Some children left behind: Variation in the effects of an educational intervention

Julie Buhl-Wiggers, Jason Kerwin, Juan Munoz-Morales, Jeffrey Smith and Rebecca Thornton

Journal of Econometrics, 2024, vol. 243, issue 1

Abstract: We document substantial variation in the effects of a highly-effective literacy program in northern Uganda. The program increases test scores by 1.4 SDs on average, but standard statistical bounds show that the impact standard deviation exceeds 1.0 SD. This implies that the variation in effects across our students is wider than the spread of mean effects across all randomized evaluations of developing country education interventions in the literature. This very effective program does indeed leave some students behind. At the same time, we do not learn much from our analyses that attempt to determine which students benefit more or less from the program. We reject rank preservation, and the weaker assumption of stochastic increasingness leaves wide bounds on quantile-specific average treatment effects. Neither conventional nor machine-learning approaches to estimating systematic heterogeneity capture more than a small fraction of the variation in impacts given our available candidate moderators.

Keywords: Treatment effect heterogeneity; Machine learning; Education programs (search for similar items in EconPapers)
JEL-codes: C18 C21 I21 I25 J24 (search for similar items in EconPapers)
Date: 2024
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Related works:
Working Paper: Some children left behind: Variation in the effects of an educational intervention (2022)
Working Paper: Some Children Left Behind: Variation in the Effects of an Educational Intervention (2021)
Working Paper: Some Children Left Behind: Variation in the Effects of an Educational Intervention (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:243:y:2024:i:1:s0304407622000355

DOI: 10.1016/j.jeconom.2021.12.010

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