidence of a price pass-through. A limitation

of these studies is the small size of the datasets that relies on one minimum-wage increase

for identification. Card and Krueger (1995) try to mitigate this shortcoming by using a

broader panel consisting of food-away-from-home Consumer Price Indices of 27 cities across

a three-year time period. Using the 1990-91 federal minimum-wage increases as a basis for

their difference-in-difference estimation, they do not find statistically significant evidence of

price increases in the aftermath of wage hikes.

Aaronson (2001) uses a much larger dataset that combines the minimum-wage histo-

ries of the United States and Canada from 1978–1995 with price data on restaurants and

fast-food outlets from Bureau of Labor Statistics (BLS) Consumer Price Index (CPI) for

food-away-from-home and what was then called the American Chamber of Commerce Re-

search Associate (ACCRA, now C2ER) cost-of-living index. Using time-series reduced-form

equation for differenced data and exploiting the variation in minimum wages across time and

states, he finds elasticities of fast-food prices with respect to minimum wages of up to 0.16

using the ACCRA data and 0.07 using the BLS CPI. One limitation of the Aaronson (2001)

study is that it does not include price data for U.S. states with the most active minimum-

wage history as most of these states did not regularly take part in ACCRA pricing surveys

during the time period of the study. This omission effectively limits the identification of the

4

minimum-wage effects to changes in the federal minimum wage.

In a related paper, Aaronson, French, and MacDonald (2008) focus on the price effects

of the federal minimum-wage increases in 1996–97. Using micro (outlet-level) data from

the BLS CPI from over 1,000 outlets in 76 metro areas and 12 non-metro areas during this

time period, they find price elasticities around 0.07. Both studies find evidence that the

price response is almost immediate as price increases happen within the two-month period

surrounding a minimum-wage increase.

In a case study of the early effect of San Francisco’s “living wage” legislation, Dube,

Naidu, and Reich (2007) find a price elasticity of about 0.06 for fast-food outlets.

This study complements the prior work by using more recent data, during a period rich

in state-level minimum-wage increases, to identify the effect of the minimum wage on fast-

food prices. We also estimate the effect of the minimum wage on cost variables to verify

that the minimum wage is sufficiently binding for this sector that we might expect prices to

increase in a competitive environment.

3 Data

We obtained minimum-wage data from several sources. Federal minimum-wage rates and

enactment dates come from the U.S. Department of Labor’s website, which also includes

historical state minimum-wage data but without the enactment dates. We corroborated the

data with history of state minimum-wage enactment dates from the Fiscal Policy Institute

for 1996-2006 as well as from state governments’ websites for the remaining years to form

a comprehensive dataset with state minimum-wage rates and their enactment dates from

1993-2012.

Five nominal federal minimum-wage increases occurred during the two decades of our

study: on October 1, 1996 (from $4.25 to $4.75 per hour); on September 1, 1997 (to $5.15);

on July 24, 2007 (to $5.85); on July 24, 2008 (to $6.55); and on July 24, 2009 (to $7.25).

5

Figure 1 shows the real federal minimum wage, in December 2012 dollars, over the 20-year

period of our study; real (CPI-deflated) buying power of the minimum wage decreased by

more than 25% between September 1997 and July 2007, before rising, in steps, to a level

higher than that of September 1997.

Sandwiched in between the federal minimum-wage increases are many instances of state

minimum-wage changes. In our analysis, the only minimum-wage changes of economic in-

terest are those where the effective minimum wage in a state changes as a result of either a

federal or a state minimum-wage hike.

A federal minimum-wage hike can change the effective minimum wage in a state if (a)

the state effective minimum wage is set at the old federal level, in which case it receives the

full brunt of the new federal minimum-wage hike; (b) the state’s effective minimum wage is

between the old and the new federal levels, in which case the effective wage floor increases

by less than the full amount of the federal increase; (c) the state’s effective minimum wage

is at or above the new federal level, but state law pegs the state minimum wage at a fixed

level above the federal wage.1 States whose effective minimum wages are at or above the

new federal level and have no mandated contemporaneous increase see no minimum-wage

spikes following an increase in the federal wage floor. Table 1 lists the number of states (out

of 50; Washington DC is omitted from the data) that are affected in each of these ways by

each of the five federal minimum-wage hikes. This variation in the way the federal minimum

wage affects states is at the heart of the identification strategy of papers that use federal

minimum-wage increases to identify the effect on prices and employment. (To make things

more complicated, some states adopted the federal minimum wage of July 24, 2007, 2008,

and 2009 effective July 1 of the same year.)

1This was the case in Alaska and Connecticut in the 1990s. Connecticut law automatically increases theminimum wage to 0.5% above the federal rate any time the federal minimum wage rate equals or becomeshigher than the State minimum.

6

The effective minimum wage in a state also increases when the states raises its minimum-

wage level above the federal minimum-wage rate by passing a law to that effect in the state

legislature, or when, due to CPI pegging, the minimum wage increases automatically on

an annual basis. Since there was no federal minimum-wage hike between 1997 and 2007,

some states experienced several minimum-wage changes during this period while others had

no minimum-wage increases. We use this periodic variability, along with the additional

variation provided by the uneven impact of the federal hikes in 1996, 1997, and the late

2000s, to identify the effects of minimum-wage changes on prices and isolate them from the

effects of other variables changing at the same time.

Table 2 lists the number of effective minimum-wage increases by state over the period

of our study. A total of 321 instances are listed, two thirds of which are either due to

federal minimum-wage increases or take effect within six months before or after a federal

increase. One hundred and one increases, in 29 states, are completely independent of the

federal increases and occur outside the six-month window. Table 3 repeats this analysis by

year, excluding the federal hikes. We do not see any minimum-wage increases during 1993;

every later year has at least one effective minimum-wage increase, with clusters in years of

federal action. On average across all states and months in our data, a state’s minimum wage

is about 4% above the federal level, with the median state’s minimum wage set at the federal

level. At the same time, 14 states had minimum wages set at or above 130% of the federal

level at some point during the sample period, and Oregon and Washington both exceeded

140% in the months immediately preceding the July 24, 2007 increase in the minimum wage.

Where possible, we supplement the state and federal data with information on city-level

minimum wages, sometimes called “living wages.” San Francisco enacted a minimum wage

in 2004 and has raised it almost every year since. Santa Fe also adopted a city minimum

wage of $8.50 an hour in June 2004 (Yelowitz, 2005); in 2008, it was expended to all private

employees (prior to that it had only applied to businesses with 25 or more employees), and

has increased on average every other year. Finally, Albuquerque, New Mexico, has had a

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city minimum wage since 2008.

We use city-level average-price data for the period 1993–2012 from the Council for Com-

munity and Economic Research (C2ER, formerly the American Chamber of Commerce Re-

search Association, or ACCRA). The data are updated quarterly, in the first week of each

quarter (in January, April, July, and October). For this analysis, we use C2ER’s city-level

average prices of three fast-food items over the period 1993–2012. These data were also

used in Aaronson’s (2001) study. The products are a McDonald’s Quarter Pounder (“McD

burger”), 13 inch thin-crust regular cheese pizza at Pizza Hut and/or Pizza Inn (“pizza”),

and fried chicken drumstick and thigh at Kentucky Fried Chicken and/or Church’s Fried

Chicken (“KFC fried chicken”); the product definitions are consistent over time and across

states. Price surveyors at participating Chambers of Commerce are requested to survey

at least five and up to ten McDonald’s, Pizza Hut and/or Pizza Inn, and Kentucky Fried

Chicken and/or Church’s Fried Chicken establishments in town, if possible.2

These quarterly publications are constructed with the help of local chambers of com-

merce in participating cities where the price data from 5–10 retail establishments are collected

by local volunteers in the first week of each quarter. The sample of cities in each quarterly

publication varies from issue to issue as participation in the price survey is strictly volun-

tary. As a result, some cities participate in the survey every other quarter, others miss an

occasional survey, and still others only report prices for a few quarters before disappearing

from the sample altogether. In 2007, C2ER stopped collecting fourth-quarter prices, so we

have price data for a maximum of 74 quarters for each product and city. We do not know

which, or even how many, outlets were surveyed in practice in each city.

After dropping any city that is included in the survey in fewer than 40 of the possible

74 quarters, we are left with a dataset that includes 284 cities in 48 states (Rhode Island

2City-level ACCRA data have been used for a variety of economic studies in the past, including studies ofsupermarket financing (Chevalier, 1995; Chevalier and Scharfstein, 1996); price convergence and deviationsfrom the “law of one price” (Parsley and Wei, 1996; Choi and Wu, 2012); inequality (Frankel and Gould,2001); and the impact of retailer entry on prices (Basker, 2005; Courtemanche and Carden, 2012).

8

and Hawaii, as well as the District of Columbia, are missing from the data). Twenty nine

cities in 18 states are included in the survey every single quarter, and 11 cities in 11 states

are included for the minimum of 40 quarters.

Some cities in the C2ER database are actually composites of several nearby cities (for

example Reno–Sparks, NV or Benton Harbor–St. Joseph, MI), but most are stand-alone

cities. A few cities, such as Kansas City and St. Louis, straddle state lines; since we do

not know the exact locations of establishments surveyed in these cities, we drop them to

eliminate ambiguity with respect to the applicable minimum wage.

The real (December 2012 dollars) average prices and the cross-sectional inter-quartile

range for the three products are shown in Figures 2(a)–2(c).3

Aaronson (2001) raises several concerns about the ACCRA/C2ER data. First, he notes

that C2ER does not aim for consistency in its product definitions over time, focusing instead

on cross-sectional consistency; as a result, survey participants vary from quarter to quarter.

While the product definitions of the fast-food items in this study have not changed over

this period, the specific outlets surveyed may have changed, which could result in spurious

variation over time in the average price of a specific item in a given city. Second, the

quarterly frequency of C2ER data could make it difficult to determine whether prices respond

immediately to a minimum-wage increase. Because C2ER prices are always collected in the

first week of the quarter, and minimum-wage increase almost always become effective on

the first day of the quarter, a lag in price adjustment of just a few days could delay our

observation of the price increase by a full quarter.

Aaronson (2001) partially addresses the first concern by smoothing out the price series

to remove temporary price changes of more than 5% that quickly return to their prior

levels. We have estimated all our regressions using this smoothing procedure, but as it did

3The inter-quartile range of the price of pizza converges to a point in the third quarter of 2010, when justover half the cities — 152 of 303 — quoted a price of $10 for a pizza. This is highly unusual, and appearsto be motivated by a chain-wide sale, although we could not find any documentation for it.

9

not meaningfully change either point estimates or significance levels, we report only the

unadjusted regressions.

Unlike Aaronson (2001), we do not supplement the price analysis by using the BLS

“food-away-from-home” CPI as a second measure of prices. First, many of the BLS cities are

actually multi-state metro areas (e.g., Washington–Baltimore, DC–MD–VA–WV; New York–

Northern New Jersey–Long Island, NY–NJ–CT–PA), and are subject to different minimum

wages in different parts of the metro area at any given point in time. This is less of an

issue for Aaronson’s analysis, since he focuses on the effect of federal minimum-wage hikes,

but is problematic when our identification comes from state-level changes. In addition,

some areas that are entirely contained within a single state, such as Phoenix–Mesa, AZ, or

Denver–Boulder–Greeley, CO, have shorter series or only semi-annual data.

Because of concerns that state minimum wages are procyclical, which may cause a

spurious positive relationship between minimum wages and prices, we also include in some

regression specifications the log of state-level GDP (Gross State Product, or GSP) from the

Bureau of Economic Analysis in chained 2005 dollars. This variable is calculated on an SIC

basis to 1996 and on a NAICS basis from 1997. GSP per capita ranges from $22,000 (in

2005 dollars) to nearly $65,000, with an average of about $38,000. Two states, Alaska and

Delaware, have GSP per capita above $60,000 for several years during the sample period;

four states, Arkansas, Mississippi, Montana, and West Virginia, have GSP per capita below

$25,000 for one or more years during the sample period. The year-to-year growth rate of

state GSP averages 1.5% over this sample; eight states experienced growth above 9% at

some point during the sample (six of them from 1996 to 1997) and five experienced a decline

greater than 9% at some point (two from 1996 to 1997 and three from 2008 to 2009).

For our wage analysis, we use individual-level data from the Current Population Survey

(CPS) Outgoing Rotation Group (ORG) survey from 1993 to 2012. Restaurant workers are

identified by 1980 industry code 641 (through 2002) and by 2002 industry code 8680 (from

2003). Hourly workers are those for whom an hourly wage is available. Full-time workers

10

are those who report 35 hours or more of “usual hours” worked at this job, and part-time

workers are those who report working fewer than 35 hours a week. We drop workers whose

hourly earnings fall below the 1st percentile or above the 99th percentile within each state,

month, and year. We are left with about 125,000 worker-month observations, of which about

half are full-time workers, 40% are part-time workers, and the remaining 10% do not report

their usual hours. Even after deleting the top and bottom 1% of the wage distribution, the

distribution is very wide, with real wages (in December 2012 dollars) ranging from $2.07 per

hour to $64.42 per hour; the average worker’s real wage is about $8.55 per hour, and the

average hours-weighted wage is $8.89 per hour.

Finally, we use payroll data from state-level County Business Patterns (CBP) data

from 1993 to 2011. CBP data are annual and include full-year and first-quarter payroll

paid by business establishments, by state and industry. Until 1997, the data are reported

using the Standard Industrial Classification (SIC) system, and we use payroll by SIC 5800

establishments (eating and drinking places). Starting in 1998, the reporting is done using

the North American Industrial Classification System (NAICS), and provides a breakdown of

NAICS 722 (all restaurants and drinking places) into multiple subsectors, including NAICS

722110 (full-service restaurants) and NAICS 722211 (limited-service restaurants).4

4 Effect of Minimum Wage on Restaurant Wages

The fast-food sector employs many minimum-wage workers. Before we turn to estimating the

effect of minimum-wage laws on prices, we verify that minimum wage laws have a noticeable

effect on the prevailing wages in this sector. We do this by combining the minimum-wage

4Establishment payroll includes “all forms of compensation, such as salaries, wages, commissions, dis-missal pay, bonuses, vacation allowances, sick-leave pay, and employee contributions to qualified pensionplans paid during the year to all employees [. . . as well as] amounts paid to officers and executives [of corpora-tions, . . . but excluding] profit or other compensation of [the owner or owners of unincorporated businesses].”Source: http://www.census.gov/econ/cbp/definitions.htm, accessed August 19, 2013.

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data with the CPS-ORG survey, to estimate

ln(wage)ijt = αj + δt + βjtimet +∑S

γs ln(minwagejs) + ρ0 ln(GSPj,y(t))

+ ρ1 ln(GSPj,y(t)−1) + εijt (1)

where wageijt is the reported hourly wage of worker i, working in state j at time t; αj

is a state fixed effect, δt is a time fixed effect, βj is a state-specific linear time trend, and

minwagejt is the minimum wage in state j in month t. The state-specific linear time trends

are intended to capture general “drift” of real wages and prices over the relatively long

(twenty-year) panel, and GSPj,y(t) is gross state product in state j in the year of month t.

We include both current and one-year lagged gross state product in the regressions for

two reasons. First, state-level linear trends can be sensitive to recessions and expansions,

particularly at the beginning and end of the sample period (Neumark, Salas, and Wascher,

forthcoming); by controlling for these expansions and recessions directly we hope to remove

their influence on the trend variables. Second, minimum wages may themselves be procyclical

and therefore endogenous to the outcome variables (Baskaya and Rubinstein, 2013).

We estimate a baseline equation in which S = {t}, i.e., only the current minimum wage

in state j is included in the regression, as well as a variant in which S = {t− 3, t, t+ 3}, i.e.,

we include the three-month lead and lag of the minimum wage in state j. (We use quarterly

rather than monthly leads and lags because most of the state minimum-wage increases in

our dataset took place on the first day of the quarter, so there is very little month-to-month

variation in this variable within a quarter.)

Not including worker demographics or fixed effects in our regressions means that we

interpret the coefficient γ not as the effect of the minimum wage on an individual’s wage

but as the effect on the average worker’s wage, allowing for endogenous changes in the

composition of workers. For example, an increase in the minimum wage may lead employers

to be more selective in hiring, resulting in a better-educated or more-experienced workforce.

12

This approach is similar to the one taken by Neumark, Schweitzer, and Wascher (2004), who

use CPS data for the period 1979–1997 to identify the effect of the minimum wage on wages

across all sectors.

The results are presented in Table 4. The estimate in the first column includes all

restaurant workers. A 10% increase in the real minimum wage, for example from $5 per hour

to an inflation-adjusted $5.50 per hour, increases the average hourly wage of a restaurant

worker by nearly 1.6%.

The effects are starkly different for part-time and full-time employees. When we restrict

the sample to only part-time workers, the estimated effect of a 10% increase in the minimum

wage is a 2.3% increase in average hourly wages; when we restrict the sample to only full-time

workers, this estimate falls to a statistically insignificant 0.7%. (Workers whose usual weekly

hours are not given are included in the full sample but omitted from the part-time/full-time

breakdowns.) This is not surprising, as full-time workers typically earn higher hourly wages:

the average real wage for a full-time restaurant worker is 18% higher than that of a part-time

worker in the CPS data, partly because full-time workers are more likely to be in managerial

positions. The minimum wage is less binding for these higher-paid workers.

The last three columns repeat the analysis adding three-month lead and lag variables.

These have the effect of increasing standard errors due to the collinearity of current and

lagged minimum wages, and eliminating the statistical significance of individual coefficients,

except in the case of part-time workers. Point estimates indicate that at least two-thirds

and possibly all of the increase in wages occurs in the quarter of the minimum-wage change,

with the remainder taking place up to one quarter later. Although the coefficients for the

full and full-time samples are not individually significant, they are jointly significant for

the full sample; the sum of the coefficients is very similar to the estimated effect in the

single-coefficient specification, and with similar levels of significance. We take the fact that

lead effects, particularly for part-time workers, are small and statistically significant as an

indication that our control variables have captured any part of the relationship between

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wages and the minimum wage that are due to reverse causality or joint determination (e.g.,

cyclicality).5

These results suggest that restaurant workers’ wages are tied to the minimum wage, but

much more so for part-time workers. As Aaronson, French, and MacDonald (2008) point

out, self-reported wages of restaurant workers in the CPS may not be representative of wages

in the fast-food sector; specifically, fast-food wages are likely to be lower, and therefore more

closely tied to the minimum wage. One reason for this is a disproportionate share of part-

time work in the fast-food sector. In the CPS sample, 44% of workers who provide their

usual hours are full-time workers, defined as those who usually work at least 35 hours per

week in their primary job. Since Katz and Krueger (1992) report that only 30–40% of fast-

food workers in their sample were full-time workers, we expect that the fraction of part-time

workers in the fast-food sector is at least as large as in the restaurant sector as a whole, and

therefore that our estimated wage effects for the full sample are a lower bound on the true

elasticity in the fast-food sector.

We next use annual-frequency state-level data on payroll by type of restaurant to assess

the degree to which minimum wages bite differentially into full-service and limited-service

restaurants’ costs.

5 Effect of Minimum Wage on Restaurant Payroll

Next, we use annual files from the Census Bureau’s County Business Patterns (CBP) which

provide, at the state level, annual and first-quarter payroll figures for various restaurant sub-

sectors. Although the CBP dates back to the 1960s, the distinction between different types

of restaurants has only been made since 1998, when CBP switched from using the Stan-

5The coefficients on current and lagged gross state product (not shown) are positive throughout. The effectof current GSP is relatively small, with elasticities ranging from 0.03-0.08 and not statistically significant; theelasticity of fast-food wages with respect to lagged GSP is larger, ranging from 0.14–0.20, and is statsticallysignificant except in the full-time regressions.

14

dard Industrial Classification (SIC) system to the North American Industrial Classification

System (NAICS).

Despite the relationship between minimum wages and restaurant workers’ wages, it

is not obvious that minimum wages should affect payrolls, at least not enough to ensure

identification in a statistical model. This is because, first, payrolls are disproportionately

affected by full-time workers’ wages, which show only a weak relationship to minimum wages

in Table 4; and second, because restaurants may adjust their employment on many margins,

including number of workers, hours per worker, and the relative share of part-time and

full-time workers, and all of these could work to reduce the impact of minimum wages on

payrolls.

We estimate

ln(pay)it = αi + δt +βitimet +γ ln(minwageit) +ρ0 ln(GSPjt) +ρ1 ln(GSPj,t−1) + εit (2)

where payit is either real annual or real first-quarter payroll in state i in year t, δt is now a

year fixed effect, and the other variables are as defined above. The minimum wage in year

t is calculated as the algebraic average of the 12 monthly minimum wages in the state; and

the first-quarter minimum wage is calculated as the algebraic average of the three monthly

minimum wages in the first quarter.

We estimate this regression separately for all restaurants and drinking places, full-service

restaurants, and limited-service restaurants. We have data on the first starting in 1993, but

the breakdown by service level is only available starting in 1998.

The results are reported in Table 5. Our preferred specification uses first-quarter data

since the minimum wage is measured with less error in that specification (minimum-wage

increases rarely occur within a quarter). We find that total state-wide restaurant payroll

increases by nearly 1.3% for every 10% increase in the average state effective minimum wage,

less than the effect on average wages in the CPS regressions, but larger than the effect when

15

we consider only full-time workers.

When we break down the payroll effect by type of restaurant, it is clear that the effect

on full-service restaurants is somewhat smaller — 1.0% — than the effect on limited-service

restaurants, which is 1.6%.6

The 0.16 elasticity of limited-service restaurants’ first-quarter payroll costs with respect

to the minimum wage is practically identical to our estimate in the previous section of the

increase in the average worker’s wage, although the latter does not include any benefits

and is not hours-weighted. (An hours-weighted estimate would be somewhat lower, since

the elasticity of part-time workers’ hours is much larger than that for full-time workers.)

One possibility is that although full-time workers’ wages adjust only modestly in response

to minimum-wage increases, restaurants use other margins, such as paid sick leave or other

benefits, to undo the resulting wage compression.

These estimates are all strongly statistically significant, and suggest that fast-food work-

ers’ wages are closely tied to the minimum wage. The fact that we find strong effects using

both the monthly CPS data and the annual CBP data further suggest that the variation we

have in effective state minimum wages over this time period is sufficient for identification,

an important concern when it comes to estimating the effect of minimum wages on fast-food

prices.

6The coefficients on current and lagged gross state product (not shown) are all positive and statisticallysignficiant. The elasticity of restaurant payroll with respect to current GSP ranges from 0.29–0.45, dependingon the specification, and is significant at the 1% level in all specifications. The elasticity of restaurant payrollwith respect to lagged GSP is slightly smaller, ranging from 0.16–0.37, significant at the 1% level except inone specification where it is significant only at the 10% level.

16

6 Effect of Minimum Wage on Fast-Food Prices

For each of the three fast-food items, we estimate

ln(price)it = αi + δt + βitimet +∑S

γs ln(minwageis) + ρ0 ln(GSPj,y(t))

+ ρ1 ln(GSPj,y(t)−1) + εit (3)

where priceit is the price in city i at the beginning of quarter t; αi is a city fixed effect, δt

is a time fixed effect, βi is a city-specific linear trend, and minwageis is the minimum wage

in city i at the beginning of quarter s. We include a one-quarter lag and a one-quarter lead

of the minimum wage as well as the current-period minimum wage. Table 6 presents the

coefficients γs from the above regression.

We find that McDonald’s burger prices and Pizza Hut pizza prices increase by about

0.9% for every 10% increase in the effective minimum wage; the first of these is highly

significant, while the second is significant only at the 10% level. In the case of KFC fried

chicken prices, the point estimate is negative, but the standard error is very large and does

not preclude positive as well as negative and zero price effects.

When we add leads and lags, we find that the increase in McDonald’s prices is concen-

trated in the quarter of the minimum-wage increase. About two thirds of the effect of the

minimum wage on pizza prices is in the quarter of the increase, with the remainder of the

increase delayed by a quarter. The coefficients are jointly significant in both regressions, but

much more so in the first. The KFC regression does show a positive correlation between the

current minimum wage and the current price, but it is flanked by two negative coefficients

in the leading and lagged quarters.

We have extended the leads and lags by one more quarter in supplementary regressions,

not shown. These estimates are noisier, but all produce positive contemporaneous-effect co-

efficients, with point estimates ranging from 0.02 (fried chicken) to 0.09 (burger). Estimates

17

of the coefficients on the two-quarter lead and lag were generally small and measured very

imprecisely, with the exception of the two-quarter lag of the minimum wage in the pizza

regression, which is both large (0.09) and statistically significant at the 1% level.7

These results are broadly consistent with the results of Aaronson’s (2001) regressions

in which he regressed log prices on the current minimum wage, a one-month lead, and a

one-month lag, using year and quarter fixed effects rather than a full set of time effects

(interactions of year and quarter), over an earlier period, and with a significantly smaller

dataset (roughly 3,000 observations compared to about 18,000 used here).

To get a sense of the magnitude of these figures, if input markets are competitive and

demand at an individual fast-food outlet has a constant elasticity ε, then price is a constant

markup over marginal cost(p = ε

ε−1c), and the marginal-cost elasticity of price

(dpdc

cp

)is

1, so a 0.9% increase in price implies a 0.9% increase in marginal cost at McDonald’s or

Pizza Hut in response to a 10% increase in the minimum wage.8 Given our estimates that

payroll costs increase by about 1.7%, this suggests a 50% labor share of costs, even higher

than the 30% labor share at limited-service restaurants reported by Aaronson, French, and

MacDonald (2008), which implies that these fast-food chains are passing the full increase

in their costs. As explained in a series of papers by Aaronson and coauthors, this effect

is inconsistent with substantial monopsony power in the low-wage labor market (Aaronson,

2001; Aaronson, French, and MacDonald, 2008; Aaronson and French, 2007).

7 Concluding Remarks

We find robust, economically meaningful, and statistically significant effects of changes in

the effective minimum wage on restaurant-workers’ wages and on restaurants’ labor costs,

7Unlike in the wage and payroll regressions, neither the log of current and nor the log of lagged per-capitaGSP are ever statistically significant in the price regressions.

8This calculation makes no assumptions about the magnitude of the elasticity of demand, which may besmall or approach infinity, but it does assume the elasticity is locally constant.

18

particularly for limited-service restaurants which encompass the fast-food sector. Our results

for prices are somewhat weaker and less robust, but overall consistent with a competitive

labor market. We show that prices of both McDonald’s burgers and Pizza Hut pizza increase

with the minimum wage, and that these increases are quite large, amounting to roughly

50% of the increase in payroll due to the minimum-wage increase. We cannot reject price

elasticities of 0.07 across all products, as reported by Aaronson (2001): two of our point

estimates, for McDonald’s burgers and Pizza Hut pizza, are somewhat higher than that

0.09, and the third point estimate is negative but very imprecise.

The rejection of the monopsony model for the 284 cities in the current sample is, in a

way, not surprising. These are all cities with at least one McDonald’s, at least one KFC or

Church’s Chicken, and at least one Pizza Hut or Pizza Inn outlet. In other words, at the

very least, there are three outlets competing for low-wage workers in the fast-food sector,

and most likely more than three. (Since most outlets are franchised, the fact that KFC and

Pizza Hut share a parent company is unlikely to limit their outlets’ competition for workers.)

The extent to which this result holds in smaller towns with only a subset of these outlets is

an open question.

Finally, we should offer a caution that the predictive power of our estimates is limited

to the range over which we identify these estimates. The real minimum wage, in Decem-

ber 2012 dollars, ranges in our sample from $5.68 to $9.30, with the interquartile range

roughly between $6.50 and $7.50. We cannot extrapolate the results to predict the effect of

a minimum-wage hike far above or below this range, except to say that if the minimum wage

falls far below the current range its effect will likely be diminished as it loses its bite; if the

minimum wage were to increase to, as some have suggested, $15 per hour, we would expect

its effect on all outcome variables to be magnified.

19

References

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Aaronson, D., and E. French (2007) “Product Market Evidence on the Employment Effectsof the Minimum Wage,” Journal of Labor Economics, 25(1), 167–200.

Aaronson, D., E. French, and J. MacDonald (2008) “The Minimum Wage, Restaurant Prices,and Labor Market Structure,” Journal of Human Resources, 43(3), 688–720.

Ater, I., and O. Rigbi (2012) “Price Control and the Role of Advertising in FranchisingChains,” unpublished paper, Tel Aviv University.

Baskaya, Y. S., and Y. Rubinstein (2013) “Using Federal Minimum Wages to Identify theImpact of Minimum Wages on Employment and Earnings across the U.S. States,” unpub-lished paper, London School of Economics.

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Card, D. E., and A. Krueger (1994) “Minimum Wages and Employment: A Case Study ofthe Fast-Food Industry in New Jersey and Pennsylvania,” American Economic Review,84(4), 772–793.

(1995) Myth and Measurement: The New Economics of the Minimum Wage. Prince-ton University Press, Princeton, NJ.

Chevalier, J. A. (1995) “Do LBO Supermarkets Charge More? An Empirical Analysis of theEffects of LBOs on Supermarket Pricing,” Journal of Finance, 50(4), 1095–1112.

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Choi, C.-Y., and J. Wu (2012) “Understanding Retail Price Differences across U.S. Cities:A Tale of Two Frictions,” unpublished paper, University of Texas at Arlington.

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Dube, A., S. Naidu, and M. Reich (2007) “The Economic Effects of a Citywide MinimumWage,” Industrial and Labor Relations Review, 60(4), 522–543.

Frankel, D. M., and E. D. Gould (2001) “The Retail Price of Inequality,” Journal of UrbanEconomics, 49(2), 219–239.

Katz, L. F., and A. B. Krueger (1992) “The Effect of the Minimum Wage on the Fast-FoodIndustry,” Industrial and Labor Relations Review, 46(1), 6–21.

20

Krueger, A. B. (1991) “Ownership, Agency, and Wages: An Examination of Franchising inthe Fast Food Industry,” Quarterly Journal of Economics, 106(1), 75–101.

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Neumark, D., J. M. I. Salas, and W. Wascher (forthcoming) “Revisiting the Minimum Wage-Employment Debate: Throwing Out the Baby with the Bathwater?,” Industrial and LaborRelations Review.

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Parsley, D. C., and S.-J. Wei (1996) “Convergence to the Law of One Price without TradeBarriers or Currency Fluctuations,” Quarterly Journal of Economics, 111(4), 1211–1236.

Wimmer, B. S. (1996) “Minimum-Wage Increases and Employment in Franchised Fast-FoodRestaurants,” Journal of Labor Research, 17(1), 211–214.

Yelowitz, A. S. (2005) “How Did the $8.50 Citywide Minimum Wage Affect the Santa FeLabor Market? A Comprehensive Examination,” unpublished paper, University of Ken-tucky.

21

Figure 1. Federal Minimum Wage, 1993–2013 (December 2012 Dollars)

(a) McDonald’s Burger (b) Pizza

(c) KFC Fried Chicken

Figure 2. Mean and Inter-Quartile Range of Real Price Series, by Product(December 2012 Dollars)

22

Table 1. Number of U.S. States affected by Federal Minimum-Wage Hikes

Full Partial Other NoYear Effect Effect Pega Increaseb Effect

1996 39 2 2 1 61997 39 4 2 1 42007 20 0 0 5 252008 18 7 0 5 202009 23 8 0 2 17Excludes Washington, DCa State minimum wage increases automatically

to stay above the federal levelb Concurrent increase not required to comply

with federal increase

23

Tab

le2.

Min

imum

-Wag

eIn

crea

ses,

by

Sta

te,

1993

–201

2

Excl

udin

gC

han

ges

Excl

udin

gC

han

ges

wit

hin

6M

onth

sof

wit

hin

6M

onth

sof

All

aF

eder

alIn

crea

seA

lla

Fed

eral

Incr

ease

Ave

rage

Ave

rage

Ave

rage

Ave

rage

Num

ber

ofP

erce

nt

Num

ber

ofP

erce

nt

Num

ber

ofP

erce

nt

Num

ber

ofP

erce

nt

Sta

teIn

crea

ses

Incr

ease

Incr

ease

sIn

crea

seSta

teIn

crea

ses

Incr

ease

Incr

ease

sIn

crea

seA

K5

10.6

126

.5M

T9

6.9

38.

3A

L5

11.3

0N

C5

11.4

119

.4A

R5

11.4

121

.4N

D5

11.3

0A

Z7

9.1

312

.2N

E5

11.3

0C

Aa

88.

34

9.9

NH

511

.30

CO

88.

03

12.8

NJ

49.

82

17.8

CT

125.

78

5.0

NM

a6

10.0

0D

E9

6.2

49.

0N

V7

10.0

214

.3F

L9

6.9

48.

4N

Y6

9.4

311

.6G

A5

11.3

0O

H7

9.3

312

.8H

I4

8.4

48.

4O

K5

11.3

0IA

412

.70

OR

125.

38

4.0

ID5

11.3

0P

A5

11.5

121

.4IL

88.

83

9.4

RI

77.

65

7.5

IN5

11.3

0SC

511

.30

KS

511

.30

SD

511

.30

KY

511

.30

TN

511

.30

LA

511

.30

TX

511

.30

MA

611

.14

12.4

UT

511

.30

MD

511

.41

19.4

VA

511

.30

ME

105.

95

5.6

VT

154.

710

5.4

MI

512

.31

35.0

WA

155.

212

5.5

MN

511

.41

19.4

WI

79.

42

12.4

MO

69.

61

26.2

WV

511

.31

13.6

MS

511

.30

WY

511

.30

Tot

al32

110

1E

xcl

udes

Was

hin

gton

,D

C.

The

stat

em

inim

um

wag

eis

the

max

imum

ofth

efe

der

alan

dst

ate

leve

la

Does

not

incl

ude

city

min

imum

-wag

ein

crea

ses

inSan

Fra

nci

sco,

Alb

uquer

que,

and

San

taF

e

24

Table 3. Minimum-Wage Increases, by Year, 1993–2012

Excluding Changeswithin 6 Months of

All a Federal IncreaseAverage Average

Number of Percent Number of PercentYear Increases Increase Increases Increase1993 0 01994 1 15.3 1 15.31995 2 7.6 2 7.61996 a 3 8.0 2 8.71997 a 7 7.6 01998 2 10.4 1 11.71999 6 9.5 6 9.52000 5 11.0 5 11.02001 5 7.5 5 7.52002 5 7.3 5 7.32003 6 9.1 6 9.12004b,d 7 4.8 7 4.82005b 10 11.5 10 11.52006b,d 15 11.2 15 11.22007 a,b 28 13.8 19 14.22008 a,b,c 21 4.1 02009 a,b,c,d 15 5.9 02010b 5 4.4 2 6.22011b 7 1.4 7 1.42012b,d 8 4.2 8 4.2Excludes Washington, DC, federal, and city-level increases.

The effective minimum wage is the maximum of thefederal and state level

a Year with a federal minimum-wage increaseb Year with minimum-wage increases in San Francisco, CAc Year with minimum-wage increases in Albuquerque, NMd Year with minimum-wage increases in Santa Fe, NM

25

Table 4. Restaurant Wages as a Function of State Minimum Wages

All Part-Time Full-Time All Part-Time Full-Time

ln(minwaget+3) 0.0450 0.0067 0.0342(0.0531) (0.0404) (0.0838)

ln(minwaget) 0.1582*** 0.2272*** 0.0705 0.0847 0.1828** 0.0491(0.0303) (0.0338) (0.0436) (0.0893) (0.0727) (0.1704)

ln(minwaget−3) 0.0358 0.0437 -0.0147(0.0624) (0.0703) (0.1138)

F testa 9.7436 18.3966 0.9772p value 0.0000 0.0000 0.4111Sum of minimum-

wage coefficients 0.1655 0.2332 0.0685F testb 28.8941 46.9504 2.7593p value 0.0000 0.0000 0.1031Observations 125,353 62,179 49,742 122,111 60,373 48,485Unit of observation is a worker-monthAll regressions include state and time FE, state-specific linear trends, and current

and lagged gross state productRobust standard errors in parentheses, clustered by state* p<10%; ** p<5%; *** p<1%a Test for joint significance of minimum wage variablesb Test for significance of sum of coefficients

Table 5. Restaurant Payroll as a Function of State Minimum Wages

Full-Service Limited-ServiceAll Restaurants Restaurants Restaurants

Annual Q1 Annual Q1 Annual Q1

ln(minwaget) 0.1242*** 0.1318*** 0.0941** 0.1047*** 0.1340*** 0.1622***(0.0372) (0.0318) (0.0354) (0.0370) (0.0482) (0.0452)

Years 1993–2011 1993–2011 1998–2011 1998–2011 1998–2011 1998–2011Observations 950 950 700 700 698 698Unit of observation is a state-yearAll regressions include state and time FE, state-specific linear trends, and current

and lagged gross state productRobust standard errors in parentheses, clustered by state* p<10%; ** p<5%; *** p<1%

26

Table 6. Fast-Food Prices as a Function of State Minimum Wages

Burger Chicken Pizza Burger Chicken Pizza

ln(minwaget+1) 0.0212 -0.0325 -0.0138(0.0256) (0.0505) (0.0289)

ln(minwaget) 0.0935*** -0.0500 0.0941* 0.0930* -0.0023 0.0615(0.0221) (0.0620) (0.0523) (0.0473) (0.0585) (0.0478)

ln(minwaget−1) -0.0207 -0.0256 0.0532(0.0420) (0.0799) (0.0327)

F testa 6.0939 0.2433 2.1830p value 0.0014 0.8657 0.1031Sum of minimum-

wage coefficients 0.0935 -0.0603 0.1009F testb 15.1324 0.6365 3.2421p value 0.0003 0.4292 0.0785Observations 18,118 18,118 18,118 17,888 17,888 17,888Unit of observation is a city-quarterAll regressions include city and time FE, and city-specific linear trendsRobust standard errors in parentheses, clustered by state* p<10%; ** p<5%; *** p<1%a Test for joint significance of minimum wage variablesb Test for significance of sum of coefficients

27