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

Fingerprint

Personnel
Monitoring
Experiments

Keywords

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

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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.

Home Bias in Online Employment. / Liang, Chen; Hong, Yili; Gu, Bin.

2018. Paper presented at 38th International Conference on Information Systems: Transforming Society with Digital Innovation, ICIS 2017, Seoul, Korea, Republic of.

Research output: Contribution to conferencePaper

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, 12/10/17 - 12/13/17, .
Liang C, Hong Y, Gu B. Home Bias in Online Employment. 2018. Paper presented at 38th International Conference on Information Systems: Transforming Society with Digital Innovation, ICIS 2017, Seoul, Korea, Republic of.
Liang, Chen ; Hong, Yili ; Gu, Bin. / 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.
@conference{346c14f2a5204c7399107f4e5d84f28b,
title = "Home Bias in Online Employment",
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.",
keywords = "Employment, Gig economy, Home bias, Quasi-natural experiment, Statistical discrimination, Taste-based discrimination",
author = "Chen Liang and Yili Hong and Bin Gu",
year = "2018",
month = "1",
day = "1",
language = "English (US)",
note = "38th International Conference on Information Systems: Transforming Society with Digital Innovation, ICIS 2017 ; Conference date: 10-12-2017 Through 13-12-2017",

}

TY - CONF

T1 - Home Bias in Online Employment

AU - Liang, Chen

AU - Hong, Yili

AU - Gu, Bin

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - Employment

KW - Gig economy

KW - Home bias

KW - Quasi-natural experiment

KW - Statistical discrimination

KW - Taste-based discrimination

UR - http://www.scopus.com/inward/record.url?scp=85041743811&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85041743811&partnerID=8YFLogxK

M3 - Paper

ER -