Subjective Judgment and Gender Bias in Advice: Evidence from the Laboratory

Juliana Silva Goncalves () and Roel van Veldhuizen
Additional contact information
Juliana Silva Goncalves: University of Sydney, Australia

No 2020:27, Working Papers from Lund University, Department of Economics

Abstract: Better understanding and reducing gender gaps in the labor market remains an important policy goal. We study the role of advice in sustaining these gender gaps using a laboratory experiment. In the experiment, “advisers” advise “workers” to choose between a more ambitious and a less ambitious task based on the worker’s subjective self-assessment. We expected female workers to be less confident and advisers to hold gender stereotypes, leading to a gender bias in advice. However, we find no evidence that women are less confident or that advice is gender-biased. Our results contribute to our understanding of the mechanisms driving gender differences in the labor market. They also call for caution when making general interpretations of research findings pointing to a gender bias in specific settings.

Keywords: Advice; Subjective judgment; Gender bias (search for similar items in EconPapers)
JEL-codes: C91 D91 J16 (search for similar items in EconPapers)
Pages: 57 pages
Date: 2020-12-14
New Economics Papers: this item is included in nep-exp, nep-gen, nep-hrm and nep-lab
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://lucris.lub.lu.se/ws/portalfiles/portal/194592700/WP20_27 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hhs:lunewp:2020_027

Access Statistics for this paper

More papers in Working Papers from Lund University, Department of Economics School of Economics and Management, Box 7080, S-22007 Lund, Sweden. Contact information at EDIRC.
Bibliographic data for series maintained by Iker Arregui Alegria ().