Digital Signals of Human Capital : Analyzing Variation Across Skill Networks

In an online labor market, we investigate the possibility that different types of human capital signals are better suited for and more effective in different skill networks. Our method consists of three components: (1) Social network analysis to identify distinct skill networks based on user profiles and project postings. (2) Mapping human capital to digital signals in online labor markets as a multidimensional construct. This multidimensional signal construct includes reputation signals, skill signals, cheap talk signals, and social distance signals. (3) Linking different signals of human capital to different skill networks in a regression analysis to identify the differential impact of these human capital signals on winner selection in different skill networks. We find a differential impact of different human capital signals across skill networks. Specifically, we find that reputation signals and social distance signals are more effective for less commodified skill sets such as creative writing, whereas in more commodified skill networks such as programming and web design, particularly skill signals are effective in combination with the price offered