Enrico Zanardo

PhD candidate in Economics
Columbia University

Job Market Paper

How to measure disagreement?

This paper defines a family of functions that measure the distance between opinions. I introduce six axioms that a measure of disagreement should satisfy, and characterize the functions that satisfy them. The disagreement measures we characterize generalize the Renyi divergences, and include the Kullback-Leibler divergence and the Bhattacharyya distance.

I analyze two applications. In the first, I find a necessary and sufficient condition under which public information reduces expected disagreement between Bayesian agents. In the second, I show that our measures of disagreement are useful to understand trading under heterogeneous beliefs. Trade volume and gains from trade are increasing in some of our measures disagreement.

Working Papers

Matching in the smallest large market (2016)

Work in Progress

When does information reduce disagreement? (with Navin Kartik)

The Value of Information in Voting Games





Columbia University
Department of Economics
1036 International Affairs Building
420 West 118th Street
10027 New York (NY)