Identifying Career Boundaries Using Minimum Description Length on a Graph

Abstract

Understanding how careers evolve is very important for us, so we can make better professional decisions; for companies, so they can better plan their internal organization career progressions; and also for the governments, so they can better plan their labour market and economic policies. In this paper, we model a career progression as a graph, called MCar, in which nodes represent occupations and edges correspond to the flow of professionals among occupations. Based on MCar, we introduce some key concepts to investigate career evolution, namely, career boundary, occupational poles, and occupational islands. Finally, we propose the use of community detection techniques on a real database with millions of professional backgrounds to objectively identify career boundaries in Brazil and to study the topologies of the resultant graph. The results obtained provide a quantitative basis for career models, showing, for instance, the presence of hubs suggesting that less regular careers are common.

Keywords

• Network
• community detection
• occupation
• boundaryless career
• protean career
• occupational pole