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; [email protected]
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.
2
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
7
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.
11
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
13
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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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