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Dworczak, P. (2020). Mechanism Design With Aftermarkets: Cutoff Mechanisms. Econometrica, 88, 2629–2661.

FT50 A*

I study a mechanism design problem in which a designer allocates a single good to one of several agents, and the mechanism is followed by an aftermarket?a post-mechanism game played between the agent who acquired the good and third-party market participants. The designer has preferences over final outcomes, but she cannot design the aftermarket. However, she can influence its information structure by publicly disclosing information elicited from the agents by the mechanism. I introduce a class of allocation and disclosure rules, called cutoff rules, that disclose information about the buyer's type only by revealing information about the realization of a random threshold (cutoff) that she had to outbid to win the object. When there is a single agent in the mechanism, I show that the optimal cutoff mechanism offers full privacy to the agent. In contrast, when there are multiple agents, the optimal cutoff mechanism may disclose information about the winner's type; I provide sufficient conditions for optimality of simple designs. I also characterize aftermarkets for which restricting attention to cutoff mechanisms is without loss of generality in a subclass of all feasible mechanisms satisfying additional conditions.

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Dworczak, P., & Pavan, A. (2022). Preparing for the Worst but Hoping for the Best: Robust (Bayesian) Persuasion. Econometrica, 90, 2017–2051.

FT50 A*

We propose a robust solution concept for Bayesian persuasion that accounts for the Sender's concern that her Bayesian belief about the environment?which we call the conjecture?may be false. Specifically, the Sender is uncertain about the exogenous sources of information the Receivers may learn from, and about strategy selection. She first identifies all information policies that yield the largest payoff in the ?worst-case scenario,? that is, when Nature provides information and coordinates the Receivers' play to minimize the Sender's payoff. Then she uses the conjecture to pick the optimal policy among the worst-case optimal ones. We characterize properties of robust solutions, identify conditions under which robustness requires separation of certain states, and qualify in what sense robustness calls for more information disclosure than standard Bayesian persuasion. Finally, we discuss how some of the results in the Bayesian persuasion literature change once robustness is accounted for, and develop a few new applications.

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Duffie, D., & Dworczak, P. (2021). Robust benchmark design. Journal of Financial Economics, 142, 775–802.

FT50 UTD24 A*

We model the design of a benchmark fixing as an estimator of fair market value. The fixing data are the transactions of agents whose profits depend on the fixing, implying incentives for manipulation. We derive the optimal linear fixing under an assumption that transaction weights are unidimensional. We also axiomatically characterize the unique linear fixing that is robust to a certain form of collusion among traders. Our analysis provides a foundation for the commonly used volume-weighted average price (VWAP) and its analogue based on unidimensional weights. We characterize the relative advantages of these fixing designs, depending on market characteristics.

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DUFFIE, D., DWORCZAK, P., & ZHU, H. (2017). Benchmarks in Search Markets. The Journal of Finance, 72, 1983–2044.

FT50 UTD24 A*

ABSTRACT We characterize the role of benchmarks in price transparency of over-the-counter markets. A benchmark can raise social surplus by increasing the volume of beneficial trade, facilitating more efficient matching between dealers and customers, and reducing search costs. Although the market transparency promoted by benchmarks reduces dealers' profit margins, dealers may nonetheless introduce a benchmark to encourage greater market participation by investors. Low-cost dealers may also introduce a benchmark to increase their market share relative to high-cost dealers. We construct a revelation mechanism that maximizes welfare subject to search frictions, and show conditions under which it coincides with announcing the benchmark.

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Dworczak, P., Kominers, S. D., & Akbarpour, M. (2021). Redistribution Through Markets. Econometrica, 89, 1665–1698.

FT50 A*

Policymakers frequently use price regulations as a response to inequality in the markets they control. In this paper, we examine the optimal structure of such policies from the perspective of mechanism design. We study a buyer-seller market in which agents have private information about both their valuations for an indivisible object and their marginal utilities for money. The planner seeks a mechanism that maximizes agents' total utilities, subject to incentive and market-clearing constraints. We uncover the constrained Pareto frontier by identifying the optimal trade-off between allocative efficiency and redistribution. We find that competitive-equilibrium allocation is not always optimal. Instead, when there is inequality across sides of the market, the optimal design uses a tax-like mechanism, introducing a wedge between the buyer and seller prices, and redistributing the resulting surplus to the poorer side of the market via lump-sum payments. When there is significant same-side inequality that can be uncovered by market behavior, it may be optimal to impose price controls even though doing so induces rationing.

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Akbarpour, M., Dworczak, P., & Kominers, S. D. (2024). Redistributive Allocation Mechanisms. Journal of Political Economy, 132, 1831–1875.

FT50 A* Open Access

Many scarce public resources are allocated at below-market-clearing prices and sometimes for free. Such ?nonmarket? mechanisms sacrifice some surplus, yet they can potentially improve equity. We develop a model of mechanism design with redistributive concerns. Agents are characterized by a privately observed willingness to pay for quality, a publicly observed label, and a social welfare weight. A market designer controls allocation and pricing of a set of objects of heterogeneous quality and maximizes the expectation of a welfare function. The designer does not directly observe individuals? social welfare weights. We derive structural insights about the form of the optimal mechanism, leading to a framework for determining how and when to use nonmarket mechanisms.

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Akbarpour, M., Budish, E., Dworczak, P., & Kominers, S. D. (2024). An Economic Framework for Vaccine Prioritization*. The Quarterly Journal of Economics, 139, 359–417.

FT50 A*

We propose an economic framework for determining the optimal allocation of a scarce supply of vaccines that become gradually available during a public health crisis, such as the COVID-19 pandemic. Agents differ in observable and unobservable characteristics, and the designer maximizes a social welfare function over all feasible mechanisms—accounting for agents’ characteristics, as well as their endogenous behavior in the face of the pandemic. The framework emphasizes the role of externalities and incorporates equity as well as efficiency concerns. Our results provide an economic justification for providing vaccines immediately and for free to some groups of agents, while at the same time showing that a carefully constructed pricing mechanism can improve outcomes by screening for individuals with the highest private and social benefits of receiving the vaccine. The solution casts light on the classic question of whether prices or priorities should be used to allocate scarce public resources under externalities and equity concerns.

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