Nathan Hancart

Department of Economics
University College London

Nathan Hancart

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About me

Hello, I'm a PhD candidate in Economics at University College London. I will be on the 2022/23 job market.

My interests are in Microeconomic theory with a focus on Information and Behavioural Economics.

You can find information on my research and my CV here.


Job Market Paper: Designing the Optimal Menu of Tests

A decision-maker must accept or reject a privately informed agent. The agent always wants to be accepted, while the decision-maker wants to accept only a subset of types. The decision-maker has access to a set of feasible tests and, prior to making a decision, requires the agent to choose a test from a menu, which is a subset of the feasible tests. By offering a menu, the decision-maker can use the agent's choice as an additional source of information. I characterise the decision-maker's optimal menu for arbitrary type structures and feasible tests. I then apply this characterisation to various environments. When the domain of feasible tests contains a most informative test, I characterise when only the dominant test is offered and when a dominated test is part of the optimal menu. I also characterise the optimal menu when types are multidimensional or when tests vary in their difficulty.

Managing the Expectations of Buyers with Reference-Dependent Preferences, 2021, R&R at Journal of Economic Theory
[draft][24 minutes presentation]

I consider a model of monopoly pricing where a risk-neutral firm makes an offer to a buyer with reference-dependent preferences. The reference point is the ex-ante probability of trade and the buyer exhibits an attachment effect: the higher his expectations to buy, the higher his willingness-to-pay. When the buyer’s valuation is private information, a unique equilibrium exists where the firm plays a mixed strategy and its profits are the same as in the reference-independent benchmark. The equilibrium always entails inefficiencies: even as the firm’s information converges to complete information, it mixes on a non-vanishing support and the probability of no trade is greater than zero. Finally, I show that when the firm can obtain costless signals on the buyer’s valuation, it can do strictly better than in the reference-independent benchmark by leveraging the uncertainty generated by a noisy learning strategy. However, this advantage vanishes as the attachment effect grows large.

The (No) Value of Commitment

I provide a sufficient condition under which a principal does not benefit from commitment in economic situations. I focus on situations described by a constrained maximisation problem. I show that commitment has no value when the marginal contribution of the constraints is null in the problem with commitment. This condition also has bite when constraints are binding. I then apply this condition in a mechanism design setting. I show that a designer does not benefit from being able to contract over actions when his preferences are partially aligned with the agent's. Verifying the condition does not necessitate verifying explicitly that the strategy under commitment is a best-response to the information revealed in the economic problem.