The Second Chance Gap
56 Pages Posted: 4 Nov 2018 Last revised: 11 Feb 2019
Date Written: September 15, 2018
Over the last decade, dozens of states have enacted “second chance” reforms that increase the eligibility of individuals charged or convicted of crimes to, upon application, shorten or downgrade their past convictions, clean their criminal records, and/or regain the right to vote. While much fanfare has accompanied the increasing availability of “second chances,” less is known about their uptake. This study introduces the concept of the “second chance gap” - the gap between eligibility and delivery of second chance relief - and sizes it in connection with several second chance initiatives and laws. Using administrative data, I estimate that less than 10% of individuals eligible for relief under the Obama Clemency Initiative and California’s Prop 47 and 64 received it under petition-based processes. Using a novel dataset of ~60,000 criminal records based on background checks performed in 2017 and 2018 on persons primarily seeking gig economy work, I estimate that, conservatively, 30-40% of adults with records, or 25-30M individuals, are entitled under state laws to clean their criminal records, partially or fully, but not have done so. These findings suggest that a large number of petition-based second chances have been missed chances, due to administrative factors like low awareness and high cost, high-friction application processes. To close second chance gaps at scale and unlock opportunities for individuals with criminal histories, this Essay argues in favor of automation. Using state legislative data, it finds that under Pennsylvania’s “fully automatic” Clean Slate model, the cost per clearance is around 5 cents as compared to costs per application (including court and defendant time) in the thousands to implement some petition-based models. Automating the administration of second chance relief can help remove the red tape, not steel bars, that stand in the way of second chances.
Keywords: criminal records, cost-benefit analysis, uptake analysis, computational policy
JEL Classification: K14, K4
Suggested Citation: Suggested Citation