For LEPs to successfully achieve their key goals of determining local economic priorities, driving economic growth and increasing productivity within their local area, a robust, evidence-based understanding of the economic makeup of the area, including industrial mix, current and projected employer demand, and sector strengths and weaknesses is essential. This emphasis on underpinning strategy with a solid evidence-base was clearly set out in the Government’s Local Industrial Strategies Policy Prospectus, which stated:
“Local Industrial Strategies will be long-term, based on clear evidence and aligned to the national Industrial Strategy. They should set out clearly defined priorities for how cities, towns and rural areas will maximise their contribution to UK productivity. Local Industrial Strategies will allow places to make the most of their distinctive strengths.”
But how can priorities and strengths be identified? Whilst anecdotal evidence can be useful up to a point, there is no substitute for solid data that can really help unpick what is going on in local economies.
Making complex data very simple
There is, however, a big problem with using data, whether it be raw Government data, Emsi data or any other organisation’s data. The Standard Industrial Classification system (SIC) categorises 586 different sectors at the 4-digit level, which makes it extremely hard to identify trends, let alone understand which industries are driving the economy. The SIC system itself puts these into groups (3-digit SIC), then divisions (2-digit SIC), and finally sections (1-digit SIC). But as anyone who has looked at these classifications will know, these groups, divisions and sections don’t really give a very good picture of what is actually driving an economy, because they are grouped together on the basis of similarity of activity, rather than by any economic linkages between them.
A much better solution is to group those 4-digit SIC industries together according to their economic links with one another, such as industries which are connected by supply chain, which tend to co-locate in the same areas, and which share a similar workforce. Our economists have undertaken this exercise, grouping the 586 4-digit SIC industries into 49 ‘coherent’ industry clusters. What this means is that not only is the process of interrogating the data made far easier than trying to make sense of all 586 sector classifications, but because our 49 groupings are based on economic similarities, gaining insights about your area’s economy is made extremely simple.
Tradable and local clusters
Having created our 49 economic clusters, we have then taken an additional step, which is to group these into two distinct cluster categories, which help you to get a much better handle on what is driving your economy.
Local Industry Clusters – These are made up of sectors that tend to serve local needs, and which don’t have much in the way of national or international exports.
Tradable Industry Clusters – These are made up of sectors that tend to export both nationally and internationally, and which are therefore the real drivers of growth.
Although only 14 of the 49 are local clusters, they tend to be the largest employers in each region, simply because they often constitute essential parts of day-to-day life (such as education and health). Across Britain as a whole, some 63.2% of all employment is in these sectors, compared to 36.8% in the tradable clusters. Furthermore, these clusters tend to be fairly evenly spread across the country, because the services they provide are needed everywhere.
By contrast, although the 35 tradable industries tend to employ less people, they are the real drivers of local economies. Not only do they trade outside the region – whether nationally or internationally – so bringing wealth into the region, which then multiplies through the local economy, but they also tend to have higher productivity, higher wages, and lower labour-intensity than local clusters. For any regional economic developer trying to understand the priorities, strengths and opportunities in their local economy, being able to quickly identify the clusters that are key to growth is clearly extremely beneficial.
Identifying an area’s strengths, threats and opportunities
Having established our clusters, and having then distinguished between tradable and local, we can now use the data to identify where the strengths, threats and opportunities lie. To do this, we use a couple of metrics. The first is to compare the growth in each cluster with growth in the same cluster nationally. The second is to capture the Location Quotient (LQ) of the cluster. LQ is the proportion that an occupation or industry makes up within a local or regional labour market, compared with the proportion the same occupation or industry makes up in the national economy. The nation is given a benchmark of 1.0, and so any occupation or industry with an LQ over about 1.2 can be seen as a regional specialism. In the chart below, we have used data from an anonymised region to show what this looks like:
As you can see from the chart, we have labelled the upper right, upper left and lower right quadrants as strengths, opportunities and threats respectively. What we mean by this is:
Strengths – Industry clusters that are growing nationally, and in which the region has a comparative advantage over other areas of the country, meaning that it is well placed to benefit.
Opportunities – Industry clusters that are growing nationally, but which the region does not yet enjoy a comparative advantage in, meaning that an economic developer could look to potentially grow them.
Threats – Industry clusters in which the region enjoys a comparative advantage over other areas, but which may be under threat as nationally they look set to decline in the coming years.
Focus on the digital cluster in the G-First LEP Region
Identifying which clusters give your region a comparative advantage is, however, only one part of the picture. What we can then do is explore those clusters in order to glean more insight.
In order to demonstrate this, we have used data from the GFirst LEP region, and in particular the Digital cluster. This is the largest tradable industry cluster in the region in terms of job numbers, and it also has a slight comparative advantage over other areas, with an LQ of 1.33. In the graphic below, we begin with some general information about the cluster in the region, including total GVA, and also the jobs multiplier (this is the knock on effect on the Gloucester economy that introducing new jobs in the Digital cluster would have. So with a jobs multiplier of 1.47, for every hundred new jobs created in the cluster, around another 47 would be created throughout the local economy in other sectors).
What we can also do is to compare the G-First region with other areas. The following chart shows projected job growth for the cluster in the area between 2018 and 2023, compared to the six LEP regions that share a border with the LEP. As you can see, G-First has the highest projected growth of these areas with 12.68% (1,741 new jobs). This is also higher than projected growth in the cluster across the nation (7.15%), indicating that there may be opportunities for the LEP to focus on trying to grow it further to increase its comparative advantage:
Underlying industries and occupations
Having looked at some general insights for the Digital cluster, we can now dig much deeper to glean some insights at a more granular level. The Digital cluster is comprised of 12 SIC-4 industries, and we can open it up to reveal a number of metrics about each of these:
There are a number of things of interest here. Firstly, although Computer consulting activities is dominant with more than 5,000 jobs, there are also significant job numbers in the second, third and fourth sectors on the list. Secondly, a number of industries are set to see significant growth over the next few years, with seven of the 12 projected to have double digit growth. Thirdly, the Gloucester area enjoys a significant comparative advantage in a number of industries, notably Other information technology and computer service activities, and Other software publishing with LQs of 2.56 and 2.47 respectively.
In addition to better understanding the make-up of the Digital cluster, we can also run a Staffing Pattern to identify the occupations that are employed in it. The table below shows the Top 10 occupations employed in the cluster in the G-First LEP region, including job numbers and the proportion that each job makes up of the cluster as a whole:
The Government has made it clear that Local Industrial Strategies should be based on clear, solid evidence, and that they should identify and capitalise on the distinctive strengths your region has. The insight we have shown throughout this section will hopefully have given you a good idea of how this can be done. By using the industry cluster data, you can establish which sectors present your region with its best opportunities for growth, and by digging deeper you can reveal more granular detail on underlying industries and occupations. Taking this approach will give you the confidence to better understand where to focus your investments and interventions, ultimately giving your region a solid basis for the future growth and higher productivity that you are working to achieve.
You can download a PDF of this four part series here. Get in touch to find out how we can help you build a solid evidence-base for your skills and industrial strategy.