Manuel Lleonart

About Me

I am a Ph.D. candidate at Universitat Autònoma de Barcelona & Barcelona School of Economics under the supervision of Professor Pau Milán. My research interests are Microeconomic Theory, Organizational Design and Industrial Organization. I'm seeking a position in the 2025-26 Job Market.

I did my visiting in the Spring of 2024 and 2025 at Paris School of Economics hosted by Francis Bloch.

Research

• Job‑Market Paper: “Optimal Monitoring Hierarchies under Collusion”

  This paper explores how organizational structure influences the effectiveness of monitoring in firms, particularly under the threat of collusion between agents and monitors. Traditional models in contract theory often assume truthful reporting under high detection probabilities, overlooking complex collusion dynamics. Departing from this view, the paper proposes a novel firm design approach that allows the principal to assign two monitors to each agent and ties their compensation to the full set of reports, reducing collusion incentives. The model extends the principal supervisor agent framework to a continuum of agents with private productivity levels and considers how the number of monitors affects detection, costs, and incentive structures. It highlights trade-offs in monitoring intensity and shows how heterogeneity in detection likelihood can shape optimal monitoring schemes. I show that \textit{collusion risk reshapes the optimal organizational structure}. When collusion is a concern, the optimal design no longer minimizes redundancy —it leverages it as an anti-collusion device. These findings provide insights into monitoring and wage structures and explain industry-specific monitoring practices, offering a novel perspective on the design of organizational incentives.

• “Feed for Good? On Regulating Social Media Platforms” (with Miguel Risco ) – PDF

  This paper builds a theoretical model of communication and learning on a social media platform, and describes the algorithm an engagement-maximizing platform implements in equilibrium. This algorithm overexploits similarities between users, locking them in echo chambers. Moreover, learning vanishes as platform size grows large. As this is far from ideal, we explore alternatives. The reverse-chronological algorithm that social platforms reincorporated after the DSA was enacted turns out to be insufficient, so we construct the "breaking-echo-chambers" algorithm, which improves learning by promoting opposite viewpoints. Finally, we advocate for horizontal interoperability as a regulatory measure to align platform incentives with social welfare. By eliminating platform-specific network effects, interoperability incentivizes platforms to adopt algorithms that maximize user well-being.

• “A Model of Punitive Voting” – PDF (Submitted)

  I analyze a two-period model of political competition where voters care about candidates’ integrity. Candidates must trade off implementing their preferred policy against maintaining their electoral promises. Voters punish candidates that deviate from electoral promises by voting for their opponent. I find that punitive voting can exert political discipline only if candidates face low levels uncertainty about voters preferences. In this case candidates’ electoral promises are a compromise between their preferred policy and voters’ preferences, and when elected they implement their promise. Finally, I show that when one candidate’s ideal policy is closer to the median voter, an equilibrium exists where one candidate is disciplined and the other is not.

Work in Progress

• “Media Competition in Spain” (with Luis I. Menéndez)

  Supported by a Google Cloud Research Grant. (Details coming soon.)

• “Balancing Sectoral Efficiency and Competition: Optimal Public Procurement Allocation” (with Santiago D. Iglesias Cuenca & Basile Grassi)

Teaching