The relationship between worker human capital and wages is a question of considerable economic interest. Skills are usually characterized using a one-dimensional measure, such as years of training. However, in knowledge-based production, the interaction between a worker’s skills is also important. Here, we propose a network-based method for characterizing worker skill sets. We construct a human capital network, wherein nodes are skills and two skills are connected if a worker has both or both are required for the same job. We then illustrate the method by analyzing an online freelance labor market, showing that workers with diverse skills earn higher wages and that those who use their diverse skills in combination earn the highest wages of all.Abstract
We propose a network-based method for measuring worker skills. We illustrate the method using data from an online freelance website. Using the tools of network analysis, we divide skills into endogenous categories based on their relationship with other skills in the market. Workers who specialize in these different areas earn dramatically different wages. We then show that, in this market, network-based measures of human capital provide additional insight into wages beyond traditional measures. In particular, we show that workers with diverse skills earn higher wages than those with more specialized skills. Moreover, we can distinguish between two different types of workers benefiting from skill diversity: jacks-of-all-trades, whose skills can be applied independently on a wide range of jobs, and synergistic workers, whose skills are useful in combination and fill a hole in the labor market. On average, workers whose skills are synergistic earn more than jacks-of-all-trades.