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Supercompliers

Author

Listed:
  • Matthew L. Comey
  • Amanda R. Eng
  • Pauline Leung
  • Zhuan Pei

Abstract

In a binary-treatment instrumental variable framework, we define supercompliers as the subpopulation whose treatment take-up positively responds to eligibility and whose outcome positively responds to take-up. Supercompliers are the only subpopulation to benefit from treatment eligibility and, hence, are important for policy. We provide tools to characterize supercompliers under a set of jointly testable assumptions. Specifically, we require standard assumptions from the local average treatment effect literature plus an outcome monotonicity assumption. Estimation and inference can be conducted with instrumental variable regression. In two job-training experiments, we demonstrate our machinery's utility, particularly in incorporating social welfare weights into marginal-value-of-public-funds analysis.

Suggested Citation

  • Matthew L. Comey & Amanda R. Eng & Pauline Leung & Zhuan Pei, 2022. "Supercompliers," Papers 2212.14105, arXiv.org, revised Dec 2024.
  • Handle: RePEc:arx:papers:2212.14105
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    References listed on IDEAS

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    1. Xuan Chen & Carlos A. Flores, 2015. "Bounds on Treatment Effects in the Presence of Sample Selection and Noncompliance: The Wage Effects of Job Corps," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 523-540, October.
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    5. Amanda Kowalski, 2019. "A model of a randomized experiment with an application to the PROWESS clinical trial," CeMMAP working papers CWP11/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    7. Lex Borghans & Anne C. Gielen & Erzo F. P. Luttmer, 2014. "Social Support Substitution and the Earnings Rebound: Evidence from a Regression Discontinuity in Disability Insurance Reform," American Economic Journal: Economic Policy, American Economic Association, vol. 6(4), pages 34-70, November.
    8. Will Dobbie & Jacob Goldin & Crystal S. Yang, 2018. "The Effects of Pretrial Detention on Conviction, Future Crime, and Employment: Evidence from Randomly Assigned Judges," American Economic Review, American Economic Association, vol. 108(2), pages 201-240, February.
    9. Guido W. Imbens & Donald B. Rubin, 1997. "Estimating Outcome Distributions for Compliers in Instrumental Variables Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 555-574.
    10. Marianne P. Bitler & Jonah B. Gelbach & Hilary W. Hoynes, 2017. "Can Variation in Subgroups' Average Treatment Effects Explain Treatment Effect Heterogeneity? Evidence from a Social Experiment," The Review of Economics and Statistics, MIT Press, vol. 99(4), pages 683-697, July.
    11. Miriam Bruhn & David McKenzie, 2009. "In Pursuit of Balance: Randomization in Practice in Development Field Experiments," American Economic Journal: Applied Economics, American Economic Association, vol. 1(4), pages 200-232, October.
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