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 Organizational Design, Microeconomic Theory, and Industrial Organization. I am in the 2025-26 Job Market.

I visited Paris School of Economics hosted by Francis Bloch in the Springs of 2024 and 2025.

Research

• Job‑Market Paper: “Firm Design when Monitors Can Be Bribed”

  This paper explains why many organizations deliberately duplicate employee oversight—even for routine, verifiable tasks—and when doing so is efficient. I study internal monitoring when supervisors themselves can be bribed. The firm jointly chooses the \emph{architecture} of oversight (how many supervisors per worker and how their spans of control overlap) and the \emph{incentives} needed to keep supervisors honest. Overlap—assigning two independent supervisors to the same worker—makes it harder to hide wrongdoing and, crucially, lowers the wage required to maintain honesty; the resulting wage savings can outweigh the extra hiring cost. The analysis shows that redundancy is optimal when baseline verifiability is low or detection decays steeply with span; improvements in monitoring technology or stronger external enforcement shift the optimum toward wider spans with less overlap. The model predicts sorting: weak-verifiability environments sustain narrower spans and more redundancy, while strong-verifiability environments sustain wider spans and greater independence. By linking organizational structure to the credibility of enforcement, the paper reframes overlapping oversight not as waste but as a rational governance choice.

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

  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.

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• “A Model of Punitive Voting” (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.

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Work in Progress

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

  This paper investigates how competition shapes ideological slant in television news. While theoretical models suggest that increased media competition can either intensify or mitigate bias—depending on whether audiences seek confirmation or accuracy—empirical evidence remains limited. We address this gap by analyzing the entry of a new Spanish TV news outlet and measuring how it alters the political slant of existing providers. Our approach combines a formal model of political media markets with a novel empirical strategy that leverages large-language-model and text-analysis techniques. We disentangle media bias into topic selection, ideological tone, and airtime allocation—capturing the three primary channels through which slant manifests. Our findings offer the first direct evidence of how heightened rivalry influences not just audience composition, but the strategic editorial decisions that shape political coverage. Supported by a Google Cloud Research Grant.

  Extended Abstract

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

Teaching

Evaluations available upon request.

As instructor

As Teaching Assistant