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Are employers in “non-STEM” occupations (e.g. Graphic Designers, Economists) interested in STEM (Science, Technology, Engineering, and Mathematics) graduates because of the knowledge and skills they acquired through their STEM education (e.g. “C++”, “Systems Engineering”) or simply because of their problem solving and analytical abilities? This is an important question in the UK where less than half of STEM graduates work in STEM occupations, and where this apparent leakage from the “STEM pipeline” is often considered as a wastage of resources. To address it, this paper goes beyond the discrete divide of occupations into STEM vs. non-STEM: using UK online vacancy postings, we measure STEM requirements at the level of jobs, by classifying each posting as either STEM or non-STEM based on its job description text instead of its occupation identifier. We find that 35% of all STEM jobs belong to non-STEM occupations, and that 15% of all vacancies posted in non-STEM occupations are in fact STEM jobs. Moreover, STEM jobs are associated with higher wages within both STEM and non-STEM occupations.

At any point in time, skills gaps, mismatches, and shortages arise because of an imperfect correspondence between the singular sets of skills required by different open vacancies and the unique combinations of capabilities embodied in every job seeker - skills diversity in unity. This paper first constructs an abstract framework for defining and thinking about these phenomena in a unified, formal and objective way. The main building block is a discrete skills space in which the locations of vacancies and workers are determined by the vectors of skills characterizing them. We define skills gaps and mismatches as two different distance measures between them, and derive a condition for each vacancy that determines whether or not it experiences a skills shortage. We then develop a job matching model with imperfect information, in which skills mismatches influence the job application decisions of the workers, while skills gaps and shortages shape the competition for workers on the resulting bipartite job applications network. The tools proposed in this paper could in future work be employed as the main ingredients of an agent-based model used to investigate how skills gaps, mismatches and shortages affect equilibrium outcomes in the context of skills diversity in unity and imperfect information.

Do employers in "non-STEM" occupations (e.g. Graphic Designers, Economists) seek to hire STEM (Science, Technology, Engineering, and Mathematics) graduates with a higher probability than non-STEM ones for knowledge and skills that they have acquired through their STEM education (e.g. "Microsoft C#", "Systems Engineering") and not simply for their problem solving and analytical abilities? This is an important question in the UK where less than half of STEM graduates work in STEM occupations and where this apparent leakage from the "STEM pipeline" is often considered as a wastage of resources. To address it, this paper goes beyond the discrete divide of occupations into STEM vs. non-STEM and measures STEM requirements at the level of jobs by examining the universe of UK online vacancy postings between 2012 and 2016. We design and evaluate machine learning algorithms that classify thousands of keywords collected from job adverts and millions of vacancies into STEM and nonSTEM. 35% of all STEM jobs belong to non-STEM occupations and 15% of all postings in non-STEM occupations are STEM. Moreover, STEM jobs are associated with higher wages within both STEM and non-STEM occupations, even after controlling for detailed occupations, education, experience requirements, employers, etc. Although our results indicate that the STEM pipeline breakdown may be less problematic than typically thought, we also find that many of the STEM requirements of "non-STEM" jobs could be acquired with STEM training that is less advanced than a full time STEM education. Hence, a more efficient way of satisfying the STEM demand in non-STEM occupations could be to teach more STEM in non-STEM disciplines. We develop a simple abstract framework to show how this education policy could help reduce STEM shortages in both STEM and non-STEM occupations.