Local Industrial Strategies and the need for economic assessments

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The UK Government is implementing its (national) Industrial Strategy (after consultation, this was announced in November 2017) by recognising the importance of ‘places’, specifically through Local Industrial Strategies (LIS). In December 2018, BEIS stated “… The entire country will be able to benefit from developing a local Industrial Strategy… We will work in partnership with places to develop Local Industrial Strategies that will be long-term plans based on clear evidence and aligned to the national modern Industrial Strategy… (it) will build on unique local strengths to ensure every community, and the country, reaches their economic potential and creates high quality good jobs” (BEIS, 2018, emphasis added to original).[1]

As with the national strategy, improving productivity is at the centre of the development of a LIS. But to know what drives productivity at a local level, indeed how different local economic partnerships (LEPs) rank on this metric, needs more information. Hence in January this year, BEIS put out a tender for work to be done between now and the end of May that provides baseline information that can made available to LEPs while they develop a LIS.

PIN is well-placed to help deliver on this need, given its access to firm-level data sources made available by the ONS via the Secure Data Service.[2]

This allows LEPs to consider how well firms in their area do on a wide range of metrics all associated with productivity, such as:

• R&D and innovation
• Exporting
• The importance of foreign-owned and outward FDI firms
• Absorptive capacity in LEPs
• Firm-level estimates of (labour and total factor) productivity

The rest of this ‘blog’ is just a few examples of the range of information available, based on these ONS data sources. This will give the reader a flavour of the richness of the data sets.[3]

Figure 1: Labour productivity (£’000 per worker) in 2016 in each LEP for selected sectors

(a) Advanced manufacturing

(b) Digital

(c) Biologics

The above show some contrasting differences across LEPs, and labour productivity has the advantage of being easy to calculate and relatively easy to comprehend; it has the disadvantage of being potentially misleading, especially if comparisons are undertaken across very different sectors (in terms of their capital- and intermediate-input intensity). That is, labour productivity will be higher when firms use relatively greater amounts of plant (e.g., chemicals) and/or intermediate inputs (such as motor vehicles, which is heavily reliant on – often overseas – supply-chains providing much of the semi-finished goods and services that ultimately combine to produce the final product). Similarly, labour intensive industries de facto have low labour productivity. In contrast total factor productivity (TFP) measures the extent to which a firm efficiently produces output relative to all factors of production (labour, capital and intermediate inputs), taking into account changes in technology over time. What is most important in productivity terms is the role of efficiency and technical progress (both captured by TFP), and less so whether a firm increases output-per-worker through outsourcing and/or substituting capital for labour (i.e., automation).

So what does TFP look like? Figure 2 provides two examples, showing that the London LEP has the highest productivity in most all parts of the distribution; and in advanced manufacturing the Tees Valley LEP does well.

Figure 2: Distribution of ln TFP for LEPs and sectors

(a) Advanced manufacturing

(b) Digital

How about some key drivers of longer-term productivity? Figure 3 shows the value of the R&D stock, perhaps not surprisingly showing it is generally concentrated in the south and south-east of England

Figure 3: R&D stocks in 2016 in each LEP for certain sectors

(a) Advanced manufacturing

(b) Digital

(c) Logistics

Figure 4: Percentage of sales exported in each LEP

(a) Advanced manufacturing

(b) Digital

(c) Logistics

Figure 5: Innovation activity in each LEP for 8 sectors aggregated (Advanced Manufacturing to Biologics)

(a) % product innovating

(b) % process innovating

(c) % ‘blue-skies’ innovating

Figure 4 shows a more diverse pattern in terms of exporting intensity (the proportion of goods and services exported abroad), while Figure 5 shows the dominance of the central LEPs (including Oxfordshire and Cambridge & Peterborough) in terms of product innovations and ‘blue-skies’ innovations (the latter are new to market – for product – and new to the industry – for process).

Presumably the work that will be done for BEIS will capture similar information to that produced here, helping LEPs develop their own, unique LIS which is based on local understanding of the productivity issues they face.

Professor Richard Harris
Professor of Economics
Durham University Business School

[1] Source: https://www.gov.uk/government/news/local-industrial-strategies-to-drive-growth-across-the-country.
[2] https://www.ukdataservice.ac.uk/get-data/how-to-access/accesssecurelab.
[3] This work contains statistical data from ONS which is Crown copyright and reproduced with the permission of the controller of HMSO and Queen’s Printer for Scotland. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research datasets which may not exactly reproduce National Statistics aggregates