Start Date
10-12-2017 12:00 AM
Description
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 roughly 54.0% of home bias is driven by statistical discrimination.
Recommended Citation
Liang, Chen; Hong, Kevin; and Gu, Bin, "Home Bias in Online Employment" (2017). ICIS 2017 Proceedings. 14.
https://aisel.aisnet.org/icis2017/Peer-to-Peer/Presentations/14
Home Bias in Online Employment
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 roughly 54.0% of home bias is driven by statistical discrimination.