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Shifting Peaks and Cumulative Consequences: Disqualifying Convictions in High-security Jobs

OBJECTIVES: Disqualifying conviction lists (DCLs) bar applicants with certain convictions within specified timeframes from employment. Using proposed federal legislative changes in the aviation sector as a case study, we examine whether convictions under the existing policy are associated with subse...

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Detalles Bibliográficos
Autores principales: Denver, Megan, Behlendorf, Brandon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8841643/
https://www.ncbi.nlm.nih.gov/pubmed/35177870
http://dx.doi.org/10.1177/00224278211043926
Descripción
Sumario:OBJECTIVES: Disqualifying conviction lists (DCLs) bar applicants with certain convictions within specified timeframes from employment. Using proposed federal legislative changes in the aviation sector as a case study, we examine whether convictions under the existing policy are associated with subsequent arrest. Then we consider the implications of proposed expansions—arrests instead of convictions and a longer look-back window—on employment restrictions. METHODS: Since DCLs exclude ineligible applicants with conviction records, we use a large, single-state sample of diverse criminal histories. We compare subsequent arrest rates across offense types, consider variations in hazard patterns, and project exclusion estimates based on current and anticipated policy reforms. RESULTS: Only half of the disqualifying offenses have consistently higher recidivism rates than non-disqualifying offense types. Over 20 percent of the sample would be barred from employment, policy extensions double this estimate, and exclusions are age-graded, shifting a peak conviction age of 20 years old to a peak “consequence age” of 28. CONCLUSIONS: Including a narrower set of offenses would reduce those automatically disqualified in our study context by nearly 20 percent, or 39,000 individuals. Instead of expanding the DCL scope, successful criteria should be both effective in prediction and narrow in application.