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

July 8-10, 2020, 9 am - 3 pm PST
Organized by: 
  • Steven Puller, Texas A&M University
  • Frank Wolak, Stanford University
The papers for this session are invited from the fields of empirical Industrial Organization (IO), Labor Economics, Public Finance, and Health Economics, Environmental and Energy Economics, and Development Economics. The unifying feature of the papers should be that they each contain a theoretical model of an economic interaction and an empirical implementation of this theoretical model using actual data. Popular topics for papers from previous years—the empirical implementation of models of auction market equilibrium, discrete choice models of differentiated product demand and oligopoly equilibrium, dynamic models of individual and group behavior, and analysis of experiment data of policy interventions in circumstances of non-random assignment or self-selection. A clear link between the theoretical economic model and econometric model should be a hallmark of the papers presented.

In this Session

Jul 8 | 9:00 am to 10:00 am

Student Beliefs and the (Perverse) Incentives of Preferential College Admissions

Presented by: Michela Tincani (University College London)
Co-Author(s): Fabian Kosse (LMU Munich) and Enrico Miglino (University College London)

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 35 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 subjective beliefs are biased, the response of students to perceived incentives can undercut the efficacy of preferential admissions as a tool for fostering upward mobility

Jul 8 | 10:00 am to 10:15 am

Chat with Michela Tincani

Jul 8 | 10:15 am to 11:15 am

The Equilibrium Effects of Public Provision in Education Markets: Evidence from a Public School Expansion Policy

Presented by: Michael Dinerstein (University of Chicago)
Co-Author(s): Christopher Neilson (Princeton University) and Sebastián Otero (Stanford University)

In a variety of 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 whether the procurement lottery randomly assigned the project to a firm or an unaffiliated individual. 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 provision of high quality schools varies with the level of public provision, and to compare the effects to the alternative policy of public financing, we specify and estimate an empirical model of demand (students choosing schools) and supply (schools choosing whether to stay open and what price to charge).

Jul 8 | 11:15 am to 11:30 am

Chat with Michael Dinerstein

Jul 8 | 11:30 am to 12:30 pm

Lunch

Jul 8 | 12:30 pm to 1:30 pm

School Competition under Social Interactions and the Design of Education Policies

Presented by: Claudia Allende (University of Chicago)

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.

Jul 8 | 1:30 pm to 1:45 pm

Chat with Claudia Allende

Jul 8 | 1:45 pm to 2:45 pm

Network Externalities of Off Platform Options and the Efficiency of Centralized Assignment Mechanisms

Presented by: Christopher Neilson (Princeton University & NBER)
Co-Author(s): Adam Kapor (Princeton University & NBER) and Mohit Karnani (MIT)

We study the efficiency of a centralized college admissions platform that operates jointly with a decentralized “off-platform” aftermarket. We exploit a policy change in 2012 in which a significant number of off-platform higher education options joined a centralized assignment system for higher-education programs in Chile. We show evidence that this policy change had impacts on real outcomes finding that the share of students declining their placed spot decreased by 8% and dropout rates at the end of the first year of college dropped by 2 percentage points (a 16% drop) following this event. To quantify the welfare impacts of the aftermarket on the efficiency of the match, we develop and estimate an empirical model of college applications, aftermarket waitlists and matriculation choices, using individual-level administrative data from Chile on almost half a million applications, including test scores and enrollment decisions at all on- and off-platform higher education options. According to model estimates, welfare increases substantially, students begin their studies sooner, and fewer students drop out by the end of the first year of study when top off-platform programs join the platform. These benefits are greater for less advantaged students and for women. Counterfactual analysis suggests that more desirable options generate larger negative externalities when not on the platform. These externalities are mostly driven by students admitted on on-platform options that decline their spots to pursue off-platform programs; and are amplified by frictions in waitlists that yield socially inefficient allocations. Our results indicate that platform design can have real impact on outcomes of interest. Specifically, our findings suggest policymakers need to consider the implications of off platform options and their characteristics when designing regulation surrounding centralized assignment systems.

Jul 8 | 2:45 pm to 3:00 pm

Chat with Chris Neilson

Jul 9 | 9:00 am to 10:00 am

Search Frictions and Efficiency in Decentralized Transport Markets

Presented by: Myrto Kalouptsidi (Harvard University)
Co-Author(s): Giulia Brancaccio (Cornell University), Theodore Papageorgiou (Boston College) and Nicola Rosaia (Harvard University)

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.

Jul 9 | 10:00 am to 10:15 am

Chat with Myrto Kalouptsidi

Jul 9 | 10:15 am to 11:15 am

Spatial Distribution of Supply and the Role of Market Thickness: Theory and Evidence from Ridesharing

Presented by: Soheil Ghili (Yale University)
Co-Author(s): Vineet Kumar (Yale University)

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.

Jul 9 | 11:15 am to 11:30 am

Chat with Soheil Ghili

Jul 9 | 11:30 am to 12:30 pm

Lunch

Jul 9 | 12:30 pm to 1:30 pm

Breaking the Commitment Device: The Effect of Home Equity Withdrawal on Consumption, Saving, and Welfare

Presented by: Patrick Moran (The University of Oxford and Institute for Fiscal Studies)
Co-Author(s): Agnes Kovac (The University of Manchester and Institute for Fiscal Studies)

Financial innovation and deregulation have given households an unprecedented ability to access home equity. To what extent is this benecial? 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 benets 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.

Jul 9 | 1:30 pm to 1:45 pm

Chat with Patrick Moran

Jul 9 | 1:45 pm to 2:45 pm

How Social Norms and Menus Affect Choices: Evidence from Tipping

Presented by: Kwabena Donkor (Stanford University)

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.

Jul 9 | 2:45 pm to 3:00 pm

Chat with Kwabena Donkor

Jul 10 | 9:00 am to 10:00 am

Imperfect Competition and Rents in Labor and Product Markets: The Case of the Construction Industry

Presented by: Yao Luo (University of Toronto)
Co-Author(s): Kory Kroft (University of Toronto & NBER), Magne Mogstad (University of Chicago, Statistics Norway, NBER & IFS) and Bradley Setzler (University of Chicago)

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.

Jul 10 | 10:00 am to 10:15 am

Chat with Yao Luo

Jul 10 | 10:15 am to 11:15 am

The Gender Pay Gap: Micro Sources and Macro Consequences

Presented by: Christian Moser (Columbia University)
Co-Author(s): Iacopo Morchio (University of Vienna)

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.

Jul 10 | 11:15 am to 11:30 am

Chat with Christian Moser

Jul 10 | 11:30 am to 12:30 pm

Lunch

Jul 10 | 12:30 pm to 1:30 pm

Discounts and Deadlines in Consumer Search

Presented by: Bradley J. Larsen (Stanford University & NBER)
Co-Author(s): Dominic Coey (Facebook, Core Data Science) and Brennan C. Platt (Brigham Young University)

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 nd 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.

Jul 10 | 1:30 pm to 1:45 pm

Chat with Bradley Larsen

Jul 10 | 1:45 pm to 2:45 pm

Disagreement Payoffs and Negotiated Prices: Evidence from Out-of-Network Hospital Payments

Presented by: Nicholas Tilipman (University of Illinois at Chicago)
Co-Author(s): Elena Prager (Northwestern University)

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-ofnetwork 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.

Jul 10 | 2:45 pm to 3:00 pm

Chat with Nicholas Tilipman