On October 10, the Supreme Court will hear oral argument in Fisher v. University of Texas. The constitutional question in that case is whether the University of Texas’s use of race in undergraduate admissions decisions violates the Equal Protection Clause.
At the risk of some oversimplification, here is a brief summary of UT’s admissions system: The system has two parts. First, under Texas’s Top 10% Law, any graduate of a Texas high school who is ranked in the top 10% of his class is guaranteed admission to UT. The portion of each matriculating class admitted under the race-neutral Top 10% Law has ranged from roughly 60% to 80%.
The second part of the system is used to fill the remaining seats in the class. Under that second part, each applicant is assigned an Academic Index (AI), calculated from class rank and standardized test scores, and a Personal Achievement Index (PAI), which is a composite of essay scores and of a so-called “personal achievement score,” which may take into account various factors including the applicant’s race. The aggregate AI/PAI scores of applicants are then plotted on a grid, with the AI score on one axis and the PAI on the other, and admissions officers draw a stair-step line on the grid to divide those applicants who will be admitted from those who won’t.
Abigail Fisher, the plaintiff in the case, is a white female who was not entitled to admission under the Top 10% Law and who was denied admission under the AI/PAI grid review.
The interested reader can learn more about the case by reading the competing briefs available at SCOTUSblog—but beware that, in addition to the briefs of the parties, there are some 90 or so amicus briefs, including an absurdly high 74 or so amicus briefs in support of UT. (I say “absurdly high” because it is inconceivable that so many separate briefs are needed to convey any relevant legal points.)
In this and follow-on posts, I would like to focus attention on the dirty secret of undergraduate and graduate admissions programs—and on a powerful new book that explains the nefarious consequences of that secret for the supposed beneficiaries of racial preferences. The dirty secret—not a dirty little secret, but a dirty huge secret—is how massive in size their racial preferences are.
UT, like every other university that employs racial preferences, conceals critical information about how they operate. UT structures its PAI in such a way that a minority applicant who gets the lowest possible rating on his essays could, solely because of points awarded for his race, achieve a PAI higher than a white applicant who gets the highest possible rating on his essays. Admissions officers, in drawing their stair-step line on the AI/PAI grid, would then know that favoring low AI/high PAI applicants over high AI/low PAI applicants would yield a much larger pool of minority admittees. More broadly, the AI/PAI part of the admissions system is sufficiently malleable to achieve whatever race targets UT’s admissions officers desire.
As UCLA law professor Richard Sander and legal journalist Stuart Taylor, Jr. discuss in their amicus brief (submitted in support of neither party), the racial preferences that UT and other universities use for African-Americans and Hispanics “are very large indeed”:
For example, among freshmen entering the University of Texas at Austin in 2009 who were admitted outside the top-ten-percent system, the mean SAT score (on a scale of 2400) of Asians was a staggering 467 points and the mean score of whites was 390 points above the mean black score. In percentile terms, these Asians scored at the 93rd percentile of 2009 SAT takers nationwide, whites at the 89th percentile, Hispanics at the 80th percentile, and blacks at the 52nd percentile.
In their new book Mismatch (and in more abbreviated form in their amicus brief), Sander and Taylor discuss the “outpouring of scholarly research in recent years showing how large racial preferences backfire against many and, perhaps, most recipients” and thus inadvertently operate as the “biggest problem for minorities in higher education.” I will explain the mismatch effect and the related cascade effect in my next post.