Misreporting and econometric modelling of zeros in survey data on social bads: An application to cannabis consumption

William Greene, Mark N. Harris, Preety Srivastava, Xueyan Zhao

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)372-389
Number of pages18
JournalHealth Economics
Issue number2
Publication statusPublished - 2018


  • Cannabis consumption
  • Discrete data
  • Misclassification
  • Ordered outcomes
  • Zero-inflated responses

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