Misreporting and econometric modelling of zeros in survey data on social bads: An application to cannabis consumption. (4th August 2017)

• Record Type: Journal Article
• Authors: Greene, William; Harris, Mark N.; Srivastava, Preety; Zhao, Xueyan
• Is Part Of: Health economics. Volume 27:Number 2(2018)
• Page Start: 372
• Page End: 389
• Publication Date: 2017-08-04
• Subjects: cannabis consumption -- discrete data -- misclassification -- ordered outcomes -- zero‐inflated responses; Medical economics -- Periodicals; Economics, Medical -- Periodicals; Health Care Costs -- Periodicals; Health Policy -- economics -- Periodicals; Health Services -- economics -- Periodicals; 362.1
• DOI: 10.1002/hec.3553
• Languages: English
• ISSNs: 1057-9230

Abstract

Abstract: 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.