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Identifying the average treatment effect in ordered treatment models without unconfoundedness

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  • Lewbel, Arthur
  • Yang, Thomas Tao

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.

Suggested Citation

  • Lewbel, Arthur & Yang, Thomas Tao, 2016. "Identifying the average treatment effect in ordered treatment models without unconfoundedness," Journal of Econometrics, Elsevier, vol. 195(1), pages 1-22.
  • Handle: RePEc:eee:econom:v:195:y:2016:i:1:p:1-22
    DOI: 10.1016/j.jeconom.2016.05.015
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    More about this item

    Keywords

    Average treatment effect; Ordered choice model; Special regressor; Semiparametric; Competition and innovation; Identification;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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