Leveraging Lotteries for School Value-Added: Testing and Estimation / Joshua Angrist, Peter Hull, Parag A. Pathak, Christopher Walters.

Format:

Book

Author/Creator:

Angrist, Joshua.

Contributor:

National Bureau of Economic Research.

Hull, Peter.

Pathak, Parag A.

Walters, Christopher.

Series:

Working Paper Series (National Bureau of Economic Research) no. w21748.

NBER working paper series no. w21748

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Other Title:

Leveraging Lotteries for School Value-Added

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2015.

Summary:

Conventional value-added models (VAMs) compare average test scores across schools after regression-adjusting for students' demographic characteristics and previous scores. This paper tests for VAM bias using a procedure that asks whether VAM estimates accurately predict the achievement consequences of random assignment to specific schools. Test results from admissions lotteries in Boston suggest conventional VAM estimates are biased, which motivates the development of a hierarchical model describing the joint distribution of school value-added, bias, and lottery compliance. We use this model to assess the substantive importance of bias in conventional VAM estimates and to construct hybrid value-added estimates that optimally combine ordinary least squares and lottery-based instrumental variables estimates of VAM parameters. The hybrid estimation strategy provides a general recipe for combining non-experimental and quasi-experimental estimates. While still biased, hybrid school value-added estimates have lower mean squared error than conventional VAM estimates. Simulations calibrated to the Boston data show that, bias notwithstanding, policy decisions based on conventional VAMs are likely to generate substantial achievement gains. Hybrid estimates that incorporate lotteries yield further gains.

Notes:

Print version record

November 2015.