Should Value-Added Models Weight All Students Equally?

Conventional value-added (VA) models estimate teacher quality as a simple average of the difference between students’ actual and predicted standardized test scores. These models therefore implicitly assume it is just as important to raise test scores of lower-achieving students as it is to raise test scores of higher-achieving students. I consider whether a weighted average of residuals might be more useful. Using data from North Carolina, I find that teacher VA measures become more predictive of teachers’ long-run impacts when the highest-achieving students are weighted more than the median student. Strikingly, even impacts on low-achieving students’ long-run outcomes are best predicted by increasing the weight on impacts on high-achieving students’ short-run outcomes. These differences in weights may reflect that either (i) small-sample efficiency (some students are more informative about teachers’ true test-score effects than others) or (ii) differences in true effects (e.g. test-score effects for different students might capture different general aspects of teaching). I find empirical evidence supporting both explanations. In particular, the large weights for high-achieving students are partially but not completely explained by the fact that their residuals are less noisy.

Awarded the Cliff R. Kern Excellence in Research Award (Binghamton University, Department of Economics), 2025.