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 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.

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 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 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.

Using data from Duke University undergraduates, we make three main contributions to the literature. First, we show that data on earnings beliefs and probabilities of choosing particular occupations are highly informative of future earnings and occupations. Second, we show how beliefs data can be used to recover ex ante treatment effects and their relationship with individual choices. We find large differences in expected earnings across occupations and provide evidence of sorting on expected gains. Finally, nonpecuniary factors play an important role, with a sizable share of individuals willing to give up substantial amounts of earnings by not choosing their highest-paying occupation.