IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp18598.html

Supercompliers

Author

Listed:
  • Comey, Matthew

    (Joint Committee on Taxation)

  • Eng, Amanda

    (Internal Revenue Service)

  • Leung, Pauline

    (Cornell University)

  • Pei, Zhuan

    (Cornell University)

Abstract

In an 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 evaluation. We propose conditions for characterizing supercompliers, and show how 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 analyses.

Suggested Citation

  • Comey, Matthew & Eng, Amanda & Leung, Pauline & Pei, Zhuan, 2026. "Supercompliers," IZA Discussion Papers 18598, IZA Network @ LISER.
  • Handle: RePEc:iza:izadps:dp18598
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp18598.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Cornelissen, Thomas & Dustmann, Christian & Raute, Anna & Schönberg, Uta, 2016. "From LATE to MTE: Alternative methods for the evaluation of policy interventions," Labour Economics, Elsevier, vol. 41(C), pages 47-60.
    2. Alberto Abadie & Joshua Angrist & Guido Imbens, 2002. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, Econometric Society, vol. 70(1), pages 91-117, January.
    3. Emmanuel Saez & Stefanie Stantcheva, 2016. "Generalized Social Marginal Welfare Weights for Optimal Tax Theory," American Economic Review, American Economic Association, vol. 106(1), pages 24-45, January.
    4. Martin E Andresen & Martin Huber, 2021. "Instrument-based estimation with binarised treatments: issues and tests for the exclusion restriction," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 536-558.
    5. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    6. Angrist, Joshua & Kolesár, Michal, 2024. "One instrument to rule them all: The bias and coverage of just-ID IV," Journal of Econometrics, Elsevier, vol. 240(2).
    7. Ismael Mourifié & Yuanyuan Wan, 2017. "Testing Local Average Treatment Effect Assumptions," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 305-313, May.
    8. Marbach, Moritz & Hangartner, Dominik, 2020. "Profiling Compliers and Noncompliers for Instrumental-Variable Analysis," Political Analysis, Cambridge University Press, vol. 28(3), pages 435-444, July.
    9. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769, December.
    10. 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.
    11. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    12. Salanié, Bernard, 2011. "The Economics of Taxation," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262016346, December.
    13. 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.
    14. David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(3), pages 1071-1102.
    15. Fan, Yanqin & Park, Sang Soo, 2010. "Sharp Bounds On The Distribution Of Treatment Effects And Their Statistical Inference," Econometric Theory, Cambridge University Press, vol. 26(3), pages 931-951, June.
    16. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    17. Peter Z. Schochet & John Burghardt & Steven Glazerman, 2001. "National Job Corps Study: The Impacts of Job Corps on Participants' Employment and Related Outcomes," Mathematica Policy Research Reports db6c4204b8e1408bb0c6289ec, Mathematica Policy Research.
    18. 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.
    19. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    20. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    21. Amy Finkelstein & Matthew J Notowidigdo, 2019. "Take-Up and Targeting: Experimental Evidence from SNAP," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(3), pages 1505-1556.
    22. Celislami, Elda & Kastoryano, Stephen & Mastrobuoni, Giovanni, 2023. "Strategic Bureaucratic Opacity: Evidence from Death Investigation Laws and Police Killings," IZA Discussion Papers 16609, IZA Network @ LISER.
    23. 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.
    24. Patrick Kline & Christopher R. Walters, 2016. "Evaluating Public Programs with Close Substitutes: The Case of HeadStart," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1795-1848.
    25. Howard S. Bloom & Larry L. Orr & Stephen H. Bell & George Cave & Fred Doolittle & Winston Lin & Johannes M. Bos, 1997. "The Benefits and Costs of JTPA Title II-A Programs: Key Findings from the National Job Training Partnership Act Study," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 549-576.
    26. John Shea, 1997. "Instrument Relevance in Multivariate Linear Models: A Simple Measure," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 348-352, May.
    27. 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.
    28. 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.
    29. Abadie A., 2002. "Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 284-292, March.
    30. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    31. 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.
    32. Aurélie Ouss & Megan Stevenson, 2023. "Does Cash Bail Deter Misconduct?," American Economic Journal: Applied Economics, American Economic Association, vol. 15(3), pages 150-182, July.
    33. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
    34. repec:mpr:mprres:2951 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
    2. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    3. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    4. Patrick Kline & Christopher R. Walters, 2019. "On Heckits, LATE, and Numerical Equivalence," Econometrica, Econometric Society, vol. 87(2), pages 677-696, March.
    5. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    6. Buhl-Wiggers, Julie & Kerwin, Jason T. & Muñoz-Morales, Juan & Smith, Jeffrey & Thornton, Rebecca, 2024. "Some children left behind: Variation in the effects of an educational intervention," Journal of Econometrics, Elsevier, vol. 243(1).
    7. Bartalotti, Otávio & Kédagni, Désiré & Possebom, Vitor, 2023. "Identifying marginal treatment effects in the presence of sample selection," Journal of Econometrics, Elsevier, vol. 234(2), pages 565-584.
    8. Chen, Xuan & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2015. "Going Beyond LATE: Bounding Average Treatment Effects of Job Corps Training," IZA Discussion Papers 9511, IZA Network @ LISER.
    9. Jinhyun Lee, 2013. "Sharp Bounds on Heterogeneous Individual Treatment Responses," Discussion Paper Series, School of Economics and Finance 201310, School of Economics and Finance, University of St Andrews.
    10. Carneiro, Pedro & Lee, Sokbae, 2009. "Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality," Journal of Econometrics, Elsevier, vol. 149(2), pages 191-208, April.
    11. Philip Marx, 2020. "Sharp Bounds in the Latent Index Selection Model," Papers 2012.02390, arXiv.org, revised Apr 2023.
    12. Sun, Zhenting, 2023. "Instrument validity for heterogeneous causal effects," Journal of Econometrics, Elsevier, vol. 237(2).
    13. Hsu, Yu-Chin & Huang, Ta-Cheng & Xu, Haiqing, 2023. "Testing For Unobserved Heterogeneous Treatment Effects With Observational Data," Econometric Theory, Cambridge University Press, vol. 39(3), pages 582-622, June.
    14. Marx, Philip, 2024. "Sharp bounds in the latent index selection model," Journal of Econometrics, Elsevier, vol. 238(2).
    15. Vitor Possebom, 2019. "Sharp Bounds for the Marginal Treatment Effect with Sample Selection," Papers 1904.08522, arXiv.org.
    16. Lee, Jinhyun, 2013. "Sharp Bounds on Heterogeneous Individual Treatment Responses," SIRE Discussion Papers 2013-89, Scottish Institute for Research in Economics (SIRE).
    17. Imbens, Guido W., 2014. "Instrumental Variables: An Econometrician's Perspective," IZA Discussion Papers 8048, IZA Network @ LISER.
    18. Kitagawa, Toru, 2021. "The identification region of the potential outcome distributions under instrument independence," Journal of Econometrics, Elsevier, vol. 225(2), pages 231-253.
    19. Sun, Zhenting & Wüthrich, Kaspar, 2025. "Pairwise valid instruments," Journal of Econometrics, Elsevier, vol. 250(C).
    20. Magne Mogstad & Andres Santos & Alexander Torgovitsky, 2018. "Using Instrumental Variables for Inference About Policy Relevant Treatment Parameters," Econometrica, Econometric Society, vol. 86(5), pages 1589-1619, September.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • H00 - Public Economics - - General - - - General
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp18598. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mark Fallak (email available below). General contact details of provider: https://edirc.repec.org/data/izaaalu.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.