Preferential Admission and MBA Outcomes: Mismatch Effects by Race and Gender

The Supreme Court decisions are Regents of the University of California v. Bakke (1978); Grutter v. Bollinger (2003) which upheld the law school at the University of Michigan’s affirmative action policies; and Gratz v. Bollinger (2003). State propositions passed in California in 1996, in Washington in 1998, Nebraska 2008, Michigan 2008, Arizona in 2010 and Oklahoma in 2012. For more details, see Fang and Moro (2010).

This idea is attributed to Thomas Sowell (1972).

The use of the Socratic method in law school as characterized in the movie, The Paper Chase, might cause embarrassment and self-doubt in front of one’s peers if a mismatch was clearly and publically displayed to an entire cohort of law school students.

For the law school affirmative action literature, see Sander (2004), Rothstein and Yoon (2008; 2009), Ayres and Brooks (2005), Chambers et al. (2005), Ho (2005), and Williams (2010). All these studies utilize the BPS dataset, commissioned and conducted by the Law School Admission Council in the 1990s (Wrightman 1998).

Using similar data to that used in this study, Montgomery and Powell (2003) investigate whether women who completed an MBA degree experience lower earnings than those who did not. However, their analysis does not address whether or not gender-related mismatch is the cause of observed earnings differentials.

Kane (1998) also distinguishes between attending historically black schools versus schools of predominantly white students.

While the precise mechanisms for these favorable outcomes are not known, possibilities include better-prepared classmates or better teachers fostering student learning (Kane 1998) or schools with large endowments permitting smaller classes and more faculty mentoring. Carrell et al. (2009) find evidence for the role of study partnerships (p. 441). Light and Strayer (2000) observe that “racial preferences in college admission boost minorities’ chances of attending college and that retention programs directed at minority students subsequently enhance their chances of earning a degree.”

Arcidiacono et al. (2011) analyze Duke University’s use of private information regarding the desirable outcomes of preferentially admitted minorities.

Unlike other higher education settings, law schools provide what amounts to a common exit exam, the bar examination. However, the content and scoring of this exam vary by state. Unfortunately, the BPS does not identify the state in which the exam was taken.

The BPS tracked two-thirds of all students who started law school in 1991 through their law school careers and bar exam experiences. Twenty-seven thousand participants completed surveys when they started law school; data were collected regarding their undergraduate grades, LSAT scores, and law school performance; and for the great majority of them, information was gathered about taking the bar exam in the 3 years after graduation.

Fixed effects go beyond a selection-on-observables approach to dealing with individual differences across race, gender, and program quality, as it eliminates the effect of time-invariant unobserved heterogeneity from biasing our estimates of the returns to an MBA for various subgroups.

We collapsed the more numerous admissions selectivity categories designated in Barron’s guide into three categories: selective undergrad, middle undergrad, and the omitted category (representing both the least selective schools and those not included in the guide).

The following is a complete listing of personal attributes included in the skill index: Initiative, High ethical standards, Communication skills, Ability to work with people from diverse backgrounds, Shrewdness, Ability to organize, Physical attractiveness, Assertiveness, Ability to capitalize on change, Ability to delegate tasks, Ability to adapt theory to practical situations, Understanding business in other cultures, Good intuition, Ability to motivate others, Being a team player, and Knowing the right people. Montgomery and Powell use a similar combination of these responses, referring to it as a “confidence index”.

Earnings (including monetary bonuses but not one-time starting bonuses) were reported in the surveys in a number of possible ways (hourly, weekly, bi-weekly, monthly, or yearly). For those not reporting an hourly wage, we used individual reports of how many hours they work in a typical week to calculate a measure of hourly wage, assuming 50 weeks worked per year. A similar calculation was done for annual salary, also assuming 50 weeks worked per year, when earnings were not reported in annual terms.

See Arcidiacono, Cooley, and Hussey (2008) for further discussion of the benefits and underlying assumptions of the use of fixed effects in a returns to MBA context.

See, for example, Bowen and Bok (1998), Kane (1998), Brewer, Eide, and Goldhaber (1999), and Arcidiacono (2005).

Adding additional observations from inferring acceptance from attending an alternative school does not substantively change the results of our admission analysis.

This set of variables is most similar to studies of admission to law school (Sander 2004), which typically include only LSAT scores and undergraduate GPA.

We exclude the less than 1 year of work experience category and include dummy variables for 1–3 years, 3–5 years and more than 5 years of work experience.

As a test of the robustness of our findings, and in order to include more observations in the selective MBA category, we ran similar regressions comparing the returns to top 25 versus non-top 25 programs for each subgroup. These results can be found in Appendix Table A2.

These findings related to those of Arcidiacono, Cooley, and Hussey (2008) who report evidence that individuals attending lower ranked programs may be less able than non-MBAs in certain difficult-to-measure dimensions like unobserved workplace skills.

See Smith et al. (1987) and the JDI website: http://showcase.bgsu.edu/IOPsych/jdi/index.html. The GMAT Registrant Survey contains three of the five JDI surveys (excluded are the Supervision and the Coworkers surveys).

If a “yes” response was indicated and the job attribute was positive, three points were given. If “can’t decide” was indicated, one point was given. If the job attribute was negative and “no” was indicated, zero points were given.

Similar results exist for more selective and less selective programs when we define these groups based on within and outside the top 50 ranked programs. These results can be found in Appendix Table A3.

Note that these “gendered” differences in concentrations disappear when using top 25 programs as the indicator of selectivity.

Grove, Hussey, and Jetter (2011) analyze the role of non-cognitive attributes and labor market preferences in accounting for the gender pay gap.

The additional programs include mentoring for women and minorities, full-time EO/AA staff, diversity efforts in managers’ evaluations, and networking for women and minorities.