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dc.contributor.authorBrown, S.
dc.contributor.authorHarris, Mark
dc.contributor.authorSrivastava, P.
dc.contributor.authorZhang, X.
dc.date.accessioned2017-03-24T11:53:17Z
dc.date.available2017-03-24T11:53:17Z
dc.date.created2017-03-23T06:59:56Z
dc.date.issued2016
dc.identifier.citationBrown, S. and Harris, M.N. and Srivastava, P. and Zhang, X. 2016. Modelling illegal drug participation. Journal of the Royal Statistical Society: Series A (Statistics in Society). In Press.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/51447
dc.identifier.doi10.1111/rssa.12252
dc.description.abstract

We contribute to the small, but important, literature exploring the incidence and implications of misreporting in survey data. Specifically, when modelling 'social bads', such as illegal drug consumption, researchers are often faced with exceptionally low reported participation rates. We propose a modelling framework where firstly an individual decides whether to participate or not and, secondly, for participants there is a subsequent decision to misreport or not. We explore misreporting in the context of the consumption of a system of drugs and specify a multivariate inflated probit model. Compared with observed participation rates of 12.2%, 3.2% and 1.3% (for use of marijuana, speed and cocaine respectively) the true participation rates are estimated to be almost double for marijuana (23%), and more than double for speed (8%) and cocaine (5%). The estimated chances that a user would misreport their participation is a staggering 65% for a hard drug like cocaine, and still about 31% and 17%, for the softer drugs of marijuana and speed.

dc.publisherWiley-Blackwell Publishing
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleModelling illegal drug participation
dc.typeJournal Article
dcterms.source.volume-
dcterms.source.issn0964-1998
dcterms.source.titleJournal of the Royal Statistical Society: Series A
curtin.departmentDepartment of Economics & Property
curtin.accessStatusOpen access


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