Where are the UK’s levelling up funds most needed?

A key challenge currently facing UK policy-makers is to understand how the Covid-19 pandemic has affected local areas. There is widespread concern that the pandemic may have exacerbated existing inequalities across localities – a core problem in the UK that policies such as the £4.8 billion levelling up fund seek to address. 

Yet there is a lack of direct evidence on the inequality effects of the pandemic at the local level. We have constructed a new Index of Covid-19 Economic Impact to address this data gap. Our index reveals that areas of ethnically diverse metropolitan living have been particularly hard hit by both high levels of existing deprivation and negative effects of the pandemic.

What data are required to target places most in need of policy intervention?

Addressing persistent uneven regional economic growth has been identified as one of the UK government’s central policy objectives. By extension, a range of policy interventions have been developed including the £4.8 billion levelling up fund, whereby local authorities submit bids for investment in town centre and high street regeneration projects, transport improvements and local cultural industries. 

Critical to the success of these policy interventions will be the extent to which they target the places that most need support to stimulate economic growth and reduce deprivation. This is not an easy task, as the UK lacks sufficiently granular, official real-time (or near-real-time) data on local economic activity.

New research from tracktheconomy.ac.uk addresses this issue using real-time, local-level indicators of economic activity sourced from the UK’s leading providers of geo-specific real-time activity data. We partnered with Experian, Fable Data and Huq to source data from consumer and company credit files, transaction-level data on consumer spending and geolocation.

These granular data sources allow us to track, at the local authority level, consumer and firm financial distress, measures of consumer mobility and consumer spending – which we used in prior research to understand the pandemic by studying local lockdowns and regional inequalities. We also draw on data from the Decision Maker Panel (derived using employment survey data from the Office for National Statistics, ONS) to measure firms’ expectations for future investment and growth at the local level.

A new Index of Covid-19 Economic Impact

Combining these data sources, we have generated a new Index of Covid-19 Economic Impact, which ranks local authorities by the severity of the economic impacts of Covid-19, as measured using the latest real-time data to the end of June 2021 relative to a pre-pandemic 2020 baseline. 

For local authorities in England, we then compare this with the underlying economic situation of the local authority pre-pandemic using pre-Covid-19 Indices of Multiple Deprivation (IMD) from the Ministry of Housing, Communities and Local Government. 

Together, these data sources provide a view of the long-term economic situation of local authorities and their Covid-19 economic experience. (The levelling up fund has partitioned funds for Northern Ireland, Scotland and Wales and in line with this, we conduct separate analysis for these using each country’s own IMD measure – no UK-wide IMD exists.)

Figure 1 shows the rank position of each local authority in England in the IMD and the Index of Covid-19 Economic Impact. The top-right quadrant shows the most affluent local authorities that have been least affected by Covid-19, which include South Cambridgeshire and Rushcliffe.

Figure 1: The correlations between 2019 IMD and Real-time Index based on 311 areas in England - by local authority

Source: Author calculations derived from Office for National Statistics, Experian, Fable Data, Huq, Decision Maker Panel (derived by Will Rossiter & Konstantinos Karagounis, Nottingham Trent University), Ministry of Housing, Communities & Local Government English Index of Multiple Deprivation (2019) and Her Majesty's Treasury Levelling Up Fund priority category datasets.

Note: Each dot is a local authority in England.

In contrast, the most deprived local authorities that have been most affected by Covid-19 are shown in the bottom-left quadrant of Figure 1. Blackpool and Great Yarmouth stand out as being particularly severely affected. 

Yet there are many data points in the top-left and bottom-right, showing local authorities suffering deprivation yet less affected by Covid-19, or affluent areas that have suffered worse effects from Covid-19. This means that the two measures are important complements for policy-makers to consider – such as when deciding which local authorities should be allocated levelling up funds.

How might we categorise areas in the bottom-left quadrant of Figure 1 that have experienced the ‘double whammy’ of underlying deprivation and negative Covid-19 impact? One lens is to use the government’s three categories of the levelling up fund: level 1 Most Need areas, level 2 Mid Need and level 3 Least Need. 

Figure 2 shows that this categorisation correlates with the IMD (from left-to-right), since deprivation is evident among Most Need areas, but it does not capture the Covid-19 experience.

Figure 2: The correlations between 2019 IMD and Real-time Index based on 311 areas in England - by Levelling Up Fund category

Source: Author calculations derived from Office for National Statistics, Experian, Fable Data, Huq, Decision Maker Panel (derived by Will Rossiter & Konstantinos Karagounis, Nottingham Trent University), Ministry of Housing, Communities & Local Government English Index of Multiple Deprivation (2019) and Her Majesty's Treasury Levelling Up Fund priority category datasets.

Note: Each dot is a local authority in England.

A different and more informative lens is to look at local authorities grouped by ONS ‘supergroups’ such as Affluent England, Countryside Living and London Cosmopolitan. Figure 3 shows clear patterns of impact of Covid-19 by ONS supergroup. Here we see that particular supergroups stand out as being worse affected: notably Ethnically Diverse Metropolitan Living, some areas of Countryside Living and parts of London Cosmopolitan. 

Figure 3: The correlations between 2019 IMD and Real-time Index based on 311 areas in England - by ONS supergroup

Source: Author calculations derived from Office for National Statistics, Experian, Fable Data, Huq, Decision Maker Panel (derived by Will Rossiter & Konstantinos Karagounis, Nottingham Trent University), Ministry of Housing, Communities & Local Government English Index of Multiple Deprivation (2019) and Her Majesty's Treasury Levelling Up Fund priority category datasets.

Note: Each dot is a local authority in England

In contrast, some ONS supergroups have higher existing levels of affluence (for example, Affluent England) and were less affected, (for example, Services and Industrial Legacy). As a result, these supergroups do not feature much or at all in the bottom-right quadrant, while others show only a small association, such as Town and Country Living.

We see these patterns replicated in data for Scotland and Wales. In Scotland, Aberdeenshire and East Dunbartonshire have been least affected by long-term deprivation and the effects of Covid-19, whereas Renfrewshire and Glasgow City have experienced the most severe effects. 

In Wales, Newport shows high levels of long-term deprivation and worst effects of Covid-19, in contrast with Flintshire and Wrexham, which have been least affected. In these nations, we see similar patterns by levelling up fund categories and ONS supergroups.

We suggest, therefore, that policy-makers should consider combining pre-Covid-19 measures of deprivation with our Index of Covid-19 Economic Impact when making decisions about additional support for local authorities, such as disbursements from the levelling up fund. 

In Table 1, we list the ten most in-need local authorities in England based on a composite measure of the IMD and our Index of Covid-19 Economic Impact. By this composite measure, the highest priority local authorities for support include Blackpool, Barking and Dagenham, Newham, Liverpool and Great Yarmouth, whereas Rushcliffe, South Cambridgeshire and Waverley would rank last in line for support. 

Table 1: English Local Authorities with Highest and Lowest Economic Need: Rankings by Composite Measure of Economic Need

Source: Author calculations derived from Office for National Statistics, Experian, Fable Data, Huq, Decision Maker Panel (derived by Will Rossiter & Konstantinos Karagounis, Nottingham Trent University) datasets.

Notes: Local authorities in England. Composite measure applies 75% weight to English Index of Multiple Deprivation (2019) and 25% weight to our Real-Time Index.

What are the key challenges of policy formulation?

There are two main challenges for policy-makers seeking to direct the levelling up fund. First, at what scale should policy intervention be initiated? 

Earlier approaches to uneven economic growth from the 1980s onwards focused on economic differences at the regional level. In many ways, this made sense at the time given the profound restructuring of the UK economy associated with deindustrialisation, mostly concentrated in the North East, North West, South Wales and Midlands. These areas were associated with heavy industries such as coal in the North East and Wales, and steel on Tyneside.

But it is important to note that similarities in economic structures – reflected by measures such as ONS local authority supergroups – are no longer particularly geographically contiguous. Commuter towns in Warwickshire may have more in common with towns in Hertfordshire than they do with the wider West Midlands, for example. Coastal towns with high levels of deprivation are found in the North West (Blackpool) and the South East (Hastings). 

This suggests that measuring and targeting policy at needs among similar but geographically diverse local areas is more important than focusing on regional economies.

The second policy challenge for levelling up stems from Covid-19 and further amplifies the importance of locally targeted approaches. Policy interventions need to address the fact that longstanding patterns of economic inequality are now being overlaid with the locally uneven economic impacts of the pandemic. 

Where can I find out more?

• The Data-Driven Discovery Initiative (3Di) is a not-for-profit research institute at the University of Nottingham funded by the ESRC and established to advance real-time economic tracking using tracktheconomy.ac.uk. We collate economic data from a variety of public and private sources to provide a unique real-time picture of economic activity from the beginning of the Covid-19 crisis to today. 
• Experian is the world’s leading global information services company, focused on creating economic opportunities for individuals, businesses and society. 
• Fable Data is Europe’s leading provider of consumer spend data, delivering real-time, large scale, anonymised data on the performance of the economy into the hands of decision makers in business and government. 
• Huq Industries provides accurate, real-time mobility measurement data for local and central government, academia, retail, real estate and financial services.
• Decision Maker Panel: Monthly survey run by the Bank of England, Stanford University and the University of Nottingham providing insights into business expectations and uncertainty.

Who are experts on this question?

• Sarah Hall, Professor of Economic Geography at University of Nottingham
• John Gathergood, Professor of Economics at University of Nottingham 
• Benedict Guttman-Kenney, Economics PhD Candidate at University of Chicago Booth School of Business 
• Neil Stewart, Professor of Behavioural Science at Warwick Business School, University of Warwick 
• Paul Mizen, Professor of Monetary Economics at University of Nottingham

Authors: John Gathergood, Benedict Guttman-Kenney, Fabian Gunzinger, Sarah Hall, Benjamin Lucas, Paul Mizen, Edika Quispe-Torreblanca, Neil Stewart and Arif Sulistiono 

Acknowledgments:

The views expressed are the authors and do not necessarily reflect the views of Experian, Fable Data Limited or Huq. We are grateful to Experian, Fable Data and Huq for sharing these data for research and providing feedback. This work is supported by the UK’s Economic and Social Research Council (ESRC) under grant number ES/V00486/1 ‘Real-time evaluation of the effects of Covid-19 and policy responses on consumer and small business finances’. 

We thank William Rossiter and Konstantinos Karagounis at Nottingham Trent University for sharing their derived data and advice. We also thank David Phillips and Kate Ogden at the Institute for Fiscal Studies for helpful discussions related to this topic. We thank participants at the BEIS/ONS/NIESR/ESCoE ‘Data after Covid-19’ conference for their feedback.