Cognitive and non-cognitive efficiency gaps between private and public schools and their determinants in the Latin America region–a hybrid data envelopment analysis and interpretable machine learning approach based on PISA 2022

Latin America’s education systems are fragmented by school type. Knowing whether private and public schools are making an efficient use of resources is critical, even more so after the post-pandemic learning crisis. In this paper, relying on data of 2,034 schools and nine Latin American countries from PISA 2022, I offer new evidence on school efficiency (both on cognitive and non-cognitive dimensions) using Data Envelopment Analysis (DEA) by school type and, then, I estimate efficiency determinants through interpretable machine learning methods (IML). This hybrid DEA-IML approach allows to accommodate the issue of big data, assessing several determinants of school efficiency. I find a cognitive efficiency gap of nearly 0.10 favouring private schools and of 0.045 for non-cognitive outcomes, and with a lower heterogeneity in private than public schools. For cognitive efficiency, leading determinants for the chance of a private school of being highly efficient are a higher stock of books and computers at home, a lack of engagement in paid work and higher school autonomy; whereas low-efficient public schools are shaped by poor school climate, large rates of repetition, truancy and intensity of paid work, fewer books at home and increasing barriers for homework during the pandemic.