Essays in Behavioral and Experimental Economics

Abstract

This dissertation examines how informational framing, cognitive complexity, and political identity shape beliefs and subsequent behavior. Across four chapters based on experimental studies, it investigates fairness judgments under inequality framings, the link between beliefs and actions under model uncertainty, misperceptions of others' political reasoning, and polarization dynamics following the 2024 U.S. presidential election. <br /> The first chapter shows that framing income changes in absolute versus relative terms — though informationally equivalent — systematically alters fairness judgments and redistributive choices, likely by inducing different underlying notions of inequality. The second chapter (co-authored with Florian Zimmermann) finds that while stated beliefs correctly reflect uncertainty over competing models, actions do not: particularly under high computational complexity, participants behave as if the most likely model were certainly correct and express unwarranted confidence in the accuracy of their estimates. The third chapter (co-authored with Guy Yanay) shows that although actual belief updating exhibits no evidence of motivated reasoning, both lay participants and academic experts systematically overestimate the degree to which others update in a partisan manner — distorted meta-beliefs that may themselves reinforce polarization. The fourth chapter (co-authored with Florian Zimmermann) exploits the 2024 U.S. presidential election as a natural experiment and documents increased polarization in ideological preferences, factual beliefs, and political salience, driven primarily by Democrats disappointed by the election outcome. <br /> Taken together, the four chapters show that beliefs are systematically shaped by how information is presented, by cognitive constraints, and by political identity. Rather than reflecting purely rational updating, beliefs emerge as context-dependent, influenced by framing, complexity, stereotypes, and identity.