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dc.contributor.authorGreene, W.
dc.contributor.authorHarris, Mark N.
dc.contributor.authorSrivastava, P.
dc.contributor.authorZhao, X.
dc.identifier.citationGreene, W. and Harris, M.N. and Srivastava, P. and Zhao, X. 2017. Misreporting and econometric modelling of zeros in survey data on social bads: An application to cannabis consumption. Health Economics. 27 (2): pp. 372-389.

When modelling "social bads," such as illegal drug consumption, researchers are often faced with a dependent variable characterised by a large number of zero observations. Building on the recent literature on hurdle and double-hurdle models, we propose a double-inflated modelling framework, where the zero observations are allowed to come from the following: nonparticipants; participant misreporters (who have larger loss functions associated with a truthful response); and infrequent consumers. Due to our empirical application, the model is derived for the case of an ordered discrete-dependent variable. However, it is similarly possible to augment other such zero-inflated models (e.g., zero-inflated count models, and double-hurdle models for continuous variables). The model is then applied to a consumer choice problem of cannabis consumption. We estimate that 17% of the reported zeros in the cannabis survey are from individuals who misreport their participation, 11% from infrequent users, and only 72% from true nonparticipants.

dc.publisherJohn Wiley & Sons Ltd.
dc.titleMisreporting and econometric modelling of zeros in survey data on social bads: An application to cannabis consumption
dc.typeJournal Article
dcterms.source.titleHealth Economics
curtin.departmentSchool of Economics and Finance
curtin.accessStatusOpen access

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