IDEAS home Printed from https://ideas.repec.org/p/sin/wpaper/12-a013.html
   My bibliography  Save this paper

Semi-parametric Estimation of Program Impacts on Dispersion of Potential Wages

Author

Listed:

Abstract

Policy evaluations require estimation of program impacts on both mean and dispersion of potential outcomes, even though the previous identi cation e orts have focused mostly on impacts on the mean. We propose the use of instrumental variables and pairwise matching to identify the average treatment e ect on conditional variance in potential outcomes. We show that it is possible to identify and estimate program impact on conditional dispersion of potential outcomes in an endogenous switching model, without using the identification-at-infinity argument, if we impose semi-parametric conditions on the error structure of the outcome equation. In the presence of a multi-valued or continuously distributed instrument, we recommend the pairwise-matching method if the error term and the latent index are symmetrically distributed around zero. Simulation results show that the proposed estimators converge at the regular parametric rate. We apply the new estimators to a well-known empirical study of the e ect of education on potential wage inequality.

Suggested Citation

  • Stacey H. Chen & Shakeeb Khan, 2012. "Semi-parametric Estimation of Program Impacts on Dispersion of Potential Wages," IEAS Working Paper : academic research 12-A013, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    • Handle: RePEc:sin:wpaper:12-a013
    as

    Download full text from publisher

    File URL: https://www.econ.sinica.edu.tw/~econ/pdfPaper/12-A013.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abrevaya, Jason & Xu, Haiqing, 2023. "Estimation of treatment effects under endogenous heteroskedasticity," Journal of Econometrics, Elsevier, vol. 234(2), pages 451-478.

    More about this item

    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:sin:wpaper:12-a013. 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: HsiaoyunLiu (email available below). General contact details of provider: https://edirc.repec.org/data/sinictw.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.