The influence of the preference for menthol cigarettes on smoking prevalence trends

3e The influence of the preference for menthol cigarettes on smoking prevalence February 14 A report for Philip Morris International

Contents Executive Summary 1 1 Introduction. 3 Data and sample description.1 Smoking prevalence.. Menthol market share 9.3 Socio-economic status. 11.4 Cigarette price 11. Smoking ban policies. 1.6 Demographics 13 3 Regression analysis 1 3.1 Simple linear correlation analysis 1 3. Multivariate regression analysis. 4 Conclusions. 8 Appendix 9 6 References. 34

Executive Summary Background Menthol has been used as a cigarette flavour for almost 9 years. It has been suggested that the use of menthol as a cigarette flavour might affect the rate of progress in reducing smoking prevalence, either by increasing rates of initiation, or by reducing rates of smoking cessation. Oxford Economics performed a study in 1 that analysed whether the availability of menthol cigarettes (market share) had an influence on youth smoking prevalence in a sample of countries and concluded that there is no evidence to support the notion that the availability of menthol cigarettes is associated with higher youth smoking prevalence 1. However, from a global perspective, the state of knowledge about the relationship between changes in preference for menthol cigarettes and other smoking prevalence rates is underdeveloped. The existing literature typically has a narrow geographical and temporal focus. It is constrained by the study of only one or a few countries at one point in time rather than examining over a longer time period and across diverse jurisdictions. Aims The aim of this study is to investigate whether changes in preference for menthol cigarettes influence smoking prevalence. In particular, we are interested in examining whether there is a statistically significant relationship between the change in the preference for menthol cigarettes (as measured by their share in the total national cigarette market) and in smoking prevalence over the period 1-1. Study design This study uses simple correlation analysis, followed by more sophisticated multiple regression analysis, to examine the relationship between over time in smoking prevalence rates, changes over time in the market share of menthol cigarettes, and additional control variables, including socio-economic, price, institutional/policy and demographic variables. The study considers both cross-sectional and longitudinal dimensions of patterns in smoking prevalence: a core sample of 48 countries is used, with time-series data on the two principal variables of interest spanning the period 1-1. This core sample is roughly evenly split between developed and emerging economies. The availability of appropriate time-series data on smoking prevalence and menthol market shares determined the choice of countries in the sample. 1 Oxford Economics, The influence of the availability of menthol cigarettes on youth smoking prevalence, December 1. Commissioned by Philip Morris International. 1

Results We find no evidence in our sample of 48 countries that an increase in preference for menthol cigarettes is associated with a slower rate of decline in smoking prevalence. In the simple correlation analysis, the association between these two variables is always statistically insignificant and is often negative (implying that an increase in preference for menthol cigarettes is associated with a greater decline in smoking prevalence rates). This lack of significant correlation was found in the full sample of adult smoking prevalence, in the developed and emerging market subsamples of adult smoking prevalence, and also when looking at an alternative dependent variable, namely youth smoking prevalence. Similar results also held in a multiple regression setting: most of our regression equations suggested a statistically insignificant effect of the change in menthol preference on smoking prevalence; when the change in menthol preference was significant it was negative, so that this cannot be taken as support for the claim that menthol has increased or slowed decline in smoking prevalence. In contrast to the generally poor statistical performance of the change in menthol preference in our regressions, certain socio-economic, price and demographic variables were able to explain a significant share of the variation in smoking prevalence rates across countries and over time. Conclusion If preference for menthol cigarettes influenced in smoking prevalence, we would expect to find a robust and statistically significant relationship between the change over time in smoking prevalence rates and the change in the market share of menthol cigarettes. Examining over the previous decade across a core sample of 48 countries, we found no evidence that an increased preference for menthol cigarettes (as measured by an increase in the market share of menthol cigarettes) is associated with a slower rate of decline of smoking prevalence. Our correlations and estimated regression equations suggest an insignificant relationship in the vast majority of cases. Some of our equations do suggest a significant relationship, but these were always negative, in contrast to the suggestion that menthol increases or prevents decline in smoking prevalence. These findings hold after controlling for socio-economic, price, institutional/policy and demographic factors; they hold for both developed and emerging economies; and they hold for both adult and youth smoking prevalence. As a result, we conclude that there is no evidence from the 48 countries in our sample to support the hypothesis that an increase in the preference for menthol cigarettes impacts smoking prevalence. Instead, cross-country and longitudinal patterns in smoking prevalence can be substantially explained by socio-economic, price and demographic factors.

1 Introduction Menthol has been used as a cigarette flavour for almost 9 years (TPSAC, 11). It has been suggested that the use of menthol as a cigarette flavour might influence progress in reducing smoking prevalence, either by increasing rates of initiation or reducing smoking cessation (Giovino et al., 13). This hypothesis forms the background to the present study, which Oxford Economics has undertaken at the request of Philip Morris International. The existing state of knowledge about the relationship between preference for menthol cigarettes and smoking behaviour is underdeveloped. First, there is little consensus in the existing scientific literature on the relationship between menthol cigarette preference and general smoking behaviours: some studies find a positive link between the two, some only find such a link for certain subpopulations (such as the youth population or ethnic minority populations), and some studies find no link at all. Second, the existing literature suffers from a number of methodological weaknesses and limitations. The existing literature has a narrow geographical focus: the vast majority of studies focus on the US, with the remaining studies focusing on only a handful of other countries. In addition, most of the existing literature either focuses on cross-sectional variation at one point in time, or examines over time within a narrowly-defined population; there are few studies examining both cross-sectional and longitudinal relationships between menthol preference and smoking behaviour (for a more detailed discussion see section.). The present study aims to contribute to and build upon this existing literature. Our study makes a number of notable contributions. First, we utilise time-series data from 48 countries, thus allowing us to investigate both cross-sectional and longitudinal aspects of the relationship between preference for menthol cigarettes and smoking prevalence. Second, our data is more comprehensive than much of the existing literature along both of these dimensions: the cross-country aspect of our study looks at 48 countries, in contrast to the existing literature s focus on either a single country or a narrow range of countries; the longitudinal aspect of the main part of our study involves consideration of a period of close to a decade, allowing us to investigate considerable time-variation in the variables of interest. Third we control for a wider range of potential covariates than most studies in the existing literature, checking the robustness of our results with the inclusion of a range of socio-economic, price, institutional/policy and demographic variables. Fourth, we also check the robustness of our results through stratification of our sample into subsamples (developed and emerging economies), and through alternative dependent variables (adult and youth smoking prevalence). The principal objective of our study is to investigate whether there is any statistically significant relationship between changes in preference for menthol 3

cigarettes and smoking prevalence. To this end, we assembled a database of time-series data on adult smoking prevalence rates for 48 countries across the previous decade (the s), including a roughly even split of developed and emerging economies. Most of this data was obtained from the OECD, although various other national and international sources were also utilised (for details see the Appendix). We also obtained time-series data over the same period on the market share of menthol cigarettes in the total national cigarette markets from Philip Morris International and Euromonitor. We took the percentage point change in these variables across the period 1-3 to 8-1 as the principal variables of interest. We first used simple correlation analysis to investigate the relationship between these variables. These raw correlations were always statistically insignificant. In addition, they were often negative, contradicting the hypothesis that menthol slows the rate of decline in smoking prevalence. We then proceeded to check the robustness of these results in a multiple regression setting which allowed us to control for a variety of other variables. In the vast majority of the regression equations (across the full adult sample, the developed and emerging subsamples, and the youth sample) the change in menthol preference entered insignificantly. In the small number of cases where it was significant, the sign was negative. In contrast to the poor statistical performance of the change in menthol cigarette preference, socio-economic, price and demographic variables were able to account for a significant part of the cross-country and longitudinal variation in smoking prevalence. Thus our study does not support the notion that changes in menthol cigarette preference have influenced in smoking prevalence over the past decade or so. In particular there is no evidence for a positive relationship, i.e. that the recent growth in menthol market shares in some countries has slowed decline in smoking prevalence in those countries. The layout of the rest of the study is as follows. Section describes the data and samples used in the study. Section 3 uses simple correlation analysis and multiple regression analysis to examine the relationship between recent changes in menthol preference and in smoking prevalence. Section 4 discusses our conclusions. Menthol market share sources: Philip Morris International based on AC Nielsen and other in-market sales data (4 countries), and Euromonitor (6 countries) 4

Data and sample description We constructed a database of time-series data covering 48 countries over the previous decade (the s). The availability of appropriate time-series data for our two principal variables of interest, the adult smoking prevalence rate and the market share of menthol cigarettes, determined the choice of countries included in the analysis, as well as the period of study..1 Smoking prevalence The dependent variable of interest is the change in the adult smoking prevalence rate over the period of study. The latter was chosen to be the period 1-3 to 8-1. We obtained the earliest possible observation on adult smoking prevalence in the three-year period 1-3 and the latest possible observation in the period 8-1, subject to these observations being from the same source and thus comparable over time. The percentage point (pp) difference between these numbers was then taken as the dependent variable. For example, in 1, the smoking prevalence rate in Japan was 4.4%; by 1 it had fallen to 19.%; thus the change in smoking prevalence over the 1-1 time period for Japan is 4.9 percentage points (pp) 3. The above procedure gave us a sample of 48 countries. The majority of the smoking prevalence data were sourced from the OECD (3 countries). For some European countries, the OECD data was inadequate, due to a lack of observations for both periods 1-3 and 8-1, so Eurobarometer data was used instead (4 countries). Data on ex-soviet countries were obtained from an academic paper, Roberts et al (1), which itself obtained them from standardised household surveys. Data on a further 9 countries were obtained from a variety of other national and international sources (see appendix for details). For the majority of countries, adult smoking prevalence rate is defined as the proportion of the population aged over 1 years who are daily smokers. In other countries there are minor variations in the precise definition, e.g. some sources define adult to be the population aged over 18 years, some sources merely refer to current or regular smokers rather than specifying daily smoking. These minor variations in definition are not detrimental to the reliability of our 3 The percentage point change is more appropriate than using the percentage change. This is especially the case for the menthol market share (discussed in detail in section.): in some cases, the observations for the initial year for menthol market share is zero, in which case a percentage change cannot be calculated, and so we would lose those countries from our sample; in other cases, the observation for the initial year is positive but very small, which means the measured proportional change is extremely large due merely to base effects, which results in heavily distorted measurement of the true extent of change in the menthol market.

Indonesia Greece Armenia Czech Republic Georgia Ghana Ireland Italy Russia Thailand Austria Estonia Kenya Germany Brazil Slovak Republic Malta Chile Korea Hong Kong Israel United States Belgium France Poland Hungary Mean Australia Finland Japan Mexico Switzerland Sweden Spain Belarus Portugal Canada South Africa Kazakhstan Turkey China United Kingdom Taiwan Ukraine Netherlands Luxembourg Iceland Denmark Norway The influence of the preference for menthol cigarettes on smoking prevalence results, however: since we are using the difference in smoking prevalence across the time period, then as long as methodological differences in datacollection and definition are assumed to only affect observed levels and not rates of change, incomparability across sources for different countries should not be an issue. In addition, excessive strictness with our operational definition of adult smoking prevalence rate would significantly restrict the available sample. The average number of years between datapoints for a given country is 8 years (to the nearest whole number), and a plurality of countries have the maximum 9 years between datapoints. Thus the longitudinal dimension of this study considers a period of close to a decade, which should be long enough to observe significant variation in prevalence over time. Chart.1 shows the percentage point change in adult smoking prevalence for our sample of countries. The mean change was a decrease of -4.1pp. The vast majority of countries saw a decrease in prevalence over the period of study. Only four countries exhibited an increase (Indonesia, Greece, Armenia and the Czech Republic), with the largest in Indonesia (3.pp). The remaining 44 countries saw a decrease in prevalence, with the largest in Norway (-11pp). Chart.1: Full sample by change in adult smoking prevalence from 1-3 to 8-1 pp change (1-3 to 8-1) 4 - -4-6 -8-1 -1 Source: OECD / Eurobarometer / Roberts et al (1) / other national and international sources / Oxford Economics 6

For the purpose of checking the robustness of our results, we split the full sample into subsamples of developed and emerging economies. A country was classified as developed if at the start of the period of study (in 1) it had a GDP per capita in purchasing power parity (PPP) dollars greater than half that of the US. This gave subsamples of 6 developed countries and emerging countries. Chart. shows the percentage point change in smoking prevalence for the subsample of developed economies. The mean change is a decrease of -4.8pp. Only Greece saw a rise among developed economies (3.pp), with prevalence decreasing in all others. The largest decline was seen in Norway with a decrease of -11.pp. Chart.3 shows the emerging subsample. The mean change among emerging countries is a decrease of -3.1pp, a smaller magnitude than among the developed countries. Three countries saw an increase in prevalence. The largest decline occurred in Ukraine with -7.pp. Chart.: Developed country subsample by change in adult smoking prevalence from 1-3 to 8-1 pp change (1-3 to 8-1) 4 - -4-6 -8-1 -1 Source: OECD / Eurobarometer / other national and international sources / Oxford Economics 7

Chart.3: Emerging country subsample by change in adult smoking prevalence from 1-3 to 8-1 pp change (1-3 to 8-1) 4 - -4-6 -8-1 Source: OECD / Eurobarometer / Roberts et al (1) / other national and international sources / Oxford Economics It has been suggested that the use of menthol as a cigarette flavour might be associated with youth smoking in particular. To account for this possibility, we used youth smoking prevalence as an alternative dependent variable. The prevalence of weekly smokers among 1 year olds was obtained from the WHO s Health Behaviour in School-Aged Children (HBSC) reports. This variable was available for 4 of the countries in our full sample. Chart.4 shows the percentage point change in youth smoking prevalence from 1 to 1. The mean change in smoking prevalence among our youth sample is a fall of -7.1pp. Only Greece saw an increase in youth prevalence (1.8pp). The remaining 3 countries saw a fall in youth prevalence, with the largest in Germany (-17.9pp). 8

Chart.4: Change in youth smoking prevalence from 1 to 1 pp change (1-3 to 8-1) - -1-1 - Source: HBSC / Oxford Economics. Menthol market share The main independent variable of interest is the change in preference for menthol cigarettes. The existing literature investigating the relationship between menthol preference and smoking behaviour focuses on the US and reports mixed findings. For example, a survey of this literature by the Tobacco Products Scientific Advisory Committee (11) in the US concluded that the evidence suggests that menthol increases smoking behaviour among youth, although not among adults. In contrast, a survey by the American Council of Science and Health (1, and an update in 13) found that even regarding youth smoking the evidence is not suggestive of any effects, and that the literature in general is methodologically weak from the standpoint of demonstrating causality. The few existing studies conducted outside the US similarly fail to find consensus in support of the view that a preference for menthol cigarettes encourages smoking behaviour. Li et al (1) use cross-sectional regressions on New Zealand data and find no effect on youth smoking prevalence. King et al (1) look at time-series data from Australia and find that a preference for menthol cigarettes plays no role in current smoking initiation. In order to more robustly examine the relationship between smoking prevalence and menthol preference and thus contribute to this literature, we measured the change in menthol cigarette preference over the period of study with the percentage point change in the market share of menthol cigarettes in the total 9

Japan Thailand Hong Kong Poland Finland Ghana Iceland Norway United Kingdom Sweden Korea Estonia Chile Mean Indonesia Brazil Kenya Georgia Denmark France Belgium United States Ireland Czech Republic Kazakhstan Mexico Germany Luxembourg Malta Taiwan Netherlands Ukraine Russia Portugal Armenia Spain Israel Austria Slovak Republic China Italy Belarus Switzerland Turkey Greece Hungary South Africa Canada Australia The influence of the preference for menthol cigarettes on smoking prevalence national cigarette markets, defined as the number of menthol cigarette sticks sold as a percentage of total sales volume. For the majority of countries, we used market share data provided by Philip Morris International (4 countries). For 6 countries where PMI data was not available for both the 1-3 and 8-1 time periods, we used market share data from Euromonitor. As before, as long as methodological differences in the compilation of market share data between these two sources are assumed to only affect observed levels, no problems of incomparability across sources should arise. Chart. summarises this variable. The mean change in the full sample is an increase of 1.8pp. Only countries saw a fall in menthol cigarette market share (Greece, Hungary, South Africa, Canada and Australia), and all these declines were of small magnitude (less than -.pp). The remaining 43 countries all saw a rise in menthol market share, with a few countries standing out as having especially large growth of more than 1pp: Japan (1pp), Thailand (1.7pp) and Hong Kong (1.7pp). Chart.: Full sample by change in menthol cigarette market share from 1-3 to 8-1 pp change (1-3 to 8-1) 14 1 1 8 6 4 - Source: Philip Morris International / Euromonitor 1

.3 Socio-economic status For the purposes of checking robustness, we also employed a number of control variables suggested by the academic literature. The existing literature finds large variation along socio-economic dimensions in smoking prevalence and its subdeterminants (rates of initiation, cessation, etc), both within-country and cross-country. Huisman et al () find negative associations within the EU between income and smoking prevalence and education and smoking prevalence, with the latter association stronger. Reid et al (1) look at data from the US, Canada, the UK and Australia, and find lower quit attempts, quit intentions and periods of smoking abstinence among lower education and lower income individuals. In addition to income and education, some of the literature finds that unemployment is a strong predictor of smoking, with Montgomery et al (1998) finding a higher smoking prevalence among the unemployed in the UK even after controlling for other socio-economic variables. There is also evidence from country-level studies that low socio-economic status groups are less sensitive to recent downward in smoking prevalence rates. Hiscock et al (1) find that prevalence declined in England since in response to tobacco control measures, except among low socio-economic status individuals. Similarly, de Walque (1) in a study of over time in US smoking finds that prevalence declined earlier and most dramatically for college graduates. We thus employed a number of socio-economic status variables as controls. We used a measure of income at the start of the period of study (i.e. the relevant year in 1-3 that corresponds to the first observation on smoking prevalence), namely the natural log of GDP per capita in US dollars at purchasing power parity (PPP) exchange rates. This variable was calculated by Oxford Economics using data from the IMF, and was available for all 48 countries. As a measure of the level of education, we used the gross secondary school enrollment ratio from the World Bank s World Development Indicators (WDI), which was available for 44 of the 48 countries in our sample. In addition to capturing socio-economic status, we use this variable as an indicator for awareness of the health risks associated with smoking. We also used the long-term unemployment rate as a proxy for labour market stress. For the purposes of this study, long term unemployment is a better measure than headline unemployment as it is less cyclical and therefore less sensitive to the choice of dates for our period of study. This variable was calculated by Oxford Economics using WDI data, and was available for 3 of the countries in our full sample..4 Cigarette price Another variable widely cited in the academic literature as affecting smoking behaviour is the price of cigarettes. Studies typically find moderate negative 11

elasticities of total demand for cigarettes with respect to price. Chaloupka and Warner () survey the cigarette demand literature and find that the elasticity of total consumption is typically in the region of -.3 to -., although the split between effects on average consumption per person and prevalence rates is less clear. Indeed, the evidence on the effects of price on smoking behaviour is generally clearer for youth smoking, with negative price effects found more frequently and of larger magnitude. Chaloupka and Warner () find widespread support for an inverse relationship between price sensitivity and age. US Department of Health and Human Services (1) surveys the more recent literature and similarly finds strong support for negative price effects for youth smokers. Nikaj and Chaloupka (13) study data across 38 countries and find a high price elasticity for total youth consumption of -1. (and an even larger one of -. for low- and middle-income countries). We therefore included a measure of the change in cigarette prices as a control variable. Data on the change in the US$ price of a pack of international brand cigarettes between 1 and 11 was obtained from the Tobacco Atlas, and was converted to PPP exchange rates by Oxford Economics using WDI data on PPP conversion factors. This variable could be constructed for 4 countries.. Smoking ban policies A notable trend in the previous decade was a move in many countries towards stricter legislative regulation of smoking. There is mixed evidence in the academic literature on the efficacy of legislative smoking bans at reducing prevalence and other smoking-related variables. Callinan et al (1) conduct a meta-analysis of 3 studies: of the 1 that studied prevalence as an outcome, 8 found a decrease in response to a legislative ban; the evidence for the effects on other smoking-related variables was found to be more mixed. The International Agency for Research on Cancer (9) in another meta-analysis stressed that the results typically depended on study methodology: natural experiment studies found mixed results, as did workplace studies, but regression studies overwhelmingly found a greater decline in prevalence where stronger laws were instituted. Bajoga et al (11) studied 1 countries and subnational jurisdictions where comprehensive smoke-free legislation was introduced: the rate of decline in prevalence was found to increase in only 8, while there was no discernible effect in the others. Nagelhout et al (1) stress the role of comprehensiveness in the design of smoke-free legislation, finding that comprehensive legislation increased quit attempts and success in England and Ireland, but more partial legislation in the Netherlands had no effect. In order to measure the change in the policy environment over the period of study we used the WHO Tobacco Control Country Profiles for the years 3 and 11, and constructed a dummy variable taking the value 1 if a country had smoke-free laws covering all public places in 11 but did not in 3. This is taken as a measure of whether the country implemented a comprehensive smoking ban (either a wholesale introduction, or moved towards one from 1

existing partial legislation) in the period of study. This variable could be constructed for 47 of the 48 countries in our full sample..6 Demographics Finally, there is much evidence that demographic structure matters for nationwide smoking patterns, with smoking patterns across birth cohorts and across gender two especially salient dimensions. Willets (4) points to large differences in smoking prevalence in Britain by birth cohort, with prevalence higher amongst groups born earlier throughout their lifetime. As an implication of this, Davy (6) finds that the recent decline in overall prevalence in the UK is not due to changing behaviour by established smokers, but rather due to older birth cohorts (with high prevalence) dying out and new cohorts taking up smoking at a lower rate. Chen et al (11) similarly finds substantial differences in youth and young adult smoking prevalence by birth cohort in the US, with in overall prevalence strongly influenced by cohort effects. Thus it might be expected that countries with a higher rate of turnover in birth cohorts (i.e. with older cohorts dying out faster and new cohorts entering the population faster) would see a faster rate of decline in overall prevalence 4. It is also possible that the gender structure of a country s population affects in overall smoking prevalence. Greaves and Tungohan (11) note that although global male smoking rates have reached their peak and are slowly waning, female rates are rising rapidly. Pampel (11) relates this fact to Lopez s (1994) model of the smoking epidemic, which is seen as having later onset and being of lesser magnitude among female compared to males; thus from stage onwards, female prevalence rates are likely to be either rising faster or falling slower than male prevalence, so that countries with a higher female share of the population should see slower declines in overall prevalence. 4 It is likely that in some of the poorest countries later cohorts would actually have higher rates of smoking prevalence than earlier cohorts. However, this is unlikely to apply to many of the countries present in our sample: most of our emerging countries are middle- rather than low-income countries. Lopez (1994), in a widely-cited paper, proposed a descriptive model of in smoking prevalence over time as a country develops economically. He described a 4-stage smoking epidemic model, whereby overall smoking prevalence first rises and then falls with economic development. Stage describes the period when overall smoking prevalence is still rising rapidly, and female prevalence is catching up to male prevalence. Stage 3 describes the period when overall prevalence begins to peak, with male prevalence starting to fall and female prevalence plateauing. Stage 4 applies to the most developed countries, where both male and female prevalence is falling, but the former at a faster pace due to peaking at a higher level. Most of the emerging countries in our sample can be considered to be in either the later part of Stage or in 13

We therefore used the population churn rate (i.e. the birth rate plus the death rate) at the beginning of the period of study as a proxy for the rate of cohort replacement. This was calculated by Oxford Economics using WDI data, and was available for 47 countries. The gender structure of the population was measured using the share of females in the total population at the beginning of the period of study, obtained for 47 countries from WDI. Stage 3, while the developed are primarily in Stage 4. Thus in most of the countries in our sample, female smoking prevalence is likely to be rising faster or falling slower than male prevalence. 14

3 Regression analysis In section 3.1 we perform simple correlation analysis between the change in smoking prevalence over the period of study and the change in the market share of menthol cigarettes. In the following section 3. we summarise the results of our multivariate regression analysis which examines the association between these two variables while controlling for a variety of other factors. 3.1 Simple linear correlation analysis First, we examine the relationship between in smoking prevalence and the change in preference for menthol cigarettes using simple correlation analysis. Chart 3.1 shows the full sample of adult smoking prevalence data plotted against the change in the menthol market share. Visual examination of the chart, does not obviously suggest the existence of a strong relationship between these variables. In particular, a positive relationship between preference for menthol cigarettes and smoking prevalence has been widely hypothesised, but the country detail of the data does not support this notion. For example, there are a number of countries which saw rapid growth in the menthol market, but still saw relatively large declines in adult smoking prevalence. Japan had the largest growth in menthol market share (1pp) but saw a larger than average decline in prevalence (of -4.9pp, compared to a full-sample mean of -4.1pp). Norway had a larger than average rise in menthol preference (.9pp compared to a mean of 1.8pp) but saw the largest decline in prevalence (-11pp). Finland had one of the largest increases in menthol preference (.4pp) but a fall in prevalence (-4.8pp) larger than the full-sample mean. And both Iceland and the UK saw larger than average growth in menthol preference (3.pp and.9pp respectively) but saw some of the largest reductions in smoking prevalence (-8.7pp and -7.4pp) On the other hand, there are a number of countries with virtually no growth in the menthol market share, but which still saw either a rise or a very small decline in adult smoking prevalence. Greece saw virtually zero change in the menthol market share and Armenia also had close to zero menthol growth (.3pp), but both were among only 4 countries to see a rise in smoking prevalence (of 3pp and 1pp respectively). Similarly, Italy, Austria and Russia saw minimal growth in menthol preference (.1pp,.pp and.4pp respectively) but still had some of the smallest declines in smoking prevalence (of -1pp, -pp and -1.3pp). 1

Chart 3.1: Change in adult smoking prevalence and menthol market share, 1-3 to 8-1 smoking prevalence, pp (1-3 to 8-1) 6 4 menthol market share, pp (1-3 to 8-1) - 4 6 8 1 1 14 - -4-6 -8-1 -1-14 -16 Source: Oxford Economics This impressionistic analysis is confirmed by examining correlation coefficients. The correlation between the change in menthol markets share and adult smoking prevalence in the full sample is.3, which is not statistically significant (p=.8), indicating that the association between the two variables is too weak to be statistically distinguishable from a chance association. As a check on the robustness of this result, we looked at the correlation coefficients for the developed and emerging subsamples separately, to account for the possibility that there may be a significant association within a sub-group of our full sample of countries. The raw correlation in the developed subsample is -.9. The p value of.66 again indicates that the correlation is not statistically significant. The correlation coefficient in the emerging subsample of.3 is again far from statistical significance, with a p value of.31, and so is again statistically indistinguishable from a chance association. The data for the developed and emerging subsamples are summarised in Charts 3. and 3.3. 16

Chart 3.: Change in adult smoking prevalence and menthol market share, developed country subsample, 1-3 to 8-1 smoking prevalence, pp (1-3 to 8-1) 4 menthol market share, pp (1-3 to 8-1) - 4 6 8 1 1 14 - -4-6 -8-1 -1 Source: Oxford Economics Chart 3.3: Change in adult smoking prevalence and menthol market share, emerging country subsample, 1-3 to 8-1 smoking prevalence, pp (1-3 to 8-1) 4 menthol market share, pp (1-3 to 8-1) - 4 6 8 1 1 - -4-6 -8-1 Source: Oxford Economics 17

As a further check, we examined the correlation between the growth in the menthol market share and the change in youth smoking prevalence. The correlation is -.4, and the p value of. indicates that the relationship is again not statistically significant. The youth data are summarised in Chart 3.4. Chart 3.4: Change in youth smoking prevalence and menthol market share, 1-3 to 8-1 smoking prevalence, pp (1-3 to 8-1) menthol market share, pp (1-3 to 8-1) - 4 6 8 - -1-1 - Source: Oxford Economics This lack of a significant correlation between the change in menthol market share and in smoking prevalence can also be visualised by looking at the entire run of time-series data on the two variables. Chart 3. plots the full time-series of observations on adult smoking prevalence and menthol market share (with missing values for smoking prevalence linearly interpolated) for the five countries which saw the largest pp increase in the market share of menthol cigarettes. Chart 3.6 does the same for the five countries at the other extreme of the distribution (ie. which saw the smallest increase or a decline in the market share of menthol cigarettes). Within both these groups of countries (ie. at both ends of the distribution of changes in the menthol market share) there is a diversity of in smoking prevalence. For example, amongst those five countries with the largest increase in the menthol market share, two witnessed a decrease in smoking prevalence that was larger in absolute value than the full-sample mean (Japan, Finland), whereas three saw a decrease that was smaller than the mean decline (Thailand, Hong Kong, Poland). 18

Similarly, amongst the five countries with the smallest decrease (or an increase) in the menthol market share, three countries had a decrease in smoking prevalence which was larger in absolute value than the mean (Australia, Canada, South Africa), and two saw a decrease that was smaller than the mean decline (Greece, Hungary). If there were a strong and robust relationship between the preference for menthol cigarettes and the rate of smoking prevalence, then it is highly unlikely that one would see such diversity in smoking prevalence amongst groups of countries with such similar in menthol preference. There is thus no evidence from simple correlation analysis in favour of the notion that preference for menthol cigarettes is associated with over times in smoking prevalence. The raw correlations are always statistically insignificant. This holds for the full sample of 48 countries, for the developed and emerging country subsamples, and also holds for both adult and youth smoking prevalence. 19

Chart 3.: Change in adult smoking prevalence in countries with menthol market share increases over time, 1 to 1 Finland Japan Japan % % %% 3 % 3 3 3 % 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 4 6 7 8 9 1 11 1 1 3 4 6 7 8 9 1 11 3 4 6 7 8 9 1 11 1 1 3 4 6 7 8 9 1 11 1 Japan Hong Kong Hong Kong % 3 % % 3 16 Canada % % 4 16 % % 4 14 33 14 3 1 3 1 3 1 1 1 1 8 1 8 16 11 6 1 1 4 1 4 1 1 1 3 4 6 7 8 9 1 11 1 3 4 6 7 8 9 1 1 3 4 6 7 8 9 1 11 1 United Kingdom Finland % 1 3 4 6 7 8 9 1 11 1 % Finland 3 % 1 % % 3 3 9 8 7 6 1 1 1 1 1 4 1 3 1 1 1 1 3 4 6 7 8 9 1 11 1 34 4 6 6 7 7 8 8 9 1 9 11 11 11 Iceland Thailand Thailand % % % % 3 4 % 3 % 4 3 4 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 4 6 7 8 9 1 11 1 1 3 4 6 7 8 9 1 11 1 3 4 6 7 8 9 1 11 1 1 3 4 6 7 8 9 1 11 1 Norway Poland Poland % % 3% 6 % % 3 Greece % 18 3 % % 3 16 4.8 14 4.7 4 1 3.6 13 1 3 1 1 1. 8 1 1.4 1 6 1.31 4 1. 1 1 3 4 6 7 8 9 1 11 1 1 3 4 6 7 8 9.1 1 3 4 6 7 8 9 1 11 1. 1 3 4 6 7 8 9 1 11 1 Source: OECD / PMI Source: OECD / PMI Source: OECD / PMI / PMI

Chart 3.6: Change in adult smoking prevalence in countries with menthol market share decreases over time, 1 to 1 Australia % % % 1 9 9 8 8 7 7 1 6 6 1 1 1 4 3 1 1 1 3 4 6 7 8 9 1 11 1 1 3 4 6 7 8 9 1 South Africa South Africa % % % % 9 9 8 8 7 7 6 1 6 1 4 1 4 1 3 3 1 1 3 4 6 7 8 9 1 11 1 3 4 6 7 8 9 Greece Greece % % % 4 % 1. 4 1. 4 4 3.7 3 3.7 3.. 1 1. 1. 1. 1 3 4 6 7 8 9 1 11 1. 3 4 6 7 8 9 1 11 1 1 Canada % % % 4 4 3 3 3 3 1 1 1 1 1 1 1 1 1 3 4 6 7 8 9 1 11 1 1 3 4 6 7 8 9 1 Hungary Hungary % % 3 % % 9 3 7 3 8 3 76 6 4 1 4 1 3 3 1 1 1 1 1 3 4 6 7 8 9 1 11 1 3 4 6 7 8 9 Source: OECD / PMI Source: OECD / PMI 1

3. Multivariate regression analysis In this section we summarise the results of our regression analysis of the change in smoking prevalence. We applied ordinary least squares (OLS) regression to our full 48 country sample, using robust standard errors to correct for heteroskedasticity. We adopted the following iterative procedure for checking the robustness of the results of the previous section in a multiple regression setting. We began with a simple regression of the change in smoking prevalence on the change in the menthol market share. We then successively added the various socio-economic status variables, these being the most firmly established variables in the existing literature as having an influence on smoking behaviour. We left in the best of the two main socio-economic variables, income and education, and then proceeded to successively add unemployment (to add further socio-economic detail), the change in the cigarette price, and the comprehensive smoking ban indicator, these being the next-best established variables in the existing literature. We then retained the best two of these latter variables, and successively added the demographic variables, leaving these until last as the least well-established in the existing literature. We then examined all the resulting regression equations and checked whether the change in the menthol market entered significantly when all these variables are controlled for in various combinations. The results of this procedure for the full sample of adult smoking prevalence are summarised in Table 3.1. Looking at the row listing the coefficients on the change in the menthol market share (shaded in grey), it is clear that menthol preference enters the vast majority of equations with an insignificant coefficient. It also enters most equations with a negative sign. In the final two equations it does enter significantly, but with a negative sign, such that this cannot be taken as supportive of the hypothesis that menthol cigarettes increase or prevent decline in smoking prevalence. The other variables investigated, however, are able to explain a significant part of the variation across countries and across time in smoking prevalence. Education consistently enters with the expected negative sign, and occasionally at high levels of statistical significance (i.e. more educated countries saw more rapid decline in adult smoking prevalence, most likely reflecting both the awareness effect of education, and the fact that education strongly proxies general levels of economic development, and thus the extent to which a country has progressed through the smoking epidemic ). Long-term unemployment also consistently enters with the expected positive sign, and is statistically significant in every equation (i.e. countries with a higher rate of long-term unemployment saw smaller declines in smoking prevalence, most likely reflecting the fact that people detached from the labour market and of low socio-economic status appear to be resistant to recent downward in smoking behaviour). When unemployment is included, education, although still of negative sign, tends to become insignificant.

Table 3.1: Adult smoking prevalence regressions, full sample SMOKING (1) () (3) (4) () (6) (7) (8) (9) (1) (11) CONSTANT -4.13 6.7 1.18 -.3-1.8 -.1.98-3.79 -..6-7.6 t statistic (-7.96) (1.4) (.6) (-8.6) (-.8) (-.).49 (-1.1) (-1.8) (1.76) (-.1) statistical significance *** *** * MENTH.4.4 -.6 -.1 -.1 -. -.7 -.18 -.1 -.3 -.3 (.39) (.47) (-.7) (-1.3) (-1.4) (-.44) (-.81) (-1.1) (-.86) (-.8) (-.) ** * LOG(GDPCAP)_1/3-1.13 (-.39) ** SCHOOL_1/3 -. -.3 -.3 -. -.1 -.3 -. -. (-.8) (-1.) (1.39) (-.78) (-.4) (-1.36) (-.8) (-.68) *** *** UNEMP_LONG_1/3.31.7..8.6.7 (4.) (3.7) (1.76) (3.88) (.86) (.1) *** *** * *** *** ** PRICE -.19 -.19 -.3 -. (-1.11) (-1.9) (-.) (-1.91) ** * BAN.8 1. (.8) (.9) CHURN_1/3 -.36 -.36 (-3.89) (-3.8) *** *** FEMALE_1/3. (.37) Observations 48 48 44 3 34 38 44 3 34 3 3 R..1.1..7.18.17.8.3.47.47 Variables: Statistical significance: SMOKING=change in adult smoking prevalence rate between 1/3 and 8/1, percentage points *=significant at 1% level MENTH=change in market share of menthol cigarettes between 1/3 and 8/1, percentage points **=significant at % level LOG(GDPCAP)_1/3=natural log of GDP per capita in dollars at PPP exchange rates ***=significant at 1% level SCHOOL_1/3=secondary school enrollment ratio (% gross) UNEMP_LONG_1/3=long-term unemloyment as % of total labour force PRICE=change in price of packet of international cigarettes (Marlboro or equivalent) between 1/3 and 8/1, US$ at PPP exchange rates BAN=indicator taking value 1 if a country implements a smoking ban in all indoor public places CHURN_1/3=population 'churn rate' (equals birth rate plus death rate) FEMALE_1/3=female population as % of total population The change in the cigarette price consistently enters with the expected negative sign, and often with a statistically significant coefficient. This confirms the findings in most existing literature that smoking behaviour is negatively related to the cost of cigarettes. The comprehensive smoking ban indicator enters with the opposite sign to what would be expected if comprehensive smoke-free legislation reduced smoking prevalence, but the coefficient is never statistically significant. The poor performance of this variable seems to be due to measurement error: our indicator variable does not appear to properly capture the change in the policy environment over our period of study. In particular, the level of funding and strictness of enforcement of de jure anti-smoking regulations is likely to vary 3

substantially across countries, but sufficient data does not exist to properly adjust for this. The population churn rate (the birth rate plus the death rate) enters significantly with the expected negative sign, suggesting that smoking prevalence declines more rapidly where older heavily-smoking cohorts are dying out and younger lower-smoking cohorts are entering the adult population faster. Although the female population ratio enters with the expected positive sign, it is not statistically significant. In order to check the robustness of these findings, we performed the same exercise on the developed and emerging subsamples. The results are summarised in Tables 3. and 3.3. The core result holds for the developed and emerging subsamples as well as the full sample. In the developed subsample, the change in menthol preference is never statistically significant, and in addition is always negative in sign. In the emerging subsample, menthol is never statistically significant, and the sign is unstable across equations. The control variables typically enter with similar signs and magnitudes to the full sample, although usually at lower levels of significance due to the smaller number of observations. Finally, we performed the same exercise on the youth sample, which consisted of the 4 countries where suitable data on youth smoking prevalence was available. The results of this exercise are summarised in Table 3.4. Menthol enters significantly in roughly half of the equations, but always with a negative sign, so that this cannot be taken as evidence in favour of the particular hypothesis that menthol increases or slows decline in smoking prevalence. GDP per capita is never significant and the sign of the coefficient is unstable across equations, but youth unemployment enters with the same positive sign as long term unemployment in the adult regressions, and is usually significant. The change in the cigarette price also enters with the expected negative sign, and indeed with a larger coefficient than in the adult regressions, although it is only occasionally significant. The smoking ban and demographic variables are always far from statistical significance. 4

Table 3.: Adult smoking prevalence regressions, subsample of developed countries SMOKING_DEV (1) () (3) (4) () (6) (7) (8) (9) (1) (11) CONSTANT -4.86 46.49.9-6.93 1.16 44.8 38.1 8.66 1.6 1.3.86 t statistic (-6.6) (1.9) (.8) (-7.8) (.89) (1.8) (1.37) (.47).83..7 statistical significance *** * *** * MENTH -.9 -.8 -.18 -.11 -.1 -.1 -.13 -.14 -.14 -.16 -.16 (-.66) (-.64) (-1.3) (-.7) (-.83) (1.1) (-.9) (-.8) (-.71) (-.93) (-.9) LOG(GDPCAP)_1/3-4.98 -.1-4.66-4.1-1.41 -.8-1.6-1.6 (-.13) (-1.8) (1.98) (-1.1) (-.8) (-1.16) (-.77) (-.77) ** ** SCHOOL_1/3 -. (-1.77) * UNEMP_LONG_1/3 1.1 1.4 1. 1..9.97 (3.74) (3.) (3.) (.34) (.76) (.3) *** *** *** ** ** ** PRICE -.17 -.1 -.11 -.11 (-1.6) (-.63) (-.68) (-.71) BAN -1.4 -. (-1.1) (-.17) CHURN_1/3 -.1 -.11 (-.36) (-.39) FEMALE_1/3 -. (-.1) Observations 6 6 4 4 4 4 6 3 4 3 3 R.1.17.11.4.47.4.1.47.47.47.47 Variables: Statistical significance: SMOKING_DEV=change in adult smoking prevalence rate between 1/3 and 8/1, percentage points *=significant at 1% level MENTH=change in market share of menthol cigarettes between 1/3 and 8/1, percentage points **=significant at % level LOG(GDPCAP)_1/3=natural log of GDP per capita in dollars at PPP exchange rates ***=significant at 1% level SCHOOL_1/3=secondary school enrollment ratio (% gross) UNEMP_LONG_1/3=long-term unemloyment as % of total labour force PRICE=change in price of packet of international cigarettes (Marlboro or equivalent) between 1/3 and 8/1, US$ at PPP exchange rates BAN=indicator taking value 1 if a country implements a smoking ban in all indoor public places CHURN_1/3=population 'churn rate' (equals birth rate plus death rate) FEMALE_1/3=female population as % of total population

Table 3.3: Adult smoking prevalence regressions, subsample of emerging countries SMOKING_EMERGE (1) () (3) (4) () (6) (7) (8) (9) (1) (11) CONSTANT -3.8.1-1.48-4. -31.8.4. -8.73-34.6-37.84-7.88 t statistic (-4.63) (.36) (-.46) (-3.7) (-1.16).3 (.3) (-.33) (-1.31) (-.88) (-.3) statistical significance *** *** MENTH..4.18.4 -.8.6.4 -.6 -. -1. -.98 (1.66) (1.66) (1.14) (.16) (-.33) (1.3) 1.8 (-.96) (-.1) (-1.66) (-.91) LOG(GDPCAP)_1/3 -.69.87 -.76 -.69 8.46 3.1 4.69 4.8 (-.91) 1. (-1.13) (-.89) (.1) (1.1) (1.3) (.69) SCHOOL_1/3 -. (-.6) UNEMP_LONG_1/3.18.36.94.4 1.6 1.6 (3.49) (1.78) (1.31) (.17) (1.8) (.94) *** * PRICE. -1.78 -. -1.9 (.4) (-.9) (-1.47) (-.61) BAN.4 1.78 (.3) (.84) CHURN_1/3 -.38 -.33 (-1.9) (-.8) FEMALE_1/3.34 (.17) Observations 1 11 11 17 8 11 8 8 R..9.6.6.38.11.9.1.4.7.73 Variables: Statistical significance: SMOKING_EMERGE=change in adult smoking prevalence rate between 1/3 and 8/1, percentage points *=significant at 1% level MENTH=change in market share of menthol cigarettes between 1/3 and 8/1, percentage points **=significant at % level LOG(GDPCAP)_1/3=natural log of GDP per capita in dollars at PPP exchange rates ***=significant at 1% level SCHOOL_1/3=secondary school enrollment ratio (% gross) UNEMP_LONG_1/3=long-term unemloyment as % of total labour force PRICE=change in price of packet of international cigarettes (Marlboro or equivalent) between 1/3 and 8/1, US$ at PPP exchange rates BAN=indicator taking value 1 if a country implements a smoking ban in all indoor public places CHURN_1/3=population 'churn rate' (equals birth rate plus death rate) FEMALE_1/3=female population as % of total population 6

Table 3.4: Youth smoking prevalence regressions SMOKING (1) () (3) (4) () (6) (7) (8) (9) (1) (11) CONSTANT -6.3-1.96-8.33-9. 3.39-6.88.6-18. 1.7-36.6 11.48 t statistic (-.7) (-.1) (-1.49) (-.86).18 (-1.43) (.3) (-.8).8 (-1.) (.94) statistical significance *** *** MENTH -.61 -.6 -.66-1.6-1.4 -. -. -1.14-1.7-1.3-1.36 (-1.48) (-1.47) (-1.46) (-3.) (-.9) (-1.46) (-1.) (-.74) (-.83) (-.71) (-3.1) *** *** ** ** ** *** LOG(GDPCAP)_1/3 -.43-1..3 -.7 1.1-1.4.3-1.39 (-.) (-.66) (1.18) (-.37) (.1) (-.49) (.89) (-.41) SCHOOL_1/3. (.4) UNEMP_YOUTH_1/3.7.4.4..9.7 (.86) (.8) (1.96) (.33) (1.78) (1.9) *** * * ** * PRICE -.3 -.9 -.9 -.31 (-.49) (-1.4) (-1.17) (-1.7) ** BAN 1.77 -.7 (.8) (-.1) CHURN_1/3.37.46 (.6) (.7) FEMALE_1/3 -.1 (-1.) Observations 4 4 3 3 3 3 4 3 R.6.6.6.7.8.17.9.31.8.3.36 Variables: Statistical significance: SMOKING_YOUTH=change in adult smoking prevalence rate between 1/3 and 8/1, percentage points *=significant at 1% level MENTH=change in market share of menthol cigarettes between 1/3 and 8/1, percentage points **=significant at % level LOG(GDPCAP)_1/3=natural log of GDP per capita in dollars at PPP exchange rates ***=significant at 1% level SCHOOL_1/3=secondary school enrollment ratio (% gross) UNEMP_YOUTH_1/3=youth unemloyment as % of total labour force PRICE=change in price of packet of international cigarettes (Marlboro or equivalent) between 1/3 and 8/1, US$ at PPP exchange rates BAN=indicator taking value 1 if a country implements a smoking ban in all indoor public places CHURN_1/3=population 'churn rate' (equals birth rate plus death rate) FEMALE_1/3=female population as % of total population 7

4 Conclusions The purpose of this study has been to investigate whether recent changes in preference for menthol cigarettes have influenced in smoking prevalence rates. We conclude that there is no evidence to support such a hypothesis. In particular, there is no evidence that the recent growth in menthol preference in certain countries has increased or slowed decline in smoking prevalence in those countries. Time-series data on a sample of 48 countries were used to examine the relationship between menthol cigarette preference (as measured by the market share of menthol cigarettes in total national cigarette markets) and smoking prevalence over the period 1 to 1. The sample of countries and the period of study were chosen so as to maximise the number of observations on the two principal variables of interest. We found no evidence that an increase in preference for menthol cigarettes is associated with a slower rate of decline of smoking prevalence. This was the case for adult smoking prevalence (in both the full sample and developed and emerging subsamples) and for youth smoking prevalence in the countries where data was available. Simple correlations were always statistically insignificant. Additionally, they were often negative in sign. Similar results also held in a more sophisticated multiple regression setting, where we controlled for a wide range of socio-economic, price, policy and demographic variables: in the vast majority of our many regression equations, menthol was insignificantly related with in smoking prevalence; and when menthol did enter significantly into our regression equations, it did so with a negative sign, the opposite relation to that suggested by the hypothesis that menthol preference is positively associated with smoking prevalence. We thus conclude that there is no evidence to support the hypothesis that changes in preference for menthol cigarettes have influenced the smoking prevalence rates. In particular, there is no evidence to support the hypothesis that an increase in preference for menthol cigarettes increase or slow decline in smoking prevalence. Instead, cross-country and longitudinal patterns in smoking prevalence can be substantially explained by socio-economic, price and demographic factors. 8

Appendix List of variables used in regressions SMOKING=change in adult smoking prevalence rate between 1/3 and 8/1, percentage points Source: OECD/Eurobarometer/Roberts et al (1)/other national and international sources/oxford Economics SMOKING_YOUTH=change in adult smoking prevalence rate between 1/3 and 8/1, percentage points Source: World Health Organisation Health Behaviour in School-Age Children (HBSC) (1, 4) MENTH=change in market share of menthol cigarettes between 1/3 and 8/1, percentage points Source: Philip Morris International Management SA (based on AC Nielsen and other in-market sales data, 4 countries) / Euromonitor (6 countries) LOG(GDPCAP)_1/3=natural log of GDP per capita in dollars at PPP exchange rates Source: IMF World Economic Outlook/Oxford Economics SCHOOL_1/3=secondary school enrollment ratio (% gross) Source: World Bank World Development Indicators (WDI) UNEMP_LONG_1/3=long-term unemloyment as % of total labour force Source: WDI/Oxford Economics UNEMP_YOUTH_1/3=youth unemloyment as % of total labour force Source: WDI PRICE=change in price of packet of international cigarettes (Marlboro or equivalent) between 1/3 and 8/1, US$ at PPP exchange rates Source: Tobacco Atlas (fourth edition, 1; first edition, )/IMF/Oxford Economics BAN=indicator taking value 1 if a country implements a smoking ban in all indoor public places Source: WHO Report on the Global Tobacco Epidemic (11), Tobacco Control Country Profiles (3) CHURN_1/3=population 'churn rate' (equals birth rate plus death rate) Source: WDI/Oxford Economics FEMALE_1/3=female population as % of total population Source: WDI 9

List of smoking prevalence and menthol market share sources Smoking prevalence Menthol market share Australia OECD (via OECD Stat Extract) Philip Morris International SA Austria Eurobarometer (Attitudes of Europeans Towards Tobacco, 1, 6) Philip Morris International SA Belgium OECD Philip Morris International SA Canada OECD Euromonitor Chile OECD Euromonitor Czech Republic OECD Philip Morris International SA Denmark OECD Philip Morris International SA Estonia OECD Philip Morris International SA Finland OECD Philip Morris International SA France OECD Philip Morris International SA Germany OECD Philip Morris International SA Greece Eurobarometer (Attitudes of Europeans Towards Tobacco, 1, 6) Philip Morris International SA Hungary OECD Philip Morris International SA Iceland OECD Philip Morris International SA Ireland Eurobarometer (Attitudes of Europeans Towards Tobacco, 1, 6) Philip Morris International SA Israel OECD Philip Morris International SA Italy OECD Philip Morris International SA Japan OECD Philip Morris International SA Korea OECD Philip Morris International SA Luxembourg OECD Philip Morris International SA Malta Department of Health Information and Research (European Health Interview Survey, 8) Philip Morris International SA Mexico National Institute of Psychiatry / Ministry of Health (National Addiction Survey, 11) Philip Morris International SA Netherlands OECD Philip Morris International SA Norway OECD Philip Morris International SA Poland OECD Philip Morris International SA Portugal Eurobarometer (Attitudes of Europeans Towards Tobacco, 1, 6) Philip Morris International SA Slovak Republic OECD Philip Morris International SA Spain OECD Philip Morris International SA Sweden OECD Philip Morris International SA Switzerland Tobacco Monitoring Switzerland (Summary of the Research Report, 11) Philip Morris International SA Turkey OECD Philip Morris International SA United Kingdom OECD Philip Morris International SA United States OECD Euromonitor Brazil OECD Philip Morris International SA China OECD Euromonitor Russia OECD Philip Morris International SA South Africa OECD Euromonitor Indonesia Southeast Asia Tobacco Control Alliance (ASEAN Tobacco Tax Report Card, 13) Philip Morris International SA Hong Kong Census and Statistics Department (Thematic Household Survey, No. 48 11, No. 16 3) Euromonitor Thailand Southeast Asia Tobacco Control Alliance (ASEAN Tobacco Tax Report Card, 13) Philip Morris International SA Taiwan Department of Health (Public Health Report, 11; Tobacco Control Annual Report, 1) Philip Morris International SA Kenya National Bureau of Statistics (Demographic and Health Survey, 8, 3) Philip Morris International SA Ghana National Statistical Service / Health Service (Demographic and Heath Survey, 8, 3) Philip Morris International SA Armenia Roberts et al. (1) Philip Morris International SA Belarus Roberts et al. (1) Philip Morris International SA Georgia Roberts et al. (1) Philip Morris International SA Kazakhstan Roberts et al. (1) Philip Morris International SA Ukraine Roberts et al. (1) Philip Morris International SA 3

Dependent and explanatory variables in the smoking regressions, Table 1 SMOKING % (T1) SMOKING % (T) SMOKING SMOKING_YOUTH % (T1) SMOKING_YOUTH % (T) SMOKING_YOUTH Australia 19.8 (1) 1.1 (1) -4.7 Austria 36 () 34 (9) -. 31. (1/) 7. (9/1) -4. Belgium 4.1 (1). (8) -3.6 3. (1/) 1.9 (9/1) -7.1 Canada.4 (1) 16.3 (1) -6.1 14. (1/) 8. (9/1) -6. Chile 33 (3) 9.8 (9) -3. Czech Republic 4.1 () 4.6 (8). 9.6 (1/) 4.9 (9/1) -4.7 Denmark 9. (1) (1) -9. 18.8 (1/) 13. (9/1) -.3 Estonia 8.3 () 6. (1) -.1 4. (1/) 19.1 (9/1) -.4 Finland 3.8 (1) 19 (1) -4.8 3. (1/) 19. (9/1) -1.7 France 7 (1) 3.3 (1) -3.7 6.3 (1/). (9/1) -6.3 Germany 4.3 (3) 1.9 (9) -.4 3.9 (1/) 1. (9/1) -17.9 Greece 39 () 4 (9) 3. 13.8 (1/) 1.6 (9/1) 1.8 Hungary 3.4 (3) 6. (9) -3.9 7. (1/) 6. (9/1) -1. Iceland.9 (1) 14. (1) -8.7 Ireland 3 () 31 (9) -1.. (1/) 13. (9/1) -7. Israel 1.9 () 18. (1) -3.4 Italy 4.1 (1) 3.1 (1) -1. 3.3 (1/). (9/1) -.8 Japan 4.4 (1) 19. (1) -4.9 Korea 6.1 (1).9 (1) -3. Luxembourg 6 (1) 18 (1) -8. Malta 3. ().4 (8) -.8 Mexico 3. () 18. (8) -. Netherlands 8.8 (1).9 (1) -7.9 3.4 (1/) 16. (9/1) -7.4 Norway 3 (1) 19 (1) -11. 3.3 (1/) 8. (9/1) -14.7 Poland 7.6 (1) 3.8 (9) -3.8 1.8 (1/) 14.1 (9/1) -7.7 Portugal 9 () 3 (9) -6. 1.8 (1/) 1. (9/1) -11.3 Slovak Republic.1 (3) 19. (9) -.6 Spain 31.7 (1) 6. (9) -. 7.8 (1/) 18.9 (9/1) -8.9 Sweden 18.9 (1) 13.6 (1) -.3 14.9 (1/) 14. (9/1) -1. Switzerland 4 (1) 19 (1) -. 4.8 (1/) 17.1 (9/1) -7.7 Turkey 3.1 (3).4 (1) -6.7 United Kingdom 7 (1) 19.6 (1) -7.4 3.8 (1/) 11.8 (9/1) -1. United States 18.7 (1) 1.1 (1) -3.6 1. (1/) 8. (9/1) -6.9 Brazil 17.6 (3) 1.1 (1) -. China 31.4 () 4.1 (1) -7.3 Russia 3.1 (1) 33.8 (9) -1.3.9 (1/) 17. (9/1) -.9 South Africa.4 (3) 13.8 (9) -6.6 Indonesia 31. (1) 34.7 (1) 3. Hong Kong 14.4 () 11.1 (1) -3.3 Thailand. (1) 3.7 (9) -1.8 Taiwan 7 () 19.6 (1) -7.4 Kenya 11.9 (3) 9.6 (8) -.3 Ghana 4.6 (3) 3.7 (8) -.9 Armenia 3 (1) 31 (1) 1. Belarus 33.4 (1) 7. (1) -.9 Georgia 8.7 (1) 8.3 (1) -.4 Kazakhstan 36. (1) 9.6 (1) -6.6 Ukraine 4. (1) 34.7 (1) -7. 34. (1/). (9/1) -11.8 No. of observations 48 48 48 4 4 4 31

Dependent and explanatory variables in the smoking regressions, Table MENTH % (T1) MENTH % (T) MENTH GDPCAP_1/3 SCHOOL_1/3 UNEMP_LONG_1/3 UNEMP_YOUTH_1/3 Australia 8.3 (1) 7.8 (1) -. 3,866 14.4 1.6 13. Austria.4 ().6 (9). 3,73 98.1.8 6. Belgium 1.7 (1).7 (8) 1. 3,44 1. 3. 1.3 Canada 3 (1).7 (1) -.3 33,673.6 1.9 Chile.1 (3).3 (9). 11,396 88. 1.1 Czech Republic 1.3 ().1 (8).9 18,94 94. 3.7 16. Denmark 9.3 (1) 1. (1) 1. 3, 13.7.9 8.3 Estonia 7. () 9.7 (1). 13,38 9.. 17.4 Finland 18.9 (1) 4.3 (1).4 7,76 17.4. 18.8 France.8 (1) 3.9 (1) 1. 8,644 17.3 3. 18. Germany 1.7 (3). (9).8 9,734 99. 4.6 1.6 Greece. (). (9).,13 98..3 6.8 Hungary 6.6 (3) 6. (9) -.1 1,31 11.9. 13.4 Iceland 18.9 (1).1 (1) 3. 31,3 17..3 4.8 Ireland 1. ().1 (9) 1. 3,8 14. 1.3 7.8 Israel 1.3 () 1. (1). 3,13 16. 1.3.9 Italy. (1). (1).1 7,99 96. 6. 7.8 Japan 11.9 (1) 3.9 (1) 1. 8,976 1. 1.3 9.7 Korea.6 (1). (1).6 19,88 97.8.1 1. Luxembourg 1. (1) 1.9 (1).7 63,839 97.. 6.3 Malta. ().6 (8).6,7 86..6 1. Mexico 4. () (8).8 11,6 76.8..9 Netherlands 3.7 (1) 4. (1). 33,998 14.. 4.4 Norway.9 (1) 3.9 (1).9 44,898 113.8. 1. Poland 8. (1) 1. (9) 7.3 11,783 11.9 7.8 41. Portugal.1 (). (9).3,7 17.3 1.7 11.6 Slovak Republic.4 (3). (9).1 14,331 88.3 1.7 33.1 Spain.6 (1).9 (9).3,6 113. 3.9.8 Sweden 8.9 (1) 11. (1).6 9,96 147.6 1.1 11.6 Switzerland 1.9 (1). (1).1 3,978 9.1.7. Turkey.4 (3).4 (1). 9, 88.7.. United Kingdom 4.6 (1) 7.4 (1).8 9,4 11.6 1.3 1.4 United States 6.3 (1) 7.3 (1) 1. 39,78 93.7.3 1.6 Brazil. (3). (1) 1.6 7,994 19. China. ().1 (1).1 3,14 64.4 Russia. (1).9 (9).4 9,83 91.6 3. 18. South Africa 8.3 (3) 8. (9) -.1 7,89 88.6 6.9 4.8 Indonesia.4 (1). (1) 1.8,786. 4.1 Hong Kong.8 () 33. (1) 1.7 3,479 77. 14.9 Thailand 3. (1) 34.3 (9) 1.7,64 6. 7.6 Taiwan 1. (). (1). 3,441 Kenya 19.6 (3) 1. (8) 1.4 1,74 4.9 Ghana 9.6 (3) 14. (8) 4.4 1,837 4.1 Armenia. (1).3 (1).3,47 87.7.7 48. Belarus.1 (1). (1).1 6,81 Georgia. (1) 1.3 (1) 1.3,469 79.7.1 Kazakhstan.4 (1) 1.3 (1).9 6,3 93.9 17.3 Ukraine.6 (1) 1.1 (1). 4,7 99. No. of observations 48 48 48 48 44 3 4 3

Dependent and explanatory variables in the smoking regressions, Table 3 PRICE (T1) PRICE (T) PRICE BAN CHURN_1/3 FEMALE_1/3 Australia PRICE. (1) (T1) 17.8 PRICE (11) (T)?PRICE 1.4 BAN 1 CHURN_1/3 19.3 FEMALE_1/3.4 SMOKING % T1 SMOKING % T SMOKING_7/9-1/1 CAPSULE Australia Austria..7 (1) 17.8 7.3 (11) 1.4 4.6 1 19.3 19.1.4 1.6 Austria Australia 16.6 1.1-1..7 Belgium.7.3 (1) 7.3 8.4 (11) 4.6 6. 19.1 1. 1.6 1.1 Austria 34 33-1..9 Belgium Canada.3.7 (1) 13.1 8.4 (11) (11) 1. 6. 1 1. 17.7 1.1. Belgium Canada Chile.7.8 (1) 13.1.6 (11) 1. 1.8 1 17.7.6.. Chile Canada 18. 1.7 -.. Czech Chile Republic.8. (1).6 3.4 (11) 1.8.9.6 19.7. 1.3 Czech Denmark Republic. Czech Republic 4.1 (1) 1.1 3.4 (11) (11).9 6.1 19.7 3.1 1.3. Denmark Estonia 4.1 (1) Denmark 1.1 (11) 4 6.1-4. 3.1 3.1..9 3.9 Estonia Finland Estonia 3.4 (1) 6. 9.9 (11) 6. 6.. 3.1. 3.9.9 1. Finland France 3.4 Finland.6 (1).6 1. 9.9 (11) (11) 6. 17.8 7.4 -.8 1. 1.9 1. 1.7 1.6 France Germany.6 France.4 (1) 1. 6. 7.7 (11) 7.4 3.3.3 1 -.9 1.9 18.9 1.6.7 1. Germany Greece.4 Germany 1. (1) 7.7. (11).3 3.9 1 18.9 18.9 1..7 Greece Hungary 1. Greece.4 (1). 4.3 (11) 3.9 4 1.9 1 -. 18.9.7.7.1. Hungary Iceland.4 Hungary 4. (1) 1..3 (11) (11) 1.9.9.7.. 49.9 Iceland Ireland Iceland 4. (1) 1. 19 1.7 (11).9 13.8 8.7 -. 1..9 49.9.. Ireland Israel 4. Ireland.6 (1) 1.7 31.7 (11) 8.7 9 3.1 1 -..9 7....7 Israel Italy.6 Israel 1.9 (1).7 18.7 7. (11) (11) 3.1 18.. -. 1 7. 19.1.7.3 1.6 Italy Japan 1.9 Italy.9 (1) 7..4 7.3 (11) (11)..1 4.4 1 -.3 19.1 17. 1.6 Japan.9 7.3 (11) 4.4 17.. 1.1 Korea 1.1 Korea Japan.9 (1).9 4.1 1.7 (11) 1.7 (11).1.8.8-4. 16.7 16.7 1.1 49.9 Luxembourg 49.9 Luxembourg Korea 1.9 (1) 1.9 (1) 4 8. (11) 8. (11) 3. 6.1 6.1 -.8.8.8.6.7 Malta.7 Malta Luxembourg 1 17-4. 1 17. 17...3 Mexico.3 Mexico Malta 1.1 (1) 1.1 (1) 1. (11) 1. (11).. 1 7.1 1 7.1 1.7 Netherlands 1.7 Netherlands Mexico.3 (1).3 (1) 8.3 (11) 8.3 (11) 6.1 6.1 1.3 1.3. Norway. Norway Netherlands 6.6 (1) 6.6 (1) 3.1 4. (11) 4. (11).8 17.9 17.9 -.3.3.3 1.6. Poland. Poland Norway.7 (1).7 (1). (11). (11) 16 1.8 1.8-6. 19.1 19.1 1. 1. Portugal 1. Portugal Poland 1. (1) 1. (1) 4.7 (11) 4.7 (11) 3. 3. 1. 1. 1.7 Slovak 1.7 Slovak Portugal Republic Republic 3 3. 19.3 19.3 1.6 1. Spain 1. Spain Slovak Republic 1.4 (1) 1.4 (1) 6. (11) 6. (11) 4.6 4.6 1 18.8 1 18.8 1. Sweden 1. Sweden Spain 3.4 (1) 3.4 (1) 6. 1.7 (11) 1.7 (11) 3.9 7.3 7.3 -.3.8.8.3. Switzerland. Switzerland Sweden 3.1 (1) 3.1 (1) 13.8 13. (11) 13. (11) 13.1 1.4 1.4 -.7 18. 18. 4.3 1.1 Turkey 1.1 Turkey Switzerland.4 (1).4 (1).7 (11).7 (11) 19.3.3-1. 1.3.3 1..8 United.8 United Turkey Kingdom.6 (1) Kingdom.6 (1) 7.4 1. (11) 1. (11) 3.8 6.4 6.4-3.6 1 1. 1 1..4 1. United 1. United United States States Kingdom 3.7 (1) 3.7 (1) 1 6.4 (11) 6.4 (11) 19.6.7.7-1.4.6.6.4.9 Brazil.9 Brazil United States.4 (1).4 (1).9 (11).9 (11).6.6.8.8.6 China.6 China Brazil.6 (1).6 (1) 16.4 1. (11) 1. (11) 14.8.8.8-1.6 19.3 19.3.9 48.3 Russia 48.3 Russia China.3 (1).3 (1) 1. (11) 1. (11).7.7 4.7 4.7 3.4 South 3.4 South Russia Africa. (1) Africa. (1) 3. (11) 3. (11).. 37.8 37.8 1. Indonesia 1. Indonesia South Africa. (1). (1) 1.1 (11) 1.1 (11).9.9 8.6 8.6. Hong. Hong Indonesia Kong Kong 34. 36.1 1.9 1 1.1 1.1. 1.8 Thailand 1.8 Thailand Hong Kong.4 (1).4 (1) 11.8 1. (11) 1. (11) 11.1 1.1 1.1 -.71.9 1.9.1 1. Taiwan 1. Taiwan Thailand 1. 4.8. Kenya Kenya Taiwan. (1). (1).3 1.4 (11) 1.4 (11) 19.1.9.9-3. 1. 1...1 Ghana.1 Ghana Kenya.4 (1).4 (1).3 (11).3 (11) 1.9 1.9 43. 43. 49.8 Armenia 49.8 Armenia Ghana 1.9 1.9.8 Belarus.8 Belarus Armenia 3.3 3.3 3.1 Georgia 3.1 Georgia Belarus.3 (1).3 (1) 1.6 (11) 1.6 (11) 1.3 1.3.1.1.7 Kazakhstan.7 Kazakhstan Georgia 4.9 4.9.1 Ukraine.1 Ukraine Kazakhstan. (1). (1).6 (11).6 (11).. 3. 3. 3.6 No. 3.6 No. Ukraine of observations 4 of observations 4 4 4 4 4 47 47 47 47 47 47 No. of observations 8 8 8 8 33

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