What happened to drug detections and drug-offences during the early months of the COVID-19 pandemic: A comparison of recorded offence rates and dynamic forecasts (ARIMA) in Queensland, Australia

Cameron Thomas Langfield, Jason Leslie Payne and Toni Makkai
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Cameron Thomas Langfield: Australian National University
Jason Leslie Payne: Australian National University

No q5zk9, SocArXiv from Center for Open Science

Abstract: Public commentary has offered mixed opinion on the likely impact of COVID-19 restrictions on drug-related offending. On the one hand, it is argued that drug users—and the drug markets in which they interact—may have become the incidental targets of law enforcement as police seek to enforce social distancing regulations by focusing their efforts on street-level pedestrian activity or open-air gatherings. On the other, interstate border closures and restrictions on person and freight traffic are thought to have interrupted illicit drug supply chains, temporarily reducing or displacing market activity at the street level and thus reducing police detections of drug users. In this study, we extend current analyses of crime during the COVID-19 pandemic to explore how the rate of police detection for drug possession and other drug-related offences has changed. Using Queensland crime data, we use Auto-Regressive and Moving Average (ARIMA) time series modelling techniques to explore historical trends and their dynamic forecasts. We then compare actual offence rates for March through June to identify any statistically significant changes. Importantly, we do not expect the impact of COVID-19 regulations to be universal, mostly because of the significant heterogeneity in local drug market dynamics that has elsewhere been documented. Our analysis has significant import for criminal justice practitioners in further understanding drug market dynamics and drug-related offending during COVID-19 restrictions.

Date: 2020-10-29
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:q5zk9

DOI: 10.31219/osf.io/q5zk9

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