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Pre-College human capital investment occurs within a competitive environment and depends on market incentives created by Affirmative Action (AA) in college admissions. These policies affect mechanisms for rank-order allocation of college seats, and alter the relative competition between blacks and whites. We present a theory of AA in university admissions, showing how the effects of AA on human capital investment differ by student ability and demographic group. We then conduct a field experiment designed to mimic important aspects of competitive investment prior to the college market. We pay students based on relative performance on a mathematics exam in order to test the incentive effects of AA, and track study efforts on an online mathematics website. Consistent with theory, AA increases average human capital investment and exam performance for the majority of disadvantaged students targeted by the policy, by mitigating so-called "discouragement effects." The experimental evidence suggests that AA can promote greater equality of market outcomes and narrow achievement gaps at the same time.

Students who attend different colleges in the U.S. end up with vastly different economic outcomes. We study the role of relative value-added across colleges within student choice sets in producing these outcome disparities. Linking high school, college, and earnings registries spanning the state of Texas, we identify relative college value-added by comparing the outcomes of students who apply to and are admitted by the same set of institutions, as this approach strikingly balances observable student potential across college treatments and renders our extensive set of covariates irrelevant as controls. Methodologically, we develop a framework for identifying and interpreting value-added under varying assumptions about match effects and sorting gains. Empirically, we estimate a relatively tight, though non-degenerate, distribution of relative value-added across the wide diversity of Texas public universities. Selectivity poorly predicts value-added within student choice sets, with only a fleeting selectivity earnings premium fading to zero after a few years in the labor market. Non-peer college inputs like instructional spending more strongly predict value-added, especially conditional on selectivity. Colleges that boost BA completion, especially in STEM majors, also tend to boost earnings. Finally, we probe the potential for (mis)match effects by allowing value-added schedules to vary by student characteristics. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

We leverage a field experiment across three distinct school districts to identify key pieces of a structural model of adolescent human capital production. Our focus is inspired by the contemporary psychology of education literature, which expresses learning as a function of the ratio of the time spent on learning to the time needed to learn. By capturing two crucial student-level unobservables—which we denote as academic efficiency (turning inputs into outputs) and time preference (motivation)—our field experiment lends insights into the underpinnings of adolescent skill formation and provides a novel view of how to lessen racial and gender achievement gaps. One general insight is that students who are falling behind their peers, whether correlated to race, gender, or school district, are doing so because of academic efficiency rather than time preference. We view this result, and others found in our data, as fundamental to practitioners, academics, and policymakers interested in designing strategies to provide equal opportunities to students.

We leverage a field experiment across three distinct school districts to identify key pieces of a structural model of adolescent human capital production. Our focus is inspired by the contemporary psychology of education literature, which expresses learning as a function of the ratio of the time spent on learning to the time needed to learn. By capturing two crucial student-level unobservables—which we denote as academic efficiency (turning inputs into outputs) and time preference (motivation)—our field experiment lends insights into the underpinnings of adolescent skill formation and provides a novel view of how to lessen racial and gender achievement gaps. One general insight is that students who are falling behind their peers, whether correlated to race, gender, or school district, are doing so because of academic efficiency rather than time preference. We view this result, and others found in our data, as fundamental to practitioners, academics, and policymakers interested in designing strategies to provide equal opportunities to students. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Because of discrete bid increments, bidders at electronic auctions engage in shading instead of revealing their valuations, which would occur under the commonly assumed second‐price rule. We demonstrate that misspecifying the pricing rule can lead to biased estimates of the latent valuation distribution, and then explore identification and estimation of a model with a correctly specified pricing rule. A further challenge to econometricians is that only a lower bound on the number of participants at each auction is observed. From this bound, however, we establish nonparametric identification of the arrival process of bidders—the process that matches potential buyers to auction listings—which then allows us to identify the latent valuation distribution without imposing functional‐form assumptions. We propose a computationally tractable, sieve‐type estimator of the latent valuation distribution based on B‐splines, and then compare two parametric models of bidder participation, finding that a generalized Poisson model cannot be rejected by the empirical distribution of observables. Our structural estimates enable us to explore information rents and optimal reserve prices on eBay. eBay electronic auctions bid increments pricing rule D44 C72

We develop theory and a tightly-linked field experiment to explore the supply side implications of corporate social responsibility (CSR). Our natural field experiment, in which we created our own firm and hired actual workers, generates a rich data set on worker behavior and responses to both pecuniary and CSR incentives. Making use of a novel identification framework, we use these data to estimate a structural principal-agent model. This approach permits us to compare and contrast treatment and selection effects of both CSR and financial incentives. Using data from more than 1100 job seekers, we find strong evidence that when a firm advertises work as socially-oriented, it attracts employees who are more productive, produce higher quality work, and have more highly valued leisure time. In terms of enhancing the labor pool, for example, CSR increases the number of applicants by 25 percent, an impact comparable to the effect of a 36 percent increase in wages. We also find an economically important complementarity between CSR and wage offers, highlighting the import of using both to hire and motivate workers. Beyond lending insights into the supply side of CSR, our research design serves as a framework for causal inference on other forms of non-pecuniary incentives and amenities in the workplace, or any other domain more generally.

Substantial price variation for homogeneous goods in online markets is a well-known puzzle that has withstood attempts by empirical researchers to explain it. Economic theory suggests two possible sources of the dispersion: either market frictions are more important than previously thought, or there are subtle differences between product listings presented to e-commerce consumers that applied econometricians have failed to detect. We use a very detailed data set consisting of posted-price listings for new Kindle Fire tablets from eBay to determine if observable listing heterogeneity can explain the price dispersion of seemingly homogeneous products. By combining a richer set of variables than previous studies with more sophisticated machine learning techniques, we can explain 42% of the dispersion. We interpret this as a bound on the influence of market frictions on price dispersion. Variables describing the amount of information in the listing are good predictors of the price, but variables describing the style of a listing's text are good predictors as well. We identify readily interpretable groups of words that are also good predictors of price. We find a high degree of heterogeneity of the marginal effects of seller reputation and including an image in the listing, but the patterns of heterogeneity largely conform to economic intuition. A smaller, but non-trivial, latitude for market frictions remains, and we discuss their possible sources.

We study a structural model of college admissions framed as a contest between a continuum of students for enrollment in a continuum of colleges where the contest outcome is decided by the students' choice of human capital (HC). Students have private information about their learning costs, and colleges have heterogeneous, observable qualities. Our economic model is inspired by methods from the empirical auctions literature and allow us to separately identify the roles of school quality, HC, and unobserved learning costs on post-college household income. We use our estimates to conduct counterfactual experiments comparing different college admissions rules including color-blind admissions, a proportional quota for minority students, and means-tested affirmative action (AA). An AA ban would result in a large migration of minority students out of the best schools and into the lowest quality schools with a corresponding reduction in household income and mean graduation rates. However, the signs and magnitudes of changes to HC investment and individual graduation rate depend on the demographics and learning cost type of the particular student in question. We also argue that a means-tested AA plan does not significantly increase racial diversity. Finally, our estimates imply that the competitive incentive to accrue HC is stronger than the productive incentive for all but the top 4% achieving students.

Summary Two-step nonparametric estimators have become standard in empirical auctions. A drawback concerns boundary effects which cause inconsistencies near the endpoints of the support and bias in finite samples. To cope, sample trimming is typically used, which leads to non-random data loss. Monte Carlo experiments show this leads to poor performance near the support boundaries and on the interior due to bandwidth selection issues. We propose a modification that employs boundary correction techniques, and we demonstrate substantial improvement in finite-sample performance. We implement the new estimator using oil lease auctions data and find that trimming masks a substantial degree of bidder asymmetry and inefficiency in allocations. Copyright © 2014 John Wiley & Sons, Ltd.

Pre-College human capital investment occurs within a competitive environment and depends on market incentives created by Affirmative Action (AA) in college admissions. These policies affect mechanisms for rank-order allocation of college seats, and alter the relative competition between blacks and whites. We present a theory of AA in university admissions, showing how the effects of AA on human capital investment differ by student ability and demographic group. We then conduct a field experiment designed to mimic important aspects of competitive investment prior to the college market. We pay students based on relative performance on a mathematics exam in order to test the incentive effects of AA, and track study efforts on an online mathematics website. Consistent with theory, AA increases average human capital investment and exam performance for the majority of disadvantaged students targeted by the policy, by mitigating so-called "discouragement effects." The experimental evidence suggests that AA can promote greater equality of market outcomes and narrow achievement gaps at the same time.

We provide a model of a decentralized, dynamic auction market platform (e.g., eBay) in which a large number of buyers and sellers participate in simultaneous, single-unit auctions each period. Our model accounts for the endogenous entry of agents and the impact of intertemporal optimization on bids. Solving our model with a finite number of bidders is computationally intractable due to the curse of dimensionality, so we prove that a continuum version of our model provides a good approximation of an equilibrium in the finite model. We use the approximation to estimate the structural primitives of our model using Kindle sales on eBay. We find that just over one third of Kindle auctions on eBay result in an inefficient allocation with deadweight loss amounting to 13.5% of total possible market surplus. We also find that partial centralization - for example, running half as many 2-unit, uniform price auctions each day - would eliminate a large fraction of the inefficiency, but yield lower seller revenues. Our results highlight the importance of understanding platform composition effects - selection of agents into the market - in assessing the implications of market design.

Researchers and experts have typically viewed electronic auctions (such as those implemented by eBay, Amazon, and Yahoo!) as either oral, ascending-price (English) auctions or second-price, sealed-bid (Vickrey) auctions. I show that significant differences exist, both theoretical and practical, between English and Vickrey pricing rules and those used in electronic auctions. In addition, in a static model, within the symmetric independent private-values (IPV) paradigm, I derive the unique symmetric equilibrium bid function, showing that the presence of bid increments can significantly alter bidder behavior. Using numerical methods, I also illustrate that these result in a highly non-linear bid function, in contrast to that predicted under either the English or the Vickrey formats.

We develop a model of college assignment as a large contest wherein students with heterogeneous abilities compete for seats at vertically differentiated colleges through the acquisition of productive human capital. We use a continuum model to approximate the outcomes of a game with large, but finite, sets of colleges and students. By incorporating two common families of affirmative action mechanisms into our model -- admissions preferences and quotas -- we can show that (legal) admissions preference schemes and (illegal) quotas have the same sets of equilibria including identical outcomes and investment strategies. Finally, we explore the welfare costs of using human capital accumulation to compete for college admissions. We define the cost of competition as the welfare difference between a color-blind admissions contest and the first-best outcome chosen by an omniscient social planner. Using a calibrated version of our model, we find that the cost of competition is equivalent to a loss of $91,795 in NPV of lifetime earnings.

We conduct a field experiment paying students according to relative performance on a mathematics exam and tracking study efforts on a mathematics website to test the incentive effects of affirmativ...

While much is known about optimal design of auctions within the context of a single item for sale, little is known about optimal design of large platform markets like eBay and auto auction houses that house large numbers of concurrent auctions. We attempt a macro-level empirical market design exercise by combining a unique dataset on tablet sales collected from eBay over the course of a year with methodologies developed by Bodoh-Creed (2011) and Backus and Lewis (2013). The former proposes a tractable approach to studying dynamic auction markets when the number of participants on both sides is sufficiently large. The model also delivers predictions on optimal fee schedule design -- specifically, in terms of listing fees and percentage charges for a sale -- for an auction house wishing to maximize profits by attracting the appropriate mix of buyers and sellers into the market. We begin by empirically investigating the key assumptions of the model which deliver (computational and empirical) tractability, and find that they are reasonable. We then estimate consumer demand, market supply, and the distributions of market entrants (this part still in progress). These figures are plugged into the Bodoh-Creed (2011) framework in order to compute optimal fee schedules and draw comparisons to actual fee schedules, as well as to make policy prescriptions.

Auction models have proved to be attractive to structural econometricians who, since the late 1980s, have made substantial progress in identifying and estimating these rich game-theoretic models of bidder behavior. We provide a guide to the literature in which we contrast the various informational structures (paradigms) commonly assumed by researchers and uncover the evolution of the eld. We highlight major contributions within each paradigm and benchmark modi cations and extensions to these core models. Lastly, we discuss special topics that have received substantial attention among auction researchers in recent years, including auctions formultiple objects, auctions with risk averse bidders, testing between common and private value paradigms, unobserved auction-speci c heterogeneity, and accounting for an unobserved number of bidders as well as endogenous entry.