Events Unrelated to Crime Predict Criminal Sentence Length

29 Pages Posted: 1 Aug 2016  

Evelina Bakhturina

New York University (NYU) - Center for Data Science

Nora Barry

Center for Data Science, NYU

Laura Buchanan

New York University (NYU) - Center for Data Science

Daniel L. Chen

University of Toulouse 1 - Toulouse School of Economics Institute for Advanced Studies/Harvard Law School LWP; Harvard Law School

Date Written: May 1, 2016

Abstract

In United States District Courts for federal criminal cases, prison sentence length guidelines are established by the severity of the crime and the criminal history of the defendant. In this paper, we investigate the sentence length determined by the trial judge, relative to this sentencing guideline. Our goal is to create a prediction model of sentencing length and include events unrelated to crime, namely weather and sports outcomes, to determine if these unrelated events are predictive of sentencing decisions and evaluate the importance weights of these unrelated events in explaining rulings. We find that while several appropriate features predict sentence length, such as details of the crime committed, other features seemingly unrelated, including daily temperature, baseball game scores, and location of trial, are predictive as well. Unrelated events were, surprisingly, more predictive than race, which did not predict sentencing length relative to the guidelines. This is consistent with recent research on racial disparities in sentencing that highlights the role of prosecutors in making charges that influence the maximum and minimum recommended sentence. Finally, we attribute the predictive importance of date to the 2005 U.S. Supreme Court case, United States v. Booker, after which sentence length more frequently fell near the guideline minimum and the range of minimum and maximum sentences became more extreme.

Suggested Citation

Bakhturina, Evelina and Barry, Nora and Buchanan, Laura and Chen, Daniel L., Events Unrelated to Crime Predict Criminal Sentence Length (May 1, 2016). Available at SSRN: https://ssrn.com/abstract=2815955 or http://dx.doi.org/10.2139/ssrn.2815955

Evelina Bakhturina

New York University (NYU) - Center for Data Science ( email )

726 Broadway
7th Floor
New York, NY 10003
United States

Nora Barry (Contact Author)

Center for Data Science, NYU ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

Laura Buchanan

New York University (NYU) - Center for Data Science ( email )

726 Broadway
7th Floor
New York, NY 10003
United States

Daniel L. Chen

University of Toulouse 1 - Toulouse School of Economics Institute for Advanced Studies/Harvard Law School LWP ( email )

21 allée de Brienne
31015 Toulouse cedex 6 France
Toulouse, 31015
France

Harvard Law School ( email )

8 Mt. Auburn St., 1st Floor
Cambridge, MA 02138
United States

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