Efficient Targeting in Childhood Interventions - Alexander Paul; Dorthe Bleses; Michael Rosholm (Institute of Labor Economics (IZA), 2020)
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.
Keywords: I18; I28; I38; 330; targeting; early childhood intervention; machine learning