Abstract
We study the relative performance of two policy-relevant value-added models—a one-step fixed effect model and a two-step aggregated residuals model—using a simulated data set well grounded in the value-added literature. A key feature of our data generating process is that student achievement depends on a continuous measure of economic disadvantage. This is a realistic condition that has implications for model performance because researchers typically have access to only a noisy, binary measure of disadvantage. We find that one- and two-step value-added models perform similarly across a wide range of student and teacher sorting conditions, with the two-step model modestly outperforming the one-step model in conditions that best match observed sorting in real data. A reason for the generally superior performance of the two-step model is that it better handles the use of an error-prone, dichotomous proxy for student disadvantage.
Keywords
Get full access to this article
View all access options for this article.
References
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
