Home Bias in Online Employment

Chen Liang, Yili Hong, Bin Gu

Research output: Contribution to conferencePaper

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, we employ the difference-in-difference model to show that the home bias does exist in online employment, and at least 54.0% of home bias is driven by statistical discrimination.

Original languageEnglish (US)
StatePublished - Jan 1 2018
Event38th International Conference on Information Systems: Transforming Society with Digital Innovation, ICIS 2017 - Seoul, Korea, Republic of
Duration: Dec 10 2017Dec 13 2017

Other

Other38th International Conference on Information Systems: Transforming Society with Digital Innovation, ICIS 2017
CountryKorea, Republic of
CitySeoul
Period12/10/1712/13/17

Keywords

  • Employment
  • Gig economy
  • Home bias
  • Quasi-natural experiment
  • Statistical discrimination
  • Taste-based discrimination

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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  • Cite this

    Liang, C., Hong, Y., & Gu, B. (2018). Home Bias in Online Employment. Paper presented at 38th International Conference on Information Systems: Transforming Society with Digital Innovation, ICIS 2017, Seoul, Korea, Republic of.