The effect of two policy options - alcohol tax reform and minimum pricing of alcohol - on young Australians Saturday nights

Transcription

1 The effect of two policy options - alcohol tax reform and minimum pricing of alcohol - on young Australians Saturday nights Paper prepared for the Sixth Annual Conference of the International Society for the Study of Drug Policy: Canterbury, UK, May 2012 Jenny Chalmers* and Matthew Sunderland Drug Policy Modelling Program, University of NSW. Sydney, NSW, 2052, Australia *Corresponding author. Tel ; fax address: j.chalmers@unsw.edu.au ABSTRACT Draft: Please do not quote or cite without permission of authors Policies which increase alcohol prices are considered to be most effective in reducing alcohol consumption and related harms. While there is substantial evidence supporting the inverse relationship between population level alcohol consumption and alcohol prices, less is known about the implications for sub-populations. Nor has much attention been paid to the consequence of alcohol pricing for illicit drug consumption. The most effective way to reduce hazardous drinking via the price of alcohol is to increase tax on alcohol. A significant anomaly in Australia s alcohol taxation system is the negligible taxation of low priced wine. The alcohol industry does not incorporate taxation in the price of each and every alcohol product. Interest in minimum pricing as a complementary policy has gained traction in recent years, particularly in the UK. This paper presents findings from a study which uses the internet to access a representative sample of 2,010 young Australians (18-30 years of age), quota sampled to ensure representation of poly drug users. Using an experimental behavioural economics approach, participants were asked how they would adjust their alcohol and illicit drug use over a night out in response to hypothetical changes in the prices of alcohol products in a bottle shop and bar. Participants were presented with three sets of prices. The first establishes base-line patterns of drinking and drug use. The second introduces a minimum price of $1.50 per standard drink. The third applies a consistent volumetric tax to all alcohol at a rate that approximates the current rate applying to spirits and alcopops. Results highlight the heterogeneity in responses to increases in the price of alcohol associated with these policy options. 1

2 Introduction Drinking heavily on single occasions is more costly to society than alcohol dependence; the costs extending beyond health-related consequences for the drinker to social costs like criminal damage, violence and lost productivity in the workplace (Babor et al., 2010). Increasing the retail price of alcohol is considered one of the most effective ways to reduce alcohol consumption (Anderson, Chisholm and Fuhr, 2009) and increasing the taxation on alcohol the most efficient way to increase price (World Health Organization, 2007). Increases in the minimum price of alcoholic beverages can also substantially reduce alcohol consumption by raising the prices of the cheapest alcohol (Stockwell et al., 2012). Both are on the policy agenda in Australia (Webster, 2012). Despite the fact that many young people consider alcohol part of the repertoire of psychoactive drugs used to enhance leisure time through intoxication (Keane, 2009; Brain et al., 2000), the research community has been largely unconcerned about the impact of alcohol pricing policy reform on the consumption of illicit drugs. Nor has much attention been paid to variation in responses to policy reform in alcohol consumption, especially in polydrug users. This paper explores the implications of taxation reform and the establishment of a minimum price for alcohol for alcohol, cannabis and ecstasy consumption in a sample of young Australians (18 to 30 years of age) using a behavioural economics experimental approach. Cannabis is the most widely used illicit drug in Australia and ecstasy, which appeared on the Australian drug-scene in the early 1990s, is the second most common illicit drug used. Among young adults (18-29 years), one in five used cannabis and around one in ten used ecstasy over the past year (AIHW, 2011). Over the past two decades, increases in the price of alcohol have outpaced increases in consumer prices in general by nearly 20 per cent in Australia. However, this is not the case for all alcoholic beverages. Whereas beer and spirits have become more expensive relative to 2

3 other consumer items, wine has become substantially less expensive Carragher and Chalmers, 2012). The Australian alcohol taxation system could better guide prices towards levels that reduce harmful drinking in Australia. The ad valorum taxing of wine is anomalous and, based on evaluations of restrictions and a levy on cask wine in Australia, has public health implications (Carragher and Chalmers, 2012). There are two major perspectives on the direction of alcohol taxation policy reform in Australia; the Henry Review volumetric model (which would see all alcoholic beverages taxed at a common rate according to alcohol beverages that accounted for spillover costs from harmful alcohol use) and the public health advocacy volumetric tiered model (which would see the volumetric rate of tax increase with the alcohol content of the product). The incumbent federal government has rejected recommendations to reform the alcohol taxation system with a volumetric model (Webster, 2012). Although taxation represents the most cost-effective alcohol policy for reducing alcoholrelated problems, particularly for countries with high levels of hazardous drinking (World Health Organization, 2007), the alcohol industry does not incorporate taxation in the price of each and every alcohol product, as evidenced by sales of alcohol below cost (Carragher and Chalmers, 2012). Thus it can subvert the pricing signals of the tax system, allowing drinkers to maintain harmful drinking practices by substituting away from higher priced (tax inclusive) products. A legislated minimum price would help circumvent this practice and target the cheapest products; in the case of Australia, cheap cask wine. Although about to be legislated in Scotland, minimum pricing was largely un-tested until a recent report on British Columbia s natural experiment which illustrated its effectiveness in that Canadian province (Stockwell et al., 2012). The Australian government is considering the practicalities of 3

4 introducing minimum pricing but Australia s public health lobby sees it as a complement to taxation reform, rather than an alternative (Webster, 2012). In this study we compare responses to the introduction of a $1.50 per standard drink minimum price with the establishment of a universal volumetric tax rate on all alcoholic beverages equivalent to a ten per cent increase in the current excise applicable to spirits and alcopops. This volumetric tax model is consistent with the Henry Tax Review recommendations and was found, in a recent Australian modelling exercise, to be the most effective of a range of volumetric tax alternatives (Doran et al., 2011). We assume that all taxation increases are passed on to retail prices, so the taxation alternative incorporates a complementary minimum price. Policy makers in many countries are concerned about binge drinking and associated antisocial behaviour (Hutton, 2012). Their concerns run parallel to the growth of the night time economy and the commodification of leisure and pleasure (Measham, 2006). The practice of drinking to, or past, the point of intoxication has been likened to purposeful but controlled loss of control (Measham and Brain, 2005), with many young people drinking for social reasons relating to pleasure (Kuntsche et al, 2005). Recent decades have also seen the normalisation of illicit drug use, particularly recreational illicit drugs including cannabis and ecstasy, among certain groups of young people. This concentration of illicit drug use in particular groups of young people has been described in terms of demographic characteristics (like gender and education), although it is clear that social networks of illicit drug users are also of paramount importance (Wilson et al., 2010). The tendency to use a variety of drugs, including alcohol, in combination is well documented (Boys et al., 1999; Parker and Williams, 2003). As a group, heavy drinkers are the most likely to use illicit drugs and the prevalence of heavy drinking on single occasions increases with illicit drug use (Hakkarainen and Metso, 2009). 4

5 In economics the association between alcohol and illicit drug consumption can be described in terms of substitution and complementarity. In this context, substitution could describe two responses to the rise in alcohol prices associated with the reforms; replacing some alcohol use with illicit drug use, or replacing some illicit drug use with alcohol use. In contrast, alcohol and illicit drugs would be regarded as complements if the price increase associated with the pricing reform induced reductions in the use of both alcohol and illicit drugs. People combine drugs (including alcohol) to compensate for the side-effects of one drug, to take advantage of the synergistic effects of drugs, or over longer time periods to compensate for drugs that are in low supply or relatively expensive (Clayton, 1986; Boys et al., 2001). Research into the economic association between alcohol use and illicit drug use based on existing data is scarce, inconclusive and largely limited to cannabis use. One group of US based studies support substitution (DiNardo and Lemieux, 2001; Model, 1991; 1993; Chaloupka and Laixuthai, 2001; Saffer and Chaloupka, 1999a) while another supports complementarity (Farrelly et al., 1999; Pacula, 1998a; 1998b; Saffer and Chaloupka, 1999b; Williams et al., 2004). US studies do not typically use cannabis prices due to their unavailability, and instead use proxies such as whether or not a state has decriminalised the use of cannabis. There is no consensus among Australian studies either. Two studies found that cannabis and alcohol were substitutes (Cameron and Williams, 2001; Clements and Daryal, 2005), another found cannabis and alcohol consumption to be unrelated (Zhao and Harris, 2004), and two studies support complementarity (Williams and Mahmoudi, 2004; Harris, Ramful and Zhao, 2006). Although, Harris et al., (2006) found no relationship between cannabis and alcohol use for frequent drinkers and Williams and Mahmoudi (2004) find that complementarity was 5

6 stronger for cannabis users who drink alcohol when using cannabis compared with cannabis users who never use cannabis and alcohol concurrently. Irrespective of whether cannabis and alcohol are consumed concurrently, men s cannabis use was found to be more responsive than women s to changes in the price of alcohol. There can also be substitution between alcohol products of different price (Gruenewald et al., 2006; Meier et al., 2009). Heavier drinkers, in particular, have been found to trade down to cheaper products when alcohol prices rise. Rather than consume less in response to rising prices Gruenwald et al. (2006) found that drinkers switched to lower-cost brands, for example switching from a high-quality wine to a cheaper wine, and switching from wine to spirits, a generally more cheaper way to drink a certain volume of alcohol. Current practice is to use economic modelling techniques to forecast the impact of untried pricing policy reforms on alcohol consumption and harms. A major modelling exercise built a causal, deterministic, epidemiological model to evaluate 18 alcohol pricing policies for the UK government (Meier et al., 2010) and a model to evaluate minimum pricing options for Scotland (ScHARR, 2009). A similar, less sophisticated Australian modelling exercise investigated the health benefits and costs of implementing a range of volumetric alcohol tax models in Australia (Byrnes et al., 2011; Doran et al., 2011). These models depend on price responsiveness estimates using point in time regional differences in alcohol prices and patterns of self-reported alcohol consumption, or changes over time in those comparisons. The credibility of the observed causal link between prices and alcohol consumption depends on the extent to which extraneous factors, related both to prices and alcohol consumption, have been accounted for. This is an all but impossible task, considering regional differences in factors, such as attitudes towards alcohol use and availability of alcohol through, for example, opening hours of licensed premises (Chaloupka et al., 2002). 6

7 An alternative approach followed in this project is the experimental behavioural economics approach. We build on the paradigm developed by Nancy Petry and Warren Bickel to understand polydrug use (Goudie et al. 2007; Petry and Bickel, 1998) to examine the implications of drug and alcohol prices for dependent users, whose behaviour may not be considered rational by some economists (Vuchinich and Heather, 2003). Research participants are given a hypothetical fixed drug budget, presented with a range of drug pricelists and asked how many units of each drug on the price-list they would buy with their drug budget. This approach ensures that the observed association between alcohol prices and illicit drug use is due to alcohol prices alone. The only study of this type relevant to the aims of this project concluded that people would reduce their consumption of alcohol and cocaine in response to an increase in the price of alcohol, consume the same amount of ecstasy, and replace some of the alcohol and cocaine with amphetamines (Sumnall et al., 2004). This paper uses latent transition analysis (LTA) to describe the impact of the two pricing policy reforms on participants consumption of alcohol, ecstasy and cannabis. Latent class analysis (LCA) is a useful, and increasing popular, approach to describe patterns of polydrug use by grouping people into mutually exclusive profiles based on their use of various drugs (eg Smith et al., 2011; Mitchell and Plunkett, 2000; Lynskey et al., 2006; Carlson et al., 2005; Wu et al., 2009; Ramo et al., 2010; Heddon et al., 2010; Whitesell et al., 2006). LCA assumes that the profiles describing the various modelled patterns of behaviour are stable. LTA extends LCA by considering how people move between profiles with time or changes in their circumstances. It also allows for the possibility of change in the profiles themselves. 7

8 Method Survey A three-part online questionnaire was developed and pre-tested. The survey ran between November 2011 and March The first part sought information on the participants demographic characteristics, life-time and recent alcohol and illicit drug use, attitudes towards drinking and illicit drug use and access to illicit drugs. In the second part participants were asked to record their consumption of alcohol and illicit drug use on the previous weekend for the 12 hour period encompassing 6pm Saturday to 6am Sunday. Based on questions from the National Drug Strategy Household Survey (Australia s only source of representative data on illicit drug and alcohol usage) on previous day alcohol use we asked participants to report their consumption by container of beer, cider, wine, spirits, pre-mixed spirits and fortified wine so we could calculate the approximate number of standard drinks consumed (AIHW, 2011). We also asked where the alcohol and illicit drugs were consumed, how much was spent on each form of alcohol and illicit drug and from where the alcohol was purchased. The hypothetical drug and alcohol price scenarios were presented in the final section. The survey read: We re now going to play a sort of game. Imagine that it is Saturday night, some Saturday soon when you re free to enjoy yourself. We will show you a list of alcoholic beverages and other drugs and their prices. We d like you to tell us how much you d buy and consume. Then, we will show you three different price lists and ask you to tell us what you would buy and consume for each one. According to the presented rules of the game, Saturday night could last from 6pm until 6am the following Sunday morning. You decide when your night ends. Unlike many of the 8

9 behavioural economics studies of hypothetical alcohol and illicit drug use (eg Jofre-Bonet & Petry, 2008; Petry & Bickel, 1998; Sumnall et al., 2004), we did not use a fixed budget. Participants were told, (Y)ou can spend as much as you want on alcohol and other drugs, but you need to be mindful of reality. Think about your income and other living expenses. Pretesting of the survey instrument showed that participants took this direction to heart. Post survey completion discussions revealed that most spent within their means. Two recent systematic reviews suggest that a 10 per cent increase in the price of alcohol is associated with a 5 per cent decline in overall consumption suggesting that overall expenditure on alcohol will rise following a price increase (Gallet, 2007; Wagenaar et al., 2009). A fixed drug budget would not allow for this. The remaining rules of the game were We expect you to buy and consume alcohol and other drugs in much the same way as you normally would, but you can only purchase the drugs and alcohol on the lists we provide. You can only consume what you actually buy on the night, but you don t have to consume all of it. If you normally buy more alcohol or other drugs than you use on a Saturday night in one go, do so. Just tell us how much you d consume that night. The quality of alcohol and other drugs do not change when the price changes. You don t have to buy illicit drugs. If you do, you ll be able to buy all the illicit drugs from a dealer that you know. You don t have to buy alcohol. If you do, you ll be able to buy it from a bottle shop and a bar. You don t have to buy alcohol from both sources. What alcohol you buy from the bar you must drink in the bar. What you buy from the bottle shop you cannot drink in the bar. For the 12 hour period between Saturday 6pm and Sunday 6am... Please tell us how much of each beverage and other type of drug you would buy and consume. 9

10 The screen following the rules presented the baseline price list for illicit drugs, the bottle shop and the bar. Participants were allowed to shop in and consume alcohol from each location once, in any order. The minimum price and taxation scenarios were then presented in random order. Under the $1.50 per standard drink minimum price scenario there were only increases in bottle shop prices for the largest packs of beer and cider, cask wine and the cheapest bottle of wine (see Appendix A). For example, the price of one 355 ml bottle of Australian brand beer, purchased in a 24 pack, increased from $1.57 (at baseline) to $2.06 and the price of a 355ml bottle of cider purchased in a 24 pack increased from $1.63 to $2.08. Under the tax reform scenario prices of all alcohol types, except for pre-mixed drinks, increased in the bottle shop and bar (See Appendix 2). At the bottle-shop, prices of premixed drinks fell slightly and there was no change to the bar prices of pre-mixed drinks. If purchased in a 24-pack the price of one 355 ml bottle of Australian brand beer, increased from $1.57 (at baseline) to $2.08. If purchased as a 6-pack the price rose from $2.50 to $3.00 and if purchased singly the price rose from $3.00 to $3.50. The bar price of a 425 ml glass of beer rose from $4.20 to $5.00. Participants The survey was conducted through Pureprofile, an opt-in internet panel representative of the Australian population (in terms of census statistics). This panel is well-respected and used by academics seeking cost effective methods to engage with representative samples of respondents (eg Bateman et al., 2011). Panel members hold an account in which they accumulate the small amounts they receive as compensation for participation in panel 10

11 surveys. We surveyed 2010 people aged 18 to 30 years, who had drunk alcohol in the past year. The survey ran from November 2011 until March According to the findings of the 2010 National Drug Strategy Household Survey most (85 %) of Australians aged 18 to 30 years consumed alcohol in the past year. Of those who consumed alcohol in the past year three-quarters (74%) per cent did not consume cannabis or ecstasy, 16% consumed cannabis but not ecstasy and 10% consumed ecstasy (AIHW, 2011). We quota sampled on the basis of gender and past-year cannabis and ecstasy use, intending to survey equal numbers of men and women (ie 400); who had not used either of the illicit drugs in the past year, who had used cannabis (but not ecstasy in the past year); and who has used ecstasy in the past year. Although Pureprofile has information on panel member demographics, it does not ask for information on alcohol and illicit drug use. Pureprofile invited panel members in the target age group to undertake our survey and the first part of the survey screened for participants who met the age, gender and past year alcohol and drug use criteria. The two quotas for the non cannabis and ecstasy using groups and the women s quota for recent cannabis use were filled. However, the remaining quotas were not met. We closed the survey at the point when fewer than 5 respondents were added to each quota in a week. Statistical analysis LCA operationalises a measurement theory which assumes that a population of interest can be divided into mutually exclusive and exhaustive groups (latent classes) on the basis of an unobserved grouping variable which is itself described by a set of observable behaviours (Collins and Lanza, 2010). In this case the observable behaviours are hypothetical Saturday night consumption of alcohol, cannabis and ecstasy. Individuals in each class share a common pattern of consumption of each of the three drugs. In this case the unobserved 11

12 grouping variable represents hypothetical combined alcohol, cannabis and ecstasy use on a Saturday night. LCA is suitable for binary or ordered category measures of alcohol and drug use. The hypothetical units of alcohol consumed (converted to standard drinks) and drugs consumed are continuous, so we use a version of LCA known as latent profile analysis (LPA) where the identified classes are instead referred to as profiles (this term is used for the rest of the article). LTA is an extension of LCA/LPA which allows for two possibilities consequent on the alcohol pricing policy reforms; the possibility that individuals move between latent profiles, as their drinking and or illicit drug use behaviour responds to the increases in alcohol prices; and the possibility that behaviour is so changed that the latent profiles themselves change (Collins and Lanza, 2010). LTA models can be specified depending on the acceptable level of measurement invariance between the two time points. A model that freely estimates the mean consumption levels for each latent profile between the two time points (known as the noninvariant model) has a different interpretation from a model that constrains the mean consumption estimates for each profile to be equivalent (known as a time invariant model). For example, it is possible that an individual may remain in the same identified profile between the two time points however the estimated alcohol and drug consumption levels within that profile differ across the two time points, whereas in a time invariant model any individual who remains in the same profile at both times points is assumed to have the same estimated alcohol and drug consumption levels. To examine invariance over time, two models were generated (a non-invariant and a time-invariant model) and compared using BIC and ssabic. Comparison of model fit between the two nested models was also tested using the Satorra-Bentler scaled chi-square difference test (Satorra, 2000). The consumption data is highly skewed, bounded by zero and, at least for the illicit drug consumption data, congregated around a few integers that are often close to zero. To 12

13 accommodate this feature of the data we used count data analysis, specifically the Poisson model. The model parameters were estimated in Mplus version 6.2 using a maximum likelihood with robust standard errors and chi-square method of estimation, suitable for estimating parameters that are robust to non-normal observations (Muthen and Muthen, 2010). To avoid problems such as model non-convergence or local maxima, the models were estimated using increasing numbers of random starting values and the models were evaluated to ensure replication of the final log-likelihood value (Jung & Wickrama, 2008). Three sets of parameters are estimated for an LTA. Latent profile membership probabilities are estimated three times (at baseline and for both of the pricing policy reforms), showing the proportions of the participants expected to belong in each of the alcohol and illicit drug using groups. Transition probabilities are estimated for both of the reforms, showing the probabilities of moving between profiles consequent on the reforms. Mean alcohol and illicit drug consumption levels are estimated for baseline and both reforms, these are characterisations of the groups in terms of alcohol and illicit drug consumption. Key to the analysis is determining the optimal number of latent profiles. We selected between models with numbers of latent profiles ranging from two to seven using the following statistical fit criteria and interpretability of the latent profiles. The model with the lowest Bayesian Information Criterion (BIC) (Schwartz, 1978) and the lowest sample-size adjusted Bayesian Information Criterion (ssabic) (Sclove, 1987) is traditionally deemed to provide the best fit (Burnham & Anderson, 2002). Classification accuracy provided by the number of profiles in each model is evaluated using an entropy measure with scores ranging from 0.0 to 1.0; values close to 1.0 indicate greater classification accuracy (Ramaswamy et al., 1993). Profile selection was also guided by the Lo-Mendell-Rubin adjusted Likelihood Ratio Test (LMR-LRT; Lo, Mendell, & Rubin, 2001). The LMR-LRT evaluates whether a model with k profiles is a significant improvement on a model with k-1 profiles. A non-significant p-value 13

14 associated with the LMR-LRT statistic indicates that the model with one less profile provides a more parsimonious fit to the data. However, the LMR-LRT value relies on multivariate normality of outcomes and may favour models with too many classes when this assumption is violated (Tofighi & Enders, 2007). Nylund and colleagues (2007) recommend using a combination of BIC, ssabic, and LMR-LRT values when determining the best fitting model. In addition to the statistical fit indices, theoretically meaningful profiles as well as a statistically relevant numbers of individuals within each profile guided model selection by examining the estimated consumption rates and proportion of the total sample within each profile. As a rule it was decided that each profile must consist of greater than 1% of the total population. Measures Hypothetical Saturday night alcohol consumption was measured by the number of standard drinks, which contains 10 grams of alcohol. We differentiated between alcohol purchased from and consumed in the bar and alcohol purchased from the bottle shop. Cannabis consumption was measured in grams and ecstasy consumption in tablets. For all drugs respondents could hypothetically consume portions of these measures. The following covariates were used as predictors of substance use behaviour at baseline; the participant s sex, annual personal income (High: $31,200+, Low: < $31,199, Don t know/prefer not to say), education level (Tertiary level, Less than tertiary level), marital status (Never married, Married/Widowed/Divorced/Separated), past year cannabis and ecstasy use (Used, Did not use), proportion of friends/acquaintances that use alcohol, cannabis and ecstasy (All/Most, Half/A few/none/don t know), age, usual alcohol consumption (Alcohol Use Disorders Identification Test (AUDIT) score, Babor et al., 2001) 14

15 and a measure of the cost of alcohol to them at baseline the average cost of a standard drink. The AUDIT is a screening instrument designed to measure three domains of alcohol use: consumption, dependence and related problems. Ranging from 0 to 40 AUDIT scores fall into four categories: abstinence or low-risk drinking (0-7), moderate-risk drinking (8-15), high-risk drinking (16-19) and possible dependence (20-40) (Babor et al., 2001). Results Sample characteristics The survey was conducted through Pureprofile, an opt-in internet panel representative of the Australian population (in terms of census statistics). Panel members hold an account in which they accumulate the small amounts they receive as compensation for participation in panel surveys. The demographic characteristics of the 2,010 participants (aged 18 to 30 years and who had drunk alcohol in the past year) are summarized in Table 1 by quota, in comparison with equivalent Australian representative samples drawn from the NDSHS. Three of the six quotas were filled men and women who had not used ecstasy and cannabis in the past year and women who had used cannabis but not ecstasy in the past year. A little over 300 participants were recruited for the equivalent men s quota and for women who had used ecstasy in the past year, but only 191 men who used ecstasy in the past year were recruited. In all quotas the participants are older on average than the population representative quotas based on the NDSHS data. Consistent with this, the participants are more likely to have a tertiary qualification and have higher personal incomes. The average age of participants varies little between quotas, a finding consistent with the population representative data. Compared with men in the corresponding drug use quota, female participants reported lower 15

16 annual incomes, a finding consistent with the population representative quotas. Also consistent with the population representative data, participants who had used ecstasy recently reported the highest incomes and participants who used cannabis recently, but not ecstasy, reported the lowest incomes. Compared with men in the corresponding drug use quota, female participants were less likely to have a tertiary qualification, a pattern not observed in the population representative data. The participants least likely to have tertiary qualifications reported recent cannabis use but not ecstasy use while the most educated participants reported no recent drug use, regardless of gender. The same pattern was observed for women in the population representative data although, amongst men in the population representative data, the least educated reported recent ecstasy use and the most educated reported recent cannabis use, but no recent ecstasy use. The indicators of the latent profiles at baseline and for the two alcohol pricing reforms and covariates used to describe the baseline profiles are summarized in Table 2 for the entire sample. Less than half the participants were men (44%), their average age was 25 and the majority (61%) had never been married. Three quarters (77%) had obtained a tertiary qualification. The majority (61%) reported personal annual earnings of at least $31,200 (61%) but 14% did not provide this information. The mean AUDIT score was 10.4, a score that indicates moderate-risk drinking (Babor et al., 2001). Unsurprisingly, the great majority of participants (85%) reported that all or most of their friends and acquaintances consumed alcohol. Whereas over half the sample reported past year cannabis use, only 12% reported that all or most of their friends and acquaintances used cannabis. The corresponding percentages for ecstasy use were 25% and 5%. 16

17 Table 1: Demographic characteristics of the sample of past year alcohol drinkers (18 to 30 years of age) and a similarly aged Australian representative sample of past year alcohol drinkers drawn from the NDSHS, by quota. Study sample (n=2010) Quota 1 (n=402) Quota 2 (n=410) Quota 3 (n=303) Quota 4 (n=401) Quota 5 (n=191) Quota 6 (n=303) Age - mean (SD) 26 (3.3) 26 (3.2) 25 (3.4) 25 (3.4) 25 (3.2) 25 (3.1) Income - % High ($31,200+) Low (Nil-$31,199) Don't know Tertiary education - % Tertiary education Less than tertiary education NDSHS Comparison sample (n=3684) Quota 1 (n=1097) Quota 2 (n=1628) Quota 3 (n=286) Quota 4 (n=306) Quota 5 (n=183) Quota 6 (n=184) Age - mean (SE) 24 (0.1) 24 (0.1) 24 (0.3) 23 (0.2) 24 (0.3) 24 (0.3) Income - % (SE) High ($31,200+) 50 (1.8) 35 (1.3) 39 (3.2) 29 (3.0) 56 (4.2) 49 (4.2) Low (Nil-$31,199) 30 (1.7) 48 (1.4) 45 (3.4) 55 (3.3) 38 (4.2) 44 (4.2) Don't know 20 (1.4) 18 (1.2) 16 (2.6) 16 (2.5) 6 (1.9) 7 (2.1) Tertiary education - % (SE) Tertiary education 56 (1.8) 58 (1.4) 58 (3.3) 51 (3.4) 53 (4.2) 56 (4.1) Less than tertiary education 44 (1.8) 42 (1.4) 42 (3.3) 49 (3.4) 47 (4.2) 44 (4.1) Notes: Quota 1 = Men who had not used cannabis or ecstasy in past 12 months. Quota 2 = Women who had not used cannabis or ecstasy in past 12 months. Quota 3 = Men who had used cannabis but not ecstasy in past 12 months. Quota 4 = Women who had used cannabis but not ecstasy in past 12 months. Quota 5 = Men who had used ecstasy in past 12 months. Quota 6 = Women who had used ecstasy in past 12 months. Means and percentages in the NDSHS sample were weighted according to the Australian population. SE = standard errors (adjusted for complex sampling bias). SD = standard deviation. N = sample size. 17

18 Table 2: Descriptive statistics of variables used in latent transition analyses Baseline Minimum pricing Tax reform 1. Indicators of latent profiles Median 25th 75th 90th 95th Median 25th 75th 90th 95th Median 25th 75th 90th 95th PCTL PCTL PCTL PCTL PCTL PCTL PCTL PCTL PCTL PCTL PCTL PCTL Bottle Shop standard drinks Pub standard drinks Cannabis grams Ecstasy tablets Baseline 2. Grouping variable n % Sex Male Female Baseline 3. Predictor variables n % Income High ($31,200 +) Low (Nil-$31,199) Don't know Education Tertiary level Less than tertiary level Marital Status Never married Married/Widowed/Divorced/Separated Past 12 months use of cannabis Yes No Past 12 months use of ecstasy Yes Proportion of friend/ acquaintances that use alcohol Proportion of friend/ acquaintances that use No All/Most Half/A few/none/don't know All/Most

19 cannabis Proportion of friend/ acquaintances that use ecstasy Half/A few/none/don't know All/Most Half/A few/none/don't know Age (mean) 2010 M=25.39 Cost per standard drink at baseline (mean) 2010 M=1.97 AUDIT score (mean) 2010 M=

20 Most of the alcohol hypothetically consumed by participants was bought from the bottle shop and not consumed on licensed premises (bottle shop alcohol). For ease of presentation we omit hypothetical from the remainder of the results section. At baseline, between half and three-quarters of the participants consumed bottle shop alcohol and median consumption was 1.38 standard drinks. Between one-quarter and one-half of the half of the participants did consume alcohol in the bar. Ten per cent of participants consumed at least 16 drinks of bottle shop alcohol and ten per cent consumed at least 9 standard drinks in the bar. The levels of drinking, both in and outside the bar, fell with both alcohol pricing reforms; more so under the taxation reform. Whereas, somewhere between half and three-quarters of the participants drank bottle shop alcohol at baseline, less than half the participants did so under the two reforms. Under minimum pricing, bottle-shop alcohol consumption fell by around 2 standard drinks at the 75 th, 90 th and 95 th percentiles. While we observed similar falls in consumption at the 75th and 90 th percentiles in response to the taxation reform, the reduction at the 95 th percentile (representing the heaviest drinkers) was almost double. Similarly, alcohol consumption in the bar fell by around 1 standard drink at the 75 th and 90 th percentiles under both reforms, whereas the reduction at the 95 th percentile was around 1 drink for minimum pricing and 2 drinks for the taxation reform. Although over half the participants had consumed cannabis in the past year, less than one quarter consumed cannabis at baseline, with 5% of participants consuming at least1 gram and 10% consuming at least ½ gram. There was no perceivable change in these patterns of consumption with the alcohol pricing reforms. Recent ecstasy users were more likely to consume ecstasy at baseline with 5% consuming at least 1 tablet and 10% at least ¼ tablet. 20

21 Although fewer than 10 % of participants used ecstasy under the pricing reforms 5% continued to consume at least 1 tablet. Latent Transition Modelling The fit statistics for each of the LPA models at baseline, minimum pricing, and tax reform conditions are provided in Table 3 for 2 to 7 profiles. For all conditions, the BIC and ssabic values fell with successive increases in the number of profiles indicating improvement in the fit of the models. For the baseline and minimum pricing conditions, a model with six profiles was selected as the best model since the seven profile model had a non-significant LMR-LRT p-value, indicating that the addition of the seventh profile does not provide a significantly better fit. Inspection of the entropy values suggested that the six profile models possessed excellent classification accuracy and each profile contained more than 1% of the total sample. The choice of the best fitting model for the tax reform condition was not so apparent. Again, the BIC and ssabic values fell with the inclusion of successive profiles. The LMR-LRT p- value indicated that a seven class model provided better fit than the six class model. Attempts were made to fit an eight class model; however this model failed to converge despite increasing the number of random starts. The seventh profile was qualitatively different from the remaining profiles and of interest to the current study, representing individuals who only consumed cannabis and ecstasy. Therefore, the seven profile model was selected as the best fitting model to represent tax reform consumption. 21

22 Table 3: Model fit statistics for separate Latent Profile analyses for each condition fitting models with two to seven profiles. Condition/Model Log-likelihood k BIC ssabic Entropy LMR-LRT Baseline 2 profiles profiles profiles profiles profiles profiles Minimum pricing 2 profiles profiles profiles profiles profiles profiles Tax reform 2 profiles profiles profiles profiles profiles profiles Note: Bold indicates the selected model. k = number of free parameters. BIC = Bayesian Information Criterion, ssabic = sample size adjusted Bayesian Information Criterion, LMR-LRT = p-value for the Lo-Mendell-Rubin Log-likelihood Ratio test. The fit statistics for the six-six profile LTA model comparing baseline and minimum pricing and the six-seven profile LTA model comparing baseline with tax reform are reported in Table 4. As can be seen, for the baseline and minimum pricing models the BIC and ssabic values indicated the time-invariant model as the best fitting model. The time invariant model assumes that the levels of consumption of alcohol, cannabis, and ecstasy within each profile are identical at baseline and under minimum pricing conditions. Any observed reduction in total sample consumption between baseline and minimum pricing can be explained by participants transitioning from higher consumption profiles to lower consumption profiles. 22

23 The Satorra-Bentler scaled chi-square difference test comparing the invariant and noninvariant models was also non-significant (S-Bχ 2 =20.26, df=24, p=0.68), supporting the noninvariant model s superior fit. For the baseline and tax reform models the BIC value indicated the time-invariant model as the best fitting model. However, the ssabic value was slightly larger for the time-invariant model. Similarly, the Satorra-Bentler scaled chi-square difference test just reached significance (S-Bχ 2 =39.11, df=24, p=0.03). Despite this, given the substantial reduction in model complexity (93 free parameters compared to 69 free parameters) at a small increase in the ssabic value (ΔssaBIC = 4), it was decided to select the time-invariant model as the best fitting LTA model for the baseline and tax reform conditions. Table 4: Model fit statistics for latent transition analyses comparing baseline condition against experimental conditions. Log-likelihood k BIC ssabic Entropy Baseline - Minimum pricing Six-Six profile Non-invariant Six-Six profile Time-invariant Baseline - Tax reform Six-Seven profile Non-invariant* Six-Seven profile Time-invariant Notes: k = number of free parameters, BIC = Bayesian Information Criterion, ssabic = sample size Each profile is described according to the four indicators. As can be seen in Figures 1 and 2, Profile 1 represents respondents who were sober (7% drank a median of 1 standard drink, 7% used the illicit drugs). Profile 2 represents respondents who drank safely (bottle shop) 23

24 and got slightly high (100% drank a median of 6 standard drinks, 13% used the illicit drugs cannabis more so than ecstasy). Profile 3 represents respondents who drank safely (bar) and got moderately high (100% drank a median of 5 standard drinks, 24% used the illicit drugs ecstasy more so than cannabis). Profile 4 represents respondents who got drunk (bottle shop and bar) and high (100% drank a median of 13 standard drinks, 31% used the illicit drugs in roughly equal proportions). Profile 5 represents respondents who got very drunk (bottle shop) and high (100% drank a median of 17 standard drinks, 30% used the illicit drugs cannabis more so than ecstasy). Profile 6 represents respondents who got extremely drunk (bottle shop and bar) and extremely high (100% drank a median of 30 standard drinks, 60% used illicit drugs). The tax reform condition also included a seventh profile that represented respondents who were all high (3% decided to drink with a median of 2 standard drinks, 100% decided to use illicit drugs). The proportion of the total sample in each profile is also presented in the figures, with the largest proportion of the sample classified as sober (Profile 1) and the smallest proportion of the sample classified as all high (Profile 7). 24

25 Mean No. of Standard Drinks Mean No. of Grams/Tablets Shop Consumption Pub Consumption Cannabis Consumption Ecstasy Consumption Figure 1: Alcohol and Illicit substance consumption estimates for each profile in the latent transition analyses (Baseline and minimum pricing profiles estimated as time invariant. Percentages under the profile labels represent total proportion of sample within the profile at baseline/minimum pricing). Mean No. of Standard Drinks Mean No. of Grams/Tablets Shop consumption Pub consumption Cannabis consumption Ecstasy consumption Figure 2: Alcohol and Illicit substance consumption estimates for each profile in the latent transition analyses (Baseline and tax reform estimated as time invariant for the first six profiles. The seventh profile only applies to the tax reform condition. Percentages under the profile labels represent total proportion of sample within the profile at baseline/tax reform). 25

26 In general, the more alcohol (in total) that was consumed by the profile members, the more likely were its members to use cannabis and/or ecstasy. Furthermore, respondents were more apt to use ecstasy (over cannabis) the higher the share of alcohol consumed in the bar. Therefore, any description of the identified profiles needs to take into account the patterns of consumption of both sources of alcohol and both illicit drugs on a hypothetical Saturday night. The estimated latent transition probabilities from baseline to both the alcohol pricing reforms are presented in Table 5; the top half refers to minimum pricing and the bottom half to tax reform. The unshaded cells show the proportions of profile members estimated to remain within that profile. The cells shaded in black represent transitions to lower alcohol consuming profiles, the cells shaded in light grey represent transitions to higher alcohol consuming profiles and the cells shaded in dark grey represent transitions to profiles characterised by similar levels of alcohol consumption. Unsurprisingly, the consumption behaviour of individuals in the sober profile was least influenced by the pricing reforms, with over 80% of this profile s members at baseline remaining in the profile at minimum pricing and tax reform conditions. At least half of the members of each of the other profiles remain within their baseline profile under the pricing reforms. In response to the tax reform, it is generally the case that the higher is the level of alcohol consumption in a profile at baseline; the less likely are its members to remain within the profile. In contrast, the tendency to remain within a baseline profile in response to minimum pricing did not vary consistently with the level of the profile s baseline alcohol consumption. 26

27 Table 5: Latent transition probabilities for the baseline minimum pricing and baseline tax reform latent transition analyses Minimum pricing Baseline Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Profile 6 Moved to lower alcohol consuming profile Profile Profile Profile Profile Profile Profile Tax reform Baseline Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Profile 6 Profile 7 Moved to lower alcohol consuming profile Profile Profile Profile Profile Profile Profile The most common destination for respondents who left their baseline profile in response to the pricing reforms was the sober profile. Their response was to more or less, forego consumption of alcohol, cannabis and ecstasy. Participants who drank safely (bottle shop) and got slightly high (Profile 2) were the most likely to make this transition; 32% in response to minimum pricing and 30% in response to the tax reform. Members of the extremely drunk (bottle shop and bar) and extremely high profile (Profile 6) were least likely to make this transition; 18% in response to minimum pricing and 15% in response to the taxation reform. In all baseline profiles, the members were less likely to forego consumption of all three substances under the tax reform than under minimum pricing. In response to minimum pricing, around 30% of the members of the drank safely profiles (Profiles 2 and 3) and got drunk profile (Profiles 4) are estimated to have moved to a profile 27

28 representing lower alcohol consumption, compared with around 45% of the members of Profiles 5 and 6 (the very drunk and extremely drunk profiles). The estimated transition probabilities follow a similar pattern for the taxation reform, although over 50% of the extremely drunk profile (Profile 6) moves to lower alcohol consumption profiles under taxation reform. The people who moved into the all high profile (Profile 7) under the taxation reform replaced their alcohol consumption with ecstasy and/or cannabis use (less than 5% of the drank safely and got drunk, 7% of Profile 5 and 6% of Profile 6). Another sub-group of people responded to both pricing reforms by reducing their consumption of alcohol and cannabis, while increasing their consumption of ecstasy. Under minimum pricing 8% of the very drunk and high profile (Profile 5) moved to the drank safely and got moderately high and got drunk and high profiles (Profiles 4 and 3). These people also replaced some of the alcohol purchased in the bottle shop with alcohol consumed in the bar. In most of the instances where people moved to lower alcohol consumption profiles, common to both alcohol pricing reforms, people also reduced their cannabis and ecstasy use. Under minimum pricing 14% of (Profile 3) moved to Profiles 2 and 4; 28% of Profile 6 moved to Profiles 2, 3, 4, and 5; and 5% of Profile 5 moved to Profile 2. Two sub-groups of people responded to the pricing reforms by substituting similar amounts of alcohol bought from one location for alcohol purchased at the other location - transitions between the drank safely profiles (Profiles 2 and 3). Participants who replaced bar purchased alcohol with alcohol purchased at the bottle shop also reduced their illicit drug use, ecstasy more-so than cannabis (8-10% moved from Profile 3 to Profile 2) while participants 28