As the United States grapples with reforms to its criminal justice system, the issue of societal reentry for individuals with criminal records continues to remain at the forefront. Advocates and policymakers have embraced fair-chance hiring policies (FCHPs) as a tool to help individuals with criminal backgrounds attain employment and thereby reduce recidivism. However, despite their adoption by twenty-one states and over one hundred localities, and support from the federal government, FCHPs have not been widely studied. The evidence is limited regarding their impact on the number of applicants with criminal records applying for and being hired in jurisdictions with such policies. There has been even less study of how FCHPs work in implementation – specifically regarding practices that effectively and reliably place qualified candidates with criminal backgrounds in jobs while accurately identifying individuals who are unlikely to remain or have success in those positions.
Our project focuses on the outcomes of FCHPs for the San Francisco Department of Human Resources (DHR). It evaluates how DHR decides whether job finalists whose criminal backgrounds may conflict with the job they are seeking have been sufficiently rehabilitated and will achieve success – with success defined by using the proxy of job tenure and, if the employee is terminated, reason for employment termination. We use multiple regression analysis to compare job success for all candidates with conviction histories against those without. Finally, we suggest data collection and management practices that will enhance DHR's process and assist other entities interested in implementing FCHPs.