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

Julie Buhl-Wiggers, Jason T. Kerwin, Juan Muñoz-Morales, Jeffrey Smith, Rebecca Thornton

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
JournalJournal of Econometrics
StateAccepted/In press - 2022


  • Education programs
  • Machine learning
  • Treatment effect heterogeneity

ASJC Scopus subject areas

  • Applied Mathematics
  • Economics and Econometrics


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