CrimProf Blog

Editor: Kevin Cole
Univ. of San Diego School of Law

Tuesday, February 12, 2019

Scurich & John on The Dark Figure of Sexual Recidivism

Nicholas Scurich and Richard S. John (University of California, Irvine and University of Southern California) have posted The Dark Figure of Sexual Recidivism on SSRN. Here is the abstract:
Empirical studies of sexual offender recidivism have proliferated in recent decades. Virtually all of the studies define recidivism as a new legal charge or conviction for a sexual crime, and these studies tend to find recidivism rates on the order of 5-15% after 5 years and 10-25% after 10+ years. It is uncontroversial that such a definition of recidivism underestimates the true rate of sexual recidivism because most sexual crime is not reported to legal authorities, the so-called “dark figure of crime.” To estimate the magnitude of the dark figure of sexual recidivism, this paper uses a probabilistic simulation approach in conjunction with a.) victim self-report survey data about the rate of reporting sexual crime to legal authorities, b.) offender self-report data about the number of victims per offender, and c.) different assumptions about the chances of being convicted of a new sexual offense once it is reported. Under any configuration of assumptions, the dark figure is substantial, and as a consequence, the disparity between recidivism defined as a new legal charge or conviction for a sex crime and recidivism defined as actually committing a new sexual crime is large. These findings call into question the utility of recidivism studies that rely exclusively on official crime statistics to define sexual recidivism, and highlight the need for additional, long-term studies that use a variety of different measures to assess whether or not sexual recidivism has occurred.

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This paper fails to address the fact that behavior, reporting, detection, and/or prosecution are likely to occur at different rates once someone has had a legal intervention for sexual offending behavior. You can't use apples to predict oranges.

Posted by: Laurie Rose Kepros | Feb 13, 2019 4:39:37 PM

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