Cios lep smart specialisation economic analysis

Strategic Economics Ltd – Bespoke Analysis & Advice

Commissioned Client Report for Cornwall & Isles of Scilly Local Enterprise Partnership / Cornwall Council

Smart Specialisation Economic Analysis Nigel F Jump, Executive Director & Chief Economist Shane Vallance, Associate Senior Economist Strategic Economics Ltd

Introduction The Cornwall and Isles of Scilly Local Enterprise Partnership (CIoS LEP), through the Cornwall Council (CC) identifies a need for a Smart Specialisation Framework (SSF) to support its future investment in innovative technologies and sectors under the relevant part of its EU funding programme (SIF) 2014-­‐‑20. Strategic Economics Ltd has been appointed to support this work, providing an evidence-­‐‑based SSF to assist annual reporting, monitoring and project development.

This report was compiled from sources and intelligence available up to the middle of September 2014. Strategic Economics and the authors have used their professional experience and expertise to provide this research for the CIoS LEP. They cannot be held responsible, however, for any errors or omissions revealed by future data revisions, new publications or policy changes or for the consequences of actions taken by the client and/or its partners on the basis of the report as it stands.

Strategic Economics is a private economics adviser that offers bespoke analysis on a range of economics subjects to private and public clients, the media and academic institutions. Please contact us for all your economics needs. See us at www.strategiceconomics.co.uk.

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Approach & Index Approach The Smart Specialisation Framework (SSF) required by Cornwall Council and the Cornwall and Isles of Scilly (CIoS) LEP will support a Smart Specialisation Programme (SSP) of investment in local development that needs to be sustained and sustainable.

Innovation in products and services, and processes and delivery will be the key driver of the SSP, with reproducible and exportable commercial activities as the final goal.

The SSP is about identifying and building markets, in terms of destructive and constructive products and services, and promoting and developing capacity and capability in technical niches in which Cornish and Scillies’ businesses can be widely competitive.

Thereby, the SSP can create and expand businesses that add value by building productivity and, therefore, it can increase employment in high skill, high wage occupations. Hopefully, a positive feedback loop from investment to productivity to employment to demand and back to investment can be established.

This ideal, however, must be couched in terms of several important caveats. By its very nature, the SSP will involve intervention to support high-­‐‑risk activities that may produce high returns, but also may not.

Moreover, at this point, it is not known exactly what interventions will be chosen, and which will yield positive returns, or when. Inherently, the SSP will invest in activities that will yield their returns over an uncertain future.

Indeed, to have lasting effect, this is desirable. The net benefits “in-­‐‑ programme” may be relatively modest compared with the benefits over the long run if sustained and sustainable economic development is to be achieved.

Economic development interventions always involve an element of choice between immediate returns and longer-­‐‑term returns. Economic, social and political pressures can bias decision-­‐‑making towards the short run. The very nature of the SSP initiative, however, is arguably to capture a persistent flow of returns that are optimised over the long term.

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Strategic Economics Ltd – Bespoke Analysis & Advice Against this background, the SSF itself aims to be, and can only be, a macro overview that guides decision-­‐‑making and evaluation. Dealing with uncertain ‘futures’, it cannot direct interventions or predict economic impacts with any precision. SSF modelling offers professional opinions rather than recommendations and, as such, should be used as a benchmark for discussion and assessment. It cannot provide a forecast of precise outcomes.

Accordingly, the SSF needs to be objective, consistent and concise. 

As a Framework, it is a scaffold on which assessment, argument and decisions can be built rather than a comprehensive compendium of all possible analysis and evidence. It must be clear and easy to communicate to a range of internal officers and external partners.



As a Smart Specialisation Framework, it focuses on five key economic activities where innovation and investment is likely to lead growth and development. These five technology-­‐‑based areas Chosen by the LEP and its partners after previous research and analysis, these five technology-­‐‑based areas are taken as read in this report.

Our approach is to establish: 

A macroeconomic framework for the period of the programme and beyond, setting the overall context for the SSF.



A growth potential framework, classifying the five ‘smart’ markets and the local assets available, and considering potential risks and evaluation evidence.



A benchmark options framework that analyses the current and future prospects for smart specialisation, focussing on output, jobs and business creation data to tell a base and aspirational story of smart specialisation futures.

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Strategic Economics Ltd – Bespoke Analysis & Advice INDEX Introduction

1

Approach & Index

2-­‐‑4

Executive Summary

5-­‐‑9

Macroeconomic Framework Global Economic Conditions The UK Economy Regional Context

10-­‐‑23

Forecasts & Projections 1. UK Real GDP Growth 2. CIoS Macro Scenarios 3. CIoS Sector Forecasts 4. CIoS Demand Trends

15-­‐‑23

Growth Potential Framework Context & Approach Classification of ‘Smart’ Markets Asset Specialisation within CIoS Risks affecting Impact Lessons from previous Evaluation Overall consideration

24-­‐‑48

Benchmark Options Framework Investment Scenarios Base trend Growth Outputs & Results 1. Gross Value Added 2. Job Creation 3. Business Creation Overall Consideration

49-­‐‑60

Conclusion

61

10-­‐‑11 11-­‐‑13 13-­‐‑15

16-­‐‑17 18-­‐‑19 19-­‐‑22 22-­‐‑23

24-­‐‑28 29-­‐‑33 33-­‐‑39 40-­‐‑43 43-­‐‑44 44-­‐‑48

49 50 50-­‐‑59 50-­‐‑54 54-­‐‑57 57-­‐‑59 59-­‐‑60

Appendices 62-­‐‑72 1. Key data definitions 62 2. Estimating GVA for Smart Specialisation 63-­‐‑65 3. SIC based definitions for Smart Specialisation 66-­‐‑72

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SMART SPECIALISATION ECONOMIC ANALYSIS Executive Summary There are no existing models or robust data sources for many of the Smart Specialisation sectors chosen by Cornwall and Isles of Scilly (CIoS) partners and analysed in this research. By their nature, these sectors involve new technologies and processes that are innovative and, therefore, not captured by current statistical sources and other intelligence.

The five sectors or technologies picked by CIoS – aerospace, marine, digital, agriculture and health – reflect a local belief that these sectors will experience significant change and have higher growth potential than underlying baselines or trends. This research does not question these assumptions about the future economy.

In order to create a Smart Specialisation Framework (SSF), therefore, it has been necessary to synthesise through assumption, experience and judgement, and by discussion with local sector representatives provided by the client, a new approach to quantitative and qualitative futures. This is done by a sequential process of building frameworks towards benchmarks for gross value added (GVA), full-­‐‑time equivalent (FTE) employment, and net business creation.

In turn, these benchmarks reflect what might be possible given the intention to invest in the smart specialisation areas. At this point, however, there is no certainty of how much will be spent and when and where it will be spent. Accordingly, we cannot forecast SSF impact other than in broad, potential terms. It is for CIoS partners to determine how to convert the modelled targets into policy, action and evaluation at a programme, project and local level.

Macroeconomic Framework The Macroeconomic Framework describes the broad economic context and outlook. It does this by reference to the current consensus on cyclical and structural factors in order to determine baseline and aspirational forecasts for growth and its components (productivity and employment).

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Strategic Economics Ltd – Bespoke Analysis & Advice The following summary table 1 presents a macroeconomic framework for the period 2015-­‐‑30. It reflects the current consensus on growth potential, historical experience of differentials, and analysis of potential ‘futures’.

Summary Table 1: Macroeconomic Forecasts (ann. avge % chge, 2015-­‐‑30) baseline aspiration baseline UK real GVA +2.3 +3.1 CIoS real GVA +2.6 UK nominal GVA +4.5 +5.2 CIoS nominal GVA +4.7 UK inflation +2.1 +2.1 CIoS employment +0.8 CIoS productivity +1.8 Source: Strategic Economics Ltd

aspiration +3.4 +5.5 +1.2 +2.2

Summary Table 2 displays the broad sector forecasts that can be derived from the overall macro view and judgements about ‘futures’ for particular industries. It suggests strong recovery in private services and construction and a better performance in manufacturing than in the recent past, in contrast with further below average trends in land-­‐‑based activities and public services.

Summary Table 2: Broad Sector Forecasts (ann. avge % chge, 2015-­‐‑30) baseline baseline Land-­‐‑Based +2.7 Information/Communications +5.8 Manufacturing +5.3 Finance & Business +6.3 Construction +6.9 Public Services +1.9 Distribution +4.8 Other Services +6.4 Total +4.7 Source: Strategic Economics Ltd

Growth Potential Framework The Growth Potential Framework considers new technologies and market potential to decide whether the five smart specialisation areas are “nascent, emergent or maturing”. The analysis concludes that aerospace is emergent/mature, marine and agri-­‐‑tech are emergent, and digital and e-­‐‑health are nascent/emergent (particularly with respect to niche markets with high growth potential).

Next, the asset base in CIoS for these five areas is considered. Marine technology is classified as having a “combined presence of assets”. In other words, it has a strong supply and value chain of national and international importance. E-­‐‑health technology is assessed as “limited-­‐‑to-­‐‑partial presence of assets” because the asset base for commercial exploitation is still

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Strategic Economics Ltd – Bespoke Analysis & Advice developing. The other three smart areas are classified as “partial presence of assets”: areas having some assets with potential but not necessarily any more or better than other areas in the United Kingdom without further investment.

Finally, risks and lessons from previous evaluations are assessed in order to derive an overall picture of growth potential. This is classified as relative growth potential that is “very strong (+++)”, “stronger than trend (++)”, and “near trend (+)”. Summary Table 3 reproduces the conclusions.

Summary Table 3: Overall Growth Potential Classification Smart Classification – Classification – asset base Specialisation market maturity Market Digital Economy Nascent/Emergent Partial presence of assets Marine Technology Emergent Combined presence of assets E-­‐‑Health Nascent Limited presence of assets Space & aerospace Emergent/Mature Partial presence of assets Agri-­‐‑tech Emergent Partial presence of assets Source: Strategic Economics Ltd

Relative growth potential ++ +++ + +/++ +/++

Benchmark Options Framework The Benchmark Options Framework takes the macro and growth potential conclusions and derives potential output figures for output (nominal GVA), jobs (FTEs) and net business creation. The approach is based on ABS statistics and trends but, immediately, the problems of data coverage and access become apparent. By definition, it is assumed that the smart specialisation areas will grow marginally quicker than the rest of the CIoS economy (the macroeconomic framework) but individually, they will perform differently according to the asset base (growth potential framework).

Using sound but pragmatic judgement and acknowledging the high degree of uncertainty, the analysis produces aggregate figures for the EU SIF programme period (2015-­‐‑20) and beyond, reflecting the likelihood that impact from funded interventions is likely to occur over a longer term.

Summary Table 4 shows the results (rounded to the nearest five units to aid communication and to emphasise the qualitative nature of the findings).

They are shown including and excluding health because the difficulties of defining e-­‐‑Health are profound. There is a local preference to include a broad

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Strategic Economics Ltd – Bespoke Analysis & Advice definition, which tends to swamp the other smart areas in aggregate, whilst there is value in also considering the others without e-­‐‑health’s effects.

Overall, the benchmark estimates that are dependent upon SIC based definitions, which do not necessarily approximate closely to each of the potential smart specialisation areas. They reflect a sound and pragmatic approach but, inevitably, retain some numerical uncertainty.

These results provide a benchmark for decision-­‐‑making and evaluation of future investment. They represent a ‘story’ of reasonable ‘futures’ assuming the SIF is used to stimulate growth in the smart sectors. They are not, however, point estimates that can be relied upon precisely. By their nature, they merely represent a metric against which to test plans and outcomes.

Summary Table 4: Rounded SSF Benchmarks Including Health GVA (£mn) FTEs (No.) 2015-­‐‑20 65 225 2015-­‐‑25 160 485 Excluding Health 2015-­‐‑20 30 120 2015-­‐‑25 80 260 Source: Strategic Economics Ltd

Net Firms (No.) 45 70 40 60

Accordingly, users of the framework should recognise it as a benchmark against which to:  Test the overall SSP at a strategic level  Design specific investment plans for the five smart areas  Monitor outputs regularly through the SSP  Evaluate future outcomes To these ends, the benchmark numbers shown in summary table 4 should not be viewed in any sense of ‘success’ or ‘failure’. In due course, it will be more important to understand why the individual benchmarks are matched, exceeded or not achieved than to see the figures as goals in their own right.

There are a number of reasons why the benchmarks raised here are suggestive rather than predictive:  The macroeconomic cycle will be changed by unforeseen ‘shocks’ and unforeseeable ‘events’, as well as developments in national and international markets and policy choices, particularly with regard to EU funding.

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Strategic Economics Ltd – Bespoke Analysis & Advice 



The technological cycle will develop in ways that cannot be predicted.

The dynamic process of invention, application and dissemination is inherently uncertain.

The business cycle of skills acquisition and training, and enterprise and competition is likely to evolve in ways that are beyond the scope of this work’s assumptions and judgements.

The usefulness of this SSF is to provide a benchmark against which to assess processes of economic change, to influence choices and to measure retrospective performance. CIoS partners’ decisions as to how to invest in the five smart specialisation sectors will evolve as the economy, technology, business aspiration and the SSP itself matures. The SSF, itself, should be seen as a dynamic format as the SSP moves from decision-­‐‑making through delivery and monitoring to evaluation.

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Macroeconomic Framework It is important to understand the wider context for future economic development in CIoS. Prospects for the local economy are dependent on market and structural performance beyond its geographical borders. Local decisions on investment should be set against the competitive context and other business conditions prevailing elsewhere. This is the crucial first step in building the SSF. Global Economic Conditions After the deep recession of 2008-­‐‑9 and the prolonged downturn thereafter, parts of the global economy are now staging something of a recovery. 

In 2013, members of the Organisation for Co-­‐‑operation and Development (OECD)1 averaged a modest increase in real GDP of 1.3%, with the US economy up by 1.9% and the Euro Area down by 0.4%. The global average growth rate was 2.8%. Real world trade was only 3% higher.



The International Monetary Fund (IMF)2 said that world output was 3% higher last year; made up of 1.3% in the so-­‐‑called advanced economies and 4.7% in the emerging and developing markets.



Meanwhile, inflation averaged 1.4% in the advanced and 5.8% in the emerging nations. Interest rates remained low (e.g. key LIBOR rates were still below 0.5%). Within the OECD, unemployment rates ranged from about 12% in the Euro area to about 7% in America and 4% in Japan. Very limited progress was made with the major structural fiscal and trade imbalances.

In summary, the ‘Great Downturn’ has lasted six years. Aspects of the credit crunch are still with us and the recovery to date is modest and partial.

Nevertheless, the outlook is improving. 

The OECD expects the recovery to gain momentum only slowly, with a reasonable rate of world growth in 2014 (3.4%) and a little more (3.9%) in 2015. Inflation will rise a little and unemployment will fall slightly (to 1.6% and 7.5% respectively in 2014 and 1.9% and 7.2% respectively in 2015). There may be some acceleration in world trade (4.4% in 2014

OECD Economic Outlook – May 2014.

IMF World Economic Outlook – April 2014

1 2

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Strategic Economics Ltd – Bespoke Analysis & Advice and 6.1% in 2015); a little progress in cutting fiscal deficits (averaging below 4% of GDP); but little change in overall current account ratios or key interest rates. 

The IMF postulates a small increase in growth for world output (3.6% this year and 3.9% in 2015). Over the next two years, the ‘advanced’ economies are forecast to average 2.2% growth per annum and the ‘emerging‘ countries 5.1% growth per annum.

Risks from the continuing euro-­‐‑zone crises remain high. Globally, a lack of confidence about investment trends persists. Against this background, the OECD and the IMF advocate further economic rebalancing on output, trade and employment and warn about some emerging issues on certain assets in some countries, such as UK housing, where there is a risk of price ‘booms’ fuelled by overly loose monetary policy conditions and high household and business liquidity.

To date, the global recovery remains patchy. The incipient upturn is not yet secure or sustainable. Over the life of the ‘smart specialisation’ period, further volatility in macroeconomic conditions must be assumed.

The UK Economy The UK downturn, which started in 2008 and lasted into early 2014, is finally over: the level of economic activity exceeded the previous peak (Q1 2008) for the first time during second quarter of 2014. At the same time, inflation has fallen back into the target range (1.5-­‐‑2.5%, averaging 2% per annum). Chart 1 below shows recent trends in UK growth and inflation.

Employment growth has rebounded more strongly than expected (about 1.3% per annum in 2012 and 2013 for the United Kingdom and, in SW England, employment growth exceeded 3% year-­‐‑on-­‐‑year in the first half of 2014), largely exceeding output growth and resulting in low productivity growth.

Supply is still being restrained by low productivity, cautious confidence on business investment and limited access to finance. Demand is being constrained by declining real incomes, restricted working hours and job uncertainties, including elements of distressed self-­‐‑employment.

Recent evidence, however, suggests the current recovery is getting more robust, at least in the short term. All business and other economic surveys now indicate investment and hiring intentions have improved significantly and order books and output are rising. This may suggest the long-­‐‑awaited recovery in productivity may be imminent.

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Chart 1: UK Real Growth & Inflation: 2007Q1-­‐‑2014Q2 (% ch yoy)

Source: ONS The current consensus3 is that the economy will grow faster in 2014 (+3.1%) but slow down again in 2015 (+2.6%) – the recovery is not yet considered fully sustainable because of issues about real earnings at home and uncertainty abroad. In the first half of 2014, real GDP increased by about 3% on the same period a year earlier and inflation averaged about 1.9% (CPI measure).

Unemployment rates averaged about 6.5% for the UK as a whole and 5% in SW England. The inflation rate is expected to average below the 2% target in the period ahead (+1.7%), whilst unemployment rates are expected to drift down, (averaging 6.1%).

There is a chance that the forecasters are slightly too cautious for this year.

There has been good momentum in the UK economy in the year to date. Still, with manufacturing and construction still 7% and 10% respectively below their pre-­‐‑recession peaks and underlying fundamentals problematic, the danger is of a loss of momentum in the year or so ahead. Indeed, as the Bank of England states, the recovery is not yet enough to be sure of a sustained upturn in jobs or a closure of the ‘output gap’.

Nevertheless, despite the uncertainty about constrained domestic budgets and the global climate, there has been an important improvement in business and household confidence. Although remaining differentials in demand trends, product and market niches, and access to finance mean that divergence in business performance and planning will persist, overall corporate finances are buoyant and investment has begun to pick up.

HM Treasury’s Comparison of Independent Forecasts (August 2014)

3

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In short, according to competitiveness and demand characteristics, some sectors or firms are “flying”, especially those selling niche, high value products and services in thriving export markets – exactly the ones the SSP wishes to support. Not all businesses, however, particularly those operating in commodity or peripheral industries, are enjoying that rate of market improvement. 

On the downside, the risk is that sovereign and banking risks (such as Euro-­‐‑zone and Chinese adjustment tensions, as well as tensions in Ukraine and the Middle East, and not to mention UK constitutional change) threaten further shocks whilst the domestic policy balance (tight fiscal and loose monetary policy) may prolong the relative stagnation. Moreover, the threat of sterling appreciation to external trade prospects cannot be dismissed.



On the upside, real growth in America and Asia could advance further, offsetting the flatness in parts of Europe and offering business opportunities for exporters and investors, especially if sterling remains competitive. In addition, stronger stock markets and more replacement demand may activate surplus cash reserves, raising spending in more businesses and households.

Regional Context Typically, the CIoS economy has lower unemployment, lower earnings, lower productivity and less engagement in international trade than the UK average.

Its industrial structure, largely historically determined, leaves it constrained relative to more ‘agglomerated’ and less geographically peripheral centres of economic activity.

Over the last year, the local survey evidence has improved. There has been a tentative improvement in business sentiment in 2014, with surveys pointing to slightly more business confidence. Evidence from car sales, housing, tourism and some export markets show modest progress, offsetting continuing malaise in the discretionary High Street and its suppliers.

As historical patterns of competitiveness shift, the macro background for businesses in CIoS is improving, but not yet strongly. Excess debt burdens are only slowly dispersing and there are demand risks at home and overseas.

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Strategic Economics Ltd – Bespoke Analysis & Advice Structurally, the key issue for the CIoS economy remains its relatively weak and volatile performance on output and incomes per head (see chart 2 below4). 

The latest available figures show output per head, measured by Gross Value Added (GVA) per head, reached £13,036 in 2012. As a relative index (UK = 100), CIoS recorded 61.2 or 38.6% below the UK average.

This was the lowest amongst the six LEP areas in SW England and amongst the lowest NUTS5 regions in the wider UK and EU economies.

This low ranking is the reason why CIoS will be eligible for further EU funding programmes, such as smart specialisation, in the period 2014-­‐‑ 20. It is notoriously difficult to shift such regional, relative output per head performances, even in the long run – not least because nowhere is standing still – an improvement locally may well be matched elsewhere. Nonetheless, using EU funding to boost long-­‐‑term productivity and value and, thereby, employment and living standards is a key driver of the SSP initiative analysed in this report.

Chart 2: Cornwall & Isle of Scillies GVA & GDHI per head: Indices (UK = 100) 1997-­‐‑2012 95.0 90.0 85.0 80.0

GVA per head

75.0

GDHI per head

70.0 65.0 60.0 55.0 1997

1999

2001

2003

2005

2007

2009

2011

Source: ONS 

The latest available figures on incomes per head, measured by Gross Disposable Household Income (GDHI) per head, show £15,654 in 2012.

As a comparable index, CIoS recorded 93.2, or just 6.8% below the UK

4 N.B. these are relative indices shifting because of factors, such as the combined effects of the

recent downturn, both locally and elsewhere 5 NUTS = the international designation of geographical areas for economic analysis and policy assessment

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Strategic Economics Ltd – Bespoke Analysis & Advice average (Chart 2). This put it above Plymouth and Torbay locally. On income per head, CIoS’ index is not as comparably low as the equivalent output measure outlined above, showing that, because of balancing transfers and other wealth effects, the local economy, overall, has larger issues with relative sustained business productivity than it does with relative average household earnings.

These headline statistics for the CIoS economy indicate that further economic rebalancing is required. In sector terms, a shift from public to private/social enterprise and from public services and banking to manufacturing and private services is still to be preferred. In market terms, a shift of expenditure from government to investment, and from domestic consumption to exports is probably desirable.

Indeed, part of the rationale for Smart Specialisation, as advocated by EU development research and policy, is the need to innovate in areas with new market and technological potential. At the margin, it seeks a shift of the CIoS economy towards higher value opportunities. By so doing, CIoS might build, for the 2020s and beyond, the relative output per head performance in a way that augurs for, and underpins, a sustained betterment of relative living standards.

Forecasts and Projections Against the current background outlined in the ‘macro perspective’ section above, we turn to the longer-­‐‑term outlook for the UK economy and, within that, the Cornish economy and its parts. This element of understanding ‘futures’ is the second step towards setting our SSF in a realistic context.

The analysis is based on the current consensus for the UK economy, which is remarkably consistent. As such, the latest trend forecasts of the UK Office for Budget Responsibility (OBR), which accompanied the UK Treasury’s April 2014 Budget, are considered, covering the next five years and beyond.

From this foundation, adapting for foreseeable risks, we project a long-­‐‑term UK growth profile with alternative scenarios and use this to generate forecasts for the CIoS economy and its main sector constituents.

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Strategic Economics Ltd – Bespoke Analysis & Advice 1. UK Real GDP Growth Central Scenario: The consensus is that the UK economy as a whole will maintain an upturn through the medium term but that long-­‐‑term structural changes (largely demographic ageing) and the impacts on growth potential of the recent long downturn itself mean that the potential growth rate will continue to moderate. This is depicted by the blue line in Chart 3 below, which indicates that the economy will achieve average growth of slightly above 2% during the next decade, continuing the gradual decline in growth rates that has persisted over the last 50 years.

According to the OBR, this reflects underlying trends in population, technological dissemination, spare capacity, incomes and international trade patterns, employment growth and productivity.

For example, to derive its long-­‐‑term growth forecast of about 2.2% per annum, the OBR assumes:    

Spare capacity is used up by 2018; Some of the productivity lost in the downturn is recovered, but not all; The “low hanging fruit” of recent technical innovation has already been assimilated; and World trade will grow at no more than 5% per annum on average (a low rate compared with history).

Chart 3: UK Real GDP Growth (% change) 1970-­‐‑2023 8.00 6.00 4.00

0.00 -­‐2.00

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022

2.00

-­‐4.00 -­‐6.00

Source: ONS and Strategic Economics Forecasts

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Strategic Economics Ltd – Bespoke Analysis & Advice Moreover, the OBR postulates that current constraints on productivity, real earnings and export growth will persist. This means average consumption growth of about 1.5% per annum, business investment 0.8% per annum, government spending of -­‐‑0.2% and flat net trade. Potential productivity growth climbs to only 2% per annum by 2017 and 2018 and potential employment growth is set to decline to 0.2% per annum. Accordingly, potential output growth is projected at about 2.2% per annum. This is not a high rate compared with UK history or the UK’s international peers.

High Scenario: There is a risk that the central view outlined above is too pessimistic. 



More economic rebalancing is possible, led by technological progress and business investment in markets, products and services, and capacity and skills.

Global and domestic demand could be higher, shifting partially to new products and services, and productivity could bounce back more sharply than most now expect – helped, perhaps, by ‘re-­‐‑shoring’ of some economic activities.

Under such conditions, we could envision a story represented by the green line in Chart 3, where growth is sustained higher for longer. In other words, the prolonged downturn may have ‘cleared the decks’, allowing a reallocation of resources to fuel a stronger recovery than now expected.

Low Scenario: Conversely, as in the red line above, significant downside risks pertain.

Economic rebalancing has been, at best, marginal to date and may not advance much further. There are serious:    

Dislocations and imbalances in credit markets, including banking and housing, and regulatory reform.

Risks in the unwinding excessively accommodative monetary policies and adapting fiscal restraint.

Issues about structural adjustments at home and abroad, including America, the Euro-­‐‑zone and China.

Constraints on productivity and, thereby, employment growth and, ultimately, real earnings and overall living standards.

Accordingly, there is a chance that we return to recession within the next five years although, barring ‘shocks’ from policy mistakes or political events, such as the Ukraine or Iraq-­‐‑Syria crises, this need not be deep or last long.

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Strategic Economics Ltd – Bespoke Analysis & Advice 2. CIoS Macro Scenarios Based on these UK alternative scenario forecasts, we now develop key macro forecasts for the local economy. Tables 1 and 2 below summarise the key trends. To derive these trends, we have made a number of assumptions:  

 

The UK trends identified above will set the founding parameters for future local development.

Historical relationships largely persist: the differentials observed between CIoS and UK trends can be volatile year-­‐‑on-­‐‑year but, by using average data from 1997 to 2012, we maintain stable average relationships going forward.

The latest population forecasts from the ONS are taken as given.

The short-­‐‑term OBR inflation forecasts are largely adopted i.e. the Bank of England Monetary Policy Committee (MPC) is assumed to maintain inflation, in the long term, close to the current inflation target.

Over the last two decades, the CIoS economy has tended to grow slightly faster than the UK average but the relationship is highly volatile year-­‐‑to-­‐‑ year. We have smoothed the volatility here.

We see the local economy growing in a range 2.2%-­‐‑2.8% per annum over the life of the latest EU programme and that it will operate in a similar growth range in the 2020s (see Table 1).

This total growth rate will be made up of 0.7%-­‐‑1.0% in employment and 1.5%-­‐‑1.9% in productivity. Importantly, this rate of productivity growth is no great shakes. Ideally, the 2014-­‐‑20 programmes of EU development funding for CIoS will raise this baseline.

Table 1: CIoS Economy forecasts: Central View 2015-­‐‑17 2018-­‐‑20 UK Nominal GVA 4.8 4.4 CIoS Nominal GVA 5.0 4.6 UK Real GVA 2.5 1.9 CIoS Real GVA 2.8 2.2 CIoS Employment 1.0 0.7 CIoS Productivity 1.8 1.5 CIoS GVA per head 4.2 3.8 Source: Strategic Economics Ltd

2021-­‐‑30 4.4 4.6 2.3 2.6 0.7 1.9 3.8

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Strategic Economics Ltd – Bespoke Analysis & Advice Table 2: CIoS Economy forecasts: High/Low range around Central View 2015-­‐‑17 2018-­‐‑20 2021-­‐‑30 UK Nominal GVA +/-­‐‑1.0 +/-­‐‑1.3 +/-­‐‑0.5 CIoS Nominal GVA +/-­‐‑1.1 +/-­‐‑1.4 +/-­‐‑0.8 UK Real GVA +/-­‐‑1.0 +/-­‐‑1.2 +/-­‐‑0.5 CIoS Real GVA +/-­‐‑1.1 +/-­‐‑1.2 +/-­‐‑0.8 CIoS Employment +/-­‐‑0.5 +/-­‐‑0.6 +/-­‐‑0.4 CIoS Productivity +/-­‐‑0.6 +/-­‐‑0.6 +/-­‐‑0.4 CIoS GVA per head +/-­‐‑1.0 +/-­‐‑1.4 +/-­‐‑1.2 Source: Strategic Economics Ltd These growth ranges provide the base case for the SSF. Quantitatively, the question, then, is whether investment of EU and matched funds in the five Smart Specialisation sectors chosen can influence the point at which the economy performs within the spread of these likely outcomes.

3. CIoS Sector Trends and Forecasts Moving towards this end, sector trends in the CIoS Economy are considered.

Charts 4 and 5 below show the historical trends in CIoS nominal GVA. They illustrate considerable year-­‐‑by-­‐‑year volatility in all sectors. In average terms, over the 1997-­‐‑2011 period, strongest growth was achieved in finance and business services and slowest in manufacturing (see Table 3).

Chart 4: Leading CIoS Production Sector trends in nominal GVA 1997-­‐‑2011 25.00 land based & utilities

20.00

manufacturing

construction

15.00 10.00 5.00 0.00 -­‐5.00

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

-­‐10.00 -­‐15.00 -­‐20.00

Source: ONS

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Strategic Economics Ltd – Bespoke Analysis & Advice Chart 5: Leading CIoS Services Sector trends in nominal GVA 1997-­‐‑2011 70.00

distribution

60.00

information services Oinance & business services

50.00

public services

40.00

other services

30.00 20.00 10.00 0.00 -­‐10.00

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

-­‐20.00 -­‐30.00

Source: ONS Table 3: CIoS Sector Growth 1997-­‐‑2011 (nominal average % change per annum) Land based & utilities 2.5 Distribution Manufacturing 1.9 Information/Comms Construction 6.8 Finance & business Public services 5.1 Other services Source: ONS

4.8 4.4 7.6 6.6

Accounting for average inflation (2.1% per annum over this period), reveals:  A real decline in manufacturing, marginal growth in land-­‐‑based production (including agriculture);  Moderate growth in distribution services (including retailing and transport), information and communication services, and public services; and  Strong growth in private business (including finance), personal and leisure services (including tourism), and construction (especially housing).

This sector pattern of growth broadly matches the overall UK trends of recent historical growth.

Looking forward, total sector growth is constrained to the forecast GVA total from the central view outlined earlier. We do, however, differentiate by sector, assuming an element of rebalancing from public and some private services towards other private services and manufacturing (see Table 4).

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Strategic Economics Ltd – Bespoke Analysis & Advice (This accords with modest adjustments common within the ‘futures’ outlooks, as often outlined by a range of forecasters.)

Table 4: CIoS sector growth forecasts (avge % chge per annum) 2015-­‐‑30 2015-­‐‑17 2018-­‐‑20 2021-­‐‑30 Land based & utilities 3.2 3.0 2.5 Manufacturing 6.0 5.3 5.0 Construction 9.7 7.3 6.0 Public services 1.4 2.0 2.0 Distribution 5.0 5.3 4.5 Information/Comms 7.7 6.8 5.0 Finance & business 6.7 7.1 6.0 Other services 7.3 7.0 4.6 Source: Strategic Economics Ltd It is important to stress that these forecasts reflect a coherent story of future development based on sound economic theory and sensible applied assumptions. It is not suggested, however, that the eventual outcomes will match the average growth rates exhibited here or that other economists will not quibble with individual estimates. They are consistent with the kind of (consensus) forward analysis currently available for planning, policy and investment purposes.

The economic ‘future’ these forecasts depict is one of near term cyclical recovery, medium term reversion to trend, and long-­‐‑term projection of underlying trend potential. This is constrained by the overall macro view painted earlier in this chapter. It then considers other trends in terms of demographics, technology and other structural supply and demand changes that are likely to occur.

The result is a story with several key conclusions. This story predicts: 1. It is possible for CIoS to slightly outperform UK macro averages over the forecast period, partly reflecting historical patterns of ‘catch up’ as well as proposed local, relatively robust, public investments in economic development.

2. Demographic aging, technological change, and environmental policies, particularly with respect to energy, materials use, and sourcing patterns, will affect the local trajectory. Smart Specialisation can be a small part of these potential adjustments.

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Strategic Economics Ltd – Bespoke Analysis & Advice 3. There will be some rebalancing in sector growth patterns, reflecting ‘insourcing’ and relative competitive trends. As a result, o Distribution services and other (personal and leisure) services should broadly follow their, and average, overall relative trends.

This is consistent with their dependence on domestic consumption and its drivers. o Financial and business services and information / communication services will probably continue to be a relative growth leader. o Land-­‐‑based activities are likely to experience better, but still relatively modest, growth rates. o Manufacturing could improve its relative growth record compared with the previous cycle, consistent with some external trade rebalancing. o Public services growth may slip back as the need to get public debts under control persists, but CIoS should remain positive, reflecting EU programme interventions. o Construction might perform cyclically well, partly to make up for previous under-­‐‑supply and partly to reflect energy efficiency and other new investment trends.

4. CIoS Demand Trends The next step is to relate such supply side stories to potential demand side ‘futures’. This involves consideration of household consumption, business investment, government spending and net trade (exports-­‐‑imports).

Historically, spatially peripheral areas, such as CIoS, have experienced demand-­‐‑led growth mainly focussed on domestic consumption and public spending. The rebalancing required to be competitive in the coming years will need to be driven more by investment and exports. This is the essence of the case for more productivity-­‐‑led growth, as reflected in the EU regional development strategies since the Lisbon Treaty, and lies behind CIoS’s SSP initiatives. It is the reason why employment growth and improvements in living standards and overall well being, are linked to a shift in relative demand towards higher value products and services.

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Strategic Economics Ltd – Bespoke Analysis & Advice

This does not mean a complete change in market emphasis. Domestic consumption will continue to contribute the bulk of economic demand for Cornish and Scillies’ businesses. Many important local sectors and employers, such as agriculture and tourism, will remain dependent on overall UK household expenditure. What it does mean is a desire for a relative shift in demand growth towards high value investment and exports.

The ‘Holy Grail’ is a pattern of raised investment-­‐‑led growth that boosts productivity through the development of innovation and skills, increases in entrepreneurship and enhanced international competitiveness. This value-­‐‑ added approach offers the prospect of more sustained, high paid employment with greater networking and agglomeration benefits that yield better incomes and opportunities for all residents.

Smart specialisation is a small, but potentially exciting source of such growth: growth that demonstrates feasibility builds capacity and disseminates best practice across other economic areas. A demand outlook that postulates a rebalancing of demand consistent with the rebalancing of supply is a prerequisite for success in gaining a strong impact from smart specialisation.

Next, the local growth potential framework is described. Later, this is brought together with the macroeconomic framework depicted here to forecast ‘futures’ and apply them to the five smart specialisation sectors.

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Growth Potential Framework Context & Approach Market and technological change are inherently uncertain processes that are difficult to predict, a priori. The following analysis aims to understand what uncertainties may exist with the identified smart specialisation areas and the challenges they represent for developing a successful framework of investment support within the CIoS Structural and Investment Fund 2014-­‐‑20 (SIF) context.

The key points that provide the context for the analysis are: 1. New market opportunities and technological change can form the basis of growth and new jobs – but it is not a given. There is always considerable ambiguity over whether CIoS (and UK) assets and value chains associated with these market opportunities and new technologies will be sufficient to enable substantial growth of access and market share -­‐‑ inroads into highly competitive international markets. The decision-­‐‑making processes of large international businesses in some markets, considering, for example, where to locate R&D and production facilities, compound these uncertainties.

2. The market opportunities encapsulated in the identified smart specialisation areas vary substantially in their current and future potential scale and maturity. Although this report considers the gap between the quality of existing CIoS assets and the potential commercial opportunities in the market place, it is for those businesses with direct commercial interests to best identify, and scale, such market opportunities. Cornwall Council and the CIoS LEP will need the continued input of re-­‐‑validation by local industries to understand the potential and real evolution of ‘smart’ markets.

3. New and emerging market opportunities are normally based around an inter-­‐‑related value chain and inter-­‐‑dependent technologies. The difficulty in classifying each of the smart specialisation areas arise in part because there is considerable overlap in the use of technologies in different industrial areas. This means that the commercial opportunity in one sector often depends on the use of technology in other areas.

For example, in Marine Technology, commercial opportunities in end-­‐‑ use applications, such as offshore wind, may well relate to the use of different technologies in combination, such as composite materials

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Strategic Economics Ltd – Bespoke Analysis & Advice combined with electronics. Also, there are significant potential links, for example, between the capabilities that the digital sector can provide and potential e-­‐‑health solutions.

4. Innovation models of commercialisation vary between each of the smart specialisation areas. They often represent a spectrum of influence between ‘science and sector (perhaps HEI) push’ and ‘industry and market pull’. The relative balance of these innovation models, and their inherently different commercialisation processes, influence the need for, and form of, investment support: (what type of support, at what scale, when and by whom). In turn, this requires the commercialisation process to be much better understood in and across each market or technology area. This places importance on a detailed ‘fine-­‐‑grained’ analysis of technological development requirements and international market perspectives for each specific sector. This is something beyond the scope (and cost) of the broad analysis contained in this report.

Whilst commercial opportunity and technological expertise (or potential) may exist in a number of forms, if it is to result in lasting competitive advantage then this implies that any expertise must be connected to the real market place: it must be part of a value chain that adopts and exploits innovation in commercial ways.

For competitive advantage to be established, then, ‘assets’6 in each of the smart specialisation areas within CIoS must be able to combine effectively technology with economic potential. These ‘assets’ have been identified in the Catalys report (op cit) and are well documented. However, as part of this analysis, it is worth considering whether there is sufficient maturity and scale in some of the smart specialisation areas to enable competitive advantage to be truly established within and beyond the EU SIF programme period.

Arguably, the competitive potential of each of the smart specialisation markets within CIoS lies with the way in which the assets in each area are co-­‐‑ ordinated and combined across the value chain. Fundamentally, it is important to understand that this co-­‐‑ordination will need to be multi-­‐‑ locational (i.e. may well mean enabling businesses and/or organisations to

Assets in the context of this report could be natural (wave and tidal resource), research and/or science base (university excellence), the development & commercialisation process (R&D centres of large firms, etc.) or industrialisation process (businesses of national and/or international significance, supply chain clusters, etc). It could also relate to the local skills/human capital base.

6

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Strategic Economics Ltd – Bespoke Analysis & Advice link effectively beyond the CIoS boundaries) and will also probably require support at different points of the development journey.

Whilst geographical clusters are often important (agglomeration economies remain powerful), they are not always necessary to ensure a strong competitive position given the increase in connectivity and the mobility of assets (including labour). Similarly, rarely can a single firm, or a single institute, prosper in an increasingly globalised economy without a network of collaboration and co-­‐‑ordination across spatial and temporal borders.

The chosen smart specialisation areas are at differing points in this journey.

For some, the assets that CIoS holds may be part of a value chain that already exists, in which case the emphasis for existing businesses may be the need to adapt to take advantage of new or emerging opportunities. In other areas, where the value chain is not well established, businesses need to focus on building these network relationships and capabilities.

Differing scales of market size, combined with the technological & commercial maturity of existing CIoS assets, leads to the concepts of nascent, emergent and mature technologies: classifications which are used to understand the growth potential of the smart specialisation areas: •

Nascent technologies: those with relatively immature value chains and supply development in the CIoS area but with major growth potential: technologies that, in the long run, may represent the greatest returns potential at high risk

•

Emergent technologies: those with existing evidence of global market developments, with value chain development sketched out for the future and, therefore, those most likely to undergo sustained and substantial market growth

•

Mature technologies: those with markets of an already significant size, predicted to see limited market growth in the forecast period beyond the average, overall sector or macro trends, given the existing scale and scope of well-­‐‑established supply chains

Later in this report, how these classifications are used in combination with analysis of current CIoS assets in each of the smart specialisation areas is addressed, leading to a judgement regarding benchmark growth potential.

Investment support under EU smart specialisation funding schemes is likely to centre upon addressing the underdevelopment of the commercialisation

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Strategic Economics Ltd – Bespoke Analysis & Advice process (as compared with the development of the underpinning science).

This requires the full engagement of industry. The CIoS LEP, and its partners, are assumed to be positioned well to incorporate the necessary business led perspective and to ensure effective engagement with businesses and industries. Investment support for the commercialisation of new technologies requires a high level of maturity and dissemination in the knowledge economy.

As stated earlier, there should be an inherent recognition that the translation of emerging technologies into the exploitation of market opportunities is an uncertain process. Even though the SIF programme covers the period 2014-­‐‑ 2020, there remains a requirement for long-­‐‑term (10-­‐‑20 year) development perspectives. Previous EU programmes have found that this means difficult decisions, ideally evidenced, about ‘sacrificing’ short-­‐‑term impact in favour of longer-­‐‑term structural support and, ultimately, greater net outcomes.

It will also require direct support and other policies to be complementary (including the dependence of the SIF on market regulation aimed to de-­‐‑risk investment). This may include working collaboratively beyond the boundaries of CIoS to aid the commercialisation process and to build a globally significant reputation and offer.

Finally, to effectively help businesses in the smart specialisation markets to improve the commercialisation process may demand more detailed investigation and analysis than is possible here of the value chains that are necessary for successful innovation and the capture of economic value.

Again, the CIoS LEP should be well placed to work with industry to better understand this process. The Offshore Renewable Delivery Programme is a good example of how this type of activity is already taking place in one particular industry.

Therefore, in order to increase the likelihood of SIF investment support having the desired, indeed, required impact, future investment needs to be mindful of: 1. The long-­‐‑term view: much of the impact may occur after 2020 2. The wider context: investment support must complement macro-­‐‑ economic policy and the regulatory environment.

3. The value chains: varying significantly between and within each smart specialisation area in terms of the current starting point of development and in facilitating collaboration/co-­‐‑ordination across

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Strategic Economics Ltd – Bespoke Analysis & Advice sectors to promote the ‘smart’ commercialisation of new technologies.

This may involve working closely in or with other geographical areas to develop mutually beneficial interdependencies.

In order to derive a judgement of how potential future developments in each of the smart specialisation markets may affect future growth prospects, as well as the intensity of SIF investment support, it is important to adopt a structured approach. The method chosen is to view each of the markets through different ‘filters’ in order to build a comprehensive picture of the demand issues that need to be considered.

This follows the following steps:

1. Classifying each ‘smart’ area according to their market growth potential over the next 5-­‐‑10 years, primarily based on an assessment of technological and market maturity.

2. Making a judgement, within the scope of the limited resources available for this study, of the assets that CIoS has in these markets, particularly focusing on relative competitiveness and taking account of factors such as infrastructure, research excellence and the present business base.

3. Looking at the overall risks that may impact growth potential and the effectiveness of investment support to meet its own objectives.

4. Bringing into consideration any other evidence, such as lessons learned from evaluation of previous interventions that may be important to the findings.

These steps are combined to formulate an overall view of growth potential for each smart specialisation area within CIoS and the likelihood that investment support may be effective. This leads to an informed judgement based on the available evidence and limited consultations. It is worth reiterating, however, that no one can forecast exactly how the market for each smart specialisation will develop over the next decade. For example, a priori, it cannot be judged how local businesses in CIoS will respond to any, as yet, unspecified incentives or whether they will seek to exploit any emerging opportunities. Nevertheless, the pragmatic approach adopted here should provide a range of useful information for the next stage of development of the smart specialisation aspects of the CIoS SIF programme.

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Strategic Economics Ltd – Bespoke Analysis & Advice Classification of ‘Smart’ Markets After reviewing published research – often including work undertaken by industry/sector representative groups in CIoS – each of the smart specialisation areas is classified in terms of technological maturity and growth potential. These classifications are not associated with any particular local geography; rather it is a reflection on the prospects for broad sectors and, where appropriate, specific niches.

Digital Economy: Overall, digital is already a relatively mature global market of substantial proportions that is subject to high levels of ‘technological disruption’. Whilst the United Kingdom has a small world market share by scale, it performs well proportionally; it is globally competitive; and it should be capable of increasing its market share in the future. There is significant growth potential through technology-­‐‑driven niche opportunities, including: wireless, security, search, ‘gamification’ and virtualisation technologies.

Moreover, digital development remains an enabling technology for a substantial array of wider economic activity – i.e. the whole area of digital drift across industrial boundaries. For example, there is a growing trend in the mainstream application of previously niche specialisms, such as gaming.

Given factors such as relatively low entry-­‐‑costs and increasing transferability of skills/specialisms, then ‘scalability’ of application is a major advantage of the industry.

In this respect, ‘smart digital’ focuses on embedded software and information design with, potentially, wide application to many sectors and a virtuous circle of company creation and creativity. It incorporates high-­‐‑value, skills-­‐‑ based activities, especially in STEM areas. Through assets and outputs at, for example, Falmouth, there is thought to be a good record of business start-­‐‑ups and graduate employability.

Classification: Parts of the smart digital sector are mature but others are still emerging. Indeed, due to the pace of technological change and trends in consumer application, parts of the smart market could be classified as nascent. Overall, we conclude nascent/emerging.

Marine Technology: In terms of marine energy, this represents a nascent technology with expansion of global markets predicted to remain relatively limited during the

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Strategic Economics Ltd – Bespoke Analysis & Advice programme period. The value chain remains relatively under-­‐‑developed, albeit R&D-­‐‑led. The United Kingdom can claim international competitive presence in development and evaluation of prototypes and demonstrators but domestic future development of the technology and subsequent commercial opportunities remains uncertain.

For offshore wind, this is an emergent industrial technology forecast to experience one of the highest global market growth rates of all the technologies: one in which the UK market currently has strengths. The UK can claim international centres of expertise in R&D and some good commercial positions in the value chain (for example, prototypes, power conversion and installation) but gaps in the supply chain remain (for example, in assembly), suggesting a requirement to scale-­‐‑up local production.

For other offshore activity – such as drilling, site investigation and associated support – market growth is driven by the increasing scarcity and difficulty in accessing oil and gas reserves. Given that it is highly unlikely that renewables will displace global oil & gas dependency over the next 10-­‐‑20 years, this market will continue to exhibit strong growth. An important driver will remain the cost pressures reflecting scarcer, more difficult to access and extract resources. Businesses that help the oil & gas industry minimise exploratory and set-­‐‑up costs will experience strong demand for their services.

Finally, recent concepts of ‘green marine’ have also been driven by cost considerations. The need for shipping to become more efficient, with an associated lower environmental impact, is driving a shift to more resource ‘light’ products. Areas such as engine design & development and the application of new materials such as composites are expected to experience strong growth. In a UK context – given that it has lost its competitive position in the ship building market generally to lower cost competitors (primarily in the Far East) -­‐‑ the shift to high-­‐‑value green technology may now represent a significant market opportunity.

Another key aspect of smart marine is about the wider deployment of marine-­‐‑ applicable and driven technology into other sectors/supply chains – multi-­‐‑use technologies driving offshore diversification and skills development that can build new products and services and offer substitution opportunities, e.g. the aforementioned shift into ‘green’ marine technologies.

Classification: Overall, the smart Marine sector is classified as ‘emergent’.

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Strategic Economics Ltd – Bespoke Analysis & Advice E-­‐‑health: Serving a very large, mature, global market in traditional terms, parts of e-­‐‑ health represent relatively immature markets – ones that are based on a complex system of emerging and applied technologies that are yet to be fully understood. The value chains (i.e. the roadmap through to commercialisation for more technologically complex solutions) within the industry are not that well developed and competitive positions have yet to be determined. Other areas, such as video conferencing, are more established.

Smart e-­‐‑health represents a potentially wide range of private and public services, offering new technological interfaces especially for the primary care market and infrastructure of an increasingly ageing population. It will be driven by accumulation and analysis of digital “big data” – suggesting high-­‐‑ risk, high-­‐‑return market-­‐‑driven solutions can be pursued. Market growth will reflect an intense political, social and economic pressure for cost savings and efficiencies in developed healthcare provision and for greater accessibility in emerging economies.

Classification: For those areas where the technological solutions have clearly yet to be fully understood, e-­‐‑health is a ‘nascent’ market opportunity. For other areas, where market solutions are already being delivered, e-­‐‑health is more ‘emergent’. Overall, the market is judged ‘nascent/emergent’ with potentially significant market opportunities in absolute and relative terms.

Space & aerospace: Overall, both space and aerospace are relatively mature markets with highly organised value chains that are driven primarily by principal organisations (multinational corporations or state bodies termed as primes). The roadmap from research through to commercialisation is well understood – particularly in the established air transport sector. The United Kingdom has the second largest sector globally, with a significant South West base centred on the Bristol City-­‐‑region. For space, the sector is dominated by the United States, although emerging players, such as China and India, are increasingly influential.

Whilst the end-­‐‑product market is mature in scale, both sectors are experiencing high levels of technological change and product development which is ‘disrupting’ the value chain. Examples of such disruptive forces include composite materials, additive manufacturing, unmanned aviation vehicles et al. As a consequence, new market niches are opening up and there are opportunities for market entry and share acquisition by new providers. In

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Strategic Economics Ltd – Bespoke Analysis & Advice smart terms, radical development in unmanned vehicles, satellite data communications, and aspects of advanced engineering, such as composite materials, are expected.

In terms of satellite communications, the key driver is the trend for ever-­‐‑ increasing capacity, flexibility and availability of service (in itself connected to exponentially growing performance of IT and other telecommunication systems and the general ‘demand’ for data in a global context). Greater levels of global integration will require more effective, cost efficient and larger scales of wireless interconnection via satellites. As such even though the technology is relatively mature, global demand for expanded satellite-­‐‑based service delivery is predicted to remain strong.

Classification: Largely mature market, but end-­‐‑user requirements centred on efficient and environmental requirements are leading to, largely replacement, technological changes that could be better classified as ‘emergent’. Space/satellite communications and aerospace are termed as ‘emergent/mature’.

Agri-­‐‑tech: Increasing global populations, higher incomes per head and changing consumer’ eating trends in emerging countries are driving increased demand for high value and novel foodstuffs. Also, environmental concerns mean that this demand needs to be met without a commensurate increase in natural resource inputs or depletion. The requirement for sustained productivity gains in food production is high for this highly significant global market.

The United Kingdom has experienced a slower rate of productivity improvements in its food sector when compared with its competitors and much of this has been attributed to the ‘valley of death’ between its world-­‐‑ class research and its commercial application (as with genetically-­‐‑ modified/GM food technologies). Value chains are well formed – with many international businesses having well-­‐‑established relationships with UK research institutions – and many areas are led by large, transnational, globally significant businesses. Smart agri-­‐‑tech is an opportunity in building resource efficiency and remote serving in foods and environmental goods and services.

This will reflect knowledge transfer through research, development and adoptive dissemination.

Classification: Overall, the agri-­‐‑food market is very large and mature. However, the requirement for improving productivity without environmental impact means that there are significant opportunities from technological improvements and the application of new practices: the scope for ‘emergent’ agri-­‐‑tech.

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Strategic Economics Ltd – Bespoke Analysis & Advice

In summary, the market classifications for the smart specialisation sectors are: Table 5: Market Maturity Classification Smart Specialisation Market Classification – market maturity Digital Economy Nascent/Emergent Marine Technology Emergent E-­‐‑Health Nascent/Emergent Space & aerospace Emergent/Mature Agri-­‐‑tech Emergent Source: Strategic Economics Ltd

Asset Specialisation within CIoS Following the assessment of the broad issues that are affecting each of the smart specialisation market areas, the CIoS position in terms of the scale, breadth and maturity of its assets for these areas is considered. To reiterate, in the context of this report, assets encapsulate capabilities across the value chain. This means the plethora of technology and knowledge contained within universities, research centres, the business base, and the workforce.

This consideration focuses on supply-­‐‑side aspects, with the specific aim of placing the CIoS within the context of national and international assets i.e. whether they are leading or following sector activity development generally -­‐‑ whether sector expertise is locally or remotely held.

The overall aim is to understand how market potential is combined with CIoS positioning in terms of its assets across the value chain to determine the competitive position for each of the smart specialisation areas. This will then inform the judgement of future growth potential.

This work classifies each of the smart specialisation areas into generic categories after an assessment of available research. It includes previous CIoS-­‐‑specific research, such as that undertaken by Catalys, as well as national resources, such as the Witty Review, and reports from other relevant sector bodies.

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Strategic Economics Ltd – Bespoke Analysis & Advice •

Combined presence of assets: combinations of identified assets across the value chain (research expertise and a business and infrastructure base that can exploit commercial opportunities) with national and potentially international importance.

•

Partial presence of assets: some identifiable assets that may be significant but not in sufficient scale and scope to be viewed as connected right across the value chain. Business base not fully developed to fully exploit commercial opportunity. Overall, CIoS is not necessarily better positioned than other UK areas.

•

Limited presence of assets: relatively limited assets of national or international significance. This does not imply that there are no important organisations/businesses within CIoS for a given smart specialisation market, simply that there is limited evidence of scale and/or maturity.

Marine Technology: Overall, only the Marine Technology smart specialisation has the combination of (internationally significant) research expertise and businesses that are already export-­‐‑focused. It stands able to exploit the commercial opportunities that are foreseen over the next 5-­‐‑10 years. Although there may be gaps in the supply base, there is a significant ‘prime’-­‐‑driven supply chain, for example companies based in the Penryn/Falmouth area. Therefore, the industry already has a combined presence of assets.

Research has shown that the supply chain within the marine industries of CIoS is also relatively well developed. Importantly, there are strong local linkages that could mean that a greater proportion of the economic benefit is retained locally. The benefits of previous policy and investment support to help the industry develop its ‘in-­‐‑county’ supply chain may well continue to be extracted in the coming decade.

R&D facilities including the Wave Hub, PRIMaRE, Plymouth Marine Laboratory and FabTest are complemented by both traditional and new marine and renewable businesses that have sufficient scale and experience of tapping into growth markets. Both the research and commercial elements are important nationally and are well placed to aid the industry’s competitive positioning.

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Strategic Economics Ltd – Bespoke Analysis & Advice Key businesses in the local sector – particularly in niche offshore markets that include (but not exclusively) both traditional energy & renewables – have demonstrated a sustained ability to capture international business. This export orientation in the marine tech market is a key strength of the CIoS asset base.

The caveat to this overall assessment is that limited evidence of the relative competitiveness of CIoS in terms of offshore wind has been found. Other regions in the United Kingdom and Europe have developed stronger offerings. As such, in this specific area, it may be difficult for SIF investment support to deliver significant economic benefit within the period 2014-­‐‑2020.

Perhaps, in marine energy terms, investment support might be focussed primarily upon the marine tech in offshore wave and tidal, green technologies and other forms of offshore deployment, where CIoS is better positioned competitively than in offshore wind.

Other Areas: On reviewing the available evidence, our view is that the other smart specialisation areas are less well developed across all aspects of their value chains. Each of the areas has strengths – either in research, infrastructure or business – but these are not, as yet, optimally connected. Without significant effort, this will affect the ability of CIoS to fully exploit global market growth over the coming period.

Agri-­‐‑Tech, Digital Economy and Aerospace all have a partial presence of assets but are not necessarily fully developed in sector terms. These areas each have their own, different strengths.

Agri-­‐‑Tech: In Agri-­‐‑Tech, CIoS has a number of important businesses that operate in value added food manufacturing. Overall, the production and processing food sectors are an important part of the CIoS economy. The extensive dairy sector in the county is an asset base to be exploited.

There is less evidence that there are businesses operating at the leading scientific edge of Agri-­‐‑tech. Whilst there is excellent research activity occurring in the CIoS – notably through the University of Exeter’s research engagement in the county (recognised as a Witty ‘hotspot’), a growing agri-­‐‑ tech focus at the Environmental Sustainability Institute many universities and at Duchy College – many research centres leading the field exist elsewhere in the United Kingdom. Given that agri-­‐‑research excellence is extremely

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Strategic Economics Ltd – Bespoke Analysis & Advice resource intensive, it is questionable whether a truly significant, internationally competitive, capability can be developed quickly and/or cheaply. Nevertheless, CIoS is undertaking some useful research work with regard to grassland management and the dairy sector and there is scope to develop this further, perhaps with partners elsewhere.

It may be more appropriate for support to be given to attract research activity into the county’s agri-­‐‑sectors. This may involve wider engagement with research excellence located elsewhere in the United Kingdom. Support for the agri-­‐‑sector in CIoS to engage with this research excellence could be a viable and relatively efficient form of SIF intervention, allowing positive returns in a shorter period of time.

Generally, there is a greater orientation towards value added manufacturing rather than agri-­‐‑tech per se, and the research base and local adoption are less developed than elsewhere/desirable. CIoS may not necessarily be able to lead the exploitation of growth in the global agri-­‐‑tech market. This does not mean, however, that it cannot make an important contribution. Careful prioritisation of support for competitive agri-­‐‑tech niches is probably needed, perhaps in association with other centres of, for example, grassland research across the EU. Given that agri-­‐‑tech normally involves a relatively long process from research through to commercialisation/adoption, the SIF may not wish to focus on technology-­‐‑related research per se. Supporting applied activities, such as improving resource efficiency in the grassland and dairy sectors would appear to fit better with overall objectives. Activity that adds value to product development in the processing sector – particularly when it is done in conjunction with producers – could also be considered. This would build on strengths already extant in local dairy processing.

Digital: For the digital economy, there are local strengths in the superfast broadband infrastructure that is in place and under development, and the strong creative nature of both its academic and business offering. Superfast broadband addresses a major supply-­‐‑side issue and the evaluation evidence indicates that the business community are beginning to take advantage through expanding markets and new product/service offerings. Developing the demand side from a wider range of local businesses remains a priority.

Historical and current strengths at Falmouth University in the creative and digital sectors – which have now been complemented by investments in

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Strategic Economics Ltd – Bespoke Analysis & Advice incubator/innovation centres aimed at start-­‐‑ups in the creative & digital sector and local specialist expertise – has created an environment of digital innovation and entrepreneurship. This capacity includes post-­‐‑production film, ‘gamification’ and other narrow, creative niches. It is important to remember that this is a ‘people driven’ industrial segment: intelligent capability may be more important than physical capacity (although there are issues about the local stock for business accommodation).

There is good local evidence of both start-­‐‑ups and relocated businesses experiencing high growth, resulting in strong demand for jobs. The sector appears ‘highly responsive’ – in terms of job and business creation – to both market growth and intervention support. The Alacrity project is an example of how business creation can be responsive to public support and subsidy.

There is a sense of a ‘digital community’ developing in CIoS with some emerging expertise in areas that focus on the mainstream/multiple application of core technology. Gamification, design thinking and content production are areas that are developing quickly.

It is more difficult to ascertain CIoS’ nationally or globally competitive position. It is an intensely competitive world marketplace with larger clusters of innovation activity elsewhere in this and other countries. CIoS does have the ‘lifestyle’ tool that is an important factor in this relatively flexible sector.

As such, in a macro context, the CIoS asset base is partially developed, but showing good signs of progress. These areas of strength may warrant further investment support.

Given the difficulties of ‘picking winners’, this investment support might emphasise the continued development of the CIoS digital community, building on previous EU investments. An example of how SIF investment support could help the digital community – which largely constitutes micro businesses – is by cutting the ‘search cost’ of market intelligence.

Space and Aerospace: In Space & Aerospace, there are opportunities for CIoS. But, again, both the research and business base are not as well developed locally as elsewhere.

There are good opportunities, however, in the form of the infrastructure assets – notably through the infrastructure at Goonhilly and the enterprise zone at Newquay airport.

The enterprise focus at Newquay is to develop clusters of activity in specific markets, e.g. unmanned aerial vehicles (UAVs). Given the particular

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Strategic Economics Ltd – Bespoke Analysis & Advice characteristics of Newquay (distant from main population or flight centres), good potential does exist in this market, particularly for testing. It is more questionable whether UAVs and markets will operate in a full commercial sense during the SIF programme period.

Similarly, the intention to develop space and satellite communications at the property assets at Goonhilly is well intentioned but, even with major investment, may struggle to deliver significant economic returns during the immediate SIF programme period. Whilst important businesses are active in the sector (e.g. Avanti), there is limited evidence of strong CIoS connections with global supply networks in space and aerospace. More extensive business clusters exist elsewhere in the United Kingdom and many of the industry ‘primes’ have long-­‐‑standing and deeper research relationships with universities and other agencies outside of CIoS. Goonhilly represents an asset ripe for further exploitation, but the risk that associated activity will take place outside of the CIoS area (via remote monitoring), should be recognised.

As such, based on the infrastructure opportunities and some encouraging signs of the Enterprise Zone at Newquay airport helping to attract businesses, the value chain is only partially developed but has good potential, indicating concentrated effort on these sites to boost long-­‐‑term growth prospects may be worthwhile. In addition, there is logic behind providing support to address skills shortages in the local workforce, as specified by what the industry primes want/need in advanced engineering generally. Intervention benefits may take time to build. The sector is well established and traditionally has high barriers-­‐‑to-­‐‑entry meaning that support may be best targeted at established businesses.

E-­‐‑Health: In terms of technological solutions7 and compared with market potential, the e-­‐‑health smart specialisation area is mostly in its infancy within CIoS (indeed, the global market as a whole). Whilst there is some development of local research-­‐‑related activity, centred on the Peninsula Medical School and the European Centre for the Environment and Human Health, this tends to be broadly based around wider health & well-­‐‑being agendas rather than specifically focused on technological solutions to deliver e-­‐‑health products/services. It is likely that the global market will be led by innovation & enterprise elsewhere, notably in the United States and South Korea, and in some emerging economies, such as India. Currently, there are relatively few

7 This report is focused principally on using digital technologies to improve health outcomes -­‐‑ as identified in the CIoS SIF smart specialisation documents.

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Strategic Economics Ltd – Bespoke Analysis & Advice businesses operating solely in this sector (although companies such as those noted in the Catalys report are experiencing high growth and solutions will be delivered via ‘mainstream’ businesses i.e. applications of analytical software for healthcare purposes).

One CIoS asset might be its relatively aged and stable population, which can provide a ‘test bed’ for businesses to apply and develop future products and/or services. There are signs of business interest in utilising these demographic characteristics to attract investment/activity to the area. The question is whether the value of such demonstration work can be led or captured locally through real inward investment as well as remote engagement.

The benefit of a relatively ‘straightforward’ healthcare structure (NHS Kernow) is also seen as an advantage. CIoS benefits from an evolving partnership structure involving private, public and academic organisations that are working together to understand and exploit the opportunities that may be available.

Whilst it is accepted that the superfast broadband infrastructure provides one platform for local businesses to enter and operate in this market, the wider technology-­‐‑side of the sector in CIoS is in its infancy. The conclusion is that e-­‐‑ health has a limited-­‐‑to-­‐‑partial presence of assets. Right now, the asset base for e-­‐‑ health, particularly in private business terms, is largely in need of further development. There may be a useful debate as to how this should be orientated, given the local skills base, with respect to business-­‐‑to-­‐‑business technological development versus serving the final market in order to manage the sector’s high risk/high return characteristics.

To conclude, the assessment of the smart specialisation asset base within CIoS is summarised in the table below: Table 6: Asset Base Classification Smart Specialisation Market Classification – asset base Digital Economy Partial presence of assets Marine Technology Combined presence of assets E-­‐‑Health Limited-­‐‑to-­‐‑partial presence of assets Space & aerospace Partial presence of assets Agri-­‐‑tech Partial presence of assets Source: Strategic Economics Ltd

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Strategic Economics Ltd – Bespoke Analysis & Advice Risks affecting Impact The next stage of this assessment is to highlight the risks that may inhibit the ability of SIF investment support to fully extract economic benefits within8 the programme period. This analysis is not extensive and may cover some of the issues already highlighted. However, it is another analytical layer of the SSF and in assessment of the SIF programme’s ability to affect the growth potential in each of the smart specialisation areas.

There are macro risks that could affect the programme significantly – both in terms of inputs and outputs, impact and outcome. These range from political factors, such as the possibility of an EU exit referendum in the United Kingdom in 2017, through socio-­‐‑environmental issues, such as demographic and welfare change, to economic risks related to policy and market changes, and technological development.

As specified earlier in the context section, there remains a risk of fundamental constraints on growth through the economic imbalances of debt, trade, policy and structure. Further ‘shocks’ from financial markets, including inflation and currency fluctuations, foreign markets, including the euro-­‐‑zone malaise, and domestic markets, including the issues of real earnings growth for demand and productivity for supply.

The key micro issues for the five sectors that could affect the ‘success’ of any SIF investment are outlined in the table below.

Table 7: SSF Risk Analysis Smart Specialisation Risks Market Digital Highly ‘disruptive’ industry, with technological changes Economy occurring at a pace that makes it difficult to ‘pick winners’.

The risks (of failure) associated with targeted support are relatively high but the rewards of success may be large. The pace of change can be so marked that any programme of public investment risks being unwieldy or ‘behind the curve’. Any investment programme needs to be sufficiently flexible to respond to the changing environment. This is likely to mean an avoidance of ‘rigid’ deliverables that do

8

It is worth reiterating that references to ‘within’ the programme period generally

encapsulates the delivery period (2014-­‐‑2020) as well as up to 5 years following: the impact period that could be attributable to the SIF investment support.

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Strategic Economics Ltd – Bespoke Analysis & Advice not allow projects to shift their emphasis in response to changing market conditions.

Marine Technology

Given the relatively fragmented nature of the market (dominated by micro and small businesses in CIoS), it is difficult to consistently identify issues that may be best addressed through targeted local support. There is a relative lack of coordinated sector representation within CIoS (encapsulating the different strands of a diverse industry).

These may inhibit the formation of a coherent view and investment strategy.

For wave and tidal energy, there are risks associated with how the Electricity Market Reform (EMR) (still not fully understood or promulgated) will impact upon near-­‐‑term viability of research, demonstration and commercial projects.

Hence, regulatory risk is a factor in the period 2014-­‐‑2020.

Due to its continued importance to investment viability, the exact nature of the price subsidy regime is a major risk factor, potentially dominating any support that could be provided through the SIF. For offshore wind, the main manufacturers have established relationships with research centres and their supply chains.

Other than the price subsidy elements (which are already addressed at a national policy level) it will be difficult to pinpoint a local market failure that needs to be addressed.

E-­‐‑Health

For some businesses, the regulatory risks identified above may be at least partially negated by opportunities elsewhere in offshore deployment. The increasing scarcity of easy-­‐‑to-­‐‑ reach oil & gas reserves will continue to drive demand. It is not expected that this will lessen during the SIF programme.

Market in early stages of development and ‘leaders’ difficult to identify. From here, it is opportune but hard for any public sector support to be well targeted (in terms of ‘surety of return’ in EU funding terms). This may be an example of a high-­‐‑risk, high-­‐‑reward emerging technology.

The UK health market involves a principal customer base, currently within the NHS but, perhaps, evolving into new delivery models. Whilst the need for efficiency and cost control is one of the factors driving e-­‐‑health, there is also the

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Space & aerospace

Agri-­‐‑tech

normal economic risk associated with having a relatively narrow, structured customer base i.e. government funding, health policy etc. This risk may be less marked for private and international markets.

Moreover, successful penetration of potential markets could yield enormous returns. It is possible that much of the market opportunity for the sector is that the current model of health provision is unsustainable, and more efficient (both in terms of treatment and particularly preventative medicine) solutions will be attractive.

Self-­‐‑monitoring and diagnostic tools are being developed by extremely large businesses (Google, Apple, Samsung etc.).

There is a danger that the size of the resources being devoted to product development by these global players will shape and dominate the overall digital technology market.

Intense global competition in both space (heavily US dominated) and aerospace. The CIoS sector appears relatively small and disparate in this context and may require substantial investment to develop a cluster with sufficient scope and ‘momentum’.

The long-­‐‑term nature of R&D programmes means that impact may take place far beyond the end of the programme period. Moreover, aspects of the supply chain are relatively mature, with considerable barriers-­‐‑to-­‐‑entry. It is unlikely that major ‘new’ players will enter the market during the SIF programme period.

Activity in aerospace is highly cyclical – driven by the product cycles of the aircraft prime manufacturers (Boeing and Airbus, and the engine makers). SIF interventions will need to be well timed in sector-­‐‑cycle terms.

UK policy on factors such as GM foods may mean that this area suffers from a relative competitive disadvantage when compared to other countries. However, the CIoS focus on grassland and dairy mitigates this.

Demographic, environmental and socioeconomic issues that are driving increased demand for food are greater elsewhere (developing countries). This may lead to more focus – by

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Strategic Economics Ltd – Bespoke Analysis & Advice both public and private organisations – on those market opportunities. Climate change issues may not be as pressing in SW England as elsewhere. Overall, the incentive for UK-­‐‑ orientated producers may be lower at present, but there are export opportunities.

Due to licencing and safety issues with food, the adoption of research for new/adapted food products is a long-­‐‑term process. Therefore, it is likely that any substantial impact may take place far beyond the end of the programme.

Source: Strategic Economics Ltd

Lessons from Previous Evaluation The initial point to make regarding any review of evaluation evidence is that it tends to be ‘sector neutral’ i.e. it does not really provide any pointers as to whether investment support for one sector is more successful than in another.

Therefore, this report highlights the key points that are of primary relevance for the SIF programme to consider in terms of where and what investment support should be given. Some connection is made as to how this may specifically relate to the identified smart specialisation areas.

•

It takes time to put partnerships and delivery vehicles in place. In those smart specialisation markets where there is less sector ‘coherence’, or where there is no single sector voice, establishing fruitful mechanisms may take more time than first envisaged.

•

It takes time to see results, and the quickest wins are not necessarily the lasting ones. The political cycle can introduce a short-­‐‑term bias.

The time it takes for projects to be fully implemented and to generate a ‘return’ on the public investment should not be underestimated. The evaluation evidence consistently highlights the difficulty in measuring, or even assessing, lasting impact within the timeframes of public programme funding. Previous European programmes have found this, and this SIF faces similar issues.

•

Success depends heavily on finding the right delivery mechanism: one that works in the particular context of, and at, that time.

•

Investment support to address market failure is, by its nature, risky.

There is always a risk that those interventions that address deep-­‐‑ 43

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•

•

rooted and longstanding market failures themselves carry a high probability of failure relative, perhaps, to short-­‐‑term ‘safer bets’. This does not mean that risky projects should be avoided but that the worthwhile benefits from those projects need to be outlined carefully and may take longer to develop/mature – affecting the programme-­‐‑ level profile of outcomes.

Fundamentally, it is important to get reasonable output/targets set at a programme level to ensure good practice at a project level. There is some evidence that unrealistic (difficult to achieve) programme level targets drive poor ‘behaviour’ from applicants. A ‘good’ proposal – one that would stand a chance of securing funding – should show its strategic fit (in terms of lasting and sustained impact), as well as the ability to deliver as many of the outputs as possible.

Size matters. Evaluation evidence appears to indicate that, in general, larger investments by economic development bodies are shown to deliver the greatest strategic economic impact. Fewer, bigger projects tend to deliver more leverage than many, smaller ones. Evaluation evidence tends to suggest that it is more difficult to determine the impact of smaller projects – or projects where the relationship with beneficiaries is ‘lighter’ (see later comment on business support).

The evidence suggests that to have lasting impact on the most structural of economic problems interventions generally have to be large scale, coordinated across a number of agencies and partners and sustained. This is always an issue in the CIoS context, unless wider networks can be entered/accessed.

•

It is important to have sufficient flexibility in a programme to be able to react to unforeseen and unforeseeable events. In the ‘real’ economy nothing stands still. As a consequence, it is not necessarily correct to develop ‘solutions’ and then simply stand back and wait for the outputs and impacts to arrive. This may apply to the digital and e-­‐‑ health sectors in particular.

•

Businesses should not be overwhelmed with offers of support. It is important that programme level targets are mindful of the ‘size of the market’. The evaluation evidence highlights numerous examples of projects within local areas competing for the same clients to deliver target outputs. This may be most pertinent to the smaller specialisation markets and, especially, in an economy of the CIoS’ size.

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Strategic Economics Ltd – Bespoke Analysis & Advice •

The evaluation evidence suggests there is tension in delivering ‘businesses supported’ outputs and more lasting economic impact.

Targeting intensive support to a limited number of businesses can deliver more lasting economic impact than light touch interventions to a large number of businesses. This is an example where evaluation evidence could help ‘shape’ programme/project delivery – suggesting that fewer, but more intensive, business supports would have a greater chance of meeting strategic objectives.

Overall consideration: It is appropriate to reflect how all of the ‘growth potential framework’ issues outlined above can be considered in combination to assess growth potential.

First, the issues surrounding maturity of the market, and how CIoS is competitively placed in those markets based on its assets, are combined to judge how this may impact upon relative growth potential9.

Given that all of the smart specialisation areas have been identified to deliver growth, it is unlikely that any will experience negative growth during the SIF programme period (depending on the course of overall macro conditions).

What this framework approach strives to identify are those areas that might have the potential to perform relatively well in terms of growth, i.e. to perform  Very strongly (+++),  Stronger than trend growth (++),  Near trend growth (+).

The following table summarises the classification analysis to date10

It is important to stress that this analysis is primarily concerned with relative growth; that is potential growth set against broad trends for the CIoS economy as a whole.

10 At this stage of the analysis, growth is outlined in proportional terms and not absolute terms.

The existing size of each of the smart specialisation areas and the potential contribution overall output growth – it’s macro impact -­‐‑ is not taken into account. This is considered under the ‘Benchmark Options Framework’ below.)

9

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Strategic Economics Ltd – Bespoke Analysis & Advice Table 8: Overall Growth Potential Classification Smart Specialisation Classification – Classification – asset base Market market maturity Digital Economy Marine Technology

Nascent/Emergent Partial presence of assets Emergent Combined presence of assets E-­‐‑Health Nascent/Emergent Limited-­‐‑to-­‐‑partial presence of assets Space & aerospace Emergent/Mature Partial presence of assets Agri-­‐‑tech Emergent Partial presence of assets Source: Strategic Economics Ltd

Relative growth potential ++ +++ + +/++ +/++

This framework suggests that Marine Technology offers the strongest growth potential over the programme period within CIoS. This is largely as a result of continued market growth driven by renewable targets and wider marine-­‐‑ tech deployment, the UK’s early mover status and the strong infrastructure already in place in the region. Given that significant progress has already been made in developing a business base and retaining economic benefit through ‘embedded’ supply chains, there is a good foundation in place for future growth.

Due to the strength of market growth in different parts of the Digital Economy, this sector offers the potential to also grow strongly during the programme period. CIoS is relatively well placed due to relatively low barriers of entry in some markets – (meaning that, given a solid entrepreneurial and skills base, SMEs can enter and compete) – and its strengths in the creative sector. The merging of expertise in both digital and creative is a CIoS strength that can be further developed. It is an ‘agile’ market segment, which means that positive returns could be felt quickly relative to those in other sectors.

Whilst both the Space & Aerospace markets and Agri-­‐‑tech offer growth potential, there are enough limitations in the competitive position of CIoS – combined with the fact that there are high barriers to entry and entrenched research-­‐‑manufacturer-­‐‑supplier relationships elsewhere – to feel that these areas could have relatively low additional growth potential at the margin without massive investment. Considerable resource would need to be invested to develop sectors of sufficient scale and this is unlikely to occur within the programme period, though it may be seen as a ‘slow burner’ for the longer term, with an emphasis on skills rather than technical innovation.

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Strategic Economics Ltd – Bespoke Analysis & Advice After reviewing the available evidence, in the near term, the lowest relative growth could occur in the e-­‐‑health market. Whilst there are notable examples of businesses in CIoS performing well in this market, there are significant uncertainties as to how the overall market will develop and its relative immaturity. The existence of considerable competition elsewhere leads to a conclusion that it may be a lesser priority investment for the SIF programme, unless it can be coupled with opportunities elsewhere (outside CIoS and/or within Digital). E-­‐‑Health is likely to be a ‘long-­‐‑term’ game with significant development in later decades after initial investment in enabling capacity, patient-­‐‑cohort systems, and intellectual R&D. The key to success for the investment support provided by the SIF to the smart specialisation markets will also depend upon the identified macro and micro risks (as well as the unforeseeable uncertainties which may arise during the programme period).

For Marine Technologies -­‐‑ specifically marine energy -­‐‑ the key risk is whether the post-­‐‑Electricity Market Reform (EMR) environment will still provide sufficient incentives for further development and deployment of the technologies. Without sufficient price guarantees, the impact of any SIF investment support may be negated or muted. The SIF programme needs to understand the outcome of the EMR before finalising the level and type of its intervention support.

If the EMR is not favourable to marine energy developers/operators, it will dampen the growth potential. The LEP might usefully engage with the industry to understand the implications of the EMR for the development of the industry over the coming decade. Initial indications are that – whilst the proposed strike price for wave & tidal is below what the industry would require/prefer – the proposed values are still workable. Nevertheless, it is important to understand the full implications with the local organisations.

In terms of the Digital Economy, the primary risk is the difficulty of the public sector in providing well-­‐‑designed programmes of support that do not pick the ‘wrong’ technologies or are simply superseded by the pace of market change. The risk is inherently connected to the creative destruction characteristics of a relatively agile market compared with a relatively unwieldy public turnaround. This indicates that i) any support at a programme-­‐‑level is better designed with sufficient flexibility for it to respond to changes and ii) that any support is better focused on generic/horizontal issues for the sector, rather than on ‘hot topics’ carrying greater risk.

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Strategic Economics Ltd – Bespoke Analysis & Advice Of course, the learning point from the evaluation evidence highlighted previously is that investment risk can be tolerated as long as all stakeholders are willing to trade risk in the hope of potential return i.e. local risk appetite.

The risks for Space & Aerospace and Agri-­‐‑Tech are associated with the view that the CIoS is not necessarily that well positioned competitively, and it will be difficult to improve that competitive position within the programme period. It is also important to note that returns (benefits) from the property-­‐‑ related interventions at Goonhilly and Newquay airport may be, and may be better if they are, long-­‐‑term in nature. Property-­‐‑related interventions tend to perform relatively poorly on traditional measures of return, such as Net Present Value, because they tend to be capital intensive and the benefits tend to take longer to flow through.

The risks of the E-­‐‑health sector relate to the wide and largely unknown potential scope of a market at an early stage of development and to the quasi-­‐‑ political and planned nature of the market. Changes in the structure of UK healthcare provision and how they might lead to market opportunities (for both market and non-­‐‑market (NHS) provision) remain unclear. The provision of e-­‐‑heath products direct to consumers/individuals is a highly sought after market. Significant investment is being made and led by most of the global technology giants.

Overall, the consideration of the risks does not really change the assessment of market growth potential for each of the smart specialisation areas.

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Strategic Economics Ltd – Bespoke Analysis & Advice

Benchmark Options Framework Investment Scenarios The EU SIF Strategy for CIoS puts great emphasis on creating the conditions for growth (skills and infrastructure), raising business growth (productivity and competitiveness), and pursuing future opportunities (new and emerging markets).

The ‘smart specialisation’ programme (SSP) drives towards the heart of this ‘future economy’ strategy by promoting supply capacity and exploiting demand changes through innovative sector developments.

Previous research identifies the five technology-­‐‑driven sectors for development considered here, namely elements of marine, space and aerospace, e-­‐‑health, agriculture and digital. Previous sections have laid out frameworks for growth in these areas.

It is understood that the SSP has a potential of £64 million available to spend over the programme period (2014-­‐‑20). Council officers have specified a range of potential outputs from the SSP (Table 9).

Table 9: EU SIF Outputs Summary Enterprises supported 234 Enterprises co-­‐‑operative research New Enterprises 65 Enterprises with new to supported market products Jobs created 212 Enterprises with new to firm products Private Leverage (£m) 10 Enterprises using ICT Site development (ha) 1 No. participants (employed)

227 40 20 0 55

In this part of the report, these elements are melded with the Macroeconomic and Growth Potential Frameworks to produce a Benchmark Options Framework. Together, these three frameworks constitute the SSF.

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Strategic Economics Ltd – Bespoke Analysis & Advice Base trend Growth To create a baseline benchmark, the following approach is adopted (see details in appendices):   

    

Use the Annual Business Survey (ABS) to extract detailed industrial data at a NUTS3 level for approximated Gross Value Added (GVA).

Use ONS work comparing this with the National Accounts (Blue Book) measure of GVA to create appropriate adjustment factors.

Find that the calculated differentials for the ‘smart specialisation’ sectors, in manufacturing, ICT and professional and technical services, are acceptably low.

Apply these adjustment factors to the SW ABS data to derive local, granular GVA estimates.

Take employment numbers and shares from the BRES to adjust the final estimates.

Adopt national and regional productivity ratios for the relevant sectors.

Build ‘smart specialisation’ definitions and apply the above approach to create a baseline.

Apply ‘futures’ estimates to this baseline.

Outputs & Results Consideration of the strengths and weaknesses of each of the smart specialisation areas in the previous chapter, creates a good position to understand the implications for potential benchmarks: the outputs and results that could be delivered as a consequence of investment support during the 2015-­‐‑2020 period.

At the outset, there is a need to emphasise the difficulty in defining each of the areas, particularly quantifiably, within the ‘building blocks’ that are available – namely the Standard Industrial Classification (SIC) system11. In order to provide the requested guidance on the quantifiable impact of potential SIF support for smart specialisation, it is necessary to define each smart specialisation sector through a SIC-­‐‑based framework. Following discussions, Cornwall Council and partners are aware of the difficulty of such an approach and have agreed the proposed definitions.

11 This is discussed in more detail in Appendix 3.

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Strategic Economics Ltd – Bespoke Analysis & Advice Any approach is only as robust as the underlying data. Published data, principally from the ONS, tends to become less robust the smaller the geographical area. Moreover, most of the data used is survey based and the confidence intervals associated with survey data always increase at smaller geographies, such as CIoS.

Since published data at an industry/sector level is always set by reference to

official SIC classifications, and given that CIoS smart specialisation areas do

not always fit well within this structure, there is inevitable uncertainty in any

quantified estimates. This uncertainty is reinforced by a lack of granular GVA

data at a sub-­‐‑ national level, requiring a number of assumptions and

judgements to be made in determining useful benchmarks.

There will be an irreducible margin of error associated with any estimates.

Users need to understand and reflect this in any subsequent work undertaken

based on this SSF. Nonetheless, the approach adopted is transparent,

pragmatic and based on sensible economic assumptions and analysis.

Therefore, it should be a useful tool for the Cornwall LEP to use, with other

intelligence/evidence, when considering its SSP/SIF options.

Given this context, the objective is to define a range of possible outcomes in terms of the impact upon Gross Value Added (GVA), Full-­‐‑time equivalent (FTE) jobs, and net business creation.

1. Gross Value Added The approach to estimating the potential SSF impact upon GVA is defined comprehensively in Appendix 2. Given the lack of detailed, industrial-­‐‑level GVA data -­‐‑ particularly at a sub-­‐‑national level -­‐‑ the approach is to estimate the current GVA associated with each of the smart specialisation areas.

A ‘baseline’ is established for the focus period, extending this from 2015 out to 2030. The baseline for each of the smart specialisation areas is a forecast for GVA growth at a CIoS macro level, before any intervention of SIF support.

Given the scale of funding available through the smart specialisation strand of the SIF, it will not change the macro story significantly. It is the hope (and expectation), however, that the SIF will impact growth in each of the smart specialisation areas positively. Ultimately, this will raise the local economy’s growth potential.

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Strategic Economics Ltd – Bespoke Analysis & Advice Consideration of the GVA impact is done in nominal terms. Therefore, estimates of real GVA growth (from the earlier Macroeconomic Framework) are converted to nominal growth by factoring for inflation over the period 2015-­‐‑203012. This produces the nominal baseline outlined in table 10 below: Table 10: Nominal GVA growth (average percentage change per annum) 2015-­‐‑17 2018-­‐‑20 2021-­‐‑30 CIoS Nominal GVA 5.0 4.6 4.6 Source: Strategic Economics Ltd The fundamental principle of the benchmark analysis is that all smart specialisation areas are expected to experience growth in excess of the overall average for CIoS growth. Indeed, the understanding is that this is the basis on which they were chosen prior to this research on the grounds that they represent strong, potential markets with growth in excess of the CIoS ‘potential trend’.

It is argued, however, that, due to the differences outlined in earlier analysis regarding the stage of market maturity, opportunity and assets within CIoS, there will be variation in the extent of this ‘above trend’ growth. Earlier analysis attempted to place a qualitative framework around this and this is now translated into a quantitative approach for benchmark options (Table 11).

Table 11: Smart Specialisation Potential GVA Growth Differentials Average annual, nominal GVA growth rate above CIoS trend growth (2015-­‐‑ 2030) Central Low Scenario High Scenario Scenario Aerospace (+/++) +1.0% +0.75% +1.5% Marine Tech (+++) +1.5% +1.25% +2.5% Digital Economy (++) +1.25% +1.0% +2.0% Agri-­‐‑tech (+/++) +1.0% +0.75% +1.5% E-­‐‑health (+) +0.75% +0.5% +1.0% Source: Strategic Economics Ltd

12 Two approaches to inflation were considered and synthesized: 1) based on a simple use of the OBR approach that CPI inflation will revert to the target set by government for the Bank of England (2% per annum) and 2) based on a more nuanced understanding of the impacts of the expected output recovery on the output gap and of continuing quantitative easing/monetary potential. Nominal forecasts are sensitive to the inflation rates assumed.

The forecast is that inflation averages 2.3% per annum for 2015-­‐‑17, 2.4% for 2018-­‐‑2020 and 2.0% for 2021-­‐‑30.

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Strategic Economics Ltd – Bespoke Analysis & Advice Referencing the central scenario, some confidence in the assumptions used is gained by noting the near historical performance of each of the smart specialisation in relation to overall growth. Data at a national level from the Annual Business Survey shows that between 2008 & 2012 average annual growth for all areas covered by the ABS was 0.7% per annum13. This is in nominal terms. In real terms, there was a contraction in output during this period of general downturn.

All smart specialisation areas, however, experienced growth in excess of this rate: the positive differential being within a range of 1.2%-­‐‑2.3%. The forthcoming period will be one of steadier growth in the overall economy and, therefore, the ‘smart differential’ would be expected to narrow compared with the 2008-­‐‑12 base period. Hence, a narrower central range, 0.75%-­‐‑1.5%, is judged realistic going forward (Table 11).

This provides confidence that the central scenario is broadly sound and defendable, a priori. Assumptions regarding the ‘low’ and ‘high’ scenarios are variances around the central view. In many respects, the ‘high’ scenario represents the ‘aspirational’ scenario – “what if everything goes well, investment decisions are excellent and productive, and businesses respond optimally to the increased market potential”14. The ‘low’ scenario represents a less successful intervention effort, either because of implementation delays or less effective and efficient investment choices.

Table 12 & 13 below present estimates in current prices. The first includes a wide definition of the e-­‐‑health sector and the second excludes this sector (pleased refer to the particular issues in defining this sector through a SIC based approach in the relevant appendix). Figures are presented for two periods: • 2015-­‐‑2020 – representing the SIF delivery period • 2015-­‐‑2025 – representing a period reflecting previous experience that the impact of the SSF is likely to be lagged beyond the delivery period In presenting these three alternative scenarios, the advice is that the central scenario is likely to carrying the highest probability and is the most useful for planning and decision-­‐‑making. In all cases, gross estimates are presented.

13 This figure will not match estimates derived from Gross Domestic Product measures of economic output at a national level because the ABS is only a partial view.

14 All the assumptions represent informed judgement as to what seems reasonable in light of an assessment of the competitive position of each the smart specialisation areas, and the forecasts of the overall macro environment during the SIF period.

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Table 12: SSF GVA Growth relative to Baseline (including e-­‐‑Health) GVA growth over baseline (trend) – total for all smart specialisation areas (including the wide definition of the e-­‐‑health sector) Central Scenario Low Scenario High Scenario (£m) (£m) (£m) 2015-­‐‑2020 62.6 45.4 92.6 2015-­‐‑2025 160.0 115.2 239.7 Source: Strategic Economics Ltd Table 13: SSF GVA Growth relative to Baseline (excluding e-­‐‑Health) GVA growth over baseline (trend) – total for all smart specialisation areas (excluding the wide definition of the e-­‐‑health sector) Central Scenario Low Scenario High Scenario (£m) (£m) (£m) 2015-­‐‑2020 30.8 24.2 49.8 2015-­‐‑2025 79.0 61.9 130.4 Source: Strategic Economics Ltd

2. Job creation The approach to estimating the potential employment impact of SIF investment support in the smart specialisation markets is linked to the GVA forecasts and is based on the same principle of adopting a positive differential above forecast trend growth. In other words, all smart specialisation areas are assumed to experience employment growth over and above the overall CIoS trend due to their inherent, higher-­‐‑growth potential.

It is important to understand that the rate of annual employment growth is lower than overall GVA growth, given that the majority of GVA growth is delivered by improvements in productivity, especially in the higher scenarios.

(The baseline assumptions regarding employment growth in CIoS are as defined in Table 1 of the Macroeconomic Framework above; reproduced here as Table 14).

Table 14: Average Employment growth (percentage per annum) 2015-­‐‑17 2018-­‐‑20 2021-­‐‑30 CIoS Employment 1.0 0.7 0.7 Source: Strategic Economics Ltd

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Strategic Economics Ltd – Bespoke Analysis & Advice Given that the forecast annual employment growth rate is lower, the assumptions regarding the positive differential are also narrower than was assumed for GVA. In the central scenario, employment growth ranges from 1.2%-­‐‑1.5% per annum, reflecting judgement about the competitive positioning of each of the smart specialisation areas (i.e. adding central rates in Tables 14 and 15).

Table 15: SSF FTEs Growth Differentials relative to Baseline Average annual employment growth rate above CIoS trend growth (2015-­‐‑ 2030) Central Scenario Low Scenario High Scenario Aerospace (+/++) +0.3% +0.2% +0.5% Marine Tech (+++) +0.5% +0.4% +0.8% Digital Economy (++) +0.4% +0.3% +0.6% Agri-­‐‑tech (+/++) +0.3% +0.2% +0.5% E-­‐‑health (+) +0.2% +0.1% +0.3% Source: Strategic Economics Ltd To determine the current employment levels for each of the smart specialisation areas, data from the Business Register & Employment Survey (BRES) has been used. BRES data is available at a sufficiently granular level to allow it to be associated with each smart specialisation area.

Job outputs in a European funding context should be expressed as Full-­‐‑ Time Equivalents. Therefore, the following steps have been taken to determine the current employment position: •

The BRES data captures employees, full-­‐‑time and part-­‐‑time jobs. To convert part-­‐‑time jobs into full-­‐‑time jobs, a conversion factor of 43% (i.e. each part-­‐‑time job equates to 0.43 of a full-­‐‑time job) is used. This is based on the fact that, according to latest data, the average part-­‐‑time worker works 43% of the weekly hours of a full-­‐‑time worker15.

•

BRES does not capture self-­‐‑employed workers. Given that some of the smart specialisation areas will have considerable numbers of self-­‐‑ employed workers (e.g. in micro businesses), this needs to be taken into account. According to the latest data,16 CIoS has considerably higher levels of self-­‐‑employed workers than the national average – 23% of those in employment aged 16+ are self-­‐‑employed (compared with 15% nationally). Therefore, the BRES data is adjusted upwards by a

15 Labour Force Survey – Apr-­‐‑Jun 2014

Annual Population Survey – Apr 2013-­‐‑Mar 2014

16

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•

further 22% to reflect potential self-­‐‑employment levels. The propensity of self-­‐‑employment to differ across the smart specialisation areas is recognised. The approach here potentially understates self-­‐‑ employment in some areas, and overstates it in others but applying this overall figure is the safest approach without much more detailed primary research. The full-­‐‑time and part-­‐‑time split is assumed to be the same as employee numbers in the BRES.

Finally, BRES data tends to be ‘chunky’ by sector and by time period.

For example, it is rounded to the nearest 100, which impacts accuracy, particularly at a sub-­‐‑regional level. The data also tends to be variable over time. Both matters are addressed by using an average over a 4-­‐‑ year period (2009-­‐‑12) to smooth out the data somewhat. Although this uses a period of recession and downturn for the benchmark, there is little alternative.

Given these assumptions -­‐‑ related to determining current employment levels for the smart specialisation definitions, and the rate of growth for both the baseline and the sectors -­‐‑ estimates of employment impact are made. Again, results are presented including and excluding e-­‐‑health, and for a range of three scenarios (Tables 16 and 17).

The difference between the employment outcomes for the scenarios is narrower than the GVA estimates, simply because the ‘differential’ between each smart specialisation area is smaller.

Using the central scenario for the approach that uses the wide definition of e-­‐‑ health, approximately 228 jobs could be created during the SIF programme period. Allowing for lagged effect beyond this period increases the estimate to approximately 487 jobs. These should be considered as gross estimates that represent achievable targets for the SIF programme.

Table 16: SSF FTE Jobs growth over baseline (including e-­‐‑Health) Job growth over baseline (trend) – overall smart specialisation areas (including the wide definition of the e-­‐‑health sector)

Central Scenario Low Scenario High Scenario 2015-­‐‑2020 228 134 329 2015-­‐‑2025 487 278 693 Source: Strategic Economics Ltd

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Strategic Economics Ltd – Bespoke Analysis & Advice Table 17: SSF FTE Jobs Growth over baseline (excluding e-­‐‑Health) Job growth over baseline (trend) – overall smart specialisation areas (excluding the wide definition of the e-­‐‑health sector) Central Scenario Low Scenario High Scenario 2015-­‐‑2020 120 78 169 2015-­‐‑2025 264 163 360 Source: Strategic Economics Ltd

3. Business creation The approach to estimating business creation differs from that taken for the GVA and employment estimates. It is a more difficult exercise to understand the implications for business creation of, as yet unspecified, investment support. Moreover, this is complicated further by trying to understand the impact at an innovative sector level.

As a consequence, the approach to the impact upon business creation of the SIF programme considers all of the smart specialisation areas in the same manner. This may understate the impact in some and overstate it in others.

Intuitively, it may understate business creation impact in the digital economy sector – where there are relatively low barriers to entry and business start-­‐‑ups tend to be high. Equally, it may overstate it in areas that are more manufacturing based, such as aerospace and marine technologies, where the existing business structure is relatively well developed and market supply chains are well established. SIF investment support would be expected to have a relatively minor impact on net business creation in these areas.

Overall, therefore, these SSF estimates should be considered holistically.

The approach is based upon understanding the relationship between GVA growth in CIoS and the rate of net business start-­‐‑ups. Net business start-­‐‑ups are the appropriate indicator to consider, given that the objective of SIF support will be to create businesses that are sustainable in the long run, not just created as a partial or temporary consequence of publically funded investment support (i.e. temporary substitution effects).

As expected, the rate of net business creation tends to be lower than GVA growth, given that much of output growth is created by existing businesses.

According to the latest data17, between 2004 & 2012, CIoS experienced an

Business demography data – ONS – 2004-­‐‑2012

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Strategic Economics Ltd – Bespoke Analysis & Advice average of 0.2% net business growth per annum18. This rate was affected by the recessionary period (2008-­‐‑9), where business closures exceeded business start-­‐‑ups. Overall, this rate was lower than the national average of 1.2% net growth over the same period.

During this eight-­‐‑year period, the average rate of real GVA growth in CIoS was 2.4% per annum. Given that the annual, average forecast for real CIoS GVA growth in the 2015-­‐‑2030 period is 2.6% (see Summary Table 1), it is reasonable to assume that the previous long-­‐‑term relationship will broadly be maintained. Therefore, the base assumption is that net business creation will remain at about 0.2% per annum for all of the smart specialisation areas, (though potentially higher in areas with less established business structures and vice versa).

In terms of determining the possible scenarios for SIF investment outcomes, the starting position remains that because all of the smart specialisation areas have been identified as high-­‐‑growth, then the rate of net business creation can be expected to exceed this baseline trend growth.

Again, it is recognised that this may not be the case for certain smart specialisation areas where entry-­‐‑to-­‐‑market is more difficult. The benefit of the approach used, however, is that these areas tend to have fewer existing businesses and, therefore, the numbers involved in extrapolating forward tend to be quite small – not affecting the overall estimates significantly. For example, there are very few businesses defined as being principally involved in the CIoS aerospace industry and this suggests very few businesses will be created in this sector as a consequence of SIF investment support.

In determining the benchmark scenarios, the following is assumed, compared with a 0.2% per annum net rate average: •

For the high scenario, the SIF programme could ‘aspire’ to achieve a rate of net business creation in excess of national rates. An annual growth rate of 1.5% per annum is assumed.

•

For the central scenario, given that SSF investment support will be targeted (and in cases like the marine sector also supplemented by other SIF investment strands) at a relatively small cohort of businesses, then an assumption of 1% net growth appears reasonable.

Given that this data cannot be disaggregated to our smart specialisation definitions, the rates of business creation for those specific areas is not known.

18

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Strategic Economics Ltd – Bespoke Analysis & Advice •

Given the overriding assumption that all smart specialisation areas will experience growth in excess of trend, the low scenario assumes 0.5% annual growth.

Given these assumptions, estimates of net business creation are outlined in Tables 18 and 19 below.

Table 18: SSF Business Creation (including e-­‐‑Health) Net business creation growth over baseline (trend) – overall smart specialisation areas (including the wide definition of the e-­‐‑health sector) Central Scenario Low Scenario High Scenario 2015-­‐‑2020 46 17 76 2015-­‐‑2025 69 25 115 Source: Strategic Economics Ltd Table 19: SSF Business creation (excluding e-­‐‑Health) Net business creation growth over baseline (trend) – overall smart specialisation areas (excluding the wide definition of the e-­‐‑health sector) Central Scenario Low Scenario High Scenario 2015-­‐‑2020 40 15 67 2015-­‐‑2025 60 22 101 Source: Strategic Economics Ltd It is worth noting that the difference between the ‘including e-­‐‑health’ and ‘excluding e-­‐‑health’ approach is smaller than for the GVA and jobs estimate.

This reflects the fact that there are relatively few business ‘enterprises’ (or public organisations) involved in mainstream health delivery and, therefore, the impact of extrapolating forward is relatively minor19.

Overall Consideration The estimates presented for the primary Outputs and Results for the SIF programme are based on a sound and pragmatic approach for CIoS. Against the caveats of data availability and consistency, and the need for judgement and careful consideration, the predictions for the SSF are highlighted in Tables 20 and 21.

Of course, alternative ‘futures’ of health delivery models could be envisaged with smaller, private business units arising, but such suggestions are beyond the scope of this research.

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Strategic Economics Ltd – Bespoke Analysis & Advice These predictions represent benchmarks that are realistic and achievable, whilst offering a sense of ambition. Looking at the central scenario for all three measurements (using the approach that uses the wide e-­‐‑health definition), these estimates indicate an ‘informed judgement’ that looks sensible in the context of the prospective macro environment over the SIF programme period.

It is important to capture the lagged effects of the SIF support, but it is recogniseed that considering these benchmarks over the longer 2015-­‐‑2025 period makes them more ‘stretching’ and potentially less reliable.

Table 20: Overall SSF Benchmark Options (including e-­‐‑Health) CIoS SIF programme targets – smart specialisation markets (inc. e-­‐‑health) GVA Job creation Net business creation (No. (£m) (No.) new enterprises supported) 2015-­‐‑2020 £62.6 228 46 2015-­‐‑2025 £160.0 487 69 Source: Strategic Economics Ltd By not including the wide definition of e-­‐‑health (remembering that it effectively encapsulates all mainstream health delivery and, therefore, will only loosely relate to e-­‐‑health technology development), the benchmarks are lower.

Table 21: Overall SSF Benchmark Options (excluding e-­‐‑Health) CIoS SIF programme targets – smart specialisation markets (exc. e-­‐‑health) GVA Job creation Net business creation (No. (£m) (No.) new enterprises supported) 2015-­‐‑2020 £30.8 120 40 2015-­‐‑2025 £79.0 264 60 Source: Strategic Economics Ltd

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Conclusion The Smart Specialisation Framework (SSF) specified in this report suggests benchmarks of growth for the five chosen markets/technologies and the CIoS economy.

Recognising the historical data deficiencies and the speculative nature of the exercise, it depends on strong assumptions and professional judgements about the state of the local economy and its prospects.

The report takes the CIoS sectors as given and accepts the arguments suggesting they possess inherent future, above average, growth potential.

The results represent baseline and aspirational benchmarks against which CIoS decisions about investment interventions, performance monitoring and outcome evaluations can be made and assessed.

They are ‘stories’ of potential rather than precise forecasts and will need to be reconsidered as economic circumstances change through the life of the Smart Specialisation Programme, especially over the longer period of its supposed impact.

The derived SSF is fit for purpose, accepting the caveats agreed with the client at the outset and developed through the process. Despite its limitations, it should prove to be a useful tool for SSP executives, administrators and deliverers in the years ahead.

Nigel Jump, Executive Director and Chief Economist Shane Vallance, Associate Senior Economist

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APPENDICES 1. Key Data Definitions Gross Value Added (GVA) is an economics measure of the value of goods and services produced in an area, industry or sector of an economy, usually in a particular time period. It is related to other output measures. In particular, GVA + taxes on products -­‐‑ subsidies on products = GDP. NUTS geography. Nomenclature of Units for Territorial Statistics (NUTS) is an economic geography developed by the European Union (with its member states) to allow comparison of regional and sub-­‐‑regional data across the EU-­‐‑ 28 member states. A number of ONS regional and sub-­‐‑regional statistical outputs are produced based on the NUTS geography. These include regional and sub-­‐‑regional GVA. In practice, each NUTS 2 or 3 sub-­‐‑region (CIoS is both) covers the same area as either a single local authority or a combination of two or more adjacent local authorities. They are not based on functional economic zones or spatial areas.

Economically active: People who are either in employment (see next definition) or unemployed but actively seeking work -­‐‑ currently not working but are willing and able to work for pay, currently available to work, and have actively searched for work.

In employment: People who did some paid work in the reference week (whether as an employee or self employed); those who had a job that they were temporarily away from (e.g. on holiday); those on government-­‐‑ supported training and employment programmes; and those doing unpaid family work.

Occupation: Occupations are classified according to the Standard Occupation Classification 2000.

Industry: Sectors in which businesses operate are classified using the Standard Industrial Classification (SIC 2007) system.

Gross disposable household income (GDHI) is the amount of money that households have available for discretionary spending or saving. This is the money left after necessary expenditure associated with income, e.g. taxes and social contributions, property ownership and provision for future pension income. It is calculated gross of any deductions for capital consumption.

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2. Estimating GVA for Smart Specialisation This research requires an understanding of the potential impact on future GVA of each of the smart specialisation markets, given future investment support. To do this, a mechanism that estimates the current baseline position (notwithstanding, the separate issue of precise definition of the smart specialisation markets themselves – see next appendix) is needed.

The difficulty is that estimates of GVA from published ONS data for specific industries only exist at a relatively broad scale, and at larger geographies than C&IoS. GVA estimates for broad industries are part of the National Accounts (or the ‘Blue Book’) produced by the ONS. These do not meet the requirements of this study, both in terms of granularity of industry and of local geography.

Therefore, an alternative approach is adopted towards making an informed estimate of GVA for the requirements of the SSF going forward.

Importantly, the estimates produced should not be used for any other purposes

other than the objectives of this research. To produce robust and defensible regional estimates of GVA for very granular industrial breakdowns is sub-­‐‑

beyond the scope of this study. For example, the SW Regional Accounts -­‐‑ the

only reasonable set of equivalent, local estimates that are available -­‐‑ required scale public investment over a number of years to produce industry-­‐‑ large-­‐‑

specific GVA estimates. These Accounts still had significant margins of error,

particularly at the sub-­‐‑ regional (e.g. CIoS) level. Given the basic data

limitations, the approach in this report represents a useful approximation but it

should not be used beyond the current remit.

The alternative approach adopted here is based on utilising the approximated estimate of GVA (aGVA) that is produced on an annual basis through the ONS’ Annual Business Survey (ABS). The Annual Business Survey is a survey-­‐‑based exercise that does produce approximated estimates at a relatively low-­‐‑level of industrial breakdown (4-­‐‑digit SIC code) at both a national and regional level. The term “approximate” reflects the fact that the ABS measure of aGVA can be used as an approximation for the National Accounts measure of GVA.

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Strategic Economics Ltd – Bespoke Analysis & Advice Recent ONS work has compared the aGVA with National Accounts measure of value added20. For those industry sections covered by the ABS, overall aGVA is between 90% and 94% of GVA between 2008 and 2011. However, there are sectors where the match is less close – principally because the ABS does not cover those areas. Unfortunately, agriculture is one such area.

Overall, the comparison work undertaken by the ONS provides some reassurance that the ABS is a reasonable starting point. Specifically, for those broad industries that are most closely associated with the smart specialisation areas, the match between aGVA and GVA holds up reasonably well. These two measurements have been reconciled across three years, taking account of the adjustment factor. The resulting percentage differential is shown in the table below.

Average % difference between UK aGVA & UK GVA (using ONS published adjustment) Manufacturing ICT Professional, scientific & technical Transport & storage Overall

3.2% 0.7% 1.1% 3.3% 2.1%

By adjusting the aGVA figures by these adjustment factors, there is reasonable confidence that the estimates of GVA for these broad industrial groups are within a 5% margin of error. Again, it is important to reiterate that this is not an expression of exact statistical robustness. Rather, it is a guide to having some confidence in the approach, and that the estimates are a reasonable proxy of industry-­‐‑level GVA – if only for the purposes of this particular SSF research.

Because the ABS is survey based – with sample sizes decreasing at smaller geographical levels – the relationship between aGVA and GVA at a regional level begins to breakdown. There is less confidence of the aGVA being truly representative of GVA at a regional or local level. As a consequence, the aGVA for the United Kingdom is used as the relevant starting point.

For each definition of the smart specialisation markets (see next appendix), the following steps are taken to produce GVA estimates at a C&IoS level:

‘A comparison between Annual Business Survey and National Accounts Measures of Value Added’ – ONS – April 2014

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Strategic Economics Ltd – Bespoke Analysis & Advice 1. Using the adjustment factors, the aGVA estimates are converted into a GVA equivalent for each of the SIC codes falling within the smart specialisation definition. This is done at a UK level. This is undertaken for 2011 & 2012 data to mitigate some data anomalies.

2. For the same definitions, ONS BRES data are used to estimate the share of national employment that the C&IoS area has for each smart specialisation market. This is done on total employee numbers. (The full-­‐‑time and part-­‐‑time breakdown are compared for each area to ensure that these broadly coincide: a +/-­‐‑ 5% difference is found).

3. Based on the C&IoS employment share for each smart specialisation definition, GVA is estimated for a C&IoS geography.

4. Effectively, this assumes that productivity levels are the same in CIoS as they are nationally for each sector. This is not a strong assumption and, therefore, a further adjustment to reflect productivity differentials is made. Two methods were considered. First, relative productivity estimates at an industrial level could be applied, using a source such as the SW Regional Accounts. However, these estimates intuitively felt ‘pessimistic’ for some industries i.e. for marine it was estimated that productivity in the CIoS was only 49% of the national average. Second, a productivity measure at a broader scale could be utilised, such as the ONS experimental statistics on sub-­‐‑regional productivity21.

Specifically, data on GVA per hour worked for 2012 is used. The GVA estimate is then adjusted further by the relative indexed GVA per hour for C&IoS (68.2%).

This approach might underplay the relative productivity position of some of the industries and overplay others within C&IoS. Recognising these qualifications, in the absence of good local industry-­‐‑level productivity measurements, this is a reasonably sound, pragmatic and transparent approach.

Following the approach set out in 1-­‐‑4 above, GVA estimates for the different definitions (next appendix) of the smart specialisation markets can be made.

‘Sub regional productivity’ – ONS -­‐‑ March 2014

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Strategic Economics Ltd – Bespoke Analysis & Advice 3. SIC based definitions for Smart Specialisation Defining the smart specialisation markets through the ‘traditional’ method of SIC classifications is a difficult process. Nevertheless, in order to fulfil the quantitative requirement of this research, and given that measurements of economic activity at an industry level are ‘associated’ with SIC classifications, it is advisable to define the smart specialisation areas this way, where it is possible. Another issue is that GVA data is only available at 4-­‐‑digit SIC level (see previous appendix on approach to estimating GVA). Data at a more granular level is not available.

A set of smart specialisation definitions was provided by the client on commission (‘SS sector business numbers and employment’), some of which were based on the definitions used in the Witty Review. As part of the process of working towards a quantitative assessment of the potential impact of SIF investment support, it is appropriate to review these definitions.

Aerospace: The review of this sector largely accepts those SIC classifications already made and these are carried forward into the new definition. There is one significant proposed additional inclusion, which covers satellite communication activities, reflecting the existing and future role of Goonhilly – including private sector delivery from the site. In that sense, the definition used in this SSF actually expands the one made in the original document.

Whilst the SIC-­‐‑based system is largely ‘suitable’ for defining the aerospace sector, it may not capture all aerospace. In particular, it excludes related activity where products or services are provided by businesses whose primary activity is more generic (e.g. products developed by engineering companies that have wider application through a client base which extends beyond aerospace).

Marine Tech & Offshore Renewables:

A SIC-­‐‑based approach to this smart specialisation market is difficult because the SIC07 classifications still do not fully capture renewable energy (RE) activity (let alone specific RE areas such as marine/tidal). Indeed, the only specific RE classification relates to the ‘production of electricity – wind farms’ and this is at a lower level than the 5-­‐‑digit system: it is part of ‘production of electricity -­‐‑ 305110’. Even then, this bracket does not differentiate between onshore and offshore wind. Therefore, it is difficult to focus on marine based activity alone.

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The revised definition of the sector suggested includes the production, transmission and distribution of electricity simply because this will capture elements of RE activity. But, since these three broad activities are by far the largest elements of output in this definition, and given that the majority of data in this sector will still reflect non-­‐‑renewable activity, the overall size of this area could be significantly over-­‐‑inflated.

For marine technology, some classifications that were previously included in the definition are excluded. These primarily relate to the transportation elements of the marine industry. The broader marine transportation activities are not the focus of the proposed smart specialisation investment support, which is aimed at developing the high-­‐‑value technological aspects of the industry. Other notable changes include the exclusion of ‘engineering activities and related technical consultancy’. This captures too wide a spectrum of engineering consultancy activity that is not related to the marine industry, e.g. road and building infrastructure. Therefore, it is excluded from the revised definition.

Even this revised definition may overstate the size of activity in the marine industry. Many of the manufacturing based activities – whilst needing to be included because they contain elements of the marine supply chain – will also encompass elements that will not be marine related, e.g. manufacture of fluid power equipment will be for purposes and sectors wider than marine.

In conclusion, the SIC-­‐‑based system does not reflect the marine technology and/or the offshore renewable industry well and the revised definition used here is still likely to overstate the size of the smart specialisation market.

Digital Economy: The major change proposed for this sector is to narrow the definition to focus on content. This should more closely match those specific market opportunities such as software development & digital media that have been identified for ‘smart specialisation’. As such, ‘telecommunications’ is excluded because its activities relate primarily to hardware (principally wired telecoms) and this does not fit well with the intended areas of SSF investment.

This approach reduces the size of the smart specialisation market considerably. The revised focus is purely upon software development and programming. Again, this is likely to overstate the scale of the smart specialisation focus – given that, for example, software development will

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Strategic Economics Ltd – Bespoke Analysis & Advice cover a wider range of business areas than those identified in the smart specialisation approach, such as gaming, pervasive media etc.

Agri-­‐‑tech: The major difference in the proposed revised approach is to remove SIC 01000 and 03200, which cover ‘general’ crop, animal and fish production i.e. large parts of traditional agricultural and fishing output. Given that the smart specialisation emphasis is upon agricultural technology, this work focuses on those activities that are likely to contain specific technological activities. As such, SIC 72190, which encapsulates research and development on natural sciences, is included.

These changes will more closely reflect agri-­‐‑tech activities but it is a difficult sector to define via SIC classifications. Indeed, this revised definition may well be too narrow. In particular, it would not capture agri-­‐‑tech activities that occur in the mainstream agricultural sector i.e. producers that are implementing technological based solutions in the food value chain. e-­‐‑health: e-­‐‑health is not served at all well by the current SIC classification system, reflecting the extreme immaturity in the sector. No previous attempt at defining the sector in SIC terms has been found and, as such, it cannot be accurately reflected in any quantitative approach.

After consultation with the client, however, and recognising the objective of this report is to provide some idea of quantitative benchmarks, a wide definition of the health sector has been included. This also takes account of comments received from the sector/partners that delivery will largely take place within the mainstream health sector. The LEP has the option to include or exclude this definition from its consideration.

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AEROSPACE Original definition Description

SIC code 3030 Manufacture of air and spacecraft and related machinery 3316 Repair and maintenance of aircraft and spacecraft 5110 Passenger air transport 5121 Freight air transport

SIC code 30300

5122 Space transport 5223 Service activities incidental to air transportation 7735 Renting and leasing of air transport equipment

51210 51220

61300

77350

77352

33160 51101 51102

52230 52242

New definition Description Manufacture of air and spacecraft and related machinery Repair and maintenance of aircraft and spacecraft Passenger air transport Non-­‐scheduled passenger air transport Freight air transport Space transport Service activities incidental to air transportation Cargo handling for air transport activities of division 51 Satellite telecommunications activities Renting and leasing of air transport equipment Renting and leasing of freight air transport equipment

MARINE TECH & OFFSHORE RENEWABLES Original definition Description

SIC code 3011 Building of ships and floating structures 3012 Building of pleasure and sporting boats 3315 Repair and maintenance of ships and boats 5010 Sea and coastal passenger water transport

SIC code 30110

5020 Sea and coastal freight water transport 5030 Inland passenger water transport

27110

5040 Inland freight water

27200

30120 33150 25300

27120

New definition Description Building of ships and floating structures Building of pleasure and sporting boats Repair and maintenance of ships and boats Manufacture of steam generators, except central heating hot water boilers Manufacture of electric motors, generators and transformers Manufacture of electricity distribution and control apparatus Manufacture of batteries and

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Strategic Economics Ltd – Bespoke Analysis & Advice transport 5222 Service activities incidental to water transportation 7734 Renting and leasing of water transport equipment 2521 Manufacture of central heating radiators and boilers 2529 Manufacture of other tanks, reservoirs and containers of metal 2530 Manufacture of steam generators, except central heating hot water boilers 2711 Manufacture of electric motors, generators and transformers 2712 Manufacture of electricity distribution and control apparatus 2720 Manufacture of batteries and accumulators 2811 Manufacture of engines and turbines, except aircraft, vehicle and cycle engines 2812 Manufacture of fluid power equipment 2813 Manufacture of other pumps and compressors 2815 Manufacture of bearings, gears, gearing and driving elements 2825 Manufacture of non-­‐ domestic cooling and ventilation equipment 3511 Production of electricity 3512 Transmission of electricity 3513 Distribution of electricity 3514 Trade of electricity 4322 Plumbing, heat and air-­‐ conditioning installation 7112 Engineering activities and related technical consultancy 7120 Technical testing and analysis

28110 28120 28130

accumulators Manufacture of engines and turbines, except aircraft, vehicle and cycle engines Manufacture of fluid power equipment Manufacture of other pumps and compressors

28150

Manufacture of bearings, gears, gearing and driving elements

35110

Production of electricity

35120

Transmission of electricity

35130

Distribution of electricity

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Strategic Economics Ltd – Bespoke Analysis & Advice The difficulty with marine-­‐tech has been to capture activity in the renewable energy sector. Some is implicitly reflected in the SIC codes that relate to electricity production, transmission and distribution. The majority of this output, however, will still represent non-­‐renewable production. Therefore, the aGVA for these three areas of activity is amended (by 0.21) to represent the proportion of energy generation in CIoS that currently comes from renewable sources. It is important to understand that this will inflate the importance of the offshore sector somewhat, given that most will constitute onshore wind and solar. It is included only as a general proxy.

DIGITAL ECONOMY SIC code 58200 61000 62000 63000

Original definition Description

SIC code 58200 62000

Software publishing Telecommunications Computer programming, consultancy and related activities Information service activities

New definition Description

Software publishing Computer programming, consultancy and related activities

AGRI-­‐TECH SIC code 01000 03200 20150 20200 28300

Original definition Description Crop and animal production, hunting and related service activities Aquaculture Manufacture of fertilisers and nitrogen compounds Manufacture of pesticides and other agrochemical products Manufacture of agricultural and forestry machinery

SIC code 01610 01629 01640

New definition Description Support activities for crop production Support activities for animal production (other than farm animal boarding and care) n.e.c.

Seed processing for propagation

20150

Manufacture of fertilisers and nitrogen compounds

20200

Manufacture of pesticides and other agrochemical products

72190

Other research and experimental development on natural sciences and engineering

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E-­‐Health Original definition

SIC code 86101 86900

New definition Description Hospital activities Other human health activities

72