@inproceedings{98c599326117491f9938647b8189005a,
title = "Home bias in hiring: Evidence from an online labor market",
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 market containing employers' consideration set of workers and their ultimate selection of workers, we first estimate employers' home bias in their online employment decisions. Moreover, we find that employers from countries with high traditional values, lower diversity, and smaller user base (or population size), tend to have a stronger home bias. Further, we disentangle two types of home bias, i.e., statistical and taste-based, using a quasi-natural experiment wherein the platform introduces a monitoring system to facilitate employers to easily observe workers' progress in time-based projects. After matching comparable fixed-price projects as a control group using coarsened exact matching, our difference-in-difference estimations show that the home bias in online employment is primarily driven by statistical discrimination.",
keywords = "Discrimination, Employment, Gig economy, Home bias, Online labor market, Quasi-natural experiment",
author = "Chen Liang and Yili Hong and Bin Gu",
note = "Publisher Copyright: {\textcopyright} PACIS 2018.; 22nd Pacific Asia Conference on Information Systems - Opportunities and Challenges for the Digitized Society: Are We Ready?, PACIS 2018 ; Conference date: 26-06-2018 Through 30-06-2018",
year = "2018",
language = "English (US)",
series = "Proceedings of the 22nd Pacific Asia Conference on Information Systems - Opportunities and Challenges for the Digitized Society: Are We Ready?, PACIS 2018",
publisher = "Association for Information Systems",
editor = "Motonari Tanabu and Dai Senoo",
booktitle = "Proceedings of the 22nd Pacific Asia Conference on Information Systems - Opportunities and Challenges for the Digitized Society",
}