Does debt increase risk? A mixed methods approach to studying the potential underlying risk factors in the relationship between debt and crime

ABSTRACT

Previous studies have found evidence for a relationship between debt and crime, and for problems in childhood, education, work, and mental and physical health as underlying risk factors. However, insight into the interplay between these possible risk factors is limited. Therefore, a mixed methods approach was applied by both creating a quantitative Gaussian Graphical Model (GGM) and conducting qualitative analyses on 250 client files including risk assessment data from the Dutch probation service, to gain more specific insight into the interaction between potential risk factors. The results show that debt is strongly related to criminal behavior and problems in many life domains for most probation clients. Debt seems to be a direct risk factor for crime, but debt and crime also appear to be highly interrelated as part of a complex interplay of risk factors. The most frequently rated factors – limited or incomplete education, no job and related lack of income, and mental and physical health problems – are highly interwoven and increase the risk of both debt and crime. The findings stress the importance of paying attention to and using interventions focusing on strongly related crime risk factors, including debt, and their complex interplay, to supervise probation clients effectively.

KEYWORDS:

• Financial problems
• debt
• crime
• probation
• risk factors
• mixed methods

Acknowledgements

The authors would like to thank the Dutch probation service for its permission to use data from their standard risk assessment instrument and client files. They would also like to thank the steering committee of this project for its invaluable feedback during the research process.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, Gercoline van Beek, upon reasonable request.

Declaration of conflicting interests

The authors declare that they have no relevant or material financial interests relating to the research described in this paper.

Ethical approval

The study design, the data collection method, and the data analysis and storage were approved by the ethics committee of the Research Centre for Social Innovation of Utrecht University of Applied Sciences. The authors also declare that they honor the International Standards for Authors of the Committee on Publication Ethics.

Additional information

Notes on contributors

Gercoline van Beek

Gercoline van Beek, Research Group Working with Mandated Clients, Research Centre for Social Innovation, Utrecht University of Applied Sciences, Utrecht, The Netherlands. ORCID: 0000-0002-4181-6845. LinkedIn: https://www.linkedin.com/in/gercoline-van-beek

Vivienne de Vogel

Vivienne de Vogel, Research Group Working with Mandated Clients, Research Centre for Social Innovation, Utrecht University of Applied Sciences, Utrecht, The Netherlands. ORCID: 0000-0001-7671-1675. LinkedIn: https://www.linkedin.com/in/vivienne-de-vogel-a26102a

Roger Leenders

Roger Leenders, Department of Organization Studies, Tilburg School of Social and Behavioral Sciences, Tilburg University, Tilburg, The Netherlands & Jheronimus Academy of Data Science, ‘s-Hertogenbosch, the Netherlands. ORCID: 0000-0002-0556-2550. LinkedIn: https://nl.linkedin.com/in/roger-leenders-974a33b

Dike van de Mheen

Dike van de Mheen, Tranzo Scientific Center for Care and Wellbeing, Tilburg School of Social and Behavioral Sciences, Tilburg University, Tilburg, The Netherlands. ORCID: 0000-0002-7918-1523. LinkedIn: https://www.linkedin.com/in/dike-van-de-mheen-25515614