Hiring and Learning in Online Global Labor Markets

Abstract

This paper uses data from freelancer.com – an online platform that allows employers and freelancers to search for, and match with, each other – to study the effect of freelancers’ country of origin on their likelihood to be hired. Having to rely on a relatively small number of characteristics, employers use the freelancer’s country of origin to infer the expected service’s quality. This setting also allows me to document how employers’ experience in past hires affects their behavior in current hires. I find that freelancers from developing countries are less likely to be hired when they have no individual reputation, and as individual reputation becomes better this country effect disappears. I show that following a good match with a freelancer, employers are more likely to hire freelancers from the good match’s country. These these findings are consistent with statistical – rather than purely taste-based – discrimination.