Bipartite Graph Theory and the STEM Labor Market
As a college student in a STEM field, I have felt the threat of the difficult job market firsthand. To put it simply, in many job markets, there are simply not enough STEM jobs to match every STEM worker with a position in their desired field. As such, it becomes necessary for workers to analyze the probability of job matching in various areas to maximize their likelihood of finding a job. As this article explains, it is not, as one would initially think, in the largest STEM job markets (very large cities like New York and Los Angeles) that matching probabilities are highest. Instead, matching rates are highest in markets such as Seattle or Austin with the highest STEM labor market density (which is calculated by calculating the ratio of STEM jobs to working age citizens).
Bipartite graph theory makes it easy for us to understand why this is the case. Imagine a bipartite graph where one side is made up of STEM graduates, and the other is made of STEM jobs. In a very large job market, which will always be in an urban area with many people, the number of applicants will always dwarf the number of jobs, leading to a low matching probability. However, in a dense labor market, these two sides will come much closer to equal. While they will likely never achieve perfect matching, because there will likely always be constricted sets of applicants fighting for the same jobs, it is intuitive that the matching probabilities will be much higher in the second case, which explains the high STEM matching probabilities in dense STEM labor markets like Seattle, Austin, Raleigh, and Washington D.C.
Original Article: https://www.sciencedaily.com/releases/2016/09/160912132741.htm