The long-run effect of cigarette taxes on smoking:
evidence from the Canadian Community Health Survey
Mahmood Zarrabi1 and M. Christopher Auld2
Prepared for the CEA conference at Vancouver, BC
June 2008
Abstract
Smoking control policies are legitimate if they effectively reduce the
smoking rate of the population. It is argued that if a smoking control
policy prevents youths from smoking, it will reduce the smoking rate of
the population in the long run. We test this argument using a sample of
95,000 individuals from the Canadian Community Health Survey. We
exploit dramatic changes in cigarette taxes across the Canadian provinces,
particularly increased in tobacco taxes in 1991-1994 and the tax
discrepancy across the provinces since then, and examine the effect of
cigarette taxes encountered at youth on probability of smoking and
smoking intensity among adults aged 19-40. We found cigarette taxes
encountered at youth have very weak effects on likelihood of smoking and
smoking intensity of respondents in adulthood, and the effects attenuated
by late 20s. Using simulation method, we find counter factual 50 percent
increase in cigarette taxes at youth would reduce the proportion of the
smokers aged 20-29 years by roughly 2 percent.
1
PhD candidate, department of economics at the University of Calgary; gmzarrab@ucalgary.ca
Associate professor, department of economics at the University of Calgary; auld@ucalgary.ca
We thank the Statistics Canada for providing the data of this research.
2
1. Introduction
Prevalence of cigarette smoking among youths and adults has been a research
object over the last few decades. However, less effort has been taken to identify and
quantify the relationship between youth and adult smoking. Becker and Murphy (1988)
argue smoking addiction requires current smoking to be correlated to past smoking. On
the other hand, the relationship between youth and adult smoking might be mapped
through the effect of an unobservable factor that persistently affects smoking behavior of
individuals over time. Assuming smoking is mainly affected by an unobservable factor
rather than to be derived by the addictive characteristic of smoking, discouraging youths
from smoking may result in smoking initiation to be postponed without a substantial
effect on the smoking rate of the population.
Regardless which theory, however, explains better the inter-temporal correlation
of smoking, it is important for policy implication to quantify the long run effects of
smoking control policies that target youths to reduce the smoking rate of the population.
This paper examines the effect of cigarette taxes encountered at youth on smoking
behavior in adulthood.
We are interested to test the effect of cigarette taxes because among different
policies prevail in most of the developed countries such as tobacco taxes, clean indoor air
laws, restriction on cigarette use for the youth and health warnings, there is a universal
agreement that cigarette tax is more effective policy to control smoking (Warner,
Chaloupka et al. ,1995). Particularly, we are concerned about the long run effect of
cigarette taxes because many studies conclude most of the smokers started smoking at
youth, cigarette tax reduces smoking initiation among youths, and youth smoking is
2
more price sensitive than adult smoking3, so they mainly conclude preventing youths
from smoking will reduce the smoking rate of the population. In addition, dramatic raise
in cigarette taxes in Canada in 1991-1994 and the tax discrepancy across the Canadian
provinces since then, besides the CCHS data recovering years 2001-2005, provide us a
sample of adolescents and adults who faced disproportion cigarette taxes as youths, and
this suits us to verify the long run effect of cigarette taxes on smoking.
Empirical studies concerning the contemporaneous effect of cigarette taxes
conclude cigarette demand is inelastic with estimated elasticities in the range -0.5 to -0.7
among youths and in the range 0-0.25 among adults (Chaloupka and Warner, 2000;
Evans et al., 1999). In this paper, we examine the long ran youth cigarette tax elasticity of
adult smoking. We use data on a sample of individuals from the Canadian Community
Health Survey (CCHS) cycles 2001, 2003, and 2005. We count for entire cigarette taxes
encountered at youth using average of cigarette taxes at age 14, 14-16, or 12-18 Canadian
youths faced in 1979-2004. Linear probability model, ordered-probit, and OLS
regressions are employed to examine the long run effect of youth cigarette taxes on
probability of smoking, smoking intensity, and smoking type of respondents aged 19-40
years in 2001-2005.
We found a weak but significant long term effect for cigarette taxes in which 10
percent increase in average cigarette taxes at age 12-18 will reduce probability of
smoking in adulthood by 1 percent. Also, cigarette taxes encountered in early life slightly
reduces smoking intensity of smokers. Moreover, we found the effect of cigarette taxes
encountered at youth attenuated by early adulthood.
3
Lewit, Coate, and Grossman (1981); Evans and Huang (1998); Harris and Chan (1999); Tauras and
Chaloupka (1999); Cawley, Markowitz, and Tauras (2004)
3
2. Related literature
The long-term effects of policies encountered early in life are often of policy and
academic interest. For instant, the effect of early initiation on adolescent smoking (Auld,
2005), the log run effect of youth tobacco control (Glied, 2002), the long term effect of
minimum wage on onset labor market outcome (Neumark and Nizalova, 2004), the long
term effect of legalizing abortion on crime rate (Donohue and Levitt, 2001), and the long
term effect of youth unemployment (Moraz and Savage, 2001).
Most of literature of cigarette tax effect is related to the contemporaneous effect
of cigarette tax/price on smoking initiation and cigarette consumption. Foster and Jones
(1999) using retrospective data find higher taxes are associated with later initiation.
Chaloupka and Wechsler (1997) predict 75 percent price increase in a pack of cigarette
would reduce number of smokers of ages 18 through 24 years by over 1.2 millions.
Warner et al. (1995) conclude that increase in cigarette price is the most effective policy
to influence smoking of groups of people for whom education has been less effective.
They conclude (p.386) “… value of increased taxation in discouraging children from
becoming addicted to nicotine was potentially the most powerful argument supporting
increased taxes”. Gruber and Koszegi (2000) show if smoking decision is timeinconsistent, then prohibiting youth from smoking may make them better off in later life.
In contrast, Douglas and Hariharan (1994) and Douglas (1998) find that current cigarette
prices are uncorrelated with smoking initiation.
Only a few researchers have studied the causal effect of past price on current
smoking. Auld (2005) uses a dynamic structural model to decompose youth smoking
pattern over time into the correlation of smoking over time (addiction) and an
4
unobservable heterogeneous effect of smoking intensity. He considers smoking at age 14
as an endogenous treatment on subsequent smoking. Using the Youth Smoking Survey of
Canada and using an endogenous switching binary response regression, he finds smoking
is highly addictive for all respondents, but for those whom were observed initiated early
is less addictive. He concludes a smoking control policy would reduce smoking rate of
the population if it could deter smoking initiation of early initiators, but not by a large
magnitude. Glied (2002) uses cross-cohort correlation to examine the effect of smoking
at age 21 on smoking at age 30 and 40, she also uses longitudinal analysis to test the
effect of cigarette tax at age 14 on overall smoking behavior, quitting behavior, or
initiation behavior. She finds for every smoker at age 21 there will be 3/4 smoker at age
30 and 0.55 smoker at age 40. Glied extracts advantages of the National Longitudinal
Survey of Youth (NLSY79) in the U.S. by observing actual cigarette taxes respondents
faced at age 14 and changes in smoking behavior of respondents over time, and finds
cigarette tax at age 14 has substantial effect on contemporaneous smoking, but the effect
attenuated by adulthood. She concludes difference in cigarette taxes respondents faced at
age 14 has no effect on their smoking behavior by age 40. Laux (2000) argues that those
who faced high taxes in youth may be more reluctant to initiate smoking as adult, and
adult smokers who faced high taxes as youth are likely to have begun smoking in later
adolescent. In contrast, some other researchers find youth tax policy has no effect after
adolescent (Orphnides and Zerovs, 1995; Survanovic et al., 1999; and Gruber and
Koszegi, 2000). Decicca, Knedel, and Mathios (2002) use model of onset smoking and
discrete-time hazard with state fixed effects, and find tax has no effect on onset smoking
between eighth and twelfth grades.
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3. Data
3.1 Canadian Community Health Survey
This paper uses a sample of individuals in the Canadian Community Health
Survey (CCHS). The CCHS is a cross-sectional survey that initially was conducted by the
Statistics Canada in 2001, and has been repeated every second year. Three cycles of the
data are available by now: cycles 2001, 2003, and 2005. Key advantages of using CCHS
data are, first the CCHS sample sizes are very large in which they enable researchers to
derive reliable estimates even from their sub-samples, e.g. this study restricts the sample
to individuals who aged 19-40 years old, and also it stratifies its estimate models by
gender. The CCHS sample sizes are 133300, 130700, and 128700 observations in cycles
2001, 2003, and 2005, respectively; second, tax changes in 1991-1994 provide lots of
variation in cigarette taxes encountered at youth for cohort who were adolescent/adult in
the CCHS cycles.
The CCHS collects information on health status, health care utilization, and health
determinants for the Canadian population. The CCHS operates on a two-year collection
cycle. The first year of the survey cycle is a large sample designed to provide a reliable
estimates at the health region level4. The second year of the survey cycle is a smaller
sample and is designed to provide provincial level results on specific focused health
topics. The CCHS targets persons aged 12 years old and older who are living in private
dwelling.
Current smoking status of a person is ascertained by a series of questions in the
CCHS. A person is considered as a daily smoker if he/she smoked each day in the last
4
There are 122 health regions across the provinces and one health region per territory in Canada, this paper
excludes information on the territories
6
month prior to the interview date. Number of cigarettes that a daily smoker smokes is
used to measure smoking intensity. Smoking type of a respondent including never
smoked, occasional smoker, or daily smoker is a discrete variable in the CCHS that takes
on value zero, 1, or 2, respectively.
Table 1 displays definition of the variables used in this paper and descriptive
statistics. Ethnicity of a person is ascertained from a question addressing the respondent’s
country of birth. Immigration status of a person is derived based on his current age and
year of landing in Canada. We count only for those immigrants who landed in Canada
at/prior an age at which the long run effect of cigarette tax encountered at that age is
going to be estimated. For example, consider a 40 years old respondent in cycle 2005, if
our model is going to estimate the effect of average cigarette taxes one faced at age 1416, a binary response variable is set to value 1 if the respondent is an immigrant who
landed in/prior to year 1979 in Canada, the binary variable takes value zero if the
respondent is not an immigrant or if he is an immigrant but landed in 1982 or later,
otherwise we set its value missing. The missing value for this person denotes he is an
immigrant who landed in Canada in a year within period 1980-81 for which a part of
cigarette taxes he faced at age 14-16 is not observable to us. Similar procedure has been
done for all immigrants aged 19-40 years old in the three cycles.
3.2 Cigarette Taxes in Canada
Tobacco in Canada has been subject to volatile taxes over time and across the
provinces. Particularly, federal government of Canada raised tobacco excise tax and duty
in 1991 which in result price of a pack of 200 cigarettes rose from roughly $35 in 1990 to
7
almost $45-$50 in 1991 across the provinces. From 1984 to 1991, overall Canadian
tobacco taxes had quadrupled while US taxes had increased by less than 50 percent,
resulting taxes averaging about seven times the US level, and consequently, raising
tobacco smuggling sailed from US-Canada borders into Canada (Studlar, 2002; p.145).
Cigarette prices in Canada had remained high till 1994 when federal government reduced
tobacco excise tax and duty. It was followed with reductions in retail tobacco tax among
most of the eastern provinces to control cigarette smuggling. These led to almost $14 to
$21 reduction in price of a pack of 200 cigarettes in Ontario, Quebec, New Brunswick,
Prince Edward Island, and Novo Scotia, while price of cigarettes remain relatively high
in most of the western provinces and in Newfoundland (Hamilton et al., 1997). Figure 1
displays changes in Canadian cigarette prices over time and across the provinces since
1979-2004. Abnormal increases in tobacco taxes in 1991-1994 and their rollback in 1994
and later on among the western provinces created a bulge in tobacco tax trend in Canada.
The large variations of cigarette taxes in Canada, and observing sample of individuals 10
years after the bump in cigarette tax trend suit our estimations to identify the long run
effect of cigarette taxes on smoking. We use three measures of taxes at youth to ensure
we count for all taxes attributed to smoking behavior of youths. We estimate
1. the long run effect of cigarette tax one faced at age 14,
2. the long run effect of average cigarette taxes one faced at age 14-16,
3. and the long run effect of average cigarette taxes one faced at age 12-18.
This paper uses cigarette price as a proxy of cigarette taxes. Cigarette prices are
highly correlated with cigarette taxes across the provinces in which change in cigarette
taxes are mainly reflected in the price changes. The cigarette taxes in Canada include
8
federal excise duty, federal excise tax, provincial tobacco tax, wholesale/retail margin,
provincial sales tax, and the federal G.S.T. from which, for example, a calculation from
cigarette prices and the cigarette taxes compiled by Non-Smoker’s Rights Association
(2003) indicates cigarette taxes count for 80%, 76%, 68%, and 69% of cigarette prices in
British Columbia, Alberta, Ontario, and Quebec in 1997, respectively.
Cigarette price indexes are obtained from the Statistics Canada. We adjusted
cigarette prices using provincial cigarette price indexes. The Statistics Canada has begun
to collect cigarette prices at provincial level since 1979. We are able to observe cigarette
taxes respondents faced as youths if we know which province they resided then, so we
use a sample of individuals aged 19-40 years old in 2001-2005. For instance, a 40 years
old respondent in 2005 was 14 years old in 1979, thus we can find cigarette tax he faced
as a youth if we know which province he resided at age 14. Figure 1 consists of four
panels displaying real cigarette price index across the provinces from 1979 to 2005, and
cigarette prices Canadian adults aged 19-40 years in 2001-2005 faced as youths.
4. Econometric methods
Parametric estimate models are used in this paper to examine the log run effect of
cigarette taxes. The long run effect of cigarette taxes, however, may not be completely
mapped by a linear function as there is 1 to 26 years lag between the tax incident and
when we observe smoking behavior of respondents. To avoid biased estimations that our
parametric methods impose by using linear function, we also estimate the effect of taxes
using semi-parametric methods.
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4.1 Parametric method
The causal effect of cigarette taxes encountered at youth on smoking behavior in
adulthood can be projected by a structural form model given as
Smoking youth ,i = α + β1 Pyouth ,i + Z iγ 1 + X youth ,iγ 2 + ui
(1)
Smoking adult ,i = δ + β 2 Padult ,i + ϕ Smoking youth ,i + Z iη1 + X adult ,iη 2 + ε i
(2)
where Smoking youth ,i and Smoking adult ,i denote smoking status of person i as a youth and
as an adult, respectively. Pyouth denotes cigarette price respondents faced as youth, Padult
denotes cigarette price respondents faced as adult. Z is a vector of time-invariant
explanatory factors may affect smoking status of a respondent including ethnicity and
immigration status, X youth and X adult are vectors of time-varying factors that might
explain smoking status of respectively a youth and an adult such as marital status, age,
family income, education, province of residence, household size, depression, and
pregnancy (for females). α and δ are intercept terms of youth and adult smoking
models, respectively. ui and ε i are error terms indicating idiosyncratic effects on youth
and adult smoking, respectively. Parameter ϕ estimates the causal effect of youth
smoking on adult smoking. Following Becker and Murphy (1988), ϕ measures how
addictive smoking is. The other notations, β1 , β 2 , γ 1 , γ 2 ,η1 , and η2 , are
structural
parameters of the model.
Since the CCHS data does not allow us to observe the smoking status of
respondents as youths, we can not estimate the structural model. We estimate a reduced
form model instead. The reduced form model is derived by substituting equation model
(1) into (2), and is given as
10
Smoking adult ,i = µ + β 2 Padult ,i + β 3 Pyouth ,i + Z i λ1 + X adult ,i λ2 + vi
(3)
where µ = δ + φα , β 3 = φβ1 , λ1 = φγ 1 + η1 , λ2 = η2 and vi = φ ui + ε i + φγ 2 X youth ,i . vi is the
reduced form model’s error term that consists of X youth plus a linear combination of the
idiosyncratic effects. We assume time-varying explanatory variables of youth smoking
model, X youth , are not correlated with cigarettes prices. By which, these omitted variables
in the reduced form do not cause endogeneity problem, and so the estimated effect of
cigarette taxes is consistent and unbiased if the classical linear regression assumptions
hold. The parameter of interest in the reduced form model is β 3 which estimates the
causal effect of cigarette tax one faced as a youth on his smoking behavior as an adult. It
consists of interaction between the causal effect of youth smoking on adult smoking and
the contemporaneous effect of cigarette taxes on youth’s smoking. β 3 is zero if either
smoking is not addictive and/or youth smoking is perfectly price inelastic.
In estimate of the reduced form model (3), we use three different dependent
variables each indicating different aspects of an individual’s smoking behavior. First we
estimate the effect of cigarette tax one faced as a youth on probability of daily smoking as
an adult using linear probability model. Second we employ OLS regression to estimate
the effect of cigarette taxes one faced as a youth on his smoking intensity as an adult. We
use smoking intensity of daily smoker respondents, and measure it as number of
cigarettes a respondent usually smokes every day. Third, we employ ordered-Probit
model to test the effect of cigarette taxes encountered at youth on smoking type of an
adult. The CCHS groups smoking type of its respondents into three categories: daily
smoker, occasional smoker, and never smoked, so dependent variable of the ordered-
11
probit model is a discrete variable taking on value zero, 1, or 2 for never-smoked,
occasional smoker, and daily smoker respondent, respectively.
Auld (2005) and Glied (2002) examine the effect of cigarette price a youth faced
at age 14 on adolescent and adult smoking. Their findings are tied to variation of cigarette
tax at age 14 among individuals in their samples, and are dependent to youths’ propensity
to initiate smoking at age 14 relative to earlier or later ages. Though, cigarette taxes are
highly correlated over time, but they are varying from year to year (figure 1), while
hazard of youth smoking stay high over the entire teenage period. For which, we count
for all cigarette taxes encountered at youth by using average cigarette taxes at age 14, 1416 and 12-18.
To control the effects of all observable-influent factors of smoking behavior, a set
of regressors are entered in the reduced form model; age variable is entered as a set of
dummies to control the age cohort effect; family income is entered as a set of dummies
indicating households with no income and households that their incomes fall within
certain categories (table 1); education variable of an adult is a dummy variable indicating
the highest level of education ever achieved by a respondent including less than
secondary education, secondary education, some post secondary education, or university
education.
Moreover, we assume CCHS’s respondents had not moved across the provinces
since youth. This assumption allows us to observe cigarette taxes respondents faced as
youths. This assumption, at first glance, seems to be very strong, but there are two
considerations which persuade us to rely on the assumption; first, although cigarette taxes
in Canada have substantially varied over time, but there had not been much disparity
12
across the provinces during 1979-1994 (figure 1); second, a calculation from the National
Population Health Survey in Canada shows roughly 10 percent of the respondents aged
12-40 years old in 1994 had moved across the provinces over a decade, i.e. from 1994 to
2003. Also, since cigarette prices are compatible in near by provinces, moving to a near
by province is less problematic to our estimations than moving from the west to the east
or vise versa.
We add two more dummies to the reduced form model to control the time effects
of the CCHS’s cycles. Error terms in the reduced form model are set to be clustered at
provincial level because respondents who live in same province face same regulatory
policies and similar environment. Furthermore, the estimate models are stratified by sex
to control the gender effect, and the estimations are robust to correct the covariance
matrix for heteroscedasticity problem.
To reexamine Glied’s finding regarding attenuation of cigarette taxes over time,
we test variation of the effect of cigarette taxes with age by modifying the reduced form
model to
Smoking adult ,i = µ + β 2 Padult ,i + β 3 Pyouth,i + β 4 Pyouth ,i .agei + β5 Pyouth,i .agei2
+ ∑ ρ j D j + Z i λ1 + X adult ,i λ2 +ν i
(4)
j
where age and age2 are respectively age and age-squared variables, Pi , youth .agei and
Pi , youth .agei2 are their interactions with cigarette prices at youth. D j indicate five-year age
group dummies including age group19-23, 24-28, 29-33, and 34-38. Other notations are
similar to the equation model (3).
Having run regression model (4), we test changes in the effect of cigarette taxes
with age by taking derivative from equation (4) respect with youth cigarette prices
13
∂Smoking adult ,i
∂Pyouth ,i
= β3 + β 4 .agei + β5 .agei2 = 0
(5)
The null hypothesis of joint signification test is H 0 : β 3 + β 4 .age + β 5 .age 2 = 0 as if age is
given.
4.2 Semi-parametric model
Linear probability model of equation (3) implies cigarette taxes encountered at
youth to affect smoking habits of respondents in adulthood via a linear channel. This
linearity assumption may cause estimated effect of cigarette taxes to be biased. For
which, we use semi-parametric technique that allows cigarette taxes at youth to affect
smoking behavior in adulthood through an unknown nonlinear function. Semi-parametric
estimate model is given as
Smoking adult ,i = f ( Pyouth ,i ) + Z i β + ε i
(6)
where f (.) is an unknown function with bounded derivatives, Z is a vector of all
explanatory variables included in the equation model (3) but youth cigarette price, and ε i
is i.i.d. mean-zero error term. It is assumed that the parametric terms are additively
separable from the non-parametric term.
We use the first order difference method to estimate the semi-parametric model
(6), also called partial linear regression model (Yatchew, 2003; Lokshin, 2006).
Moreover, we run sensitivity analysis of the non-parametric estimates to bandwidth.
Since the first difference method is inefficient because the differenced error terms may
14
have an MA(1) structure, we estimate the model with the second order difference and
compare the results.
5. Results
This section discusses the estimates of the models defined by equations (3), (4),
and (6). We group the results into four categories: First category includes parametric
estimates of the effect of cigarette taxes encountered at youth on probability of smoking,
smoking intensity, and smoking type of respondents in adulthood. Second category
discusses variation of the effect of cigarette taxes encountered at youth with current age
of respondents. Third category illustrates simulated the long run effect of cigarette taxes
on the proportion of the smokers using counter factual cigarette taxes. The last category
displays whether the effect of cigarette taxes encountered at youth affect smoking
behavior of an adult through a linear or a non-linear channel. All the estimate models in
the four categories are stratified by sex.
5.1 Parametric estimates
Estimated linear probability model of the effect of cigarette taxes a youth faced at
age 14, 14-16, or 12-18 on probability of smoking in adulthood is displayed in the first
two columns of table 2. The estimated effects show 10 percent increase in average
cigarette taxes at age 12-18 reduces probability of daily smoking in adulthood by 0.9
percent. Magnitude for the effect of average cigarette taxes at age 14-16 is the same for
females, but the effect on males is very weak and insignificant. Moreover, cigarette tax at
15
age 14, as Auld (2005) and Glied (2002) find, is not a determinant factor of probability of
smoking in adulthood for both genders.
The estimated effect of cigarette taxes one faced as a youth on his smoking
intensity as an adult is displayed in the last two columns of table 2. We use OLS
regression to examine the long-run effect of cigarette taxes on smoking intensity, and find
average cigarette taxes youths faced reduces their smoking intensity as adults, however
average cigarette taxes at age 12-18 is more robust than average cigarette taxes at age 1416 or at age 14 for males.
Contemporaneous cigarette taxes, however, are not determinant factors of adult
smoking propensity and estimated sign of the coefficients mainly do not match with our
expectation, while they affect adult’s smoking intensity with negative sign as we
expected. Their effects on smoking intensity, however, are more robust on males than
females.
Table 3 displays estimated the long run effect of cigarette taxes on smoking type
of a person using ordered-Probit model. The first two columns display how likely an
adult who has never smoked would remain a non-smoker if she had faced one percent
higher cigarette taxes at youth. We find the effect of average cigarette taxes at age 12-18
is stronger than the effect of average cigarette taxes at age 14-16 or at age 14, and also
the effect is more robust on females than males. Ten percent increase in average cigarette
taxes encountered at age 12-18 can increase probability of remaining a non-smoker by
0.4 percent. Columns three and four of the table display the effect of cigarette taxes
encountered at youth on likelihood of occasional smoking in adulthood. The CCHS does
not allow us to observe smoking status of a respondent in time prior to the interview date,
16
so if a person is found to be an occasional smoker at interview, it does not necessary
mean the respondent has been an occasional smoker all the time precede to interview.
Having said, we found the estimated effect of average cigarette taxes at age 12-18 is
stronger than the effect of average cigarette taxes at age 14-16 or at age 14, and it is more
robust on women than men. Ten percent increase in average cigarette taxes at age 12-18
reduces likelihood of occasional smoking in adulthood by 0.6 percent for females and 0.4
percent for males. Moreover, the last two columns in table 3 display the long run effect of
cigarette taxes at youth on likelihood of daily smoking in adulthood. The estimated
effects are consistent with the results from linear probability model in which 10 percent
increase in average cigarette taxes one faced at age 12-18 reduces probability of daily
smoking in adulthood by almost 1.3 percent for females and 1 percent for males.
All the estimate models include depression variable as an explanatory variable,
however a person’s mood may be correlated with her personality trait and so the
depression variable might be correlated with our models’ error terms. If so, the
correlation between depression variable and the error term causes an endogeneity
problem, and as a consequence the estimated parameters are spurious. However, if an
adult depression status is not correlated to cigarette taxes at youth and to concurrent
cigarette taxes, then excluding the depression variable should solve the endogeneity
problem. We find exclusion of depression variable does not have a substantial effect on
the estimated parameters (the results are not reported for space reserving).
5.2 Change in the effect of cigarette taxes with age
17
This section discusses whether the effect of cigarette taxes attenuates over time.
To do so, we investigate the joint effects of cigarette tax and its interactions with age and
age-squared. The estimated parameters are displayed in table 4. Also, figure 2 illustrate
variation of the effect of average cigarette taxes encountered at youth with current age of
respondents in adulthood.
We examine variation of the tax effect with age using the joint effects of cigarette
tax plus its interactions with age and age-squared as if age is given. The estimated results
show the effect of cigarette taxes encountered at youth is decreasing with age at a
decreasing rate. The joint effect forms an inverse u-shape against age and is illustrated in
figure 2 for female and male separately. The figure illustrates the effect of average
cigarette taxes encountered at age 12-18 on likelihood of daily smoking in adulthood, and
shows the effect of cigarette taxes attenuated by late 20s (the effects on smoking intensity
and smoking type of respondents present a similar pattern, but are not reported for space
reserving).
5.3 Simulation results
As we discussed in the last two sections, average cigarette taxes encountered at
youth is very weak determinant of adult smoking behavior with elasticity close to 0.1. To
visualize the magnitude of the long-run effect of taxes, we simulate the proportion of
daily smokers using actual and counter factual cigarette taxes. Perhaps, it is an interesting
to find that what would be the proportion of the smokers if federal government of Canada
had not raised cigarette taxes in 1991-1994. We simulated the proportion of the smokers
using our model assuming cigarette taxes in 1991-1994 had remained constant at 1990-
18
level. The result is graphed in top panel of figure 3 that displays difference in the
proportion of the smokers for both sexes using actual cigarette taxes and the counter
factual cigarette taxes. It shows the difference is almost intangible.
We also simulated the proportion of the smokers using counter factual 50 percent
increase in cigarette taxes at youth. It is displayed in bottom panel of figure 3. The figure
shows the proportion of the smokers among adults aged 20-29 years would be reduced by
roughly 2 percent if they had faced 50 percent raise in cigarette taxes at age 12-18.
5.4 Semi-parametric estimates of cigarette taxes
The parametric estimates of section 5.1 imply cigarette taxes encountered at youth
to affect onset life smoking behavior of a person through a linear channel. To release the
linearity assumption, we estimate the effect of cigarette taxes using semi-parametric
estimate models. Figures 4 displays semi-parametric estimates of the effects of average
cigarette taxes encountered at age 12-18, stratified by sex. The figure displays Lowess
smoother estimates which are plots of weighted estimated daily smoking in adulthood
against cigarette taxes encountered at youth (the estimated effects of average cigarette
taxes at age 14-16 or at age 14 are not reported. Also, the effects on adults’ smoking
intensity and smoking type are not reported to conserve space). The estimated effects are
consistent with that of the parametric models displayed in table 2 in which average
cigarette taxes at age 12-18 are slightly reducing likelihood of daily smoking in
adulthood. The differencing test for parametric specification of the unknown functional
form f (.) in equation model (6) signifies that cigarette taxes encountered at youth affect
19
onset life smoking behavior of a person via a non-linear channel (results are not
reported).
We tested the semi-parametric models using different bandwidths and found no
change in the effect of cigarette taxes with change in bandwidth. Moreover, we used the
second order difference method and found the results are insensitive to the order of
differencing (the results are not reported).
As a sample, table 5 displays estimated parameters of entire explanatory variables
used in the equation models (3), (4), and (6). The table displays parametric estimates of
linear probability and ordered-Probit models.
6. Conclusion
This paper tests the effect of cigarette taxes one faced as a youth on his smoking
behavior as an adult. We exploit dramatic changes in cigarette taxes across the Canadian
provinces, particularly the increase in cigarette taxes in 1991-1994 and its discrepancy
across the provinces later on. Using the CCHS cycles 2001, 2003, and 2005, we observe
a cohort who are adolescents and adults in our sample and faced disproportion tobacco
taxes as youths. We use parametric and semi-parametric estimate models to examine
respectively linearity and non-linearity effect of cigarette taxes. We tried to quantify the
long run effect cigarette taxes encountered at youth using parametric and simulation
methods. We examined the long run effect of cigarette taxes on daily smoking behavior,
smoking intensity, and smoking type of a person.
We found a weak but significant long term effect for cigarette taxes using both
parametric and semi-parametric estimates. Using parametric estimates, we found
20
probability of daily smoking in adulthood reduces by 0.9 percent if average cigarette
taxes one faced at age 12-18 increases by 10 percent. Also, we found cigarette taxes
encountered at youth affect smoking intensity of a person in adulthood. The long run
effect of cigarette taxes at youth, as we found, attenuated by late 20s of a person’s life.
A simulation method shows the proportion of the smokers would not be
significantly different from what prevails now if the federal government of Canada had
not raised cigarette taxes in 1991-1994. We also find, using simulation method, large
changes in cigarette taxes at youth, for example 50 percent increase, will slightly affect
the proportion of the smokers in adulthood.
This paper finds a cigarette tax policy that is conducted to reduce the smoking rate
of the population by controlling youth smoking is not as effective as what has been
claimed to be. Perhaps, because there is an unobservable factor persistently affects
smoking propensity of smokers, and so a youth smoking policy most likely postpones
smoking initiation of youths from a time that they can not afford smoking, mainly
because of low income, to later time when they have enough income to afford smoking.
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Bureau of Economic Research, Working paper 10656
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T.E.; Schelling, T. C.; and Townsend, J. (1995), Criteria for Determining an Optimal
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23
Table 1: Summary Statistics of the Canadian Community Health Survey:
Respondents aged 19-40 years (Pooled cycles 2001, 2003, and 2005)
Population
Variables
Description
African
Age
Asian
Canadian
Dsmoker
Depress
=1 if respondent was born in Africa
Age of respondents
=1 if respondent was born in Asia
=1 if respondent was born in Canada
=1 if respondent is a daily smoker
A discrete variable values from 0 to 8
indicating mood of a respondent
=1 if respondent is not a subject to be asked
about his/her mood
=1 if highest level of education completed is
less than high school
=1 if highest level of education completed is
high school
=1 if has some post secondary education
=1 if graduated from an university
=1 if respondent was born in Europe
=1 if respondent is female
# number of people that are living in the
household of respondent
=1 if household has no income
=1 if household’s income < 5000
=1 if hh income is between 5,000 - 9,999
=1 if hh income is between 10,000 – 14,999
=1 if hh income is between 15,000 – 19,999
=1 if hh income is between 20,000 – 29,999
=1 if hh income is between 30,000 – 39,999
=1 if hh income is between 40,000 – 49,999
=1 if hh income is between 50,000 – 59,999
=1 if hh income is between 60,000 – 79,999
=1 if household’s income >= 80,000
Ddepress
Education1
Education2
Education3
Education4
European
Female
HHsize
HH-Income1
HH-Income2
HH-Income3
HH-Income4
HH-Income5
HH-Income6
HH-Income7
HH-Income8
HH-Income9
HH-Income10
HH-Income11
Mean
Female
Male
S.D.
Mean
S.D.
Mean
S.D.
0.008
29.42
0.04
0.89
0.25
0.088
5.77
0.21
0.31
0.43
0.007
29.38
0.043
0.89
0.24
0.084
5.69
0.203
0.31
0.42
0.009
29.48
0.044
0.89
0.27
0.093
5.85
0.21
0.31
0.45
0.65
1.85
0.81
2.05
0.47
1.52
0.37
0.48
0.37
0.48
0.37
0.48
0.11
0.31
0.09
0.29
0.12
0.33
0.19
0.11
0.59
0.03
0.54
0.39
0.32
0.49
0.18
0.5
0.18
0.11
0.62
0.03
-
0.38
0.32
0.49
0.18
-
0.21
0.11
0.56
0.03
-
0.41
0.31
0.5
0.18
-
2.89
0.004
0.013
0.032
0.05
0.062
0.11
0.12
0.11
0.14
0.16
0.21
1.36
0.065
0.11
0.17
0.21
0.24
0.31
0.32
0.32
0.34
0.36
0.41
2.97
0.004
0.014
0.038
0.06
0.07
0.11
0.12
0.11
0.13
0.15
0.19
1.34
0.063
0.12
0.19
0.24
0.26
0.31
0.33
0.31
0.34
0.34
0.39
2.79
0.005
0.011
0.024
0.033
0.051
0.098
0.12
0.12
0.14
0.24
0.24
1.38
0.07
0.1
0.15
0.18
0.22
0.3
0.32
0.32
0.35
0.43
0.43
Population
Variables
Description
Hispanic
=1 if respondent was born in the South
America
=1 if respondent is an immigrant who landed
in Canada at age 12 or earlier
=1 if respondent is an immigrant who landed
in Canada at age 14 or earlier
=1 if respondent is an immigrant who landed
in Canada at age 14 or earlier
=1 if respondent is an immigrant
=1 if respondent is married or is in common
law relationship
# number if cigarettes a daily smoker
smokes every day
=1 if respondent was born in the North
America but Canada
Respondents aged 19-40
Contemporaneous price index of cigarette
Average cigarette prices index one faced at
age 12-18
Cigarette price index one faced at age 14
Average cigarette prices index one faced at
age 14-16
=1 if a female respondent is pregnant
=1 if province of residence is Newfoundland
=1 if province of residence is Prince Edward
=1 if province of residence is Nova Scotia
=1 if province of residence is New
Brunswick
=1 if province of residence is Quebec
=1 if province of residence is Ontario
=1 if province of residence is Manitoba
=1 if province of residence is Saskatchewan
=1 if province of residence is Alberta
=1 if province of residence is British
Columbia
Imm_P12-18
Imm_P14
Imm_P14-16
Immigrant
Married
Ncig_d
Namerican
Age
Pcig
Pcig_12-18
Pcig_14
Pcig_14-16
Pregnant
Province1
Province2
Province3
Province4
Province5
Province6
Province7
Province8
Province9
Province10
Mean
S.D.
Female
Male
Mean
S.D.
Mean
S.D.
0.012
0.11
0.013
0.11
0.011
0.11
0.034
0.18
0.032
0.18
0.036
0.19
0.039
0.19
0.038
0.19
0.04
0.2
0.039
0.1
0.19
0.3
0.038
0.1
0.19
0.3
0.04
0.1
0.2
0.3
0.54
0.5
0.57
0.5
0.51
0.50
14.84
7.7
13.35
7.02
16.33
8.11
0.011
29.42
1.05
0.1
5.77
0.21
0.012
29.38
1.05
0.11
5.69
0.21
0.01
29.48
1.06
0.1
5.85
0.21
0.66
0.59
0.17
0.21
0.66
0.6
0.16
0.22
0.65
0.59
0.17
0.21
0.62
0.03
0.018
0.037
0.2
0.17
0.13
0.19
0.62
0.055
0.031
0.019
0.039
0.2
0.23
0.17
0.14
0.19
0.61
0.028
0.017
0.035
0.2
0.17
0.13
0.18
0.036
0.21
0.32
0.059
0.057
0.12
0.19
0.41
0.47
0.23
0.23
0.32
0.037
0.21
0.32
0.058
0.06
0.11
0.19
0.41
0.47
0.23
0.23
0.32
0.034
0.21
0.32
0.059
0.055
0.12
0.18
0.41
0.47
0.24
0.23
0.32
0.12
0.32
0.12
0.32
0.12
0.33
25
Population
Variables
Description
Smokt
Is a discrete variable
=0 if never smoked
=1 if occasional smoker
=2 if daily smoker
=1 if cycle 2001
=1 if cycle 2003
=1 if cycle 2005
Wave1
Wave2
Wave3
Number of
Observations
Mean
0.67
0.07
0.26
0.31
0.33
0.36
95,408
S.D.
0.46
0.47
0.48
Female
Male
Mean
S.D.
Mean
S.D.
0.69
0.07
0.24
0.32
0.33
0.36
0.46
0.47
0.48
0.65
0.08
0.27
0.31
0.33
0.36
0.46
0.48
0.33
50,684
-
44313
-
26
Table 2: The estimated effects of cigarette taxes encountered at youth on
smoking propensity and smoking intensity of adults
Contemporaneous price of
cigarette
Smoking Propensity1
Male
Female
0.101
0.082
(.24)
(.12)
Smoking Intensity2
Male
Female
-0.31***
-.08
(0.1)
(.095)
Price of cigarette at age 14
.024
(.037)
-.01
(.049)
-.019
(.014)
-.023*
(.012)
Number of observations
44,313
50,684
12,044
11,968
Contemporaneous price of
cigarette
0.059
(.11)
0.11
(.24)
-.31**
(.098)
-.072
(.1)
Average price of cigarettes at
age 14-16
-.01
(.03)
-.092*
(.054)
-.006
(.017)
-.034**
(.017)
Number of observations
44,092
50,436
12,001
11,951
***
Contemporaneous price of
cigarette
0.15
(.16)
-.023
(.21)
-0.3
(.09)
-0.023
(.11)
Average price of cigarettes at
age 12-18
-.092
(.06)
-.093*
(.052)
-.047**
(.024)
-.031
(.027)
Number of observations
37,987
43,929
10,332
10,472
* p<0.1; ** p<0.05; *** p<0.01
1. Linear probability model. Dependent variable is a binary response variable indicating an adult smoking
status.
2. OLS regression. Dependent variable is number of cigarettes a daily smoker smokes every day.
Standard errors are robust and clustered at provincial level. Estimated parameters using linear probability
model and OLS regression are price elasticities. Figures in parenthesis are standard errors of the estimators. All
regression models include immigration status, marital status, country of birth, household size, education
(dummies), age (dummies), family income (dummies), province of residence (dummies), depression status,
pregnancy status for female respondents, and two dummy variables indicating cycles of the CCHS data.
27
Table 3: The estimated effects of cigarette taxes encountered at youth on
smoking behavior in adulthood1
Contemporaneous price of
cigarette
Never Smoked
Male
Female
0.016
0.092
(.076)
(.068)
Occasional Smoker
Male
Female
-0.23
-.086
(.11)
(.063)
Daily Smoker
Male
Female
-.046
-.21
(.22)
(.15)
Price of cigarette at age 14
-.005
(.017)
0.012
(.017)
.005
(.015)
-.016
(.024)
0.012
(.037)
-.033
(.049)
Number of observations
44,313
50,684
44,313
50,684
44,313
50,648
Contemporaneous price of
cigarette
0.096
(.065)
0.007
(.075)
-.089
(.06)
-0.01
(.1)
-.21
(.14)
-0.021
(.21)
Average price of cigarettes at
age 14-16
0.009
(.016)
0.041**
(.017)
-.008
(.014)
-.056**
(.024)
-.019
(.036)
-.11**
(.049)
Number of observations
44,092
50,436
44,092
50,436
44,092
50,436
Contemporaneous price of
cigarette
0.068
(.08)
0.056
(.07)
-0.063
(.077)
-0.075
(.09)
-0.15
(.18)
-0.15
(0.19)
Average price of cigarettes at
age 12-18
0.044
(.029)
0.046***
(.018)
-.04
(.027)
-.062***
(.024)
-.097
(.065)
-.127***
(.049)
Number of observations
37,987
43,929
37,987
43,929
37,987
43,929
* p<0.1; ** p<0.05; *** p<0.01
1. Ordered-Probit estimates of adult’s smoking type. Standard errors are robust and clustered at provincial
level.
Estimated parameters are price elasticities. Figures in parenthesis are standard errors of the estimators. All
regression models include immigration status, marital status, country of birth, household size, education
(dummies), age (dummies), family income (dummies), province of residence (dummies), depression status,
pregnancy status for female respondents, and two dummy variables indicating cycles of the CCHS data.
28
Table 4: Estimated variation of the effect of cigarette taxes at youth
with current age of adults
Contemporaneous Cigarette
price index
Cigarette price index at age
14
o Interaction of the
cigarette price index
with age variable
o Interaction of the
cigarette price index
with age-squared var.
Number of observations
Contemporaneous Cigarette
price index
Average cigarette prices
index at age 14-16
o Interaction of the
cigarette price indexes
with age variable
o Interaction of the
cigarette price indexes
with age-squared var.
Number of observations
Contemporaneous Cigarette
price index
Average cigarette prices
index at age 12-18
o Interaction of the
cigarette price indexes
with age variable
o Interaction of the
cigarette price indexes
with age-squared var.
Number of observations
Extension of
Smoking1
Male Female
0.016
0.02
(.029) (.053)
-.65*** -.698***
(.122) (.189)
Smoking Intensity2
Smoking Type3
Male
-4.82***
(1.48)
-8.31
(7.71)
Female
-.866
(1.37)
4.08**
(1.55)
.043*** .048***
(.008) (.013)
0.217
(.417)
-.687***
(.104)
-.001*** -.0007***
(.0001) (.0002)
0.004
(.007)
.02***
(.002)
-.008*** -.005***
(.001) (.0009)
44,313 50,684
0.01
0.022
(.028) (.052)
-.67*** -.8***
(.11)
(.19)
44,313
-4.82***
(1.51)
-7.07
(5.06)
50,648
-.762
(1.34)
3.28
(2.26)
44,313 50,684
-.207** -.033
(.143)
(.107)
-3.95*** -2.96***
(1.02)
(.677)
0.117
(.378)
-.66***
(.128)
0.006
(.007)
-.019***
(.002)
-.007*** -.005***
(.001) (.0007)
44,092 50,436
-4.74*** -.246
(1.46)
(1.4)
-14.82** -2.42
(3.56)
(5.39)
44,092 50,436
-.153
-.129
(.137) (.131)
-5.08*** -3.92***
(1.13)
(.782)
.04***
(.007)
.054***
(.013)
-.001*** -.0009***
(.0001) (.0002)
44,092 50,436
0.035
-.008
(.042)
(.046)
-1.01*** -.97***
(.14)
(.19)
.07***
(.012)
.069***
(.014)
0.72
(.41)
-.173
(.273)
-.001*** -.001***
(.0002) (.0002)
-.007
(.007)
0.009
(.0058)
37,987 43,929
37,987 43,929
Male Female
-.206* -.056
(.11)
(.146)
-4.73*** -2.77***
(.935) (.699)
.39***
(.066)
.339***
(.073)
.43***
(.079)
.248***
(.047)
.24***
(.038)
.32***
(.074)
-.009*** -.007***
(.001) (.0017)
37,987 43,929
* p<0.1; ** p<0.05; *** p<0.01
1. Linear probability estimate. Dependent variable is a binary response variable indicating an adult smoking
status.
2. OLS regression. Dependent variable is number of cigarettes a daily smoker smokes every day.
3. Ordered-probit model. Dependent variable is a discrete variable indicating smoking type of a respondent
including never smoked, occasional smoker, or daily smoker.
Standard errors are robust and clustered at provincial level. Figures in parenthesis are standard errors of the
estimators. All regression models include immigration status, marital status, country of birth, household size,
education (dummies), age (dummies for ordered-probit models and 5-year age cohort for the other models),
29
family income (dummies), province of residence (dummies), depression, pregnancy for female respondents,
and two dummy variables for the CCHS’s cycles.
Table 5: Sample of the estimated effects of explanatory variables used in this paper on
smoking habits of adults
Linear Probability Model1
Female
Male
Variables
Current cigarette price
Average cigarette prices
at age12-18
Immigrant
Married
Canadian
North American
Hispanic
European
African
Asian
Household Size
Education < secondary
Education = secondary
Some post secondary
education
University education
Age 19
Age 20
Age 21
Age 22
Age 23
Age 24
Age 25
Age 26
Age 27
Age 28
Age 29
Age 30
Age 31
Age 32
Age 33
Age 34
Age 35
Age 36
Age 37
Age 38
Age 39
Age 40
Province – NF
β
s.e.
β
s.e.
Ordered-Probit2
Female
Male
β
s.e.
β
s.e.
-.005 .047
-.034 .018
.038 .042
-.039 .025
-.108 .132
-.143 .056
-.114 .14
-.119 .081
.063
-.039
.102
.007
-.12
.021
-.131
-.082
.005
.235
.052
-
.009
.004
.044
.05
.044
.05
.052
.052
.001
.02
.01
-
-.012
-.032
.105
.042
-.002
.011
-.049
.005
.003
-.177
-.24
.007
.005
.038
.047
.042
.033
.04
.037
.002
.014
.026
.272
-.186
.575
.233
-.418
.31
-.51
-.32
.012
-.47
-.633
.066
.016
.197
.239
.192
.233
.249
.233
.004
.047
.058
-.002
-.133
.342
.133
.077
.358
-.182
.038
.005
-.444
-.634
.04
.016
.178
.222
.177
.184
.214
.177
.006
.037
.722
-.064
-.066
-.019
-.017
-.006
.023
.01
.014
.011
.012
.005
.01
.01
-.002
-.004
-.0002
.014
.006
.007
.017
0.3
.018
.03
.025
.033
.024
.024
.01
.018
.024
.015
.012
.02
.02
.01
.02
.015
.007
.019
.008
.012
0.004
-.31
-.083
-.058
-.032
-.029
.007
.014
.021
.032
.014
.025
.01
.014
.007
.006
-.002
.007
.01
-.009
.032
-
.017
.021
.02
.019
.014
.012
.013
.014
.014
.016
.009
.02
.018
.014
.015
.013
.01
.012
.013
.016
-
-.833
-.185
-.046
-.043
.018
.075
.032
.046
.036
.015
.016
.019
.004
-.057
-.055
-.052
-.009
-.054
-.049
-.079
.132
.052
.106
.084
.115
.081
.086
.064
.077
.083
.041
.056
.077
.083
.056
.064
.067
.053
.071
.04
.037
.012
-.822
-.249
-.19
-.095
-.125
-.001
.002
.041
.063
.027
.033
-.008
-.003
-.044
-.025
-.07
-.045
-.041
-.119
-.078
-
.051
.049
.044
.038
.04
.028
.025
.022
.031
.035
.034
.043
.04
.042
.043
.036
.04
.042
.068
.05
-
30
Table 6 (Cont’d)
Variables
Province – PE
Province – NS
Province – NB
Province – QB
Province – ON
Province – MN
Province – SK
Province – AB
Province – BC
Cycle 2001
Cycle 2003
Household with no
income
Income < 5,000
Income 5,000 - 9,999
Income 10,000 - 14,999
Income 15,000 - 19,999
Income 20,000 - 29,999
Income 30,000 - 39,999
Income 40,000 - 49,999
Income 50,000 - 59,999
Income 60,000 - 79,999
Income >= 80,000
Depression
Dummy for missing
values in the Depression
variable
Pregnant
Constant
R-Squared
Number of obs.
Linear Probability Model1
Female
Male
Ordered-Probit2
Female
Male
β
s.e.
β
s.e.
β
s.e.
β
s.e.
-.013
.015
.038
.012
-.000
.023
.014
-.032
.034
.01
-.082
.002
.004
.006
.009
.005
.005
.003
.003
.02
.006
.024
-.014
-.037
-.026
-.018
-.01
-.064
-.048
-.006
-.086
.063
.011
-.064
.002
.003
.005
.006
.008
.003
.005
.004
.004
.016
.004
.04
-.012
.032
.158
.039
.039
.106
.07
-.069
.047
.022
-
.006
.012
.015
.024
.014
.015
.009
.009
.056
.019
-
-.081
-.107
-.111
-.08
-.068
-.154
-.112
-.021
-.231
.051
.01
-.232
.007
.009
.015
.02
.027
.011
.016
.013
.012
.051
.014
.124
.039
.056
.016
-.02
-.043
-.088
-.1
-.11
-.14
.02
.039
.03
.02
.021
.017
.021
.018
.017
.02
.023
.001
.005
-.025
-.037
-.018
-.04
-.056
-.09
-.108
-.131
-.153
.021
.031
.023
.031
.026
.02
.028
.031
.027
.029
.002
.002
.003
.247
.329
.352
.26
.159
.092
-.033
-.084
-.132
-.232
.062
.122
.084
.127
.104
.108
.09
.106
.097
.096
.106
.11
.002
.015
-.085
-.142
-.062
-.134
-.17
-.255
-.311
-.376
-.441
.06
.093
.067
.084
.073
.064
.087
.092
.081
.085
.097
.006
.011
-.078
.22
.008
.08
.49
.047
-.41
-
.043
-
-
-
.11
43,929
-
.09
37,987
-
.08
43,929
-
.05
37,987
-
1. Dependent variable is a binary response variable indicating an adult daily smoking status.
2. Dependent variable is an index variable indicating adult’s smoking type including never smoked, occasional
smoker, and daily smoker.
Standard errors are robust and clustered at provincial level.
31
Figure 1: Cigarette price index over time and those the youth faced by the provinces
NF
PE
NS
NB
QB
ON
MN
SK
AB
BC
1980
1985
1995
1990
Year
2000
2005
R e a l c ig a re tte p ric e in d e x - b a s e y e a r = 1 9 9 2
1
.8
.6
.4
.2
30
Current Age
35
40
25
30
Current Age
35
40
Average cigarette prices by the Canadian provinces respondents
aged 19-38 years in 2001-2005 faced at age 12-18
NF
PE
NS
NB
QB
ON
MN
SK
AB
BC
25
NF
PE
NS
NB
QB
ON
MN
SK
AB
BC
20
Average cigarette prices by the Canadian provinces respondents
aged 19-40 years in 2001-2005 faced at age 14-16
20
R eal cigarette price index - base year = 1992
.2
.4
.6
.8
1
1.2
Cigarette price by the Canadian provinces respondents
aged 19-40 years in 2001-2005 faced at age 14
R ea l cig are tte price in de x - ba se ye a r = 1 99 2
.4
.6
.8
1
1 .2
price in de x - ba1se yea r = 1 9912.5
0R ea l cig are tte .5
Real cigarette price indexes by the Canadian provinces since 1979-2004
NF
PE
NS
NB
QB
ON
MN
SK
AB
BC
20
25
30
Current Age
35
40
32
Figure 2: Pattern of changes in the effect of cigarette taxes with age
The effect on probability of smoking
-.15
-.1
-.05
0
.05
The effect of cigarette taxes female respondents faced at age 12-18
and its variation with age on probability of smoking in adulthood
20
25
30
35
Current Age
Confidence Interval / 95%
Zero Line
Point Estimation
The effect on probability of smoking
-.2 -.15 -.1 -.05
0
.05
The effect of cigarette taxes male respondents faced at age 12-18
and its variation with age on probability of smoking in adulthood
20
25
30
35
Current Age
Confidence Interval / 95%
Zero Line
Point Estimation
33
Figure 3: Simulated the proportion of the smokers using actual and counter factual
cigarette taxes
.3
The proportion of the smokers
.35
.4
.45
Simulated the proportion of the smokers by age using actual and
counter factual cigarette taxes in 1991-1994 replaced with tax level in 1990
22
24
Female Smokers
Male Smokers
26
Current Age
28
30
Female Smokers_Counter Factual
Male Smokers_Counter Factual
.3
The proportion of the smokers
.35
.4
.45
Simulated the proportion of the smokers by age using actual cigarette taxes
and counter factual 50 percent increase in cigarette taxes at age 12-18
22
24
Female Smokers
Male Smokers
26
Current Age
28
30
Female Smokers_Counter Factual
Male Smokers_Counter Factual
34
Figure 4: Lowess Smoother Estimators: plots of weighted estimated probability of
smoking at age 19-38 against average cigarette taxes at age 12-18
-.5
Probability of Smoking
0
.5
1
1.5
Semi-Parametric Estimate of Average Cigarette Prices Female
Respondents Faced at Age 12-18 on Probability of Smoking at Age 19-38
.4
.6
.8
1
Average Cigarette Prices at Age 12-18
1.2
bandwidth = .8
-.5
Probability of Smoking
0
.5
1
1.5
Semi-Parametric Estimate of Average Cigarette Prices Male
Respondents Faced at Age 12-18 on Probability of Smoking at Age 19-38
.4
.6
.8
1
Average Cigarette Prices at Age 12-18
1.2
bandwidth = .8
35