The Utility of Skills in Online Labor Markets

In this work, we define the utility of having a certain skill in an Online Labor Market (OLM), and we propose that this utility is strongly correlated with the level of expertise of a given worker. However, the actual level of expertise for a given skill and a given worker is both latent and dynamic. What is observable is a series of characteristics that are intuitively correlated with the level of expertise of a given skill. We propose to build a Hidden Markov Model (HMM), which estimates the latent and dynamic levels of expertise, based on the observed characteristics. We build and evaluate our approaches on a unique transactional dataset from oDesk.com. Finally, we estimate the utility of a series of skills and discuss how certain skills (e.g. ‘editing’) provide a higher expected payoff once a person masters them over others (e.g. ‘microsoftexcel’)