Interpreting Tests of School VAM Validity
Joshua Angrist, Peter Hull, Parag Pathak, Christopher Walters
American Economic Review · 2016 · Volume 106 · Issue 5
Abstract
We develop over-identification tests that use admissions lotteries to assess the predictive value of regression-based value-added models (VAMs). These tests have degrees of freedom equal to the number of quasi-experiments available to estimate school effects. By contrast, previously implemented VAM validation strategies look at a single restriction only, sometimes said to measure forecast bias. Tests of forecast bias may be misleading when the test statistic is constructed from many lotteries or quasi-experiments, some of which have weak first stage effects on school attendance. The theory developed here is applied to data from the Charlotte-Mecklenberg School district analyzed by Deming (2014).
Keywords
State and Local Government: Health, Education, Welfare, Public Pensions, Analysis of Education
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