With the American Supreme Court preparing to hear two cases on the use of race-based affirmative action in university admissions, the stage is set for a landmark opinion that would prevent race from being considered in admissions in any capacity. What is the economic rationale for affirmative action policies, and what are the potential economic implications of banning them?
The financial returns to attending college are well established. Not all colleges are the same however, and there are widely held beliefs that the returns from attending prestigious universities are larger than others (a view backed by recent research). This is reflected in the demand for places at some colleges exceeding capacity. Affirmative action policies were designed to increase the probability that students from underrepresented minorities would gain admission to selective schools, essentially by lowering academic admission requirements for them.
While these policies may improve equity in enrollment, are they efficient? Allocative efficiency requires that the benefit to the selected students is greater than for those who were not selected.
This naturally leads to the question of whether certain students are better suited to certain courses of study? In other words, are there complementarities between students and universities, so that the most academically prepared students gain more out of the most academically demanding courses? If there are no complementarities, and the most prestigious, high-return courses benefit all students equally, then policymakers concerned with reducing earnings inequality may wish to implement affirmative action policies, as there would be no consequence on net returns. In contrast if there are complementarities, then allocating students to courses to which they are well matched would produce net gains for society. Those gains would need to be weighed up against the goal of reducing inequality.
Key to determining the consequences of affirmative action policies is establishing if there is a student-to-university match effect. A first step toward this is to define what we mean by “matching.” We recently set out a simple metric for measuring student match. A student who has similar qualifications to other students on the same course is well matched. Students who have higher qualifications than their peers are undermatched, and those with lower qualifications are overmatched. A student who is overmatched may still gain more than enrolling on a lower ranked course, depending on the extent of the match effects. A student who is undermatched, on the other hand, is likely to achieve lower benefits from college than otherwise (due to both mismatch effects and attending a lower return university).
In our recent IZA World of Labor article we review the literature on the effects of mismatch. A small number of papers have studied the impact of overmatching, with specific reference to the affirmative action policy of the university system of California, where on average underrepresented minority (URM) freshman are admitted with significantly lower SATs. There are two examples that find opposing results on the impact of overmatching in this context. The first finds that the match effects outweigh the university effects. The authors argue less-prepared minority students at top-ranked campuses would have higher science graduation rates had they attended lower-ranked campuses. A more recent study investigated the abolition of this affirmative action policy and found that this caused underrepresented minority applicants to enrol at lower-quality universities, with subsequent reductions in their degree attainment, and ultimately lower wages.
The finding that overmatched students are gaining from attending prestigious institutions does not exclude the possibility that match effects exist, merely that university quality effects may outweigh them. An alternative explanation is that such students were not overmatched at these universities, but instead that measurements of academic preparedness systematically undervalue these students. This is more likely to occur in applications systems which rely on costly standardized testing, and out of the classroom activities, which advantage those from wealthier backgrounds. The bottom line is that qualification measures that are closely tied to the school and university curriculum and less to the background of the student, are more likely to result in better matches.
© Richard Murphy and Gillian Wyness
Richard Murphy is assistant professor of economics at the University of Texas at Austin and a Research Affiliate of IZA.
Gillian Wyness is professor of economics at University College London, UK.
We recognize that IZA World of Labor articles may prompt discussion and possibly controversy. Opinion pieces, such as the one above, capture ideas and debates concisely, and anchor them with real-world examples. Opinions stated here do not necessarily reflect those of the IZA.