Jennifer Doleac, Associate Professor of Economics, Texas A&M University.
We evaluate how adopting risk assessment tools (algorithmic predictions of future offending) affects sentencing, recidivism and race/age disparities for felony offenders.
We find that scoring right above the “low-risk" cutoff increases the likelihood of incarceration by 6-7 percentage points and increases the sentence length by approximately 23-34%, depending on the crime of conviction.
Using a difference-in-difference framework, with defendants who were ineligible for risk assessment as a control, we find no evidence that the adoption of risk assessment affected average sentencing for nonviolent offenders: to whatever extent sentences decreased for lower risk defendants this was counterbalanced by an increase for those with higher risk scores -- in particular, for young defendants.
We find little evidence that risk assessment led to a decline in recidivism, and explore several potential reasons why not.
Our results on racial disparities in sentencing are mixed. Statewide, we find no evidence that risk assessment affected racial disparities. However, racial disparities increased after the adoption of risk assessment in the subset of judicial circuits that appear to use risk assessment most.
About the speaker
Jennifer Doleac is an Associate Professor of Economics at Texas A&M University, and Director of the Justice Tech Lab. She is also a Research Fellow at IZA, and a Research Affiliate at the Institute for Research on Poverty, the University of Chicago Crime Lab, and the Wilson Sheehan Lab for Economic Opportunities.
Jennifer Doleac studies crime and discrimination, with particular emphases on prisoner reentry and the effects of technology on public safety. She organizes the Texas Economics of Crime Workshop (TxECW), and hosts Probable Causation, a podcast about law, economics, and crime.
Learn more about Jennifer Doleac's research