Carl Heese

I'm an Assistant Professor in Economics at the University of Vienna, with research interest in microeconomic theory (primary), political theory and behavioral economics (secondary). I received my Ph.D. from the University of Bonn.

Here is a video about our department that I shot together with my colleagues Karl Schlag and Si Chen.

  Email: carl.heese(at)
  Contact: +43 677 63699700
  Office: Oskar-Morgenstern-Platz 1, 1090 Wien

Working Papers

Voter Attention and Distributive Politics

CRC discussion paper , 20-min video presentation


This paper studies the heterogeneous acquisition of political information and its effect on election outcomes. Policy consequences are uncertain and distributive. In this setting, when the voters’ information about consequences is exogenous, outcomes are full-information equivalent (\cite{bhattacharya}). When information is costly, the information choices are strategically interdependent, creating multiple equilibria. There are equilibria where outcomes depend only on the prior information. There are multiple additional equilibria where outcomes depend on the joint distribution of primitives (cost, preferences, prior beliefs); correlation and dispersion matter. In particular, we characterize when small groups with special, high stake interests may win the election, and show how an interest group may be harmed from having more dispersed, but more informative prior beliefs.

Persuasion and Information Aggregation in Elections

(with Stephan Lauermann, discussion paper , NEW version (August 2021) )


This paper studies a large majority election with voters who have heterogeneous, private preferences and exogenous private signals. We show that a Bayesian persuader can implement any state-contingent outcome in some equilibrium by providing additional information. In this setting, without the persuader's information, a version of the Condorcet Jury Theorem would hold (Feddersen and Pesendorfer, 1997). Persuasion does not require detailed knowledge of the voters' private information and preferences: the same additional information is effective across environments. The results require almost no commitment power by the persuader. Finally, the persuasion mechanism is effective also in small committees with as few as 15 members.

Fishing for good news: Motivated Information Acquisition (with Si Chen)

My co-author was awarded the Econ Job Market Best Paper Award 6th Edition for this paper.
(UniCredit Foundation & European Economic Association), CRC discussion paper, NEW version (August 2021)


The literature on motivated reasoning argues that people skew their beliefs to feel moral when acting selfishly. Leveraging techniques from the Bayesian persuasion literature, we study the information acquisition of decision-makers with a motive to form positive moral self-views and a motive to act selfishly. Theoretically and experimentally, we find that a motive to act selfishly makes individuals dynamically `fish for good news’: they are more likely to continue (stop) acquiring information, having so far received mostly information suggesting that acting selfishly is harmful (harmless) to others. We also find that fishing for good news may improve social welfare and that more intelligent individuals have a higher tendency to fish for good news.

Work in Progress

Delegated Authority, Signaling Motives, and Bad Compromises in Collective Decisions

Conference Presentation Slides (ResearchGate)

Social Learning with State-Dependent Observations



In this note, I study a variant of the canonical binary-state binary-choice social learning model (Bikhchandani et al., 1992). An individual would like to choose an action only in the high state. When making her own decision, she observes previous decision-makers who chose the action. Importantly, the likelihood of observing the action of previous decision-maker depends on the state. I show that when observing the action is more likely in the low state, the individual faces an inference problem: does she observe many actions because the state is high and previous decision-makers had private information about this or because the state is low and previous actions are more visible. In this situation, learning is confounded (Smith and Sorensen, 2000).