Hiring and Learning in Online Global Labor Markets

This paper uses data from the online employer-freelancer matching platform freelancer.com to study the determinants of a match between an employer and a freelancer. Having to rely on a relatively small number of characteristics, employers use the freelancer's country of origin and reputation scores to infer the expected service quality. 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. This setting also allows me to study how employers' experience in past hires affects their behavior in current hires. I show that following a good match with a freelancer, employers are more likely to hire freelancers from the good match's country. I discuss how these findings contribute to our understanding of matching, learning, and discrimination in online settings.