This paper addresses how changing the admission and financial aid rules at colleges affects future earnings. I estimate a structural model of the following decisions by individuals: where to submit applications, which school to attend, and what field to study. The model also includes decisions by schools as to which students to accept and how much financial aid to offer. Simulating how black educational choices would change were they to face the white admission and aid rules shows that race-based advantages had little effect on earnings. However, removing race-based advantages does affect black educational outcomes. In particular, removing advantages in admissions substantially decreases the number of black students at top-tier schools, while removing advantages in financial aid causes a decrease in the number of blacks who attend college.

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. (JEL I23, I26, I28, J15, J31, J44, K10)

We adapt the expectation–maximization algorithm to incorporate unobserved heterogeneity into conditional choice probability (CCP) estimators of dynamic discrete choice problems. The unobserved heterogeneity can be time-invariant or follow a Markov chain. By developing a class of problems where the difference in future value terms depends on a few conditional choice probabilities, we extend the class of dynamic optimization problems where CCP estimators provide a computationally cheap alternative to full solution methods. Monte Carlo results confirm that our algorithms perform quite well, both in terms of computational time and in the precision of the parameter estimates.

A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a finite mixture distribution. A barrier to using finite mixture models is that parameters that could previously be estimated in stages must now be estimated jointly: using mixture distributions destroys any additive separability of the log-likelihood function. We show, however, that an extension of the EM algorithm reintroduces additive separability, thus allowing one to estimate parameters sequentially during each maximization step. In establishing this result, we develop a broad class of estimators for mixture models. Returning to the likelihood problem, we show that, relative to full information maximum likelihood, our sequential estimator can generate large computational savings with little loss of efficiency.

An important issue with conducting economic analysis in the lab is whether the results generalize to real-world environments where the stakes and subject pool are considerably different. We examine data from the game show The Weakest Link to determine whether the gender of one's opponent affects performance. We then attempt to replicate the competitive structure of the game show in the lab with an undergraduate subject pool. The results in the lab only match when we both employ high stakes in the lab (≥ $50) and limit our analysis to young contestants in the game show (age < 33).

We use public documents from the Students for Fair Admissions v. Harvard University lawsuit to examine admissions preferences for recruited athletes, legacies, those on the dean’s interest list, and children of faculty and staff (ALDCs). More than 43% of white admits are ALDC; the share for African American, Asian American, and Hispanics is less than 16%. Our model of admissions shows that roughly three-quarters of white ALDC admits would have been rejected absent their ALDC status. Removing preferences for athletes and legacies would significantly alter the racial distribution of admitted students away from whites.

We estimate a model where workers are heterogeneous both in their own productivity and in their ability to facilitate the productivity of others. We use data from professional basketball to measure the importance of peers in productivity because we have clear measures of output and members of a worker’s group change on a regular basis. Our empirical results highlight that productivity spillovers play an important role in team production. Despite this, we find that worker compensation is largely determined by own productivity with little weight given to productivity spillovers.

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.

We examine how informational frictions impact schooling and work outcomes by estimating a dynamic structural model where individuals face uncertainty about their academic ability and productivity, which determine their schooling utility and wages. We account for different college types, majors, occupational search frictions, and work hours. Individuals learn from grades and wages, which may affect their choices. Removing informational frictions would increase graduation by 4.4 percentage points and by an additional 2 points without search frictions. Providing students with full information about their abilities would increase the college and white-collar wage premia while reducing the graduation gap by family income.

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.