Smoking Costs

7/28/2019 Smoking Costs

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FEDERAL RESERVE BANK of ATLANTA WORKING PAPER SERIES

Even One Is Too Much: The EconomicConsequences of Being a SmokerJulie L. Hotchkiss and M. Melinda Pitts

Working Paper 2013-3

July 2013Abstract: It is well known that smoking leads to lower wages. However, the mechanism of this negativerelationship is not well understood. This analysis includes a decomposition of the wage gap between

smokers and nonsmokers, with a variety of definitions of smoking status designed to reflect differences in

smoking intensity. This paper finds that nearly two-thirds of the 24 percent selectivity-corrected

smoking/nonsmoking wage differential derives from differences in characteristics between smokers and

nonsmokers. These results suggest that it is not differences in productivity that drive the smoking wage

gap. Rather, it is differences in the endowments smokers bring to the market along with unmeasured

factors, such as baseline employer tolerance. In addition, we also determine that even one cigarette per

day is enough to trigger the smoking wage gap and that this gap does not vary by smoking intensity.

JEL classification: J31, I19, C31

Key words: smoking wage penalty, wage decomposition, wage differentials


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Even One is Too Much: The Economic Consequences of Being a Smoker

I. Introduction and Background

The health consequences of smoking have been well documented (Chaloupka and

Warner 2000). Cigarette smoking has been shown to decrease life expectancy and

increase health care utilization and expenditures. The CDC estimates that health care

expenditures attributable to smoking were over $95 billion per year in the period 2000-

2004 (Adkihari et al. 2008). However, there are other costs associated with cigarette

smoking besides poor health and smoking-attributable health care expenditures. This

research explores the labor market costs associated with cigarette smoking, specifically

the impact of cigarette smoking on wages.

There are several different mechanisms through which smoking could impact

earnings. For example, it is reasonable to expect that any action that lowers a persons

stock of health would have negative implications for wages, either through absenteeism

(Weng et al. 2013) or lower productivity (Kristein 1983). In addition, there could also be


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2010). However, when the estimation is performed separately for men and women, it

appears that the wage penalty is driven by the negative effect on mens wages as no wage

penalty was found for female smokers, at least in The Netherlands (van Ours 2004).

While it is generally accepted that smokers earn lower wages, the mechanism

behind this wage differential is less clear. Levine et al. (1997)suggeststhatthelower

wagesforsmokersisduetosuchissuesasemployerdiscrimination,increasedcosts

ofemployingsmokers,orlowerproductivitybysmokers.Inthispaper,a

decompositionofthewagedifferentialbetweensmokersandnonsmokers,acrossa

rangeofcriteriaforsmokingstatus,isusedtogainafurtherunderstandingintothe

shareofthewagedifferentialthatisattributedtoselectionintosmoking,differences

inendowments,anddifferencesinthereturntothoseendowments.A secondary

goal of this research is to examine the impact of the choice of the smoking status criteria,

including how to capture smoking intensity (i.e., number of cigarettes consumed as well

as daily versus nondaily smoking status), as well as how to treat former smokers.

Understanding the impact of smoking at different levels of intensity will aid in the


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enough for the wage penalty to kick in. In other words, it is simply the fact that an

individual smokes, not the level of cigarette consumption that matters for the

determination of the smoking wage penalty. Furthermore, the mechanism behind the

wage differential does not change with smoking intensity.

II.EmpiricalModel

Anindividualischaracterizedashavinghis/herwagedeterminedinoneof

twosectors,the"smoking"sector(S)orthe"nonsmoking"sector(NS).Because

smokingbehaviorisgenerallyobservableintheworkplace,employerscanlikely

differentiatesmokersfromnonsmokers,andpenalize(ornot)smokerswithlower

wages.Aworkersdecisiontobeasmoker,ornot,however,isnotexogenous.If

thereareunobservedindividualcharacteristicsrelatedtoboththewagestructure

andsmokingbehavior,estimationofthewagepenaltywouldbebiased.If,for

example,peoplewithhigherskillschoosetosmoke,naiveestimationofthewage

penalty would be biased downward because it wouldnt be taking into account that


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!,! (j=ns,s)isthelogofhourlywages,areindividualcharacteristicsthat

areexpectedtoinfluencebothwagesandaperson'ssmokingdecisionand! arethe

returnstomeasuredworkerscharacteristics(j=nsfornonsmokersandj=sfor

smokers).Althoughthemarketisabletodifferentiatesmokersfromnon-smokers,

employerscannotobservethelatentpropensitythatworkershavetosmoke.A

person'spropensitytosmokeisdeterminedbythesamecharacteristicsthat

determinethatperson'swage,,aswellassomecharacteristics,Z,thataffectthe

decisiontosmokebutdonotdeterminewages.!,! (j=ns,s)and! arerandomerror

termsthatareassumedtobedistributedasatri-variatenormal.Estimationis

performedinmultiplestages.

A.SelectionintoSmokingandNonsmoking

Sinceaperson'ssmokingpropensity,!,isunobserved,equation(3)cannot

bedirectlyestimated.Instead,undertheassumptionofnormalitythedecisionof

smokingcanbeestimatedviamaximumlikelihoodprobit,whereaworkeris

d d k f h l bl d k f


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wageequationssuchthat:

(1') !",!|!, = 0 = !"!

! + !"!",! + !",! (2')

!,!|! , = 1 = !!! + !!,! + !,!

Estimationofthisspecificationofthewageequationsproducesunbiasedestimates

ofthe,since,basically,self-selectionintosmokinghasbeenremovedfromthe

errorterm.

B.DecompositionoftheSmokingWageDifferential

Theobservedwagedifferentialbetweensmokersandnonsmokerscanbe

expressedas:

(5) !"

!=

!"

!!"+

!"!"

!

!

!+

!!

=

!!"

!+

!"!"

!+

!"!"

!!.

Thefirsttermontherighthandsideoftheequationisreferredtoasthecoefficient

effectandtellsushowthedifferentevaluationofasmoker'sandnonsmokers

characteristicscontributetotheobservedwagedifferential;thesecondtermisthe


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"yes,"or"no."However,fromtheperspectiveofthelabormarket,workersmaybe

penalizedmoreseverelythemoreintensivetheirsmokinghabit.Forexample,

smokinganoccasionalcigaretteontheweekendcouldhaveverydifferent

implicationsforaperson'shealthand/orproductivitythan,say,someonesmokinga

packofcigarettesperday.Thepack-a-daysmokermaytaketimeawayfrom

productiveactivitiestofeedhis/herhabit,thusloweringproductivity,aswellas

exhibitamorevisiblesmokingbehavior,bothofwhichmayreducethewagean

employeriswillingtopay.

Todeterminewhethertheintensityofsmokingimpactsthemeasured

selectivity-correctedwagedifferential,thecompleteestimationprocessisrepeated

multipletimes,changingthedefinitionofasmokerbasedonthenumberof

cigarettesapersonsmokespermonthwiththebasisofcomparisonremaining

currentnonsmokers.Afulldecompositionoftheresultsisalsopresentedto

determinewhetherendowmentorcoefficientdifferencesplayadifferentroleat

different levels of smoking intensity


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programs. The survey includes questions related to smoking, use of tobacco products,

and tobacco-related norms, attitudes, and policies (NCI 2012). The CPS provides

information on the employment and socioeconomic characteristics of the individual,

which, along with the TUS supplement, can be matched to information on other family

members.

A. Who should be considered a Smoker?

While there seems to be agreement in the literature that smoking leads to lower

wages, there does not appear to be agreement over how to define a smoker or how to

capture the penalty. Levine et al. (1997) and Auld (1998) only consider daily smokers as

smokers, with no regard for number of cigarettes. Anger and Kvasnika (2010) consider

anyone a smoker if they indicate they are a current smoker. Braakman (2008) and van

Ours (2004) use the number of cigarettes in order to capture intensity.

In this research, the impact of how the criteria used to define a smoker affects

outcomes is examined by defining the statusofsmokerandnon-smokerin

differentways,takingintoaccountcurrentsmokingstatus,theintensityofsmoking


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percent).Approximately83percentofsmokersaredailysmokerswhileaboutone

percentsmokeslessthan30cigarettespermonth.Aboutone-halfofsmokers

consumeonepackofcigarettesperday,onaverage.Again,thisshareishigherfor

males,(56.3percentofsmokers)thanforfemales(42.6percent).

[Table 1 about here]

Whileallsmokerswhostatethattheyaredailysmokersalsoreportsmoking

atleast30cigarettespermonth(consistentwithsmokingatleastoneperday),only

29percentofnondailysmokersreportsmokinglessthan30cigarettespermonth.

Infact,almosthalfofnondailysmokersconsumebetween30and149cigarettesper

month,withapproximately15percentsmokingbetween150and299cigarettesper

month.3Thissuggeststhattheremaybebingeingofcigaretteconsumption.Ifthis

bingeingisnotdoneatwork,thentherecouldbedifferentimplicationsfor

productivityordiscrimination(andthuswages)thanfordailysmokers.Inorderto

differentiatebetweenthesetwotypesofsmokers,alloftheanalysisisperformed

separately both for all smokers and for daily smokers only


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presence of former smokers). However, Blondal et al. (1999) find that the probability of

relapse of a former smoker who quit more than one year ago is negligible. Thus, in order

to abstract from any contamination of the nonsmoker group with the inclusion of newly

minted former smokers, we eliminate from the analysis anyone who quit smoking within

the previous year.4

However,thisstillleavesthecomplicationofformersmokerswhoquitmore

thanayearago.Ifthemechanismthroughwhichcurrentsmokingaffectscurrent

wagesispurelyoneofcurrentproductivity(e.g.,takingsmokingbreaksor

discrimination),thenincludingformersmokerswithnonsmokersshouldnotbias

theestimationofawagepenalty.However,AngerandKvasnicka(2010)findthat

smokingcessationismorepositivelycorrelatedwithlabormarketoutcomesthan

smokinginitiation;i.e.formerssmokersearnmorethancurrentsmokers.Infact,

theyfoundformerssmokersalsoearnedmorethanneversmokers.Thusthe

analysisisrepeatedwithoutanyformersmokerstodeterminethisimpact.

B. Sample Means


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spousessmokerelativetononsmokers'spouses.Inaddition,nonsmokersfacea

slightlyhigheraveragecostperpackofcigarettesintheirstateofresidencethan

smokers.

[Table2abouthere]

Inadditiontobasicsocioeconomicanddemographicinformation,theTUS-

CPSalsoincludesinformationonwhetherapersonworkspart-timeaswellasif

theyworkindoorsoroutdoors.Themajorityofthesample(70percent)work

indoors,withtheshareslightlyhigherforsmokers(77percent)andslightlylower

fornonsmokers(68percent).Forindoorworkers,thesurveyhasafollowup

questionregardingtheexistenceofsmokingrestrictionsonthejob.Forthose

workerswhoworkindoors,aslightlygreatershareofsmokersworkinafacility

withnorestrictions(19percent)comparedtolessthan15percentofnonsmokers.

IV.Results

A First stage Estimation of the Probability of Smoking


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restrictionssmokelessthanthoseworkingindoorswithnorestrictions.However,

thisnegativeeffectisdiminishedforpart-timeworkers,aswouldbeexpected

(fewerhoursinwhichtheworkerisexposedtotherestriction).Finally,thepriceof

cigarettesisnegativelycorrelatedwithsmokingforallagesofwomenandforallbut

theyoungestandoldestofthemeninthesample.

Theseresultsarelargelysimilarnotonlyacrossgenderbutalsoacross

smokingintensity.Malesappeartobemorepricesensitivethanfemalesandless

sensitivetoindoorsmokingrestrictions.Pricemattersmorewhenthethresholdfor

classifyingsomeoneasasmokerislessthanorequalto150cigarettespermonth.5

Themostimportantresultisthatthevariablesincludedtoidentifythesmoking

equation(i.e.,spousesmoking,thepriceofcigarettes,andpriceinteractedwithage)

aregenerallysignificantacrossallgroups.

B.EstimationofLogWageEquations

ResultsforthelogwageregressionsarereportedinAppendixA,TableA3.In

general the result are as expected Older workers earn higher hourly wages at a


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characteristicsthatleadtohigherwagesinaparticularsectorarepositively

correlatedwithcharacteristicsdeterminingtheworker'sdecisiontosmokeornotto

smoke.

C.DecomposingtheSmokingWageDifferential

Decompositionofthewagedifferentialbetweensmokersandnonsmokers

forthefullsample,aswellasbygender,arepresentedinTable3.Ingeneral,

nonsmokersearn17.5percentmorethansmokers,withaselectivity-corrected

wagegapof23.6percent.Theselectivity-correctedwagegapisslightlyhigherfor

malesat24.2percent,withtheselectivity-correctedwagegapof22.0percentfor

females.

[Table3abouthere]

Itisimportanttodistinguishbetweenawagedifferential(orgap)andawage

penaltyassociatedwithsmoking.Thetwoconceptsarefundamentallyrelated,but

differintheirconstruction.Thegapinwagesisthedifference(correctedornot

corrected for selection into smoking/nonsmoking) between the average


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percentin1986toalmost12percentin2001,dependingonclassificationof

nonsmoker.Asarobustnesscheck,asimilarspecificationwasestimatedusing

thesedata,andthepenaltyassociatedwithsmokingisintheballparkofwhatothers

havereported,rangingfrom3.6percentto6.8percent.

Individualselectionintosmoking(andnonsmoking)hastheeffectof

reducingtheobservedwagegap,makingitsixpercentagepointslowerthanthe

wagegapthatcontrolsforindividualself-selectionintosmoking(ornot).The

majorityoftheselectivity-correctedwagegap(61percentforthefullsample;62

percentformen;and68percentforwomen)isaccountedforbydifferencesinthe

endowmentsofnonsmokersrelativetosmokers.Thelargestcontributingfactorto

differencesinendowmentsbetweensmokersandnonsmokersiseducation.Aswas

seeninthesamplemeans,nonsmokersbringsignificantlygreaterlevelsof

educationtothelabormarket.Thisisconsistentwiththehigherrateoftime

preferenceamongsmokers,assuggestedbyLevineetal.(1997).Overall,the

contribution of differences in endowments suggests that smokers are different from


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theresultofreducingthewagegap.However,therelativelylargeandsignificant

(exceptformales)differenceintheconstanttermssuggeststhatthereissomething

fundamentallydifferent(andunexplainedbytheincludedregressors)aboutthe

labormarketsinwhichsmokersandnonsmokersfindthemselves(alsosee

Braakman2008).Ofcourse,twooftheunmeasuredcharacteristicsofthelabor

marketsistoleranceofemployersforemployeeswhosmokeandon-the-job

productivity.Thenextsetofresultswillhelpgetusdisentangletheroleof

employerpreferencesandproductivitydifferencesinthedeterminationofthewage

gap.

D.SmokingIntensity

Asmentionedearlier,thereareseveralhypothesesaboutwhysmokersearn

lowerwagesthannonsmokers.Onehypothesisisthatsmokersarelessproductive,

eitherbecausetheyaremorefrequentlyabsentfromthelabormarket(dueto

healthreasons,seeMuchaetal.2004)ortheyspendlessworkingtimeinproductive

activities (due to having to take smoking breaks see Halpern et al 2001) Since


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(perhapsbecauseofhigherhealthcarecosts,etc.).

Smokingintensityisnottypicallyaddressed;anindividualisseengenerally

asasmokeroranon-smoker.Levineetal(1997)utilizeddailysmokerstatusand

Braakman (2008) includedacountofthenumberofcigarettesconsumedperday.

Theseapproaches,however,donotallowonetoidentifyathresholdofcigarette

consumptionatwhichanemployerconsiderssomeonea"smoker."Athreshold

approachalsoallowsforintensitytoplayaroleindeterminingthecontributionof

endowmentsandcoefficientstothewagedifferentialbetweensmokerand

nonsmokers.VanOurs(2004)alsotakesathresholdapproachbutincludesthe

thresholdsinasingleregression,whichdoesnotallowthecontributionoftheother

regressorstovarybythreshold.Inthisresearch,theaboveanalysisisrepeatedfor

smokersofvaryingdegreesofsmokingintensity--atleast30,150,300,and600

cigarettespermonth.Smoking600cigarettespermonthamountstoroughlyone

packperday.Thecomparisongroup,forallanalysis,isthosethatdonotcurrently

smoke thus allowing the contributions of the regressors to vary Table 4 contains


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acrossintensity.Inaddition,theshareoftheselectivitycorrectedwagegap

accountedforbydifferencesinendowmentsbetweensmokersandnonsmokersare

allroughly60percent.Allinall,thereisverylittledifferenceinthedecomposition

resultsacrosslevelsofsmokingintensity.Thissuggeststhatthesmokingwage

penaltyisnotbeingdrivenbydifferencesinproductivity,but,rather,bythe

endowmentstheybringtothemarket(e.g.,educationalattainment)andby

unmeasuredfactors,suchasbaselineemployertolerance,whichshowupin

differencesintheestimatedconstantterm.

Theanalysiswasalsorepeatedfordailysmokersonlyinordertodetermine

ifbingesmokers,whowouldpresumablyhavelesssmokingintensityduringwork

hours,werereducingthesizeofthewagepenalty.6Itdoesappeartobethecase

thatdailysmokinghasagreaterimpactonwagesthanwhenallsmokersare

included,withadifferenceofalmosttwopercentagepointsformenandexactlytwo

percentagepointsforwomen.However,onceanindividualsmokesmorethan150

cigarettes per month which is 85 percent of all smokers the penalty and the share


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andStafford(2009)findthatformersmokersarefundamentallydifferentfrom

currentsmokers,andthisdifferenceleadstodifferencesinwages.

Inordertodeterminetherobustnessoftheresultstotheexclusionofformer

smokers,theaboveanalysiswasrepeatedcomparingformersmokerstonever

smokersaswellascomparingneversmokerstocurrentsmokers.7Inthisfirstcase,

formersmokersactuallyearnasevenpercentwagepremiumoverindividualsthat

neversmoked.Thus,includingtheformersmokerswiththenonsmokersincreases

theobservedwagepenaltyforsmokers.Sinceapproximately21percentof

nonsmokersareformersmoker,theirexclusionhasthepotentialofbeingnontrivial.

However,repeatingthefullanalysisexcludingformersmokersresultsinonlya

slightlylowerselectivity-correctedwagedifferential(21.3percentversus23.6

percentwhenformersmokersareincluded)andaslightlyhighershareofthewage

penaltybeingattributedtoendowments(66.7percentversus61percentwhen

formersmokersareincluded).Intheend,theinclusionorexclusionofformer

smokers does not fundamentally change any conclusions the selectivity corrected


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thispapertellsusthatroughly60percentofthewagedifferentialbetweensmokers

andnonsmokerscomesfromdifferencesinthecharacteristicstheseworkersbring

tothelabormarket,especiallyeducationalattainment.Theseresultsconfirmwhat

isfoundintheearlierliterature.

Newinsightsfromtheanalysisinthispapertellusthatevenonecigaretteis

enoughtotriggerasmokingwagedifferential,thatthewagedifferentialdoesnot

changewhenconsideringlowandhighintensitysmokers,andthat,regardlessof

intensity,roughlythesameamountofthesmokingpenaltyisaccountedforby

differencesinendowments.Inaddition,thelargestfactorcontributingtothe

differenceinthecoefficientsinthedeterminationofthewagedifferentialcomes

fromtheestimatedconstanttermsofthewageequation--theportionthatistruly

unexplainedbyregressorsincludedinthemodel.Theseresultssuggestthatthe

smokingwagegapisnotbeingdrivenbydifferencesinproductivity,but,rather,by

theendowmentssmokersbringtothemarket(e.g.,educationalattainment)andby

unmeasured factors such as baseline employer tolerance which shows up in the


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Similartoearlierresearch,wealsofindthatformersmokershaveattributes

thataremorehighlyrewardedinthelabormarketthaneithercurrentsmokersor

neversmokers,thusbiasingupwardthepenaltyforsmokers.Takenconversely,this

impliesthatthepenaltyfornotquittingishigherthanthepenaltyforsmoking

initiation.

Thelackofdifferenceacrossintensitysuggeststhatsimplyclassifyingan

individualasasmokershouldbeasufficientcontrolforsmokingstatus.However,it

isimportanttoseparateoutdailyandformersmokersinordertogetanaccurate

pointestimateforthepenaltyforcurrentsmoking.


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307-319.


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Table1:DistributionofSmokingIntensity

TotalNever

Smoker

Everyday

Smoker

Someday

Smoker

Former

SmokerTotal

NonSmokers 76,321 0 0 20,673 96,994

1-29cigpermonth 0 0 1,161 0 1,161

30-149cigpermonth 0 574 1,972 0 2,546

150-299cigpermonth 0 1,416 615 0 2,031

300-599cigpermonth 0 6,217 263 0 6,480

600+cigpermonth 0 12,070 52 0 12,122

Total 76,321 20,277 4,063 20,673 121,334


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Table2:SampleMeans

Variable Full Smoker Nonsmoker FormerSmoker

Males Females

HourlyWage 15.627 13.101 16.261 16.892 17.7 13.588

(0.048) (0.133) (0.049) (0.122) (0.083) (0.046)

Smoke(=1) 0.201 - - - 0.206 0.196

(0.001) (0.002) (0.002)

Age 42.076 41.094 42.322 46.914 41.755 42.392

(0.032) (0.067) (0.037) (0.079) (0.046) (0.046)Female(=1) 0.504 0.492 0.507 0.464 - -

(0.001) (0.003) (0.002) (0.003) Black(=1) 0.095 0.085 0.098 0.059 0.079 0.112

(0.001) (0.002) (0.001) (0.002) (0.001) (0.001)Hispanic(=1) 0.070 0.076 0.044 0.036 0.076 0.063

(0.254) (0.265) (0.205) (0.186) (0.265) (0.243)

Married(=1) 0.613 0.500 0.641 0.665 0.664 0.562 (0.001) (0.003) (0.002) (0.003) (0.002) (0.002)

LessthanHigh

school(=1)

0.091 0.135 0.08 0.077 0.101 0.081

(0.001) (0.002) (0.001) (0.002) (0.001) (0.001)

Some

College(=1)

0.278 0.296 0.273 0.3 0.262 0.294

(0.001) (0.003) (0.001) (0.003) (0.002) (0.002)

BAorGraduate

degree(=1)

0.314 0.144 0.357 0.306 0.329 0.3

(0.001) (0.002) (0.002) (0.003) (0.002) (0.002)

Parttime(=1) 0.394 0.439 0.383 0.426 0.335 0.453 [0.001] [0.003] [0.002] [0.003] [0.002] [0.002]

Outdoor

Work(=1)

0.304 0.226 0.323 0.201 0.423 0.186

(0 001) (0 003) (0 002) (0 003) (0 002) (0 002)


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Table3:WageDecomposition

FullSample Male FemaleTotalWagegap 0.175*** 0.188*** 0.171***

[0.007] [0.011] [0.010]

Wagegap 0.236*** 0.242*** 0.220***

Selectivitycorrected [0.014] [0.019] [0.019]

Differencesin

endowments

0.144*** 0.150*** 0.150***

[0.003] [0.004] [0.004]

Differencesin

Coefficients

0.092*** 0.092*** 0.070***

[0.013] [0.018] [0.019]

Selection -0.061*** -0.054*** -0.049***

[0.013] [0.017] [0.018]

Differencesin

endowments 0.144*** 0.150*** 0.150***

Occupation 0.044*** 0.043*** 0.049***

[0.001] [0.002] [0.002]

Industry 0.005*** 0.002 0.008***

[0.001] [0.001] [0.001]

WorkCharacteristics 0.004*** 0.005*** 0.003*** [0.001] [0.001] [0.001]

Education 0.077*** 0.075*** 0.076***

[0.002] [0.002] [0.002]

Demographics -0.001 0.009*** -0.002*

[0.001] [0.002] [0.001]

StateFE 0.011*** 0.010*** 0.011***

[0.001] [0.001] [0.001]

TimeFE 0.006*** 0.006*** 0.006***

[0.000] [0.001] [0.001]

Differencesin

Coefficients 0.092*** 0.092*** 0.070***

O ti 0 050*** 0 042*** 0 061***


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Smokes>=30 Smokes>=150 Smokes>=300 Smokes>=600

TotalWagegap 0.182*** 0.190*** 0.188*** 0.183*** [0.007] [0.007] [0.007] [0.008]

Wagegap 0.240*** 0.243*** 0.244*** 0.252***

Selectivitycorrected [0.014] [0.015] [0.015] [0.020]

Differencesinendowments 0.148*** 0.152*** 0.151*** 0.145***

[0.003] [0.003] [0.003] [0.004]

DifferencesinCoefficients 0.093*** 0.091*** 0.094*** 0.107***

[0.014] [0.014] [0.015] [0.019]

Selection -0.059*** -0.053*** -0.057*** -0.069***

[0.013] [0.013] [0.014] [0.018]

Differencesinendowments 0.148*** 0.152*** 0.151*** 0.145***

Occupation 0.045*** 0.048*** 0.049*** 0.053***

[0.001] [0.001] [0.002] [0.002]

Industry 0.005*** 0.005*** 0.004*** 0.001

[0.001] [0.001] [0.001] [0.001]

WorkCharacteristics 0.004*** 0.004*** 0.004*** 0.005***

[0.001] [0.001] [0.001] [0.001]

Education 0.079*** 0.084*** 0.086*** 0.095*** [0.002] [0.002] [0.002] [0.002]

Demographics -0.003** -0.008*** -0.013*** -0.033***

[0.001] [0.001] [0.001] [0.002]

StateFE 0.012*** 0.013*** 0.014*** 0.016***

[0.001] [0.001] [0.001] [0.001]

TimeFE 0.006*** 0.006*** 0.007*** 0.009***

[0.001] [0.001] [0.001] [0.001]

DifferencesinCoefficients 0.093*** 0.091*** 0.094*** 0.107***

Occupation -0.052*** -0.068*** -0.073*** -0.078***

[0.011] [0.012] [0.012] [0.015]

Industry -0.039 -0.043 -0.054* -0.047


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AppendixA:SupplementalTables

TableA1:ProbabilityofSmoking

Variables Full

Sample

Males Females

Age 0.081*** 0.102*** 0.063***

[0.011] [0.015] [0.015]

(0.020) (0.025) (0.015)

AgeSquared -0.001*** -0.001*** -0.001*** [0.000] [0.000] [0.000]

(-0.000) (-0.000) (-0.000)

Female -0.216*** - -

[0.009]

(-0.052)

Black -0.248*** -0.178*** -0.313***

[0.016] [0.024] [0.023]

(-0.060) (-0.043) (-0.074)

Hispanic -0.416*** -0.305*** -0.573***

[0.022] [0.029] [0.034]

(-0.100) (-0.074) (-0.136)

Married -0.662*** -0.624*** -0.723***

[0.010] [0.014] [0.015]

(-0.159) (-0.151) (-0.171)

LessthanHighSchool 0.203*** 0.204*** 0.185***

[0.016] [0.022] [0.023]

(0.049) (0.049) (0.044)

SomeCollege -0.178*** -0.177*** -0.187***

[0.011] [0.016] [0.015] (-0.043) (-0.043) (-0.044)

BAorGraddegree -0.677*** -0.702*** -0.658***

[0.012] [0.017] [0.018]


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AvgPriceofCigarette 0.263* 0.540*** 0.024

(realtermsbyState) [0.141] [0.197] [0.201]

(0.063) (0.131) (0.006)

Price*Age -0.019*** -0.028*** -0.011 [0.006] [0.009] [0.009]

(-0.004) (-0.007) (-0.003)

Price*Age Squared 0.000*** 0.000*** 0.000

[0.000] [0.000] [0.000]

(0.000) (0.000) (0.000)

Constant -1.401*** -2.111*** -0.946***

[0.238] [0.339] [0.336]

SampleSize 121,334 60,168 61,166

LogLikelihood -52238 -26079 -25914

Notes:Standarderrorsareinbrackets,marginaleffectareinparentheses,***

p


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TableA2:ProbabilityofSmokingbyIntensity

Variables Smokes>=30 Smokes>=150 Smokes>=300 Smokes>=600Age 0.085*** 0.086*** 0.085*** 0.096***

[0.011] [0.011] [0.012] [0.014]

(0.020) (0.019) (0.017) (0.015)

AgeSquared -0.001*** -0.001*** -0.001*** -0.001***

[0.000] [0.000] [0.000] [0.000]

(-0.000) (-0.000) (-0.000) (-0.000)

Female -0.216*** -0.229*** -0.248*** -0.342***

[0.010] [0.010] [0.010] [0.012]

(-0.050) (-0.050) (-0.051) (-0.053)Black -0.254*** -0.314*** -0.400*** -0.600***

[0.017] [0.018] [0.019] [0.025]

(-0.059) (-0.068) (-0.082) (-0.093)

Hispanic -0.481*** -0.594*** -0.667*** -0.817***

[0.023] [0.025] [0.027] [0.035]

(-0.112) (-0.129) (-0.136) (-0.127)

Married -0.671*** -0.682*** -0.686*** -0.688***

[0.010] [0.011] [0.011] [0.013]

(-0.156) (-0.148) (-0.140) (-0.107)LessthanHighSchool 0.205*** 0.207*** 0.210*** 0.238***

[0.016] [0.017] [0.017] [0.019]

(0.048) (0.045) (0.043) (0.037)

SomeCollege -0.187*** -0.200*** -0.201*** -0.227***

[0.011] [0.011] [0.012] [0.014]

(-0.044) (-0.044) (-0.041) (-0.035)

BAorGraddegree -0.710*** -0.765*** -0.785*** -0.834***

[0.013] [0.014] [0.014] [0.017]

(-0.165) (-0.166) (-0.160) (-0.129)Part-timeWorker -0.011 -0.004 -0.006 -0.035

[0.027] [0.028] [0.029] [0.033]

(-0.003) (-0.001) (-0.001) (-0.005)


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Variables Smokes>=30 Smokes>=150 Smokes>=300 Smokes>=600Price*age^2 0.000*** 0.000** 0.000* 0.000

[0.000] [0.000] [0.000] [0.000]

(0.000) (0.000) (0.000) (0.000)

Constant -1.507*** -1.552*** -1.515*** -1.847***

[0.244] [0.255] [0.264] [0.314]

SampleSize 120,173 117,627 115,596 109,116

LogLikelihood -50240 -45934 -42523 -30882

Notes:Standarderrorsareinbrackets,marginaleffectareinparentheses,***p


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TableA3:LogWageRegression

FullSample Males Females

Smoker

Non-

Smoker Smoker

Non-

Smoker Smoker

Non-

Smoker

Age 0.042*** 0.043*** 0.048*** 0.051*** 0.035*** 0.034***

[0.002] [0.001] [0.003] [0.001] [0.003] [0.001]

AgeSquared -0.000*** -0.000*** -0.001*** -0.001*** -0.000*** -0.000***

[0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

Female -0.213*** -0.206*** - - - - [0.007] [0.004]

Black -0.098*** -0.095*** -0.149*** -0.141*** -0.052*** -0.059***

[0.011] [0.006] [0.017] [0.009] [0.015] [0.007]

Hispanic -0.103*** -0.112*** -0.124*** -0.133*** -0.066*** -0.084***

[0.015] [0.007] [0.020] [0.010] [0.024] [0.010]

Married 0.043*** 0.054*** 0.097*** 0.104*** -0.007 0.012**

[0.006] [0.004] [0.009] [0.006] [0.008] [0.005]

LessthanHighSchool

-0.135*** -0.190*** -0.145*** -0.212*** -0.118*** -0.156***

[0.009] [0.007] [0.013] [0.009] [0.013] [0.009]SomeCollege 0.072*** 0.090*** 0.056*** 0.068*** 0.078*** 0.098***

[0.007] [0.004] [0.011] [0.007] [0.010] [0.006]

BAorGraddegree 0.257*** 0.321*** 0.213*** 0.278*** 0.301*** 0.357***

[0.011] [0.005] [0.016] [0.008] [0.015] [0.007]

Part-Time -0.077*** -0.087*** -0.045*** -0.085*** -0.090*** -0.078***

[0.008] [0.004] [0.013] [0.008] [0.010] [0.005]

Constant 1.643*** 1.821*** 1.468*** -0.005 1.545*** -0.003

[0.059] [0.030] [0.083] [0.017] [0.087] [0.016]

0.046*** -0.019 0.045*** 1.595*** 0.040*** 1.755***

[0.010] [0.012] [0.014] [0.043] [0.014] [0.046]

R2 0.239 0.368 0.194 0.339 0.226 0.352SampleSize 24,340 96,994 12,372 47,796 11,968 49,198


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- A6 -

TableA4:LogWagebySmokingIntensity

Smokes>=30 Smokes>=150 Smokes>=300 Smokes>=600

Smoker NonSmoker Smoker NonSmoker Smoker NonSmoker FormerSmoker NonSmoker

Age 0.042*** 0.043*** 0.042*** 0.043*** 0.041*** 0.043*** 0.041*** 0.043***

[0.002] [0.001] [0.002] [0.001] [0.002] [0.001] [0.003] [0.001]

AgeSquared -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** -0.000***

[0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

Female -0.213*** -0.206*** -0.213*** -0.206*** -0.220*** -0.207*** -0.230*** -0.207***

[0.007] [0.004] [0.007] [0.004] [0.008] [0.004] [0.010] [0.004]

Black -0.099*** -0.095*** -0.094*** -0.096*** -0.084*** -0.096*** -0.097*** -0.097***

[0.011] [0.006] [0.013] [0.006] [0.014] [0.006] [0.021] [0.006]

Hispanic -0.113*** -0.112*** -0.115*** -0.113*** -0.120*** -0.113*** -0.133*** -0.114***

[0.017] [0.007] [0.019] [0.007] [0.021] [0.007] [0.032] [0.007]

Married 0.044*** 0.054*** 0.049*** 0.054*** 0.050*** 0.054*** 0.054*** 0.054*** [0.006] [0.004] [0.007] [0.004] [0.007] [0.004] [0.008] [0.004]

Lessthan

HighSchool

-0.132***

[0.010]

-0.190*** -0.128*** -0.190*** -0.127*** -0.190*** -0.119*** -0.190***

[0.007] [0.010] [0.007] [0.010] [0.007] [0.012] [0.007]

Some

College

0.072*** 0.090*** 0.073*** 0.090*** 0.070*** 0.090*** 0.065*** 0.090***

[0.007] [0.004] [0.008] [0.004] [0.008] [0.004] [0.010] [0.004]

BAorGrad

degree

0.256*** 0.320*** 0.254*** 0.320*** 0.249*** 0.320*** 0.231*** 0.320***

[0.012] [0.006] [0.013] [0.006] [0.014] [0.006] [0.018] [0.005]

PartTime 0.235 -0.087*** -0.072*** -0.087*** -0.071*** -0.087*** -0.074*** -0.087***

[0.008] [0.004] [0.009] [0.004] [0.009] [0.004] [0.012] [0.004]

Constant 0.044*** 1.820*** 0.039*** 1.820*** 0.041*** 1.819*** 0.047*** 1.817***

[0.010] [0.030] [0.011] [0.030] [0.011] [0.030] [0.013] [0.030]

1.645*** -0.019 1.612*** -0.020* 1.619*** -0.023* 1.616*** -0.026* [0.061] [0.012] [0.064] [0.012] [0.067] [0.012] [0.087] [0.014]

R2 0.235 0.368 0.229 0.368 0.231 0.368 0.222 0.368Sample

Size 23179 96994 20633 96994 18602 96994 12122 96994Notes:Standarderrorsareinbrackets,***p

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