TY  - RPRT
        AU  - Klein, Tobias J.
        TI  - Heterogeneous Treatment Effects: Instrumental Variables without Monotonicity?
        PY  - 2007/Apr/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 2738
        UR  - https://www.iza.org/publications/dp2738
        AB  - A fundamental identification problem in program evaluation arises when idiosyncratic gains from participation and the treatment decision depend on each other. Imbens and Angrist (1994) were the first to exploit a monotonicity condition in order to identify an average treatment effect parameter using instrumental variables. More recently, Heckman and Vytlacil (1999) suggested estimation of a variety of treatment effect parameters using a local version of their approach. However, identification hinges on the same monotonicity assumption that is fundamentally untestable. We investigate the sensitivity of respective estimates to reasonable departures from monotonicity that are likely to be encountered in practice and relate it to properties of a structural parameter. One of our results is that the bias vanishes under a testable linearity condition. Our findings are illustrated in a Monte Carlo analysis.
        KW  - monotonicity
KW  - heterogeneity
KW  - identification
KW  - program evaluation
KW  - dummy endogenous variable
KW  - selection on unobservables
KW  - instrumental variables
        ER  -