Research paperPreferences for policy options for cannabis in an Australian general population: A discrete choice experiment
Introduction
Although countries vary to some extent on cannabis policy (Hall and Pacula, 2003, Room et al., 2008) the primary international policy response to preventing cannabis use is prohibition (Macleod & Hickman, 2010). Some notable exceptions to prohibition are the recent legalisation of cannabis in Colorado and Washington in the United States and its’ decriminalisation in Portugal (Hughes & Stevens, 2010). In spite of this, an estimated 128.9 million to 190.7 million people across the globe used cannabis in 2010 (UNODC, 2010). In Australia cannabis remains the most commonly used illicit drug, with one in three persons (35.3%) aged 14 and older having used it in their lifetime, and one in ten having used in the last year (Australian Institute of Health & Welfare, 2011). These numbers would suggest that prohibition may not function well as a deterrent for preventing use. Prohibition can further place excessive burden on the police and courts and this is frequently used as an argument to legalise or at least decriminalise cannabis (Egan and Miron, 2007, Ludgwig and Cook, 2001, Miron, 2005, Nadelmann, 1989, Wodak et al., 2002). For those who receive a criminal record for a cannabis offence, research suggests that there may be negative impacts on subsequent employability (Pager, 2003); stigmatisation from family and friends (Ali et al., 1999, Hathaway et al., 2011, Lenton and Ovenden, 1996); and they may be less likely to enter and complete treatment for dependence (Ahern, Stuber, & Galea, 2007). Indeed, some jurisdictions in Australia have introduced programs that divert cannabis use offenders away from the criminal justice system and into education or treatment options (Hughes and Stevens, 2007, Lenton and Heale, 2000, van Laar and van Ooyen-Houben, 2009, Weatherburn and Jones, 2001). The afore mentioned Portuguese decriminalisation seeks to provide a more humane framework for those detected with drugs through improved prevention, harm reduction and treatment activities in order to minimise social exclusion and marginalisation of drug users (Hughes & Stevens, 2010).
There is also an established research literature on the harms associated with cannabis use (Degenhardt et al., 2008, Hall and Pacula, 2003, Room et al., 2008). For example, there appears to be an association between cannabis use and traffic accidents (Mann, Stoduto, MacDonald, & Brands, 2008), and evidence that cannabis might precipitate schizophrenia in vulnerable individuals (Cohen et al., 2008, Ferguson et al., 2008, Fergusson et al., 2005, Moore et al., 2007). Additionally, among those who have ever used cannabis the risk of dependence is between 7% and 9% (Anthony et al., 1994, Perkonigg et al., 2008).
As a result of these different consequences of cannabis use and associated cannabis policies there have been long-standing debates over the legal status of cannabis (Nadelmann, 1989, Room et al., 2008, Wodak et al., 2002). Determining the optimal cannabis law for any given society appears to be a challenging and fraught exercise. The simple application of research evidence is not adequate given the potential for greater health harms associated with relaxation of laws and the potential for criminal justice harms associated with prohibition. What is needed, but currently absent, is evidence that takes into account trade-offs between the multiple wide ranging social harms and the legal status of cannabis and uses this to inform socially acceptable policy solutions.
Considerations of public opinion, in this context, become central. Research suggests a connection between public opinion and public policy decisions particularly when the issue has salience (Burstein, 2003, Monroe, 1998). Many polls have summarised, in a dichotomous fashion, the extent of support or opposition for different cannabis policies such as legalisation or decriminalisation. For example, USA Gallup polls have shown increases in support for cannabis legalisation, from 12% in 1969 to 50% in 2011 (Newport, 2011). However, there are concerns that public opinion polling is simplistic and highly driven by the type of question posed (Matthew-Simmons, Love, & Ritter, 2008). Surveys do not always distinguish between whether they are referring to consumption or supply of cannabis, and whether the policy of interest is legalisation or decriminalisation (Newcombe, 2004). Further, as many individuals are unaware of the current legal status of cannabis in their jurisdiction (Chanteloup et al., 2005, MacCoun et al., 2009, Pacula et al., 2004) it is difficult to interpret the results of these surveys.
In this context, and given the potential influence of public opinion in determining public policy, it is necessary to have a more nuanced understanding of societal preferences for cannabis policies. This ought to link the legal status of cannabis preferences with associated attributes such as prevalence of use, health consequences and criminal justice sector consequences.
We argue the best research tool available for examining trade-offs between different cannabis policies and their consequences is the Discrete Choice Experiment (DCE) approach. It is a stated preference technique that uses an attribute-based survey method to measure the value of multiple impacts on a single scale. In the current context, DCE draws upon economic theory to allow the identification and quantification of key characteristics which are likely to influence society's preferences for the legal status of cannabis.
The DCE approach uses the choices respondents make between hypothetical (yet realistic) options to estimate utility. In stating a choice the respondent is revealing the alternative that yields highest utility to them and the DCE approach, thus, relies on an individual's knowledge or perceptions of her/his own preferences and on her/his ability to make trade-offs between alternatives in terms of having more of one attribute level and less of another. Underpinning this behaviour is economic consumer theory which assumes individuals derive wellbeing (utility) not from a good (or in this case a policy) itself but rather from the attributes (characteristics) of that good (policy) (Amaya-Amaya et al., 2008, Lancaster, 1966, Louviere et al., 2000).
The aim of this study was to identify and quantify the factors influencing societal preferences for alternative cannabis policies using a DCE survey to vary key characteristics of cannabis's legal status and wider social consequences (cannabis-related healthcare and criminal justice expenditures, rates of cannabis use and location of cannabis purchase). A further aim was to demonstrate the usefulness of the findings for policy analysis. By analysing preference variation by population characteristics policy makers gain a better understanding of social preferences and, in turn can use this information to influence cannabis reform. To the best of our knowledge this is the first application of DCE to examine cannabis policy options in broad social policy terms. This study examined Australian social preferences for cannabis policy with respect to its legal status and four associated impacts: prevalence of cannabis use; cannabis-related health expenditure; criminal justice system expenditure; and, the location of purchase of cannabis.
Section snippets
Methods
The design of the DCE survey followed good practice, for example (Amaya-Amaya et al., 2008, De Bekker-Grob et al., 2012, Gerard et al., 2008, Hensher et al., 2005, Lancsar and Louviere, 2008). The key methodological steps involved are: (i) attribute and level selection; (ii) experimental design; (iii) sample and modality of collection; (iv) data analysis and (v) interpretation.
Results
A total of 1670 persons logged on to undertake the survey during December 2009. After the first 100 respondents had completed the survey the data were examined to confirm trading between attributes was occurring, and that surveys were being completed. Of the 1670 persons who logged on, 350 (21%) belonged to stratum already full and were not permitted to complete the survey beyond the cannabis use, age and gender questions (screening questions); 222 (13.3%) did not complete the survey, they
Discussion
This study has demonstrated how DCEs can be used to assess societal preferences for cannabis policies in Australia and, potentially more widely. It demonstrated that it is feasible to use DCE to elicit preferences from the general public on options for cannabis policy taking into consideration some of the key consequences. The data collected were of good quality and produced theoretically valid utility models.
Respondents were able to indicate their preferences between policy options where the
Conclusion
The multiplicity of views on illicit drugs policy means decision making in this area is fraught with challenges and as such participatory systems become increasing important in democracies. By analysing preference variation by population characteristics policy makers gain a better understanding of social preferences and, in turn can use this information to influence cannabis reform. This paper is the first known use of DCE in the illicit drugs policy area. The results confirm that it is a
Conflict of interest statement
The authors state they have no conflict of interest with respect to this paper. All authors have seen and approved the manuscript being submitted.
Acknowledgments
This work was supported by grant from the Australian Research Council [DP0880066]. This work forms part of the Drug Policy Modelling Program, a program funded by the Colonial Foundation Trust and auspiced by the National Drug and Alcohol Research Centre, which receives core funding from the Commonwealth Government of Australia. Professor Ritter is funded through an NHMRC Senior Research Fellowship (APP1021988). Dr Gerard is funded by a National Institute for Health Research Career Development
References (79)
- et al.
Stigma, discrimination and the health of illicit drug users
Drug and Alcohol Dependence
(2007) - et al.
Conjoint Analysis Applications in Health – A Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force
Value in Health
(2011) - et al.
Assessing the influence of design dimensions on stated choice experiments
Transportation Research Part B
(2005) - et al.
Cohort trends in the age of initiation of drug use in Australia
Australian and New Zealand Journal of Public Health
(2000) - et al.
What influences participation in genetic carrier testing? Results from a discrete choice experiment
Journal of Health Economics
(2006) Modelling heterogeniety in patients’ preferences for the attributes of a general practitioner appointment
Journal of Health Economics
(2008)- et al.
Cannabis use and risk of psychotic or affective mental health outcomes: A systematic review
The Lancet
(2007) - et al.
The impact of offering two versus three alternatives in choice modelling experments
Ecological Economics
(2009) - et al.
The social impacts of the cannabis expiation notice scheme in South Australia: Summary report (No. 34)
(1999) - et al.
Discrete choice experiments in a nutshell
Using discrete choice experiments to value health and health care
(2008)