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Predicting and Preventing Shootings among At-Risk Youth

Author

Listed:
  • Dana Chandler
  • Steven D. Levitt
  • John A. List

Abstract

Each year, more than 250 students in the Chicago Public Schools (CPS) are shot. The authors of this paper worked with the leadership of CPS to build a predictive model of shootings that helped determine which students would be included in a highly targeted and resource intensive mentorship program. This paper describes our predictive model and offers a preliminary evaluation of the mentoring intervention performed by Youth Advocate Programs, Inc. (YAP). We find little evidence that the intervention reduces school misconducts or improves educational outcomes. The scale of intervention was too small to generate meaningful findings on shootings.

Suggested Citation

  • Dana Chandler & Steven D. Levitt & John A. List, 2011. "Predicting and Preventing Shootings among At-Risk Youth," American Economic Review, American Economic Association, vol. 101(3), pages 288-292, May.
    • Handle: RePEc:aea:aecrev:v:101:y:2011:i:3:p:288-92
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    References listed on IDEAS

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    1. Robert J. LaLonde, 2003. "Employment and Training Programs," NBER Chapters, in: Means-Tested Transfer Programs in the United States, pages 517-586, National Bureau of Economic Research, Inc.
    2. Robert A. Moffitt, 2003. "Means-Tested Transfer Programs in the United States," NBER Books, National Bureau of Economic Research, Inc, number moff03-1, June.
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    2. Fairley, Kim & Sanfey, Alan G., 2020. "The role of demographics on adolescents’ preferences for risk, ambiguity, and prudence," Journal of Economic Behavior & Organization, Elsevier, vol. 179(C), pages 784-796.
    3. Ginevra Buratti & Alessio D'Ignazio, 2024. "Improving the effectiveness of financial education programs. A targeting approach," Journal of Consumer Affairs, Wiley Blackwell, vol. 58(2), pages 451-485, June.
    4. Andini, Monica & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Salvestrini, Viola, 2018. "Targeting with machine learning: An application to a tax rebate program in Italy," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 86-102.
    5. Pietro Battiston & Simona Gamba & Alessandro Santoro, 2020. "Optimizing Tax Administration Policies with Machine Learning," Working Papers 436, University of Milano-Bicocca, Department of Economics, revised Mar 2020.
    6. Michael Allan Ribers & Hannes Ullrich, 2024. "Complementarities between algorithmic and human decision-making: The case of antibiotic prescribing," Quantitative Marketing and Economics (QME), Springer, vol. 22(4), pages 445-483, December.
    7. Monica Andini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Viola Salvestrini, 2017. "Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy," Temi di discussione (Economic working papers) 1158, Bank of Italy, Economic Research and International Relations Area.
    8. Lelys Dinarte-Diaz, 2024. "Peer Effects on Violence: Experimental Evidence from El Salvador," CESifo Working Paper Series 10975, CESifo.
    9. Battiston, Pietro & Gamba, Simona & Santoro, Alessandro, 2024. "Machine learning and the optimization of prediction-based policies," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    10. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    11. Sean Tanner & Jenna Terrell & Emily Vislosky & Jonathan Gellar & Brian Gill, "undated". "Predicting Early Fall Student Enrollment in the School District of Philadelphia," Mathematica Policy Research Reports 63a18bf538bd41f98d72ff91d, Mathematica Policy Research.
    12. Michael Allan Ribers & Hannes Ullrich, 2023. "Machine learning and physician prescribing: a path to reduced antibiotic use," Berlin School of Economics Discussion Papers 0019, Berlin School of Economics.
    13. Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020. "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers 16/20, Sapienza University of Rome, DISS.
    14. Potash, Eric, 2018. "Randomization bias in field trials to evaluate targeting methods," Economics Letters, Elsevier, vol. 167(C), pages 131-135.
    15. Ville A. Satopää & Marat Salikhov & Philip E. Tetlock & Barbara Mellers, 2021. "Bias, Information, Noise: The BIN Model of Forecasting," Management Science, INFORMS, vol. 67(12), pages 7599-7618, December.
    16. Dinarte Diaz, Lelys, 2024. "Peer Effects on Violence: Experimental Evidence from El Salvador," IZA Discussion Papers 16830, Institute of Labor Economics (IZA).
    17. Dinarte Diaz,Lelys Ileana, 2020. "Peer Effects on Violence : Experimental Evidence from El Salvador," Policy Research Working Paper Series 9187, The World Bank.

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