Gender Differences in Smoking: Evidence from Canadian Public

Gender Differences in Smoking: Evidence from Canadian Public

Gender Differences in Smoking:
Evidence from Canadian Public Smoking Bans
Joshua Lewis
∗
Department of Economics, University of Toronto
February 12, 2012
Abstract
Despite a large literature emphasizing the indirect costs associated with female smoking,
there has been limited research assessing whether men and women respond differently to
anti-smoking policies. This paper fills that gap, examining the effect of Canadian public
bans on male and female smoking rates and cigarette consumption, the unique rollout
of Canadian bans allowing me to address several identification issues associated with
non-random adoption of local bylaws. I find that public bans led to large declines in female
smoking rates but had virtually no impact on male smoking. These results are robust to a
variety of alternative estimation strategies, and are consistent with previous studies that
have found females to be much more likely to engage in ‘social smoking.’ Moreover, the
results highlight how public bans can be an effective tool to reduce female smoking, a key
objective of policy makers.
Keywords: Public smoking bans; Gender differences in smoking, Non-random policy
adoption.
JEL Classification Numbers: H75, I10, I18.
∗
I am grateful to Dwayne Benjamin, Rob McMillan, and Mark Stabile for their guidance and supervision
during this research. I also benefited from valuable comments from Andrew Bird, Branko Boskovic, Victor Couture, Sacha Kapoor, Frank Lewis, Hugh Macartney, and seminar participants at the University of
Toronto. All remaining errors are my own. This paper represents the views of the author and does not necessarily reflect the views of Statistics Canada. The data used in this article can be obtained through application
to Statistics Canada’s Research Data Centre program at http://www.statcan.ca/english/rdc/index.htm.
Email: [email protected]
1
1
Introduction
Smoking poses a variety of unique dangers for women. It increases risks associated with
pregnancy and menstrual function; it has been associated with lower bone density and greater
incidence of hip fracture among postmenopausal women; and it has been linked to an elevated
risk of cervical cancer.1 In addition to these health risks, maternal smoking is harmful for
child health. Since mothers are typically the primary caregiver during the first years of
life, maternal smoking differentially exposes young children to environmental tobacco smoke
(ETS) (World Health Organization, 1999).2 Maternal smoking is particularly hazardous to
fetal health, and smoking during pregnancy accounts for 20 percent of all low birthweight
births (US Department of Health and Human Services, 1990).
Given the consequences of poor health at birth for long-term health, education, and
socioeconomic outcomes (Currie, 2009), policies that reduce female smoking could generate
substantial future benefits. However, there is only limited research examining whether antismoking policy can be used to target women. Previous studies have focused exclusively
on taxes, researchers generally finding that women are less responsive to taxes than men.3
More recent studies have investigated how taxes affect smoking prevalence among pregnant
women, and find participation elasticities ranging from -0.35 to -0.7 (Evans and Ringel, 1999;
Gruber and Köszegi, 2001; Ringel and Evans, 2001), generally larger than those found among
the overall population (Chaloupka and Warner, 2000).4
Public smoking bans may also influence female smoking behaviour. These policies –
typically restricting smoking in restaurants and bars – have proliferated in both the United
1
See the U.S. Department of Health and Human Resources (2001) for a more thorough discussion of the
risks associated with female smoking.
2
The 2006 Surgeon General Report concluded that there is a causal relationship between postnatal ETS
exposure and Sudden Infant Death Syndrome, lower respiratory illness, middle-ear diseases, and asthma.
3
See Lewit and Coate (1982), Chaloupka and Pacula (1998), for example.
4
Colman, Grossman, and Joyce (2003) estimate price elasticities for prenatal quitting and postpartum
relapse equal to 1, although they still find that 75 percent of quitters resume smoking within a year of giving
birth. Lien and Evans (2005) demonstrate that large tax hikes in the U.S. did lead to declines in both
smoking among pregnant women and the incidence of low birthweight, but the authors caution that these
policies could at best have modest effects on infant health.
2
States and Canada over the past two decades. While there is mixed evidence as to the effect
of these policies on smoking prevalence,5 there are several reasons why these policies may
have disproportionately influenced female smoking. First, females were more likely to be
‘social smokers’ (Moran, Wechsler, and Rigotti, 2004; Nichter, et al., 2006), and public bans
targeted establishments in which ‘social smoking’ was common. Second, women were more
likely to be employed in establishments that were covered by a public ban, so may have
been more ‘exposed’ to the policy change. Third, smoking cessation is negatively related to
cigarette consumption (Cohen et al., 1989; Wagner et al., 1990), and males tend to be heavier
smokers than women. Despite these differences, however, previous research has focused solely
on the effect of these policies on aggregate smoking prevalence.
This paper investigates the effect of Canadian public smoking bans on male and female
smoking behaviour. These policies were adopted rapidly in Canada, and the proportion
of the population covered rose from just 12 percent in 2000 to over 95 percent in 2006.
There were also large regional differences in the timing of ban adoption. I exploit variation
in the timing of when municipalities and counties adopted bans to estimate fixed effects
regressions. These models control for time-invariant determinants of smoking as well as a
variety of demographic and economic covariates. These baseline results suggest that strict
public smoking bans were associated with roughly a 12 percent decline in female smoking
rates, but had virtually no impact on the smoking behaviour of men.
There are several potential concerns with the baseline estimation strategy. Municipalities
and counties that voluntarily adopted bans tended to have higher levels of education and
income, and had lower initial smoking rates. Differential trends in smoking across adopting
and non-adopting regions could bias the results. For example, wealthy jurisdictions may have
5
In the U.S., Chaloupka (1992), Czart et al. (2001), and Wasserman et al. (1991) find a negative
relationship between clean indoor-air laws and smoking behaviour. More recently, researchers have estimated
models that control for state fixed-effects (Tauras, 2006; Yurekli and Zhang, 2000), thereby controlling for
unobservable heterogeneity. These more recent studies associate clean indoor-air laws with declines in adult
cigarette consumption. Meanwhile, Carpenter, Postolek, and Warman (2011) find limited evidence that
Canadian public bans reduced overall smoking prevalence, but that these policies led to large reductions in
exposure to second-hand smoke at restaurants and bars.
3
experienced relative declines in smoking rates, in which case the baseline estimates would
be upward biased. A second concern is that smoking bans were the result of endogenous
policy. While the fixed effects model controls for fixed unobservable heterogeneity, timevarying determinants of ban adoption could still lead these estimates to be biased (Besley
and Case, 2000). If public bans were more likely to be adopted in jurisdictions where citizens
had stronger preferences for reducing smoking, the baseline results would overestimate the
impact of these policies; alternatively, if bans were implemented in an effort to curb rising
local smoking rates, the baseline model would understate their true effectiveness.
Several unique characteristics of the Canadian experience allow me to address these
identification issues. First, I rely on the widespread adoption of local bans to estimate models
that control flexibly for differential trends across provinces, and rely solely on within-province
variation. Allowing for these differential cross-province trends increases the magnitude of
the estimates, indicating that failing to fully account for cross-provincial trends would lead
to an underestimate of the impact of these policies. This point has relevance for studies from
the U.S., which have relied on cross-state policy variation.
Second, I rely on extensive local adoption of bans to estimate propensity-score matching
regressions, which parametrically match communities along baseline observable characteristics. This estimation strategy improves on the standard fixed effects approach by comparing
how bans affected smoking across observationally similar communities. Differential trends
are less likely to bias these estimates, and random assignment of public bans is a more
credible assumption.
Third, I exploit the fact that Canadian bans were implemented at both the local and
provincial level to address the issue of endogenous policy adoption. In particular, I study
the effect of public bans in regions that did not voluntarily adopt bylaws, but were forced
to comply with provincial legislation. These policies should be exogenous since they were
imposed on local communities by a higher level of government.
All three alternate estimation strategies support the baseline findings. Across a variety
4
of specifications, strict public bans were associated with a significant 9 to 13 percent decline
in female smoking rates, but had no effect on the smoking behaviour of males. These results
are consistent with previous studies that have found females to be more likely to engage in
‘social smoking.’ Further, these findings highlight how public bans may be an effective tool
at reducing adult female smoking prevalence, a key objective of policymakers.
The paper proceeds as follows: Section 2 provides background information about public
smoking bans in Canada; Section 3 discusses the different factors influencing male and female
smoking; Section 4 presents the data and the empirical strategy; Section 5 reports the main
empirical results; Section 6 provides robustness checks; and Section 7 concludes.
2
Public smoking bans in Canada
Between 1995 and 2006, there was a dramatic increase in the proportion of Canadians covered
by public smoking restrictions. While municipalities first implemented bans as early as 1995,
the majority of these policies were adopted between 2000 and 2004. During this four year
period, over 100 municipalities and counties introduced smoking bylaws. More recently,
provincial governments have enacted province-wide public smoking restrictions, and by 2006,
over 95 percent of Canadians were covered by some form of a ban.
There was considerable variation in the scope of these policies. Typically, restrictions
applied to establishments such as restaurants, bars, bingo halls, bowling alleys, billiard halls,
and casinos. Local policies would often exempt certain types of establishments from coverage,
although the vast majority of bans applied to both restaurants and bars. Public bans were
typically enforced by inspectors who imposed fines on non-complying establishments. Fines
varied across regions, ranging from $200 up to $10,000 (CBC, 2009). Despite substantial
variation in these fines, there was widespread enforcement of these policies, and a high level
of compliance by restaurant and bar owners (Griffith, 2008). .
There was also variation in stringency of public smoking bans. Municipalities and counties
5
differed in allowances for designated smoking rooms (DSRs); some banned smoking outright,
others allowed DSRs, and still others required DSRs that had separate ventilation. Health
Canada developed a 3-level standard to rate the restrictiveness of both local and provincial
smoking bans. ‘Bronze’ bylaws restrict smoking in the majority of public places, including
restaurants. DSRs are permitted under the ‘bronze’ classification, and there are allowances
for two or more exemptions from following types of establishments: bars, bingo halls, billiard
halls, bowling alleys, and casinos. The ‘silver’ standard has the same requirements as the
bronze, except that only one type of establishment may be exempt. ‘Gold’ bylaws restrict
smoking in all public places, with no exemptions or allowances for DSRs. Just over 10
percent of municipalities also went through a ‘transitional phase’ - a period in which the
local bylaw was typically not fully enforced.6
The first municipalities to implement smoking bylaws were in British Columbia in the
mid-1990s. Beginning in 2000, a large number of municipalities in Ontario began adopting
bylaws, and the vast majority of Ontarians were covered by either a municipal or county
bylaw before the province-wide ban was implemented in 2006. Fewer municipalities in the
western provinces enacted public restrictions. Meanwhile, in Quebec, local jurisdictions did
not have the authority to implement public smoking restrictions, so none of its residents
were covered until a provincial-wide ban was implemented in the spring of 2006.
In 2002, PEI was the first province to adopt a ban, and within four years, eight other
provinces had public restrictions in place. A community was required to enforce the more
stringent of either the provincial or local ban. For example, a municipality that had a ‘bronze’
ban in place would be required to meet all the additional requirements associated with a
‘gold’ provincial ban. Depending on the relevant local bylaw, the provincial legislation either
represented no change or an increase in effective regulation.
Figure 1 highlights how public smoking bylaws have evolved in Canada. It reports the
proportion of the adult population covered by local bans (municipal or county) and provincial
6
See www.hc-sc.gc.ca/hc-ps/tobac-tabac/about-apropos/role/municip/ban-interdiction-eng.php
more details on the Health Canada ratings.
6
for
bans. I distinguish between ‘all bans’ and ‘strict bans’.7 During the 1990s, municipalities
and counties gradually adopted smoking restrictions, and 12 percent of the population was
covered by 2000. Over the next four years, there was a dramatic expansion in coverage, and
by 2004, over 60 percent of the population was covered by local restrictions. By 2006, public
ban coverage was virtually universal in Canada. Over time Canadians have been covered
by more stringent bans, in part because new provincial legislation has tended to be more
restrictive, but also because a number of local governments replaced weaker bylaws with
more restrictive policies.
3
Gender differences in smoking
There are a several reasons why women may have been more responsive to public smoking
restrictions. First, heavy smoking is negatively related to successful cessation (Cohen et al.,
1989, Wagner et al., 1990). This is believed to be due to heavy smokers facing stronger
internal cues triggering smoking, and facing greater withdrawal symptoms (Killen et al.,
1988; Goldberg et al., 1993). Since females smoked significantly less than males,8 they
may have found it easier to quit in response to a ban. Second, several studies have found
that females are more likely to be ‘social smokers’ (see Moran, Wechsler, and Rigotti, 2004;
Nichter et al., 2006). Gritz et al. (1996) show that co-residence with a smoker has a larger
negative effect on the probability cessation for women than men. Since ‘social smoking’ was
common in establishments covered by public smoking bans, these policies may have had
a larger influence on female smoking behaviour. Third, women were more likely to have
been employed in industries affected by public smoking bans. In 2008, females comprised
60 percent of employment in the accommodations and food services industry (Statistics
Canada, 2009). Bitler, Carpenter, and Zavodny (2009) find that state clean indoor air laws
7
The former includes any form of public smoking restriction (‘gold’, ‘silver’, ‘bronze’, and ‘transitional
stage’ classifications), while the latter is restricted to ‘gold’ and ‘silver’ bans.
8
In this sample, the smoking rate for males was four percentage points higher than it was for females,
and male smokers consumed roughly 3 additional cigarettes per day.
7
that restricted smoking in bars led to significant reductions in smoking among bartenders.
Differential exposure to these policies resulting from gender differences in employment may
have led to larger reductions in smoking prevalence among women.
On the other hand, there are several factors that may have limited the response among
women. First, concern over weight gain may have limited the influence of bans on female
smoking behaviour.9 Second, there are several physiological factors that may make it more
difficult for women quit.10 And third, men were more likely to frequent the venues covered
by bans (Demers et al., 2002; Kairouz and Greenfield, 2007), so they may in fact have been
more exposed to these policies.
Given the host of separate factors influencing on male and female smoking behaviour, it
is not clear how public bans affected smoking prevalence among each group. To shed light
on this question, the subsequent empirical analysis focuses on the effects of public bans by
gender.
4
Data
To examine the impact of public bans on smoking, I use data from the Master Files of the
National Population Health Survey (NPHS). The NPHS is a longitudinal survey conducted
by Statistics Canada that collects information on a variety of health measures. Beginning in
1994, roughly 17,000 respondents aged 12 years and older were interviewed every two years.
I use data from all seven available cycles, covering the period 1994/95 to 2006/07.
Participants were asked several questions on smoking behaviour. The survey provides
information on current smoking status (daily smoker, occasional smoker, or non-smoker).
9
Smoking has been associated with lower body mass index (BMI) scores for both men and women
(Rásky et al., 1996), and smokers often express concerns about weight gain from quitting. These concerns
are particularly acute among women. Pirie et al. (1991) found that more than 58 percent of female smokers
expected significant weight gain if they quit, compared with on 26 percent of males. Meanwhile, Klesges and
Klesges (1988) found that among quitters who relapsed, women were 3 times more likely to cite weight gain
as the primary reason for relapse.
10
In particular, nicotine withdrawal symptoms vary with hormonal fluctuations throughout the menstrual
cycle and over a woman’s lifecycle (O’Hara et al., 1989; Pomerleau et al., 1992; Perkins et al., 2000). The
onset of menopause has also been shown to be a major obstacle to cessation (Jarvis, 1994).
8
Daily smokers reported cigarette consumption (usual number of cigarettes smoked per day).
I use this information to construct three measures of smoking prevalence: a dummy variable
for smoker status, a dummy variable for daily smoker status, and cigarette consumption for
daily smokers. The survey provides information on key demographic characteristics including
age, sex, marital status, and education. The master files include geographic identification,
which is used to assign ban coverage to the sample.
Data on municipal and county bans come from Health Canada (2008), while Shields
(2007) provides information on provincial bans. These sources provide a comprehensive list
of all Canadian public bans covering the sample period (1994-2006). For each ban, there
is information about which establishments were covered, whether there were allowances for
DSRs, how the ban was classified under the Health Canada rating system, and the precise
time when the law was first enforced. I use this information to construct two measures of
ban status. ‘All bans’ is a dummy variable equal to one for communities with any form
of public smoking restriction, whether they were in a transitional phase, or had a ‘bronze’,
‘silver’, or ‘gold’ ban in place. Meanwhile I construct a dummy variable for ‘strict bans’,
which is equal to one for communities with either a ‘silver’ or ‘gold’ ban, and zero otherwise.
For the baseline analysis, I do not distinguish whether smoking ban was adopted at either
the local or provincial level.
Table 1 presents the descriptive statistics.11 Columns 1 reports the means for the full
sample. Smoking rates and daily smoking rates were 26 and 22 percent, and daily smokers
consumed 16.6 cigarettes per day. The mean age in the sample was 45.5 years, and two-thirds
were married. Thirty-five percent of the sample had a high school degree or less, 29 percent
had some post-secondary education, and 37 percent had a post-secondary degree. Columns
2 and 3 report the sample means by gender.12 Females were somewhat older, and slightly
less likely to have a post-secondary degree. Males were much more likely to smoke (male
participation rates were 28 percent compared to 24 percent for females), and male smokers
11
12
All means are adjusted by sample weights.
Sample weights adjust for the relatively lower response rate among males.
9
consumed roughly 3 additional cigarettes per day.
In the last three columns of Table 1, I report the means for the sample currently covered by a public ban.13 Among these individuals, smoking rates were 5 percentage points
lower, and smokers consumed roughly 2 fewer cigarettes per day. Although these differences
in means provide suggestive evidence that public bans reduced smoking, it is unlikely that
the differences in smoking prevalence were solely driven by the policies. First, public bans
were not randomly adopted, and municipalities that implemented bans tended to be more
educated and have higher levels of income. As a result, differences in individual characteristics may been responsible for the differences in smoking prevalence across adopting and
non-adopting communities, rather than public bans per se. Alternatively, a downward trend
in adult smoking, coupled with the gradual expansion of public bans could have generated
the negative correlation between public ban coverage and smoking prevalence observed in
Table 1. In the next section, I evaluate how much of these differences in smoking can be
attributed to public bans.
5
Empirical Methodology
In the baseline model, I study how public bans affected smoking rates and daily cigarette
consumption among continuing smokers. Simple OLS regressions – relating the presence of
a public to smoking prevalence – are likely to yield biased estimates of the effect of public
bans. As was clear from Table 1, these policies were not randomly adopted. Unobservable
determinants of ban adoption may also have influenced smoking prevalence. For example,
health-conscious communities may simultaneously have been more likely to implement a
ban and have lower smoking rates. In this case, simple OLS regressions would overstate the
causal impact of public bans on smoking prevalence.
In light of these concerns, I employ a quasi-experimental research design, where changes in
smoking in ban-adopting communities are compared to changes in smoking in non-adopting
13
In particular, communities with the variable ‘all bans’ equal to one.
10
communities. In this difference-in-differences framework, I include both county-level fixed
effects to control for time-invariant unobservables, and a full set of year dummies to control
for changing trends in smoking behaviour.
The basic model is
yicpt = β0 + γ · Banicpt + β1 · Xicpt + β2 · Y eart + β3 · (P rovincep × trendt ) + β4 · CDc + icpt (1)
where yicpt is the smoking outcome for an individual i in county c, in province p, in year t.
The vector Xicpt includes controls for individual characteristics such as marital status, sex,
age, and education. Y eart is a set of yearly dummies, while the term (P rovincep × trendt )
is a province-specific linear trend. CDc is a set of county fixed effects, which are meant to
capture fixed heterogeneity across counties. The variable of interest, Banicpt , is equal to one
if an individual was covered by a public smoking ban and zero otherwise.14 The coefficient γ
captures the relative change in the smoking outcome following a ban. The model is estimated
using two alternative specifications for the Banicpt variable: ‘all bans’ and ‘strict bans.’ For
individuals who were simultaneously covered by both provincial and local legislation, the
more restrictive policy takes precedence.15 Equation (1) is estimated for the individuals
aged 18 years and older, who resided in urban areas.16 All reported standard errors are
clustered at the county level (Bertrand, Duflo, and Mullainathan, 2004).
14
The ban variable is subscripted by the individual, i, to reflect the fact that bans were assigned to
individuals at either the municipal, county, or provincial level.
15
The baseline estimates ignore whether the ban was implemented at the local or provincial level.
16
Residents in rural areas are excluded primarily because geographic identification is less precise, making
it difficult to assign the relevant smoking ban. Individuals from northern territories are also excluded from
the sample.
11
6
Empirical Results
6.1
The overall impact of public bans
Before reporting the main findings on the gender-specific response to public bans, I first
report the estimates of the effect of public bans in the overall population. These regressions
are similar the analysis in previous studies on clean indoor-air laws on overall smoking
prevalence.
Table 2 reports the estimates of γ for the full sample, which captures the relative change
in the particular smoking outcome following the adoption of ban. The model is estimated
for three smoking outcomes: daily smoker status, smoker status, and daily cigarettes among
daily smokers. Columns (1), (3), and (5) report the estimates excluding the county fixed
effects, CDc , while columns (2), (4), and (6) report the estimates of the model with the full set
of controls. The top row of Table 2 reports the estimates using the broad definition of public
bans (‘All bans’). There is little evidence that these bans had any effect on smoking rates or
the cigarette consumption of continuing smokers. For the regressions on smoker status and
daily smoker status, the point estimates are all small and statistically insignificant. Similarly,
the estimates for daily cigarette consumption imply an insignificant 2 percent decline.
The second row of Table 2 reports the estimates using ‘Strict bans.’17 Again, there is no
evidence that bans were associated with any change in cigarette consumption of continuing
smokers. There were significant declines in both smoking rates and daily smoking rates. The
point estimates in columns (1) and (3) imply declines of 9 to 10 percent in smoking rates.18
When county fixed effects are included, these estimates fall to roughly 6 percent.
The inclusion of county fixed effects significantly reduces the magnitude of the point
estimates. Ban adoption was non-random, and ban-adopting communities would have experienced lower smoking rates even in the absence of the policies. Failing to account for this
17
Classified as either ‘gold’ or ‘silver’ under the Health Canada standard.
The percent declines were calculated as the point estimate divided by the mean of the dependent
variable. For daily smoker status: −0.022/0.22 = 10%; for smoker status: −0.024/0.26 = 9%.
18
12
unobservable heterogeneity would lead us to overstate the causal effects of bans by roughly
one third.
Only strict bans had a significant impact on smoking prevalence. This result is not surprising, given that weaker bans exempted certain types of establishments and often permitted
DSRs. These allowances may have offset any perceived increase in the cost of smoking, and
reduced the incentives for smokers to quit.
These results suggest that public bans had a moderate impact on smoking rates, but
no effect on smoking intensity.19 These results differ somewhat from studies of U.S. clean
indoor-air laws, which have associated these policies with reductions in both smoking rates
and cigarette consumption. Unlike the U.S. policies, which restricted smoking in both workplaces and public places, the Canadian bans applied almost exclusively to smoking in public
venues.20 Since individuals were less exposed to public bans throughout the course of the
day, it is not surprising that these policies primarily affected smoking rates, rather than daily
cigarette consumption. Based on the estimates in Table 2, one might conclude that there is
limited scope for these policies to influence smoking behaviour; however, as will be seen in
the next section, these estimates mask large changes in smoking prevalence among women.
6.2
Gender differences in the response to public bans
I explore whether males and females differed in their response to public bans. To investigate
this issue empirically, I re-estimate equation (1) separately for men and women. Table
3 reports these estimates. The top half of the table shows the results for males. Bans
had no effect on participation rates: point estimates range from 0.016 to -0.017, and are
all statistically insignificant. Strict bans were associated with a moderate decline in daily
cigarette consumption. The point estimate in column (5) is -1.2 and is significant at the
19
Carpenter, Postolek, and Warman (2011) find similarly modest effects of Canadian public-place bans
on smoking rates – their point estimates range from -0.016 to 0.002 –, and no impact of these policies on
cigarette consumption.
20
Most workplaces already had smoking restrictions in place before local and provincial bans were adopted
(Carpenter, 2009).
13
5 percent level. Once fixed effects are included, the point estimate falls to -0.7 and it is
statistically insignificant. Overall, bans had very little impact on male smoking. I can
rule out declines in smoking participation greater than 5 percent, and declines in cigarette
consumption of more than 7 percent.
The lower half of Table 3 reports the estimates for females. Women did not reduced
smoking intensity: the estimates for daily cigarette consumption are all statistically insignificant. Instead, bans affected smoking participation. For daily smoker status, the point
estimates in column (1) are significant and range from -0.026 and -0.028. Controlling for
fixed effects reduces the magnitude of these estimates, but strict bans were still associated
with a significant 12 percent decline in daily participation rates. A similar pattern emerges
in the regressions on smoker status. In column (3), the point estimates are -0.026 and -0.037.
Once fixed effects are included, only strict bans had a significant effect on smoking rates.
The estimate -0.029 corresponds to a 12 percent decline in smoking participation rates.
Males and females responded quite differently to the bylaws. Bans had virtually no impact
on either male smoking rates or cigarette consumption. Meanwhile, females experienced
significant reductions in both smoking rates and daily smoking rates. One explanation for
these differences is that men were heavier smokers, so it was easier for women to quit once a
ban was adopted. Alternatively, public bans targeted establishments where ‘social smoking’
was common, an activity that has been found to be more common among women. These
results have important implications for policy. Given the large externalities associated with
maternal smoking, policies that effectively reduce smoking participation among women may
yield health benefits both to women themselves, as well as to the future generations of
children.
14
7
Robustness checks
Time-varying determinants of ban adoption could lead to variable Banicpt to be correlated
with the error term in equation (1). This situation could arise if bans were more likely to be
enacted in communities that had a stronger desire for reducing smoking rates, which would
lead us to overestimate the causal impact of bans on smoking. Alternatively, bans may have
been adopted in an effort to curb rising local smoking rates, which would lead the fixed
effects estimates to understate the true impact of these policies.
Even in the absence of endogenous adoption, the identification strategy could be compromised because of differential trends in smoking. Ban-adopting communities systematically
differed from non-adopting communities: they were more densely populated, had higher levels of education and income, and had lower initial smoking rates. If there were differential
trends in smoking between these two types of communities that were not fully accounted for
by the vector of time-varying controls, the estimates of equation (1) would be biased. This
issue is of particular concern, give the strong link between both education and income and
health outcomes (Grossman, 2006; Lindahl, 2005; Lleras-Muney, 2005).
The Canadian experience allows me to investigate both these identification issues. I
rely on the fact that public bans were widely implemented at the local level, and that local
bans were often followed by provincial-level policies to conduct several alternative estimation
strategies.
7.1
The impact of local public bans
In this section, I examine the impact of public bans that were voluntarily implemented at
the local level. I focus exclusively on municipal- and county-level bans.21 By focusing on
local variation in policy, I can estimate models that control flexibly for differential trends
in smoking. I also use the widespread local adoption of bans to estimate propensity-score
matching regressions. The drawback to ignoring provincial bans is that these estimates may
21
In the empirical analysis, all provincial smoking bans are now coded as a zero.
15
understate the true impact of local policies, since some members of the control group were
actually covered by provincial restrictions.
7.1.1
Controlling flexibly for provincial trends
Because this analysis relies solely on local policy changes, I can control flexibly for provincial
trends in smoking behaviour. I estimate a generalized version of equation (1), replacing
the provincial linear trend, (P rovincep × trendt ), with a province-year interaction term
(P rovincep × Y eart ).22 This analysis relies solely on within-province variation in policy, so
the estimates are robust to all forms of cross-provincial trends in smoking. These estimates
are also robust to changes in other provincial legislation that may have influenced smoking
outcomes. I estimate the generalized version of equation (1) separately for females and males.
I report the estimates using ‘Strict bans’, although the estimates using ‘All bans’ are also
consistent to the baseline findings.
Table 4 reports the estimates for females. For references, columns (1) reports the baseline
results. Column (2) displays the estimates relying solely on local bans. As expected, these
estimates are smaller and less significant than the original results. The point estimates
imply a 7 to 10 percent decline in smoking participation (compared to 12 percent in the
baseline regressions). Column (3) reports the estimates from the regressions which include
the flexible province-year interaction term. When I control flexibly for provincial trends, the
size and significance of these estimates increases dramatically: local bans were associated
with a significant 13 percent decline in smoking rates. Again, there is no evidence that these
policies affected the cigarette consumption of female smokers.
These results highlight how differential trends in smoking across provinces are not entirely
captured by the controls for linear provincial trends, and may lead us to underestimate the
impact of public bans. This finding has implication for studies from the US, which have
relied on cross-state variation in policy, since linear controls for state-specific trends may not
22
The term (P rovincep × Y eart ) could not be included in the baseline regressions, since provincial bans
would not be identified.
16
fully capture differential trends in smoking.
Table 5 reports the corresponding estimates for males. Columns (2) and (3) display
the estimates relying only on local bans. There is no evidence that these policies had any
impact on smoking participation decisions. The point estimates are all close to zero and
insignificant. The point estimates for cigarette consumption associate bans with roughly a
5 percent decline, which is marginally significant.
7.1.2
Propensity-score matching regressions
While the fixed effects strategy controls for time invariant unobservable heterogeneity, it
does not address time-varying determinants of ban adoption. To address this issue, I exploit
the widespread local adoption of bans to estimate propensity-score matching regressions
(Imbens, 2004). This approach parametrically matches communities along initial observable
characteristics.
Let e(Xic ) denote the propensity score (the predicted probability that an individual was
covered by a local smoking ban) based on a set of county-level observable characteristics.
The goal is to find a set of explanatory variables such that, conditional on the propensity
score, smoking bans were randomly assigned: more formally, Pr(Adoptedi = 1|e(Xic )) =
Pr(Adoptedi = 0|e(Xic )), where Adoptedi is an indicator for whether individual i was ever
covered by a local public ban. I estimate the individual propensity score as a probit function
of the characteristics of their county of residence in 1994: Pr(Adoptedi = 1) = Φ(Xic ). Where
Xic is a set of county-level means which include smoking rates, population size, employment
rate, and mean education and income level.23 I then use the propensity score, e(Xic ), to
estimate re-weighted regressions.24 These models also include the flexible province-year
interaction term.
23
These means were calculated in 1994 to ensure that they were not directly affected by the policies
themselves. Counties with fewer than 30 observations in 1994 were omitted from the sample. The results
are not sensitive to this choice of cutoff.
q
Adoptedi
1−Adoptedi
Individual weights are given by the following formula: wi =
e(Xic ) + 1−e(Xic ) . This approach
places greater weight on ban-adopters and non-adopters that are observationally similar to each other.
24
17
Column (4) of Table 4 reports these estimates for females. Again, there is no evidence
that bans led to any change in cigarette consumption. The point estimates for smoker status
and daily smoker status are smaller and marginally significant. These estimates correspond
to an 8 percent decline in smoking rates. The matching estimates are roughly two thirds
the magnitude of the baseline results. Since some members of the control group would have
been covered by provincial legislation, it is not surprising that these estimates are smaller.
Column (4) of Table 5 reports the corresponding estimates for males. There is no evidence
that bans had any impact on male cigarette consumption. Similarly, bans were not associated
with any decline in smoking rates. The estimates on both daily smoker status, and smoker
status are both positive.
7.2
The impact of provincial bans in ‘non-adopting’ communities
Canadian public bans were implemented at two distinct levels of government. I use this additional source of policy variation to address the issue of endogenous ban adoption. Specifically,
I estimate the impact of these policies in regions that did not voluntarily adopt local bans,
but were ‘forced’ to implement provincial legislation. These regressions rely solely on variation in the timing of provincial bans. Since these bans were imposed on local jurisdictions
by a higher level of government, local characteristics were unlikely to have influenced the
adoption decision.
I exclude municipalities and counties that ever voluntarily adopted a local bylaw, and
re-estimate equation (1). The estimates for females are reported in the last two columns
of Table 4. Overall, the coefficients are less precisely estimated; excluding regions that
voluntarily adopted local bylaws reduces the sample size by roughly half, and there is much
less variation in policy at the provincial level. Column (5) reports the estimates which
exclude controls for county fixed effects, while column (6) reports the fully specified model.
There was no significant change in female cigarette consumption. Provincial bans were
associated with a significant decline in smoking participation. Despite the large standard
18
errors, the point estimates are all statistically significant, and they imply declines in smoking
rates ranging from 19 to 21 percent. The last two columns of Table 5 report the estimates
for males. Unlike the baseline results, there is no evidence that bans reduced the cigarette
consumption of smokers. The estimates for smoking participation are all negative, but highly
insignificant.
Moving from columns (5) to (6) we can see that including county fixed effects does little to
change the magnitude of the point estimates. Given that the provincial smoking restrictions
were not voluntarily adopted by local jurisdictions, it is comforting to find that unobservable
heterogeneity has less of an impact on these regressions.
The results based on ‘forced adopters’ of public bans support the baseline estimates.
Females responded to provincial bans by reducing smoking rates, while males were insensitive
to these policies. Interestingly, the point estimates for females were much larger than those
found in the baseline model, which is consistent with heterogeneous treatment effects: bans
were most effective in regions that were least likely to voluntarily adopt them. Since regions
that never adopted local bans tended to have higher initial smoking rates, there may have
been more individuals available to respond to these policies. Alternatively, both smoking
behaviour and income have been linked to patronage at licensed establishments (Biener and
Siegel, 1997; Single and Wortley, 1993), so residents in communities that were ‘forced’ to
enact these policies may have been more exposed to the bans. Finally, it is possible that local
bans were adopted in regions where more restaurant and bar owners had already voluntarily
implemented smoking restrictions, so provincial bans had a larger impact on the ‘effective’
regulation of smoking in these venues.
8
Conclusion
In this paper, I investigate the impact of public smoking bans on male and female smoking
behaviour. I find large gender differences in the response to these policies. Across several
19
different estimation strategies public bans were associated with significant 9 to 13 percent
declines in female smoking rates, but had virtually no impact on male smoking behaviour.
These results can be rationalized by the fact that females were more likely to engage in ‘social
smoking’. In addition, baseline cigarette consumption was lower among women, which may
have facilitated cessation.
These results have implications for policy. Most of the public now accurately perceives
the risks associated with smoking (Viscusi, 1990), although parents may still not be fully
aware of the hazards that prenatal smoke exposure and ETS exposure pose for children.25
Even if governments inform the public about these risks, intervention may still be necessary
to reduce female smoking if families do not fully internalize the externalities associated with
maternal smoking.26
While strict public bans are now virtually universal in Canada, less than half the states
in the US have adopted tough restrictions on smoking in restaurants and bars. Public bans
are almost nonexistent in many developing nations, where smoking rates have been on the
rise in recent years. This research suggests that public smoking bans may be an effective
tool to reduce female smoking.
25
Information campaigns have been credited with reducing prenatal smoking in recent years, although
the post-pregnancy relapse rate is still very high (US Department of Health and Human Resources, 2001).
26
Economists typically assume that parents internalize these externalities when making optimal tax calculations (Manning et al., 1991; Viscusi, 1995). Evans et al. (1999) demonstrate that if these costs were not
internalized optimal taxes should be between 42 and 72 cents per pack more. Similar arguments apply to
other government policies designed to reduce adult smoking.
20
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24
0
Proportion of adults covered
.2
.4
.6
.8
1
Figure
1:asPublic
coverage
in Canada
.2roportion
0
P
1
.8
.6
.4
.2
Proportion
1994
1996
1998
2000
2002
2004
2006
Year
Any
Strict
Source:
Note:
ban
provincial
Strict
ban
provincial
Canada
-apropos/role/municip/ban-interdiction-eng.php).
National
ofbans
Population
adults
guidelines
ban
ban
arecovered
classified
Health
(seeSurvey
www.hc-sc.gc.ca/hc-ps/tobac-tabac/about
'gold'
(cyclesban
or
1 to
'silver'
7).
bans
according to Health
1994
1996
1998
2000
Year
Any ban
Strict ban
2002
2004
2006
Any provincial ban
Strict provincial ban
Source: National Population Health Survey (cycles 1 to 7).
Note: Strict bans are classified as 'gold' or 'silver' bans according to Health
Canada guidelines (see www.hc-sc.gc.ca/hc-ps/tobac-tabac/about
-apropos/role/municip/ban-interdiction-eng.php).
25
Table 1: Descriptive Statistics
Full sample
Currently has a ban
All
60,888
Male
26,704
Female
34,184
All
16,041
Male
7,130
Female
8,911
Smoker
Daily smoker
Daily cigarettes
(among daily smokers)
Age
0.26
0.22
16.6
0.28
0.23
18.0
0.24
0.20
15.2
0.21
0.16
14.8
0.24
0.18
15.9
0.19
0.15
13.5
45.5
44.3
46.6
46.6
45.4
47.7
Married
0.64
0.65
0.63
0.65
0.64
0.66
Male
0.48
-
-
0.48
-
-
Less than high school
High school
Some college
Post-secondary degree
0.20
0.15
0.29
0.37
0.19
0.14
0.29
0.39
0.20
0.16
0.28
0.36
0.15
0.14
0.29
0.43
0.14
0.13
0.29
0.44
0.15
0.15
0.28
0.42
Observations
Notes: Weighted means reported. Data are from the National Population Health Survey
cycles 1 to 7 (1994 -2006). The sample is restricted to individuals aged 18 years and
older. The mean for daily cigarettes is calculated for the sample of daily smokers.
Table 2: The effect of public bans on smoking prevalence - Full sample
Daily Smoker
Smoker
Daily Cigarettes
(among daily smokers)
(1)
(2)
(3)
(4)
(5)
(6)
All bans
-0.014
-0.001
-0.009
0.003
-0.49
-0.36
[0.008]*
[0.007]
[0.011]
[0.010]
[0.35]
[0.32]
Strict bans
Full controls
County fixed effects
Observations
-0.022
[0.009]**
-0.013
[0.008]*
-0.024
[0.008]***
-0.016
[0.008]**
-0.28
[0.34]
-0.11
[0.29]
Yes
Yes
Yes
60,888
Yes
Yes
Yes
60,888
Yes
Yes
Yes
13,760
60,888
60,888
13,760
Notes: Data are from the National Population Health Survey cycles 1 to 7 (1994 -2006). The sample
is restricted to individuals aged 18 years and older. All regressions are weighted and include controls
for education (12 dummies), age (14 dummies), gender, marital status, population (6 dummies), year,
province, and a linear provincial trend. Regressions are clustered at the county level. ***, **, * denote
statistical significance at the 1%, 5%, and 10% levels, respectively.
26
Table 3: The effect of public bans on smoking prevalence among males and females
Daily Smoker
Smoker
Daily Cigarettes
(among daily smokers)
(1)
(2)
(3)
(4)
(5)
(6)
Males
All Bans
-0.001
0.015
0.010
0.016
-0.72
-0.24
[0.013]
[0.010]
[0.015]
[0.012]
[0.46]
[0.50]
Strict Bans
Full controls
County fixed effects
Observations
Females
All Bans
Strict Bans
Full controls
County fixed effects
Observations
-0.017
[0.013]
-0.002
[0.011]
-0.012
[0.012]
-0.002
[0.010]
-1.24
[0.54]**
-0.71
[0.57]
Yes
Yes
26,704
Yes
Yes
26,704
Yes
26,704
Yes
Yes
26,704
6,575
Yes
Yes
6,575
-0.026
[0.011]**
-0.015
[0.008]**
-0.026
[0.013]**
-0.011
[0.012]
-0.15
[0.45]
-0.32
[0.37]
-0.028
[0.012]**
-0.023
[0.009]***
-0.037
[0.012]***
-0.029
[0.010]***
0.64
[0.42]
0.46
[0.41]
Yes
Yes
Yes
34,184
Yes
Yes
Yes
34,184
Yes
Yes
Yes
7,185
34,184
34,184
7,185
Notes: Data are from the National Population Health Survey cycles 1 to 7 (1994 -2006). The sample is
restricted to individuals aged 18 years and older. All regressions are weighted and include controls for education (12 dummies), age (14 dummies), marital status, population (6 dummies), year, province, and a linear
provincial trend. Regressions are clustered at the county level. ***, **, * denote statistical significance at the
1%, 5%, and 10% levels, respectively.
27
28
7,185
Observations
7,185
34,184
0.68
[0.64]
34,184
-0.023
[0.010]**
-0.014
[0.010]
(2)
Fixed effects
7,185
34,184
0.92
[0.78]
34,184
-0.031
[0.013]**
-0.025
[0.012]**
(3)
Fixed effects
+ flexible trend
6,040
29,601
0.43
[0.59]
29,601
-0.018
[0.009]*
-0.015
[0.010]
(4)
Matching
+ fixed effects
+ flexible trend
4,222
17,718
-0.80
[1.24]
17,718
-0.051
[0.025]*
-0.037
[0.021]*
(5)
‘Forced
adopters’
4,222
17,718
-1.02
[1.17]
17,718
-0.052
[0.027]*
-0.040
[0.023]*
(6)
‘Forced
adopters’
+ fixed effects
Provincial bans
Notes: Data are from the National Population Health Survey cycles 1 to 7 (1994 -2006). The sample is restricted to individuals aged
18 years and older. All regressions are weighted and include controls for education (12 dummies), age (14 dummies), marital status,
population (6 dummies), year, province, and a linear provincial trend. Regressions are clustered at the county level. ***, **, * denote
statistical significance at the 1%, 5%, and 10% levels, respectively.
34,184
0.46
[0.41]
34,184
-0.029
[0.010]***
Observations
Smoker
Observations
Daily cigarettes
(among daily
smokers)
-0.023
[0.009]***
Daily smoker
(1)
Fixed effects
Table 4: Robustness checks: The impact of strict bans on females
Baseline Results
Local bans
29
26,704
-0.002
[0.010]
26,704
-0.71
[0.57]
6,575
Observations
Smoker
Observations
Daily cigarettes
(among daily
smoker)
Observations
6,575
26,704
-1.03
[0.58]*
26,704
-0.009
[0.015]
-0.003
[0.017]
(2)
Fixed effects
6,575
26,704
-0.91
[0.52]*
26,704
0.008
[0.017]
0.007
[0.017]
(3)
Fixed effects
+ flexible trend
5,590
23,233
-0.33
[0.50]
23,233
0.021
[0.014]
0.015
[0.012]
(4)
Matching
+ fixed effects
+ flexible trend
4,006
13,955
-0.39
[1.32]
13,955
-0.020
[0.028]
-0.011
[0.031]
(5)
‘Forced
adopters’
4,006
13,955
0.34
[1.42]
13,955
-0.018
[0.028]
-0.014
[0.030]
(6)
‘Forced
adopters’
+ fixed effects
Provincial bans
Notes: Data are from the National Population Health Survey cycles 1 to 7 (1994 -2006). The sample is restricted to individuals aged
18 years and older. All regressions are weighted and include controls for education (12 dummies), age (14 dummies), marital status,
population (6 dummies), year, province, and a linear provincial trend. Regressions are clustered at the county level. ***, **, * denote
statistical significance at the 1%, 5%, and 10% levels, respectively.
-0.002
[0.011]
Daily smoker
(1)
Fixed effects
Table 5: Robustness checks: The impact of strict bans on males
Baseline Results
Local bans