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Heterogeneous Treatment Effects: Instrumental Variables Without Monotonicity?

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Author Info
Klein, T.J. (Tilburg University, Center for Economic Research)

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Abstract

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 a local 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. Approximations to respective bias terms are derived. In an empirical application the bias is calculated and bias corrected estimates are obtained. The accuracy of the approximation is investigated in a Monte Carlo study.

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Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2008-45.

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Date of creation: 2008
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Handle: RePEc:dgr:kubcen:200845

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C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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  1. Wooldridge, Jeffrey M., 2003. "Further results on instrumental variables estimation of average treatment effects in the correlated random coefficient model," Economics Letters, Elsevier, vol. 79(2), pages 185-191, May. [Downloadable!] (restricted)
  2. James J. Heckman & Sergio Urzua & Edward J. Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," NBER Working Papers 12574, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  3. Chesher, Andrew & Schluter, Christian, 2002. "Welfare Measurement and Measurement Error," Review of Economic Studies, Blackwell Publishing, vol. 69(2), pages 357-78, April.
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  4. Angrist, Joshua D & Graddy, Kathryn & Imbens, Guido W, 2000. "The Interpretation of Instrumental Variables Estimators in Simultaneous Equations Models with an Application to the Demand for Fish," Review of Economic Studies, Blackwell Publishing, vol. 67(3), pages 499-527, July.
  5. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January. [Downloadable!] (restricted)
  6. Heckman, James J. & Vytlacil, Edward J., 2000. "The relationship between treatment parameters within a latent variable framework," Economics Letters, Elsevier, vol. 66(1), pages 33-39, January. [Downloadable!] (restricted)
  7. Ichimura, Hidehiko & Thompson, T. Scott, 1998. "Maximum likelihood estimation of a binary choice model with random coefficients of unknown distribution," Journal of Econometrics, Elsevier, vol. 86(2), pages 269-295, June. [Downloadable!] (restricted)
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  8. Pedro Carneiro & Sokbae 'Simon' Lee, 2005. "Ability, sorting and wage inequality," CeMMAP working papers CWP16/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
  9. Tobias J. Klein, 2007. "College Education and Wages in the U.K.: Estimating Conditional Average Structural Functions in Nonadditive Models with Binary Endogenous Variables," IZA Discussion Papers 2761, Institute for the Study of Labor (IZA). [Downloadable!]
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  10. Kiefer, Nicholas M & Skoog, Gary R, 1984. "Local Asymptotic Specification Error Analysis," Econometrica, Econometric Society, vol. 52(4), pages 873-85, July. [Downloadable!] (restricted)
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