Effect of local smoke-free restaurant policies on restaurant revenue in Massachusetts

16 Pages·1997·0.68 MB·English

by  Pope, Gregory (Gregory C.)

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CENTER FOR HEALTH ECONOMICS RESEARCH
300fifth Avenue. 6th floor
Walthom. MA02154
MASS. RS 3{, ((661)77))448877--00220020 Fax
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EFFECT OF LOCAL SMOKE-FREE RESTAURANT
POLICIES ON RESTAURANT REVENUE
IN
MASSACHUSETTS
JsS Cf Submitted bv:
'
GregoryC. Pope, M.S.
ocr WilliamI BaitOSCh, M.P.A.
17
799?
CenterforHealthEconomicsResearch
300 FifthAvenue, 6thFloor
MA
Wafcham, 02154
To:
Massachusetts Department ofPublicHealth
Tobacco Control Program
April 22, 1997

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EFFECT OFLOCAL SMOKE-FREE RESTAURANTPOLICIES ON
RESTAURANT REVENUEIN MASSACHUSETTS
Research evidence Unking environmental tobacco smoking (ETS), or"second-
hand" smoke, to smoking-related illnesses have led many cities and towns across
Massachusetts to adopt policesthat completely eliminate or severelyrestrict smokingin
restaurants. At least 124 localities inMassachusettshave passed sometype ofrestaurant
smoking restriction. Forty-two ofthese communities can be considered highlyrestrictive
meaning they offera smoke-free dining environment that either completelyeliminates
smoking inrestaurants oronlyallows smoking in separate, enclosed, and separately
ventilated sections ofrestaurants.
One ofthe most controversial aspectofsmoke-free restaurant policies is the
possibilitythat prohibiting smoking is economically harmful to restaurantsbecausepatrons
who smokemaymigrateto communitieswithout suchrestrictions orchoose to dine at
home. This study examineswhetherlocal policies restricting smokinginrestaurants affect
restaurant business.
RESEARCH HYPOTHESES
Ournullhypothesis isthat thereis no differencebetween pre- and post-banmeals
revenue. Two alternativehypotheses are possible. One hypothesisisthatlocal smoking
bans reduce restaurant receipts as smokers patronize establishmentsinthese communities
less frequently. Anotherhypothesisisthat smokingbans stimulate patronagefromthose
preferring a smoke-free dining environment causing anincrease inpost-banreceipts.
DATA
This study analyzed taxable meals receipts datacollectedbythe Massachusetts
Department ofRevenue (DOR)fortheperiod January 1992 throughDecember 1995.
These datacaptureinformationpertaining tothe state's five percenttax on meals. They
includetaxable receipts from all eating and drinking establishments that areprimarily
engaged inthe business ofserving meals and include receiptsfrom thesale ofalcoholic
DOR
beverages, provided aggregatetown-level datafor all communitieswithat least ten
restaurants.
Datawere adjusted forinflationusingthe ConsumerPriceIndex (CPI) forBoston
urban consumerswith 1995 as thebaseyear. This allowed usto make comparisons across
time that control forthe changingvalue ofthe dollar.
EXPERIMENTALAND CONTROL COMMUNITIES
Experimental communities included cities and town in Massachusetts that adopted
a highly restrictive policybetween January 1992 andDecember 1995, includingthose
communities that adopted a policy and subsequently revoked it (Le. Chicopeeand
Norwell). Highlyrestrictiverestaurant policies have generated the most economic
concern. They include any city ordinance, townby-law, or local board ofhealth
regulation that completelyrestricts smoking in restaurants or confines smoking to
separate, enclosed, and separatelyventilated sections ofrestaurants. In order fora town
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to be considered highly restrictive, itsrestaurant policy had to applyto taverns andbar
sections ofrestaurants. However, a community's restaurant policy could exemptbars
(meaning establishments primarily dedicatedto serving alcoholicbeverages and inwhich
the consumption offood is incidental to the consumption ofbeverages) and still be
considered highlyrestrictive.
Weidentifiedthe smoking status of309 ofMassachusetts' 351 chiesandtowns
representing 98 percent ofthe state's population. Thoselocalitiesnot included failed to
submitlocal datacollectionforms totheDepartment ofPublicHealth and could notbe
reached viatelephone. Weidentified 33 (X>rrimuniriesthat adoptedasmoke-free policy
between January 1992 and December 1995. These citiesand towns are listed in Table 1.
Thirty-one ofthese communities had dataavailable for the entirereportingperiod and
were included in our analysis. DOR suppressed dataforDover andNorfolkto preserve
confidentialitybecause ofthe small numberofrestaurants inthesetowns. West
Springfield and Sunderlandwere excludedfromthe descriptive analysis becausethey
chant have six months ofpost-ban data. Theyare, however, included in themultivariate
analysis.
Those cities andtownsthat didnot enact a smoke-free policyserved as control
communities. Thisgroup included 222 cities and townsthat didnot adopt arestaurant
policy orthat passed relativelyweakpolices(e.g., simplydesignating apercentage ofseats
non-smoking). Typically, critics ofsmoke-freepoliciesdo not claimthat weakrestrictions
cause economichardship. Cities andtownswithfewerthan 10 restaurantsbetween 1992
and 1995 orwhose smoking policy statuswasunknownwere excluded fromthe control
group.
DESCRIPTIVE ANALYSIS
Methods
Table 2 shows inflation-adjusted pre- andpost-ban datafor each ofthe smoke -free
cornmunitieswith at least 6 months ofpost-bandata. Post-bantaxablereceipts arethe
totaltaxable meals receipts generated in each experimental communityinthe 6 months
irnmediateryfollowingthe implementation ofarestaurant smokingban. Pre-banreceipts
arethetotal taxablemeals revenuethatwerereported inthe corresponding sixmonths one
year earlier. Examiningthe same sixmonthperiod one yearapart controls for seasonal
variations inrestaurantbusiness. For example, ifa communityadopted a smoke-free
policy on January 1, 1995, its post-ban periodwould beJanuary 1995 throughJune 1995.
Its pre-banperiod would be January 1994 through June 1994. Pre- andpost-ban datafor
control communities was extracted forthe sametime period as the individual experimental
<x>rnmunity. Thesetimeperiods differ across experimental communities.
Results
The percentage changeinadopting townrestaurant receipts between pre- and
post-ban periods ranges from -12 percentto over 100 percent. This is a reflection ofthe
volatilenature ofthe restaurant business, with restaurantsfrequently going inor out of
business, and experiencing large swingsin patronage. The largest changes tendto be
among the smallest towns with thefewest restaurants.
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It is more meaningful to focus on the aggregate results across all townsratherthan
the results foranyindividual town. Aggregatereceipts, suchasfor the combined control
communities, aremuchmore stable because they smooth out fluctuationsinindividual
town receipts. Forthe experimental (adopting) communities as awhole, inflation-adjusted
restaurant receipts were 5 percent greaterinthe six months following impositionofa
smoke-freepolicythaninthe same sixmonths one year earlier, pre-ban. Incontrast, there
was virtually no changein sales in control communitiesthat did not adopt smoke-free
restrictions. The comparison ofexperimentaland control communitiesindicatesthat
adoption ofhighly-restrictiverestaurant smoking policies led to an increaseofabout 5
percent in restaurant receipts inthe six months following the imposition oftheban.
We now turnto amultivariate analysisto examine the statistical precisionofthis
estimated salesgain.
MULTIVARIATEANALYSIS OF SMOKING RESTRICTION POLICIES
Methods
To estimate theaverage effect ofsmoke free policies controlling forseasonaland
year effects, we used multivariate regression. A"panel data" frameworkwasusedin
which 48 months ofinflation-adjustedrestaurant sales datafrom each of255 townswas
combined. "Fixedeffects" were entered foreachtown. Thetownfixed effectsremove all
town-specificfactors affectingrestaurant salesthat do notvary significantlyovertime,
such as town size. Dummyvariables foreach quarter oftheyearwere enteredto eliminate
seasonal influences onrestaurant sales. Inone ofourmodel specifications, wealso
control fortimeperiod effectsthroughtheuse ofa "year" factor(i.e., 1992, 1993, 1994,
and 1995) in additiontothetownfactors. Timeperiod effects control forgeneral
economic trends, data collectionmethods, andotherfactorsthatvary ova*timeacross all
towns.
Removing theinfluence ofthese otherfactors allowsus to obtain anunbiased
estimate ofthe effect ofsmokingrestrictions on restaurant sales. Adummyvariablecalled
ADOPTwas includedinthe regressionto capture the effect ofsmoking restrictions.
ADOPT is coded "one" forall town-monthsin whicha smoking restrictionwasineffect,
and "zero" for all town-monthsinwhich a smokingrestriction was notin effect. The
regressioncoefficient ofADOPT estimates the change in restaurant sales duringthe
months a smoking restrictionwas in effect in atown, comparedto months a restriction
wasnot in effect. The222townsthat never adopted a smoke-free policyareincludedin
theregression, and function as "control" wrnmunities. That is, changes in restaurant sales
in non-adoptingtowns controlforgeneral economic and othertrendsthat affectrestaurant
sales across all towns. The regression averages smoking restriction effects across all
adoptingtowns, resulting in a single estimate ofthe average or typical effect fora town.
There was some evidence ofautocorrelation oferrorterms in our models, so we
also estimated amodel corrected for autocorrelation. Autocorrelationrefers to serial
(temporal) correlation ofmodel errorterms, and can bias statistical results. All models
reporting here use thenatural logarithm oftaxable meals receipts as the dependent
variable.
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Preliminary Results
Results from three regression models are presented in Table 3. They are: (1)
townfixed effects; (2) town fixed effects corrected forautocorrelation, and(3) townand
year fixed effects. All modelsindicatethat smoke-freerestaurantrestrictions increased
restaurant receipts in towns adoptingsmoke-free policies, by 5 to 9 percent. This is
consistent with the descriptiveresults. The range ofuncertaintyofthe estimatesis to 12
percent, indicatingthat themeasured positiveeffect ofsmoking restrictions isunlikelyto
be due to randomfluctuations (noise inthe data). Thereis no support forthehypothesis
that restaurant smoking restrictions reduced restaurantreceipts.
FurtherWork
In further work, we will examinethe sensitivity ofourresultsto trends intown
economic conditions that mayaffect restaurant sales. Economicconditions willbe
measured alternativelybytowntaxable retail sales, orbycountypopulation and disposable
personal income. Controllingforthese factorswill remove anybiasthat may arisefrom
more favorable economictrends intowns that adoptedsmokingrestrictionsthanthose
that did not.
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