[Intl-tobacco] Smoking in Russia

[Robert Weissman]

Smoking in Russia: The 'Marlboro Man' rides but without 'Virginia Slims'
for now Source: Comparative Economic Studies Publication date:
2003-03-01 Arrival time: 2003-05-13

http://brownw.newsreal.com/pages/brownw/Story.nsp?story_id=38739715&ID=brownw&scategory=Smoking+%26+Health&

 Based on two rounds of a nationally representative household survey,
this paper presents an exploratory study of risk factors and the
economics of the decision to smoke by adults in Russia in the second
half of the 1990s. With an overall smoking prevalence of 32.2%, smoking
is much more prevalent among men (61.4%) than among women (10.3%). The
risk of smoking is on the rise in Russia due mainly to the growing
incidence of female smoking, especially in major urban centres, where
the impact of modern culture and Western tobacco companies is more
profound. The low estimated price elasticities of the decision to smoke
for men (-0.085) and for women (-0.628) suggest that an excise tax on
cigarettes is not an effective means to reduce the prevalence of
smoking. The decision to smoke is also found to be very income
inelastic. Formal education, occupation, alcohol consumption, and
obesity are associated with smoking in a way similar to developed countries.

 Comparative Economic Studies (2003) 45, 87-103. doi:10.1057/ palgrave.ces.8100001

 Keywords: Russia, smoking, Russian health

 JEL Classifications: I1, P5, R2

 INTRODUCTION

 According to the World Health Organization (WHO) (Lopez, 1997), smoking
has become, or soon will be, the leading cause of death throughout the
world and in Eastern Europe in particular. In this respect Russia faces
an especially grave danger, as 300,000 Russians die each year from
smoking-related illnesses. The country's population is in absolute
decline with low male life expectancy, in part, because of smoking
(Stone, 2000). Further, smoking may increase the risk of certain anxiety
disorders among young men (Johnson et al., 2000) leading to the higher
stress believed to contribute to higher mortality (Stone, 2000).

 In the literature, smoking has been found to negatively impact health
and mortality in several developed countries (Chaloupka and Warner,
1999). For example, smoking-related illnesses are the leading
preventable cause of death in the US. Americans who smoke live about 6
years less than those who have never smoked. Including quitters, the
medical cost for smokers in the US is roughly 70 billion and 2.4 million
life years.

 From the conspicuous cigarette billboards by the roads to the lack of
nonsmoking areas at public facilities and retail businesses, Russia is
one of the most smoker friendly countries in the world. A national
household survey used here reveals about 176 billion cigarettes consumed
each year. In 1998, Russian households spent 2.6% of their disposable
income on tobacco, which is more than they spent on alcohol (Monitoring
Economic Conditions in the Russian Federation, 2002).

 Young people in Russia exhibit the same naivete towards smoking as that
found in the West (Gruber and Zinman, 2000). Russian men become regular
smokers before high school graduation (Prokhorov and Alexandrov, 1992).
Mandatory military service for men, with cigarettes as part of the
'food' ration, contributed to several generations of young male
addiction similar to the `Lucky Strike' generation of US GIs. Until the
early 1990s, the mass advertising and brand proliferation of a market
economy were absent but so was any effective anti-smoking effort. While
Soviet anti-smoking 'campaigns' and propaganda existed, they were
largely ignored.

 In the second half of the 1990s, despite increased awareness of health
effects with warnings on cigarette packs and anti-smoking advertising,
smoking is believed to be dangerously on the rise especially with the
younger portion of the population (Prokhorov, 1997). Western tobacco
companies with declining markets in developed countries see Russia as a
prime unregulated area to target to make up for declining demand
elsewhere. According to the WHO, 50 billion cigarettes are being
imported by Russia each year (Interfax, 2001).

 Although the literature on the economics of smoking is quite large for
developed countries, formal analysis of smoking in developing and
transition economies is quite new. New insights on the issue of smoking
in Russia can draw on the methods and models already developed elsewhere
(Chaloupka and Warner, 1999) and the Russia Longitudinal Monitoring
Survey (RLMS), a national household survey, provides a limited
longitudinal data set on smoking. This paper presents an exploratory
study of risk factors and the economics of adult smoking in Russia in
the second half of the 1990s. Our results are consistent with earlier
work using a different survey (McKee et al., 1998). The next section
describes the data used, then subsequent section describes the patterns
of smoking, the penultimate section analyses the influences on the
decision to smoke using a probit model, and the last section provides
the conclusion.

 DATA

 This study is based on data drawn from Phase II of the RLMS, a
household-- based nationally representative survey designed to measure
systematically the effects of Russian reforms on households and
individuals. Carefully designed by an interdisciplinary partnership of
leading Russian and American experts, the survey is of exceptionally
high quality for a country undergoing such dramatic upheaval.1 In Phase
II of the RLMS, a multi-stage probability sample was employed. First, a
list of 1,850 consolidated rayons (administrative subdivisions similar
to counties in the United States) was used to serve as primary sampling
units (PSUs). Three very large population units Moscow city, Moscow
oblast (province), and St Petersburg city - constituted
self-representing (SR) strata. The remaining non-self-representing
rayons (NSR) were allocated to 35 equal-sized strata. One rayon was then
selected from each NSR stratum using the method `probability
proportional to size' (PPS). That is, the probability that a rayon in a
given NSR stratum was selected was directly proportional to the measure
of population size. The NSR strata all have approximately equal sizes
because they were purposefully designed that way to improve the
efficiency of estimates. The number of households drawn into the sample
was 4,718. Within each selected PSU, the population was stratified into
urban and rural substrata, and the target sample size was allocated
proportionately to the two substrata. All interviewers went through
extensive training before conducting the survey. In both urban and rural
substrata, interviewers were required to visit each selected dwelling up
to three times to secure the interviews. They were not allowed to make
substitutions of any sort. The interviewer then conducted interviews
with as many adults as possible, acquiring data about their individual
activities. The household response rate exceeded 80 percent and
individual questionnaires were obtained from over 97 percent of the
individuals listed on the household rosters. The multivariate
distribution of the sample by sex, age, and urban- rural location
compares quite well with the corresponding multivariate distribution of
the 1989 census.

 The study uses two rounds of the survey, Round 7, for which data were
collected in October-December 1996, and Round 8, with the data collected
in October 1998-January 1999. The smoking patterns in earlier rounds of
the RLMS Phase II, Round 5 (1994) and Round 6 (1995), were found to be
very similar to those in Round 7. In 1996, the economy was relatively
stable while in August 1998 a major financial crisis occurred, causing
high inflation and a drop in household real incomes. Thus, the data from
1998 can be compared with those from 1996 to analyse the effect on the
smoking patterns of the 1998 macroeconomic shock.

 The sample includes individuals aged 18 years old or older. Most data
are drawn from the adult individual questionnaires, which include a wide
variety of smoking-related questions, as well as socio-demographic
characteristics. The data on cigarette prices are derived from separate
'community' files, which contain information on the highest and lowest
prices of both domestic and imported cigarettes collected in the stores
on each of the 159 RLMS sites at the same time as the household
interviews were conducted. Since individuals are likely to shop for
cigarettes in their PSUs but not necessarily on their sites, the site
price data were aggregated to produce average PSU prices. The cigarette
price measure is then calculated as the geometric average of four prices
registered in the PSU during the survey: the lowest price of domestic
cigarettes, the highest price of domestic cigarettes, the lowest price
of imported cigarettes, and the highest price of imported cigarettes.

 DESCRIPTIVE PROFILE OF A RUSSIAN SMOKER

 The patterns of smoking are fairly consistent across the two RLMS
rounds (Tables 1-3). Using the pooled (Rounds 7 and 8) sample, the
overall percentage of smokers in Russia, 32.2%, is significantly higher
than that in the United States, 24.7% (CDC, 1999), but the main
distinction between the two countries is a startling gender difference
in the prevalence of smoking in Russia. Overall, most Russian men
(61.4%) smoke while most Russian women (89.7%) do not smoke, whereas in
the US the percentage of male smokers (27.6 %) only marginally exceeds
that of female smokers (22.1 %). In any age bracket, the percentage of
smoking men in Russia is significantly higher than that in the US, while
the percentage of smoking women is s\ignificantly lower. Almost half of
Russian male non-smokers (48.9%) are quitters, while a vast majority of
female current non- smokers (93.4%) never smoked.

 Further, men are much longer and heavier smokers than are women. As may
be seen from Table 3, men start smoking earlier, smoke for a longer
time, are less likely to quit, and when they do quit, do so later in
their life than women.3 An average male smoker smokes about 15
cigarettes per day, while an average female smoker smokes only about
nine cigarettes. The RLMS data also indicate that a vast majority of
female smokers, 81.5%, prefer filtered cigarettes, while 48.2% of male
smokers smoke more harmful unfiltered cigarettes, papirosi,4 or a pipe.
Only 19.6% of men never smoked, whereas the percentage of women
never-smokers is 83.7%.

 Table 1

 Table 2

 Table 3

 The Russian age-smoking profiles are also remarkably gender specific,
supporting findings in an earlier study (McKee et al., 1998). Like many
other countries analysed in the literature (eg Moore and Hughes, 2000),
prevalence of smoking in Russia is lower among older individuals of both
genders, but the reasons why it declines are contingent on gender.
First, given the considerably higher prevalence, duration, and intensity
of smoking among men, it is likely that the male mortality rate is
higher for smokers than for non-smokers (ie male smokers are likely to
be under-represented in older age groups). In 1990, smoking in Russia
accounted for 42% of all male deaths in the age group 35-69 years, but
only for 4% of female deaths in the same group (Peto et al., 1994).
Second, the percentage of women who never smoked in the 45 or older age
group (93.6%) is significantly higher than that for women aged 18-44
(73.3%), which suggests that the female age-smoking profile is subject
to a considerable cohort effect; that is, the prevalence of smoking
among younger women has increased over time. For men, the percentage of
never-smokers in both age groups is the same (19.6%), which suggests
(given the higher mortality rate for smokers) a small decline in
popularity of smoking among younger ages.

 The explanation of these startling gender differences in prevalence and
patterns of smoking lies in Russia's historical and cultural background.
Traditionally, smoking in Russia was a `male habit.' During the Soviet
period, society's attitude toward women who smoke was strongly negative.
Most men and women regarded smoking by women as frivolous, incompatible
with their roles as wives, mothers, and `homemakers.' stereotyping women
smokers as drunkards and `fallen women.' These stereotypes weakened
somewhat in the late days of the Soviet era and, after the Soviet system
collapsed, faded away amid growing skepticism and cynical distrust of
the authorities, especially among younger people. At the same time
intellectual and political liberalisation led many young women to view
smoking as a manifestation of their freedom and independence, while
economic liberalisation simultaneously opened Russian markets for
Western tobacco companies to promote an image of `Virginia Slims' female
smokers enjoying cigarettes.

 This explanation is further supported by the evidence that smoking
prevalence among women is higher in large political and cultural centres
that are likely to be more liberalised and more impacted by Western
culture, and lower in small towns and rural areas where traditional
stereotypes are likely to be stronger. In Table 2, the percentage of
smoking women is at its highest (22.7%) and close to US levels in the
two main population centres of Moscow and St Petersburg, while it is
lowest (5.2%) in rural communities. In the next section, we use a probit
model to further analyse factors that influence an individual's decision
to smoke.

 INFLUENCES ON THE DECISION TO SMOKE

 Methodology

 In the literature on smoking, the decision to smoke has been found to
be negatively correlated to education, although causality is unclear. In
our model, two dummy variables account for formal educational
attainment: education below complete secondary and college degree, with
complete secondary education (most common in Russia) as the reference
level. The literature also suggests that a white collar occupation is
usually a negative smoking risk factor. Specifically for Russia, there
is anecdotal evidence that smoking breaks during the work day (perekury)
are a deep-rooted tradition for blue collar male workers, but not as
common for white collar occupations. Dummy variables for white-collar
occupations (managers, professionals, associate professionals and
technicians, and clerks) and blue-collar occupations (the rest of
one-digit ILO categories) are included in vector zi with individuals who
do not work as the reference level.

 The literature on smoking also finds a link between smoking and
drinking (eg Hu et al., 1995; McKee et al., 1998) and that both are
partly responsible for the steep decrease in life expectancy in Russia
in the 1990s (Notzon et al., 1998; DaVanzo and Grammich, 2001). The
alcohol consumption variable in our model is defined as a natural
logarithm of grams of pure alcohol consumed last month, which is
calculated based on the RLMS data on frequency of drinking and the
average volumes of different kinds of alcoholic beverages consumed per day.

 Table 4

 In the literature another variable often linked to smoking is obesity
(Hu et al., 1995). Current smokers appear to be less heavy than
non-smokers, and return to a non-smoker weight after they quit. To
examine this relation, we constructed the Quetelet obesity index,
calculated from the RLMS data on measured height and weight as follows.
Qi = Wi/Hi^sup 2^ , where Qi is the obesity index for individual i, W is
the individual i's weight in kilograms, and H is the height in meters.
The obesity dummy variables in vector zi reflect three groups of
individuals classified according to a Quetelet index: normal weight (the
reference level), if Qi =< 27.8 for men and Qi =< 27.3 for women;
overweight, if 27.8 > Qi >= 31.0 for men, and 27.8 > Qi >= 32.2 for
women; and severely overweight, if Qi >= 31.0 for men and Qi >= 32.2 for
women. Body weight using the Q index is expected to have a negative
influence on smoking.

 Results

 The probit estimates of smoking risk factors and their marginal effects
estimated at the means of the regressors for pooled Rounds 7 and 8 are
presented in Table 5. The coefficients of the dummy variable for Round 8
are significantly positive for both men (at the 0.10 level) and women
(0.01 level). That is, prevalence of smoking with both genders tends to
increase in 1998 compared to 1996, other things being equal.8 The
marginal effects of 1998 on the probability of being a smoker are about
the same for men and women. However, the growth rate in prevalence of
smoking among women is much higher - 46.3% compared to 4.8% for men.9
One possible explanation for the increase is stress caused by the
economic crisis, which is likely to affect both genders. Another
explanation, consistent with our findings in the previous section, is
growing economic and cultural liberalisation, which along with the
cohort effect is likely to increase the prevalence of smoking among women.

 Table 5

 The probit results support our hypothesis about significant gender
differences in influences on the decision to smoke and the explanation
we have suggested in the previous section.10 Marital status is not
significant for men but very important for women. The same is true about
the community regressor. For instance, for a woman living in Moscow or
St Petersburg, the probability of being a smoker is about twice that for
a woman living in a small city or town.

 The estimated price and income elasticities of the decision to smoke
are also gender specific. The probit coefficient of log real income is
significantly negative for men and significantly positive for women.
That is, a wealthier man is less likely to smoke while a wealthier woman
is more likely to smoke. For both women and men, however, the decision
to smoke is very income inelastic. The elasticities calculated according
to equation (2) are -0.007 for men and 0.047 for women. These
elasticities may be interpreted as a 0.7% percentage decrease in the
probability of being a smoker ((Delta)S/ S) x 100) for men and a 4.7%
percentage increase in the probability of being a smoker for women in
response to doubling income.

 The gender-specific smoking patterns described in the previous section
help explain these results. Since men are much longer and heavier
smokers than are women, men are more addicted to smoking. Also, since
men tend to smoke cheaper lower-quality cigarettes and papirosi while
women prefer more expensive high-quality cigarettes, men are likely to
spend a smaller fraction of their income on cigarettes than are women.
Hence, men's decision to smoke is almost perfectly income inelastic,
whereas women's decision is slightly more income elastic. The positive
relation between income and the probability of smoking for women may be
because of the fact that relatively wealthy Russian women are more
independent and therefore less likely to be influenced by traditional stereotypes.

 The price elasticity of the decision to smoke is calculated at - 0.085
for men and -0.628 for women with the probit coefficients indicating a
significantly negative relation. Our explanation of the higher price
elasticity of women's decision to smoke compared to men's decision is
similar to that for the income elasticities: first, men smoke more;
second, cigarettes account for a smaller fraction of their budget.11

 The results in the probit model allow us to refine the age- smoking
profiles described in the previous section. The coefficients of age are
significantly positive while the coefficients of age square are
significantly negative for both genders, which indicates concavity of
both age-smoking profiles. For men, the probability of being a s\moker
increases with age (the marginal effect of age is positive) until around
40 years and then starts to decrease (ie, the marginal effect of age
becomes negative).12 For women, the probability of smoking is maximised
at 27 years.

 For both men and women, education is an important influence on the
decision to smoke. Compared to the individuals with secondary education,
the probability of smoking is significantly lower for those with higher
education and significantly higher for those with education below the
secondary level. The effect of higher education on men is quite
remarkable: an average man with a college education is 15.9 percentage
points less likely to smoke than a man with secondary education. This
relation, however, is not necessarily causal. Moore and Hughes (2000)
found, for example, that in the US current non-smokers are more likely
to have finished college and less likely to have dropped out of high
school. Farrell (1986) suggests that an unmeasured correlate of formal
education is the true causal factor. A white collar occupation also
significantly decreases the probability of being a smoker for both genders.13

 Consistent with the literature, the obesity factor in our model is
strongly negatively associated with smoking and cannot be omitted from
the model. However, examining whether smoking leads to losing weight,
obese people quit smoking for health reasons or quitters gain weight is
beyond the scope of this study.

 Our results also indicate a strong association of smoking and drinking.
The coefficients of the alcohol consumption variable in the probit model
are significantly positive for both genders. Here again, it is not our
purpose to solve the chicken-and-egg problem: whether drinking leads to
smoking or vice versa. Most likely both habits are a manifestation of a
certain social and cultural environment and way of life.

 CONCLUSION

 With an overall adult smoking prevalence of 32.2%, the gender
difference in the percentage of smokers in Russia is startling. For men
this percentage is 61.4%, while for women it is only 10.3%. Men are also
much longer and heavier smokers than are women. The age- smoking
profiles are gender specific too. For men, the probability of being a
smoker is maximised at 40 years, whereas for women the probability of
smoking reaches its peak at 27 years. Further, it has been found that
the female age-- smoking profile is subject to the cohort effect, while
the male profile is not.

 Russia's cultural traditions, which consider smoking a `male habit'
with a strongly negative attitude toward women who smoke, may explain
these startling gender differences in prevalence and patterns of
smoking. This explanation is supported by the evidence that smoking
prevalence among women is significantly higher in large political and
cultural centres, which are more liberalised and impacted by Western
culture, than in small towns and rural areas where traditional
stereotypes are stronger. It is also consistent with our finding that
married women are significantly less likely to smoke.

 Other things being equal, the risk of smoking is on the rise in Russia
due mainly to a growing incidence of female smoking. Cultural
liberalisation and opened markets for Western tobacco companies appear
to be moving Russia away from a more traditional society, where women
are simply not supposed to smoke, towards a `Virginia Slims' era where
female smoking is in fashion. The examples of Moscow and St Petersburg,
where the influence of Western culture and Western tobacco companies is
the strongest and where for a women the risk of being a smoker is about
twice that in a small city or town, show what may happen to the rest of
Russia if appropriate policies are not implemented.

 Market reforms and the importing of Western culture must be accompanied
by the importing of Western anti-smoking education to make Russian women
more aware of the adverse consequences of smoking and resist the
`Virginia Slims' image. Given the very high prevalence of smoking among
Russian men, the macho image/Marlboro Man smoker should be targeted as
well. Considering that most Russians start smoking before age 20,
teenage anti-smoking education appears to be especially important.14
Other measures, like Estonia's total ban on tobacco advertising (Stone,
2000) based on OECD experience (Lugesen and Meads, 1991), may be
suggested.14 However, as noted in the literature (Leon and Shkolnikov,
1998), there is no mechanism readily apparent to create rapid changes in
smoking behaviour. For example, despite decades of strong anti-smoking
efforts about 25% of Americans still smoke with the recent rise in
teenage smoking suggesting a new cohort of addicted adults in the future
(Gruber and Zinman, 2000).

 While improved formal education at both the secondary and postsecondary
levels appears to reduce cigarette consumption, the lack of a definitive
causal link between formal education and smoking in the literature
indicates more research is needed before linking education reforms with
anti-smoking efforts. The estimated price elasticities of the decision
to smoke, very low (-0.085) for men and higher but still inelastic
(-0.628) for women, do not allow us to recommend raising the excise tax
on cigarettes as an effective means to reduce prevalence of smoking. To
answer whether or not a change in the excise tax will be effective in
terms of reducing the quantity of cigarettes consumed by smokers,
further analysis of the price elasticity of demand for cigarettes is
needed. Such analysis is possible using household expenditure data from
the RLMS, but is left for a separate paper.

 Consistent with the literature, our results indicate a strong positive
association of smoking with another health risk, alcohol consumption.
This association needs to be further studied in the context of the rare
population decline during peacetime in Russia. Most likely both habits
are a manifestation of a certain social and cultural environment, and
should be addressed simultaneously.

 Acknowledgments

 We thank all participants at the Georgia Southern University's
economics research seminar in October 2000 and Timothy Brock (The Ohio
State University) for their comments.

 1 The survey has been coordinated by the Carolina Population Center
(CPC) at the University of North Carolina at Chapel Hill in
collaboration with Paragon Research International and the Russian
Academy of Sciences. The following outline of the RLMS sampling
techniques draws on the detailed project descriptions provided by the
CPC team (http://www.cpc.unc.edu/projects/rlms/project.html). The data
may be obtained from the RLMS Web site (http:// www.cpc.unc.edu/ projects/rlms/data.html).

 3 Despite the fact that because of higher female life expectancy
(around 72 years while male life expectancy is around 60 years), an
average adult woman is 4 years older than an average adult man, an
average female smoker is about 6 years younger than an average male
smoker. The average duration of smoking among current smokers is about
15 years for women and 24 years for men. Among those who ever smoked,
quitters account for 36.6% of women and only for 23.4% of men. The
average duration of smoking for quitters is about 9 and 22 years for
women and men, respectively.

 4 A papirosa is a shorter but somewhat thicker variant of an unfiltered
cigarette with a short paper pipe attached.

 5 Likelihood ratio tests confirmed our supposition that influences on
the decision to smoke are gender specific. The null hypothesis (ie that
the slope coefficients in the male and female equations are equal) was
rejected at the 99% confidence level.

 6 This formula is derived from the following definition of elasticity:
... ... The formula assumes constant (Cobb-Douglas type) elasticity.

 7 The likelihood ratio tests showed no differences in the coefficients
of the explanatory variables between Rounds 7 and 8 (failed to reject
the null hypothesis at the 95 % confidence level).

 8 This does not contradict the data in Table 1, which show no
significant changes in the percentages of smokers from 1996 to 1998,
because the percentages in Table I also reflect the negative influence
of other factors (particularly a more than 80 % increase in cigarette
prices), which are controleed for in the probit model.

 9 Calculated as g = ((Delta)S/S)100, where (Delta)S is the marginal
effect of Round 8, (phi)(x'zi)xR, and S = (phi)(x'zi) is estimated at
the means of the regressors.

 10 The 1.375 coefficient of the gender dummy variable (1 if male) in
the model with pooled genders is significant at the 0.01 level and the
estimated marginal effect of the male gender on the probability of being
a smoker is 0.487.

 11 This budget effect is also visible if we calculate price
elasticities of the decision to smoke separately for 1996 and 1998. In
1996, these elasticities were -0.046 and -0.430, while in 1998 they were
-0.120 and -0.919 for men and women, respectively. The greater
sensitivity of the decision to smoke to cross-section price differences
in 1998 may be explained by the fact that in 1998 the (geometric)
average real cigarette price was more than 80% higher than that in 1996
and therefore the fraction of household income spent on cigarettes was
greater in 1998. This difference in elasticity, however, does not appear
to be statistically significant given the standard errors of the
corresponding probit estimates.

 12 The marginal effects of age are calculated as ..., where S is the
probability of being a smoker, A is age, (phi)(*) is estimated at A and
the means of other regressors, xA and xA2 are the probit coefficients of
age and age square, respectively.

 13 Education and occupation are likely to be correlated. Our likelihood
ratio tests showed, however, that, for both genders, neither of these
factors can be omitted without losing the model's explanatory power (the
null hypothesis is rejected at the 99% confidence level). That is,
botheducation and occupation contribute significantly to explaining the
probability of being a smoker. Yet, for men, education appears to take
priority over occupation. The exclusion of education leads to a lower
likelihood ratio index than does the exclusion of occupation (0.118
compared to 0.128). For women education and occupation appear to be of
the same importance.

 14 Unfortunately, the RLMS data limitations do not allow us to examine
teenage smoking. In the survey, most teenagers were interviewed using
the Child Questionnaire, which did not include questions on smoking.

 15 On 8 Feburary 2001, the Lower House of the Russian parliament
recognised the health threat and voted to ban tobacco advertising in
print media, billboards and public transportation. On 14 January 2002,
Article 6 of the federal law on limitation of tobacco smoking (to be
activated after six months following its publication) forbade smoking in
public transportation facilities, at work, on aircraft during flights of
less than 3 h, in enclosed sport facilities, in organisations
specialised in the field of health, education, and culture and in all
buildings where state institutions are represented (RFE/RL, 2002).

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 CONSTANTIN OGLOBLIN & GREGORY BROCK

 Department of Finance and Economics, P.O. Box 8151, Georgia Southern
University, Statesboro, GA 30460-8151, USA. E-maiLs: coglobli@gasou.edu, gbrock@gasou.edu

 Copyright Association for Comparative Economic Studies Mar 2003

 Publication date: 2003-03-01