IDEAS home Printed from https://ideas.repec.org/p/boc/bocoec/759.html
   My bibliography  Save this paper

Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models

Author

Listed:
  • Yingying Dong

    (California State University, Irvine)

  • Arthur Lewbel

    (Boston College)

Abstract

Regression discontinuity models, where the probability of treatment jumps discretely when a running variable crosses a threshold, are commonly used to nonparametrically identify and estimate a local average treatment effect. We show that the derivative of this treatment effect with respect to the running variable is nonparametrically identified and easily estimated. Then, given a local policy invariance assumption, we show that this derivative equals the change in the treatment effect that would result from a marginal change in the threshold, which we call the marginal threshold treatment effect (MTTE). We apply this result to Manacorda (2012), who estimates a treatment effect of grade retention on school outcomes. Our MTTE identifies how this treatment effect would change if the threshold for retention was raised or lowered, even though no such change in threshold is actually observed.

Suggested Citation

  • Yingying Dong & Arthur Lewbel, 2010. "Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models," Boston College Working Papers in Economics 759, Boston College Department of Economics, revised 15 Dec 2012.
    • Handle: RePEc:boc:bocoec:759
    Note: Previously circulated as "Regression Discontinuity Marginal Threshold Treatment Effects"
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/EC-P/wp759.pdf
    File Function: main text
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    Keywords

    regression discontinuity; sharp design; fuzzy design; treatment effects; program evaluation; threshold; running variable; forcing variable; marginal effects.;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

    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:boc:bocoec:759. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Christopher F Baum (email available below). General contact details of provider: https://edirc.repec.org/data/debocus.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.