Frontiers in Market Design
Friday, Jan. 3, 2020 8:00 AM - 10:00 AM (PST)
- Chair: Eric Budish, University of Chicago
Approximating the Equilibrium Effects of Informed School Choice
AbstractThis paper studies the potential small and large scale effects of a policy designed to produce a more informed consumer demand in the context of the market for primary education. We develop and test a personalized information provision intervention that targets families of public Pre-K students entering the elementary school system in Chile. Using a randomized control trial, we find that the intervention shifts parents’ choices toward schools with higher average test scores, higher test score value added, higher prices, and schools that tend to be further distances from their home. Tracking students using administrative data, we find that student academic achievement was higher among treated families five years later. To quantitatively gauge how average treatment effects might vary in the context of a scaled up version of this policy, we embed the randomized control trial within a structural model of school choice and competition where price and quality are chosen endogenously and schools have capacity constraints. We use the estimated model of demand and supply to simulate policy effects under different assumptions about equilibrium constraints. In counterfactual simulations, we find that capacity constraints play an important role mitigating the policy effect on impact but that in several scenarios, the supply-side responses leads to increased quality which contributes to a overall positive average treatment effect. Finally, we show how model estimates can be used to inform the design of a large scale experiment such that reduced form estimates can capture equilibrium effects and spillovers.
The Efficiency of A Dynamic Decentralized Two-Sided Matching Market
AbstractThis paper empirically studies a decentralized dynamic peer-to-peer matching market. We use data from a leading ride-sharing platform in China to estimate a continuous-time dynamic model of search and match between drivers and passengers. We assess the efficiency of the decentralized market by how much centralized algorithms may improve welfare. We find that a centralized algorithm can increase the number of matches by making matches less frequently and matching agents more assortatively.
A Theory of Stock Exchange Competition and Innovation: Will the Market Fix the Market?
AbstractThis paper builds a model of stock exchange competition tailored to the institutional and regulatory details of the modern U.S. stock market. The model shows that under the status quo market design: (i) trading behavior across the seemingly fragmented exchanges is as if there is just a single synthesized exchange; (ii) as a result, trading fees are perfectly competitive; (iii) however, exchanges are able to capture and maintain economic rents from the sale of speed technology such as proprietary data feeds and co-location — arms for the high-frequency trading arms race. We document stylized empirical facts consistent with each of the three main results of the theory. We then use the model to examine the private and social incentives for exchanges to adopt new market designs, such as frequent batch auctions, that address the negative aspects of high-frequency trading. The robust conclusion is that private innovation incentives are much smaller than the social incentives, especially for incumbents who face the loss of speed technology rents. A policy insight that emerges from the analysis is that a regulatory “push,” as opposed to a market design mandate, may be sufficient to tip the balance of incentives and encourage “the market to fix the market.”
When Do Cardinal Mechanisms Outperform Ordinal Mechanisms?: Operationalizing Pseudomarkets
AbstractSchool choice and item assignment mechanisms that solicit cardinal preferences have the potential to improve properties of assignments compared to more traditional ordinal (rank ordered list) mechanisms. We explore what configurations of preferences and capacities lead to outperformance by either cardinal or ordinal mechanisms. We first discuss the canonical cardinal-preferences pseudomarket mechanism, highlight some overlooked details, and introduce the first ever computational algorithm for the pseudomarket mechanism. We then structurally estimate cardinal preferences using the lotteries in the existing school choice mechanism in Seattle, Washington. We use the estimated cardinal preferences to compare the performance of the pseudomarket mechanism to an ordinally efficient ordinal mechanism known as probabilistic serial. Finally we provide theoretical results comparing cardinal and ordinal mechanisms.
- D4 - Market Structure, Pricing, and Design