Understanding the Skill Provision in Gig Economy from A Network Perspective

Abstract

The recent emergence of gig economy facilitates the exchange of skilled labor by allowing workers to showcase and sell their skills to a global market. Despite the recent effort on thoroughly examining who workers in the gig economy are and what their experience in the gig economy are like, our knowledge on how exactly do workers provide their skills in gig economy, and how worker's strategies on skill provision and expansion relate to their success in gig economy is still lacking. In this paper, we conduct a case study on a prominent gig economy platform, Fiverr.com, to better understand the provision of skills on it through large-scale, data-driven analysis. In particular, we propose the concept of "skill space" from a network perspective to characterize the relationship between different skills by measuring how frequently workers provide different skills together. Through our analysis, we reveal interesting patterns in worker's provision of skills on Fiverr. We then show how these patterns change over time and differ across subgroups of workers with different characteristics. In addition, we find that providing a set of skills that are highly related with each other correlates with a better overall performance in gig economy, and when workers expand their skillsets, expanding to a new skill that is highly-related to the existing skills takes less time and is associated with better performance on the new skill. We conclude by discussing the implications of our findings for gig economy workers and platform in general.