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Estimating the effect of treatments allocated by randomized waiting lists

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  • Clement de Chaisemartin
  • Luc Behaghel

Abstract

Oversubscribed treatments are often allocated using randomized waiting lists. Applicants are ranked randomly, and treatment offers are made following that ranking until all seats are filled. To estimate causal effects, researchers often compare applicants getting and not getting an offer. We show that those two groups are not statistically comparable. Therefore, the estimator arising from that comparison is inconsistent. We propose a new estimator, and show that it is consistent. Finally, we revisit an application, and we show that using our estimator can lead to sizably different results from those obtained using the commonly used estimator.

Suggested Citation

  • Clement de Chaisemartin & Luc Behaghel, 2015. "Estimating the effect of treatments allocated by randomized waiting lists," Papers 1511.01453, arXiv.org, revised Oct 2018.
  • Handle: RePEc:arx:papers:1511.01453
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    Cited by:

    1. Clément de Chaisemartin, 2022. "Trading-off Bias and Variance in Stratified Experiments and in Staggered Adoption Designs, Under a Boundedness Condition on the Magnitude of the Treatment Effect," Working Papers hal-03873919, HAL.
    2. Pablo Blanchard & Matías Brum & Paula Carrasco & Cecilia Parada & Ivone Perazzo, 2023. "Employment effects of a social and labor inclusion program," Documentos de Trabajo (working papers) 23-02, Instituto de Economía - IECON.
    3. Clément de Chaisemartin & Luc Behaghel, 2020. "Estimating the Effect of Treatments Allocated by Randomized Waiting Lists," Econometrica, Econometric Society, vol. 88(4), pages 1453-1477, July.
    4. Joshua D. Angrist, 2022. "Empirical Strategies in Economics: Illuminating the Path From Cause to Effect," Econometrica, Econometric Society, vol. 90(6), pages 2509-2539, November.
    5. Kirill Borusyak & Peter Hull & Xavier Jaravel, 2023. "Design-based identification with formula instruments: A review," CeMMAP working papers 12/23, Institute for Fiscal Studies.
    6. Thomas Le Barbanchon & Diego Ubfal & Federico Araya, 2023. "The Effects of Working While in School: Evidence from Employment Lotteries," American Economic Journal: Applied Economics, American Economic Association, vol. 15(1), pages 383-410, January.
    7. Le Barbanchon, Thomas & Ubfal, Diego & Araya, Federico, 2020. "The Effects of Working While in School: Evidence from Uruguayan Lotteries," IZA Discussion Papers 13929, Institute of Labor Economics (IZA).
    8. Daniel Da Mata & Rodrigo Oliveira & Diana Silva, 2021. "Who benefits from job training programmes?: Evidence from a high-dosage programme in Brazil," WIDER Working Paper Series wp-2021-121, World Institute for Development Economic Research (UNU-WIDER).

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    More about this item

    JEL classification:

    • 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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