Reputation systems and recruitment in online labor markets: insights from an agent-based model

Martin Lukac () and André Grow ()
Additional contact information
Martin Lukac: Department of Methodology, London School of Economics and Political Science
André Grow: Max Planck Institute for Demographic Research

Journal of Computational Social Science, 2021, vol. 4, issue 1, No 10, 207-229

Abstract: Abstract Online labor markets—freelance marketplaces, where digital labor is distributed via a web-based platform—commonly use reputation systems to overcome uncertainties in the hiring process, that can arise from a lack of objective information about employees’ abilities. Research shows, however, that reputation systems tend to create winner-takes-all dynamics, in which differences in candidates’ reputations become disconnected from differences in their objective abilities. In this paper, we use an empirically validated agent-based computational model to investigate the extent to which reputation systems can create segmented hiring patterns that are biased toward freelancers with good reputation. We explore how jobs and earnings become distributed on a stylized platform, under different contextual conditions of information asymmetry. Our results suggest that information asymmetry influences the extent to which reputation systems may lead to inequality between freelancers, but contrary to our expectations, lower levels of information asymmetry can facilitate higher inequality in outcomes.

Keywords: Reputation systems; Online labor markets; Inequality; Agent-based modeling; Economic sociology; Gig economy (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1007/s42001-020-00072-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:jcsosc:v:4:y:2021:i:1:d:10.1007_s42001-020-00072-x

Ordering information: This journal article can be ordered from
http://www.springer. ... iences/journal/42001

DOI: 10.1007/s42001-020-00072-x

Access Statistics for this article

Journal of Computational Social Science is currently edited by Takashi Kamihigashi

More articles in Journal of Computational Social Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().