In the 3rd data pilot in Arloesiadur, our innovation analytics project for Welsh Government, we draw on the cutting edge of economic geography to analyse industrial clustering, measure the complexity of local economies in the UK, explore the links between complexity, productivity and wealth, and generate predictions about the future specialisations of local economies based on their current situation.
In the third data pilot in Arloesiadur, our innovation analytics project for Welsh Government, we draw on cutting edge economic geography research to identify related industries for clustering analysis, measure the complexity of local economies in the UK, explore the links between complexity and better economic outcomes such as productivity and wealth, and generate predictions about the future specialisations of UK local economies based on their current profiles.
One of the key economic lessons of the last 20 years is that, in spite of globalisation and the Internet, place still matters: industries tend to cluster in particular places; the industrial composition of those places shape future opportunities and constraints . Too much specialisation creates fragile local economies reliant on the fortunes of a small number of sectors. Too much variety could reduce the scope for spillovers between them.
The questions for policymakers are:
These are very topical issues: many analyses have linked the discontent behind the results of the European Referendum to regional economic divides, and the same seems to apply to the advance of Donald Trump in the USA. National and local industrial strategies to drive industrial growth are back on the agenda.
The challenge is how to design and implement these strategies in a way that creates new markets, strengthens industrial ecosystems and drives growth, rather than prop up inefficient industries or satisfy vested interests. Ideas such as ‘smart specialisation’ or ‘entrepreneurial discovery’, originated in academia and adopted by the European Commission are an important contribution to these debates. In a nutshell, they ask regions to focus on those industries where they have established strengths rather than trying to build new clusters from scratch, and to identify what the new opportunities are in a process driven by entrepreneurs instead of officials.
This blog post reports the findings of an innovation analytics pilot where we apply new approaches developed by economic geographers and complexity scientists with the goal of generating data that can inform policy decisions in this important area. The pilot is part of Arloesiadur, our project to develop an innovation dashboard for Welsh Government.
We discuss some of the findings in the rest of the blog. Although we introduce key concepts and intuitions for the methods used in the relevant sections, we don’t go into their detail. If you want further information, check the GitHub repository with the scripts we have used to process and analyse the data. We've made a visualisation of the data available here.
Before starting, we should point out this is an exploratory pilot with limitations that we highlight where relevant, so the (suggestive) results should be taken with caution. Our goal is to take this analysis further when we start building the Arloesiadur platform in the coming months, incorporating any feedback or suggestions you might have.
The UK Interdepartmental Business Register contains information about the geographical distribution of 543 4-digit SIC codes – however, many of these SIC codes are interrelated (e.g. PR, Marketing and Advertising). In order to identify higher-level aggregates that simplify analysis, we have drawn on the approach that US Economists Mercedes Delgado, Michael Porter and Scott Stern recently applied in the US. The basic idea is to calculate different measures of ‘similarity’ between industries – based on their propensity to co-locate, employ people from the same occupations, and trade – and use these measures to identify segments of highly interrelated industries we might expect to see clustering in the same place. This approach allows us to generate a mutually exclusive, completely exclusive allocation of SIC-codes to a manageable (64) set of industries we can map in a comparable basis across UK Local Authority Districts .
It is worth noting that these 64 segments don’t just replicate the SIC structure: 71.8% of the clusters contain 4-digit SIC codes from an assortment of different SIC divisions. Having said this, the top division in each segment accounts for 71.6% of the 4-digit SICs codes, which is what one would expect if SIC structure captured, to a strong degree, meaningful similarities between sectors. One example that illustrates how our segmentation method brings together interrelated sectors from across the SIC structure is the case of rural services, a segment where our clustering analysis places those Wholesale services related to the sale of live animals (46.23), agricultural machinery (46.61), and veterinary activities (75.00) amongst other things.
After generating a list of industry segments, we have looked at their geographical distribution across UK Local Authority Districts. We use the resulting sectoral specialisation patterns to generate measures of complexity. In doing this, we have attempted to translate the analysis of economic complexity that Cesar Hidalgo and Ricardo Hausman pioneered using export data at the country level, to the analysis of local specialisation patterns in UK local authorities.
In a nutshell, this approach, called ‘the method of reflections’ is a recursive algorithm that takes the specialisation profile of a local authority and weights it by the extent to which the sectors in which it specialises tend to appear in diversified areas. After several iterations, the ranking of areas becomes stable. We have taken those scores and normalised them to generate an indicator of complexity.
The violin plot below shows the distribution of economic complexities in local authority districts in different Regions of Britain.
It shows that London and the South tend to have, on average, local authority districts with higher levels of economic complexity, consistent with the idea that these are more diversified economies. At the same time, there is a great degree of variation in the economic complexity within regions and nations of the UK – we find more complex, generally urban, and less complex, often rural economies inside all of then. The shape of each of the violins shows that some regions such as London or the North West tend to have a ‘normal’ distribution of economic complexity, with most of their local authorities in the middle, while Wales, Scotland and Northern Ireland have a few highly complex areas, and many less complex areas. The South East and the South West are closer to a bimodal distribution, with many economically complex areas, and many non-complex ones.
Why would anyone care about any of this? Hidalgo and Hausman answer the ‘so what’ question by showing that more economically complex countries tend to perform better along a wide range of measures: they tend to have higher GDP per capita, faster growth and lower inequality.
What about the measures of complexity that we have generated for UK local authority districts?
Although replicating the econometric analyses undertaken by Hidalgo, Hausman and their collaborators goes beyond the scope of this paper, we have carried out some preliminary analyses of the link between economic complexity and measures of local economic performance, and the results are suggestive. We find a strong correlation (p=0.67) between our indicator of complexity and an area’s average annual earnings based on the Annual Survey of Hours of Earnings, which fits with the idea that those local authorities with more complex economies tend to be more productive.
Is this result simply driven by the fact that economically complex areas tend to be predominantly urban, and wealthier? This doesn’t seem to be the case. In the chart below, we have plotted economic complexity against normalised annual average salaries for English Local Authority Districts, using different colours for ‘urban’ (blue) and ‘rural’ (orange) areas based on ONS definitions. The slope of the relationship seems to be positive for both types of areas (although the urban areas tend to be, in general, more economically complex, as shown by the displacement of the blue dots towards the right of the chart relative to the orange ones).