[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp12989.html
   My bibliography  Save this paper

Efficient Targeting in Childhood Interventions

Author

Listed:
  • Paul, Alexander

    (Aarhus University)

  • Bleses, Dorthe

    (Aarhus University)

  • Rosholm, Michael

    (Aarhus University)

Abstract
Many targeted childhood interventions such as the Perry Preschool Project select eligible children based on a risk score. The variables entering the risk score and their corresponding weights are usually chosen ad hoc and are unlikely to be optimal. This paper develops a simple economic model and exploits Danish administrative data to address the issue of efficient targeting in childhood interventions. We define children to be in need of an intervention if they suffer from an socially undesirable outcome, such as criminal behavior, at around age 30. Because interventions are most effective very early in life, we then test if and to what extent indicators available at birth can predict the emergence of these outcomes. We find fair to good levels of prediction accuracy for many outcomes, especially educational attainment, criminal behavior, placement in foster care as well as combinations of these outcomes. Logistic regressions perform as well as other machine learning methods. A parsimonious set of indicators consisting of sex, parental education and parental income predicts almost as accurately as using the full set of predictors. Finally, we derive optimal weights for the construction of risk scores. Unlike the ad hoc weights used in typical childhood interventions, we find that optimal weights vary with the outcome of interest, differ between father and mother for the same predictor and should be disproportionately large when parents are at the bottom of the education and income distribution.

Suggested Citation

  • Paul, Alexander & Bleses, Dorthe & Rosholm, Michael, 2020. "Efficient Targeting in Childhood Interventions," IZA Discussion Papers 12989, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp12989
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp12989.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    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. Foutzopoulos, Giorgos & Pandis, Nikolaos & Tsagris, Michail, 2024. "Predicting full retirement attainment of NBA players," MPRA Paper 121540, University Library of Munich, Germany.
    2. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
    3. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    4. Michael Bucker & Gero Szepannek & Alicja Gosiewska & Przemyslaw Biecek, 2020. "Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring," Papers 2009.13384, arXiv.org.
    5. Jian Lu & Raheel Ahmad & Thomas Nguyen & Jeffrey Cifello & Humza Hemani & Jiangyuan Li & Jinguo Chen & Siyi Li & Jing Wang & Achouak Achour & Joseph Chen & Meagan Colie & Ana Lustig & Christopher Dunn, 2022. "Heterogeneity and transcriptome changes of human CD8+ T cells across nine decades of life," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Bennett, Donyetta & Mekelburg, Erik & Strauss, Jack & Williams, T.H., 2024. "Unlocking the black box of sentiment and cryptocurrency: What, which, why, when and how?," Global Finance Journal, Elsevier, vol. 60(C).
    7. Fogliato Riccardo & Oliveira Natalia L. & Yurko Ronald, 2021. "TRAP: a predictive framework for the Assessment of Performance in Trail Running," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(2), pages 129-143, June.
    8. Yadid M. Algavi & Elhanan Borenstein, 2023. "A data-driven approach for predicting the impact of drugs on the human microbiome," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    9. Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
    10. Ma, Shaohui & Fildes, Robert, 2021. "Retail sales forecasting with meta-learning," European Journal of Operational Research, Elsevier, vol. 288(1), pages 111-128.
    11. Florian Pargent & Florian Pfisterer & Janek Thomas & Bernd Bischl, 2022. "Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features," Computational Statistics, Springer, vol. 37(5), pages 2671-2692, November.
    12. Vanessa Ress & Eva‐Maria Wild, 2024. "The impact of integrated care on health care utilization and costs in a socially deprived urban area in Germany: A difference‐in‐differences approach within an event‐study framework," Health Economics, John Wiley & Sons, Ltd., vol. 33(2), pages 229-247, February.
    13. Satre-Meloy, Aven & Diakonova, Marina & Grünewald, Philipp, 2020. "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Applied Energy, Elsevier, vol. 260(C).
    14. Andree,Bo Pieter Johannes & Chamorro Elizondo,Andres Fernando & Kraay,Aart C. & Spencer,Phoebe Girouard & Wang,Dieter, 2020. "Predicting Food Crises," Policy Research Working Paper Series 9412, The World Bank.
    15. Fitzpatrick, Trevor & Mues, Christophe, 2021. "How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments," European Journal of Operational Research, Elsevier, vol. 294(2), pages 711-722.
    16. Nicole Ellenbach & Anne-Laure Boulesteix & Bernd Bischl & Kristian Unger & Roman Hornung, 2021. "Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 212-231, July.
    17. Gianluca De Nard & Simon Hediger & Markus Leippold, 2022. "Subsampled factor models for asset pricing: The rise of Vasa," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1217-1247, September.
    18. Y Cui & E J Tchetgen Tchetgen, 2024. "Selective machine learning of doubly robust functionals," Biometrika, Biometrika Trust, vol. 111(2), pages 517-535.
    19. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    20. Rui Wang & Naihua Xiu & Kim-Chuan Toh, 2021. "Subspace quadratic regularization method for group sparse multinomial logistic regression," Computational Optimization and Applications, Springer, vol. 79(3), pages 531-559, July.

    More about this item

    Keywords

    machine learning; early childhood intervention; targeting;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:iza:izadps:dp12989. 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: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.html .

    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.