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

Martin Lukac and André Grow

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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: agent-based modeling; economic sociology; gig economy; inequality; online labor markets; reputation systems (search for similar items in EconPapers)
JEL-codes: J01 R14 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2020-08-02
New Economics Papers: this item is included in nep-ban, nep-cmp, nep-hme and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published in Journal of Computational Social Science, 2, August, 2020, 4(1), pp. 207 - 229. ISSN: 2432-2717

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:114454

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