Session 2: Empirical Implementation of Theoretical Models of Strategic Interactions and Dynamic Behavior

We use a randomized policy, novel belief data and a structural model to study how preferential college admissions shape upward mobility when students are not fully informed about admission chances. In treatment schools, students who graduated in the top 15 percent of their school were guaranteed college admission. Enrollment in the treatment group increased by 34 percent relative to the control group, but pre-college achievement and effort fell by 0.10 and 0.09 standard deviations, respectively. Survey data show that students hold optimistic beliefs about their school rank and their score in the college entrance exam. We estimate a structural dynamic model of student choices and find that subjective beliefs can rationalize the reduction in pre-college effort as expected-utility-maximizing. Counterfactual simulations show that the  effort response compressed policy impacts on enrollment by 11 percent. When students are not fully informed, their response to perceived incentives can undercut the efficacy of preferential admissions as a tool for fostering upward mobility.

In markets with private options, the optimal level of public provision may require balancing a tradeoff between reducing private options’ market power with the possibility of crowding out potentially high-quality products. These considerations are particularly relevant in many developing countries’ education systems where private schools capture high market shares while public schools are overcrowded. We study the equilibrium effects of public provision in the context of a large expansion of public schools in the Dominican Republic. Over a five-year period, the government aimed to increase the number of public school classrooms by 78%. Using an event study framework, we estimate the effect of a new public school on neighborhood outcomes and competing private schools, where we instrument for how quickly the public school construction project finished with the characteristics of the contractor randomly assigned to build the project. We find that a new public school increased public sector enrollment significantly. As public enrollment increased, a large number of private schools closed while the surviving schools lowered prices and increased school quality. To study how the level of public provision affects the overall level of quality in the market, we specify and estimate an empirical model of demand (students choosing schools) and supply (schools choosing whether to enter, stay open and what price to charge). We use the model estimates to calculate the level of public provision that maximizes learning. Due to equilibrium competitive effects, we find that the optimal level is non-monotonic in the quality of the increased public schooling.

This paper studies families’ preferences for peers in the school and the implications of those preferences for the distribution of academic outcomes. I develop an equilibrium model of school competition and student sorting under social interactions. In the model, families differ by human capital and income. Academic achievement depends on own characteristics, school productivity, and the characteristics of the peers. Geographic differentiation gives schools local market power to increase prices and decrease quality in the absence of close substitutes. On top of that, social interactions generate interdependencies in demand that add a new dimension for school differentiation. This modifies school incentives through two channels: increased differentiation strengthens market power for some schools (direct channel) and incentivizes a screening strategy that exploits heterogeneous responses to prices and quality to intensify that differentiation (strategic channel). To study the empirical importance of these mechanisms, I estimate the model using administrative microdata from Peru. I address endogeneity of prices, quality, and peers by combining a regression discontinuity in the assignment of a scholarship with instruments that exploit the timing and local variation of a generous teacher payment reform and shocks to student sorting generated by a teachers’ strike. I find that social interactions have sizable effects, increasing the income gap in academic achievement by 30 percentage points. I use the predictions of the model to analyze the effects of counterfactual education policies in equilibrium. I then decompose the mechanisms to provide guidance on how to design education policies that improve the distribution of outcomes.

In many settings, market designers must contend with the presence of firms which they do not directly control and who do not participate in the “designed” portion of the market. We study the case of a decentralized “off-platform” aftermarket which operates jointly with a centralized college admissions platform in Chile. We evaluate the impacts of this aftermarket on college enrollment, persistence, graduation, and students’ welfare. To do so, we exploit a policy change in 2012 which caused a significant number of off-platform higher education programs to join the platform. We show that the share of students declining their placed spot decreased by 8%, dropout rates at the end of the first year of college dropped by 2 percentage points (a 16% drop) and graduation within six years increased by 2 to 5 percentage points following this event. To quantify welfare impacts and decompose channels, we develop and estimate an empirical model of college applications, aftermarket waitlists, and matriculation choices using detailed individual-level administrative data from Chile on almost half a million applicants. According to model estimates, welfare increases substantially, especially for less advantaged students and for women, following the policy change. Counterfactual analysis suggests that more desirable options generate larger negative externalities when not on the platform. These externalities are driven by students who receive and decline on-platform offers, and are amplified by substantial frictions in waitlists. Our results indicate that platform design and the scope of the platform can have real impacts on outcomes of interest.

In this paper we explore efficiency and optimal policy in decentralized transportation markets that suffer from search frictions, such as taxicabs, trucks and bulk shipping. We illustrate the impact of two externalities: the well-known thin/thick market externalities and what we call pooling externalities. We characterize analytically the conditions for efficiency, show how they translate into efficient pricing rules, as well as derive the optimal taxes for the case where planner is not able to set prices. We use our theoretical results to explore welfare loss and optimal policy in dry bulk shipping. We find that the constrained efficient allocation achieves 6% welfare gains, while the first-best allocation corresponding to the frictionless world, achieves 14% welfare gains. This suggests that policy can achieve substantial gains even if it does not alleviate search frictions, e.g. through a centralizing platform. Finally, simple
policies designed to mimic the optimal taxes perform well.

This paper develops a strategy with simple implementation and limited data requirements to identify spatial distortion of supply from demand –or, equivalently, unequal access to supply among regions– in transportation markets. We apply our method to ride-level, multi-platform data from New York City (NYC) and show that for smaller rideshare platforms, supply tends to be disproportionately concentrated in more densely populated areas. We also develop a theoretical model to argue that a smaller platform size, all else being equal, distorts the supply of drivers toward more densely populated areas due to network effects. Motivated by this, we estimate a minimum required platform size to avoid geographical supply distortions, which informs the current policy debate in NYC around whether ridesharing platforms should be downsized. We find the minimum required size to be approximately 3.5M rides/month for NYC, implying that downsizing Lyft or Via–but not Uber–can increase geographical inequity.

Financial innovation and deregulation have given households an unprecedented ability to access home equity. To what extent is this beneficial? On one hand, access to home equity enables households to better smooth consumption and self-insure against risk. On the other hand, if housing acts as a savings commitment device, then more liquidity may weaken commitment. In this paper, we evaluate the costs and benefits of greater access to home equity by estimating a model that captures these two opposing channels. Model estimates are validated using a reform that abruptly legalized home equity withdrawal in Texas. In both the data and the model, we observe a 3% increase in nondurable consumption following the reform. According to our estimates, weakened commitment and consumption smoothing each account for half of the observed increase in consumption. Finally, we find that the cost of weakened commitment dominates and that welfare has declined due to the introduction of home equity withdrawal.

Trade-offs between personal choice versus social expectations and choosing from a menu versus computing a preferred option affect decision-making. I use changes in the tip menus used in New York City taxis to analyze these trade-offs. I nonparametrically estimate that the cost of computing a tip when passengers deviate from the menu is $1.89 (15.53% of the average taxi fare of $12.17) on average. I then estimate a model where tipping choices depend on perceptions of a social norm tip, the shame from given less (norm-deviation cost), and the difficulty of calculating a tip (cognitive cost). The distribution of beliefs about the social norm tip averages about 20% of the taxi fare and the norm-deviation cost is between $0.30 and $0.38 for tipping five percentage points less. Cognitive costs average between $1.10 and $1.32. Compared to using no menu, taxi companies appear to have learned over time to use a nearly tip-maximizing menu that, on average, per ride, increases tips by 14.65% and the overall welfare from tipping by $1.08.

We quantify the importance of imperfect competition in the U.S. construction industry by estimating the size of rents earned by American firms and workers. To obtain a comprehensive measure of the total rents and to understand its sources, we take into account that rents may arise both due to markdown of wages and markup of prices. Our analyses combine the universe of U.S. business and worker tax records with newly collected records from U.S. procurement auctions. We first examine how firms respond to a plausibly exogenous shift in product demand through a difference-in-differences design that compares first-time procurement auction winners to the firms that lose, both before and after the auction. Motivated and guided by these estimates, we next develop, identify, and estimate a model where construction firms compete with one another for projects in the product market and for workers in the labor market. We find that American construction firms have significant wage- and price-setting power. This imperfect competition generates a considerable amount of rents, two-thirds of which is captured by the firms.

We assess the sources and consequences of the gender pay gap using a combination of theory and measurement. We start by documenting three empirical facts. First, women are more likely than men to work at low-paying employers. Second, for women as for men, pay is not the sole determinant of workers’ revealed-preference rankings of employers. Third, both pay and the revealed-preference rank differ between women and men within the same employer. To interpret these facts, we develop an empirical equilibrium search model featuring endogenous gender differences in pay, amenities, and recruiting intensities across employers. The estimated model suggests that compensating differentials explain one fifth of the gender gap, that there are significant output and welfare gains from eliminating gender differences, and that an equal-pay policy fails to close the gender pay gap.

We present a new equilibrium search model where consumers initially search among discount opportunities, but are willing to pay more as a deadline approaches, eventually turning to full-price sellers. The model predicts equilibrium price dispersion and rationalizes discount and full-price sellers coexisting without relying on ex-ante heterogeneity. We apply the model to online retail sales via auctions and posted prices, where failed attempts to purchase reveal consumers’ reservation prices. We find robust evidence supporting the theory. We quantify dynamic search frictions arising from deadlines and show how, with deadline-constrained buyers, seemingly neutral platform fee increases can cause large market shifts.

Recent policy proposals seek to regulate the prices that hospitals can levy for care delivered outside of a patient’s insurance network. In this paper, we study the potential effects of such regulations on equilibrium in-network prices, network breadth, and hospital service line closures. The bulk of existing empirical work on insurer-provider negotiations assumes that no out-of- network transactions occur. Under common empirical conditions, accounting for the presence of these transactions implies substantively larger hospital margins than those implied by canonical models from the literature. We operationalize the model by proposing a novel, data-driven measure of off-contract prices paid by insurers to hospitals. Using our model, we conduct a series of counterfactuals to evaluate current policy proposals that would cap out-of-network reimbursements. In counterfactual simulations, reducing out-of-network reimbursements results in considerably lower negotiated prices with in-network hospitals, but at the cost of narrower networks and some outright service line closures.