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Katherine Anderson, University of Pittsburgh,

News and Events | Comments Off on Katherine Anderson, University of Pittsburgh,

will be the presenter at this Friday’s Research Colloquium on Computational Social Science/Data Science. Dr. Anderson is a visiting assistant professor at the University of Pittsburgh’s Department of Informatics and Networked Systems in the School of Computing and Information. She uses the tools of network analysis and computational modeling to look at how skills and ideas interact in collaborative environments.

Her talk entitled “Skill Networks and Measures of Complex Human Capital” will begin at 3:00 in the Center for Social Complexity Suite located on the 3rd floor of Research Hall.  The talk will be followed by a Q&A session along with light refreshments. 

For a list of upcoming and previous seminars, please visit:  https://cos.gmu.edu/cds/calendar/

 Abstract:  The relationship between worker skills and wages is a problem of tremendous economic interest, making it critical to have effective measures of the skills, knowledge, and experience that a worker brings to production: a bundle of worker characteristics that economists refer to as human capital. Traditional models of human capital measures either divide workers into broad categories (e.g., laborers and management) or treat skills as a uni-dimensional measure of speed, education, or experience. However, in knowledge based production, the value a worker brings to production depends on both her individual skills and the interaction between them. Here, I present a network-based method for characterizing worker skills. I construct a human capital network, in which nodes are skills and two skills are connected if a worker has both or both are required for the same job. A worker’s human capital can be measured according to the position of her skills on the network. I illustrate this method using a novel dataset, gathered from an online freelance labor market. I show that workers with diverse skills earn higher wages than their peers with more specialized skills, and that those who use their diverse skills in combination earn the highest wages of all. I also show that network-based measures of human capital capture variation in wages beyond that captured by the skills individually. Finally, I will show how these same techniques can be used outside of the economic context, to quantify the relationship between the skills of collaborators.