Welcome to my website! My name is Jean-Michel Benkert, and I am an Assistant Professor at the Department of Economics of the University of Bern. In 2017, I have obtained a PhD in Economics from the University of Zurich. In between, I’ve held various roles in the fields of strategy and innovation in the private sector.
Within economics, my research focuses on microeconomic theory, with an emphasis on behavioral theory and mechanism design. You can access my current and past research papers below and find my Google Scholar profile here.
Email: jean-michel.benkert@unibe.ch
Work in Progress (presentation stage)
Strategic Attribute Learning (with Ludmila Matysková and Egor Starkov)
We study a model in which a payoff-relevant state comprises of multiple, unknown attributes. The principal delegates learning about the attributes to a biased agent, who chooses how to allocate a budget of informative tests across attributes. We derive the optimal learning strategy in this setting. Depending on the bias, the agent may learn about different attributes or choose different intensities than the principal’s optimum. Notably, the agent may abstain from learning altogether. We study how the agent’s optimal learning strategy and the principal’s payoff change with the agent’s absolute and relative bias and show that the principal may prefer more biased agents. Furthermore, we analyze the principal’s preferred organizational setup regarding delegation of learning and/or decision rights. Finally, we provide an application to an insurance setting.
Link to Ludmila’s website – Link to Egor’s website
A Theory of Recommendations (with Armin Schmutzler)
We study the value of recommendations in disseminating economic information, focusing on the impact of preference heterogeneity as a key impediment. We consider Bayesian expected payoff maximizers who evaluate non-strategic recommendations that are given when the payoff of consumption exceeds or falls below some threshold. We derive conditions under which different types accept these recommendations and assess the overall value of the recommendation system. Our analysis highlights the importance of disentangling objective information from subjective preferences. We consider the design of value-maximizing and sales-maximizing recommendation systems as well as the role of a polarized population. Finally, we extend our model in several directions, including multiple recommendation levels and endogenous recommendation thresholds.
Link to Armin’s website
Working Papers
Startup Acquisitions: Acquihires and Talent Hoarding (with Igor Letina and Shuo Liu, February 2024)
We study how competitive forces may drive firms to inefficiently acquire startup talent. In our model, two rival firms have the capacity to acquire and integrate a startup operating in an orthogonal market. We show that firms may pursue such acquihires primarily as a preemptive strategy, even when they appear unprofitable in isolation. Thus, acquihires, even absent traditional competition-reducing effects, need not be benign, as they can lead to inefficient talent allocation. Additionally, our analysis underscores that such talent hoarding can diminish consumer surplus and exacerbate job volatility for acquihired employees.
Link to paper – Link to Igor’s website – Link to Shuo’s website
Bilateral Trade with Loss-Averse Agents (revised July 2023), R&R, Economic Theory
We introduce expectations-based loss aversion, which can explain the empirically well-documented endowment and attachment effect, into the classical bilateral-trade setting (Myerson and Satterthwaite, 1983). We derive optimal mechanisms for different objectives and find that, relative to no loss aversion, the designer optimally provides agents with full insurance in the money dimension and with partial insurance in the ownership dimension. Notably, the latter is achieved either by increasing or decreasing the trade frequency, depending on the distribution of types. Finally, we show that the impossibility of inducing materially efficient trade persists with loss aversion.
Link to paper
Publications
On the Equivalence of Optimal Mechanisms with Loss and Disappointment Aversion (Benkert, J.-M., Economics Letters, 2022, Vol. 214, 110428)
We consider a standard, quasi-linear mechanism design setting in which agents’ outcomes consist of a binary part and a transfer, thus encompassing applications such as auctions, bilateral trade or public good provision. We augment preferences by allowing for loss aversion (Köszegi and Rabin, 2007) and disappointment aversion (Bell, 1985, Loomes and Sugden, 1986). While the preferences induced by these models only have a trivial intersection given by classical expected utility (Masatlioglu and Raymond, 2016), we show that the optimal mechanisms for the two types of preferences are equivalent across a broad range of problems and thus display a remarkable robustness.
Link to paper
Designing Dynamic Research Contests (Benkert, J.-M. and I. Letina, AEJ: Microeconomics, 2020, Vol. 12(4), pp. 270-289)
This paper studies the optimal design of dynamic research contests. We introduce interim transfers, which are paid in every period while the contest is ongoing, to an otherwise standard setting. We show that a contest where: (i) the principal can stop the contest in any period, (ii) a constant interim transfer is paid to agents in each period while the contest is ongoing, and (iii) a final prize is paid once the principal stops the contest, is optimal for the principal and implements the first-best.
Link to paper – Link to Igor’s website – Coverage by the AEA Research Highlight
Informational Requirements of Nudging (Benkert, J.-M. and N. Netzer, Journal of Political Economy, 2018, 126, pp. 2323-2355)
A nudge is a paternalistic government intervention that attempts to improve choices by changing the framing of a decision problem. We propose a welfare-theoretic foundation for nudging similar in spirit to the classical revealed preference approach, by investigating a framework where preferences and mistakes of an agent can be elicited from her choices under different frames. We provide characterizations of the classes of behavioral models in which the information required for nudging can or cannot be deduced from choice data.
Link to paper – Link to Nick’s website
Optimal Search from Multiple Distributions with Infinite Horizon (Benkert, J.-M., G. Nöldeke and I. Letina, Economics Letters, 2018, Vol. 164, pp. 15-18.)
With infinite horizon, optimal rules for sequential search from a known distribution feature a constant reservation value that is independent of whether recall of past options is possible. We extend this result to the case when there are multiple distributions to choose from: it is optimal to sample from the same distribution in every period and to continue searching until a constant reservation value is reached.
Link to paper – Link to Georg’s website – Link to Igor’s website