How to counteract biased self-assessments? An experimental investigation of reactions to social information

B-Tier
Journal: Journal of Economic Behavior and Organization
Year: 2023
Volume: 206
Issue: C
Pages: 1-25

Authors (4)

Fellner-Röhling, Gerlinde (not in RePEc) Hromek, Kristijan (not in RePEc) Kleinknecht, Janina (not in RePEc) Ludwig, Sandra (Universität Ulm)

Score contribution per author:

0.503 = (α=2.01 / 4 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

In a lab experiment, we investigate whether social information can improve the accuracy of self-assessments of relative performance. In particular, we compare the effectiveness of different types of social information: subjects either learn their close peers’ (i) average absolute performance, (ii) average self-assessment or (iii) average bias of self-assessments. Additionally, we explore the demand for the different types of social information. Our results suggest that social information can help debiasing subjects’ self-assessments, but not all types of information are equally effective. Only learning about the average bias of peers improves self-assessments. Subjects are, in general, willing to pay for social information but mostly prefer information about their peers’ absolute performance, which is not helpful. Nevertheless, self-selected information on peers’ average bias triggers a stronger reaction to the information.

Technical Details

RePEc Handle
repec:eee:jeborg:v:206:y:2023:i:c:p:1-25
Journal Field
Theory
Author Count
4
Added to Database
2026-01-25