Inferring Supplier Quality in the Gig Economy: The Effectiveness of Signals in Freelance Job Markets

Inferring quality of labor suppliers is a challenge in the gig economy. Many online freelance job markets address this challenge by incorporating signals. We test effectiveness of two kinds of information signals as indicators of supplier quality: skill signal (which reflects suppliers’ skill and potential), and achievement signal (which reflects suppliers’ past achievement). We theorize that two job characteristics in cross-national labor demand settings strengthen effectiveness of these signals: job duration, and cultural distance. Econometric analysis on a dataset from a leading online freelance job marketplace containing information on jobs posted by buyers and completed by suppliers located across several countries supports our hypotheses. We find that both skill and achievement signals are more effective at inferring supplier quality in jobs involving longer duration, and in jobs involving greater cultural distance between buyers and suppliers