Do Value-Added Estimates Add Value? Accounting for Learning Dynamics
Tahir Andrabi, Jishnu Das, Asim Ijaz Khwaja, Tristan Zajonc
Abstract
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Tahir Andrabi, Jishnu Das, Asim Ijaz Khwaja, Tristan Zajonc
Abstract
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This paper illustrates the central role of persistence in estimating and interpreting value-added models of learning. Using data from Pakistani public and private schools, we apply dynamic panel methods that address three key empirical challenges: imperfect persistence, unobserved heterogeneity, and measurement error. Our estimates suggest that only one-fifth to one-half of learning persists between grades and that private schools increase average achievement by 0.25 standard deviations each year. In contrast, value-added models that assume perfect persistence yield severely downward estimates of the private school effect. Models that ignore unobserved heterogeneity or measurement error produce biased estimates of persistence. (JEL I21, J13, O15)
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This paper illustrates the central role of persistence in estimating and interpreting value-added models of learning. Using data from Pakistani public and private schools, we apply dynamic panel methods that address three key empirical challenges: imperfect persistence, unobserved heterogeneity, and measurement error. Our estimates suggest that only one-fifth to one-half of learning persists between grades and that private schools increase average achievement by 0.25 standard deviations each year. In contrast, value-added models that assume perfect persistence yield severely downward estimates of the private school effect. Models that ignore unobserved heterogeneity or measurement error produce biased estimates of persistence. (JEL I21, J13, O15)
Key concepts: Econometrics, Persistence (discontinuity), Imperfect, Value (mathematics), Contrast (vision), Economics, Yield (engineering), Estimation