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         Stop Smoking, Your Paycheck Will Thank You!

                 Wage Effects from Smoking Cessation

                             Silke Angera,*            Michael Kvasnickab

    a
        German Institute for Economic Research (DIW Berlin), Mohrenstr. 58, 10117 Berlin, Germany,
                     sanger@diw.de, Tel: +49-30-89789-526, Fax: +49-30-89789-109
                   b
                       RWI Essen, Berlin Office, Hessische Str.10, 10115 Berlin, Germany




ABSTRACT

A growing body of literature has investigated the wage penalty attached to smoking.
Little research, in contrast, has been done on the wage effects of smoking cessation.
Using survey panel data from Germany, we study the relative earnings of smokers
and former smokers over an extended period of time. Our results from pooled OLS
regressions of wages on smoking status for ever smokers (smokers, former smoker)
point to a large wage gain from smoking cessation. This gain disappears once we
control for individual fixed effects, which suggests that the apparent wage gain
attached to smoking cessation is the result of a pure selection effect. However, when
adding controls for (past) smoking duration and time elapsed since quitting,
individuals who did not smoke very long or who quit many years ago do appear to
benefit in their earnings from quitting smoking. Both a selection effect and a causal
effect of smoking cessation hence seem to underlie the average wage gains attached
to quitting in our data. The prospect of a higher paycheck for short-time smokers and
long-term quitters provides an additional incentive to smokers to quit smoking, an
argument health authorities may want to utilize in public smoking cessation
campaigns.



JEL:                     J31, I19, C51
Keywords:                Smoking cessation, smoking wage penalty, earnings regressions
Running Title: Wage Effects from Smoking Cessation

*
Corresponding author
INTRODUCTION


        A small but growing body of literature has studied the consequences of

smoking for wages. Although covering different countries and time periods, and

making use of different empirical strategies and data sets, empirical studies tend to

concur in their findings of a significant wage penalty attached to smoking (see, for

example, Levine, Gustafson, and Velenchik, 1997; van Ours, 2004; Auld, 2005). In

contrast, relatively little is known on the effects that smoking cessation has on the

wages of smokers. Lack of research in this area is surprising. Wage gains, if indeed

materializing, would provide an additional incentive to smokers to quit and a

valuable argument policy makers and health authorities could fruitfully employ in

smoking cessation campaigns.

       Existing research on the smoking wage penalty provides little if any

information on the effects of smoking cessation. In particular, estimates of the

smoking wage penalty may not be viewed as the mere negative of the wage gain that

is likely to accrue on average to smokers who quit. Most existing studies on the

smoking wage penalty sub-sum former smokers and never smokers in the group of

current non-smokers, which is used as the comparison group for current smokers in

the calculation of the smoking wage penalty. This shortcoming is likely to bias

causal inference for two reasons. First, former and never smokers might

systematically differ in productivity-relevant characteristics, that is self-select into

these two smoking states. Second, smoking may have adverse wage effects that

persist even when individuals quit, as caused for instance by irreversible health

damage or irreparable career setbacks. Treating former smokers who have been

exposed to smoking and never smokers who have not nevertheless alike, blurs the

very distinction between treatment and control group and makes it difficult to

                                                                                           2
interpret in any meaningful sense wage differentials calculated on this basis. It is for

these two reasons that existing estimates of the smoking wage penalty are likely to be

biased.1 For the same reasons, they are also inadequate as a measure from which to

infer the wage effects of smoking cessation.

          In this paper, we study the wage effects of smoking cessation. Using panel

survey data from Germany, we analyze the relative earnings of smokers and former

smokers over an extended period of time. Restricting the analysis to ever smokers

throughout, that is excluding never smokers, provides for greater homogeneity in our

estimation sample and enables us to focus exclusively on the margin of direct

relevance to our research focus, the decision of whether or not to quit and the wage

effects of this change in smoking status. Results from pooled OLS wage regressions

point to a significant wage premium for past smokers relative to smokers. Fixed

effects panel regressions of wages on smoking status show that this premium

partially stems from a selection effect. However, additional analyses that control also

for (past) smoking duration and time elapsed since quitting produce evidence for a

positive effect of smoking cessation on wages for individuals who did not smoke

very long or who have quit long ago.

          Our paper contributes to the literature on smoking and earnings in various

ways. To the best of our knowledge, our study is the first in-depth analysis of the

wage effects of smoking cessation. While few studies on the smoking wage penalty

provide some tentative co-findings on wage differentials for past smokers, none of

them addresses wages effects of smoking cessation and the underlying mechanisms

per se.




1
  Anger and Kvasnicka (2009) show that the smoking wage penalty is reduced by as much as a third,
if past smoking of individuals is controlled for.

                                                                                                    3
       Second, in contrast to the previous literature on smoking and earnings we

restrict the estimation sample to ever smokers only. This restriction makes

individuals in the estimation sample more homogenous by providing for some

common initial condition (smoking initiation) and allows us to focus exclusively on

the effects of smoking cessation.

       Third, we provide evidence on the short-term and long-term wage effects of

smoking and smoking cessation by considering both (past) smoking duration and

time elapsed since quitting as explanatory variables in the analysis. And finally, we

explicitly control for recall error, a measurement error known to be pervasive in

retrospective data on smoking that causes attenuation bias.




SMOKING, QUITTING AND EARNINGS


       Several reasons are cited in the literature why smoking may adversely affect

earnings, including reduced individual productivity of smokers due to higher rates of

absenteeism and health problems, and potential discrimination of smokers by

employers and co-workers (see, for example, the discussion in Levine et al., 1997).

        All of these channels may have persistent effects on earnings. Health damage

may be irreversible, career setbacks irreparable, and missing educational investments

not to be regained. If so, then smoking lowers both current and future earnings

capacity of individuals. Former smokers will be affected by their smoking history

even after quitting, and wage penalties for smoking will show no tendency of decline

even after years of smoking cessation. However, adverse health effects may also in

part subside with time and career setbacks be compensated to some degree or



                                                                                        4
altogether if individuals quit smoking. If so, then quitting smoking may well entail

significant wage gains for individuals.

        Smoking cessation, however, is unlikely to be random. Smoking is strongly

addictive and successful smoking cessation hence very demanding in terms of drive

and will power, attributes that are also of great importance for worker productivity

and hence wages. Empirical studies on the wage effects of smoking cessation have to

control thoroughly for this potential self-selection of more productive workers into

quitting. Otherwise, wage differentials calculated between smokers and former

smokers is likely to suffer from selection bias.




PREVIOUS LITERATURE


        Empirical studies on the earnings effects of tobacco use have found

significant wage penalties attached to smoking, ranging from 2% to 24% (Levine,

Gustafson, and Velenchik, 1997; Heineck and Schwarze, 2003; van Ours, 2004;

Auld, 2005). As noted, however, most existing analyses focus exclusively on the

current smoking status of individuals and calculate the smoking wage penalty as the

average difference in wages between smokers and current non-smokers.2

        The group of past smokers has been long neglected as distinct group in

empirical analyses of smoking and wages, although it is the relevant comparison

group for a smoker that considers to quit. Levine et al. (1997) show, albeit

descriptively, that workers in the U.S. who quit smoking between 1984 and 1991

exhibit higher wage growth than workers who did not change their smoking status in

2
  A notable exception is the study by Lee (1999) who, however, uses only very crude earnings
information: the average earnings in the occupational category of an individual.

                                                                                               5
this period. More recently, Anger and Kvasnicka (2009) first point to the general

failure in the literature to control for past smoking behavior of individuals, and show

that confounding past smokers and never smokers leads to biased estimates of the

smoking wage penalty. They also provide first evidence for a smoking wage

premium for past smokers, but did not control for unobserved heterogeneity in their

analysis which is based on cross-sectional data for Germany.

       Most previous studies on the smoking wage penalty are based on cross-

sectional data or use only two or three years of panel data (e.g. Heineck and

Schwarze 2003, Levine et al. 1997). More recent studies by Brune (2007) and

Braakmann (2008) use longer-running panel data from the British Household Panel

Study (BHPS). Brune (2007) shows that the smoking wage penalty found in a cross-

sectional analysis is substantially reduced (to 2%) when using panel data methods

that control for unobserved individual heterogeneity. Using OLS regressions, he

provides indirect evidence on the wage premium for past smokers, as there are no

wage differentials discernable in his data between never smokers and quitters that

have stopped smoking more than 2 years ago. However, one of the shortcomings in

his study is that the identification of past smokers is based on retrospective data

without taking into account measurement error. Braakman (2008) uses annual data

from the BHPS, and finds likewise a substantial reduction in the smoking wage

penalty, as soon as unobserved heterogeneity is controlled for. Moreover, he finds

only small returns from starting or stopping to smoke within the observation period

relative to remaining smoker or non-smoker. However, he does not fully exploit the

smoking history of individuals, and treats past smokers who stopped smoking before

the sampling period as never smokers.




                                                                                      6
       Previous studies on smoking and wages base their analysis unexceptionally

on all individuals, i.e. current smokers, former smokers, and never smokers despite

the differences in initial conditions for these groups. However, to analyze the wages

of smokers, the group of never smokers does not seem to be the appropriate

counterfactual, as current smokers merely have the choice between remaining a

current smoker and becoming a former smoker by quitting. In order to isolate wage

effects of smoking from selection effects into smoking the sample should be

restricted to ever smokers. Likewise, the analysis of wage effects of smoking

cessation should also be based on ever smokers only.




DATA AND SUMMARY STATISTICS


       We use data from the German Socio-Economic Panel (SOEP), a

representative longitudinal survey of individuals in Germany conducted annually

since 1984 (see Haisken-DeNew and Frick, 2005 for a description of the dataset). In

the 2002 wave of the SOEP, extensive information on the current and past smoking

behavior of individuals was sampled, including whether or not individuals had

smoked regularly in the past. Unlike many previous studies, we may hence

distinguish between three groups of workers in our data: current smokers, past

smokers, and individuals that have never smoked. In addition, current and past

smokers are asked at what age they had started to smoke, and past smokers are asked

for the year when they had quit smoking. Hence, the data allows us to calculate the

duration of smoking for all ever smokers as well as the quitting duration for past


                                                                                        7
smokers. By matching the retrospective information on smoking behavior revealed in

2002 to the information on earnings and socio-economic characteristics in earlier

waves, we end up with an overall panel dataset for the period 1984-2002.

       We restrict the estimation sample to workers who have ever regularly smoked

cigarettes in their life, i.e. who are current smokers or former smokers. We choose

this restriction in order to have a more homogeneous sample of individuals, since in

contrast to never smokers, all of them have once started smoking. Given the initial

condition of tobacco consumption at any point in life, one does not have to deal with

the problem of non-random selection into smoking, which would require to model

the starting decision.

       Furthermore, we select only male workers that are of German nationality,

aged 27-55, work between 10 and 60 hours a week, earn a gross hourly wage of at

least €4, and live in West Germany. These restrictions are imposed to ensure

comparability with estimation samples used in the existing literature. Our results,

however, do not hinge on these restrictions. Furthermore, we drop cases with missing

information on the relevant variables. As we need the information on the starting age

for ever smokers and additionally the stopping year for past smokers to calculate the

length of the smoking duration and the time since they have quit, we drop all ever

smokers without information on the time when they started to smoke from the

sample. We also drop past smokers for whom the calendar year in which they

stopped smoking is not available. Hence, due to more missing information of past

smokers, they are slightly underrepresented in our sample, and the descriptive

statistics cannot be generalized to the whole population. However, since we assume

that the missing information on starting age and stopping year is random, we do not

expect a selection bias here.


                                                                                        8
        A potential problem in the retrospective data on past smoking behavior

(starting age, stopping year) may be that the data suffer from recall error, since

survey respondents may misstate the true timing of an event (heaping). Lillard, Bar,

and Wang (2008) provide an extensive overview of measurement errors in

retrospective data, differentiating between calendar heaping, age heaping, and time

heaping. They show that a mismatch between the true and a reported date causes

attenuation bias. Figure 1 displays the age when started to smoke for all ever smokers

in our sample, as reported by the respondents. For the great majority of ever smokers

the start of tobacco consumption takes place during adolescents. While most of them

report a starting age of 16 or 18, it is clearly visible that respondents who started to

smoke later than 16 or 18 tend to report an age which is a multiple of five (20, 25,

30, 35, 40). Hence, the event of starting to smoke is rounded to multiples of age of

five, although the start of tobacco consumption should naturally occur over years.

Likewise, past smokers seem to round the reported calendar year, when asked for the

year when they stopped smoking. Figure 2 shows that the event of stopping to smoke

is “heaped” to multiples of 5, and even stronger to multiples of 10, as there are clear

peaks for the calendar years 1960, 1970, 1980, 1990, and 2000.3 To solve the

problem of measurement error from heaping, we follow Lillard, Bar, and Wang

(2008) who suggest to include parametric controls in the regressions to account for

the presence and for the type of heaping. Hence, we include a set of dummy variables

which indicate whether a respondent might possibly have rounded his reported age of

starting to smoke or year of quitting to units of 5 in age, time, or calendar year in all

our regressions.

3
  The year 2001 was most frequently reported as stopping year, which can presumably be explained
by the survey on smoking behavior having been conducted in 2002. This phenomenon is not
necessarily due to recall error, as many past smokers might actually have stopped “last year”. We do
not have information on how many of them restarted smoking after the interview, but it is unlikely
that stopping was a definite decision for all of them.

                                                                                                       9
                            – Figure 1 about here –

                            – Figure 2 about here –



           Our dependent variable is the log of gross hourly wages (calculated from

gross monthly earnings and actual weekly hours of work). Covariates included in all

regressions are age, age squared, two sets of indicator variables for the respectively

highest schooling and professional degree obtained, and year dummy variables.

Variables that we use in our 2SLS regressions as instruments for past smokers

include a dummy for early age of smoking initiation (< 16 years), used also by van

Ours (2004), and dummies for co-residing with at least one no-time smoker (NS),

respectively one past smoker (PS). Current smoking status (either smoker or non-

smoker, i.e. past smoker in our sample) is determined by both past and current

factors influencing respectively the probabilities of smoking initiation and

(successful) smoking cessation. Our first instrument intends to capture systematic

differences between individuals at young age that affect their probability of early

smoking initiation when still residing with parents, such as parental smoking

behavior (see, for example, Bantle and Haisken-DeNew, 2002). The co-resident

variables, in turn, are used to capture potential influences on current smoking

behavior and (successful) smoking cessation probabilities of current co-residing

household members.4 Our instruments are strong (F-tests) and valid

(overidentification test), i.e. uncorrelated with the error term in the wage equations.

           Summary statistics on workers in our estimation sample are provided in Table

1. As mentioned above, the share of current and past smokers is not representative


4
    Clark and Etilé (2006) provide evidence on spousal correlation in smoking behavior.

                                                                                          10
for the whole population, as relatively more past smokers had to be dropped from the

sample due to missing information on the year when they had stopped smoking. As is

evident, past smokers in our sample differ markedly from current smokers in

productivity-related characteristics. While there are no significant differences in

educational degrees, past smokers in the sample are on average older and have higher

professional qualifications. Current and past smokers furthermore exhibit

substantially different likelihoods of co-residing with either a no-time smoker or a

past smoker, a feature we exploit in our 2SLS regressions. Finally, note that average

hourly wages of past smokers clearly exceed those workers who still smoke.



                        –   Table 1 about here –




ESTIMATION METHODS


       In the following, we analyze the wage effects from smoking cessation using a

standard Mincer-type specification of the earnings regression which is augmented by

measures of (past) smoking behavior. Let yit be the log of individual i’s hourly wage

at time t, xit individual characteristics, and sit the smoking status of the individual.

The estimated functions are based on the typical form:

        yit = xit β + sitγ + α i + uit
               '


       where x is a vector of individual characteristics assumed to be related to

wages, s is the dummy variable for being a past smoker, β and γ are the

corresponding parameter vectors to be estimated, αi is the individual specific effect,

and uit denotes the idiosyncratic error term.

                                                                                           11
       We include covariates as outlined above and estimate the wage of smoking

cessation using three different models. First, as a benchmark we use a pooled OLS

model which does not take into account unobserved worker heterogeneity, as it

assumes the unobserved individual specific effect αi to be identical for all persons.

Any violation of this assumption will lead to biased estimates. In addition to a pooled

OLS model, we will hence estimate a random effects model, which assumes the

individual specific effect αi to differ across individuals but to be constant over time.

The individual effect αi is assumed to be randomly distributed across individuals and

not to be correlated with the set of explanatory variables. To allow for correlation

between αi with the explanatory variables, a fixed effects model will be estimated.

       Moreover, not only the individual specific effect αi but also the idiosyncratic

error term may be correlated with the smoking variable, as earnings and smoking

behaviour may be simultaneously determined. Another problem remains the

inaccurate measurement of the smoking status due to potential recall error with

respect to the retrospective questions on starting age and stopping year. Changes in

the smoking status over time may hence be significantly affected by measurement

error. To account for potential unobservable factors that might affect both smoking

behavior and individual earnings and to deal with the problem of measurement error,

we in addition instrument past smoking status by applying two stage least squares

regressions (2SLS). OLS and 2SLS are the two most commonly used methods in the

literature on smoking and wages (cf. van Ours, 2004; Heineck and Schwarze, 2003;

Levine et al. 1997). We use the instruments for past smoking status discussed above




                                                                                        12
which show to be sufficiently correlated with smoking cessation, but are uncorrelated

with the error term in the wage equation.5




REGRESSION RESULTS


        Table 2 contains the results of our regression analyses from the pooled OLS

model (Model 1), the random effects model (Model 2) and the fixed effects model

(Model 3). In line with previous findings by Anger and Kvasnicka (2009), the simple

POLS regression shows that past smokers experience a sizable wage premium

relative to current smokers. The coefficient is highly statistically significant and

amounts to 5.4%, a wage mark-up that reduces to 2.4% if one controls for

unobserved heterogeneity by using a RE model. In the FE model, the coefficient on

past smoking changes sign, yet misses statistical significance. All of the applied

models above have been tested against each other. The Breusch and Pagan multiplier

test revealed for all estimates superiority of the random effects over the pooled OLS

model, whereas the result of the Hausman specification test was in favor of the fixed

effects model. This implies that the wage gains from smoking cessation found in the

POLS model can be explained by a pure selection on (time-invariant) unobserved

characteristics which are correlated with the explanatory variables.



                           –    Table 2 about here –




5
  As in any application using IV methods, the appropriateness of our instruments may or may not be
questioned. However, we use these instruments as they showed not to be endogenous in our data. The
tests of overidentifying restrictions have been carried out using the Stata ado file by Baum, Schaffer,
and Stillman (2003).

                                                                                                     13
       Instrumenting past smoking status to account for potential endogeneity and

measurement error confirms these findings. Table 3 shows that smoking cessation is

associated with a wage premium in the 2SLS model (Model 1), whereas estimating

IV panel regressions leads to a much smaller coefficient on past smoking (Model 2),

a change in sign (Model 3), and insignificant results in both cases. Again, the results

in Table 3 do not support the existence of a causal wage effect of smoking cessation.

Workers who stop smoking seem to have favorable unobserved (time-invariant)

characteristics, which drive higher earnings of past smokers.



                       –   Table 3 about here –



       If former smokers earn more due to non–random selection out of smoking,

they should have the same positive unobserved characteristics even shortly before

quitting. As an alternative identification strategy we therefore investigate whether

past smokers are remunerated for being a past smoker before they stop smoking, an

indicator that should capture productive unobserved worker characteristics. We

restrict our sample to current smokers and employ future quitting as control variable,

a dummy variable for quitting within the next 3 years. Table 4 shows results from

POLS, RE, and FE models with the same control variables as used in the regressions

above. The estimates from the POLS (Model 1) reveal that past smokers earn a wage

premium even before they stop smoking. The wage premium for future quitters is

about 3% and statistically significant at the 10% level. Although the wage mark-up is

clearly smaller and less significant than the wage effect of smoking cessation for past

smokers who have already quit (Table 2), it is evident that (future) quitting captures

unobserved individual characteristics which are remunerated well in the labor


                                                                                       14
market. The results of the RE and FE models (Models 2 and 3) support this finding,

as the future quitting coefficient becomes very small and statistically insignificant, as

soon as unobserved worker heterogeneity is controlled for. Overall, the results above

uniformly speak against any causal wage effects from smoking cessation, as

unobserved individual characteristics seem to drive earnings differences between

current and past smokers. The apparent wage gain attached to smoking cessation

seems to be the result of a pure selection effect.



                       –   Table 4 about here –




Durations of smoking and quitting


       So far we have looked at average wage effects for all past smokers who

turned out to possess individual productivity enhancing characteristics, which are on

average superior to those of workers who do not stop smoking. However, the group

of past smokers is heterogeneous itself, and while average wage gains attached to

quitting can be attributed to a selection effect, some worker groups might benefit

from a causal wage effect of smoking cessation. The group of past smokers consists

of individuals with both long and short smoking and quitting durations. Smoking for

a short period may have smaller adverse effects on health outcomes and career

opportunities. As a result, the earnings capacity of short-time smokers might be

reduced by less than those of workers who smoke for decades. Likewise, past

smokers who quit a long time ago might have enough time to recover in terms of

health, career, and wages from any negative effects of smoking. Of course, smoking

and quitting durations may again relate to productivity relevant individual

characteristics.

                                                                                      15
       To investigate short-term versus long-term effects from smoking behavior we

first analyze whether wage effects from smoking and smoking cessation vary by

smoking duration. As shown in Table 2, smokers have a higher average smoking

duration (23.4 years) than past smokers (17.8 years). Controlling for smoking

duration in the regression models reveals that past smokers benefit from wage gains

even when (time-invariant) unobserved worker characteristics are taken into account

(Table 5, Models 2 and 3). The fixed effects estimation, which is our preferred

model, shows a wage premium for past smokers of 15.3% which is however reduced

by every additional year an individual has smoked in the past by 1.4%. The wage

penalty attached to smoking duration for former smokers implies that even past

smokers suffer a wage penalty if they have smoked for more than 11 years. This is in

contrast to current smokers for whom the length of tobacco consumption does not

seem to affect earnings if unobserved effects are controlled for, as the main effect of

smoking duration is very small and not statistically significant (Models 2 and 3). As

this wage impact for past smokers come into effect beyond any selection, the

duration of past smoking seems to causally affect earnings. Hence, negative wage

effects from smoking are persistent and have the power to harm earnings even after

quitting.



                       –   Table 5 about here –



       Second, we analyze whether wage effects from smoking cessation vary by the

time elapsed since a past smoker has quit smoking. The average quitting duration of

past smokers is about 11.7 years (Table 2), which would give the average past

smoker some time to recover from any causal wage penalty attached to smoking.


                                                                                     16
According to the results in Table 6, the wage premium for past smokers increases

with quitting duration, and is of similar size and statistical significance in all of the

models. The wage premium attached to an additional year of quitting is about 0.5%,

which amounts to a wage premium for the average former smoker of almost 6%.



                        –   Table 6 about here –



       Furthermore, we analyze non-linear wage effects from the length of quitting

in depth by differentiating between past smokers with different quitting durations.

This further allows us to analyze the persistence of wage effects from smoking and

smoking cessation. Hence, we split all former smokers according to their time since

quitting, and compare their wage differentials relative to current smokers. We use

quitters who have stopped smoking less than 5 years ago as reference category,

which represent about 20% of all former smokers, and include indicators for having

quit 5 to 10 years ago (25% of former smokers), 10 to 20 years ago (37% of former

smokers), and more than 20 years ago (18% of former smokers). Table 7 shows the

according estimates from the POLS, RE, and FE models. For past smokers who quit

smoking only recently (past smoker reference group), the POLS reveals a wage

premium of 3.3% (Model 1) which however vanishes completely when unobserved

worker characteristics are controlled for (Models 2 and 3). The results from the

preferred model, the FE estimates, reveal that there is no wage premium for recent

quitters. However, as is evident from the FE model, there are causal wage effects

from smoking cessation for past smokers who quit at least 5 years ago. The wage

premium attached to smoking cessation increases with the length of the quitting

period, being as high as 6% for former smokers who quit more than 20 years ago.


                                                                                            17
This finding again points to persistent causal wage effects of smoking which exist

even after quitting, and become weaker after time.



                             –    Table 7 about here –



           To sum up, the results of the above regressions point to an average wage

premium for past smokers compared to current smokers, which however vanishes

when controlling for (time-invariant) unobserved worker heterogeneity. Although the

first part of the analysis suggests that the apparent wage gain attached to smoking

cessation is the result of a pure selection effect, further in-depth investigation reveals

that causal effects come into play when the full (past) smoking history is taken into

account. The differentiation between past smokers according to their smoking

duration and quitting durations allows the identification of long-term and short-term

effects. The finding in the second part of the analysis that workers who did not

smoke for very long or who quit a long time ago benefit in their earnings from

quitting smoking point to causal effects of smoking and quitting on earnings. Both a

selection effect and a causal effect of smoking cessation hence seem to underlie the

average wage gains attached to quitting in our data.

           As a robustness check, we include further control variables for marital status,

occupation, blue collar worker, tenure, public sector, firm size, region, and industry

dummies in our regressions, but did not find any significant changes in the results.

Our findings are also robust to various changes in the estimation sample, such as the

expansion of the age cohort to older workers, the omission of the minimum hourly

wage restriction, and the increase in the lower threshold for weekly hours.6


6
    Results are available from the authors upon request.

                                                                                        18
DISCUSSION AND CONCLUSION


       While numerous studies have investigated the consequences of smoking for

wages, there is a lack of evidence on the earnings effects of smoking cessation.

Quantifying the wage effects of smoking cessation is not trivial, as they are unlikely

to be the mere negative of the smoking wage penalty found in the literature. Most

existing studies on the smoking wage penalty do not differentiate between former

smokers and never smokers, which is a major shortcoming because former and never

smokers might systematically differ in unobserved productivity-relevant

characteristics. Furthermore, smoking may have adverse wage effects that persist

even when individuals quit.

       In this paper, we study the wage effects of smoking cessation using panel data

from Germany. In order to achieve greater homogeneity in our estimation sample we

restrict the analysis to ever smokers, and analyze the earnings of former smokers

relative to current smokers. Results from pooled OLS wage regressions point to a

significant wage premium for past smokers relative to smokers. Fixed effects panel

regressions of wages on smoking status show that this premium partially stems from

a selection effect. However, additional analyses that control also for (past) smoking

duration and time elapsed since quitting produce evidence for a positive effect of

smoking cessation on wages for individuals who did not smoke very long or who

have quit long ago. We hence conclude that both a selection effect and a causal effect

of smoking cessation seem to underlie the average wage gains attached to quitting.




                                                                                     19
       The findings in this paper are conditional on being a male full-time worker in

the sample. If smokers are more likely to suffer health problems which hinder them

to work (full-time), or if smokers are more strongly affected by sample attrition due

to unobserved individual characteristics, the wage premium attached to smoking

cessation will be underestimated. However, we leave the correction for these

selection effects for future research. Additional future research should be directed

towards the analysis of the unobserved factors which drive the selection out of

smoking to further illuminate the selection effect of smoking cessation.

       Our findings of a causal effect of smoking cessation for short-time smokers

and long-term quitters provide an additional incentive to smokers to quit.

Furthermore, the revealed wage gains from smoking cessation provide a valuable

argument policy makers and health authorities could fruitfully employ in smoking

cessation campaigns. The simple message to smokers should be to stop as soon as

possible and to quit forever.




ACKNOWLEDGEMENTS


We are grateful to seminar participants at the University of Aberdeen and at the

University of Dundee. All remaining errors are our own.




                                                                                       20
REFERENCES


Anger, S., Kvasnicka, M (2009) Does smoking really harm your earnings so much?

       Biases in current estimatesof the smoking wage penalty, Applied Economics

       Letters, forthcoming.

Auld M. C. (2005) Smoking, drinking, and income, Journal of Human Resources,

       40(2), 505-518.

Bantle C., Haisken-DeNew J. P. (2002) Smoke signals: The intergenerational

       transmission of smoking behavior, DIW Discussion Paper, 277.

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                                                                               21
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                                                                               22
Figure 1: Age Heaping in Retrospective Measure of Age When Started Smoking



       .8
       .6
   Density
    .4 .2
       0




              10              20                         30               40
                              Ag e W he n S ta rted To S m o ke


 Source: SOEP 2002




Figure 2: Calendar Year Heaping in Retrospective Measure of Year When
Stopped Smoking
       .25
       .2
       .15
   Density
       .1
       .05
       0




             19 60   1 97 0              1 98 0                19 9 0   20 00
                              W h en G ave U p S m oki ng , Ye ar


 Source: SOEP 2002



                                                                                23
Table 1: Summary Statistics on Workers Who Have Ever Smoked

                                      Current smokers   Past smokers

      Dependent variable
       Hourly gross wage (in €)         13.0 (5.1)       14.8 (7.1)

      Controls
       Age (in years)                   39.7 (8.4)       45.8 (8.7)

      Education (share)
       No school degree                    0.55             0.55
       Primary                             0.22             0.20
       Secondary                           0.20             0.24
       Higher                              0.02             0.01

      Professional degree (share)
       No Professional degree              0.11             0.07
       Vocational training                 0.76             0.74
       University                          0.14             0.18

      Smoking history
       Smoking duration                  23.4 (8.5)      17.8 (8.7)
       Quitting duration                     -           11.7 (7.7)

      Instruments
       Co-residing with (share)
        No-time smoker                     0.33             0.49
        Past smoker                        0.08             0.20
       Starting age less 16 (share)        0.24             0.23
      N (Total = 9,766)                    6,790           2,976

      SOEP 1984-2002




                                                                       24
Table 2: Regression Results for the Wages of Past Smokers

                                  POLS             RE          FE

                                 Model 1         Model 2     Model 3

Past smoker                     0.0542***       0.0243**     -0.00160
                                (0.00834)        (0.0115)    (0.0142)
Age                             0.0489***       0.0484***   0.0776***
                                (0.00307)       (0.00255)   (0.00265)
Age sqr                         -0.0005***     -0.0005***   -0.0005***
                                 (0.0000)        (0.0000)    (0.0000)
Lower secondary school          0.0760***       0.0496**     -0.00953
                                 (0.0184)        (0.0231)    (0.0304)
Intermediate secondary school   0.193***        0.129***     -0.0106
                                 (0.0190)        (0.0245)    (0.0333)
Higher secondary school         0.261***        0.182***     -0.00419
                                 (0.0203)        (0.0261)    (0.0359)
Vocational training             0.0727***       0.0558***   0.0364**
                                (0.00973)        (0.0123)    (0.0145)
University                      0.279***        0.221***     0.0499*
                                 (0.0146)        (0.0203)    (0.0294)
Constant                        0.824***        0.849***     0.127**
                                 (0.0669)        (0.0580)    (0.0648)
Year dummies                        +               +           +
Heaping controls                    +               +           +
Observations                      9,766           9,766       9,766

      SOEP 1984-2002




                                                                         25
Table 3: Regression Results for the Wages of Past Smokers using Instrumental

Variables

                                   IV           IV RE           IV FE

                                 Model 1        Model 2        Model 3

Past smoker                     0.155***        0.0247          -0.223
                                 (0.0448)       (0.0878)        (0.142)
Age                             0.0491***      0.0482***       0.0787***
                                (0.00309)      (0.00256)       (0.00279)
Age sqr                         -0.0005***    -0.0005***      -0.0005***
                                 (0.0000)       (0.0000)       (0.0000)
Lower secondary school          0.0689***      0.0561**         -0.0146
                                 (0.0188)       (0.0226)       (0.0310)
Intermediate secondary school   0.184***       0.141***         -0.0128
                                 (0.0196)       (0.0242)       (0.0339)
Higher secondary school         0.247***       0.195***        -0.00884
                                 (0.0214)       (0.0258)       (0.0365)
Vocational training             0.0695***      0.0582***       0.0356**
                                (0.00990)       (0.0122)       (0.0147)
University                      0.278***       0.231***         0.0470
                                 (0.0147)       (0.0203)       (0.0299)
Constant                        0.834***       0.841***          0.106
                                 (0.0675)       (0.0581)       (0.0672)
Year dummies                        +              +               +
Heaping controls                    +              +               +
Observations                      9,766          9,766           9,766

      SOEP 1984-2002




                                                                           26
Table 4: Regression Results for the Wages of Future Quitters

                                      POLS           RE           FE

                                     Model 1       Model 2      Model 3

Future Quitting within next 3 yrs    0.0288*       0.0216       0.00806
                                     (0.0163)      (0.0139)     (0.0152)
Age                                 0.0497***     0.0485***    0.0765***
                                    (0.00375)     (0.00323)    (0.00338)
Age sqr                             -0.0005***   -0.0005***    -0.0005***
                                     (0.0000)      (0.0000)     (0.0000)
Constant                            0.815***      0.854***     0.218***
                                     (0.0793)      (0.0697)     (0.0770)
Schooling degrees                       +             +            +
Professional degrees                    +             +            +
Year dummies                            +             +            +
Heaping controls                        +             +            +
Observations                          6,790         6,790        6,790

          SOEP 1984-2002




                                                                           27
 Table 5: Regression Results for the Wages of Smokers and Past Smokers
           According to Smoking Duration
                                      POLS            RE             FE

Past smoker                          0.112***      0.0919**        0.153**
                                     (0.0354)       (0.0464)       (0.0739)
Past smoker x smoking duration       -0.00443       -0.00648      -0.0137**
                                    (0.00319)      (0.00414)      (0.00646)
Past smoker x smoking duration       4.50e-05      0.000103       0.000253*
squ
                                     (0.0000)       (0.0000)       (0.0000)
Smoking duration                     0.00424*      -0.000271       -0.00235
                                    (0.00245)      (0.00261)      (0.00319)
Smoking duration squ                -0.0001**       -0.0001        -0.0001
                                     (0.0000)       (0.0000)       (0.0000)
Age                                 0.0441***      0.0497***      0.0814***
                                    (0.00398)      (0.00388)      (0.00449)
Age sqr                            -0.000417***   -0.000448***   -0.000483***
                                     (0.0000)       (0.0000)       (0.0000)
Schooling degrees                          +           +              +
Professional degrees                       +           +              +
Year dummies                               +           +              +
Heaping controls                           +           +              +
Constant                             0.868***      0.814***         0.0535
                                     (0.0721)       (0.0670)       (0.0785)
Observations                          9,766          9,766          9,766


           SOEP 1984-2002




                                                                              28
Table 6: Regression Results for the Wages of Past Smokers According to
        Quitting Duration
                               POLS          RE            FE
                              Model 1      Model 2      Model 3

  Quitting duration          0.00509***   0.00484***   0.00448**
                             (0.00131)    (0.00158)     (0.00188)
  Quitting duration squ       -0.00006     -0.00005     -0.00005
                              (0.0000)     (0.0000)     (0.0000)
  Age                        0.0499***    0.0506***    0.0799***
                             (0.00309)    (0.00260)     (0.00273)
  Age sqr                    -0.0005***   -0.0005***   -0.0005***
                              (0.0000)     (0.0000)     (0.0000)
  Constant                    0.811***     0.818***      0.0962
                              (0.0672)     (0.0586)     (0.0655)
  Schooling degrees              +            +            +
  Professional degrees           +            +            +
  Year dummies                   +            +            +
  Heaping controls               +            +            +
  Observations                 9,766        9,766        9,766

            SOEP 1984-2002




                                                                    29
  Table 7: Regression Results for the Wages of Past Smokers According to
           Quitting Duration Periods


                                         POLS          RE           FE
                                        Model 1      Model 2      Model 3

Past smoker                            0.0332***      0.0106      -0.00725
                                        (0.0129)     (0.0123)     (0.0146)
Past smoker x quitting 5 to 10 yrs      0.00328     0.0267***    0.0258**
                                        (0.0150)     (0.0102)     (0.0107)
Past smoker x quitting 10 to 20 yrs    0.0370***    0.0401***    0.0327**
                                        (0.0139)     (0.0115)     (0.0131)
Past smoker x quitting > 20 yrs        0.0384**     0.0625***    0.0599***
                                        (0.0167)     (0.0162)     (0.0191)
Age                                    0.0492***    0.0494***    0.0786***
                                       (0.00308)    (0.00258)    (0.00268)
Age sqr                                -0.0005***   -0.0005***   -0.0005***
                                        (0.0000)     (0.0000)     (0.0000)
Constant                               0.823***     0.837***       0.116*
                                        (0.0671)     (0.0583)     (0.0651)
Schooling degrees                          +            +            +
Professional degrees                       +            +            +
Year dummies                               +            +            +
Heaping controls                           +            +            +
Observations                             9,766        9,766        9,766

           SOEP 1984-2002




                                                                            30

				
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