Modelling Illegal Drug Participation
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 mis-report or not. We explore mis-reporting in the context of the consumption of a system of drugs and specify a multivariate probit model. Compared to observed participation rates of 12.2, 3.2 and 1.3% (marijuana, speed and cocaine, respectively) 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 mis-report their participation is a staggering 65% for a hard drug like cocaine, and still some 31% and 17%, for the softer drugs of marijuana and speed.