Abstract
This article describes a unique researcher and policymaker partnership aimed at assessing the racial and ethnic impact of proposed criminal justice legislation. Using data from several Florida agencies, Florida State University projected trends in population and criminal justice outcomes to assess the potential impact on racial and ethnic disparities of ten bills. This article describes the project phases, provides examples of the bill analyses, and discusses the lessons learned and impact on racial/ethnic disparities. The results of the analyses show that race-neutral reform initiatives often fall short in reducing racial/ethnic disparities. Criminal justice downsizing efforts should explicitly aim to reduce racial and ethnic disparities and incorporate this goal in their design. Specific provisions that exclude individuals with prior records or those convicted of violent offenses could prove detrimental to the goal of reducing disparities. The benefits and challenges of researcher and policymaker partnerships for designing effective and race-sensitive criminal justice policy are discussed.
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Notes
FDLE data were not available during the pre-legislative session meeting when the forecasts were presented to the Committee. We obtained FDLE data during the legislative session and the data were utilized for the development of the racial and ethnic impact statements.
Even though most of the datasets obtained contained information from previous years, there were concerns about the validity and reliability of older registries. This was an issue emphasized by the FDLE. To assuage such concerns, we agreed to base all of our forecasts on the most recent 10 years of data.
ARIMA models allow analysts to model external interventions that exogenously alter the course of the series. Modeling interventions requires making assumptions about potential factors that may impact the series, such as the priorities of different administrations, or specific policies enacted. Our forecasts were based exclusively on past values of the series without the addition of interventions to the ARIMA models.
To assess the amount of potential racial and ethnic bias we evaluated the percentage of excluded cases by sentence assigned. No substantial differences were encountered in the percentages of cases excluded among those sentenced to prison (16.1%), jail (15.4%), and probation (16.5%). The percentage of cases excluded was slightly higher for those individuals that did not receive any of those sentences (19.0%).
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Chouhy, C., Swagar, N., Brancale, J. et al. Forecasting the Racial and Ethnic Impacts of ‘Race-Neutral’ Legislation through Researcher and Policymaker Partnerships. Am J Crim Just 48, 123–150 (2023). https://doi.org/10.1007/s12103-021-09619-8
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DOI: https://doi.org/10.1007/s12103-021-09619-8