THE EFFECTIVENESS OF DISPLAY BANS:

Transcription

1 THE EFFECTIVENESS OF DISPLAY BANS: The case of Canada Jorge Padilla LECG 10 December

2 TABLE OF CONTENTS I. Introduction... 3 A. My approach... 5 B. Results... 7 C. Structure of the report... 8 D. Academic credentials and professional experience... 8 II. Data... 9 A. The Canadian Tobacco Use Monitoring Survey... 9 B. Regulatory measures adopted in Canada C. Tobacco prices III. Methodology A. Comparisons of smoking rates across provinces B. Smoking prevalence before and after the ban C. Multiple regression analysis IV. Multiple regression results A. Smoking prevalence of 15 to 79 year-olds B. Individuals under the legal smoking age C. Robustness analysis V. Conclusions Annex 1: Data description Annex 2: Empirical methodology Annex 3: Controlling for contraband tobacco Annex 4: Robustness analysis I Annex 5: Robustness analysis II: Estimation results using individual data

3 I. INTRODUCTION I have been asked by Philip Morris International (PMI) to analyse the impact of display bans on smoking prevalence in Canada. Display bans are regulations that prohibit the visual display of tobacco products within the point of sale. They are the most restrictive of all point-of-sale regulations, which include limitations on height and visibility of displays, prohibition of selfservice displays, and restrictions on logos, banners, and window posters. A display ban was first introduced in Canada in 2002 in Saskatchewan but was struck down by the Court of Appeal in 2003 and reinstated in 2005 after a favorable Supreme Court of Canada ruling. 1 Manitoba and Nunavut also implemented their own point of sale display bans in 2005, as did Prince Edward Island in 2006 and Northwest Territories and Nova Scotia in British Columbia, Ontario, and Alberta banned the display of tobacco products in 2008; New Brunswick and Yukon introduced display bans in 2009 and Newfoundland introduced them in January Whether display bans have an impact on smoking rates is an empirical question. In this report I examine the impact of display bans on smoking prevalence using data for all ten Canadian provinces. Smoking prevalence in Canada has been steadily declining since the early 1970 s. In 1974 the smoking prevalence of the total population 15 years and over was over 45% and in 2008 it had fallen below 20%. 4,5 Over the same period, the smoking rate of 1 The display ban came into effect on March 11, 2002 but was struck down by the Saskatchewan Court of Appeal on October 3, 2003; the ban was reinstated when the Supreme Court of Canada reversed the Court of Appeal on January 19, In Manitoba, the display ban came into effect on January 1, 2004 but was not enforced until August 15, 2005, several months after the Supreme Court of Canada decision upholding the similar Saskatchewan ban. 3 Tobacconists are exempted from the display ban in Alberta, Saskatchewan, Ontario, New Brunswick and Prince Edward Island as long as minors are not permitted access. In Quebec and British Columbia the additional condition that products cannot be seen from the outside is imposed. In Nova Scotia, tobacconists are exempt from display provisions provided that only the Health Canada emissions panel is visible. In Manitoba two specific tobacconists are exempt from display restrictions. 4 Percentage of current smokers (on either a daily or occasional basis). See Physicians for a Smoke Free Canada, 3

4 fifteen to nineteen-year olds also sharply decreased from 53% in 1974 to 15% in The decline of smoking rates occurred across all provinces. In Saskatchewan, for example, smoking rates declined 24% (i.e., five percentage points from 21% to 16%) between 1999, the earliest available data by province in the Canadian Tobacco Use Monitoring Survey (CTUMS hereafter), and 2008 and in Manitoba they declined by 16% (i.e., three percentage points from 19% to 16%). 6 The question I examine here is to what extent the recent decline in smoking prevalence in each province can be attributed to the display ban. This is not straightforward because smoking prevalence can be affected by many different factors in addition to a display ban, for example other regulations, social trends and the price of cigarettes. The display ban regulation was preceded by many other federal, provincial and municipal tobacco control measures, such as advertising bans, mandatory health warnings and sponsorship restrictions in 1989, and bans on smoking in public places and workplaces. 7 Those interventions may have continued to affect smoking prevalence in the different provinces even after the introduction of display bans. In addition, cigarette prices in Canada have been continuously increasing since the mid 1990s, both in real and nominal terms. The increase in tobacco prices, driven mainly by tax increases, is also likely to have had a negative impact on smoking prevalence. For these reasons a simple comparison of the smoking rate before and after the implementation of the display ban is likely to exaggerate its impact. To accurately estimate the impact of display bans on smoking rates, I need to take into account the impact of cigarette prices and other tobacco control measures on smoking prevalence. This is what I have done using standard statistical techniques. 5 The Canadian Tobacco Use Monitoring Survey does not distinguish between smokers who use lawfully purchased cigarettes and smokers who use contraband products so the decline of smoking prevalence may be over or underestimated depending on the amount of underreporting and how it has varied over time (see Annex 3). Evidence of underreporting has been found, amongst others, in Warner, K. E. (1978), Possible Increases in the Underreporting of Cigarette Consumption, Journal of the American Statistical Association, 73(362): , West Virginia Health Statistics Center (2000), Estimated Number of Adult Cigarette Smokers and Cigarette Consumption West Virginia, , available at and Pérez-Stable E. J., B. V. Marín, G. Marín, D. J. Brody, and N. L. Benowitz (1990), Apparent Underreporting of Cigarette Consumption among Mexican American Smokers, American Journal of Public Health, 80: See Health Canada, 7 Canada has a long history of mandatory and voluntary tobacco control initiatives (see Section II - B). 4

5 Canada can be considered the ideal country to analyse the impact of display bans for two reasons: first, because within the same country there are differences in the proportion of individuals affected by this measure at each point in time since display bans were not introduced across provinces simultaneously. This implies that there is always a benchmark population unaffected by display bans; and second, Health Canada developed a survey, the CTUMS, to provide continual data on tobacco use and smoking habits that has been systematically conducted since A. My approach There are two main approaches that can be used to estimate the impact of a display ban on smoking prevalence. I can compare smoking prevalence before and after the display ban; or I can compare smoking prevalence in provinces with a display ban (e.g., Saskatchewan and Manitoba in 2005) with smoking prevalence in provinces without a display ban (e.g., Alberta, British Columbia, New Brunswick, Newfoundland, Nova Scotia, Ontario, Prince Edward Island and Quebec in 2005). 8 By combining both approaches, as I have done here, I am able to obtain a more precise and robust estimate of the impact of display bans on smoking prevalence. In order to estimate the effect of the display ban I compared the smoking rate in the different Canadian provinces after the implementation of the display ban with the smoking rate in those same provinces before this ban was implemented. I have also compared smoking rates in provinces with display bans with smoking rates in provinces which had not introduced a display ban before 2009 taking into account other factors that may explain changes in smoking prevalence over time or between 8 As explained below, this comparison needs to take account of other factors that may explain the differences in smoking prevalence rates across provinces. To do so I use regression analysis. 5

6 provinces. There are standard statistical techniques that allow me to take account of these factors. The most commonly used approach, which is the one I have used here, is a multiple regression analysis. A simple regression analysis estimates the relationship between two variables. In other words, it identifies how a change in one of the variables (the explanatory variable ) affects the other (the independent variable ). For example, I might identify how a change in the price of cigarettes will affect smoking prevalence. In practice, however, I would expect smoking prevalence to be affected by many different explanatory variables, not just the price of cigarettes. A multiple regression analysis estimates the relationships between the independent variable and many explanatory variables. In this case, it allows me to identify the impact on smoking prevalence of a change in price, the introduction of different regulations, including a display ban, factors specific to different provinces and many other relevant factors. By combining data across provinces and over time and holding all of these other variables constant, I can identify the impact of a display ban on smoking prevalence. A multiple regression analysis allows me to draw two conclusions. First, I can estimate the impact of each potential explanatory variable on smoking prevalence. Second, I can test whether this relationship is statistically significant, in other words whether there is a low probability that this estimate reflects a random occurrence. I have used publicly available survey data on individuals smoking habits and their characteristics (e.g., age, gender, marital status, education level, etc.) from the CTUMS together with tobacco prices and dates of introduction of the regulatory tobacco control measures for all ten Canadian provinces from 1999 until I have used these data and standard multiple regression statistical techniques to estimate how smoking prevalence in all provinces is affected by several sociodemographic factors (e.g., gender, marital status and education), tobacco control 9 The CTUMS does not include data for Yukon, Northwest Territories, and Nunavut, which are territories, not provinces. The first year for which CTUMS is available is 1999 and the last full year for which data available is

7 measures (e.g., ban on smoking in public areas and workplaces and federal tobacco control measures), and tobacco prices for each of the years in the sample period. I estimated the impact of these factors on the average smoking prevalence across all ten provinces for two age groups: (i) the group of individuals aged 15 and above (i.e., the entire CTUMS sample), and (ii) the group of individuals under the legal smoking age. 10 In addition, to assess the reliability of my results, I conducted a similar analysis using the individual survey responses. For each individual responding to the CTUMS survey I know if he/she is a smoker and whether or not he/she is an occasional or daily smoker. The survey also provides information on a variety of social and demographic characteristics. Including these characteristics in the estimation model allows me to take into account individual characteristics that may have an influence on observed smoking habits. B. Results As I previously explained, smoking prevalence is affected by many factors. To isolate the impact of the display ban it is therefore necessary to conduct a statistical analysis that allows controlling for all these factors. This is what I have done here. Using multiple regression techniques I have been able to (a) estimate the impact of the display ban on smoking prevalence taking into account the many other factors that may affect smoking rates; and (b) assess whether this estimated relationship between the display ban and smoking prevalence truly exists or is the result of random chance. I found that display bans had no statistically significant effect on smoking prevalence. In contrast, I found that tobacco prices had a negative and statistically significant relationship with smoking prevalence The minimum age is 18 years old, except in British Columbia, New Brunswick, Newfoundland, Nova Scotia, Prince Edward Island and Ontario, where the legal age to smoke is 19 years old. 11 Although the impact of prices on smoking prevalence may be over or under estimated due to the presence of contraband products in the market, several tests I conducted suggest that the bias is likely to be small. Using different proxies for tobacco contraband, I continue to find that price increases, but not display bans, have a negative economically and statistically significant impact on smoking prevalence (see Annex 3). 7

8 These results are reliable and robust. They are reliable because the estimated impact is based on the comparison of smoking prevalence rates before and after the bans were implemented and across provinces. The results are robust because they do not change when additional control variables are added to the regression model and when different methods are used to estimate the regression model. The results also remain unchanged when the regression model is estimated using individual level data taking into account individual characteristics that may influence smoking decisions. In summary, my analysis does not support the claim that a display ban is likely to lead to a reduction of smoking rates. The data show that the real price of cigarettes reduces smoking prevalence. C. Structure of the report This report is structured as follows. In Section II, I describe the data I have used. In Section III, I explain why a simple comparison of smoking prevalence in a given province before and after the introduction of a display ban may produce erroneous conclusions. I also explain why a simple comparison of smoking prevalence in provinces with and without a display ban is not sufficient to correctly assess the impact of this regulatory measure. I then describe how multiple regression analysis techniques can be used to rigorously analyse the impact of display bans on tobacco consumption. In Section IV, I summarise the results of the regression analysis and discuss my findings. Lastly, I offer some concluding remarks in Section V. D. Academic credentials and professional experience I have significant experience in empirical analysis and the economics of the tobacco industry. I earned M. Phil and D. Phil degrees in economics from the University of Oxford. I am a Research Fellow of the Centre for Economic Policy Research (CEPR, London) and the Centro de Estudios Monetarios y Financieros (CEMFI, Madrid). I am or have been a member of the editorial boards of Competition Policy International, the Review of Economic Studies, the Spanish Economic Review and Investigaciones Económicas. I received the 1990 Young Economists Essay Award of the European Association for Research in Industrial Economics. 8

9 I have written several papers on competition policy and industrial organisation in, among other journals, the Antitrust Bulletin, the Antitrust Law Journal, the European Competition Journal, the European Competition Law Review, the European Economic Review, the Fordham International Law Journal, the International Journal of Industrial Organization, the Journal of Competition Law and Economics, the Journal of Economics and Management Strategy, the Journal of Economic Theory, the RAND Journal of Economics, the University of Chicago Law Review, and World Competition. I am also co-author of the book, The Law and Economics of Article 82 EC, published by Hart Publishing, I have provided written and/or oral testimony in competition policy matters before the Cypriot, Dutch, French, German, Irish, Israeli, Italian, Portuguese, Spanish, Turkish, UK and US competition authorities and/or courts, as well as in cases before the European Commission, the European Court of First Instance and the UK Competition Appeals Tribunal. I have provided consultancy advice to both the Office of Fair Trade (OFT) and the European Commission and authored reports published by both the OFT and the European Commission. II. DATA In this section, I describe the data I employed for the analysis. I used survey data from Statistics Canada which contains different questions regarding smoking behaviour and individual characteristics. In addition, I used publicly available data on tobacco prices and the dates of introduction of federal and provincial regulatory measures in Canada. A. The Canadian Tobacco Use Monitoring Survey I used data from the CTUMS conducted by Statistics Canada from February 1999 until December

10 The CTUMS has been conducted annually since 1999 and it covers individuals 15 years of age and above in all ten Canadian provinces. 12 Nearly 2000 individuals are interviewed over the telephone every month, from February to December. This survey is particularly appropriate for my objective since it contains individual responses regarding smoking patterns of daily smokers together with individual and household characteristics. To construct a measure of smoking prevalence and to conduct the analysis on individual smoking patterns, I focused on individuals that responded Every day to the following question: At the present time do you smoke cigarettes every day, occasionally or not at all. 13 B. Regulatory measures adopted in Canada Canada has a long history of mandatory and voluntary tobacco control initiatives many of which were implemented prior to 1999, the first year in my data. The tables below show the dates of key tobacco control measures implemented from 1999, the first year in my data, onwards. Table 1 shows the dates of the provincial regulations (i.e., display ban and ban on smoking in public areas and workplaces) and Table 2 shows the dates of the federal regulatory measures. 12 The population of Yukon, Northwest Territories, and Nunavut and full-time residents of institutions are not included in the survey. 13 See Annex 1 for a more detailed explanation of the data. 10

11 Table 1: Ban on displays and smoking in public areas and workplaces by province, Total ban on smoking in Region Display ban [1] public places and workplaces [4] Alberta July 2008 January 2008 British Columbia April 2008 May 2008 Manitoba August 2005 [2] October 2004 New Brunswick January 2009 October 2004 Newfoundland and Labrador January 2010 July 2005 Nova Scotia April 2007 December 2006 Ontario April 2008 June 2006 Prince Edward Island June 2006 October 2009 Quebec June 2008 June 2008 Saskatchewan January 2005 [3] January 2005 Notes: [1] The indicator variable for bans implemented from the 15 th of each month onwards takes the value of 1 starting the following month and zero before; [2] in Manitoba, the display ban came into effect on January 1, 2004 but was not enforced until August 15, 2005, several months after the Supreme Court of Canada decision upholding the similar Saskatchewan ban; [3] in Saskatchewan, the display ban came into effect on March 11, 2002 but was struck down by the Saskatchewan Court of Appeal on October 3, 2003; the ban was reinstated when the Supreme Court of Canada reversed the Court of Appeal on January 19, 2005;[4] in some provinces the legislation imposing a total ban on smoking in public places may have removed only the limited amount of places to smoke permitted under previous legislation Source: Various provincial legislation. Measure Table 2: Federal mandatory tobacco control measures, Date of implementation Restrictions on sponsorship advertising October 2000 [1] Introduction of pictorial health warning labels January 2001 [2] Ban on sponsorship promotion October 2003 Notes: [1] Restrictions on advertising and brand sponsorship were originally introduced in the 1989 Tobacco Products Control Act (the ban was struck down by the Supreme Court of Canada in 1995); [2] pictorial health warning labels were preceded by various voluntary codes (1972, 1975,1995,and 1996) introducing and amending health warnings and by federal laws in 1989 introducing mandatory health warnings and in 1994 introducing additional messages on health warnings and modifying the size and the presentation of the messages; the 2001 pictorial health warnings were gradually phased in; they were first required on cigarette brands with more than 2% market share in December 2000 and by June 2001 they were required on all other tobacco products. Source: Canadian tobacco legislation. C. Tobacco prices I obtained information on tobacco prices in each province from Statistics Canada. I constructed the real price of tobacco by dividing the consumer price index for tobacco by the general consumer price index in each province. 11

12 III. METHODOLOGY In this section, I explain why the analysis of the expected impact of display bans on smoking rates across the Canadian provinces requires the use of multiple regression techniques. More precisely, I explain why a simple comparison of smoking prevalence in provinces with display bans with smoking prevalence in provinces without display bans is not sufficient to assess the impact of this tobacco control measure. I also explain why a simple before and after comparison of smoking prevalence in provinces where display bans have been introduced is also not sufficient to correctly assess the impact of this regulatory measure. A. Comparisons of smoking rates across provinces A simple approach to assess the impact of display bans on smoking prevalence is to compare the smoking prevalence in provinces that have implemented a display ban with the smoking prevalence in provinces without a display ban. Figure 1 shows the average adult smoking prevalence in all ten Canadian provinces between 1999 and 2008 and the average rate of decline of smoking prevalence over the period. As described in the previous section, eight out of these ten Canadian provinces implemented display bans before Saskatchewan and Manitoba were the first to introduce a display ban in 2005; 14 Quebec, Ontario and British Columbia introduced a display ban at the very end of the sample period (i.e., between March and May 2008). New Brunswick and Newfoundland were the only two provinces without a display ban within the period of analysis. 15 New Brunswick, Newfoundland, Nova Scotia, Quebec and Saskatchewan are the provinces with the highest average smoking rates (above 19%) while British Columbia and Ontario show the lowest smoking prevalence (12.6% and 15%, respectively). This comparison does not reveal any relation between display bans and smoking prevalence. The smoking prevalence in Saskatchewan, one of the first provinces to introduce a display ban, is as high as the smoking prevalence in New 14 Nunavut also introduced display bans in 2005 but CTUMS data is not available for this territory. 15 New Brunswick implemented display bans in January 2009 and Newfoundland implemented them in January

13 Brunswick and Newfoundland, provinces without a display ban during the period of analysis. On the contrary, the provinces with the lowest average smoking rate introduced a display ban in 2008, the last year of the sample period. Figure 1: Average smoking rates (% daily smokers) and cumulated average annual growth rate (CAGR) of smoking rates, Jan Aug June 2006 April 2007 April 2008 April 2008 June 2008 July 2008 Jan Jan Date Display Ban % Saskatchewan Manitoba Prince Edward Nova Scotia British Columbia Ontario -4.9 Quebec Alberta New Brunswick % daily smokers CAGR smoking rate Newf. & Labrador Source: Author s calculations using CTUMS data. In addition, if display bans had an effect on smoking prevalence I would expect the rate of decline to be greater in provinces with a display ban relative to provinces without a display ban or to those that introduced a display ban at the end of the sample period. However, the graph shows no such trend. Manitoba and Saskatchewan, which are the provinces that first introduced a display ban, show the lowest rate of decline of smoking prevalence, 1.9% and 2.7% respectively, while the average rate of decline is 3.5%. In comparison, the rate of decline in provinces that introduced a display ban in 2008 or later is above average (3.9%). B. Smoking prevalence before and after the ban A second approach to assess the impact of the introduction of display bans on smoking rates is to focus only on provinces where these bans have been implemented 13

14 and compare the smoking prevalence in those provinces before and after the introduction of the display ban. Figure 2 shows the average adult smoking prevalence in the four provinces that introduced a display ban prior to 2008 (Saskatchewan, Manitoba, Prince Edward Island and Nova Scotia). Smoking prevalence in each of these provinces has been declining, as in the rest of the remaining 6 provinces. In all four provinces, the steepest smoking rate decline occurred between 2001 and 2002, three years or more before the display ban. Figure 2 also shows a clear negative relationship between smoking prevalence and cigarette prices in Manitoba, Nova Scotia, Prince Edward Island and Saskatchewan, and this is also the case in the remaining provinces. Figure 2: Average smoking rates amongst daily smokers Manitoba Nova Scotia % % Daily smokers CPI Tobacco Daily smokers CPI Tobacco Prince Edward Saskatchewan % % Daily smokers CPI Tobacco Daily smokers CPI Tobacco 14

15 Manitoba Nova Scotia % % Daily smokers CPI Tobacco Daily smokers CPI Tobacco Prince Edward Saskatchewan % % Daily smokers CPI Tobacco Daily smokers CPI Tobacco Source: Author s calculations using CTUMS and Statistics Canada data. The introduction of display bans does not appear to have accelerated the decline of smoking rates. Had the ban been effective, I would expect to see a permanent fall in smoking prevalence rates and/or the rate of decline of the smoking prevalence to speed up after the ban. However, as shown in Figure 2, with the exception of Manitoba, there is no clear break in the declining trend in the provinces after display bans were introduced. In Manitoba, the observed decline in 2006 occurred after the ban on smoking in public areas and workplaces (at the end of 2004) and the ban on point of sale displays (at the beginning of 2005). It is unclear therefore which of the two measures may have contributed to the observed decline and how much other factors, such as shifts in societal trends, may have also contributed. The smoking prevalence decline in the provinces that introduced a display ban prior to 2008 (Manitoba, Nova Scotia, Prince Edward Island, and Saskatchewan) was lower than the decline in provinces that introduced a display ban in 2008 or after (see Figure 3). Smoking prevalence in provinces with a display ban prior to 2008 fell from 17% in 2005 to 16% in 2008 (a 6% decline), and in provinces with a display 15

16 ban in 2008 or after, smoking prevalence declined from 15% to 13% (a 10% decline). Average smoking rates in Canada fell from 15% to 13% (a 10% decline). Figure 3: Average smoking rates (daily smokers) Source: Author s calculations using CTUMS data. The graphical analysis of smoking rates before and after the introduction of a display ban, as well as the comparison of average smoking rates between provinces with and without a display ban, suggest that display bans did not have an effect on smoking prevalence in Canada. However, smoking prevalence is driven by many factors, some that can be clearly identified and quantified, such as price increases, and others that are difficult to measure such as societal trends. So while a priori display bans could have been responsible for part of the observed decline, it is clearly not the only explanation for the observed negative trend. Two additional explanations for the observed decline cannot be overlooked. First, the display ban regulation was preceded by various other regulatory measures including bans on smoking in public areas and workplaces and numerous voluntary initiatives and federal mandatory regulations. All of these may be responsible for part of the observed reduction in smoking prevalence in each province before and after the implementation of the display ban. Second, cigarette prices have been increasing in all Canadian provinces throughout the period of analysis, driven primarily by tax increases. Average retail prices were more than two times higher in 2008 than in One would expect that this increase 16

17 in tobacco prices would have had a negative impact on smoking prevalence and the data suggests that this is indeed the case (see Figure 2). C. Multiple regression analysis It should now be clear that, in order to properly assess the impact of display bans on smoking prevalence, I need to take account of all factors that may explain changes in smoking prevalence over time and between provinces, and not just the implementation of the display ban. This requires using multiple regression techniques. 16 These techniques make it possible to estimate the relationship between two variables (e.g., smoking prevalence and a display ban), when the variable under investigation (smoking prevalence in this case) is potentially influenced by many other factors (e.g., cigarette prices). Using a multiple regression analysis, I can quantify the impact on smoking prevalence of a display ban. More precisely, I can estimate by how much smoking prevalence would change if a display ban was introduced while all other factors influencing the smoking rate were kept constant. And, furthermore, I can test whether this relationship is statistically significant. Statistical significance is important because the relationships I identify are estimates of the true relationship. There is likely to be some difference between my estimate and the underlying true relationship. This is the case with any statistical analysis. There are various reasons for this: The data I use is based on a survey which, although it is a very reliable source, will inevitably contain some small discrepancies with respect to the underlying facts. For example, the CTUMS may not accurately estimate the decline of smoking prevalence if smokers of contraband products do not admit to smoking See Stock, J. H., and M. W. Watson (2003), Introduction to Econometrics, Boston, MA: Addison Wesley, Chapter 11. See also D. Rubinfeld (2000), Reference Guide on Multiple Regression in Reference Manual on Scientific Evidence, Second Edition, Federal Judicial Center. 17 The CTUMS does not distinguish between smokers who use lawfully purchased cigarettes and smokers who use contraband products. 17

18 Smoking prevalence is affected by many factors, not all of which can be measured such as societal trends. This means that my analysis does not capture every conceivable explanatory effect. For these reasons, it is possible to find a small relationship between two variables when in practice no such relationship exists. To guard against this possibility, statisticians and social scientists have developed tests to assess how likely it is that a relationship between two variables really exists. The commonly accepted practice is that a relationship is said to be statistically significant only if these tests show that there is a high probability that this relationship really exists. If this threshold is not met then one cannot be confident that the observed relationship between the two variables is real. In summary, the use of multiple regression allows me to (a) estimate the size of the impact of the display ban on smoking prevalence taking into account that many other factors may explain the decline in smoking rates; and (b) assess whether the estimated relationship between the display ban and smoking prevalence truly exists or is driven by chance. Multiple regression techniques can be used to compare smoking prevalence before and after the display ban or to compare smoking prevalence across provinces with and without a display ban or to combine both approaches. I chose to combine both approaches because by using both the time and the cross-sectional dimension of the data, and controlling for the other factors that may have been affecting smoking prevalence, I can more precisely and reliably estimate the impact of interest. The standard practice in econometric analysis to evaluate the reliability of the empirical results is to conduct sensitivity analysis. This is necessary to ensure that findings are not just a chance occurrence and to provide assurance that results are reliable. An estimated coefficient is said to be robust when its statistical significance and its sign does not change in response to small modifications of the data or the estimation model. I have performed several robustness tests that confirm that my results on the impact of display bans on smoking prevalence are robust. 18

19 IV. MULTIPLE REGRESSION RESULTS In this section I describe the results of my multiple regression analysis and discuss my findings. I estimated a multiple regression model where the year-old average smoking prevalence rates across all ten Canadian provinces between January 1999 and December 2008 are explained by several socio-demographic factors (e.g., gender and age), tobacco control measures (e.g., ban on smoking in public areas and workplaces and federal measures implemented during the period) 18, tobacco prices and province specific effects that do not vary over time. 19 In addition, I estimated the same regression model for smoking prevalence rates of individuals under the legal smoking age (15 to 17 years). A. Smoking prevalence of 15 to 79 year-olds Table 3 below shows the results of the multiple regression analysis of the impact of display bans on year-old smoking prevalence across provinces in Canada. The table reports the estimated coefficients, which measure the direction and the magnitude of the effect on smoking incidence of a change in the explanatory variable. Underneath the regression coefficient, I report the significance level of the estimated coefficient. Values of 0.05 or below indicate that the estimated coefficient 18 I include in the regression analysis only one indicator for federal tobacco control measures implemented during the period of analysis which takes the value of one from October 2000 onwards and zero otherwise. I do not attempt to estimate the impact of each measure separately because disentangling the impact of any single federal measure based only on the variation over time (and not across provinces) of smoking prevalence is very difficult as unobserved confounding factors cannot be controlled for, especially with a data sample that does not cover the entire time span over which tobacco control measures have been implemented (see footnote 20). 19 This approach, usually referred to as difference-in-difference (DID) estimator, has become widespread in empirical economics. The DID approach is often associated with so-called natural experiments, where policy changes can be used to effectively define control and treatment groups. (See Imbens, G. and J. Wooldridge (2009), Recent Developments in the Econometrics of Program Evaluation, Journal of Economic Literature, Vol. 47, No. 1, p. 67.) The main advantage of using this approach is that it allows disentangling the effect of the policy change from other factors that may have changed over time and are unrelated to the policy change. Furthermore, by taking into account characteristics of the different provinces included in the sample and including province fixed effects, the estimated impact is not affected by any systematic differences in smoking rates between provinces. (See Wooldridge, J. (2002), Econometric Analysis of Cross Section and Panel Data, Cambridge: MIT Press, p. 130.) In the empirical tobacco control literature this approach has been applied in the past to estimate the impact of specific measures, such as advertising bans, using data for several OECD countries. (See for example, Hamilton, J. (1975), Effects of tobacco advertising restrictions, Proceedings of the Third World Conference on Smoking and Health, DHEW, Washington, DC, pp ; Stewart, M. (1993), The effect on tobacco consumption of advertising bans in OECD countries, International Journal of Advertising, Vol. 12, pp ; and Nelson, J. (2003), Cigarette demand, structural change and advertising bans: international evidence, Contributions to Economic Analysis and Policy, Vol. 2, Issue 10, Article 10). 19

20 is statistically different from zero. That is, the estimated coefficient is not the product of random chance with a level of confidence of 95% or more. 20 The first column in Table 3 reports the estimation results when I only include the provincial tobacco measures in the estimation model. Column 2 reports the results when I include as an additional control variable an indicator for federal tobacco control measures implemented after Federal tobacco control measures are not the focus of my research, however, it is important to include them in the regression model to test the robustness of the display ban results. I find that display bans did not have a significant impact on smoking rates. The estimated coefficient shown in the first row of the table is not statistically significant. This means that it is not possible to assert that the impact is different from zero. In plain words, the results offer no support for the claim that a display ban is likely to cause a reduction in smoking prevalence, neither in the overall population nor in the under smoking age population. 21 As I show in the next section, this result is robust to modifications to the method of estimation, the control variables included in the model and the data used for estimation. My results show that higher tobacco prices, driven to a great extent by higher government taxes, reduce smoking prevalence. According to my estimates a real 20 Estimated coefficients indicate the mean of the distribution of the true (unknown) coefficient. Significance tests investigate the probability that, given the estimated coefficient (mean) and the standard deviation, the true coefficient is actually zero. Generally hypotheses are tested at either the 95% or the 99% confidence level. A coefficient marked with ** indicates that the null hypothesis that the true coefficient is zero is rejected since, given the estimated coefficient and its standard error this will occur with a probability that is lower than 5%. For more information see for example Peter Kennedy, A Guide to Econometrics 5 th Ed., Oxford, England. Blackwell Publishing, I used standard errors that are robust to heteroskedasticity to evaluate the significance level of the estimated parameters. I found no evidence of autocorrelation in the estimated residuals. 21 The empirical approach I have selected measures the impact of display bans on the level of smoking prevalence. An alternative approach would be to estimate the impact on the trend of smoking prevalence, that is, whether the declining trend in smoking prevalence changes after the implementation of the display ban. The simultaneous estimation of the shift on the level and on the trend of smoking prevalence would require including additional trend variables (one starting in 1999 and others starting the month the display ban is implemented in each province). Given the strong positive trend in prices across all provinces, the trend variables would be highly correlated with tobacco prices. This multicollinearity problem would make parameters difficult to interpret. Nevertheless, if I disregard this problem and re-estimate the regression model including these additional trend variables I find that the results regarding the effectiveness of the display ban do not change. However, the estimated coefficient of the relative price loses statistical significance. I prefer the specification including relative price rather than trends because price is a policy variable and it is important to be able to estimate the price sensitivity of smoking prevalence. For a similar approach see Gospodinov, N. and I. Irvine (2004), Global health warnings on tobacco packaging: evidence from the Canadian experiment, The B.E. Journal of Economic Analysis and Policy, Vol. 4, No. 1 (Topics), Article 30. Available at: 20

21 price increase has a negative and statistically significant effect on smoking prevalence. The estimated coefficient of the real price of cigarettes indicates that average smoking rates declined by 0.8 percentage points in response to a 10 percentage point increase of real prices (see row 9, column 1). 22 That is, given an average smoking rate of 20%, an increase of the real price of tobacco from 110 to 120 is estimated to reduce smoking rates to 19.2%. Between 2001 and 2008, the real price of tobacco increased by 43 percentage points leading to a 3.4 percentage point reduction of smoking rates. 23 The estimated impact of prices on smoking prevalence may be over or under estimated due to the presence of contraband products in the market. Contraband provides consumers with an inexpensive, albeit illegal, source of tobacco products. The price difference between legal and illegal cigarettes can be as much as $64 to $100 on a carton of 200 cigarettes, depending on the provincial or territorial tax rate. 24 After declining in 1994 when cigarette taxes declined, contraband tobacco has resurged over the past 10 years fueled by new tobacco tax increases. Recent estimates indicate contraband products accounted for between 27% and 33% of cigarettes purchased in Canada in I have been unable to include contraband in the estimated models because reliable estimates of tobacco smuggling are not available for all provinces and all periods included in the regression model. However, results using different proxy measures for contraband suggest that results remain unchanged; price continues to be a significant factor and the effect of the display bans on smoking prevalence continues to be statistically insignificant (see Annex 3). 22 The estimated impact of a relative price increase on smoking incidence is not an elasticity measure that quantifies the response of tobacco consumption to an increase in tobacco prices. It quantifies the impact of price increases on the number of smokers. Using this estimated coefficient together with the average relative price of tobacco and the average smoking rate in Canada, I can calculate the price sensitivity of smoking prevalence. I find an average smoking prevalence price elasticity of 0.41 which is in line with the estimates found in the literature (see for example Anindya S. and T. Wirjanto (2009), Estimating the impacts of cigarette taxes on youth smoking participation, initiation and persistence: Empirical evidence from Canada, Health Economics, DOI: /hec See footnote See National Post, An economic cancer, September 17, 2010, available at 25 Physicians for a smoke-free Canada, 2008 and GfK, Illicit usage of cigarettes, Research Dynamics Study, 2008, respectively. 21

22 My results also show that the impact of the other tobacco control measures which I included as control variables, the total ban on smoking in public places and work places 26 and an indicator of federal measures implemented after 1999, are not statistically significant. These results, based on sound estimation techniques, identify price as a factor that has reduced smoking prevalence in the past. However, the future effectiveness of tax driven price increases will depend, amongst other things, on the existing tax level because raising taxes too much can, as occurred in Canada in the early to mid nineties and in the UK in the late nineties, spur illicit trade and have undesired consequences on smoking prevalence. The additional control variables I included in the model are statistically significant and have the expected effect on smoking prevalence. I found that smoking prevalence rates of men were on average three percentage points higher than those for women. In addition, I found that the relationship between smoking prevalence rates and age followed an inverted-u shape. The estimated coefficients of the age group variables measure the difference between the average smoking rate of year olds (the omitted variable) with respect to each of the age groups shown in the table. Thus, my results indicate that all age groups shown in the table had higher average smoking prevalence rates than year olds. Moreover, year olds had the highest smoking prevalence rate followed by year olds. 26 Although some provinces implemented partial smoking bans before the total ban, I only included in the estimation model the total ban on smoking in public places and workplaces because it is the most homogeneous of all smoking bans across Canadian provinces. 22

23 Table 3: Regression results, years Row No. Column 1 Column 2 1 Display ban [0.2046] [0.1872] 2 Total ban on smoking in public areas and workplaces [0.4073] [0.3530] 3 Post 1999 federal tobacco control measures [0.0641] 4 Males ** ** [0.0001] [0.0001] 5 Age group years ** ** [0.0000] [0.0000] 6 Age group years ** ** [0.0000] [0.0000] 7 Age group years ** ** [0.0000] [0.0000] 8 Age group 45 years & over ** ** [0.0034] [0.0034] 9 Real CPI tobacco ** ** [0.0000] [0.0000] 10 Constant ** ** [0.0000] [0.0001] Province FE Yes Yes Observations R-squared Notes: [1] p-values corresponding to robust standard errors allowing residual correlation within provinces; *and ** indicate that the estimated coefficient is significant at the 95% and 99% confidence level, respectively; [2] the real CPI Tobacco is constructed by dividing the consumer price index for tobacco by the general consumer price index; [3] the constant is the sample average smoking rate in the province of reference (i.e., the province not included in the fixed effects which in this case is Alberta) and the age group of reference; [4] the low R-squared is common when estimating panel data models based on individual data; 27 [5] results remain qualitatively unchanged when I consider smoking rates without distinguishing between males and females, and smoking rates without distinguishing between sexes and age groups; the impact of the display ban remains statistically insignificant. Source: Author s analysis using data from CTUMS and Statistics Canada. B. Individuals under the legal smoking age I found very similar results when I used the same econometric models to estimate the impact of display bans on the smoking prevalence of 15 to 17 year olds. 28 Table 4 shows the average estimated impact of tobacco control measures on smoking prevalence for this narrower age group. I continue to find that the display ban was not an effective tobacco control measure. The estimated impact is negative but very small and not statistically significant (see 27 What level of R 2, if any should lead to a conclusion that the model is satisfactory? Unfortunately, there is no clear-cut answer to this question, since the magnitude of R 2 depends on the characteristics of the data being studied and, in particular, whether the data vary over time or over individuals. Typically, an R 2 is low in crosssection studies in which differences in individual behaviour are explained. See ABA Section of Antitrust Law (2005), Econometrics: Legal, Practical and Technical Issues, p In British Columbia, New Brunswick, Newfoundland, Nova Scotia, Ontario and Prince Edward Island I used data for year-olds as the minimum smoking age is