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Instrumental variable estimation of treatment effects for duration outcomes

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Author Info
Bijwaard, G.E. (Erasmus Econometric Institute)

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Abstract

In this article we propose and implement an instrumental variable estimation procedure to obtain treatment effects on duration outcomes. The method can handle the typical complications that arise with duration data of time-varying treatment and censoring. The treatment effect we define is in terms of shifting the quantiles of the outcome distribution based on the Generalized Accelerated Failure Time (GAFT) model. The GAFT model encompasses two competing approaches to duration data; the (Mixed) Proportional Hazard (MPH) model and the Accelerated Failure Time (AFT) model. We discuss the large sample properties of the proposed Instrumental Variable Linear Rank (IVLR), and show how we can, with one additional step, improve upon its efficiency. We discuss the empirical implementation of the estimator and apply it to the Illinois re-employment bonus experiment.

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File URL: http://hdl.handle.net/1765/10344
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Paper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number EI 2007-20 Revision_Date: 2009-07-29.

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Date of creation: 21 Jun 2006
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Handle: RePEc:dgr:eureir:1765010344

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Related research
Keywords: treatment effect; duration model; censoring; instrumental variable;

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  1. Van den Berg, Gerard J., 2000. "Duration Models: Specification, Identification, and Multiple Durations," MPRA Paper 9446, University Library of Munich, Germany. [Downloadable!]
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  2. Gerard J. van den Berg & Bas van der Klaauw & Jan C. van Ours, 2004. "Punitive Sanctions and the Transition Rate from Welfare to Work," Journal of Labor Economics, University of Chicago Press, vol. 22(1), pages 211-210, January. [Downloadable!]
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  3. Jaap Abbring & Gerard van den Berg, 2005. "Social experiments and instrumental variables with duration outcomes," IFS Working Papers W05/19, Institute for Fiscal Studies. [Downloadable!]
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  4. Ridder, Geert, 1990. "The Non-parametric Identification of Generalized Accelerated Failure-Time Models," Review of Economic Studies, Blackwell Publishing, vol. 57(2), pages 167-81, April. [Downloadable!] (restricted)
  5. Angrist, Joshua D. & Krueger, Alan B., 1999. "Empirical strategies in labor economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 23, pages 1277-1366 Elsevier. [Downloadable!] (restricted)
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  6. Woodbury, Stephen A & Spiegelman, Robert G, 1987. "Bonuses to Workers and Employers to Reduce Unemployment: Randomized Trials in Illinois," American Economic Review, American Economic Association, vol. 77(4), pages 513-30, September. [Downloadable!] (restricted)
  7. Meyer, Bruce D, 1996. "What Have We Learned from the Illinois Reemployment Bonus Experiment?," Journal of Labor Economics, University of Chicago Press, vol. 14(1), pages 26-51, January. [Downloadable!] (restricted)
  8. Jaap H. Abbring & Gerard J. van den Berg, 2003. "The Nonparametric Identification of Treatment Effects in Duration Models," Econometrica, Econometric Society, vol. 71(5), pages 1491-1517, 09. [Downloadable!] (restricted)
  9. Ham, John C & LaLonde, Robert J, 1996. "The Effect of Sample Selection and Initial Conditions in Duration Models: Evidence from Experimental Data on Training," Econometrica, Econometric Society, vol. 64(1), pages 175-205, January. [Downloadable!] (restricted)
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