Booker Disparity and Data-Driven Sentencing

75 Pages Posted: 6 Mar 2017 Last revised: 18 Apr 2017

Joshua M. Divine

United States Court of Appeals for the Eleventh Circuit

Date Written: 2016

Abstract

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 substantially depends 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 effect of the old regime continues to decrease.

This Article is the first to propose a system—data-driven appellate review—that curbs sentencing disparity without re-introducing unwarranted uniformity. Congress should establish a rebuttable presumption that outlier sentences among similar offenders are unreasonable. The U.S. Sentencing Commission collects data on over 70,000 criminal cases annually. This data provides the tool for defining categories of similar offenders. Culling outlier sentences through data-driven appellate review would increase judicial awareness of sentences issued by peer judges and would therefore curb the increase in inter-judge disparity without resorting to unwarranted uniformity.

Keywords: sentencing guidelines, booker, sentencing, criminal law

Suggested Citation

Divine, Joshua M., Booker Disparity and Data-Driven Sentencing (2016). Hastings Law Journal, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2927558

Joshua M. Divine (Contact Author)

United States Court of Appeals for the Eleventh Circuit ( email )

AL
United States

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