Can fighting grade inflation help the bottom line?

Abstract

This article uses a rich set of student transcript data to estimate the economic cost incurred by a university when it does not adopt a ‘mean-shift grading policy’ to fight grade inflation. We show that even in the face of moral hazard constraints a university can enhance its profitability by fighting grade inflation with a distribution-shifting policy.

Acknowledgements

Thanks to Glenn Davis, USU Registrar, and Caryn Beck-Dudley, former Dean of the USU COB, for granting access to the student transcript data used in this study, and Randy Shelton and Krystin Deschamps for compiling the data. Prithvi Jutur was instrumental in writing the Excel macros that further compiled the data into the data set used for this study. We also greatly benefitted from discussions with colleagues David Dickinson and David Aadland, discussions that ultimately provoked us to write this article.

Notes

¹ φ, which is purposefully unsigned, accounts for the relative sizes of the benefits (e.g. reduced bottlenecks) and costs (e.g. vacant classrooms on campus) of unutilized institutional capacity. Appropriate curvature and bounding conditions on the profit function ensure that the integral in the objective function converges (Chiang, 1992).

² We assume the possibility of both demand-side moral hazard (e.g. reduced demand exhibited by potential incoming students, strategic behaviour by the institution's competitors) and supply-side moral hazard (e.g. excessive use of failing grades by certain faculty members, strategic behaviour among departments within the institution). Although estimating the potential effects of demand-side moral hazard on an institution's longer-term profitability is beyond the scope of this article, we are able to illustrate how large the effects would have to be in order to reduce by half the ‘naïve’ fail rate, that is, the profit-maximizing number of failed courses when demand-side moral hazard is ignored.

³ Similar to potential moral hazard, the effect of reduced unutilized capacity on cannot be determined empirically because of data limitations. We consider three numerical scenarios to capture the sensitivity of to various degrees of reduction.

⁴ We conducted a Hausman test for joint determination following the method originally proposed by Wu (1973). The results, available from the authors upon request, indicate that DROP and #FAIL are jointly determined at the 1% level of significance.

⁵ The zero-inflation model follows Long (1997, pp. 242–7), Greene (2003) and Vuong (1989).

⁶ The results from this first-step regression are available from the authors upon request.

⁷ Further information is available on request.

⁸ These figures are based on a typical student enrolling for four three-credit courses per semester. The university reports that approximately 80% of the incoming freshmen for 2004–2005 were residents. Therefore, the weighted average of the resident and nonresident tuition per-course tuition cost is approximately $580.

⁹ We assume a 3% discount rate.