Can Monitoring Help Flatten the World? An Empirical Examination of Online Hiring

44 Pages Posted: 7 Dec 2021

See all articles by Chen Liang

Chen Liang

University of Connecticut - School of Business

Yili Hong

University of Miami Herbert Business School

Bin Gu

Boston University - Department of Management Information Systems

Date Written: October 12, 2021

Abstract

With the prevalence of online employment platforms, employers are increasingly hiring remote workers from those platforms and using platform-provided monitoring systems to keep track of workers’ activities. We propose that the effect of monitoring goes beyond the findings from the extant monitoring literature that has predominantly examined the productivity effects. Specifically, we hypothesize that monitoring systems would reduce employers’ bias against foreign workers (home bias), thus flattening the global labor markets. Employers tend to have home bias owing to the high transaction risks and coordination costs in dealing with foreign workers. As monitoring systems automate the collection of workers’ activities during the project process, they inform employers on the work process and project progress in real time and facilitate the coordination process with remote workers. Thus, employers may reduce their home bias because the risks of hiring foreign workers and the associated coordination costs have been substantially reduced. With a unique large-scale data set from a major global online employment platform, we leverage a difference-in-differences model and the exogenous event of the introduction of a platform-provided monitoring system for time-based projects to estimate the impact of monitoring systems on employers’ home bias. We find that employers significantly reduce their home bias after the introduction of the monitoring system. Further, the decrease in employers’ home bias is smaller for employers who had positive hiring experiences with foreign workers before the introduction of the monitoring system. In addition, the decrease in employers’ home bias is larger in high-routine projects than in low-routine projects, with the latter being more difficult to monitor. Our findings are robust to alternative empirical specifications and provide important managerial implications for improving the design of online employment platforms.

Keywords: monitoring, home bias, online employment, hiring experience, task routineness

Suggested Citation

Liang, Chen and Hong, Yili and Gu, Bin, Can Monitoring Help Flatten the World? An Empirical Examination of Online Hiring (October 12, 2021). Available at SSRN: https://ssrn.com/abstract=3941309 or http://dx.doi.org/10.2139/ssrn.3941309

Chen Liang

University of Connecticut - School of Business ( email )

2100 Hillside Road, Unit 1041
UConn School of Business OPIM
Storrs, CT Connecticut 06269
United States
06269 (Fax)

Yili Hong (Contact Author)

University of Miami Herbert Business School ( email )

P.O. Box 248126
Florida
Coral Gables, FL 33124
United States

Bin Gu

Boston University - Department of Management Information Systems ( email )

595 Commonwealth Avenue
Boston, MA 02215
United States

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