ORCID

Abstract

ObjectivesTo provide a detailed understanding of how the prevalence and frequency of offending vary with age in the Cambridge Study in Delinquent Development (CSDD) and to quantify the influence of early childhood risk factors such as high troublesomeness on this variation.MethodsWe develop a statistical model for the prevalence and frequency of offending based on the hurdle model and curves called splines that allow smooth variation with age. We use the Bayesian framework to quantify estimation uncertainty. We also test a model that assumes that frequency is constant across all ages.ResultsFor 346 males from the CSDD for whom the number of offenses at all ages from 10 to 61 are recorded, we found peaks in the prevalence of offending around ages 16 to 18. Whilst there were strong differences in prevalence between males of high troublesomeness and those of lower troublesomeness up to age 45, the level of troublesomeness had a weaker effect on the frequency of offenses, and this lasted only up to age 20. The risk factors of low nonverbal IQ, poor parental supervision and low family income affect how prevalence varies with age in a similar way, but their influence on the variation of frequency with age is considerably weaker. We also provide examples of quantifying the uncertainty associated with estimates of interesting quantities such as variations in offending prevalence across levels of troublesomeness.ConclusionsOur methodology provides a quantified understanding of the effects of risk factors on age-crime curves. Our visualizations allow these to be easily presented and interpreted.

Publication Date

2022-03-29

Publication Title

Journal of Quantitative Criminology

Volume

39

ISSN

0748-4518

Acceptance Date

2022-03-22

Deposit Date

2022-04-29

Embargo Period

2023-03-29

Funding

For funding the CSDD, we are grateful to the Home Office, the Department of Health, the Department of Education, the Rayne Foundation, the Barrow Cadbury Trust, and the Smith-Richardson Foundation. For carrying out criminal record searches, we are grateful to Gwen Gundry in the 1960s and 1970s, Lynda Morley in the 1980s, Sandra Lambert in the 1990s, Debbie Wilson in the 2000s, Owen Thomas in 2011–2012 and Lisa Robinson in 2017. We also thank Tim Cole, UCL GOS Institute of Child Health, for introducing us to his work on SITAR models, and Mario Cortina-Borja, Hugh Davies, John Eales, and David Teague for interesting conversations about statistical inference and criminal behavior.

Keywords

Age-crime curves, Hurdle model, Longitudinal study, Troublesomeness, Visualization of prevalence and frequency

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