Estimation of Causal Effects in Experiments with Multiple Sources of Noncompliance

John Engberg, Dennis Epple, Jason Imbrogno, Holger Sieg and Ron Zimmer ()

No 14842, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: The purpose of this paper is to study identification and estimation of causal effects in experiments with multiple sources of noncompliance. This research design arises in many applications in education when access to oversubscribed programs is partially determined by randomization. Eligible households decide whether or not to comply with the intended treatment. The paper treats program participation as the outcome of a decision process with five latent household types. We show that the parameters of the underlying model of program participation are identified. Our proofs of identification are constructive and can be used to design a GMM estimator for all parameters of interest. We apply our new methods to study the effectiveness of magnet programs in a large urban school district. Our findings show that magnet programs help the district to attract and retain students from households that are at risk of leaving the district. These households have higher incomes, are more educated, and have children that score higher on standardized tests than households that stay in district regardless of the outcome of the lottery.

JEL-codes: C21 H75 I21 (search for similar items in EconPapers)
Date: 2009-04
New Economics Papers: this item is included in nep-ecm
Note: CH ED PE
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