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.
Liang, Chen; Hong, Yili; and Gu, Bin, "Home Bias in Hiring: Evidence from an Online Labor Market" (2018). PACIS 2018 Proceedings. 49.