IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/17408.html
   My bibliography  Save this paper

Heaping-Induced Bias in Regression-Discontinuity Designs

Author

Listed:
  • Alan I. Barreca
  • Jason M. Lindo
  • Glen R. Waddell

Abstract

This study uses Monte Carlo simulations to demonstrate that regression-discontinuity designs arrive at biased estimates when attributes related to outcomes predict heaping in the running variable. After showing that our usual diagnostics are poorly suited to identifying this type of problem, we provide alternatives. We also demonstrate how the magnitude and direction of the bias varies with bandwidth choice and the location of the data heaps relative to the treatment threshold. Finally, we discuss approaches to correcting for this type of problem before considering these issues in several non-simulated environments.

Suggested Citation

  • Alan I. Barreca & Jason M. Lindo & Glen R. Waddell, 2011. "Heaping-Induced Bias in Regression-Discontinuity Designs," NBER Working Papers 17408, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:17408
    Note: ED EH LS TWP
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w17408.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, vol. 142(2), pages 615-635, February.
    2. Dickert-Conlin, Stacy & Elder, Todd, 2010. "Suburban legend: School cutoff dates and the timing of births," Economics of Education Review, Elsevier, vol. 29(5), pages 826-841, October.
    3. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    4. McCrary, Justin, 2008. "Manipulation of the running variable in the regression discontinuity design: A density test," Journal of Econometrics, Elsevier, vol. 142(2), pages 698-714, February.
    5. Buddelmeyer, Hielke & Skoufias, Emmanuel, 2003. "An Evaluation of the Performance of Regression Discontinuity Design on PROGRESA," IZA Discussion Papers 827, Institute of Labor Economics (IZA).
    6. Justin McCrary & Heather Royer, 2011. "The Effect of Female Education on Fertility and Infant Health: Evidence from School Entry Policies Using Exact Date of Birth," American Economic Review, American Economic Association, vol. 101(1), pages 158-195, February.
    7. Douglas Almond & Joseph J. Doyle & Amanda E. Kowalski & Heidi Williams, 2010. "Estimating Marginal Returns to Medical Care: Evidence from At-risk Newborns," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(2), pages 591-634.
    8. Cook, Thomas D., 2008. ""Waiting for Life to Arrive": A history of the regression-discontinuity design in Psychology, Statistics and Economics," Journal of Econometrics, Elsevier, vol. 142(2), pages 636-654, February.
    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. Jeffrey Smith & Arthur Sweetman, 2016. "Viewpoint: Estimating the causal effects of policies and programs," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 871-905, August.
    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. Mazzutti, Caio Cícero Toledo Piza da Costa, 2016. "Three essays on the causal impacts of child labour laws in Brazil," Economics PhD Theses 0616, Department of Economics, University of Sussex Business School.
    4. Wael S. Moussa, 2017. "Closer to the Finish Line? Compulsory Attendance, Grade Attainment, and High School Graduation," Education Finance and Policy, MIT Press, vol. 12(1), pages 28-53, Winter.
    5. Mauricio Villamizar‐Villegas & Freddy A. Pinzon‐Puerto & Maria Alejandra Ruiz‐Sanchez, 2022. "A comprehensive history of regression discontinuity designs: An empirical survey of the last 60 years," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1130-1178, September.
    6. David S. Lee & Thomas Lemieux, 2010. "Regression Discontinuity Designs in Economics," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 281-355, June.
    7. Jin-young Choi & Myoung-jae Lee, 2017. "Regression discontinuity: review with extensions," Statistical Papers, Springer, vol. 58(4), pages 1217-1246, December.
    8. Nirav Mehta, 2019. "An Economic Approach to Generalizing Findings from Regression-Discontinuity Designs," Journal of Human Resources, University of Wisconsin Press, vol. 54(4), pages 953-985.
    9. Insu Chang & Heeran Park & Hosung Sohn, 2021. "Causal Impact of School Starting Age on the Tempo of Childbirths: Evidence from Working Mothers and School Entry Cutoff Using Exact Date of Birth," European Journal of Population, Springer;European Association for Population Studies, vol. 37(4), pages 997-1022, November.
    10. Hieu T. M. Nguyen & Blane D. Lewis, 2020. "Teenage Marriage and Motherhood in Vietnam: The Negative Effects of Starting School Early," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 39(4), pages 739-762, August.
    11. Philip M. Gleason & Alexandra M. Resch & Jillian A. Berk, "undated". "Replicating Experimental Impact Estimates Using a Regression Discontinuity Approach," Mathematica Policy Research Reports c7ecf8b35c8d402491d09b2d0, Mathematica Policy Research.
    12. Ivan A Canay & Vishal Kamat, 2018. "Approximate Permutation Tests and Induced Order Statistics in the Regression Discontinuity Design," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(3), pages 1577-1608.
    13. Humlum, Maria Knoth & Kristoffersen, Jannie H.G. & Vejlin, Rune, 2017. "College admissions decisions, educational outcomes, and family formation," Labour Economics, Elsevier, vol. 48(C), pages 215-230.
    14. Frandsen, Brigham R. & Frölich, Markus & Melly, Blaise, 2012. "Quantile treatment effects in the regression discontinuity design," Journal of Econometrics, Elsevier, vol. 168(2), pages 382-395.
    15. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 3-30, Spring.
    16. Ari Hyytinen & Jaakko Meriläinen & Tuukka Saarimaa & Otto Toivanen & Janne Tukiainen, 2018. "When does regression discontinuity design work? Evidence from random election outcomes," Quantitative Economics, Econometric Society, vol. 9(2), pages 1019-1051, July.
    17. Michael Clemens & Erwin Tiongson, 2012. "Split Decisions: Family finance when a policy discontinuity allocates overseas work," RF Berlin - CReAM Discussion Paper Series 1234, Rockwool Foundation Berlin (RF Berlin) - Centre for Research and Analysis of Migration (CReAM).
    18. Juan Carlos Calcagno & Bridget Terry Long, 2008. "The Impact of Postsecondary Remediation Using a Regression Discontinuity Approach: Addressing Endogenous Sorting and Noncompliance," NBER Working Papers 14194, National Bureau of Economic Research, Inc.
    19. Chad D. Meyerhoefer & Muzhe Yang, 2011. "The Relationship between Food Assistance and Health: A Review of the Literature and Empirical Strategies for Identifying Program Effects," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 33(3), pages 304-344.
    20. Jill Furzer & Elizabeth Dhuey & Audrey Laporte, 2022. "ADHD misdiagnosis: Causes and mitigators," Health Economics, John Wiley & Sons, Ltd., vol. 31(9), pages 1926-1953, September.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

    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:nbr:nberwo:17408. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.