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Student sorting and bias in value added estimation : Selection on observables and unobservables

Author: Jesse Rothstein
Publisher: Cambridge, Mass. National Bureau of Economic Research 2009.
Series: Working paper series (National Bureau of Economic Research), no. w14666.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Non-random assignment of students to teachers can bias value added estimates of teachers' causal effects. Rothstein (2008a, b) shows that typical value added models indicate large counter-factual effects of 5th grade teachers on students' 4th grade learning, indicating that classroom assignments are far from random. This paper quantifies the resulting biases in estimates of 5th grade teachers' causal effects from  Read more...
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Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jesse Rothstein
OCLC Number: 1027350304
Notes: January 2009.
Description: 1 online resource.
Details: Mode of access: World Wide Web.
Series Title: Working paper series (National Bureau of Economic Research), no. w14666.
Responsibility: Jesse Rothstein.

Abstract:

Non-random assignment of students to teachers can bias value added estimates of teachers' causal effects. Rothstein (2008a, b) shows that typical value added models indicate large counter-factual effects of 5th grade teachers on students' 4th grade learning, indicating that classroom assignments are far from random. This paper quantifies the resulting biases in estimates of 5th grade teachers' causal effects from several value added models, under varying assumptions about the assignment process. If assignments are assumed to depend only on observables, the most commonly used specifications are subject to important bias but other feasible specifications are nearly free of bias. I also consider the case where assignments depend on unobserved variables. I use the across-classroom variance of observables to calibrate several models of the sorting process. Results indicate that even the best feasible value added models may be substantially biased, with the magnitude of the bias depending on the amount of information available for use in classroom assignments.

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