Student Sorting and Bias in Value Added Estimation: Selection on Observables and Unobservables

Jesse Rothstein

No 1059, Working Papers from Princeton University, School of Public and International Affairs, Education Research Section.

Abstract: Non-random assignments of students to teachers can bias value added estimates of teachers' causal effects. Rothstein (2008) shows that typical value added models indicate large counter-factual effects of 5th grade teachers on students' 4th grade learning, implying that assignments do not satisfy the imposed assumptions. This paper quantifies the resulting biases in estimates of 5th grade teachers' causal effects from several value added models, under varying assumprions about the assignment process. Under selection on observables, models for gain scores without controls or with only a single lagged score control are subject important bias, but models with controls for the full test score history are nearly free of bias. I consider several scenarios for selection on unobservables, using the across-classroom variance of observed variables to calibrate each. Results indicate that even well-controlled models may be substantially biased, with the magnitude of the bias depending on the amount of information available for use in classroom assignments.

JEL-codes: C12 C52 H75 I21 J33 J45 (search for similar items in EconPapers)
Date: 2008-06
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Related works:
Working Paper: Student sorting and bias in value added estimation: Selection on observables and unobservables (2009)
Working Paper: Student Sorting and Bias in Value Added Estimation: Selection on Observables and Unobservables (2008)
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