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Home Bias in Global Employment

Chen Liang (), Yili Hong () and Bin Gu ()
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Chen Liang: Department of Information Systems, W.P. Carey School of Business, Arizona State University, USA
Yili Hong: Department of Information Systems, W.P. Carey School of Business, Arizona State University, USA
Bin Gu: Department of Information Systems, W.P. Carey School of Business, Arizona State University, USA

No 17-06, Working Papers from NET Institute

Abstract: We study the nature of home bias in online employment, wherein the employers prefer workers from their own home countries. Using a unique large-scale dataset from a major online labor platform, we identify employers’ home bias in their online employment decisions. Moreover, we find that employers from countries with high traditional values, lower diversity, and smaller (user) population size, tend to have a stronger home bias. Further, we investigate the nature 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 40.93% of home bias is driven by statistical discrimination.

Keywords: home bias; global employment; statistical discrimination; taste-based discrimination; quasi-natural experiment; Gig economy (search for similar items in EconPapers)
JEL-codes: J71 J78 J23 J82 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cta and nep-lma
Date: 2017-09
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