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Estimating the Impact of the Age of Criminal Majority: Decomposing Multiple Treatments in a Regression Discontinuity Framework

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Listed:
  • Michael Mueller-Smith
  • Benjamin Pyle
  • Caroline Walker

Abstract

This paper studies the impact of adult prosecution on recidivism and employment trajectories for adolescent, first-time felony defendants. We use extensive linked Criminal Justice Administrative Record System and socio-economic data from Wayne County, Michigan (Detroit). Using the discrete age of majority rule and a regression discontinuity design, we find that adult prosecution reduces future criminal charges over 5 years by 0.48 felony cases (? 20%) while also worsening labor market outcomes: 0.76 fewer employers (? 19%) and $674 fewer earnings (? 21%) per year. We develop a novel econometric framework that combines standard regression discontinuity methods with predictive machine learning models to identify mechanism-specific treatment effects that underpin the overall impact of adult prosecution. We leverage these estimates to consider four policy counterfactuals: (1) raising the age of majority, (2) increasing adult dismissals to match the juvenile disposition rates, (3) eliminating adult incarceration, and (4) expanding juvenile record sealing opportunities to teenage adult defendants. All four scenarios generate positive returns for government budgets. When accounting for impacts to defendants as well as victim costs borne by society stemming from increases in recidivism, we find positive social returns for juvenile record sealing expansions and dismissing marginal adult charges; raising the age of majority breaks even. Eliminating prison for first-time adult felony defendants, however, increases net social costs. Policymakers may still find this attractive if they are willing to value beneficiaries (taxpayers and defendants) slightly higher (124%) than potential victims.

Suggested Citation

  • Michael Mueller-Smith & Benjamin Pyle & Caroline Walker, 2023. "Estimating the Impact of the Age of Criminal Majority: Decomposing Multiple Treatments in a Regression Discontinuity Framework," Working Papers 23-01, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:23-01
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    File URL: https://www2.census.gov/library/working-papers/2023/adrm/ces/CES-WP-23-01.pdf
    File Function: First version, 2023
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    References listed on IDEAS

    as
    1. Sokbae Lee & Bernard Salanié, 2018. "Identifying Effects of Multivalued Treatments," Econometrica, Econometric Society, vol. 86(6), pages 1939-1963, November.
    2. Keith Finlay & Michael Mueller-Smith & Brittany Street, 2022. "Measuring Intergenerational Exposure to the U.S. Justice System: Evidence from Longitudinal Links between Survey and Administrative Data," Working Papers 2211, Department of Economics, University of Missouri.
    3. Hunt, Priscillia Evelyne & Saunders, Jessica & Kilmer, Beau, 2019. "Estimates of Law Enforcement Costs by Crime Type for Benefit-Cost Analyses," Journal of Benefit-Cost Analysis, Cambridge University Press, vol. 10(1), pages 95-123, April.
    4. Charles E. Loeffler & Ben Grunwald, 2015. "Decriminalizing Delinquency: The Effect of Raising the Age of Majority on Juvenile Recidivism," The Journal of Legal Studies, University of Chicago Press, vol. 44(2), pages 361-388.
    5. James J. Heckman & Stefano Mosso, 2014. "The Economics of Human Development and Social Mobility," Annual Review of Economics, Annual Reviews, vol. 6(1), pages 689-733, August.
    6. Deborah Wagner & Mary Lane, 2014. "The Person Identification Validation System (PVS): Applying the Center for Administrative Records Research and Applications’ (CARRA) Record Linkage Software," CARRA Working Papers 2014-01, Center for Economic Studies, U.S. Census Bureau.
    7. Caetano, Carolina & Escanciano, Juan Carlos, 2021. "Identifying Multiple Marginal Effects With A Single Instrument," Econometric Theory, Cambridge University Press, vol. 37(3), pages 464-494, June.
    8. Steven D. Levitt, 1998. "Juvenile Crime and Punishment," Journal of Political Economy, University of Chicago Press, vol. 106(6), pages 1156-1185, December.
    9. James J. Heckman & Rodrigo Pinto, 2018. "Unordered Monotonicity," Econometrica, Econometric Society, vol. 86(1), pages 1-35, January.
    10. Michael Mueller-Smith & Kevin T. Schnepel, 2021. "Diversion in the Criminal Justice System," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(2), pages 883-936.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    juvenile and criminal justice; regression discontinuity; machine learning; recidivism; employment;
    All these keywords.

    JEL classification:

    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • K14 - Law and Economics - - Basic Areas of Law - - - Criminal Law
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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