A principal wishes to distribute an indivisible good to a population of budget-constrained agents. Both valuation and budget are an agent’s private information. The principal can inspect an agent’s budget through a costly verification process and punish an agent who makes a false statement. I characterize the direct surplus-maximizing mechanism. This direct mechanism can be implemented by a two-stage mechanism in which agents only report their budgets. Specifically, all agents report their budgets in the first stage. The principal then provides budget-dependent cash subsidies to agents and assigns the goods randomly (with uniform probability) at budget-dependent prices. In the second stage, a resale market opens, but is regulated with budget-dependent sales taxes. Agents who report low budgets receive more subsidies in their initial purchases (the first stage), face higher taxes in the resale market (the second stage) and are inspected randomly. This implementation exhibits some of the features of some welfare programs, such as the affordable housing program in Singapore.
A principal allocates an object among several agents, each of whom wants the object and privately knows the value to the principal of assigning it to him. The object is allocated based on the reports of the agents. The principal can inspect an agent’s report at a cost and imposes a limited punishment on the agent who receives the object. An optimal mechanism specifies two thresholds vl ≤ vu. If every agent reports a value below vl, the object is assigned to a random agent, and no one is inspected. If some agent reports a value above vl but all reports are below vu, the agent with the highest reported value receives the object and is inspected with some probability. If several agents report values above vu, one of them is randomly selected to receive the object and be inspected with certainty. An agent is punished if and only if his report is found to be false. When the number of agents is small, vu is equal to the upper-bound of the support of the value distribution. When the number of agents is large, vl = vu.
This paper studies the design of ex ante efficient mechanisms in environments where a single object is for sale, and agents have positively interdependent values and can covertly acquire information at some cost before participating in a mechanism. We find that ex ante efficient mechanisms discourage agents from acquiring excessive information by pooling or randomization. The optimal pooling regions are those where the semi-elasticity of information acquisition is large. There exists an ex ante efficient mechanism that can be implemented by standard auctions with restrictions on the set of allowable bids. In special cases, this implementation is simple and appealing: standard auctions with discrete bids.
An Efficient Ascending Auction (Oct. 2016)
This paper proposes an ascending auction that yields an efficient outcome when the seller is restricted to sell bundles whose elements form a basis of a matroid and agents have interdependent values. This ascending auction generalizes Bikhchandani et al. (2011) who assume agents have independent private values; and Perry and Reny (2005) who study multi-unit good auctions. The key feature of the auction is that agents are permitted to express different demands against different elements.
Endogenous Labor Market Cycles (with Cheng Wang) (March 2019)
This paper shows that in a perfectly stationary physical environment of the labor market, moral hazard and competition in long-term contracts can generate cycles in the tightness of the market, which in turn may induce job creation and destruction, and two periods or much longer cycles in employment and output. We claim that the model may shed light on the unemployment volatility puzzle, which has inspired many discussions in the literature.
Approximation in Mechanism Design with Interdependent Values, Games and Economic Behavior, Volume 103, May 2017, Pages 225-253. (A one-page abstract of an earlier version of this paper appeared in Proceedings of the 14th ACM Conference on Electronic Commerce. ACM, pp. 675-676.)