IDEAS home Printed from https://ideas.repec.org/a/wly/empleg/v20y2023i2p377-408.html
   My bibliography  Save this article

How accurate are rebuttable presumptions of pretrial dangerousness?: A natural experiment from New Mexico

Author

Listed:
  • Cristopher Moore
  • Elise Ferguson
  • Paul Guerin

Abstract

In New Mexico and many other jurisdictions, judges may detain defendants pretrial if the prosecutor proves, through clear and convincing evidence, that releasing them would pose a danger to the public. However, some policymakers argue that certain classes of defendants should have a “rebuttable presumption” of dangerousness, shifting the burden of proof to the defense. Using data on over 15,000 felony defendants who were released pretrial in a 4‐year period in New Mexico, we measure how many of them would have been detained by various presumptions, and what fraction of these defendants in fact posed a danger in the sense that they were charged with a new crime during pretrial supervision. We consider presumptions based on the current charge, past convictions, past failures to appear, past violations of conditions of release, and combinations of these drawn from recent legislative proposals. We find that for all these criteria, at most 8% of the defendants they identify are charged pretrial with a new violent crime (felony or misdemeanor), and at most 5% are charged with a new violent felony. The false‐positive rate, that is, the fraction of defendants these policies would detain who are not charged with any new crime pretrial, ranges from 71% to 90%. The broadest legislative proposals, such as detaining all defendants charged with a violent felony, are little more accurate than detaining a random sample of defendants released under the current system, and would jail 20 or more people to prevent a single violent felony. We also consider detention recommendations based on risk scores from the Arnold Public Safety Assessment (PSA). Among released defendants with the highest risk score and the “violence flag,” 7% are charged with a new violent felony and 71% are false positives. We conclude that these criteria for rebuttable presumptions do not accurately target dangerous defendants: they cast wide nets and recommend detention for many pretrial defendants who do not pose a danger to the public.

Suggested Citation

  • Cristopher Moore & Elise Ferguson & Paul Guerin, 2023. "How accurate are rebuttable presumptions of pretrial dangerousness?: A natural experiment from New Mexico," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 20(2), pages 377-408, June.
  • Handle: RePEc:wly:empleg:v:20:y:2023:i:2:p:377-408
    DOI: 10.1111/jels.12351
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/jels.12351
    Download Restriction: no

    File URL: https://libkey.io/10.1111/jels.12351?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. David Arnold & Will Dobbie & Crystal S Yang, 2018. "Racial Bias in Bail Decisions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(4), pages 1885-1932.
    2. Frank McIntyre & Shima Baradaran, 2013. "Race, Prediction, and Pretrial Detention," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 10(4), pages 741-770, December.
    3. Richard A. Berk & Susan B. Sorenson & Geoffrey Barnes, 2016. "Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 13(1), pages 94-115, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David Arnold & Will Dobbie & Peter Hull, 2022. "Measuring Racial Discrimination in Bail Decisions," American Economic Review, American Economic Association, vol. 112(9), pages 2992-3038, September.
    2. Stevenson, Megan T. & Doleac, Jennifer, 2019. "Algorithmic Risk Assessment in the Hands of Humans," IZA Discussion Papers 12853, Institute of Labor Economics (IZA).
    3. Araujo, Aloisio & Ferreira, Rafael & Lagaras, Spyridon & Moraes, Flavio & Ponticelli, Jacopo & Tsoutsoura, Margarita, 2023. "The labor effects of judicial bias in bankruptcy," Journal of Financial Economics, Elsevier, vol. 150(2).
    4. Daniel Martin & Philip Marx, 2022. "A Robust Test of Prejudice for Discrimination Experiments," Management Science, INFORMS, vol. 68(6), pages 4527-4536, June.
    5. Arcidiacono, Peter & Kinsler, Josh & Ransom, Tyler, 2022. "Asian American Discrimination in Harvard Admissions," European Economic Review, Elsevier, vol. 144(C).
    6. J. Aislinn Bohren & Alex Imas & Michael Rosenberg, 2019. "The Dynamics of Discrimination: Theory and Evidence," American Economic Review, American Economic Association, vol. 109(10), pages 3395-3436, October.
    7. Bharti, Nitin Kumar & Roy, Sutanuka, 2023. "The early origins of judicial stringency in bail decisions: Evidence from early childhood exposure to Hindu-Muslim riots in India," Journal of Public Economics, Elsevier, vol. 221(C).
    8. Ash, Elliott & Chen, Daniel L. & Ornaghi, Arianna, 2020. "Stereotypes in High-Stakes Decisions : Evidence from U.S. Circuit Courts," The Warwick Economics Research Paper Series (TWERPS) 1256, University of Warwick, Department of Economics.
    9. Bordalo, Pedro & Gennaioli, Nicola & Kwon, Spencer Yongwook & Shleifer, Andrei, 2021. "Diagnostic bubbles," Journal of Financial Economics, Elsevier, vol. 141(3), pages 1060-1077.
    10. Xiaochen Hu & Xudong Zhang & Nicholas Lovrich, 2021. "Public perceptions of police behavior during traffic stops: logistic regression and machine learning approaches compared," Journal of Computational Social Science, Springer, vol. 4(1), pages 355-380, May.
    11. Jácome, Elisa, 2022. "The effect of immigration enforcement on crime reporting: Evidence from Dallas," Journal of Urban Economics, Elsevier, vol. 128(C).
    12. Matteo Bizzarri & Daniele d'Arienzo, 2023. "The social value of overreaction to information," CSEF Working Papers 690, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    13. Tanvir Ahmed Khan, 2023. "Can Unbiased Predictive AI Amplify Bias?," Working Paper 1510, Economics Department, Queen's University.
    14. Ash, Elliott & MacLeod, W. Bentley, 2021. "Reducing partisanship in judicial elections can improve judge quality: Evidence from U.S. state supreme courts," Journal of Public Economics, Elsevier, vol. 201(C).
    15. Sabrina T. Howell & Theresa Kuchler & David Snitkof & Johannes Stroebel & Jun Wong, 2021. "Lender Automation and Racial Disparities in Credit Access," NBER Working Papers 29364, National Bureau of Economic Research, Inc.
    16. Quinn A. W. Keefer, 2022. "Sex Differences in High-Level Managerial Jobs: Evidence From Professional Basketball," Journal of Sports Economics, , vol. 23(3), pages 301-328, April.
    17. Ash, Elliott & Asher, Sam & Bhowmick, Aditi & Bhupatiraju, Sandeep & Chen, Daniel L. & Devi, Tatanya & Goessmann, Christoph & Novosad, Paul & Siddiqi, Bilal, 2022. "Measuring Gender and Religious Bias in the Indian Judiciary," TSE Working Papers 22-1395, Toulouse School of Economics (TSE).
    18. Bhattacharya, D. & Rabovic, R., 2020. "Do Elite Universities Practise Meritocratic Admissions? Evidence from Cambridge," Cambridge Working Papers in Economics 2056, Faculty of Economics, University of Cambridge.
    19. Ayaita, Adam, 2021. "Labor Market Discrimination and Statistical Differences in Unobserved Characteristics of Applicants," EconStor Preprints 236615, ZBW - Leibniz Information Centre for Economics.
    20. Elsa Augustine & Johanna Lacoe & Steven Raphael & Alissa Skog, 2022. "The Impact of Felony Diversion in San Francisco," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 41(3), pages 683-709, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:empleg:v:20:y:2023:i:2:p:377-408. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1111/(ISSN)1740-1461 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.