Correcting Selection Bias in Standardized Test Comparisons

Abstract: This paper tackles the critical issue of sample selection bias in cross-country comparisons using international assessments such as PISA (Program for International Student Assessment). While PISA is widely used to rank educational performance, its reliance on samples of students still in school at age 15 introduces survival bias, potentially distorting comparisons. To address this, I developed an econometric framework based on a quantile selection model. Under a stochastic dominance assumption, the selection-corrected quantile function is partially identified, with point identification achieved under parametric restrictions on the joint distribution of test scores and selection. Applying this method to PISA 2018 data, I demonstrate that correcting for selection bias leads to significant changes in country rankings based on mean performance. These findings underscore the importance of accounting for sample selection bias to ensure accurate and meaningful international educational comparisons.