Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments October 12, 2021 erstellt
Beschreibung:
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