County Guide for Reducing Jail Populations and Costs

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National Association of Counties on 9/7/2022

County officials are implementing data-driven and evidence-based policies, practices and programs to decrease jail populations, reduce associated costs and meet the social and safety needs of communities. Annually, county jails process 8 million admissions and spend $29 billion on correctional facilities.[1] The Pew Charitable Trusts reported in 2021 that county corrections costs increased 521 percent from 1977 to 2017.

When determining local jail population drivers, counties may choose to look at neighboring counties’ data or others within their state and/or nationally as comparisons. The Jail Data Initiative at New York University, in partnership with The Pew Charitable Trusts, is gathering data on jail populations around the country. Using online data rosters from roughly a third of the jails in the United States, the project analyzes daily populations, lengths of stay, charge and demographic profiles of those incarcerated, admissions, release statistics and more. Visit their website to explore and compare local data metrics.

Through collaborative efforts such as local public safety planning boards or criminal justice coordinating councils, counties are looking at data from various departments and entities to identify factors that drive jail population growth and exploring solutions to improve outcomes.

Common drivers of jail populations include:[2]

  • Bookings and/or arrests, especially for low-level charges such as misdemeanors
  • Pretrial length of stay
  • Technical violations of community supervision, and
  • Recidivism.

Counties are also increasingly interested in identifying and reducing or eliminating racial disparities in their criminal legal systems. Counties can use data to better understand their jail populations and identify disparities that indicate further review. Counties can disaggregate data for each of the four jail population drivers by self-identified race, ethnicity, age, sex, sexual orientation and gender and then compare to rates in the local population. If the data demonstrate an overrepresentation of a specific group(s), county leaders can work to identify possible disproportionate sources and determine policy and program responses specific to the population(s).


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