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Instrumental Variable Estimation of Treatment Effects for Duration Outcomes

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  • Bijwaard, Govert

    (NIDI - Netherlands Interdisciplinary Demographic Institute)

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.

Suggested Citation

  • Bijwaard, Govert, 2007. "Instrumental Variable Estimation of Treatment Effects for Duration Outcomes," IZA Discussion Papers 2896, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp2896
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    1. 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 211-241, January.
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    3. 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 513-530, September.
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    5. 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.
    6. Abbring, Jaap H & van den Berg, Gerard J, 2005. "Social experiments and intrumental variables with duration outcomes," Working Paper Series 2005:11, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    7. Bijwaard, Govert E. & Ridder, Geert, 2005. "Correcting for selective compliance in a re-employment bonus experiment," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 77-111.
    8. Ashenfelter, Orley & Ashmore, David & Deschenes, Olivier, 2005. "Do unemployment insurance recipients actively seek work? Evidence from randomized trials in four U.S. States," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 53-75.
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    Cited by:

    1. Chen, Songnian, 2019. "Quantile regression for duration models with time-varying regressors," Journal of Econometrics, Elsevier, vol. 209(1), pages 1-17.
    2. David MARGOLIS, 2008. "Unemployment Insurance Versus Individual Unemployment Accounts and Transitions to Formal Versus Informal Sector Jobs," Working Papers 2008-35, Center for Research in Economics and Statistics.
    3. Anja Lambrecht & Katja Seim & Catherine Tucker, 2007. "Stuck in the Adoption Funnel: The Effect of Delays in the Adoption Process on Ultimate Adoption," Working Papers 07-40, NET Institute, revised Oct 2007.

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    More about this item

    Keywords

    censoring; duration model; treatment effect; instrumental variable;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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