Complex network approach to career planning

Abstract

The labor market is a complex system. Beyond matching the demands for various expertise with individuals who are competent, there is also the need of individual to increase their skill correspondingly. In this thesis, we seek to discover the underlying structure of the real world job transition phenomena where people from move one job to another job in the context of complex network. It was found that the degree distribution of the phenomena had a fat-tail (weighted) degree distribution which characterizes a scale-free network with scaling exponent ~ 1.2 - 1.4. Nevertheless, the configuration model of a scale-free network is not a total fit with the data as there's some behavioral deviation. In addition, we also wanted to discover the assortativity mixing of job transition approaching from the skills perspective. Binomial/proportion hypothesis testing was employed to determine whether it was crucial for individual to gain certain expertise on the skill (which overlap between original and target job) for higher chances of advancing in their career. It was found that by examining certain example of job transition; there is clear evidence that by posing the skills required relative to other factors such as the previous job and the significance of the skill in the target job increase the probability of advancing more than 50% compared to their counterparts who do not possess the skills