Policy paper

Investment Zones place selection: methodology note

Published 15 March 2023

Introduction

This note provides further detail on each step of the selection methodology developed to identify the areas most suited to benefit from an Investment Zone (IZ) in England^{[footnote 1]} and support the programme objectives. It summarises what was done at each stage, and the rationale for doing so.

Core principles

The IZ methodology is underpinned by a set of core principles that have guided the approach adopted:

a. Objectives: The methodology and criteria used to identify areas best suited to establish an IZ should flow directly from the policy objectives. IZs are ultimately aimed at leveraging local research and innovation strengths to boost productivity, increase innovation and level up the economy. Specifically, they will focus on growing knowledge-intensive clusters linked to research institutions and building on existing local strengths aligned to national priority sectors. IZs will take a whole ecosystem approach to ensure the benefits and opportunities of growth are felt by local communities within IZ areas.

b. Place-led: A place-led approach should be undertaken to identify areas with the potential to support these objectives, and which have the fundamental characteristics to create the environment in which the private sector can prosper and clusters flourish.

c. Geography: IZs will be based on functional economic areas (FEAs) that reflect the unique ecosystem of firms, institutions, supply chains and labour supply that exists in each place. Our methodology will therefore seek to identify FEAs^{[footnote 2]} that are of a sufficient scale to support mature clusters and drive wider regional and national growth, as well as the highest tier of local governance to enable strategic decision- making across these areas. At the same time, it should also aim to identify those areas which also have significant pockets of deprivation within them, that an IZ could help alleviate. This variation is consistent with the IZ policy model, which will involve both spatially specific interventions, as well as wider horizontal interventions across economic areas.

d. Programme size: There will be a limit to the number of areas initially invited to agree an IZ within the boundaries of overall programme affordability.

e. SWNI: A separate approach to identifying IZ areas in Scotland, Wales and Northern Ireland will need to be co-developed with the Devolved Administrations to reflect the specific context of each.

Guided by these principles, the methodology developed comprises of two main stages. At each stage, three criteria are applied that flow directly from the policy objectives and are rooted in the evidence of what conditions are necessary to support cluster growth.

The first stage identifies and ranks FEAs based on their potential to benefit from an IZ and support IZ objectives. Of this list, the top 20 suitable areas (based on scores) are taken forward and assessed against three further qualitative criteria to give a final ranking of places. These additional criteria aim to capture the characteristics of places that will maximise the impact of IZs.

Note: The ranking of areas in the first stage determines the shortlist of areas that progress to the second stage (thereby acting as a gateway), but otherwise does not form part of the stage 2 final scores.

Stage 1: Identifying the places that have the potential to benefit from Investment Zones

First, all FEAs in England are scored according to the following three criteria to determine the potential for places to benefit from IZs. Travel To Work Areas^{[footnote 3]} are used as a proxy for FEAs.

1. Economic (i.e. productivity) potential– as the overarching policy ambition is to boost productivity. A key route to aggregate productivity growth is by focussing on large areas with significant scope for ‘catch up’ economic growth. This criterion therefore targets IZs towards places that have the greatest scope for increased productivity and output;^{[footnote 4]}

2. Innovation potential - as increasing innovation and adoption of new ideas is one of the key drivers of long-term productivity and prosperity. This criterion therefore targets IZs towards places that already have areas within them with high business dynamism and high-skilled labour;^{[footnote 5]}

3. Levelling up need – as a key outcome of IZ policy is to support levelling up in pockets of deprivation within areas. This criterion therefore targets places that have high-deprivation areas within them.

Each of these elements are critical to IZ objectives. As such, all are given equal weight in our assessment.

Specific metrics are used to assess areas against each of these criteria, set out in Table A.1 below with justification. We consider the metrics identified as reflective of these Stage 1 criteria based on data available.

• To measure the potential for economic benefit, we estimate the size of output that is lost each year in a FEA, given its levels of productivity, relative to average productivity for a place of its size. Pre-Covid productivity is used, to capture the long- run differences in productivity between places.
• To measure the potential for innovation, we look for FEAs that contain areas with an existing concentration of high growth business, using the standard ONS definition, as well as a concentration of high-skilled labour, measured through the share of workers in Knowledge Intensive Business Services (KIBS). These metrics are not specific to those sectors targeted for IZs – these are assessed in Stage 2. Instead, this looks for evidence that a place has the general pre-conditions to support innovation and indicate a positive environment for cluster development.
• To measure levelling up need, we look for FEAs that contain ‘pockets’ which have both significant levelling up need (using established measures from the Levelling Up White Paper), and high levels of deprivation (using the Indices of Multiple Deprivations). Both elements are important to assessing outcomes for the population of TTWAs – the former reflects key high-level socio-economic performance and the latter reflects the extent of deprivation.

Note: The unit of geography used for the analysis varies across the criteria. While economic potential is measured at TTWA level, innovation potential and levelling up need are measured at lower-tier local authority level (as the most granular data available).

This variation is by design. We want to identify and target FEAs that not only underperform overall on productivity, but that also have existing pockets of innovation potential and levelling up need within them. This is on the basis that an IZ could build on, or benefit from, the former; and help alleviate the latter.

As a result, the granularity of analysis is critical across both criteria. Without it, areas of deprivation within FEAs might risk being masked by surrounding areas of affluence. At the same time, we would fail to differentiate between a concentration of high-growth businesses and high-skilled labour within an FEA, and the same number spread over a much wider area. This distinction is critical if we are to understand the potential for agglomeration benefits for innovation in a place. Emphasis is on identifying that these pockets exist within an area, rather than where they exist. While government will not require selected areas to target intervention at the pockets identified – on the basis local areas will have a deeper understanding of their area than can be discerned through quantitative analysis alone – these places will be highlighted at the outset of the co-development process as potential opportunities.

Table A.1 Quantitative analysis domains and metrics

Domain (weight)	Component (weight)	Metric	Commentary
Potential for economic benefit (1/3)	Potential for economic benefit (1/3)	Below average GVA per worker x working population in TTWA	This metric [footnote 6] captures the economic underperformance of a place, and hence at a high level the potential space for improvement if constraints to productivity are alleviated.
Potential for innovation (1/3)	Potential for innovation (1/3)	Knowledge Intensive Business employment as % of total jobs % businesses that are high growth[footnote 8], and have more than 10 employees	Often used as a proxy for business dynamism and high skilled labour, key ingredients for innovation[footnote 7]. An existing concentration of high skilled workers is considered to be a good indicator for the potential supply of high skilled workers specifically for the IZ, given the liveability of places and assortative mixing are both key determinants of the choice of location for all types of high skilled workers. Businesses that grow quickly beyond their very initial phase (10+ employees) are often innovative[footnote 9], attracting funding and higher- skill employees. Used alongside KIBs to represent the business component of innovation.
Levelling Up Need (1/3)	Core levelling up metrics (1/6)	NVQ3+ % 3 year average (2019-21) Healthy Life Expectancy 3-year average (2016/17- 2018/20) Total median weekly pay (gross) (£) 3 year average (2020-22) GVA per hour worked 3 year average (2018-20)	These metrics show how places fare according to high-level socio-economic factors, which stem from the 6 capitals framework set out in the Levelling Up White Paper (LUWP). These specific measures were used in LUWP and are regularly used by the government in order to inform policy development.
Levelling Up Need (1/3)	Established measure of deprivation (1/6)	IMD LAD overall score	The Index of Multiple Deprivation (IMD) is the official measure of relative deprivation in England and is part of a suite of outputs that form the Indices of Deprivation (IoD). It follows an established methodological framework in broadly defining deprivation to encompass a wide range of an individual’s living conditions. People can be regarded as deprived if they lack any kind of resources, not just income.

All the metrics used to evidence the criteria are based on up to date and published datasets.

Next, two thresholds are applied to the ranked list of FEAs to rule out less suitable areas:

• Minimum size: places that fall within the smallest FEAs in England likely do not have the scale to support the mature clusters that will result from a successful IZ policy. By definition, fewer people live in such areas, and so the benefits of IZs would also be felt less widely. As a consequence, the smallest areas, i.e. those that fall in the bottom 30% of TTWAs by population are excluded at this point^{[footnote 10]}.
• Average productivity: places with potential to improve productivity may already have relatively high productivity. Such places are not the target of the IZs policy, given the focus on addressing constraints on productivity and disparities between regions. We therefore exclude all areas with productivity above the national average.

This analysis results in a list of TTWAs in rank order of their potential to benefit from an IZ.

Finally, FEAs are mapped to the highest level of governance across each area, and the top 20 independent areas are shortlisted for further consideration. This is to avoid over reliance on the quantitative scores alone.

We do this so we know what the highest tier of governance is across the TTWA (as this will be assessed later in stage 2). Where devolution deals have been agreed and established, the boundary of the MCA/MCCA/County is treated as the best representation of the FEA over which assets can be leveraged, intervention will be coordinated and benefits felt^{[footnote 11]}. Where a devolution deal hasn’t been agreed, we use the highest level of governance available.

Stage 2: Ranking shortlisted places by their sectoral base, research credentials and leadership & capacity to identify the most suitable areas

Stage 2 applies three further qualitative criteria to this shortlist to assess which of these areas also have the necessary characteristics to support a successful IZ and make the most impact:

• Sector strength – because evidence is clear that government cannot create clusters from scratch^{[footnote 12]} and a core programme objective is to support the growth of key sectors where they already exist. This criterion therefore acts as a threshold to ensure a place has evidence of an existing strength in at least one of the five priority sectors^{[footnote 13]}.
• Knowledge anchor – because we know knowledge intensive clusters are driven by research-intensive institutions^{[footnote 14]} and the government wants to leverage local research strengths to spread the benefits of the innovation economy more widely. This criterion therefore targets IZs towards places with strong research-intensive institutions in order to boost innovation capacity and raise the productive potential of areas.
• Strong local leadership and capacity – because successful cluster development requires collaboration, coordination and creativity between partners. Above all, it demands strong local leaders with the vision and stable institutional and accountability structures to work strategically across a functional economic area to deliver inclusive growth^{[footnote 15]}. This criterion therefore targets places with strong local leadership, governance and partnerships already in place, to enable the development and delivery of proposals to progress at pace and ensure coordinated decision making across partners to maximise policy outcomes.

Note: The success of the model, as set out in the IZ policy prospectus, is rooted in collaboration between local partners and coordinated decision making. As such, before shortlisted areas are assessed against the above criteria, we consider all lead and constituent authorities to identify any live governance concerns. Where concerns are identified, places are considered on a case-by-case basis looking across a consistent set of governance tests relevant to the needs of the IZ programme. A place does not progress to qualitative assessment where governance, leadership and/or weak stakeholder partnerships are considered material in context of IZ programme.

An objective scoring framework is used to score areas against these three criteria (see Table 2 below):

• To score on sectoral cluster activity we use Standard industrial classification of economic activities (SIC) codes data on employment share for 4 of the 5 sectors which is a common way to map sectoral cluster activity. SIC code data does not align to green growth industries therefore we have developed an approach to identify offshore wind clusters as a proxy for a Green Industries cluster. We apply this criteria as a Yes/No threshold to indicate a place has objective evidence of one of the priority sectors from which an IZ can build.
• To score on a place’s knowledge anchor, we reward a place for either having a research-intensive university (as defined in Table 2) and/or an Innovate UK Catapult (a sectoral focused research and innovation hub that provides business support, spinout/scale up and adoption of innovation) within its TTWA or administrative boundary. Having both indicates enhanced opportunities for research and commercialisation of innovation so we score maximum points.
• We use Level 3 devolution deals (L3 deals) as a proxy for the leadership and capacity to reflect that an IZ demands strong local leaders with the vision and stable institutional and accountability structures to work strategically across an FEA. Devolution deals are a good indication that local partners are united behind a shared vision for their area. Areas with devolution deals also have a suite of tools (such as devolved skills funding; gainshare arrangements; and transport settlements) that can complement the toolkit available for IZs, maximising the value of the interventions.

Table A.2 Qualitative analysis metrics, scoring and rationale

Criteria	Metric	Qualitative scoring
Sector strength	Employment data: Business Register and Employment Survey SIC code data Offshore wind cluster analysis based on DESNZ’s Renewable Energy Planning Database	1 - One or more LTLAs from the FEA16 in the top 10 in terms of employment share nationally17 in one of the 4 priority sectors or evidence of an offshore wind cluster 0 – No employment share or offshore wind cluster evidence Scores are then doubled weighted to generate maximum score of 2

Rationale and commentary

A common way to map cluster activity is through SIC codes which generate employment share, gross value added (GVA) and turnover for each code. The publicly available data on turnover and GVA from the ONS is not disaggregated at a detailed enough SIC level for us to specify the priority sectors. In the interest of undertaking a methodology using publicly available data, employment share has been used. The top 10 list in terms of employment share is generated based on LTLAs in England only (given scope of this selection approach) and those remaining after productivity and size thresholds applied in quant analysis.

We take a different approach to identifying Green Industry clusters, for the following reasons:

1. SIC code data cannot be used to accurately define Green Industries; the codes do not reflect the industries in this priority sector (for example, SIC codes do not differentiate between green energy and fossil fuels).

2. Many Green Industries are also emerging clusters^{[footnote 18]}. As such, activity is difficult to identify in public data available, and the most consistent way to reflect places with strengths in Green Industries is to focus on places that already have the infrastructure to facilitate these activities. We know that offshore wind is the most developed of the green industries, having evolved in the UK over the past 20 years.

3. We know from the government’s Net Zero Strategy that HMG investment is particularly focused on Net Zero and Renewable Energy initiatives. We know from evidence of industrial clusters^{[footnote 19]} that places tend to have similar infrastructure that facilitates multiple renewables activities such as offshore wind, CO2 storage (carbon capture) and natural gas pipelines (hydrogen).

4. We have developed an evidence-based approach to identify offshore wind clusters as a proxy for Green Industries in lieu of accurate green growth employment share data.

We use DESNZ’s Renewable Energy Planning Database to identify major offshore wind projects and public evidence on port infrastructure facilitating these projects to identify Green Ports, as a proxy for an offshore wind cluster. We recognise the limitations of this approach and that this is a narrow definition of Green Industries. Other Green Industry activity are also reflected in other sector strengths such as Advanced Manufacturing and Digital and Technology (e.g. automated vehicles activity is partly captured by our assessment of advanced manufacturing sector strengths).

This criterion is of equal importance to the other two criteria so we double weight to bring total scores into alignment.

Criteria	Metric	Qualitative scoring
Knowledge anchor	Peer Groups for annual TRAC, TRAC fEC and TRAC(T)1 benchmarking 2017-2018 Research England grant allocations 22/23 IUK Catapults website	2 – TRAC Group A-C university / Top 20 universities in terms of Research England grant allocations and an IUK Catapult within the TTWA boundary -1 – TRAC A-C university / Top 20 universities in terms of RE grant allocations or IUK Catapult within the TTWA boundary 0 – Neither a TRAC A-C university, Top 20 university in terms of RE grant allocations or IUK Catapult within the TTWA boundaries

Rationale and commentary

For a university to count as ‘research intensive’ for this exercise they need to feature in Group A-C^{[footnote 20]} from the latest Transparent Approach to Costing (TRAC) rating or feature in the Research England’s 22/23 top 20 universities in terms of grant allocations; both indicators of universities’ capacity for research.

Catapults are used as they are (a) long established, (b) comparable, (c) a definitive and exhaustive list is available, and (d) they are specifically focused on promoting R&D through business-led collaboration to exploit market opportunities, and are therefore judged highly relevant to the IZ programme and the ability of an area to make a success of it.

Criteria	Metric	Qualitative scoring
Leadership and capacity	Devolution Deals 2023 Research Briefing	2 – Established Level 3 deal 1 – An agreed Level 3 deal 0 – No deal agreed

Rationale and commentary

We refer to Table 1 in the January 2023 Research Briefing which summarises all agreed devolution deals to date, to determine whether a place’s lead authority scores 0, 1 or 2.

Results

Of the places that proceeded to qualitative assessment, the following 8 places have been invited to co-develop proposals based on having the highest scores. These areas could be characterised as the large city regions outside of London and/or nationally significant industrial clusters:

MCA/County/TTWA

• Greater Manchester MCA
• Proposed North East MCA
• South Yorkshire MCA
• West Midlands MCA
• Liverpool City Region MCA
• West Yorkshire MCA
• Tees Valley MCA
• Proposed East Midlands MCCA

Note: As part of routine sensitivity testing, numerous scenarios were tested across the quantitative and qualitative stages. The 8 areas invited to develop IZ proposals consistently scored in the top 20 in absolute terms.

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1. We are engaging with the Devolved Administrations to agree the approach to place selection for Scotland, Wales and Northern Ireland. ↩

2. The functional area over which the local economy and its key markets operate do not adhere to administrative boundaries. Instead, key economic markets broadly correspond to sub-regions or regional labour markets - known as functional economic areas (FEAs). There is no single approach to defining FEAs. ↩

3. These are adapted from the ONS’ 2011 TTWAs, to align with 2021 administrative boundaries. ↩

4. Centre for Cities estimate that if the eight largest closed their output gap, the UK economy would be £47.4 billion larger in total. The underperformance of the UK’s largest cities is also referenced in the Levelling Up White Paper (C 1.2.6). ↩

5. This is a well-documented in economic research. The Levelling Up White Paper references this heavily, notably in 1.3.1: “Empirical evidence lends strong support to physical, human, intangible and financial capital as drivers of productivity and economic growth.” ↩

6. This has been used previously by Centre for Cities for this measure. ↩

7. Businesses growing employee size of more than 20% in 3 years 2019-2021. ↩

8. OECD (2009) Clusters, Innovation and Entrepreneurship, Nesta (2012) The Effects of Cluster Policy on Innovation. ↩

9. Research by Nesta indicates innovative firms grow twice as fast in both employment and turnover. ↩

10. This threshold excludes very small TTWAs. The TTWA being excluded are less than 1/8 the size of the top performing TTWA in the quantitative analysis. ↩

11. Where more than one TTWAs in the same devolution deal boundary, we treat this as one independent area for consideration in Stage 2. ↩

12. For example, in Centre for Cities (2017), How do we encourage innovation through clusters?; NESTA (2012), The effects of cluster policy on innovation. ↩

13. Digital and Tech, Green Industries, Life Sciences, Advanced Manufacturing and Creative Industries. As referenced in the Chancellor of the Exchequer’s speech of 23 January 2023. ↩

14. Innovate UK. 2015. The Knowledge Transfer Partnership programme: an impact review, HEFCE 2015. Assessing the Economic Impacts of the Higher Education Innovation Fund: a mixed-method quantitative approach. ↩

15. Variously: OECD (2009) Clusters, Innovation and Entrepreneurship; NESTA (2012) The effects of cluster policy on innovation; OECD (2007) Competitive Regional Clusters: National Policy Approaches. ↩

16. DESNZ (2021) Net Zero Strategy: Build Back Greener; DESNZ (2019) Energy Innovation Needs Assessments. ↩

17. HMG (2021) Industrial Decarbonisation Strategy. ↩

18. Institutions with a research income of between 5% and 15% of total income, 15% or more of total income, and 20% or more of total income with a medical school. ↩