In Part 1 of this series we looked at how universities that are looking to increase their impact on productivity in the “knowledge economy” first need to “know that economy”, which they can do by using good, objective data for their region.
In Part 2, we picked up on comments by Professor Ewart Keep who made the point that fixing the problem of inadequate supply of skills is not a catch all solution to the skills and productivity problem. Whilst agreeing that dealing with inadequate supply is not going to solve the skills and productivity problem, we argued that there is a deeper problem, which is that claims of inadequate supply of a particular occupation or skill are often based on assumptions rather than data. We went on to illustrate this by giving some examples of how drilling into the specific data often shows our assumptions to be incorrect.
In this final piece, we again want to pick up on one of Professor Keep’s comments, this time in relation to stimulating demand:
“Elsewhere in the developed world, including within the OECD’s own thinking, the definition of the problem has moved on, and there is now a recognition that policy has to also seek to stimulate demand for a more highly skilled workforce, and to help employers to think through how they can deploy those skills more productively within the workplace.”
There are many ways of stimulating demand, but for purposes of this article the question we want to concentrate on is how – if at all – data can be used for this purpose.
In one sense, the answer would seem to be that it can’t. Labour Market Information (LMI) is, after all, simply data on current numbers employed, in terms of occupations and industries, along with forecasts of future demand.
However, where data can be useful is not so much in stimulating demand, but rather in establishing where the best opportunities for stimulating demand might be. In a recent piece for the LEP Network, we wrote about how economic development agencies in Detroit have been using LMI education data to demonstrate economic progress, identify shortfalls, and strategise for the future:
“What they are doing is to take the knowledge and understanding of their area that the data gives them to then build a compelling prospectus for why Detroit is a good place to invest in and to do business.”
The point being made was that through data, Detroit’s economic developers were able to identify the city’s strengths and weaknesses, and to use this information to stimulate demand in areas where the city had an economic advantage.
One example of this is in the auto and aerospace industries. Although the auto industry has been declining over the past decade or so, aerospace has been growing with 33% of the global top 100 aerospace companies now having a presence in the Detroit region. There are skills which are common to both industries – for example logistics – and so whilst the one industry may be in decline, by tapping into the data, economic developers can encourage investment in the other industry, confident that there are people in the region with the skills that it needs to grow.
All well and good, you might say, but how can this help universities to impact on productivity? The answer is that by identifying the strengths and competitive advantages of their area, a university can work alongside their Local Enterprise Partnership (LEP) and Local Authority to encourage more investment, whether in existing industries or in other industries with similar skillsets. The university itself can then act as a hub for these industries, investing more resources into the faculty and curriculum to feed the skills demands. In other words, the university, working alongside regional economic developers, can identify the existing specialisms of the region, and can then work to both strengthen them and expand them, so bettering the chances of impacting on productivity and regional prosperity.
There are a couple of specific measures of an area’s economy built into our Analyst tool that can prove to be of great help. The first is what is known as location quotient (LQ), which is basically a statistical measure of industry or occupation concentration in a particular area compared to the rest of the country (note: this is not a measure of sheer numbers of people employed, but rather the proportion of people employed in a particular occupation or industry in a particular area relative to the rest of that area’s economy).
The following table shows the top ten graduate level jobs in terms of LQ for the Solent LEP region (note: Britain as a whole is assigned the benchmark LQ of 1.0, and so an occupation with an LQ of more than 1.0 indicates that it has a higher than average concentration of jobs in the region):
|Description||2015 Jobs||2020 Jobs||2015 Location Quotient|
|Journalists, newspaper and periodical editors||1,942||2,257||1.62|
|Social and humanities scientists||504||521||1.43|
|Engineering professionals n.e.c.||2,259||2,296||1.31|
|Speech and language therapists||475||513||1.31|
|Production and process engineers||1,145||1,145||1.28|
As you can see, the region has a higher than average concentration of engineering and journalist occupations, which may not come as a big surprise given that some of the universities in the area are well known for their engineering and journalism degrees. But it may well be that there are other economic advantages in this type of data that a university, working with economic developers, could exploit.
Another measure that can be used is the regional competitive effect. This is similar to LQ, in that it highlights the uniqueness of a regional economy, but it does so in terms of job growth rather than total jobs in an industry. What this figure does is to explain how much of the change in a given industry or occupation is due to some unique competitive advantage that the region possesses, rather than the growth being explained by national trends in the economy as whole. Positive numbers denote a positive competitive effect (and vice versa), and so the larger the number, the higher the competitive effect.
So if we run a report for graduate level occupations, again for the Solent LEP region, looking at the top ten in terms of competitive effect, we find the following:
|Description||2015 Jobs||2020 Jobs||Competitive Effect|
|Journalists, newspaper and periodical editors||1,942||2,257||269|
|Primary and nursery education teaching professionals||11,797||12,191||255|
|Secondary education teaching professionals||8,759||9,094||124|
|Further education teaching professionals||3,739||3,845||124|
|Higher education teaching professionals||3,395||3,543||107|
|Sales accounts and business development managers||12,832||13,406||93|
|IT specialist managers||6,120||6,468||78|
|Information technology and telecommunications professionals n.e.c.||4,831||5,166||68|
Once again, this is the sort of information that universities, together with their local and regional economic development agencies, could potentially use to increase investment in sectors where a competitive advantage already exists.
As we come to the end of this series, we can summarise what we have said as follows:
1. If universities are to impact on productivity in the Knowledge Economy, they must firstly gain an in depth knowledge of the economy they are hoping to impact on
2. Inadequate supply cannot be dealt with by assumptions and estimates, but rather with hard data which lifts the lid on the needs of specific occupations and industries (SOC and SIC 4) in specific locations
3. Although data cannot by itself be used to stimulate demand, it can point to areas of competitive advantage in a local or regional economy, which can be exploited by universities together with LEPs and Local Authorities, to increase investment and resources, and so drive productivity and growth
Please contact Jamie Mackay (email@example.com) to discuss how our data can help your university increase its impact on regional productivity.