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Endogeneity in weakly separable models without monotonicity

Songnian Chen, Shakeeb Khan and Xun Tang

Journal of Econometrics, 2024, vol. 238, issue 1

Abstract: We identify and estimate treatment effects when potential outcomes are weakly separable with a binary endogenous treatment. Vytlacil and Yildiz (2007) proposed an identification strategy that exploits the mean of observed outcomes, but their approach requires a monotonicity condition. In comparison, we exploit full information in the entire outcome distribution, instead of just its mean. As a result, our method does not require monotonicity and is also applicable to general settings with multiple indices. We provide examples where our approach can identify treatment effect parameters of interest whereas existing methods would fail. These include models where potential outcomes depend on multiple unobserved disturbance terms, such as a Roy model, a multinomial choice model, as well as a model with endogenous random coefficients. We establish consistency and asymptotic normality of our estimators.

Keywords: Weak Separability; Treatment Effects; Monotonicity; Endogeneity (search for similar items in EconPapers)
JEL-codes: C14 C31 C35 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:238:y:2024:i:1:s030440762300283x

DOI: 10.1016/j.jeconom.2023.105567

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