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How Do Retail Prices React
to Minimum Wage Increases?
James M. MacDonald
and Daniel Aaronson

Working Papers Series
Research Department
Federal Reserve Bank of Chicago
December 2000(WP-00-20}

FEDERAL RESERVE BANK
OF CHICAGO

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How Do Retail Prices React to Minimum Wage Increases?

James M.MacDonald
Daniel Aaronson*
December 2000

*Economic Research Service, U.S. Department of Agriculture, and Federal Reserve Bank
of Chicago, respectively. Work was performed under a memorandum of understanding
between the Economic Research Service and the Bureau of Labor Statistics(BLS), which
permitted access to the confidential BLS data used in this paper. We thank Bill Cook and
Scott Pinkerton of BLS for their advice and help, and thank Chin Lee and Gerald Schluter
for comments on earlier drafts. The views expressed herein are not necessarily those of
the U.S. Department of Agriculture, the Bureau of Labor Statistics, the Federal Reserve
Bank of Chicago or the Federal Reserve System.

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How Do Retail Prices React to Minimum Wage Increases?

A textbook consequence of an industry-wide cost shock is that it will be passed on
to consumers through an increase in prices. The minimum wage offers a compelling
natural experiment of such a cost shock, particularly among industries that employ lowwage labor.
We assess the effect of recent minimum wage increases on restaurant prices,
using specific item prices collected by the Bureau of Labor Statistics(BLS). We find that
price responses follow textbook expectations in several dimensions. First, restaurant
prices rise, by amounts that are broadly consistent with the modest costs imposed by
minimum wage increases. Second, prices respond rathez quickly, within asix-month
window around the wage incxease. Third, price increases are greater among fastfood
outlets and in low-wage locations, where minimum wage increases would be expected to
have greater effects on costs.
But other elements of the price response are more complicated. A restaurant does
not raise all of its prices by announts reflecting the costs of minimum wage increases
{from 0.3 to 1.8 percent, depending on outlet type and location). Rather, it raises fewer
prices(up to 25 percent of its items), but by 3 to 6 percent,on average. That response
suggests that there maybe some item-specific costs to changing price, or that demand
elasticities vary across items. Furthermore, we end that items at certain prices (such as
fastfood items with prices ending in 99 cents) are less likely to be raised in the face of a
minimum wage increase, and that outlets with recent price reviews are less likely to
respond to minimum wage changes.

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Ttus pattern of restaurant price responses relates closely to notions of price
stickiness. As has been known since Keynes,the dynamics of a price change can be
complicated; in particular, prices z~nay not react instantaneously to cost changes. Figure 1
exemplifies the issue; it plots monthly changes in the Food Away from Horne component
of the Consumer Price Index(CPn from 1995 to 1997, along with Producer Price Indexes
for two key restaurant inputs: pork and ground beef. While input prices are quite volatile,
the Away from Home CPI moves slowly and ~x~ethodiECally. Figure 1 suggests that prices
may respond very slowly to cost changes, and the literature suggests conditions under
which prices may not react at all. We present further evidence of sluggish price changes
in our analysis below and relate those findings to other firm level results on nominal price
rigidity. We then contrast the usual co-movements of prices and costs with the rapid and
full response to a laxge, national, well-identified cost shock like the minimum wage.

Recent Analyses of Minimum Wage Effects on Prices
Recently, Card and Krueger(1995), and Aaronson (2001)analyzed the impact of the
1980s and i990s minimum wage legislation on restaurant prices.l Cazd and Krueger(1995)
used their own surveys of fast food outlets in New Jersey and Pennsylvania, and reported
that prices increased in New Jersey outlets, but not in Pennsylvania's, after an increase in
New Jersey's minimum wage. But within New Jersey,they did not find larger increases in
areas that were more likely to be affected by the changes. They did, however,find the
expected incidence at the national level when they looked at 1990 and 1991 Federal
minimum wage changes. Using the Food Away from Home CPI indexes for 271arge
metropolitan areas, Card and Krueger found that prices rose more, between 1989 and 1992,

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in those cities with higher proportions oflow-wage workers in 1989. But the before-andafter nature of their comparisons meant that they could not evaluate issues oftiming, and
their limited data generated improbable estimates of magnitude.2
Figure 2 updates Aaronson's(2001)findings, based on the CPI Food Away from
Home index for 1978-973 The graph reports regression-adjusted price changes in the four
months surrounding a minimum wage increase. The solid line captures the mean predicted
change, while the dashed lines include the 95%confidence interval. Prices rise by
statistically significant amounts in periods around minimum wage increases, and the
increases approximate the expected cost effects o£ the minimum wage changes. The striking
feature of the graph concerns timing: most of the pz~ce response occurs within a month or
two of the minimum wage change. Despite the fact that minimum wage laws are often
passed well before the enactment date, there is only a sma11 price increase in arxticipation of
the "event," and no additional price reaction at long lags.

Price Data
Our dataset is the distinctive feature of our study. We use prices sampled over a
three year period (January, 1995 through December, 1997) to construct the Food Away
from Home component of the CPI. BLS Feld personnel collected prices for nearly 7,500
food items at over 1,000 different outlets. Outlets were drawn from 88 Primary Sampling
Units(PSUs), which in turn included 76 Metropolitan Statistical Areas and 12 other areas
representing the urban non-metro United States. PSUs were assigned to one of three
reporting cycles; outlets in the eve largest were surveyed each month, while others were
surveyed in two bimonthly cycles of odd and even numbered months. Because most
prices were collected bimonthly, we compaze price changes over two month periods.a
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We make use of a BLS "type of business" code assigned to each outlet. In
"limited service"(LS)outlets meals are served fox on or off prer~ses consum~t~on,
patrons typically pay at the register before they eat, and patrons do not typically place
orc}ers while seated at a table, booth, or counter. In "full service"(FS} outlets, food is sold
primarily for on premises consumption, waiter/waitress service is provided, orders are
taken while patrons are seated at a table, booth or counter, and patrons typically pay after
they eat.'~Ve group all other outlet types, such as schools, department stores, convenience
stores, gas stations, ox vending machines,into an "other" category. Full service outlets
account fox about half of atl price quotes, while LS outlets account for about 29%.S IS
outlets employ higher proportions of teenage and unskilled workers; thus their prices
should be more sensitive to minimum wage increases.
Enumerators price multiple items at most outlets—about half of the sample's
outlets have seven price quotes, and about a third have eight. Once an outlet is selected,
specific items are selected for pricing with probability proportional to sales. During our
1995-97 period, an "item" most commonly was a meal, as BLS aimed to price complete
meals as typically purchased at an outlet (for example, a meal item at an LS outlet might
consist of a hamburger,french fries, and a soft drinl~). Our dataset codes items broadly, as
breakfast, lunch, dinner, or snacks (corresponding to BLS "entry level item" codes}.
Because we did not obtain exact meal descriptions for our dataset, we can't compute
meaningful measures of average prices across outlets. But BLS strives to price identical
items over time, and codes in our database describe temporal item substitutions due to
discontinuances and alterations. Our ana.rysis focuses on price changes for identical
items; we do not use price comparisons where one quote reflects BLS item substitution6

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Aaronson {2001) and Card and Krueger(1995} used published BLS price indexes
for 271arge metro areas, whereas we use actual prices in $8 urban and metro areas.
Consequently, we can assess more observations over a widen zar~ge of labor market
conditions. We can also identify prices in LS outlets, whereas published price indexes
cover all outlets. But the database has some clear limitations. Because BLS introduced a
complete outlet and item resampling in January 1998, we only use data through
December 1997, and cannot look for long tags in response to the September, 1997
Federal minimum wage increase. And because our dataset contains no specific item
descriptions, we cannot tie price changes to item-specific measures of input price changes
(such as ground beef or chicken price indexes).

Characteristics of the data
We report descriptive statistics to introduce this distinctive data, to provide
support for some of our modeling decisions, and to compare restaurant pricing behavior
to evidence of nominal price rigidity reported in other firm-level data sets.

Frequency of price changes
Prior studies of nominal pricing rigidities often fnd that prices remain fixed for
long periods(Caziton 1986, Cecchetti 1986, Kashyap 1995). That pattern is likely to hold
in restaurants, where firms often review prices at regular time intervals, and change item
prices only at those intervals (Hershey,2000). If restaurants change prices only at
designated review times, then we will need to account for the possibility of Lead and
lagged responses to minimum wage increases.

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Consider figure 3, which shows the proportion of prices in our dataset that
remained unchanged in each bimonthly period. Full service meals show remazkable
stability—on average, $7.4 percent of prices remained unchanged in any two month
period, and that share shows little vaziation over time. Aside from the sharp changes
immediately following minimum wage increases in 1996 and 1997, LS rxxeal prices also
appear to be quite stable; across all outlet types and bimonthly comparison periods, 86.6
percent of prices remained unchanged.
Another way to look at the incidence of price changes is to consider a price's
typical life span. Duration descriptions are limited by the short sample period (with
resultant left and right censoring of duration measures), but we can still offer some useful
information. We looked at LS items whose prices increased just after the minimum wage
increase in October 1996. Ten months later, 56% of those new prices remained
unchanged. The pattern was quite similar among items that didn't change price just after
the minimum wage increase: 49°70 of those prices remained unchanged 10 months later.
The duration measures are consistent with figure 3, and the findings together suggest that
the half-life of an LS restaurant price in this low-inflation period was about 10 months~

The distribution of price changes
During months without a minimum wage increase, the mean bimonthly saznplewide price change was 0.37 percent, and varied little (from 0.36 to 0.38) across outlet
types. During months with a minimum wage hike, the mean price change more than
doubled in limited service outlets, rising to 0.88 percent, but rose only slightly (to 0.45}
in ail other outlets. We return to quantifying this minimum wage effect later in the paper.

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Since most prices are unchanged in bimonthly comparisons(figure 3), samplewide price increases must combine zero increases for mQSt items with substantial mean
price increases among Ehose items whose prices change.$ Table 1 reports on the size
distribution of price changes. The top panel summarizes the results for linnited service
restaurants, while the bottom panel covers full service. Columuns report data separately for
price increases and decreases, and for months with and without minimum wage changes.
Several features are noteworthy. First, there are both "large" and "small" changes
in price but increases are clearly skewed to the right. Among limited service outlets,
roughly one-quarter of all price increases are less than 2 percent and an additiona130
percent are between 2 and 4 percent. A tail of larger increases raises the mean price
increase, conditional on an increase, to 5.3 percent when minimum wages aren't
increasing and 4.8 percent when they aze. Similar magnitudes obtain among full service
outlets: just over half of all increases are less than 4 percent, while mean increases are
just under 5 percent9
One-ninth of price increases exceed IO percent, with a somewhat smaller
incidence during minimum wage increases. At first glance, these wide distributions seem
in contrast to some(S,s} models that predict firms will move when a threshold between
optimal and actual prices is crossed. However,it is possible that the cost of changing
prices varSes cross-sectionally ox should be modeled as a random variable as in Caballero
and Engel(1999), where the cost of changing prices is sometimes low,leading to small
price adjustments, and sometimes high, leading to large price adjustments.
Consistent with cross-sectional vaziation in (S,$) pricing, the mean size of price
increases is remarkably stable over our short time frame. Figure 4 reports mean

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bimonthly price increases between March 1995 and December 1997, among quotes
increasing price. Aside from a sharp spike among limited service outlets in the summer
of 1997 related to large increases in bacon usage and pork belly prices, the mean
bimonthly price increase stays within a range of4 to 6 percent. The standard deviation of
bimonthly mean price increases is 0.5 percent for full service outlets and 1.3 percent for
limited service outlets, with the bacon episode accounting for half the difference in
standard deviations. Furthermore, there's little change in the distribution of LS price
increases when minimum wages are increasing, and Iater analyses find no minimurx~
wage effect on the mean size of price increases.j0 Lastly, although price cuts are rare,
they tend to be larger in magnitude than price increases (table 1}. Over one quarter of
price cuts exceed 10 percent, and cuts frequently exceed 20 percent. Although small
price cuts occur, less than half are below 4 percent
Because mean price increases vary little from month to month, the evidence
suggests that firms may respond to industry-wide cost shocks by adjusting the likelihood
of price increases rather than their size. Moreover, because many price cuts reflect sales,
and hence are of larger size but limited duration, we may need to explicitly account for
prior price changes in our later modeling of rrunimum wage effects.

Across- and within-store synchronization
Az~ important feature of modeling the inflation process is the mechanism by which
prices move together. The sticky price literature generally assumes that price changes are
staggered across stores but synchronized within-stores. Lath and Tsiddon (1992,1996}
confirm this pattern using micro data from the food component of the Israeli CPI.

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Kashyap(2995)notes that catalog price changes are not synchronized with common price
shocks,such as changes in the money supply, suggesting prices are staggered acrossstores. Although we do not provide any formal tests, same simple descriptive statistics
suggest that staggering probably occurs both across- and within-stores in the U.S.
restaurant industry.
Figure 3, which shows the monthly proportion of quotes that do not change price
far the previous two months, provides evidence of across-store staggering. If stores
pez~ectly synchronized their price changes and price durations over 10 months, we would
erect to see a series of eight Os followed by a 1. This would imply a standard deviation
of 33 percent, approximately 7 to 16 times the size of the standard deviations that we see
in the data. But instead, figure 3 shows remarkable stability in the share of no-change
quotes. The standard deviation of this proportion is 4.8 percent for limited service outlets
(2.8 percent without the minimum wage nnonths included) and 2.0 percent for full servit;e
outlets. For this pattern to occur, and given the price durations noted above, price changes
must be staggered, not synchronized, across oatlets.
We looked at within–store staggering by looking at price changes among those
outlets with many quotes--7 or 8 price comparisons in a bimonthly period. Since most
sample outlets have 7 or 8 quotes, we still had a lot of data, 8,809 outlet-mornths and
b4,750 bimonthly price comparisons. Table 2sorts the data by outlet type, number of
quotes, and time (whether or not a minimum wage is increasing}, and for each category
reports the distribution of within-outlet price increases. If perfect synchronization occurs
within stares, observations should bunch at zero and the maximum number of price
increases(7 ox 8). Price changes do bunch at zero—most outlets change no sample prices

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in most months, even when minimum wages increased. But when outlets do increase
prices, they cleazly stagger price increases across items—there's no cleaz bunching at the
maximum number of price increases.
Table 2 also provides some suggestive evidence of how prices change in response
to minimum wage increases. First, note there is little obvious change among FS outlets,
but a sharp change among LS outlets: far fewer LS outlets keep all prices stable during
rrunimum wage changes, ana far more LS outlets incxease many prices.

Price points
The final noteworthy element is the existence of psychological price points
(Kashyap 1995). Almost 12 pexcent of LS item Prices end in 99 cents. Over 30 pexcent
end in 9(about three times random chance). Only Z percent of full service restaurant
prices end in 99 cents, but 8 percent end in 95 cents and the three most common price
endings--95,00, and 25 cents-- encompass 20 percent of all observations.
Price changes also cluster: charges of 5, 10, 20, and 30 cents account for half of
all observed LS price changes(10 cents alone accounts for 26 percent), while those four,
plus 25 cents,5Q cents, and a dollar, account far half of all FS price changes.
Nominal price points suggest a discontinuous threshold whereby firms will lose
marginal revenue if prices are raised.i~ If price points aze important, we would expect to
see quote durations that are Ionger and price changes that are higher when starting from
these thresholds. Furthermore, critical masses for price changes suggest that outlets may
put off price changes until costs rise to a price changing point; if true, then price increases
will lag cost increases.

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Restaurant price data are generally consistent with other studies that report
evidence of nominal price rigidity. First, price durations are long and price changes are
rare. Second, the size of price changes varies cross-sectionally, with both small and large
price increases common,but the cross-section distributions are stable over our three year
period. Third, price staggering appears to occur both within- and across-stores. Finally,
the use of certain price points is common and, as we show later, results in long quote
durations and larger price increases. We next look more closely at how restaurants might
respond to increases in minimum wages.

Measuring the Effects of Minimum Wage Changes
President Clinton signed a bill raising the minizx~um wage on August 20, 1996,
after a debate that .lasted through the previous six months. The increase took place in two
stages: an October 1, 1996 increase from $4.25 an hour to $4.75 an hour(11.8%), and a
second increase l 1. rrxonths later, on September 1, 1997, to $5.15 (8.4%}.
Price responses to the change should display temporal and geographic variations.
Consider the timing of the 1996-97 Federat increase. When the law was passed (August
20, 1996) businesses knew minimum wages would be increased in 6 weeks {October 1,
1996) and again in a year(September 1, 1997). An outlet that reviewed prices quarterly
might raise prices just before a minimum wage rise, or it might wait until the next review
several months later.i2 Our azaalysis will consider lead and lag temporal effects.
Changes in the Federal minimum affect specific geographic areas in different
ways, because some states maintain higher minimums, and because prevailing market
wages vary widely. States with no minimum wage standards and those with minimums

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pegged to the Federal level experience the full change in the Federal minimum, while
states with minimums above the new Fedezal level remain unaffected by the policy
changes. Some State minimums fall between old and new Federal minimums; these states
experience increases that are less in percentage terms than the first group of states.
Finally, some State minimums changed during 1995-97, and businesses in those states
faced minimum wage increases at times that differed from other states.
We captare varying geographic effects by defining the measure MW,t:the
percentage increase in the effective minimum wage in state i during month t.13 Because
of State policies, percentage increases in the Federal minimum wage exceed sample mean
values of MW;t, which is 1Q.7'% in October, 1996, compared to the 11.8°Io Federal
increase, and 7.0% in September, 1997, compared to the 8.4% Federal increase.
Market wages can vary widely across and within states. In some areas, prevailing
low-skill wages may exceed minimum wages, and increases in the minimum wage should
have little effect on market wages for low-skilled labor, and consequently little effect on
costs and prices. Tn other regions, minimum wages may exceed market wages, and
changes in the minimum wage will have stronger effects on obsexved wages,costs and
prices. We expect increases in effective miniznuxn wages to have greater impacts on costs
and hence an prices in low-wage areas.
Our database includes unique outlet codes and precise outlet locations (addresses,
zip codes, and telephone numbers)so we can link items to outlets and we caz~ link outlet
records to related geographic information. We use data from the Current Population
Survey(CPS)to summarize hourly wage distributions in the outlet's Metropolitan
Statistical Area(MSA).14 We use observations for all of 1996 for each MSA,and

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calculate the distribution of MSA hourly wages. We use the 20`h percentile howly wage
(WAGE2Q)as our measure of low-skill wages in a metro area.

Model and Basic Empirical Results
Our basic statistical model can be summarized as follows:
1}

In (Pk~,t/Pk~.t-z)=f(PPI, IPRICE, MEALTYPE,MW).

Pk~,t is the price of the item k at outlet j in month t. The dependent variable is
therefore the percentage change in price over a bimonthly period. Table 3 details the
explanatory variables. PPI, a vector of two--month percentage changes in the Producer
Price Index for Processed Foods, measures input price shocks faced by sample outlets,
and specific measures include contemporaneous aswell asone- and two-period lags. The
vector IPRICE captures some dynamics of changes in the item's price over time,
reflecting pricing strategies as well as seasonal price patterns in restaurants. Some sale
prices fall by substantial amounts in our sample(table 1), and are likely to increase
substantially some time after the promotion. Similarly,items with price increases
frequently decline again at some later period, because the original increase may have
reflected seasonal cost increases or because rivals didn't match an increase. In order to
capture these dynamics, we enter a variable, IPUP,equal to the percentage increase in the
item's price in a previous period, ox zero if the price did not increase. Similarly,
IPDOWN is the percentage decrease in price in a pxevious period, or zero if there was no
decrease. We enter one-, two- and three-period lags on the item price variables.'s

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The BIS uses ELI {entry level item)codes in item selection, and Food Away
from Home codes identify dinner,lunch,snack, and breakfast/brunch items. We dropped
snack observations(a small part of the database}, since they were disproportionately
offered through unusual outlet types,like vending machines or gas stations. The model
includes dummy variables fox lunch and breakfast meals, with dinner as the base.
Finally, MW is the change {increase)in the effective minimum wage in period t at
outlet j. We also use lead and lag measures of MW to assess price effects one period
before and one period after periods including minimum wage increases. We found no
evidence of longer leads or lags, either with these two month periods or with analyses of
those prices at outlets that are surveyed monthly.
We present the basic analysis of price effects in Table 3, using ali outlets. BLS
generally collected several prices at an outlet in 1995-97(the mode is 7); because quotes
from the same outlet are unlikely to be statistically independent, standazd error
calculations in all analyses account for quote clustering, using Huber-White robust
estimation techniques. Adjustment matters here, as robust standard errors are generally
two to three times larger than OIS standard errors.
Item price effects are important and statistically significant in table 3; prior price
cuts lead to current period price increases, and past price increases indicate current price
cuts (responses aze dampened, however; full reversion to a previous price would imply
coefficient values, in absolute terms, of 1, while these fall well below I and usually
below 0.1). PPI effects are small--food is 20 percent of outlet costs, and outlets purchase
many different combinations offood products--while unreported meal type effects were
small and not statistically significant.

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We multiply all reported minimum wage coefficients and standard errors by 10,to
save space (results should therefore be read as the effects of 10 percent, rather than 1
percent, minimum wage increases). In equation 1 of table 3, the minimum wage effect is
positive and highly significant(t=5.09}, suggesting that outlet prices rise by 0.33 percent,
on average,for contemporaneous 10 percent increases in the minimum wage. Equation
{2) adds lead and lag effects for the minimum wage change (MW;,t_~ measures price
responses to prior period minimum wage increases while MW;,~+~ tests for price responses
to next period minimum wage increases). Each effect is positive; the one period lag effect
is highly significant(t=3.18}, while the one period lead is significantly greater than zera
at the 90%confidence level(t=1.72}. Lead and lag effects raise the estimate on the
contemporaneous effect, and the full effect is more than double that in equation 1,16

~'he Incidence of Minimum Wage Price Effects
Table 4 examines minimum wage effects in more detail, with models that account
for differences in outlet types and in prevailing area wages. The table reports minimum
wage effects, but the models retain all other explanatory variables in table 3.'~
Equations 1-3 explore outlet type effects, running regressions for full (equation 1)
and limited service outlets (equations 2 and 3). Full service minimum wage effects are
statistically significant but quite small in a specification that mirrors table 3's equation 2.
In contrast, the effects in limited service outlets are much larger(equations 2 and 3}. The
coefficient an MW;t in equation {2}, positive and highly significant(t=6.44), suggests that
LS prices rise by 0.8 percent in response to contemporaneous 14 percent increases in
minimum wages (four times the FS effect). Equation (3)lead and lag effects are positive,

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large, and statistically significant(t=2.25 for the lead effect and t=2.56 for the lag
effect).18 Again,inclusion of lead and lag effects raises the contemporaneous estimate.
Equations 4-6 add an interaction term between MW,~ and WAGE20,the hourly
wage at the 20~' percentile of an area's wage distribution. High values of WAGE20
should indicate high wage areas, which maybe less strongly affected by changes in the
minimum wage. In the all-outlet sample (equation 4)the main MW coefficient remains
positive and highly significant, while the interaction Term is negative, fairly large, and
statistically significant(t=2.07). Minimum wage increases have larger effects an pxices in
low wage areas. Among full service outlets (equation 5), the coefficients on MW and the
interaction term are of the expected sign but are only marginally significant.
Equation (6)adds the interaction term to the LS sample. As in the all-outlet
sample,IS wage effects are negative, substantive, and statistically significant(t=2.09).
Price effects are larger in low wage areas. Finally, equation(7)adds interaction terms
between WAGE20 and the lead and lag MW effects. Inclusion of those measures has no
effect on the contemporaneous effects. The Zagged effects have the expected sign
(positive on MW;,~_~ and negative on the interaction) but are not quite significant. The
lead effects lose all power,suggesting that there is no interaction and that inclusion of it
simply created multicollinearity with the main effect.

How Large are Minimum Wage Price Effects?
So far, we've found that restaurant prices increased when minimum wages rose,
that increases were larger among limited service outlets and in low-wage areas, and that
the response occurred in a six month window around the wage increase. The patterns

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suggest that minimum wages affect outlet costs in different ways, and that price changes
vary in response to those different cost effects. We have no information on factor shares
for the outlets in our sample, and no data that would a11ow for estimation of factorsubstitution possibilities. As a result, we can't directly estimate the effects of minimum
wages on outlet costs, and therefore cannot directly identify the extent to which costs are
passed through in the form of higher prices.
Instead, we rely on Lee and O'Roark's(1999) calculations of likely pass-through
under various assumptions about factor shazes, substitution possibilities, and firm
behavior. They used 1992 Current Population Survey wage and employment data, and
U.S. Input-Output tables, to calculate the direct and indirect(through purchases)shares of
low wage tabor in "eating and drinking places"{SIC 581}. Assuming full pass-through of
costs to prices, no substitution, and no spillovers of nninimum wage increases to other
wages, a 10 percent minimum wage increase would lead to a 0.74 percent price increase
among eating and drinking places in Lee and O'Roark's model (table 5).
Eating and drinking places include bars that serve no food, and our transactionbased sample includes supermarkets that serve food eaten on premises, schools, gas
stations, and the like. But there's still a wide overlap between the Food Away from Home
all-outlet sample and SIC 581, and there's a strikingly close correspondence between the
Lee-O'Roark estimate of the full pass-through price increase and our estimated price
increase for the all-outlet sample~.74 vs. 0.73 in table 5. We base our calculation on
the surri of the lead, lag and contemporaneous coefficients, the most comparable
approach.19 Further comparison can be found in Aaronson (2000) who used aggregated

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1978-95 BLS indexes to estimate along-run price response of 0.72, quite close to our
0.73 for disaggregated 1995-97 BLS data.
Table 5 also reports price effects by outlet type and by prevailing wage level. In
each case, we report short tun (current period)estimates as well as longer run estimates
that add lead and lag effects. LS price effects are more than twice as large as the a11-outlet
estimate—a 1.56 percent price increase in response to a 10 percent minimum wage
increase, white FS price effects are much lower,0.40 percent if we exclude the Lead effect
(negative and not significant). In turn, LS effects are larger still in low wage areas-1.83
percent when wages of 5.50 an hour set the area 20`~ percentile.

How Do Oatlets Raise Prices?
We've seen that prices at LS outlets rise by nearly 1.6 percent in response to a 10
percent inczease in minimum wages. Restaurants can arrange that increase in a variety of
ways. They could raise all prices by 1.6 percent, or they could raise fewer prices by
greater amounts. If they choose the latter, they've got to decide which item prices to
increase. We pursue that question in this section.
We begin by assessing the incidence of price increases, rather than the average
price response to minimum wage change. Table 6 reports minimum wage effects for the
LS sample, based on logit estimates of the model used throughout, but with the dependent
variable now a dummy variable set equal to one for price increases.
The contemporaneous minimum wage effect is positive and highly significant
(t=3.b7} in equation 1, while the lagged effect is positive and marginally signifcant
(t=1.76) and the lead effect is small and not significant. The wage interaction is

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statistically significant(t=2.29)and works in the expected direction, with a smaller
incidence of price increases,following a minimum wage increase,in high wage areas.
In equation (2), we investigate the linkage between discrete restaurant price
reviews and the formation of leads and lags in price responses. Most outlets change no
sampled prices in most months: that decision may reflect the underlying econorxucs, but it
may also reflect periodic rather than continuous price reviews. Tf price reviews are
periodic, then outlets that have recently changed prices may not arespond to a minimum
wage increase until another price review.
We define a variable P_REV,and set it equal to one if the outlet changed price for
any sampled item in the previous period (that is, prices were reviewed in the previous
period). We then interacted P_REV with each of our MW terms. The results are striking.
Minimum wage changes are less likely to lead to a price increase if the outlet had
reviewed prices in the period prior to the increase. In the lagged period,firms are
considerably mare likely to raise price if the outlet raised price just after the minimum
wage changes.20
Equations 3-6 explore the role of pricing points. We define a variable PP99 and
set it equal to one if the LS item price at the beginning of tie period ended in 99 cents.
The coefficient on PP99 in equation (3)is negative and quite significant—firms are
considerably less likely to raise a price ending in 99 cents. Equation {4)interacts PP99
with our minimum wage variable, and the results indicates that the logit coefficient on
PP99 changes very little when minimum wages increase. We add an interaction with the
actual value of the beginning period price in equation (5); the marginally significant price
suggests that restaurants are particularly unlikely to raise prices on low priced 94 cent

confidential

items (priced at 0.99 or 1.99). The last equation combines the PP99 and P_REV measures
and fnds that results are robust to the inclusion of

all.21

Because the coefficients in logit models can't be directly interpreted, table 7
reports the predicted probabilities of a price increase under different conditions. We set
the base probability of a price increase to that predicted on the basis of the intercept(no
changes in PPI or IPRICE measures over the previous several periods). The base
probability of a price increase is 10.3 percent for items that aren't price points, and 1Q
percent minimum wage increases have large contemporaneous effects, raising
probabilities to 23 and 33 percent in high and low wage areas, respectively. Probabilities
decline noticeably if the outlet had just had a prior period price review,to 16 and 24
percent, respectively. Lagged effects are of interest; if the outlet changed no prices in the
immediate wake of a minimum wage increase, the probability of a lagged increase is
quite small, 12.9 percent. But for outlets that responded immediately with price increases
on some items then there's a substantial lagged possibility(21 percent)of a pxice
increase. Price point of€ects are quite large. The probability that an item's price will be
increased if price ends in 99 cents falls by 35 percent, to 6.7 percent, in the base case.
While probabilities rise after a minimum wage incxease, price point items remain
substantially more stable than other items.
Table 8 considers the second aspect of price construction—how much are item
prices increased, when outlets decide to raise price? The sample is restricted to LS items
with price increases and the dependent variable is the log difference in prices. Equations
(1)and (2)support the cruder inferences drawn on figure 4 and table 2: minimum wage
effects have virtually no effects on the size of price increases, as the coefficients are small

confidential

and not nearly significant. As table 2 suggests, firms respond to minimum wage increases
by raising more prices, not by rraising prices more.
In equation (3), the coefficient on PP99 is positive and significant; when firms
raise prices on price point items, they xaase them by more, although the coefficient is not
that large (suggesting that PP99 item prices rise by 7.2 percent, on average). When we
interact PP99 with its actual beginning price, we find that typical percentage price
increases are larger among low-priced items (about 8.9% for 99 cent items, and 8.3 far
%a

$1.99 items, versus 5% for $G.99 items), when they are increased. Finally, we can see
that P_REV plays a role, too; price increases are somewhat smaller for those items with
prior month price reviews.22

Discussion
Food prices respond quickly to minimum wage increases; most of the observed
response(about b0%)occurs in the two-month period immediately after a minimum
wage increase, while the rest occurs in the periods two months after or two months
immediately preceding the wage increase. The timing is close to that observed by
Aaronson using aggregated BLS data over the 1978-95 period; he found that about 56%
of the price increase occurred in the two months immediately after the increase. Actual
price changes could occur even more quickly than our estimations suggest,if they occur
eazly in the lead and lag periods (footnote 18). Since other evidence indicates that
restaurant prices respond only slowly to cost changes, the minunum wage response
suggests that prices will respond quickly to common and permanent industry cost shocks.

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Prices rose by amounts that were predicted on the basis of full pass-through of
costs. While the finding does not prove that casts are fully passed through(wage
spillovers, along with less than full pass-through, would give the same result), it does
provide greater confBence for the results. Moreover,the incidence of increases accords
with what we know of the low-wage labor market,Prices rose far more in LS outlets than
in other restaurants, reflecting greater reliance on low wage labor in the former, and
prices rase more in low wage areas than in high wage areas.
Our paper's title implies an interest in the size, speed, and incidence of price
increases following minimum wage increases, but it also connotes an interest in the
mechanics of how restaurant increase prices. Our xesults imply that prices in limited
service restaurants zise by 1.56 percent, on average,in response to a 10 percent minimum
wage increase(and by 0.73 percent in the a11-outlet sample}. But outlets don't construct
that response by raising all prices 1.56 percent; instead they increase price by about S
percent, on average, on a much smaller set o€items. That pattern suggests one source of
stickiness, item-specx~c casts of changing prices. We found two other specific sources, a
marked reluctance to change price on items with prices at a psychological pricing point
(ending in 99 cents), and a temporally discrete approach to price review.
Despite evidence of stickiness, the rather small cost increases associated with
minimum wage increases did generate a quick aggregate price response. As a widely
publicized and permanent industry-wide cost shock, we suspect that minimum wage
changes do not generate the sort of coordination failures that other cost or demand
changes produce.

confidential

References
Aaronson, Daniel,2Q01, "Price Pass-Through and the M~iniznum Wage," The Reviex~ of
Economics and Statistics, forthcoming.
Card, David and A]an Krueger, 1995,Myth and Measurement: The New Economics of
the Manimum Wage,Princeton, NJ: Princeton University Press.
Carlton, Dennis,"The Rigidity of Prices," American Econorrcic Review (June, 1986): 637658.
Cecchetti, Stephen, 1986,"The Frequency of Price Adjustment: A Study of the
Newsstand Prices of Magazines," The Journal ofEcoraorrcetrics, pp. 255-274.
Hershey, Robert D., Jr.,"The Cost of Not Living On a $5.15 Minimuxr~", The New York
Times, September 19, 2000: p. C1.
Kashyap, Anil, 1995, "Sticky Prices: New Evidence Frorn Retail Catalogs," The
Quarterly Journal ofEconomics, pp. 245-274.
Lach,Saul and Daniel Tsiddon,"The Behavior of Prices and Inflation: An Empirical
Analysis of Disaggregated Price Data," J'ournad ofPolitical Economy (April, 1992}: 349389.
Lach,Saul and Daniel Tsiddon, "Staggering and Synchronization in Price-Setting:
Evidence from Multi-Product Firms," American Economic Review, (December, 1.996):
1175-1196.
Lee, Chinkook and 4'Roark, Brian,"The Impact of Minimum Wage Increases on Food
and Kindred Products Prices: An Analysis of Price Pass-Through," Economic Research
Service, US Department of Agriculture. Technical Bulletin No. 1877. July, 1999.
Rubin, Aiissa J.,"Populaz Minimum Wage Hike Gets Solid Senate Approval,"
Congressional Quarterly, July 13, 1996, pp. 1964-65.
U.S. Department of Labor, Bureau of Labor Statistics, ~landbook ofMethods,
Washington: U.S. Government Printing Office, 1997.
Weisman, Jonathan, "Republican Defectors Help Propel Minimum Wage Bill to
Passage," Congressional Quarterly, May 25, 1946, pp. 1461-54.

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Table 1: Bsmonthly Pxice Changes, by Outlet Type and Minimum Wage Change

Month w/ Min Wage ?
Type of Price Change
Limited Service Outlets
% of Monthly Quotes
Mean Price Change
Incidence of Price Changes
0-2%
2-4%
4-6°/a
6-8%
8-10%
10-20%
>20%
Full Service Outlets
of Monthly Quotes
Mean Price Change
Incidence of Price Changes
0-2%
2-4%
4-6%
6-8%
8-10%
10-24%
>20%

(1)
No
Increase

11.4%
5.31%
25.5
3U.6
Ib.8
9.4
6.3
7.7
3.8

10.8%
4.76%
32.7
25.1
15.3
1.~.1
6.2
9.2
1.6

(2
Yes
Increase

3)
No
Decrease

4
Yes
Decrease

22.4%
2.7%
8.36%
4.83%
--Share of Quotes(%)-29.3
22.8
18.3
~ 34.1
i I.8
18.9
7.0
8.0
6.7
4.b
I6.2
7.9
12.8
I.5

2.6%
8.I9%

11.2%
1.7%
7.45%
4.88%
--Share of Quotes(%)-25.3
26.2
19.8
27.6
12.2
18.$
11.3
10.7
7.4
7.1
17.1
8.4
1.2
6.8

1.7%
9.29%

24.5
15.3
15.3
11.2
6.t
18.4
9.2

21.4
143
8.9
9.8
12.5
21.4
11.6

Notes: A month with a minimum wage increase is one immediately (within two months)
following any state or federal change that effectively raised the minimum.wage in a state.

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Table 2: Incidence of Price Increases Among Outlets with Many Quotes

Price Quotes at Outlet
Minimum Wage T
?

(Z)
7
No

(21
7
Yes

t3)
8
No

(4)
8
Yes

Limited Service Outlets
Outlet-Months
Bimonthly Price Pairs
Number of Price Increases
0
1
2
3-5
6
7
8

1852
287
1070
162
12,964
2,009
8,568
1,296
-Share of Outlet-Months(%}77.1
60.3
67,5
59.3
7.6
6.6
12,1
14.2
3.3
5.6
5.7
4.9
6.7
12.5
9.7
11.1
1.2
4.5
2.1
1.2
4.2
10.5
1.7
5.6
1.1
3.7

Full Service Outlets
Outlet-Months
Bimonthly Price Pairs
Number of Price Increases
0
1
2
3-5
6
7
8

3086
505
1,578
259
21,602
3,535
12,624
2,152
-Share of Outlet-Months(%)80.3
S1.2
81.Q
?8.1
5.7
5.3
5.6
5.9
2.4
3.0
2.7
2.2
6.0
5.8
4.6
6.fi
1.8
0.8
1.1
1.9
3.8
4.0
2.0
1.9
3.0
3.3

Notes: Months with increasing minimum wages are October and November, 1996, and
Septennber and October, 1997.

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Table 3: Magnitude and Timing of Price Responses to Minimum Wage Increase
Description

Coefficients (s.e)
C2)
(~)
0.315
0.379
{0.032)
(0.026)

PPI~

%change in the Producer Price
Index for Processed Foods, period t

0.050
(0.029)

0.070
(0.030)

PPI~_~

%PPI change, period t-1

-O.Q70
(0.037}

-0.040
(0.038)

PPI~_Z

% PPI change, t-2

U.061
(0.028)

0.046
(0.030)

IPUPL_1

%increase in item price, one period
Iag; zero if no increase

-0.089
(0.013)

-0.090
(0.013)

IPDOWNt_~

%decrease in item price, one period
lag; zero if no decrease

0.388
(0.445)

0.389
(0.045)

IPUPt_Z

%increase in item price, two period
lag; zero if no increase

-O.U70
(0.012)

-0.070
(O.aI2)

IPDOWN~_2

%decrease in item price, two period
lag; zero if no decrease

0.096
(0.023)

0.096
(0.023}

IPUPt_3

%increase in item price, three
period lag; zero if no increase

-0.03b
(0.010)

-0.036
(0.009}

IPDOWNt_3

%decrease in item price, three
period lag; zero if no decrease

0.060
(0.030)

0.060
(0.034)

MW;~

%change in miuumum wage in state
i, period t.

0.331
(0.065)

0.4Q6
(0.063)

MW;,~_i

%minimum wage change, state i,
period t-1.

0.207
(0.006)

MW;,t+~

%minimum wage change, state i,
period t+1

0.115
(0.007)

Variable
Intercept

0.065
RZ
6$,887
N
Note: equations also included categorical meal type variables

confidential

0.066
68,887

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Table 4: Incidence of Price Response to 10% Minimum Wage Increase

Outlet Type
Variables
MW,t

C1)
FS

C2)
IS

0.191
(0.086)

0.845
(Q.125)

C3)
LS

(4)
All

~5)
FS

~6~
LS

(7}
LS

Coefficients and standard errors
0.937
1.449
1.048
(0.134)
(0.531)
(0.692)

2.b98
{0.883)

2.69fi
(4.883)

MW;,~_~

0.212
(0.083)

0.305
(0.119}

0.200
(0.068)

0.227
(0.088)

0.263
(0.121)

x.222
(Q.797)

MW;,t+F

-0.062
(Q.075)

0.319
(0.142}

0.124
{0.070}

-O.Q41
(0.080)

0.296
(O.15S}

-0.262
(0.810}

-0.162
{O.Q78)

-0.129
(0.099)

-0.278
(0.133)

MW;=*WAGE20

MW;,~.1*WAGE20

-0.147
(0.120)

MW ;,t+~*WAGE20

0.086
(0.128)

N
R2

35,759
.017

21,064
.151

21,Q64
.152

61,716
.Ob8

32,822
.Q18

18,024
.1 G4

Notes: Each regression also includes other variables listed in table 1. FS refers to full
service outlets, while LS refers to Iimited service outlets. MW;,t-1 and MW;>t+l refer to 1
period before and 1 period after minimum wage changes. All standard error estimates are
adjusted for nonindependence within outlets.

2s
confidential

28,024
.165

confidential

Table 5: Compazing estimates of the price effect of a 10% minimum wage increase
Authors
Lee O'Roark
Aaronson
MacDonald &Aaronson
&

Scope of estimate
All outlets
All outlets
All outlets (lr, sr)
Full Service (lr, sr)
Limited Service (lr, sx)
Limited Service, high wage (lr, sr)
Limited Service,low wage {lx, sr)

%Price Rise
0.74
0.72
0.73, Q.41
0.34, 0.19
1.56,0.94
1.31, Q.6S
1.83, 1.17

Notes: `Ir' refers to Long run response, while `sr' refers to short run (one period) response.
Hi wage cities have WAGE20 equal to 7.37 an hour, while low wage cities have
WAGE20 equal to 5.50 an hour(median of cities in top and bottom quartiles,
respectively.

29
confidential

confidential

Table 6: Logit Model Coefficients: Probability of Price Increase, Limited Service
Variables
(1)
2.939
(0.800)

Coefficients and standard errors
(2)
(3)
(4)
(5)
2.983
2.854
2.852
2.841
(Q.802)
(0.799)
(0.799)
(0.798)

{6)
2.896
(0.801}

MW;,t.~

0.3I3
{0.178}

0.220
(0.193)

0.308
{0.179)

0.308
(Q.179)

0.308
(0.179)

0.212
(.I94)

MW;,C+I

O.OSS
{0.176)

0.224
(Q.206)

0.048
(0.176)

O.Q49
(0.176}

0.214
(0.205)

MW;t*WAGE20

-0.2$4
(0.124)

-0.273
(0.125)

-0.272
(0.125)

-.270
(0.125)

-0.269
(0.124)

-0.260
(4.124)

~VIW;t

MWIt*P_REV

-Q.496
(0.278)

-0.482
(4.279)

MW;,=_~*P_REV

0.616
(0.313)

0.632
(0.301)

MW;,;+i*P_REV

-0.461
(0.304)

-0.454
(Q.305)

PP99

-0.485
(0.129)

PP99*MW;~

-0.4b4
(Q.137)

-x.726
(0.2Q4)

-0.482
(0.130)

-O.U98

~a.2g~>
PP99*P1

0.062
(0.03b)

Notes: Standard errors are in parentheses. Estirz~ates drawn from logit estimation; ;full
results reported in Appendvc. Dependent variable is dummy variable equal to one if price
increased in period. MW+1 and MW-1 refer to 1 period before and 1 period after minimum
wage change.P REV is a dummy variable equal to 1 if any outlet prices were changed in
the previous period. PP99 is a dummy variable equal to 1 for items with beginning prices
ending in .99. P1 is the beginning period price. WAGE20 is the 20~` percentile wage in
the sampling area.

confidential

Table 7: Predicted probabilities that an item's price will increase
Events
Price Review Last Period?
P1 ends in 99 cents?

(I)
No
No

(2)
Yes
No

{4}
Yes
Yes

(3}
No
Yes

Predicted Probability of Price Increase(%)
6.7
6.7
10.3
10.3

Base Probability
10% Minimum Wage Increase
In high wage area
In low wage azea

23.4
33.1

15.4
23.5

15.9
23.5

1 Period After Increase

12.4

21.Q

8.0

I4.1

1 Period Befoxe Increase

12.4

8.2

8.0

5.2

10.5
15.9

Notes: Predicted probabilities from logit model of price increase. Full model reported in
appendix table 1. Low wages are set to $S.SO an hour; high wages are set to $737.

confidential

Table 8: Minimum Wage Price Effects, Among Items that Raised Price, Limited Service
Variables
(~)
0.020
(0.338)

Coefficients and standard errors
~2)
(3)
(4)
0.750
0.949
x.837
(1.966)
(1.977)
(1.9b5)

(5)
0.646
{1.958)

MW,,~_i

-0.007
(0.431

-0.052
(0.494}

-0.076
(0.495)

-0.114
(0.493)

-Q.148
(0.495)

MW;,~+i

0.844
(0.589}

0.926
(0.696)

0.918
(Q.700)

0.876
(0.702}

0.940
(0.696)

-0.095
{0.314)

-0.123
{0.312)

-0.107
(0.309)

-0.0$4
(0.306)

MW;~

MW;~*WAGE20

P REV

-0.691
(0.303)

PP99

1.712
(0.448}

PP99*P1

R2
N

0.460
2841

0.476
2,316

0.483
2,316

4.083
(0.924)

4.195
{0,930)

-0.592
(0.165)

-0.620
(0.170)

0.487
2,316

0.489
2,316

Notes: All equations also include other variables listed in table 1. MW+1 and MW-~ refer
to 1 period before and 1 period after minimum wage change. P_REV is a dummy variable
equal to 1 if any outlet prices were changed in the previous period. PP99 is a dummy
variable equal to .l for items with beginning prices ending in .99. P1 is the beginning
period price. WAGE20 is the 20th percentile wage in the sampling area.

confidential

Figure 1: Comparing Changes in the Away Prom Home CPI and Meat PP3s
and beet — —

15~

1
If;

~, 3~

1~ ,

1 ls

.~ ~
-10 •

r■

~ rl

Jan-95 Apr-95

Jul-45

Oct-95 Jan-96 Apr-96 Jul-96

Oct-96 Jan-97 Apr-97

Jul-97

Oct-97

Figure 2: Response of Food Away from Home CPI to
Minimum Wage Increase, 1978-97

0.035
0.030
0.025
,~ 0.024
0
~
60

~.~15

Q.~~~

O.Ot?5
0.000
-0.005
-0.010
-0.015
t-4

t-3

t-2

t-1

t
Month

confidential

t+i

t+2

t+3

t+4

confidential

Figure 3: Percent of Items with Unchanged Price, Full and Limited Service Outlets

too

so
~ ~a
r0
K
W
so
ao
Mar-9S

Jun-95

Sep-45 Dec-95 Maz-96 !un-46

confidential

Sep-96

Dec-96 Mar-97 Jun-97

Sep-97 Dec-97

confidential

figure 4: Mean Price Increases, Fu21 and Lamited Service Outlets
'
" LS

1000

8.00

8 6.00
;c

4.00

2.00
Mnr-95 Jun-9S

Sep-95 Dec-95 Mar-96 Jun-96 Sep-96 Dec-96 Maz-97 Jun-97 Sep-97 Aec-97

confidential

'Price responses are critical for measuring the welfare implications of minimum wage
legislation.
2

They estimated puce increases of 3.3% fora 10% increase in minimum wages,

suggesting that low wage labor accounted for one third of restaurant costs, if cost
increases were fully passed through to prices. But total payroll accounts for less than 30%
of restaurant sales, according to Retail Census data. Low wage labor more likely
accounts for one sixth of costs (Aaronson, 2000).
3

The calculations, presented in Aaronson (2000), are based on the published monthly and

bimonthly Food Awa;+ from Home indexes for 27 U.S. cities.
4 To

maintain consistency in our price analyses, we randomly assigned outlets in.the five

largest PSUs to odd or even two month cycles. Counts are based on all itenns for which
prices were obtained in October or November, 1996,just after the October 1, 1996
increase in the Federal minimum wage. A more complete description of the sample's
construction, and particularly of outlet and item selection procedures, can be found in the
BLS Handbook ofMethods.
5

BLS replaced an old ordering with these type of business codes in July, 1996, and

began to report Food Away from Home price indexes for type of business groupings in
January, 1998. Businesses surveyed early in our sample period were assigned these codes
retroactively.
6

It's possible that firms could respond to a minimum wage increase by reducing quality,

instead of raising price. We don't have measures of quality, but our dataset notes whether
an item is the same as the item priced in the previous month,or whether BLS has had to

confidential

substitute another item. The data show no evidence of any increased incidence of item
substitutions following minimum wage increases.
~ The uaeasures are consistent in the following sense: if 93.3 percent of prices were
unchanged each month (implied by 86.6 percent bimonthly stability), and if the
probability of change was independent of the length of time that the price had been
unchanged, then 1 Q months later we would expect 50 percent of prices(1-0.933~°)to be
unchanged. With the ten month survival rates so close to 50 percent, the probability of
price change does appear to be independent of the length of time that it had been fixed.
$ Price changes are also highly clustered near the mean, with excess kurtosis of62.E and
80.8 for price changes among LS and FS outlets, respectively. Distributions of increases
and decreases are also quite peaked compared to normal distributions, with excess
kurtosis of 14.2(LS)and 8.6(FS)for increases, and 1..6(LS)and 6.8(FS)for decreases.
Kashyap(1995)also reports positive excess kurtosis in his sample.
9 By comparison, Kashyap (1995} reports that more than 20 percent of the price increases
in his study of catalog prices are less than 3 percent. His study looks at changes over six
month intervals rather than the two month intervals that we report.
10 We applied Kolmogorov-Smirnov D-tests fox differences in the price distributions in
table 1. We found no significant differences in price decreases in months with minimum
wage increases compared to other months, and no significant differences in the
distribution of increases among LS outlets. FS outlets with price increases do show a
statistically significant shift, driven by the higher incidence of 0.2% increases in months
without minimum wage increases.

confidential

"Price points maybe connected to coordination failures. In markets with a limited
number of sellers, prices are strategic complements. A seller maybe reluctant to raise a
price unless they're sure that other sellers will also raise price. That uncertainty may be
particularly pronounced for items with price points, if consumers are particularly
sensitive to changes in those prices.
1z Firms clearly had more foreknowledge of the 1997 increase. The 1996 increase could
not have been predicted until sk~ortly before the House of Representatives vote on May
23, after a week of legislative maneuvezing that almost consigned the bill to defeat
;'Neisman, 1996; Rubin, 1996}. Even then, the final timing didn't become•clear until
adoption of the conference report on August 2. Businesses therefore knew of the 1996
increase 2-4 months prior to implementation, while they knew of the 1997 increase,
which was specified in the 1996 bill, 12-13 months before implementation.
I3 boring

1995-97, 12 states had minimum wages above the Federal standazd. We

summarize state-specific minimum wage changes with information from the Monthly
Labor Review's annual (January issue} surveys of state labor legislation. Tf the effective
minimum wage in a state increased by 10% on October I, then MW will equa10.10 for
August-October price comparisons and for September-November price comparisons(and
0.0 for othex comparisons).
'a For the 12 non-metro urban areas, we use wage data for the non-metro parts of the
outlet's state. CPS codes are unavailable for 9 MSAs,so sample sizes decline when area
wage data are included in the analysis.
is Adding
lags reduces sample size. Three period lags(up to 6 months, with two month
periods) were always statistically significant, while a fourth period was not.

confidential

16 Tl~is pattern closely matches that found by Aaronson using aggregated data for 197$95. He found a sharp price spike at the minimum wage increase, statistically significant
individual month effects at the one month lead and lag, and a full effect captured in a
window of six months surrounding the minimum wage increase (three before and three
afterj. Our two month contemporaneous,lead, and lag periods sum to six months.
17 Estimated minimum wage effects are quite robust to the inclusion or exclusion of the
other explanatory variables, and the pattern of coefficients on the other explanatory
'variables changes little as the model changes to capture different minimum wage effects.
_

As a final check, v`+e estimated models with fixed firm and PSU effects. Key minimum
wage coefficients were unaffected by the inclusion of fixed effects, and the fixed effects
themselves added almost nothing to the model's fit.
8 BLS enumerators collect prices in 3 roughly week-long collection periods during the
first 22 days of a month. Outlets are visited during the same collection period each
month, although not necessarily on the same day each month. Therefore, prices observed
at "one period lags" after the October 1, X996 minimum wage increase include, at the
near minirnam,comparisons made for the first week of December coanpared to the first
week of October and, at the far rnaacimum,comparisons for the third week of January
compared to the third week of November. One period lead effects include, at the near
minimum,comparisons of the third week of September to the third week of July and, at
the far ma~cimam, comparisons ofthe first week of August to the first week of June. In
short, some "lead" and "lagged" price changes could have been made quite close to the
date of the minimum wage change, as little as 10 days before oz after.

confidential

While our findings are consistent with full pass-through, they are also consistent with

less than full pass-through, coinciding with spillovers.
20

We also looked at outlet price increases two periods before a minimum wage increase,

but adding another period provided no new information for the analysis. The results
suggest the following sequence. Outlets that reviewed prices two periods before a
minimum wage increase did not then review again in anticipation of the minimum wage
change. Those that did raise price in anticipation of the nninimum wage increase(one
period before} had only modest contemporaneous price increases, while those without
anticipatory price increases before the minimum wage change raised pxices substantially.
2i

We looked at other LS price points. About 3 percent of prices end in 98 cents, and

over half end in the digits 8 or 9,but additional price point measures were not nearly
significant, once we entered PP99.
22

Price point effects were also importazit in unreported FS outlet models. There, prices

that ended in 25,50, or 95 cents were much Tess likely to be increased, and were changed
by more when they were changed.

confidential

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Full text of Working Papers (Federal Reserve Bank of Chicago) : How Do Retail Prices React to Minimum Wage Increases?, Working Paper 2000-20

FRASER