Over the last 20 years, numerous states and the federal government enacted mandatory minimum reforms, especially for drug offenses. Yet little is known about how effective these reforms have been at the state-level in lowering drug sentences. Using quasi-experimental methods and administrative data, this study evaluates the impact of state-level mandatory minimum reforms on drug sentences and their concomitant racial-ethnic disparities. We find that state-level mandatory minimum reforms do not lower drug sentences in general or change racial-ethnic disparities statistically significantly. These findings suggest that the profound racial-ethnic bias sparked by state-level mandatory minimums are not fully ameliorated by subsequent state-level reforms.
Funding source: Brown University
Award Identifier / Grant number: Unassigned
We thank the editor and two anonymous reviewers for their valuable comments and suggestions, which greatly improved our paper. We also want thank participants at the 2020 Eastern Economic Association Conference in Boston, MA for comments and suggestions on the early version of this paper.
Research funding: We are grateful to the Population Studies and Training Center at Brown University, which receives funding from the NIH (P2C HD041020), for general support.
|State||Date||Type of Drug Crimes||Coverage||Consistent||Repeal|
|State||Date||Type of Drug Crimes||Coverage||Consistent||Repeal|
|South Carolina||6/2/2010||Drug possession||2000–2015||Yes||Yes|
Column (1) reports the exact implementation date for states that modified or repealed mandatory minimum sentencing laws (MMLs). We were unable to find the day and month of MML laws for Indiana, Michigan, New York, and Pennsylvania, and thus we assume they were implemented on January 1. Column (2) lists the crimes for which MMLs were modified or lifted. Column (3) lists all states that consistently report data to NCRP (see Section 4 for more detail). Column (4) indicates whether the MMLs were fully repealed.
Data sources: Sentencing Project, “The State of Sentencing: Developments in Policy and Practice”, https://www.sentencingproject.org/issues/sentencing-policy/, various years; Subramanian and Delaney (2013); Austin (2010); https://famm.org/; and authors’ own research on state statutes and legislative histories.
|Sentence length (in days)||58.807||80.254||56.679||77.358||63.703||84.108||55.768||88.377|
|Less than HS Degree||0.238||0.426||0.187||0.390||0.313||0.464||0.184||0.387|
|Age at prison admission||34.782||9.663||35.684||9.334||34.435||9.996||34.006||9.282|
|Prior felony incarceration||0.231||0.422||0.210||0.407||0.281||0.449||0.127||0.333|
|New court commitment||0.523||0.499||0.543||0.498||0.515||0.500||0.535||0.499|
This table contains summary statistics by race and ethnicity for all variables used in the analysis. We restrict the sample to drug offenses. New court commitment, probation and parole revocation refer to the reason for prison admittance. We report percent of the data with missing or other race, ethnicity, and educational attainment. The sample is restricted to the thirteen states that consistently reported data, as identified by Neal and Rick (2016): California, Colorado, Georgia, Illinois, Michigan, North Dakota, New Jersey, New York, Ohio, South Carolina, Utah, Washington, and Wisconsin. Data are from the National Corrections Reporting Program (1997–2016).
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The online version of this article offers supplementary material (https://doi.org/10.1515/bejeap-2020-0215).
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