Booker Disparity and Data-Driven Sentencing
78 Pages Posted: 6 Mar 2017 Last revised: 22 Sep 2017
Date Written: 2016
Sentencing disparity among similar offenders has increased at a disconcerting rate over the last decade. Some judges issue sentences twice as harsh as peer judges, meaning that a defendant’s sentence depends substantially on which judge is randomly assigned to a case. The old mandatory sentencing guidelines repressed disparity but only by causing unwarranted uniformity. The advisory guidelines swing the pendulum toward the opposite extreme, and this problem promises to grow worse as the lingering effects of the old regime continue to decrease.
This Article proposes a system — data-driven appellate review — to curb sentencing disparity without re-introducing unwarranted uniformity. In a system where sentencing judges possess significant discretion, only meaningful appellate review can restrain the tendency for sentencing practices to deviate substantially between judges. Discretionary sentencing decisions are currently reviewed for abuse of discretion, but presuming that outlier sentences among similar offenders are unreasonable would mitigate the problem of inter-judge disparity. The data the U.S. Sentencing Commission collects on over 70,000 criminal cases annually can be used to define categories of similar offenders. Culling outlier sentences through data-driven appellate review would increase judicial awareness of sentences issued by peer judges, restricting inter-judge disparity without incurring unwarranted uniformity.
Keywords: Sentencing Guidelines, Booker, Sentencing, Criminal Law
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