Jacopo Perego

3022 Broadway
New York, NY 10027
Economics Division - Uris 616
(212) 851 5864

I am an Assitant Professor of Economics at Columbia GSB. Before joining Columbia, I was a Cowles Postdoc Associate at Yale University. I received my Ph.D. in Economics from New York University in 2017.

My research focuses on information economics, from micro theory to more applied work, especially in experimental economics and political economy.


Joint work with Laurent Mathevet and Ina Taneva.
Revise and Resubmit at Journal of Political Economy

Abstract: Information provision in games influences behavior by affecting players' beliefs about the state, as well as their higher-order beliefs. We characterize the extent to which a designer can manipulate players' beliefs by disclosing information. Building on this, our next results describe the structure of optimal belief distributions, including a concave-envelope representation that subsumes the single-agent result of Kamenica and Gentzkow (2011). Our belief-based approach to information design applies to various equilibrium selection rules and solution concepts. We use it to compute the optimal information structure in an investment game under adversarial equilibrium selection.

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Joint work with Sevgi Yuksel.

Abstract: We study the competitive provision and endogenous acquisition of political information. Our main result identifies a natural equilibrium channel through which a more competitive market for information increases social disagreement. A critical insight we put forward is that competition among information providers leads to a particular kind of informational specialization: firms provide relatively less information on issues that are of common interest and relatively more information on issues along which agents' preferences are more heterogeneous. This enables agents to find information providers that are better aligned with their preferences. While agents become better informed on an individual level, the social value of the information provided in equilibrium decreases, thereby decreasing the probability that the society will implement socially optimal policies.

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Joint work with Sevgi Yuksel.

Abstract: We study a dynamic learning model in which heterogeneously connected Bayesian players choose between two activities: learning from one's own experience (work) or learning from the experience of others (search). Players who work produce an inflow of information which is local and dispersed around the society. Players who search, instead, aggregate the information produced by others and facilitate its diffusion, thereby transforming what inherently is a private good into information that everyone can access more easily. The structure of social connections affects the interaction between equilibrium information production and its social diffusion in ways that are complex and subtle. We show that increasing the connectivity of the society can lead to a strict decrease in the quality of social information. We link these inefficiencies to frictions in peer-to-peer communications. Moreover, we find that the socially optimal allocation of learning activities can differ dramatically from the equilibrium one. Under certain conditions, the planner would flip the equilibrium allocation, forcing highly connected players to work, and moderately connected ones to search. We conclude with an application that studies how resilient a society is to external manipulation of public opinion through changes in the meeting technology.

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Joint work with Guillaume Frechette and Alessandro Lizzeri.

Abstract: We introduce a simple sender-receiver framework that casts under the same umbrella a class of communication models that includes as special cases Cheap Talk (Crawford and Sobel, 1982), Disclosure (Grossman, 1981), and Bayesian Persuasion (Kamenica and Gentzkow, 2011). Within this framework, we generate novel comparative statics and offer a broader and unified perspective on these celebrated models. Our theory predicts that, as the sender's ability to commit to communication strategies increases, information transmitted should decrease if messages are verifiable (rules), but increase, if messages are unverifiable (no rules). In the limit, under full commitment, verifiability is irrelevant for the amount of information transmitted. We bring these novel comparative statics to the laboratory. We find that, qualitatively, subjects respond to the degree of commitment in a manner that is consistent with the theory. However, we find important deviations from the theoretical benchmark. Commitment works best when messages are unverifiable. In particular, we find that that subjects find it easier to lie about bad news than to hide good news, when equilibrium requires so.

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  2018 - present
Assistant Professor - Columbia GSB, Economics Division
  2017 - 2018
Postdoc Associate - Cowles Foundation, Yale University.


  2011 - 2017
Ph.D. in Economics - New York University.
MSc Economics - Bocconi University, Italy.


GSAS Dean's Dissertation Fellowship - New York University.
Mc Cracken Scholarship - New York University.
Bonaldo Stringher Scholarship, Bank of Italy.
Angelo Costa Master Thesis Award - Rivista di Politica Economica.


Graduate (TA)

Real Analysis, PhD First Year, NYU.
Micro II, PhD First Year, NYU.
Game Theory, MSc, Bocconi.

Undergrad (TA)

  2015, 2016
Microeconomics, BA, NYU Stern.
Experimental Econonomics, BA, NYU.
Principles of Micro, BA, Bocconi.