After making the point in Part 1 that productivity and employability are really two sides of the same coin, we went on in Part 2 and Part 3 to look at how Universities might use Market Intelligence to help increase productivity, before looking in Part 4 at how they might use Market Intelligence to improve objective employability.
What we meant by objective employability was not the increase of skills, understandings and personal attributes that make graduates more likely to gain employment, but rather market intelligence on demand for occupations that makes it more likely that they will study for a job that will actually exist when they graduate. But what if a student graduates and there are no jobs in the field they studied in? Or what if they have studied in a field that doesn’t necessarily relate directly to a particular career or occupation? Where does objective employability fit into this?
In Part 4 we gave the illustration of two people – Anthony and Karla – who live in the same region and intend to go to university in that region. In the illustration, Anthony did Biochemistry, but after graduating finds out there are no jobs for Biochemists in the region. Karla, on the other hand, starts off wanting to do Biochemistry, but changes her mind to do Dentistry after researching labour market demand for graduate positions, and, after seeing that whereas demand for Biochemists is falling, the demand for Dentists is likely to be high. So Karla gets her job, but what about Anthony? Does he simply look for any graduate position he can find or failing that any job he can find? Or is there a better solution? The answer is yes there is a better solution; and it lies in tapping into transferrable skills.
In the US, there is a system known as Occupational Information Network (O*NET), which is a database ranking the knowledge and skills needed for any occupation. Emsi have adapted this for the UK labour market, and we have done so in such a way that allows comparison of the knowledge and skills required in one occupation with those required in any other occupation. What this means is that we can find out which occupations are most similar in terms of knowledge and skills, and also what the knowledge and skills gaps are.
Going back to Part 4, in our regional graph of the numbers employed as Biological scientists and biochemists between 2015 and 2020, we saw that the lowest demand is set to be in Kent, with job numbers expected to fall by around 39 during that period. We suggested there that someone thinking of going to university to do biochemistry, and then staying in the Kent region afterwards, might be better off first looking at occupational demand in the area to see where the jobs are set to be. But what if – like Anthony – they have done Biochemistry only to find that there are no jobs? This is where the idea of transferable skills comes in.
The graph below uses O*NET data to pick out the Top 10 nearest fit occupations to Biological scientists and biochemists, in terms of skillsets and knowledgebase, with the closest on the left (Research and development managers). Not only this, but the graph also uses our data to show the demand for each of these occupations between 2015 and 2020:
This sort of intelligence has obvious benefits both at the front end of a person’s student journey – when they are thinking of what they might like to study – and at the back end, when it comes to graduating and they are applying for jobs. At the front end, such intelligence can inform them of alternative career choices that may have more demand from employers than their initial choice. At the back end, it can inform them what similar occupations they might be able to apply for if they cannot get a job in an occupation directly related to their field of study.
But there’s more. The O*Net data allows us to dig deeper to compare the knowledge required in one occupation with another. So for instance, someone who had graduated in Biochemistry, only to find that there are no Biological scientist and biochemists jobs available in their region, might want to do a comparison of what they have learnt with some of the most compatible occupations to find out where their knowledge gaps are. So for example, if we compare the knowledge base for Biological scientist and biochemist with the closest compatible occupation, Research and development manager, we find the following:
The red parts are the gaps between knowledge needed as a Biological scientist or biochemist, and that which is needed as a Research and development manager. Where this information is invaluable is in making a graduate who cannot get a job in their field of study aware of where they might upskill in order to be able to move into a related career. Imagine taking these insights into a job interview!
In the final part of this series, we will attempt to draw all the strands of this series together, to summarise how market intelligence can be used by Higher Education institutions to Improve Lives.
For more information on how we can help your university improve productivity and the employability of its students, contact Jamie Mackay at firstname.lastname@example.org.