Jean-Michel Benkert

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.

We provide a model of investment in innovation that is dynamic, features multiple heterogeneous research projects of which only one potentially leads to success, and in each period, the researcher chooses the set of projects to invest in. We show that if a search for innovation starts, it optimally does not end until the innovation is found -- which will be never with a strictly positive probability.

We introduce expectations-based loss aversion, which can explain the empirically well-documented endowment and attachment effect, into the classical bilateral-trade setting. We derive optimal mechanisms for different objectives and find that relative to no loss aversion, the platform designer optimally provides agents with partial insurance in the ownership dimension and with full insurance in the money dimension. Notably, the former 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.

This paper shows that optimal mechanism-design problems with loss aversion and disappointment aversion are formally equivalent under a suitable transformation. The equivalence allows mechanisms derived under one specification of reference-dependent preferences to be interpreted under the other and clarifies how the two behavioral models affect incentive design.

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 the principal can stop the contest in any period, a constant interim transfer is paid to agents in each period while the contest is ongoing, and a final prize is paid once the principal stops the contest, is optimal for the principal and implements the first-best.

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.

This paper studies the information a policymaker needs in order to evaluate and design nudges when choices may not reveal welfare-relevant preferences. The analysis shows that welfare-improving nudges require enough information to identify how behavior maps into welfare; otherwise, interventions that appear beneficial from observed choices alone can reduce welfare. The results characterize the informational requirements for nudging and clarify when choice-architecture interventions can be justified.