My da|ra Login

Detailed view

metadata language: English

Replication data for: Interpreting Tests of School VAM Validity

Resource Type
  • Angrist, Joshua
  • Hull, Peter
  • Pathak, Parag
  • Walters, Christopher
Publication Date
  • 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).
  • Is supplement to
    DOI: 10.1257/aer.p20161080 (Text)
  • Angrist, Joshua, Peter Hull, Parag Pathak, and Christopher Walters. “Interpreting Tests of School VAM Validity.” American Economic Review 106, no. 5 (May 2016): 388–92.
    • ID: 10.1257/aer.p20161080 (DOI)

Update Metadata: 2020-05-18 | Issue Number: 2 | Registration Date: 2019-10-12

Angrist, Joshua; Hull, Peter; Pathak, Parag; Walters, Christopher (2016): Replication data for: Interpreting Tests of School VAM Validity. Version: V0. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset.