IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/1014.html
   My bibliography  Save this paper

Optimal Financial Aid Policies for a Selective University

Author

Listed:
  • Ronald G. Ehrenberg
  • Daniel R. Sherman

Abstract

Recent federal cut-backs of financial support for undergraduates have worsened the financial position of colleges and universities and required them to debate how they will allocate their scarce financial aid resources.Our paper contributes to the debate by providing a model of optimal financial aid policies for a selective university-one that has a sufficient number of qualified applicants that it can select which ones to accept and the type of financial aid package to offer each admitted applicant.The university is assumed to derive utility from "quality-units" of different categories (race, sex, ethnic status, income class, alumni relatives, etc.) of enrolled students. Average quality in a category declines with the number of applicants admitted and the fraction of admitted applicants who enroll increases with the financial aid package offered the category.The university maximizes utility subject to the constraint that its total subsidy of students (net tuition revenue less costs including financial aid)is just offset by a predetermined income flow from nonstudent sources (e.g.,endowment). The model implies that the financial aid package to be offered to each category of admitted applicants depends on the elasticity of the fraction who accept offers of admission with respect to the financial aid package offered them, the propensity of the category to enroll, the elasticity of the categorys average quality with respect to the number admitted, and the relative weight the university assigns in the utility function to applicants in the category.While the latter must be subjectively determined by university administrators, the former parameters are subject to empirical estimation.The paper concludes with a case study of one selective institution's dataand illustrates how they may be estimated. Based upon data from the university's admissions and financial aid files, as well as questionnaire data which ascertained what alternative college most admitted freshman applicants were considering and the financial aid packages at the alternative, probit probability of enrollment equations are estimated as are equations that determine how average quality varies with the number admitted for each category. These estimates are then applied to illustrate what the"optimal" financial aid policy would be for the university.

Suggested Citation

  • Ronald G. Ehrenberg & Daniel R. Sherman, 1982. "Optimal Financial Aid Policies for a Selective University," NBER Working Papers 1014, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:1014
    Note: LS
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w1014.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Manski, Charles F & Lerman, Steven R, 1977. "The Estimation of Choice Probabilities from Choice Based Samples," Econometrica, Econometric Society, vol. 45(8), pages 1977-1988, November.
    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. Steimetz, Seiji S.C. & Brownstone, David, 2005. "Estimating commuters' "value of time" with noisy data: a multiple imputation approach," Transportation Research Part B: Methodological, Elsevier, vol. 39(10), pages 865-889, December.
    2. Iain M. Cockburn & Megan J. MacGarvie, 2011. "Entry and Patenting in the Software Industry," Management Science, INFORMS, vol. 57(5), pages 915-933, May.
    3. Lurkin, Virginie & Garrow, Laurie A. & Higgins, Matthew J. & Newman, Jeffrey P. & Schyns, Michael, 2017. "Accounting for price endogeneity in airline itinerary choice models: An application to Continental U.S. markets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 228-246.
    4. Seiji S. C. Steimetz, 2009. "White‐Knuckle Externalities," Economic Inquiry, Western Economic Association International, vol. 47(2), pages 304-316, April.
    5. Esmeralda Ramalho, 2004. "Covariate Measurement Error in Endogenous Stratified Samples," Economics Working Papers 2_2004, University of Évora, Department of Economics (Portugal).
    6. Richard Disney & Eleonora Fischera & Trudy Owens, 2010. "Has the Introduction of Microfinance Crowded-out Informal Loans in Malawi?," Discussion Papers 10/08, University of Nottingham, CREDIT.
    7. Trent Geisler & Herman Ray & Ying Xie, 2023. "Finding the Proverbial Needle: Improving Minority Class Identification Under Extreme Class Imbalance," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 192-212, April.
    8. Steven Berry & James Levinsohn & Ariel Pakes, 2004. "Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market," Journal of Political Economy, University of Chicago Press, vol. 112(1), pages 68-105, February.
    9. Keith Head & Yao Amber Li & Asier Minondo, 2019. "Geography, Ties, and Knowledge Flows: Evidence from Citations in Mathematics," The Review of Economics and Statistics, MIT Press, vol. 101(4), pages 713-727, October.
    10. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    11. Sarlin, Peter & von Schweinitz, Gregor, 2021. "Optimizing Policymakers’ Loss Functions In Crisis Prediction: Before, Within Or After?," Macroeconomic Dynamics, Cambridge University Press, vol. 25(1), pages 100-123, January.
    12. Esmerelda A. Ramalho & Richard Smith, 2003. "Discrete choice non-response," CeMMAP working papers 07/03, Institute for Fiscal Studies.
    13. Ignacio A. Inoa & Nathalie Picard & Andr� de Palma, 2015. "Effect of an Accessibility Measure in a Model for Choice of Residential Location, Workplace, and Type of Employment," Mathematical Population Studies, Taylor & Francis Journals, vol. 22(1), pages 4-36, March.
    14. Stefano Usai & Emanuela Marrocu & Raffaele Paci, 2017. "Networks, Proximities, and Interfirm Knowledge Exchanges," International Regional Science Review, , vol. 40(4), pages 377-404, July.
    15. James Hansen & James McDonald & Panayiotis Theodossiou & Brad Larsen, 2010. "Partially Adaptive Econometric Methods For Regression and Classification," Computational Economics, Springer;Society for Computational Economics, vol. 36(2), pages 153-169, August.
    16. Nicolas Jacquemet & Stephane Luchini & Jason Shogren & Verity Watson, 2019. "Discrete Choice under Oaths," Post-Print halshs-02136103, HAL.
    17. Lancaster, Tony & Imbens, Guido, 1996. "Case-control studies with contaminated controls," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 145-160.
    18. Amanda Coston & Edward H. Kennedy, 2022. "The role of the geometric mean in case-control studies," Papers 2207.09016, arXiv.org.
    19. Jalan, Jyotsna & Ravallion, Martin, 1999. "Income gains to the poor from workfare - estimates for Argentina's TRABAJAR Program," Policy Research Working Paper Series 2149, The World Bank.
    20. Imbens, Guido W. & Lancaster, Tony, 1996. "Efficient estimation and stratified sampling," Journal of Econometrics, Elsevier, vol. 74(2), pages 289-318, October.

    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:nbr:nberwo:1014. 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.