Published on 21 March 2022
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Levelling Up for the long term

There is little continuity between what the new White Paper says and statements in existing Government economic policy documents, which does not bode well for the need for consistent and coordinated policies to tackle deep-seated and complex problems, say Professor Diane Coyle and Adam Muhtar.

The UK Government’s long-awaited Levelling Up White Paper sets out its flagship plan to transform the country by spreading opportunity and prosperity to all its localities, aiming to reverse long-standing geographic inequalities. But there is little continuity between what the new White Paper says and statements in existing Government economic policy documents, which does not bode well for the need for consistent and coordinated policies to tackle deep-seated and complex problems.

The Levelling Up White Paper contains numerous policies aiming to tackle a wide range of issues, from the distribution of R&D around the country to health inequalities. It represents these policies as the start of a “decade-long project to level up Britain,” (HM Government, 2022). Many of them are long overdue—if executed well, these policies have the potential to improve the lives of its beneficiary communities and improve many policy outcomes over the long term. 

 While the targets, or missions, set out in the White Paper are laudable, this is not the first time the Government has announced ambitious multi-decade long strategies, only for them to be scrapped only a few years down the line or left to silently fade away. We checked for consistency of the new White Paper with existing Government economic strategy documents.

In March last year, the Government announced Build Back Better: Our Plan for Growth, followed by the Innovation Strategy in July. Prior to these, the key document was the Industrial Strategy of November 2017, published in November 2017, and scrapped in the 2021 Budget. Like the Levelling Up White Paper, many of the policies announced in these documents either explicitly or implicitly span decades. 

This constant churn in policies runs counter to the need for consistency and coordination across government (Coyle and Muhtar, 2021; Industrial Strategy Council, 2020). In particular, policies aimed at tackling deep-rooted and complex structural problems such as regional inequality, or chronic under-investment in innovation need to span many authorities, departments and agencies, and need to be in place for long periods. There is rarely a quick fix and solving such challenges requires persistence. Policy consistency is also crucial for learning, as time is needed to assess what works and what does not. Consistency does not imply rigidity, as incremental improvements over time can be built into the policy process itself. This is better than dumping existing approaches and announcing sweeping policies following the ebb and flow of the political cycle. 

The cover image of the new Levelling Up White Paper is strikingly similar to that of the 2017 Industrial Strategy. But how much consistency over time is there in this suite of documents? We explored this by looking at what they say, the continuity of the language they use: how often do key terms from one recur in the next? We selected high-level terms and more detailed policy descriptors from the previous documents and checked how often they appeared in the Levelling Up White Paper. These included phrases such as ‘Industrial Strategy’ and ‘Plan for Growth’ and more detailed ones such as ‘National Productivity Investment Fund’ and ‘National Home Building Fund’.[1]

The figure below shows heatmaps of selected terms from past economic documents that are mentioned in the Levelling Up White Paper. The lighter the colour, the more frequent the mentions. The figures are mainly dark: the headline message is the absence of comparable language with even the current Government’s key economic documents from last year, still less comparability with the 2017 Industrial Strategy.

Looking at some of the detail, last week’s White Paper makes most frequent reference to the funds available—the Levelling Up Fund and Towns Fund for instance. A small number of terms referring to other policies set out in the Plan for Growth appear more than a small number of times: freeports, Shared Rural Network, Integrated Rail Plan, Net Zero Strategy. Most of the specific policies in the Plan for Growth do not recur, and ‘Plan for Growth’ is itself not mentioned in the Levelling Up White Paper.

Similarly with the even more recent Innovation Strategy, apart from that term itself, only ‘Strength In Places Fund’ and ‘Help to Grow’ appear more than zero times. This is surprising as a more even spread of R&D spending and innovation around the UK is a significant element of the Levelling Up White Paper. ‘Innovation’ is mentioned 169 times in 322 pages, and there are new terms such as ‘Innovation Accelerators’. Yet specifics in the Innovation Strategy document such as ‘National AI Strategy’ or ‘Business Innovation Forum’ do not get a mention.

Somewhat less surprisingly, (and despite their cover imagery) there is also almost no overlap in terminology between the 2017 Industrial Strategy White Paper and the 2022 Levelling Up White Paper: apart from ‘Transforming Cities Fund’, ‘Northern Powerhouse’ and ‘West Midlands Engine’ are the terms that recur most. Yet this applies also in a large degree to the sectors of the economy identified as priorities for policy, and one would not expect this to change much in five years. For example, AI was prominent in 2017 but far less so in the Levelling Up document.

It could be argued that this exercise overstates the lack of policy continuity. Sometimes the same kind of policy is simply relabelled to appear more novel than it is; and our analysis has not yet taken account of context. And the three older documents are all national policy statements so the natural emphasis could perhaps be different. On the other hand, the tools for implementing Levelling Up policies lie in the hands of national government – at least until some of the devolution measures it proposes are put in place. The Innovation Strategy and the Plan for Growth are the policy frameworks intended to implement policies across the UK. This makes the linguistic discontinuity striking, another illustration of the lack of coordination and consistency in UK economic policy.

Read Working Paper: You’re not speaking my language

[1] When extracting texts, case sensitivity and abbreviations for each term are taken into account to ensure all permutations of the words’ spelling are captured. Context of the term’s use are also accounted for to ensure that only the right instances are captured; for instance, the frequency count for the term ‘Industrial Strategy’ strips out instances where the phrase in is used in non-policy contexts, such as in the ministerial department name (Department for Business, Energy and Industrial Strategy), the minister responsible for the department (Secretary of State for Business, Energy and Industrial Strategy), or the oversight body (Industrial Strategy Council).


Coyle, D. and Muhtar, A. (2021) “UK Industrial Policy: Learning from the past?” Productivity Insights Paper No. 002, The Productivity Institute.

HM Government (2022) “Government unveils levelling up plan that will transform UK”. Available at

Industrial Strategy Council (2020) Industrial Strategy Council Annual Report 2020. Available at

The views and opinions expressed in this post are those of the author(s) and not necessarily those of the Bennett Institute for Public Policy.


Diane Coyle 2018

Professor Diane Coyle

Bennett Professor of Public Policy and Co-Director of the Bennett Institute for Public Policy

Professor Coyle co-directs the Institute with Professor Kenny. She is heading research under the progress and productivity themes. Biography Professor Dame Diane Coyle is the Bennett Professor of Public Policy...

Adam Muhtar

Affiliated Researcher

Adam Muhtar is a Data Scientist at the Bank of England, working on projects that aim to generate actionable insights from high-dimensional unstructured data using natural language processing, network science,...

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