Department of Methodology, London School of Economics and Political Science
Laboratory of Digital and Computational Demography, Max Planck Institute for Demographic Research
Abstract
Online labour markets—freelance marketplaces, where digital labor is distributed via a web-based platform—commonly use reputation systems to overcome the cultural and geographical boundaries as well as make it easier for employers to assess the abilities and trustworthiness of potential employees. Research shows, however, that reputation systems have a tendency to create winner-takes-all dynamics, in which differences in candidates' reputations become disconnected from their objective differences. In this paper, we use an empirically validated agent-based computational model to investigate to what extent reputation systems can create segmented outcomes biased towards freelancers with high reputation. We explore how jobs and earnings are redistributed on a stylised platform under different contextual conditions of information asymmetry. Results suggest that information asymmetry influences the extent to which the reputation systems may lead to inequality between freelancers and, contrary to expectations, lower levels of information asymmetry can facilitate higher inequality in outcomes.
Download the paper here (open access)
Keywords : reputation systems, online labor markets, inequality, agent-based modeling, economic sociology, gig economy
- NetLogo:
LukacGrow_JCSS_NetLogoModel.nlogo - Simulation results:
20190213 RecMechOLM info_asym_auctiontype-table.csv - R analysis script:
LukacGrow_JCSS_Visualisations.R