Identifying the average treatment effect in ordered treatment models without unconfoundedness

Arthur Lewbel; Thomas Tao Yang

doi:10.1016/j.jeconom.2016.05.015

Identifying the average treatment effect in ordered treatment models without unconfoundedness

Arthur Lewbel^*, Thomas Tao Yang

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

We show identification of the Average Treatment Effect (ATE) when treatment is specified by ordered choice in cross section or panel models. Treatment is determined by location of a latent variable (containing a continuous instrument) relative to two or more thresholds. We place no functional form restrictions on latent errors and potential outcomes. Unconfoundedness of treatment does not hold and identification at infinity for the treated is not possible. Yet we still show nonparametric point identification and estimation of the ATE. We apply our model to reinvestigate the inverted-U relationship between competition and innovation, and find no inverted-U in US data.

Original language	English
Pages (from-to)	1-22
Number of pages	22
Journal	Journal of Econometrics
Volume	195
Issue number	1
DOIs	https://doi.org/10.1016/j.jeconom.2016.05.015
Publication status	Published - 1 Nov 2016

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10.1016/j.jeconom.2016.05.015

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abstract = "We show identification of the Average Treatment Effect (ATE) when treatment is specified by ordered choice in cross section or panel models. Treatment is determined by location of a latent variable (containing a continuous instrument) relative to two or more thresholds. We place no functional form restrictions on latent errors and potential outcomes. Unconfoundedness of treatment does not hold and identification at infinity for the treated is not possible. Yet we still show nonparametric point identification and estimation of the ATE. We apply our model to reinvestigate the inverted-U relationship between competition and innovation, and find no inverted-U in US data.",

keywords = "Average treatment effect, Competition and innovation, Identification, Ordered choice model, Semiparametric, Special regressor",

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AB - We show identification of the Average Treatment Effect (ATE) when treatment is specified by ordered choice in cross section or panel models. Treatment is determined by location of a latent variable (containing a continuous instrument) relative to two or more thresholds. We place no functional form restrictions on latent errors and potential outcomes. Unconfoundedness of treatment does not hold and identification at infinity for the treated is not possible. Yet we still show nonparametric point identification and estimation of the ATE. We apply our model to reinvestigate the inverted-U relationship between competition and innovation, and find no inverted-U in US data.

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KW - Competition and innovation

KW - Identification

KW - Ordered choice model

KW - Semiparametric

KW - Special regressor

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JF - Journal of Econometrics

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Identifying the average treatment effect in ordered treatment models without unconfoundedness

Abstract

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