IDEAS home Printed from https://ideas.repec.org/p/isu/genres/12019.html

Model Uncertainty and Race and Gender Heterogeneity in the College Entry Decision

Author

Listed:
  • Tobias, Justin

Abstract

This paper uses a flexible modeling strategy to examine the roles of measured ability, family characteristics and proxies for secondary schooling quality as determinants of the decision to enter college. While previous work on this topic has been careful to determine which explanatory variables to include when modeling college entry decisions, few studies have been concerned about appropriate distributional assumptions, (i.e. choice of link function). In this paper, I extend my binary choice analysis to the class of Student-t link functions, which enables me to approximately regard the often-used probit and logit models as special cases. Unconditional estimates which average over competing models and integrate out model uncertainty are also obtained. Using NLSY data, I apply these methods and find that the link functions and estimated impacts of ability and family characteristics on the probabilities of enrolling in college are not constant across race and gender groups.

Suggested Citation

  • Tobias, Justin, 2002. "Model Uncertainty and Race and Gender Heterogeneity in the College Entry Decision," Staff General Research Papers Archive 12019, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:12019
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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. Stephen L. DesJardins & Dennis A. Ahlburg & Brian P. McCall, 2006. "An Integrated Model of Application, Admission, Enrollment, and Financial Aid," The Journal of Higher Education, Taylor & Francis Journals, vol. 77(3), pages 381-429, May.
    2. Justin L. Tobias & Mingliang Li, 2004. "Returns to Schooling and Bayesian Model Averaging: A Union of Two Literatures," Journal of Economic Surveys, Wiley Blackwell, vol. 18(2), pages 153-180, April.
    3. Kathlyn E. Lucia & Robert W. Baumann, 2009. "Differences in the College Enrollment Decision across Race," The American Economist, Sage Publications, vol. 53(1), pages 60-74, March.

    More about this item

    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:isu:genres:12019. 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: Curtis Balmer (email available below). General contact details of provider: https://edirc.repec.org/data/deiasus.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.