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Cited by 2Names, Identity, and Discrimination ResearchDigital Economy and Work TransformationEthics and Social Impacts of AI

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Home Bias in Online Employment

Open access
Liang, Chen, Hong, Yili (Kevin), Gu, Bin·2018·Proceedings of the Annual Hawaii International Conference on System Sciences
DOI 10.24251/hicss.2018.437
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Abstract

We study the nature of home bias in online employment, wherein the employer prefers workers from his/her own home country. Using a unique large-scale dataset from one of the major online labor platforms, we identify employers’ home bias in their online employment decisions. Moreover, we investigate the cause of employers’ home bias using a quasi-natural experiment wherein the platform introduces a monitoring system to facilitate employers to keep track of workers’ progress in time-based projects. After matching comparable fixed-price projects as a control group using propensity score matching, our difference-in-difference estimations show that the home bias does exist in online employment, and at least 54.0% of home bias is driven by statistical discrimination.

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  • Discrimination can also be derived from the information asymmetry between workers and consumers since workers usually receive less information about consumers.

    “Discrimination can also be derived from the information asymmetry between workers and consumers since workers usually receive less information about consumers (Liang et al., 2018).”

    From Socio‐technical issues in the platform‐mediated gig economy: A systematic literature review: An Annual Review of Information Science and Technology (ARIST) paper (2024) · cites this paper as Liang et al., 2018

    Section 2.2, “Mechanisms of Discrimination” (page not provided), describes the opposite information gap: employers have limited information about workers’ capability and effort and may use observable signals to infer their characteristics. It does not say that workers receive less information about consumers. (LLM, conf=0.88)

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Research works printed but not linked: 31

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Metadata

Authors
Liang, C., Hong, Y. (., & Gu, B.
Affiliations
Arizona State University (US)
ORCID
Venue
Proceedings of the Annual Hawaii International Conference on System Sciences
Publisher
Hawaii International Conference on System Sciences
Published
2018
ISSN
  • 1530-1605 (ISSN-L)
  • 2572-6862
DOI
10.24251/hicss.2018.437
OpenAlex
W2771037556
Cited by
2
References
0
Citations by year
2024: 1 · 2023: 1
FWCI
0.00
Type
conference paper
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Hawaii International Conference on System Sciences (HICSS) (publishedVersion, cc-by-nc-nd)
  • Journal of the Association for Information Systems (submittedVersion)
  • ScholarSpace (University of Hawaii at Manoa) (submittedVersion, cc-by-nc-nd)
Topics
  • Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
  • Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Keywords
home bias, online labour platforms, statistical discrimination, hiring decisions, labor market discrimination

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