Accounting for Student Disadvantage in Value-Added Models

Year: 
2018
Working Paper Number: 
WP 18-13
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 dataset 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.

JEL Codes: 
C1, I2
Authors: 
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