Factors that predict the referral of adult Modern Day Slavery cases to the UK's National Referral Mechanism

Abstract

There is a high drop-out or attrition rate of Modern Day Slavery (MDS) cases in the Criminal Justice System although there has been a paucity of academic research examining the factors that could be related to this attrition. Similar work has been carried out examining attrition in rape cases (Feist A, Ashe J, Lawrence J et al. (2007) Investigating and detecting recorded offences of rape. Home Office Online Research Report, 18/07. London: Home Office). The aim of this study was to examine whether factors of MDS cases were associated with and could predict referral to the National Referral Mechanism (NRM) (either by the police or other agencies). Two hundred and sixteen suspected cases of MDS were examined, 29 of which had been referred to the NRM. Content analysis was used to extract variables from the cases. These pertained to aspects of the offence (e.g. types of exploitation, offender strategies), the victim (e.g. gender, ability to speak English), and the offender (e.g. details on any recruiter, transporter and exploiter). Cases were more likely to be referred when the victim was locked, controlled or had their movement restricted, not recruited in the UK, when the trafficking flow was non-domestic, when debt bondage had occurred and when the recruiter was not in the UK when they recruited the victim. Cases were less likely to be referred when sexual exploitation was suspected, there was more than one victim and when the exploitation was thought to be occurring/have occurred in a brothel, massage parlour and or via a website. These findings may indicate that certain victims are more likely to come forward and/or that the cases are more likely to be considered MDS by those investigating the cases. Recommendations for practice include a consideration of the best way to handle suspected cases of sexual exploitation, considering adopting investigative strategies from domestic violence investigations, proactively addressing the gaps in the data and better linking of data. Limitations and future research ideas are discussed.