This paper reviews the literature on affirmative action in undergraduate education and law schools, focusing especially on the trade-off between institutional quality and the fit between a school and a student. We discuss the conditions under which affirmative action for underrepresented minorities (URM) could help or harm their educational outcomes. We provide descriptive evidence on the extent of affirmative action in law schools, as well as a critical review of the contentious literature on how affirmative action affects URM law-school student performance. Our review then discusses affirmative action in undergraduate admissions, focusing on the effects such admissions preferences have on college quality, graduation rates, college major, and earnings. We conclude by examining the evidence on “percent plans” as a replacement for affirmative action.

We examine differences in minority science graduation rates among University of California campuses when racial preferences were in place. Less prepared minorities at higher ranked campuses had lower persistence rates in science and took longer to graduate. We estimate a model of students' college major choice where net returns of a science major differ across campuses and student preparation. We find less prepared minority students at top ranked campuses would have higher science graduation rates had they attended lower ranked campuses. Better matching of science students to universities by preparation and providing information about students' prospects in different major-university combinations could increase minority science graduation.

This article develops a dynamic model of retail competition and uses it to study the impact of the expansion of a new national competitor on the structure of urban markets. In order to accommodate substantial heterogeneity (both observed and unobserved) across agents and markets, the article first develops a general framework for estimating and solving dynamic discrete choice models in continuous time that is computationally light and readily applicable to dynamic games. In the proposed framework, players face a standard dynamic discrete choice problem at decision times that occur stochastically. The resulting stochastic-sequential structure naturally admits the use of conditional choice probability methods for estimation and makes it possible to compute counterfactual simulations for relatively high-dimensional games. The model and method are applied to the retail grocery industry, into which Walmart began rapidly expanding in the early 1990s, eventually attaining a dominant position. We find that Walmart's expansion into groceries came mostly at the expense of the large incumbent supermarket chains, rather than the single-store outlets that bore the brunt of its earlier conquest of the broader general merchandise sector. Instead, we find that independent grocers actually thrive when Walmart enters, leading to an overall reduction in market concentration. These competitive effects are strongest in larger markets and those into which Walmart expanded most rapidly, suggesting a diminishing role of scale and a greater emphasis on differentiation in this previously mature industry.