Regional success after Brexit: The need for new measures

Hearne, David; de Ruyter, Alex Book — Published Version Regional success after Brexit: The need for new measures Brexit Studies Series Provided in Cooperation with: Emerald Publishing Limited Suggested Citation: Hearne, David; de Ruyter, Alex (2019) : Regional success after Brexit: The need for new measures, Brexit Studies Series, ISBN 978-1-78756-737-5, Emerald Publishing Limited, Bingley, https://doi.org/10.1108/9781787567351 This Version is available at: https://hdl.handle.net/10419/231286 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ REGIONAL SUCCESS AFTER BREXIT BREXIT STUDIES SERIES Series Editors: Alex De Ruyter, Jon Yorke and Haydn Davies, Centre for Brexit Studies, Birmingham City University, UK With the vote on 23 June 2016 for the UK to leave the European Union it has become imperative for individuals, business, government and wider society to understand the implications of the referendum result. This series, published in collaboration with the Centre for Brexit Studies at Birmingham City University, UK, examines a broad sweep of topics related to Brexit. It aims to bring together academics from across the disciplines to confront and examine the challenges withdrawal from the EU brings. The series promotes rigourous engagement with the multifaceted aspects of both the ‘leave’ and ‘remain’ perspectives in order to enhance understanding of the consequences for the UK, and for its relationship with the wider world, of Brexit, and aims to suggest measures to counter the challenges faced. Published Titles Alex De Ruyter and Beverley Nielsen, Brexit Negotiations After Article 50: Assessing Process, Progress and Impact Forthcoming Titles Arantza Gomez Arana, Brexit and Gibraltar: The Negotiations of a Historically Contentious Region Stefania Paladini and Ignazio Castellucci, European Security in a Post-Brexit World REGIONAL SUCCESS AFTER BREXIT: THE NEED FOR NEW MEASURES BY DAVID HEARNE AND ALEX DE RUYTER Birmingham City University, UK United Kingdom – North America – Japan – India Malaysia – China Emerald Publishing Limited Howard House, Wagon Lane, Bingley BD16 1WA, UK First edition 2019 © David Hearne and Alex de Ruyter Except where otherwise noted, this work is licensed under a Creative Commons Attribution 4.0 Licence (CC BY 4.0). Anyone may reproduce, distribute, translate and create derivative works of this book (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at https:// creativecommons.org/licenses/by/4.0/ British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-78756-736-8 (Print) ISBN: 978-1-78756-735-1 (Online) ISBN: 978-1-78756-737-5 (Epub) An electronic version of this book is freely available, thanks to the support of libraries working with Knowledge Unlatched. KU is a collaborative initiative designed to make high quality books Open Access for the public good. More information about the initiative and links to the Open Access version can be found at www.knowledgeunlatched.org v CONTENTS Lists of Tables and Figures vii About the Authors ix 1. Thinking Inside the Box: Defining the Problem 1 2. Thinking Outside the Box (Part 1): Real Living Standards 19 3. Thinking Outside the Box (Part 2): Real Labour Productivity 39 4. Policy Implications 75 Appendix 1: GDHI 99 Appendix 2: The EKS Method 100 Appendix 3: FISIM 103 Bibliography 107 Index 131 This page intentionally left blank vii LISTS OF TABLES AND FIGURES Tables Table 1. Types of Region 7 Table 2. Estimated Regional GDP Proportions 55 Table 3. Median Full-time Salaries (£) by Sector in 2016 61 Table 4. Estimated Regional PPPs 67 Table 5. The Impact of Different Rental Cost Deflators 70 Table 6. Per Capita Funding (£) for Transport and Education, UK Government Office Region (GORs) 78 Figures Figure 1. Comparative Economic Performance 17 Figure 2. Estimated Regional Consumer Price Levels 29 Figure 3. Real Regional Incomes in the UK 31 Figure 4. GDHI Per Capita in Combined Authorities (UK = 100) 32 Figure 5. Relative Costs of Gross Fixed Capital Formation 60 Figure 6. Estimated Absolute Lower Bound PPPs by Region 65 Figure 7. Relative Regional Productivity in the UK 71 4Regional Success After Brexit Although we know that regional imbalances in the UK span almost every domain, good policy requires more knowledge than this. In particular, it is necessary to quantify ‘success’ both in terms of living standards and the functional economic geography of an area. Existing measures fail to capture important aspects of both of these and the proposed ‘deflated’ measures can extend our understanding of these. This book therefore builds upon official data and international best practice to develop a series of measures with which to assess regional living standards and economic performance before exploring the ramifications of these in light of the UK’s vote to leave the EU. We begin by critiquing what has become the de facto measure of regional economic performance – GVA per capita – and draw upon existing research to do so. The main body of the book is concerned with deriving measures to best capture the true differences in both living standards and productivity across regions, particularly given that both academic evidence (Los et al., 2017) and a majority of experts believe that Brexit threatens to exacerbate these (De Ruyter, Hearne, & Tsiligiris, in prep.). Regional statistics in the UK do not take into account differences in the cost of living across the country. 5Defining the Problem This impacts a wide variety of measures including GVA, household incomes and wages. Happily, methodological developments over recent decades and the emergence of a greater variety of official data sources enable us to make an initial attempt to develop deflators to adjust for these issues. Although some of the methodological distinctions between different deflators are subtle, the overall issue and direction of adjustment is clear. This is key to developing appropriate policy measures, both to mitigate the impact of Brexit on more vulnerable regions and household and to address many of the insecurities and inequalities that played a factor in the vote to leave the EU. The final portion of the book therefore discusses the policy questions raised by these issues. Brexit affords an opportunity to reassess funding formulae and we argue that this must take the findings of this book into account. Particular attention needs to be paid to the likely evolution of regional policy and funding in the light of Brexit. 1.3 DEFINING THE REGION Recent years have seen a growing awareness of the importance of regional differences within the UK. Indeed, even the Chief Economist of the Bank of England has recently acknowledged the importance of regional differences across the UK economy (Haldane, 2018). It is clear that not only is the UK spatially unbalanced in an economic and social sense, but as continued interest in the so-called ‘West Lothian question’ shows, there is also a clear political imbalance between the devolved administrations in Scotland, Wales and Northern Ireland and the English regions. As noted by Benneworth (2006), there are historical antecedents to the present devolution agenda. Added to this is the 6Regional Success After Brexit need for a distinction between the region as an economic unit and the region as a facet of identity (Roberts & Baker, 2006). Indeed, the rise of a more assertive English identity that the Brexit vote has made clear (Henderson et al., 2016) could be seen as threatening this nascent regionalisation of politics. The overwhelming vote against a regional assembly in the North East of England in 2004 (Wood, Valler, Phelps, Raco, & Shirlow, 2006) might be seen in the same vein. Brexit itself exhibits a significant regional dimension with some recent research finding that regional differences in measured (psychological) character traits might have been important in the referendum (Garretsen, Stoker, Soudis, Martin, & Rentfrow, 2018). Nevertheless, in spite of the fact that regional identity in Britain remains somewhat inchoate, the fact remains that the region is often the more sensible level on which to carry out economic policy. In fact, identity in the UK is often local more than regional – witness the fierce rivalry between underland and Newcastle (those who ‘mackem’ vs. those who ‘tackem’) or Birmingham and the Black Country. This may, in part, be a result of the historic political centralisation of the UK which has seen regional boundaries adjusted numerous times over the past century without adequate study as to what the functional economic geography looks like (Roberts & Baker, 2006). We are left with three potential ways in which to ‘regionalise’ the UK. In practical terms, it is not feasible to use TTWAs as they presently stand. Their major attraction is that they potentially capture the economic geography of an area better than any alternative. Unfortunately, for our purposes the 75% threshold is probably not appropriate, particularly given that mean values can be significantly affected by the commuting patterns of a relatively modest number of high income individuals. Given this, their failure to align with any administrative or political boundary is also a disadvantage. 7Defining the Problem Table 1. Types of Region. Type of Region Definition Strengths Weaknesses Travel to Work Area (TTWA) Defined by the ONS as regions in which 75% of the economically active population work within the area and, simultaneously, 75% of the working population are also resident in the area. Captures areas with large commuting flows (such as Bromsgrove, Redditch and Tamworth into Birmingham). An official attempt to capture the functional economic geography of an area. Potentially understates the importance of commuting, particularly for high-income individuals. A 90% figure might be more appropriate. Watford, St. Albans, Basildon and Stevenage are all London commuter towns but are not within the London TTWA. TTWAs also have no political significance (unlike city-regions or even Local Enterprise Partnership (LEPs)), not least because commuter flows change over time. City-Regions Those areas which have become a ‘Combined Authority’. Many have elected a ‘metro-mayor’ plus London, Scotland, Wales and Northern Ireland. Areas of political significance with some powers over policy making. They also have an interest in local economic performance. This appears to be the level at which further devolution of powers is likely to occur. Leaves large swathes of the country unaccounted for. London and the devolved nations are different in size and nature to the city-regions. Ignores the impact of substantial inward commuting in most of these areas (and the potential for more of this). Some have sought to mitigate this (e.g. by having ‘observer’ councils from the surrounding areas). ‘Standard’ Regions (e.g. NUTS2) Standard statistical regions used by the EU when determining regional funding. Nowadays largely coterminous with official regions within the UK. A wider range of data is typically available, particularly for larger regions. As a result, practitioners are typically familiar with the units of analysis. The regions in question rarely overlap neatly with the economic geography of an area. These regions do not always overlap with the relevant political units (most notably Combined Authorities) either. Regional identity is often lacking (in contrast to, say, German Länder). 8Regional Success After Brexit Fundamentally, however, there is a relative paucity of data (particularly price data) on these areas, making them unusable for our purposes. The attraction of using city-regions lies in their political salience. The emphasis of the so-called ‘New Economic Geography’ on agglomeration chimes nicely with this political zeitgeist, even though this may be more relevant to present-day developing countries than the UK (Krugman, 2011). Indeed, although the benefits of agglomeration are considered axiomatic by some in the policy community (Swinney, 2016), the empirical evidence is far from incontrovertible. For example, Frick and Rodriguez-Pose (2018) find that small cities (up to 3 million inhabitants) are most conducive to rapid economic growth and some French data suggest that agglomeration effects are likely to be modest on a plantlevel (Martin, Mayer, & Mayneris, 2011). Indeed, although some have found that agglomeration might support productivity growth (Rice, Venables, & Patacchini, 2006), recent work suggests that historical development paths are crucial (Beugelsdijk, Klasing, & Milionis, 2018). Research suggests that, in the UK at least, the performance of cities and urban areas is intimately linked to the regions in which they are located (McCann, 2016). In addition, choice of residential location within a region (and the associated differences in cost) may in large part be due to differences in amenities offered. This, together with consumer preferences may partly explain differences between urban centres and their associated peri-urban areas and rural fringes. As a result, we initially consider differences at the level of nomenclature of territorial units for statistics (NUTS) Regions, before reconsidering the impact of our results at a more granular level. In doing so, we find some significant differences from published figures and suggest that this has salience for post-Brexit funding. 9Defining the Problem 1.4 EXISTING MEASURES – GDP AND GVA Gross domestic product (GDP) per capita (and its sister measure GVA per capita) has come to be widely used by academics and policy makers as a crude proxy for both living standards and economic performance. It has been widely criticised, not least because it ignores environmental degradation and resource use (Dasgupta, 2008). If used as a measure of welfare, GDP is not value free: it assumes that an additional £1 of income is worth the same whether it accrues to a multi-millionaire or someone who is starving. Nevertheless it remains widely used, in part because it is a well-defined measure and is highly correlated with other measures of wellbeing and progress (e.g. the human development index). Indeed, some have even argued that GDP per capita is a better measure of happiness than most alternatives (Dipietro & Anoruo, 2006), although this is far from a majority view. GVA (formerly known as GDP at basic prices), is equal to GDP but excludes taxes and subsidies. In spite of its problems, regional GVA per capita remains used in the policy community. The first part of this book draws upon the work of the ONS (Dunnell, 2009) and Gripaios and Bishop (2006), amongst others, arguing that GVA per capita is not a suitable measure of either regional productivity or regional wellbeing. The second part of the book develops official figures (including both regional GVA and regional gross disposable household income (GDHI) by assessing how subnational variations in purchasing power affect them. It investigates how this alters our perspective on relative regional performance. All of this has direct policy relevance for regional and national policy makers, particularly in light of Brexit. Areas with GVA per capita of below 90% of the EU average are eligible for higher levels of funding from the EU’s structural funds than those above this threshold (Department 10 Regional Success After Brexit for Communities and Local Government, 2014). In the UK, this includes a total of 13 regions (including Shropshire & Staffordshire in the West Midlands). Moreover, the present devolution agenda has meant that a number of local bodies have used GVA per capita as a yardstick against which they should be judged. As such, several LEPs have used it as a key performance metric in recent years (Greater Birmingham and Solihull Local Enterprise Partnership, 2016; Leicester and Leicestershire Local Economic Partnership, 2014). Similarly, the West Midlands Combined Authority (WMCA) uses GVA in its vision for 2030 – aiming for GVA per head of 5% above the national average. Indeed, the WMCA strategic economic plan explicitly states, ‘increased GVA provides evidence for real growth in the West Midlands’ economy’ (WMCA, 2016). GVA per capita was quoted in the Government’s Industrial Strategy Green Paper as a measure of productivity and thus as justification for the ‘essential’ process of rebalancing growth across the country (Department for Business Energy & Industrial Strategy, 2017). In the academic literature, spatial imbalances in the UK economy are widely commented on (Gardiner, Martin, Sunley, & Tyler, 2013; Martin, Pike, Tyler, & Gardiner, 2016; Rice & Venables, 2003), particularly in the fields of economic geography and regional studies. Even within the academic community, GVA per capita continues to be used as a shorthand for regional economic performance (see e.g. Huggins & Thompson, 2017; Ivanov & Webster, 2007; Lee, 2017). 1.5 COMMUTING AND ITS IMPACT Whatever its merits and demerits as a statistic when applied nationwide, GVA per capita is not well suited to regional analysis, particularly for geographically small regions. For 11Defining the Problem this reason, the ONS explicitly counsels against using GVA per capita (Dunnell, 2009). To see why, note that it divides the amount produced by those working in an area by the number of people living in an area. For somewhere like the UK with large flows of commuters, this can produce a seriously biased picture. A clear example of this relates to the comparison of strongly remain-voting Tower Hamlets in London and leave-voting Essex Thames Gateway (home to Basildon, Castle Point and Rochford). The former enjoys a GVA per capita almost 350% of the national average compared to the latter at just 72%. A superficial examination might, on this basis, suggest a relationship between incomes and the vote for Brexit. However, careful reflection of the data suggests that this might not be the case: residents in Tower Hamlets are only 18% better off than their counterparts in Essex Thames Gateway, suggesting that this effect is primarily due to commuter flows. This objection is not new: for over a decade, researchers have noted that commuter flows seriously impact GVA measures, particularly in London (Roberts, 2004). It is this that leads to GVA per capita in Westminster to be almost 800% above the UK average. In fact, GVA per capita is higher in Islington (represented by the constituencies of Jeremy Corbyn and Emily Thornberry) than in Kensington and Chelsea. Taken to its extreme, GVA per capita in the City of London is £5.2 million (with a population of circa 8,000 and a workforce of some 483,000). Previously measures of socalled ‘residence-based GVA’ were produced (ONS, 2017g). This attempted to allocate that portion of GVA attributable to wage earners to their region of residence rather than their region of work. The calculation as it stood led to regional output being apportioned on the basis of neither residency nor workplace but some conceptually unclear hybrid measure of the two. As a result, it is no longer produced by the ONS. 12 Regional Success After Brexit An obvious corollary of this is that these distortions have a real impact on EU funding flows. GVA per capita in parts of Outer London is far below the national average as a large number of residents commute into Inner London each day. Perversely, therefore, were EU structural funding to be reassessed now, some of the wealthiest parts of Europe (North and East London) would receive higher levels of structural funding than other (much poorer) regions. An attenuated version of this phenomenon is visible in the West Midlands: one of the reasons Shropshire and Staffordshire have such low GVA per capita is due to an outflow of commuters into the metropolitan area (and to a much lesser extent north into Cheshire). The UK’s exit from the EU potentially provides an opportunity to reassess some of these funding flows to ensure that they are targeted at the places (and people) that need them most. 1.6 DEMOGRAPHICS AND THE LABOUR MARKET Demographic factors can also have a notable effect on any figures compiled on a per-capita basis. Most obviously, economic output is generated by those in work. The presence of children and the retired in an area will thus increase the denominator without affecting the numerator. This will be true even if they carry out activity that is socially useful, for example, volunteering, that is, not captured by official economic statistics. Part of the confusion comes about because this is an acceptable practice on a national level. Germany and Japan, for example, struggle with rapidly ageing populations and pensions need to be paid by those still working. This may take the form of the return on assets acquired over a working lifetime or direct transfers but, in either case, effectively entails a transfer from the employed to the retired (Barr, 13Defining the Problem 2002). Crucially, in the absence of a large net balance of foreign assets, most of this transfer comes within countries. In contrast, on a regional basis, it is possible (indeed normal) for transfers to take place between regions. The Government may choose to tax workers in London in order to pay the pensions of those living in the South West. Alternatively, the workforce may use part of their income to purchase assets (a house, future pension investments, etc.) from those retirees who are moving to the South West. In either event, this involves a transfer of resources produced in London to be consumed in the South West. This process is both normal and healthy, but it has the effect of flattering the figures for London and depressing those for the South West. As such, whilst it is not true that GVA ‘excludes’ certain categories of income such as pensions as argued by Gripaios and Bishop (2006), it does measure economic output where it is generated rather than where the income flows to and is thus not a reliable measure of regional welfare. This effect is quantitatively significant: London has a considerably higher proportion of its population of working age than other parts of the country. Similar effects are visible in other cities. Moreover, this effect has intensified over the past two decades, accounting for a non-trivial portion of the growing disparity between London and the rest of the UK. In 1997, for example, 66.2% of London’s population was of working age, compared to 62% in the South West. By 2016, these figures had diverged to 67.9% and 60.9%, respectively. This has a non-trivial impact: it accounts for 17.8% of the disparity in GVA per head between these two regions (authors’ calculations based on ONS, 2017f, 2018a). Even accounting for these factors, however, important facets of labour market performance have a distinct impact on GVA. Lower employment in an area will, ceteris paribus, depress regional GVA. What’s less clear, however, is the extent to which this can be mitigated and whether attempting to do 20 Regional Success After Brexit regions. We draw on the latest data to develop and appropriate correction for this and demonstrate its impact. • Operating surplus – This addresses a further relatively minor technical issue in the regionalisation process used to account for imputed rent. • Inequality – GDHI takes no account of inequality and this section discusses the possible scale of the issue. • IMD – Here we briefly discuss the IMD, noting its advantages and disadvantages, and show that it requires similar corrections to GDHI in order to take account differences in the cost of living across Britain. 2.2 INTRODUCTION GDHI per capita is a measure of average regional living standards. It includes household income from all sources and, unlike GVA, is calculated on the basis of residency. Thus, if a pensioner living in Devon receives investment income that is ultimately generated by profits from a company in London then it is counted as disposable income in the South West. As such, it is a superior measure of average living standards, but should not be used to measure productivity, regional output or to describe the economic geography of an area. GDHI is defined by the ONS as being ‘the amount of money that individuals in the household sector have available for spending or saving […] after expenditure associated with income, for example taxes and social contributions’ (West et al., 2016, p. 34). The regional GDHI figures published by the ONS are compiled on a ‘top-down’ basis. In practical terms, this means that the ONS begins with national aggregates for each component part of GDHI and then uses a variety of indicators to apportion them in turn to each region. 21Real Living Standards GDHI does not map neatly to the ‘cash income’ of individuals as it includes implicit income, such as the implicit rent earned by owner–occupiers. In fact, this is a strength rather than a weakness – the fact that no money physically changes hands should not blind us to the fact that owner–occupiers receive ‘income’ in the form of not having to pay rent. In effect, they pay rent to themselves. A further major strength of GDHI per capita is that, unlike measures of deprivation, it includes the entire population in its scope. Whilst attention is rightly focussed on the very poor, a true measure of overall regional welfare should include those ‘just about managing’ (Parkinson, 2016), the middle classes and the well-off. Indeed, Sayer (2017) sees the focus on income and the ‘left behind’ as overdone (although the evidence of Becker, Fetzer, & Novy, 2017) seems to contradict this. GDHI does have major weaknesses. Firstly, like gross domestic product per capita, it tells us nothing about inequality. Most importantly of all, it measures income in purely nominal terms. Whilst it may appear sensible to measure income in terms of pounds and pence, the reality is that the cost of living varies enormously across regions. In many ways this is intuitively obvious: anyone who has spent time in both London and other parts of the country will be well aware of how much further your money goes in the latter. A classic example of this is the cost of an average pint of beer – in London this is £4.20, whereas in Herefordshire it’s just £3.31.1 2.3 PRICE LEVELS An accurate measure of regional living standards must adjust for these differences in regional price levels. In this chapter, we build on the methodology introduced in Hearne (Forthcoming-b) and use the very latest data to show that accounting 22 Regional Success After Brexit for different regional price levels more than halves the disparity between London and Yorkshire in 2016. This has very obvious ramifications for regional policy and particularly for post-Brexit funding flows. It also refines our existing perceptions of the UK’s ‘regional problem’ (Hardill, Benneworth, Baker, & Budd, 2006) and spatial imbalances and suggests both challenges to, and further scope to develop, the Government’s ‘Northern Powerhouse’, ‘Midlands Engine’ and ‘Industrial Strategy’ agendas. Interestingly, whilst the UK practice of using nominal data is standard within Europe, it is by no means universal internationally. The Bureau of Economic Analysis in the United States, for example, publishes estimates of regional price parities and finds that prices in the state of New York are 34% higher than those of Mississippi. The effects are dramatic: instead of New Yorkers being 69% better off than Mississippians, in real terms the gap is a less extreme 26% (Aten, Figueroa, Mbu, & Vengelen, 2017). Our work seeks to develop figures for the UK in line with this international best practice. The pattern of areas with high nominal incomes also experiencing higher than average price levels is well established, particularly within the academic literature on international economics (see Asea & Corden, 1994, for an overview). There is growing evidence that the same is true on a regional level, with examples as diverse as Italy (Nenna, 2001) and China (Jiang & Li, 2006), amongst others. It’s thus likely, prima facie, that the same is true within the UK. There is now a mature, high quality academic literature investigating regional differences and inequalities both within the UK and internationally (see McCann, 2016, for an in-depth treatment of the UK case, work from Beugelsdijk, Klasing, & Milionis, 2018, for an example of the Europe-wide debate and Lemoine, Poncet, & Ünal, 2015, for a discussion of the Chinese case). 23Real Living Standards Nevertheless, in spite of its importance, only a modest portion of this work has focussed on regional prices. This has not been lost on many observers: as Blien, Gartner, Stüber, and Wolf (2009, p. 17) note, ‘[T]hough the value of information on regional prices is obvious, there is a lack of empirical data in many countries’. Official interest in regional prices in the UK initially surfaced in the 1960s (Retail Prices Index Advisory Committee, 1971), but little was done. In fact, only with the advent of Eurostat’s need for Spatial Adjustment Factors did official attention return to the matter. In the interim, and particularly during the 1990s, academic attention was paid to the development of regional price indices. Regional prices diverged significantly over the course of the 1980s (Borooah, McGregor, McKee, & Mulholland, 1996), and this had a noteworthy effect on the spatial distribution real wages for both manual and non-manual workers (Martin & Tyler, 1994). Indeed, Johnston, McKinney, and Stark (1996) found that the cost of living in London went from being 5% greater than the UK average to 7.5% between the beginning and end of the 1980s. Hayes (2005) attempted to create a pure ‘price index’ and found that from 1979 to 1996, inflation across regions was highly correlated but that there was some regional heterogeneity. All of these authors made use of the regional price data collected by the Croner-Reward Cost of Living Surveys, which were the only data available on regional prices during the period in question. Today, we have the luxury of using official data, which whilst only collected every six years do have considerably larger sample sizes. The Croner-Reward data in question were discontinued and all the academic articles in question refer to prices in the mid-1990s or earlier. A second change over the past two decades is methodological. Much previous work (see e.g Borooah et al., 1996; Rienzo, 2017) drew on the methodology used by the retail 24 Regional Success After Brexit price index (RPI), which was at the time the gold standard measure of inflation in the UK. Today, the ONS has moved away from using the RPI as new, internationally comparable, measures of inflation have been developed which incorporate the latest developments and techniques. Deciding on the most appropriate method to compare prices across regions is unfortunately not as straightforward as it might at first appear. This is primarily due to the fact that not all prices differ by the same amount: the cost of broadband is broadly similar in London or Newcastle, but the cost of putting a roof over one’s head definitely isn’t. Londoners typically spend a greater proportion of their income on housing and live in smaller properties than those in the North East. This problem is compounded by the fact that consumption patterns can differ across regions. For example, the presence of a high quality mass transit network in London combined with high levels of congestion means that transport spending differs in both amount and composition (ONS, 2017d). We use the same methodology and data sources outlined in Hearne (Forthcoming-b). Since that work, however, the ONS have released relative regional consumer price levels (RRCPLs) for 2016 allowing us to provide an up-to-date assessment of relative regional living standards. The 2016 ONS RRCPLs are used as the base for our calculations. The ONS do not include housing costs in the RRCPLs, so our work needs to appropriately incorporate housing costs in order to assess the true cost of living in each region. Additionally, as outlined in Hearne (Forthcoming-b), our figures are compiled on the basis of the spending of residents of a region, whereas the ONS’ RRCPLs are compiled on the basis of what is spent in a region rather than on the basis of what is spent by residents of a region. As a result, they do not include money spent by residents outside of the region but they do include that spent by non-residents inside the region (e.g. by tourists). Whilst this is conceptually correct for their 25Real Living Standards purposes, it is not appropriate for assessing living standards. As a result, we adjust for both factors. As in 2010, a detailed breakdown of regional prices is not available at the division level (ONS, 2011). Two options are available at this point in order to operationalise our calculation of relative regional prices. The first is to assume that prices for all goods except housing are identical in English regions outside London (allowing one to use the detailed breakdowns available for Scotland, Wales, London and ‘Rest of England’). The second alternative is to use the aggregated figures available by region, but accept that division-level results are not available. As non-housing costs inside England (excluding the capital) appear to differ by over 5% the second approach seems the most sensible. We therefore break up each region’s total spending into five constituent parts: (1) Expenditure categories accounted for by the RRCPLs (this represented the majority of total consumer spending in every region – typically around 65%). (2) Expenditure on things whose prices were assumed not to vary across regions (primarily holiday expenditure). (3) Expenditure on privately rented housing. (4) Expenditure on socially rented housing. (5) Owner occupiers housing costs. Health and (private) education spending by consumers fell into the second category – prescription charges are uniform across England and prices for items such as glasses and contact lenses are unlikely to vary much. In the absence of any further data, private education and health care is assumed to be equally costly irrespective of location. This is unlikely to have a major effect on the total price index as both items combined account for around 2.5% of total spending in the UK. 26 Regional Success After Brexit Two main data sources are used. The living costs and food (LCF) survey is used to ascertain what proportion of total spending is accounted for by each category.2 In order to do so, two transformations are needed. The LCF survey gives an inventory of average total expenditure per household in each region. Unfortunately, not all forms of expenditure are relevant for the calculation of price levels. As a result, we exclude those things that are not relevant to a price index (specifically, mortgage interest payments, savings and cash transfers and gifts). Similarly, the housing services enjoyed by owner–occupiers are implicit rather than explicit. This is important: estimates of incomes and price levels need to be constructed on a systematic and consistent basis. In order to do so, it is necessary to distinguish the cost of putting a roof over one’s head (what we’re interested in) from the cost of buying a house as an asset. In essence, we need to split the ‘owner’ from the ‘occupier’. The conceptually correct way to do this is as follows: the occupant pays rent to the owner. As both are the same person in this case no money actually changes hands – the transaction is implicit. From the perspective of the occupant, the implicit rent paid can be thought of as the true cost of putting a roof over one’s head. As this is a service, it needs to be included in any measure of the cost of living. From the perspective of the owner, the rent received is part of the return earned from owning the asset (i.e. the property). This can be thought of as similar to the dividend from a share or coupon on a bond.3 The remainder of the return is the capital gain or loss realised upon the sale of the property. How much should this ‘implicit’ rental payment actually be? The obvious solution is also the correct one: the price of renting an identical property on the private market. Whilst, in practical terms, this is likely to be almost impossible for certain types of properties in some areas (e.g. the rental market for 27Real Living Standards four bedroom houses in most areas is rather thin), when comparing across larger regions this does not present a problem. The proportion of total expenditure accounted for by imputed rents can be estimated by multiplying total spending on gross rents in the LCF survey by: ÷ Proportionofowneroccupiersinregion  Proportionofrentersinregion The proportion of owner–occupiers by region can be ascertained from the Family Resources Survey (FRS). This also allows one to break down rents into the private and social renting sectors. The FRS was also used to estimate relative rental prices for both sectors (private and social rents). Private rents are the appropriate yardstick to use for the cost of owner-occupied housing. 2.4 OPERATING SURPLUS One additional, relatively minor, issue relates to the method used by the ONS for apportioning imputed rents. Gross operating surplus represents around 10% of primary resources in the UK as a whole (typically rather more in London and the South East) and ‘relates to the household sector’s rental income from buildings, including the imputed rental of owner–occupier dwellings’ (West et al., 2016, p. 36). As noted previously, regional GDHI is calculated on a top-down basis by allocating a proportion of each national component (operating surplus, mixed income, compensation of employees, etc.) to regions and then summing them. As the ONS points out, ‘[t]he national operating surplus total is regionalised using estimates of median property prices by region’. This approach implicitly assumes that regional rents are perfectly correlated with median regional property prices. 28 Regional Success After Brexit In theory this should be the case: property is an asset and if the returns on that asset are greater in one region than in another then there is a clear incentive to purchase property in the region which offers greater returns. Several factors, however, suggest that this may not be the case in practice. Firstly, rents from a property form only part of the expected returns – they are the running yield, with the remainder of the expected returns accounted for by anticipated capital appreciation. This speculation appears to have played a significant part in divergent trends in property prices in recent years. Secondly, imperfections in capital markets and frictions associated with buying and selling property may mean that any equilibrating forces act only slowly. Experimental statistics from the ONS suggest that house prices have diverged to a much greater extent than rents since 2010. In particular, between January 2011 and January 2018, rents in the North East increased by 4.3% compared to 23% in London (ONS, 2018c). In contrast, house prices increased by 5.1% in the North East and 69% in London over the same period (ONS, 2018h). As a result, using house prices rather than rents will lead to GDHI overstating improvements in living standards in London relative to those in the North East. Whilst the effects are modest relative to the changes induced by accounting for price differences, they compress the gap between London and the North still further. After recalculating the figures using the relative rents in the FRS, we find that Londoners are only 20% better off than their counterparts in the North East and those in the South East around 16% better off than the inhabitants of Yorkshire. Naturally, the FRS is not an infallible data source either: sample sizes are relatively small and relative regional rents vary substantially from year to year. Moreover, data from the Valuations Office Agency (2017) and Unison (2017) suggest that regional disparities in rents are significantly greater than the FRS would 29Real Living Standards indicate. As a result we are faced with an alternative – the ‘Operating Surplus’ portion of GDHI contains an implicit measure of relative regional housing costs. If we follow the regionalisation procedure outlined by the ONS (West et al., 2016) in reverse (by using data on housing stock by region) then we can calculate the relative regional housing costs implied by the GDHI. 2.5 THE RESULTS This completes work to find a set of appropriate expenditure weights for each region. We can then use the relative prices provided by the FRS or those ‘back calculated’ from the GDHI for housing costs and the RRCPLs for everything else. The Èltetö-Köves-Szulc procedure (explained in greater depth in Appendix 2) can then be used to calculate relative prices, based upon the five categories.4 Whilst in theory the slightly different aggregation procedure we use relative to the OECD (as a result of data limitations) could have an impact on the price levels we calculate, in practice the difference is likely to be tiny (and in all probability dwarfed by measurement error in the ONS surveys used). Fig. 2. Estimated Regional Consumer Price Levels. Source: authors’ calculations. 36 Regional Success After Brexit 2.9 CONCLUSION This chapter has shown that relative regional price levels have a considerable impact on regional incomes. This has important consequences given that local authorities with lower nominal per capita household disposable income tended to vote in favour of Brexit. The same pattern is visible for wages. It should also be absolutely critical in any future decision regarding funding flows, which is a theme we will return to in our policy chapter. This will certainly be true in a post-Brexit environment, but we argue that our evidence, combined with known flaws in the GVA per capita measure, should also cause the EU to fundamentally reassess its own structural fund and cohesion fund. The issues that exist in the UK are also visible across Europe, and many of the policy recommendations we make in Chapter 4 are likely to be applicable in a variety of European states. As such, although these recommendations are framed in terms of Brexit, they have a pan-European (and, indeed, international) dimension. Our evidence suggests that present allocation mechanisms are poor. However, given the present state of the data it is not possible to reliably determine if such funds are making a difference at the macrolevel. In the British context, London’s GDHI per capita has risen from 22% above the UK average in 1997 to 40% above the UK average in 2016. Is this due to improved economic performance (and can the rest of the country learn from it?) or does it merely reflect a rise in average prices (especially housing)? These questions matter, and they matter more than ever in a post-Brexit context. Improving measurement to the point of being able to offer longerterm solutions is a key area for future research and a major policy recommendation of this book. Moreover, returning to the theme of Rodríguez-Pose (2018) and others (Goodwin & Heath, 2016; Kriesi & Pappas, 2015): is the current wave of populism spanning the 37Real Living Standards globe, of which Brexit is just an example, a manifestation of regional policy gone wrong? Is the problem not so much one of individuals being ‘left behind’ but rather regions becoming ‘places that don’t matter’ (Goodwin & Heath, 2016; Kriesi & Pappas, 2015)? Data on household incomes can only partially answer this question: incomes may be related to where individuals live, but they also fundamentally reflect the characteristics of those who choose (or are compelled by either policy or a lack of wherewithal) to live there. The next chapter seeks to address this question more directly: how productive are different regions in the light of subnational price variations? The subsequent chapter then asks the crucial question: in light of both Brexit and this new evidence, what policy options can we take to enhance regional economic performance? NOTES 1. https://www.telegraph.co.uk/news/2017/09/07/london-nolonger-uks-expensive-place-buy-pint-beer/ 2. As is standard in such analysis (Deaton & Dupriez, 2013; Ley, 2005; OECD, 2012), we used plutocratic rather than democratic weights here (see Fisher & Fisher, 2005, for a discussion of the value-judgements implicit in the choice of weights). The resultant price levels are thus well suited to deflating GDHI per capita. The trade-off is that the weights may not mirror the experience of a ‘typical’ individual as they give more weight to those with higher expenditure (typically high-income individuals). 3. Obviously there are some stark differences – dividends can be altered or suspended at the directors’ discretion, whilst coupon payments are unchanging. Rent typically falls somewhere inbetween, with infrequent changes. The running yield on property is often higher than many other assets due to the costs and difficulties associated with purchase or sale of property as well as maintenance costs and the risk of non-occupancy, rent arrears, etc. 38 Regional Success After Brexit 4. Bilateral Laspeyres and Paasche indices are calculated for each region-pair (with the former being a weighted arithmetic mean and the latter a weighted harmonic mean), before computing a bilateral Fisher index (the geometric average of Laspeyres and Paasche indices) and using the modified Eurostat-OECD EKS procedure to obtain a series of transitive relative price levels. 5. This is admittedly a strong assumption, but is not unsupportable: local price variations mostly reflect differences in the quality of amenities. In contrast, price differences over larger areas mostly reflect differences in the cost of living. The litmus test is whether the labour market is largely self-contained: can one live in place A and work in place B? Groceries in town A may be more costly than in town B due to the fact that town A has a preponderance of Waitrose stores whilst town B is served by Lidl. As the two are proximate, those living in town A can shop at Lidl in town B (and vice versa). Although measured prices appear different, they are in fact identical. In contrast, a pint of (the same) beer is significantly more costly in London than in Yorkshire, even if the pub is otherwise identical due to differences in the cost of providing the service (staff costs, rents, etc.). It is not feasible to travel from London to Yorkshire simply to enjoy a cheaper pint! Measured price differences do indeed reflect real price differences in this case. The same logic applies for housing costs, etc. 39 3 THINKING OUTSIDE THE BOX (PART 2): REAL LABOUR PRODUCTIVITY 3.1 OVERVIEW In this chapter, we consider the impact of regional price differences on gross value added (GVA). We attempt to develop regional purchasing power parities (PPPs), focussing on the creation of both lower-bound and central estimates thereof. We conclude that nominal figures understate the size of the real economy in northern regions and commensurately overestimate the size of the economy in London. This has important ramifications for regional policy, particularly in a post-Brexit environment. Moreover, similar patterns are likely to be visible across Europe, suggesting that future European Union (EU) policy will also want to take subnational price-differences into account. There are strong policy implications from this chapter, which we explore in more depth in Chapter 4. 40 Regional Success After Brexit • Introduction – This section outlines the importance and appropriate uses of GVA and briefly discusses the regionalisation process adopted by the Office for National Statistics (ONS). • Price levels: As with other measures, GVA fails to adequately account for price differences across regions. – The theory of price-level comparisons: outlining the Eurostat-OECD methodology – From GVA to gross domestic product (GDP)… — A discussion of taxation and methods of apportionment — Setting upper and lower bounds… – Price comparisons in the household sector — Methods of apportioning household final consumption expenditure (HHFCE) — Calculating relative price levels (RRCPLs + rents – challenges re: national and domestic) – Price comparisons in the Government sector — Apportionment (easier?) — Calculating relative price levels (straightforward outside London but depends on London weighting and importance of wages) – Price comparisons for investment — Gross capital formation (GCC) and Gross fixed capital formation (GFCF): assume prices are constant for all industries except construction — Apportionment = data on construction AND remainder can use one of several methods (ONS data or DIY by industry?) 41Real Labour Productivity – How to treat non-profit institutions serving households (NPISH)? – Net exports • Technical Issues: Two further technical issues remain to be discussed – Financial intermediation services indirectly measured (FISIM) and imputed rents – FISIM – In Appendix 3, we outline why the size of the financial services sector is overstated and how this affects regional GVA – Imputed rents – Here we reprise the discussion of the previous chapter regarding the regionalisation of imputed rents and their implications for regional GVA • Putting it all together: Establishing credible upper and lower bounds for price levels and GDP. • Showing the impact on productivity. • Conclusion: Time to reassess regional success? 3.2 INTRODUCTION GVA can be thought of as a ‘pure’ measure of economic output1 and is also sometimes referred to as GDP at basic prices. Like GDP, it is a measure of the value added within an economy. However, whereas GDP measures value added at market prices (i.e. the price paid by the end user), GVA measures value added at the prices received by the producer. The difference between the two is therefore equal to the value of taxes less subsidies on goods. In the UK, the majority of this is accounted for by valueadded tax (VAT) with a lesser portion being accounted for by various duties (predominantly on fuel, alcohol and tobacco). The importance of GVA should thus be clear. On an official level, GVA per capita is used to determine eligibility for 42 Regional Success After Brexit EU structural funding. The UK Government’s Industrial Strategy Green Paper uses regional GVA per capita to illustrate the need for an industrial strategy with a spatial dimension (Department for Business Energy & Industrial Strategy, 2017). By the time of the publication of the White Paper, the UK Government was discussing regional differences in labour productivity directly – GVA per hour worked (HM Government, 2017). More broadly, GVA is used as a key performance indicator for many Local Enterprise Partnerships and is being used as one of several internal targets by some combined authorities (see e.g. WMCA, 2016). Similarly, in the academic literature, GVA growth disparities and differences in labour productivity (GVA per hour) are widely used (and conceptually correct) both as justifications and objects of research in their own right. Indeed, within the economic profession, productivity is widely regarded as the key determinant of long-run living standards (Krugman, 1997). Why does this matter beyond academic debate? Simply put, policy is made on the basis of these figures. As already discussed, they matter for funding allocations (particularly at an EU level) but they also influence policy in other subtle but important ways. If London’s price-adjusted productivity is lower than official figures suggest then it becomes extremely difficult to justify the comparatively high levels of spending on transport and education that the capital enjoys. Our calculations suggest that such monies might give a better ‘bang for buck’ (at least in productivity terms) if invested in the ‘Brexit heartlands’ of the Midlands and North of England – a theme we investigate in greater detail in Chapter 4. Figs. 5–7 also add nuance to the argument that the vote for Brexit was driven by relative prosperity, as can be seen in the results of the previous chapters. Here, we consider the most appropriate and feasible strategies for deflating regional GDP. 43Real Labour Productivity GDP can be calculated in three different ways, namely on the basis of income, output and expenditure. Whilst in theory all three should be equal, it is clearly impossible to measure every single aspect of the economy with perfect accuracy. As a result, the ONS uses a ‘balancing’ framework in which all three are constrained to be equal. This process takes the most robust elements of each of the three in order to ascertain the most accurate figures possible. At the time of writing, the balancing process typically takes place two years in arrears. Prior to this, the ONS uses information from each approach as it becomes available (one of the reasons why GDP figures are typically revised). Naturally, less information is available on a regional level. Accurately apportioning taxation to UK regions is extremely challenging. We also lack information on intra-UK exports and imports (e.g. goods or services produced in the North West but sold in the South East and vice versa). As a result, figures on regional GDP are not produced by the ONS. imports Subsidies GDP IIncomefromallsources Taxesonproduction & G DP OOutputofallindustriesVAT Othertaxes Subsidies G DP EHouseholdconsumptionGovernmentconsumption OtherconsumptionInvestmentExports Imports () () () = +− =+ +− =+ ++ +− What are available, however, are figures that exclude taxes and subsidies, that is, GVA. As can be surmised, GVA can be calculated either on the basis of the income method or on the basis of measured output (at basic prices). The Regional Accounts team use a ‘top down’ methodology to apportion GVA to each region (West et al., 2016). This is done for both the income and output methods and takes place by component, industry and region (West et al., 2016). In essence, the national totals are ‘regionalised’ using appropriate ‘regional indicators’ (West 44 Regional Success After Brexit et al., 2016). These include a variety of measures, although the majority of indicators come from direct surveys of businesses (particularly the Annual Business Survey, the Business Register and Employment Survey and the Annual Survey of Hours and Earnings). The results then undergo a complex balancing procedure in order to ensure the resulting figures are as accurate and robust as possible (West et al., 2016). 3.3 PRICE LEVELS The ONS therefore produce the best possible measure of nominal GVA given the constraints they face (both in terms of resources and due to the need to satisfy international and European standards). For many purposes, nominal GVA is indeed the appropriate measurement to use. Nevertheless, when assessing relative regional economic success, or relative productivity levels, it is real GVA (deflated by an appropriate PPP) that is needed. Crucially, whilst the ONS now produce estimates of real (as opposed to nominal) GVA growth over time, these are based on national deflators at an industry level rather than regional ones. The upshot of this is that, given that industry inflation levels don’t vary dramatically by region2 the real GVA estimates produced by the ONS are likely to be a robust way of comparing a given region’s economic performance over time. Unfortunately, they are not suitable for comparing the level of GVA across a set of regions at a given point. Given the absence of true regional industry-level price levels, it appears that calculating regional productivity adjusted for regional price differences is impossible. We argue that this is not the case. On the contrary, given appropriate assumptions, it is possible to develop a credible estimate of the lower bound for the impact of price differences on relative regional productivity levels. 45Real Labour Productivity One of the key empirical contributions of this book is to do precisely that. We then suggest a further set of assumptions to derive a preferred estimate of real regional productivity. It should be stressed at this point that these estimates should be seen as the beginning of a broader discussion of the issue rather than the final word. Further debate over the precise magnitude of the effect identified is to be welcomed and encouraged and future methodological innovations will hopefully enable researchers to capture it more fully. Nevertheless, our estimates undoubtedly represent a dramatic adjustment relative to the status quo, which does not adjust for prices at all. As mentioned above, given the absence of regional price levels by industry it is not possible to calculate real GVA directly. What can be done, however, is to use a variety of data sources to calculate estimates of PPPs for regional GDP. This is the approach used to compare real GDP across countries and has proved a rich source of information for macroeconomists concerned with differences across countries and over time. In this chapter, we build on the approach adopted by Eurostat and the OECD, although due to differences in the data that are available our results are not precisely comparable to theirs. On a cross-country basis, these effects are highly significant (even for countries that share a common currency). As an example, in pure Euro terms, France’s GDP per capita is a full 38% higher than Spain’s. In PPP terms, however, the gap falls to below 14%. In other words, most of the nominal disparity between French and Spanish GDP is purely due to price differences in the two countries. Since, for most purposes,3 when comparing areas we are interested in the amount produced rather than its price – it is the PPP-adjusted figures that should be of interest to us. As outlined in the previous chapter, we adopt the EurostatOECD Èltetö-Köves-Szulc (EKS) method to calculate PPPs for each Government Office Region. In order to do so, we 52 Regional Success After Brexit and services to households at prices that are not economically significant (or free). Examples include religious societies, clubs (including sports clubs), trade unions, political parties or organisations, etc. Charities tend to belong in this sector. Regionalising the spending of NPISH is extremely challenging. Given the absence of better data, we regionalise the nominal spending of NPISH by population, sourced from the ONS’ official population statistics (ONS, 2018a). Given the small size of the sector and the fact that in our present estimates we assume zero cost differences across regions in the sector, this is an acceptable compromise. The NPISH sector is an area where future research may seek to refine these estimates. 3.4.4 Gross Capital Consumption Gross capital consumption is primarily comprised GFCF (97%), plus changes in inventories and acquisitions less disposals. Given the minimal importance of the latter, we focus on GFCF and assume that changes in inventories plus acquisitions less disposals are proportional to GFCF. This is consists of transport equipment, other machinery and equipment (including information technology (IT) equipment), intellectual property, dwellings and other buildings. The ONS provide regional estimates of GFCF to Eurostat, although the ONS has serious concerns about the quality of data (ONS, 2017i). It is noteworthy that, for Scotland at least, these estimates differ substantially from those used in the Scottish National Accounts (Scottish Government, 2018). Nevertheless, they remain the best estimates that we have available at present. Of total UK GFCF, some £72,945m was on dwellings. Since total UK GFCF in the real estate sector was £91,536m, it’s clear that dwellings represent some 79.7% of total real 53Real Labour Productivity estate investment. In the absence of any indication to the contrary, we assume that this proportion is the same for each region. Transfer costs (which represent the bulk of the rest of real estate GFCF) and investment in equipment are likely to be proportional to spending on dwellings so this seems an eminently reasonable assumption to make.7 For all other industries, we use data from the supply and use tables (ONS, 2017e) to ascertain on a national level what proportion of GFCF was spent on inputs from the construction industry. This varies from 4% in the professional services and support industries to some 52% in ‘other services’ (which includes creative arts, libraries and museums, sports organisations, etc.). Once again, in the absence of any further information we assume that these proportions are equal in every region. Doing so we can divide capital expenditure into two parts for each region: the first being one in which prices vary (namely dwellings plus that proportion of GFCF spent on construction by industries other than real estate). The second part of capital expenditure is one for which prices do not vary. The relative weights for each region will differ due to differences in the industrial composition of regional GFCF. As a result, these weights can be used as an input into the EKS method. 3.4.5 Government Expenditure Government expenditure is regionalised by using figures from the Country and Regional Public Sector Finances (ONS, 2017c). This estimates UK government expenditure by sector for each country and region for the 2016/17 tax year (which is closest to the 2016 calendar year). As we are solely interested in government final expenditure, we exclude those categories of expenditure that pertain to transfers or intermediate consumption. 54 Regional Success After Brexit Doing so yields estimates for the entire UK that are extremely close to the ONS Blue Book estimate of government spending (ONS, 2017h). Similarly our estimates for Scotland are extremely close to the official figures given by the Scottish Government (2018), which gives a degree of confidence in the robustness of our estimates. At present, we lack comprehensive data on relative regional prices in the government sector and so there are no benefits to regionalising components of government spending at present. In future, the same source data are likely to prove useful in seeking to regionalise the various components of government spending. 3.4.6 Apportioning Regional GDP The table overleaf presents the results of this apportionment. To reiterate: =+ −GDP GVATaxesonproduction Subsidiesofproduction To recap, GVA is regionalised using the Regional Accounts data, whilst VAT (which comprises the bulk of relevant taxes) is apportioned from ONS estimates of regional VAT payments (ONS, 2017c) and the remainder from estimates of other taxes and subsidies on production (ONS, 2017c) HHFCE can be apportioned from either nominal GDHI data (ONS, 2018f) or the aforementioned VAT statistics. In either case the results are similar, but we prefer the former due to the greater stability of estimates over time. Output of the non-profit sector is regionalised using an estimate of population (ONS, 2018a), although this sector is small. Gross capital consumption is regionalised using data from Eurostat (2018b) on regional gross fixed capital formation (which forms around 97% of total gross capital consumption), whilst government expenditure is regionalised using data from the Country and Regional Analysis (HM Treasury, 2018; ONS, 2017c). Net exports are thus the residual 55Real Labour Productivity Table 2. Estimated Regional GDP Proportions. HHFCE NPISH Gross Capital Consumption Government Net Exports North East 73.1% 4.0% 20.8% 26.5% −24.4% North West 64.0% 3.4% 16.3% 21.7% −5.4% Yorkshire and the Humber 67.8% 3.7% 16.6% 22.8% −11.0% East Midlands 68.9% 3.7% 19.2% 21.0% −12.8% West Midlands 66.1% 3.6% 18.0% 22.0% −9.7% East 71.3% 3.2% 18.9% 18.3% −11.8% London 48.7% 1.7% 13.2% 13.0% 23.4% South East 66.8% 2.8% 17.5% 15.5% −2.5% South West 71.9% 3.4% 20.9% 19.7% −15.9% Wales 73.4% 4.0% 18.3% 27.8% −23.5% Scotland 64.3% 2.4% 18.4% 23.3% −8.5% Northern Ireland 67.5% 3.8% 17.7% 29.0% −18.1% See note8. 56 Regional Success After Brexit left over after completing this process. Table 2 shows what proportion of each region’s nominal GDP is accounted for by each sector. These weights are important inputs into the EKS process used to estimate real regional GDP later. 3.5 REGIONAL PRICES 3.5.1 The Household Sector This is both the most important and the easiest sector to derive prices for. We first note that, unlike for household incomes, GDP is calculated on a ‘domestic’ basis. In the absence of information on exactly how much is spent by consumers in each region, we use the data from the LCF survey to ascertain the proportion of total spending accounted for by each category. We adopt the same procedure as the previous chapter and utilise the same data sources. Indeed, the ONS RRCPLs are, if anything, more suited to this use (with the same methods and sources as we have). We then calculate the proportion of total spending accounted for by housing in the same manner as the previous chapter. Housing costs can be derived in one of three ways. Firstly, the costs of social housing are estimated directly from the Family Resources Survey (FRS). This is true across approaches. Private sector and imputed rents then both use the figures given in the FRS covering the 2016/17 financial year, which aligns most closely to the 2016 calendar year. These are the most conservative estimates of rental cost differences, showing that rents in London are 111% higher than those in the North East of England. There are good reasons to consider this a highly conservative estimate of London rents. Firstly, due to its comparatively small sample size, the figures for the FRS tend to 57Real Labour Productivity fluctuate quite significantly year-by-year. In the previous year, for example, renting in London appeared 140% more costly than in the North East. Indeed, the FRS data imply that rents in London fell by almost 7% between 2015 and 2016, contradicting evidence from the ONS’ own Index of Private Housing Rental Prices (ONS, 2018c). In addition, the FRS data apply to median rents, whereas for the purposes of deflating GVA, mean rents are the more relevant measure. The other potential option is to directly use GVA itself. This contains an implicit deflator because rental income (including imputed rent) is estimated for the real estate sector for each region as a component of regional GVA. Specifically, a component of GVA is the ‘rental income of households and NPISH’, which includes imputed rents. As is pointed out in the GVA methodology guides, these are regionalised using estimates of median property prices by region from ONS and the devolved administrations, these are multiplied by regional dwelling stock obtained from DCLG, the Welsh Government, the Scottish Government and the Department of Finance and Personnel Northern Ireland. (West et al., 2016, p. 15) Since the figures for regional dwelling stock are readily available from the Ministry of Housing, Communities and Local Government (formerly the Department for Communities and Local Government – DCLG), it is straightforward to derive implicit estimates of relative regional rents from the Regional Accounts. These show a rather wider spread of regional rents than the FRS survey (with implicit rents in London being around 3.4 times those in the North East). Interestingly, this is a broadly similar order of magnitude to figures from the Valuation Office Agency (2018) on regional rents in England. 58 Regional Success After Brexit 3.5.2 NPISH In the absence of further information, we assume that there are zero price regional price differences for NPISH. The NPISH sector comprises around 3% of GDP and principally contains institutions of higher and further education (universities and colleges), charities, trade unions, religious organisations and political parties. Given that these organisations do not charge market prices for their services, their output has traditionally been valued at cost (ONS, 2014), although the ONS is currently reassessing the classification of universities as a result of changes to the tuition fee regime (ONS, 2018b). It is likely that costs in London and the South East are at least as high as elsewhere since wages and salaries are higher in London and the South East than elsewhere (ONS, 2017b) and there is some evidence that commercial rents may also be higher in these regions (Colliers International, 2017). As such, we can be confident that our assumption of zero regional price differences for the NPISH sector is conservative. 3.5.3 Gross Capital Consumption We assume that there are no regional price differences for GFCF comprised transport equipment, other machinery and equipment (including IT equipment) and intellectual property. The law-of-one-price can be expected to apply to these, which collectively comprise around 44.1% of GFCF for the UK as a whole. A further 5.2% of national GFCF consists of ‘costs of transferring ownership on non-produced assets’ (overwhelmingly buildings), which again are unlikely to vary much by region. The remainder of GFCF represents buildings. The data we have indicate that the costs of construction are typically higher in London and the South East than elsewhere 59Real Labour Productivity in the country (Building Cost Information Service (BCIS), 2015). Specifically, we take the figures of the BCIS for 2015 (BCIS, 2015) as estimates of the relative cost of fixed capital in the form of buildings (whether residential or otherwise), with the exception of Northern Ireland where an unusually small sample size leads to estimates that are implausibly low (around half of the UK average). Estimates of relative construction costs range from 91% of the UK average in the North West to 112% of the UK average in London. Given known data on salaries (ONS, 2017b), these estimates are plausible and are the best data we have available to us at the present time. We use the EKS procedure as outlined above using the weights derived in the previous section together with the BCIS cost data for construction. As can be seen, costs vary relatively little across regions (partly by design). Nevertheless, there is a trend for higher prices in the South and East of the country (particularly in London and the South East). More puzzling are the above average prices of construction reported in the North East and East Midlands. It is unclear what might be driving this – it is entirely possible that measurement error in the source dataset is to blame, particularly as they are not official statistics. Nevertheless, the figures suggesting that investment is on average around 10% cheaper in Wales, Yorkshire or the West Midlands relative to the capital and surrounding areas is certainly plausible. This is one area that future work on regional prices may want to concentrate on, although the ultimate impact is likely to be modest (at least in the UK where gross capital consumption accounts for under 20% of GDP). 3.5.4 Government Expenditure Although major strides have been made to evaluate the output of government, this remains challenging (Pont, 2008). 60 Regional Success After Brexit In any event, national data are regionalised by the ONS using input costs – predominantly wages (West et al., 2016), suggesting that the appropriate measure of relative regional prices would be relative input costs. Outside of London, most public sector salaries are set on a national salary scale. Indeed, there are few data on relative prices in the government sector. Nevertheless, there exists a patchwork of rather partial information on prices of certain elements of government expenditure. In particular, large parts of the public sector have negotiated a ‘London weighting’, whereby employees are paid more if they are located in the capital. This applies to staff in the NHS and education (Unison, 2017). Similarly, the Annual Survey of Hours and Earnings (ONS, 2017b) suggests that public sector employees in London earn some 30–40% more than their counterparts elsewhere in the country. It is difficult to assess the extent to which this is due to the London weighting rather than the fact that higher managerial functions are more prevalent in London than elsewhere (particularly in the civil service). Equally, salaries in those parts of the economy that are dominated by public sector employees (notably health and education) Fig. 5. Relative Costs of Gross Fixed Capital Formation. 61Real Labour Productivity are around 20–30% higher in London than elsewhere in the country (ONS, 2017b). Outside of London, salaries for equivalent jobs are largely equal across the country (with a handful of minor exceptions in the East and South East in places that make up the so-called ‘fringe’ of Greater London). Given these very limited data, the best course of action open to us is to assess how robust our results are to a variety of different assumptions about the cost of providing government services. In particular, in our central scenario we assume that costs are identical across the country. For all regions apart from London this is a sensible assumption. Given the preponderance of ‘current expenditure’ in government spending (ONS, 2018d) and the fact that most public sector salaries are subject to a national pay scale, this is logical. Table 3. Median Full-time Salaries (£) by Sector in 2016. Region Public Sector Public Admin and Defence Education Health Care North East 27,341 27,140 28,389 27,126 North West 29,326 30,554 28,810 26,824 Yorkshire and Humberside 27,878 29,888 28,061 26,791 East Midlands 28,011 27,962 28,775 27,028 West Midlands 28,462 32,124 27,409 26,336 East of England 30,748 31,781 30,778 27,972 London 36,632 37,630 35,000 34,334 South East 29,896 31,020 30,824 28,177 South West 29,588 30,044 29,591 26,911 Wales 28,490 29,923 27,664 28,180 Scotland 30,886 31,062 30,992 30,372 UK 30,540 31,914 30,347 28,408 Source: Annual Survey of Hours and Earnings (ONS, 2017b). 68 Regional Success After Brexit productivity (GDP per hour worked) is smaller than that of nominal productivity. 3.7.1 Model 1: An Absolute Minimum Here we deliberately seek to underestimate the size of the effect to derive an ‘absolute lower bound’. We thus assume that GDP is directly proportional to GVA and measure housing costs using the FRS. We further assume that there are no price differences in any sector apart from the household one.10 It should be noted that if we are to deflate housing costs using the FRS then this should also be the measure used to estimate regional rents. For our absolute minimum we do not do this and therefore it should be noted that this model deliberately underestimates the price differences across regions. Simply deflating regional GDP without adjusting the imputed rents portion of GVA already leads to an increase of some 6% in GDP in Yorkshire and an 8% fall in London’s GDP. Indeed, even using this ‘absolute lower bound’ estimate significantly attenuates the productivity gap between NUTS1 regions and sees Scotland overtake the South East as the second most productive region in the UK. 3.7.2 Model 2: A Conservative Estimate This model uses the same formulation as above with one key difference that should make it a more accurate measure of real regional productivity. In particular, we continue to assume that prices are uniform across all parts of the economy apart from the household sector. The critical change pertains to the treatment of housing costs. Instead of using survey data from the FRS, we use the deflator directly implied from the GVA data on rents. We then compare this to an alternative estimate that involves reallocating rental income (including imputed rental income) to 69Real Labour Productivity UK regions using the FRS survey data (and data on total dwelling stock) before deflating this by the ‘absolute lower bound’ measure of prices calculated above. If one is to use the ‘lower bound’ figures as a measure of prices then this is the conceptually correct thing to do. Interestingly, the impact of using the FRSbased deflator combined with using the same source to allocate real estate rental income in GVA is almost identical to simply using the deflator implied by the regional GVA figures directly. As a result, we feel confident in using the latter for our estimates. 3.7.3 Models 3 and 4: Our Central Scenarios In model 3, we use the ‘conservative’ model above but add price differences in the gross capital consumption and government sectors. The methodology is outlined in the previous sections, but fundamentally, the difference between this and the more conservative ‘model 2’ are extremely modest. Our fourth and final model is somewhat more ambitious. Rather than simply assuming that regional GDP is proportional to regional GVA, we attempt to apportion VAT and other taxes/subsidies on production to different regions. To do so, the ONS’ estimates of Country and Regional Public Sector Finances were used (ONS, 2017c). These contain direct estimates of the VAT attributable to regions together with estimates of a number of other taxes (from which taxes on products can be isolated and summed). It should be noted that all of these data are experimental estimates. 3.8 CONCLUSION: TIME TO RE-EVALUATE REGIONAL SUCCESS? As can be seen, the overall impact is to significantly attenuate estimates of productivity differences across the UK. Even the 70 Regional Success After Brexit ‘absolute lower bound’ with its deliberate underestimate of price differences accounts for about half of the total impact. Using a more realistic conservative estimate accounts for a further quarter of the total effect. As such, estimates of differences in the cost of government expenditure and gross capital consumption largely amount to little more than tinkering around the edges. Attempting to apportion VAT and other taxes to regions takes a further bite out of London’s dominance (largely because in the EU most VAT is assigned to the government of the place where consumption occurs rather than where production occurs), although the absence of good data means that any realistic attempt to do so entails a degree of guesswork. Several other factors stand out. London remains the most productive region in the UK by a significant margin. Table 5. The Impact of Different Rental Cost Deflators. GDP Impact (FRS Deflator Only) GDP Impact (FRS Deflator & FRS-based Imputed Rents) GDP Impact (GVA-based Housing Costs) North East 5% 8% 8% North West 5% 5% 5% Yorkshire & Humberside 6% 7% 7% East Midlands 5% 7% 6% West Midlands 3% 5% 5% East 1% 2% 2% London −8% −12% −11% South East −2% −3% −3% South West −1% 2% 1% Wales 6% 9% 9% Scotland 4% 5% 5% Northern Ireland 8% 9% 10% 71Real Labour Productivity The ranking of various regions also changes – Scotland overtakes the South East of England to become the second most productive region in the UK whilst Wales and Northern Ireland are within 2% of the South West in any sensible scenario. Indeed, even in our conservative scenario, the South East is just 5% more productive than the North West in real terms, as opposed to some 15% when measured in nominal terms. This should cause us to fundamentally reassess our perceptions of regional economic differences in the UK. Rather than an unproductive North and a hyperproductive South, London and Scotland stand out. Indeed, in contrast to traditional perceptions the UK appears to have a ‘hollow middle’ alongside Wales and Northern Ireland. These are all areas that have been hit particularly hard by de-industrialisation. Indeed, this further reinforces the work of Beatty and Fothergill (2017) suggesting that not only do areas that experienced large-scale job loss in the 1980s and 1990s still have higher rates of worklessness, but that they might also pay a price in terms of productivity. We would tentatively suggest that Fig. 7. Relative Regional Productivity in the UK. 72 Regional Success After Brexit the strong performance of Scotland is possibly an indication that, done right, devolution can have a significant positive impact on productivity. A considerable amount of space has been devoted to questions over the measurement of an issue that should prove important to all involved in regional policy post-Brexit. To reiterate the point made at the outset – we need price-adjusted measures in order to better assess what regions need to do in order to respond to the Brexit vote. The record of the ONS in producing high-quality statistics is exemplary and in many areas they are world leading and their data underlies all of the estimates derived in this book. Unfortunately, nominal statistics on regional incomes and GVA, whilst very high quality and extremely useful for many tasks need to be complemented by real (price-adjusted) measures in order to assess regional success and failure.11 Now that we have considered two of the largest facets of regional economic performance, namely how much real disposable income residents have and how productive its workforce is (again in real rather than nominal terms), we are in a strong position to re-evaluate regional disparities. At this point, we are therefore able to consider the policy ramifications of our findings and what this means for regions in the light of Brexit. These crucial (and fascinating) issues are what the remainder of the book is devoted to. NOTES 1. It is ‘gross’ in the sense of not making any allowance for depreciation. 2. The notable exception to this is the real estate industry (SIC2007 code 68) where housing costs have increased more rapidly in some regions than others. 73Real Labour Productivity 3. There are notable and important exceptions to this rule. When calculating government debt (or the deficit) as a proportion of GDP, for example, it is nominal figures that are of interest. 4. Nevertheless, a great deal of interesting research has been done recently on input–output models, from global models (Steen-Olsen et al., 2016) to subnational analyses (Kim, Kratena, & Hewings, 2015). The work of Los, Timmer, and de Vries (2016) is important in being able to use such tables to understand the value-added component of gross exports. 5. There are some interesting exceptions to this rule, particularly for businesses that sell only a small amount in the second country (below the VAT threshold, even if they are above the VAT threshold in terms of the goods they sell in their own country). In addition, selling to businesses in a second EU country that do not possess a valid VAT number typically involves levying VAT at the rate applicable in one’s home country. The interested reader is referred to Your Europe (2018) for further details. 6. There is no evidence that individuals in and around London are running down their stock of wealth more rapidly than those elsewhere to finance a more lavish lifestyle. 7. It is possible that transfer and equipment costs are broadly constant on a per-dwelling basis rather than a total expenditure basis but this is likely to have almost zero impact on our final estimates in practice. 8. It should be noted that these proportions are heavily affected by the very same factors that make GVA per capita a problematic measure. They are thus heavily distorted by tourism (including domestic tourism) and commuting. Any money spent by commuters in their place of work counts as an ‘export’ from their work region and an ‘import’ to their home region. The result is that, for example, any tube fares purchased by a commuter from Watford count as an export from London to the East of England as would any meals, coffee, etc., purchased in their workplace. Pensioners have a similarly distorting effect: a region full of retirees will produce very little measured economic output, but its consumption will be substantial. The inevitable result is that net exports will be negative. A progressive tax and benefits system that redistributes 74 Regional Success After Brexit from high income earners to those on lower incomes will have a similar effect. 9. A corollary of this assumption is that the apportionment of other expenditure components across regions is irrelevant. They all have the same relative price levels and can therefore be treated as a homogenous ‘lump’ (whether comprised government expenditure, investment, net exports, etc.). 10. In other words, we assume that there are no regional price differences in the not-for-profit sector or for government spending or investment (including building). This generates a deliberate underestimate of price differences, suitable for calculating a lower bound. 11. The absence of rigourous, timely and complete measures of regional prices (plus the absence of any measure of regional imports and exports or, alternatively, regional price levels by industry) that would meet their exceptionally high standards is one probable reason why the ONS does not produce the statistics that we have attempted to. Freed of the need to be 100% accurate and produce a single reference estimate, together with a little ingenuity, we have been able to posit a range of numbers within which we can be relatively confident the actual answer lies between. 75 4 POLICY IMPLICATIONS 4.1 OVERVIEW It is clear that these findings have major policy implications and work will need to be done to understand these further. This chapter is a first attempt to tease some of those fundamental policy points out and to steer the emergent debate. Brexit (at the time of writing) is an omnipresent issue in this context.1 We have argued that real (price-adjusted) figures, as calculated in the previous two chapters, are indispensable for policy makers who wish to address the regional divides so powerfully evident in the Brexit vote. Indeed, use of nominal figures can distort funding flows: London’s high transport spending (covered later in this chapter) might be understandable in light of its high nominal productivity. If, however, we adjust for price differences (as economic theory and international comparisons suggest we ought) then this disparity becomes much harder to justify. As such, our results have significant implications for the geography of productivity and incomes and this will affect funding flows and other appropriate policies. Brexit presents some opportunities to adjust some of these but also major industrial challenges (Bailey & De Propris, 2017; Chen et al., 2018). 76 Regional Success After Brexit As such, we also discuss the ‘geography of discontent’ linking regional economic development and the Brexit vote (Los, McCann, Springford, & Thissen, 2017). It is perhaps no accident that the NUTS1 regions that voted most heavily to leave the European Union (EU) are those where we find priceadjusted productivity to be lowest. In order to do this, the chapter is structured as follows: • Introduction and outline – This section introduces the reader to some of the major policy implications of our work, noting the existing policy environment. • The current spending bias towards London and the South East – Here we examine the extent to which London and its environs dominate national infrastructure spending, noting that our figures imply that rebalancing towards the regions would benefit the UK as a whole. • The Brexit overhang – This section explicitly examines the likely post-Brexit funding environment and considers what an optimal funding mechanism might look like. • The case for ‘meaningful devolution’ – In this section, we examine the role of devolution in regional economic performance, making the case for greater devolution of powers. • Moving beyond ‘people versus place’ – Here we outline how the academic debate needs to move on in light of our revised figures. • Conclusion. 4.2 RECAP We commenced this book by noting the nature of regional disparities and how they are conventionally treated by 77Policy Implications government bodies. In so doing, we noted how gross value added (GVA)-based measures (particularly GVA per capita) were (and continue to be) key metrics by which such disparities are calculated, and how the Government’s Industry Strategy Green Paper made reference to them. Our research findings have highlighted that measures traditionally used in the allocation of regional funding may distort funding flows. Particularly egregious is the ongoing use of GVA per capita, despite the fact that the ONS (Dunnell, 2009) and Gripaios and Bishop (2006), amongst others, have demonstrated that it is not a measure of either regional productivity or regional wellbeing. In addition, commuting and demographics both grossly distort GVA per capita when measured on a subregional level: GVA per capita is higher in Islington (represented by the constituencies of Jeremy Corbyn and Emily Thornberry) than in Kensington and Chelsea. By using a variety of official data, we constructed a series of different regional price indices suited to different purposes in order to show that some of the gaps between different parts of the UK are narrower than hitherto believed. However, this was not to suggest that regional disparities are trivial or non-existent. Indeed, we also found that whilst the relative positions of different regions changed dramatically, gaps in living standards remain substantial. We found that the poorest region is not in the North of England, rather it lies in the old industrial heartlands of the Midlands. In contrast, we found that Scotland overtook the South East of England in terms of productivity (and was only marginally behind in terms of incomes). This is extremely noteworthy given the geography of the Brexit vote: Scotland voted heavily to remain whilst the West Midlands showed the highest leave vote in the country (closely followed by the East Midlands). However, given the nature of the data that has been used in terms of assessing regional performance, it should not be surprising that funding has – we would argue – disproportionately 84 Regional Success After Brexit Our analysis has already shown that these regions outperform their ‘official’ productivity: how much better could they do with the kind of funding received by London and the South East (or even Scotland). Given this, it’s hardly surprising that such regions are both amongst the poorest in the country and those that voted most strongly for Brexit. 4.4 THE BREXIT OVERHANG: WHAT MECHANISM FOR FUNDING ‘THE REGIONS’ AFTER BREXIT? And it is to the immediate Brexit context that we now turn. There has been a lively debate in the literature over the effectiveness of EU structural funding in the UK and elsewhere (Becker, Egger, & von Ehrlich, 2010). Currently, the UK benefits from EU regional funding, under the premises of the European Structural and Investment Funds (ESIF). Under this regime, approximately half of the UK share of ESIF over 2014–2020 (approximately £24 billion) were allocated to areas that are identified as ‘less developed’ or ‘transitional’ (Bentley, 2018). In light of this, it is noteworthy that Bachtler (2017) argued that such EU Structural Funding has provided a long-term anchor for policy. However, as noted, eligibility for structural funding correlates poorly with many measures of deprivation. Our analysis has argued that the measures underpinning this funding allocation are flawed. A key finding here was that regions such as Shropshire and Staffordshire qualify for higher levels of funding on the basis of their GVA per capita but this ignored the household income (gross disposable household income, GDHI) side of the story – to reiterate, what we regard as a better measure to calculate deprivation. In this context, poorer areas such as the Black Country in the West Midlands were eligible for less money on the basis of having a GVA per 85Policy Implications capita that exceeded EU thresholds. Yet, Brexit premises that this funding regime will no longer be applicable to the UK and thus offers the opportunity to revisit funding formulas. This is of particular importance in light of findings that the effectiveness of cohesion spending is critically dependent upon the proper identification of specific regional needs (Crescenzi, Fratesi, & Monastiriotis, 2017). Thus, whilst Brexit poses acute economic challenges, particularly in light of evidence suggesting that it might have starkly divergent regional impacts (Chen et al., 2018), it also presents opportunities for more appropriate targeting of regional policies (particularly in light of the devolution agenda). With this in mind, the UK Government proposed a ‘Shared Prosperity Fund’ (BBC, 2017a) to substitute for the monies allocated under EU regional funding schemes. The Shared Prosperity Fund was first proposed by the Conservative Party in its 2017 Election manifesto (Conservative Party, 2017, p. 30) and described as a fund ‘taken from money coming back to the UK as we leave the EU, to reduce inequalities between communities in our four nations’. In particular, that: [t]he money that is spent will help deliver sustainable, inclusive growth based on our modern industrial strategy. We will consult widely on the design of the fund, including with the devolved administrations, local authorities, businesses and public bodies. The UK Shared Prosperity Fund will be cheap to administer, low in bureaucracy and targeted where it is needed most. (Conservative Party, 2017, p. 35; our emphasis) Inferred from the above is that somehow EU funds are expensive to administer and highly bureaucratic. This does raise the issue of how one would administer such monies in a ‘streamlined’ manner. However, the more substantive issue for 86 Regional Success After Brexit us is that of how areas with the most ‘need’ would be identified. Our analysis has suggested that measures such as real GDHI per capita (with our estimated regional price levels) would be better in this regard to a replacment ‘social fund’. However, the thresholds relative to mean earnings would be contingent on the monies available. The nature of targetting priority areas in itself might be influenced by other factors. Should ‘deprived’ areas be prioritised, wherever they are in the UK? Or, to reiterate, is there a case that the Core Cities outside of London, which our analysis suggests have performed better than conventional measures depict, be given favourable treatment as nascent ‘agglomeration economies’ in themselves? The above notwithstanding, practical discussion on the implementation of a new funding regime to compensate for the loss of ESIF post-2020 has been muted (Huggins, 2018). As Huggins notes, the launch of the UK Government’s industry strategy paper in November 2017 provided an opportunity to substantiate the nature of the Shared Prosperity Fund, but very little detail was provided (Huggins, 2018, p. 144). Thus, even as late as August 2018 (at the time of writing) a high degree of uncertainty remains as to regional funding levels after Brexit. In a sense, this should not be surprising. We would argue that this is so for a number of reasons. First, the obvious caveat is that any post-Brexit regional monies will be contingent on the size of the ‘divorce bill’ to be paid to the EU.4 Second, given that the vast consensus of the economic impact of Brexit is that it will result in foregone revenue to the UK Treasury, there will be added impetus for Treasury to ‘claw back’ these monies (reinforcing long-standing practices in this regard). In addition, the thrust of regional policy in England in recent years having been for greater centralisation and control, we have little reason to expect that the fund will have any substance to it. Finally, there remains the question of how much money would 87Policy Implications actually be ‘new’, rather than recycled from existing funding tranches. European regional funding takes place on a matched basis by the UK Government (Huggins, 2018) so in a very real sense, on current allocations, half of any incipient Shared prosperity fund (SPF) would consist of moneys already allocated. The implication of the above is that there is a risk that any post-Brexit regional funding settlement will be distinctly lacking in any real semblance of ‘regional rebalancing’. Indeed, infrastructure concerns continue to be dominated by projects proposed for London and the South East (more narrowly defined here as a London–Cambridge–-Oxford ‘golden triangle’), as HS2, Crossrail, Heathrow expansion, and the advocacy by the National Infrastructure Commission and the Highways Agency for a new expressway between Oxford and Cambridge to service a million additional homes (Monbiot, 2018) show. That such proposals appear a fait accompli, without any significant economic or social justification, or democratic debate (Monbiot, 2018), in turn only serves to reiterate the urgent need to address the regional balance of power, and resourcing in the UK. It is to this that we turn in the next section of the case for ‘meaningful devolution’ for the English regions, to match those of the devolved nations. 4.5 THE CASE FOR ‘MEANINGFUL DEVOLUTION’ The creation of the Scottish Parliament and Welsh Assembly in 1998 arguably marked the most significant reconfiguration of powers in the UK since its creation in 1707, as a ‘historically centralized “union state”, […] which recently has been transformed by processes of devolution’ (Pike & Tomaney, 2009, pp. 18–19). Scotland has a devolved administration in the form of the Scottish Government, which has a clearly defined role and is widely considered to be democratically 88 Regional Success After Brexit accountable to the public, as evidenced by steadily increasing voter turnout at Scottish elections since 2003 (Aiton et al., 2016). In contrast, since the 1970s, domestic regional policy across the UK has been progressively reduced in its scope of operation (Bachtler, 2017). Over the past decade, regional governance arrangements in England in particular (excepting London) have become highly fragmented and particularly prone to the vagaries of policy changes. This has seen the abolition of Regional Development Agencies, the creation of Local Enterprise Partnerships (LEPs), Combined Authorities, various ‘enterprise zones’ and the emergence of the ‘Northern Powerhouse’ and ‘Midlands Engine’ brands (Bachtler, 2017; Bentley, Bailey, & Shutt, 2010). Unlike the Scottish Parliament, these opaque entities have struggled to secure legitimacy in the eyes of a sceptical public struggling to understand their functions and perceiving them primarily in terms being unelected and hence unaccountable.5 This is of concern given that a wide and growing range of literature suggests that governance and institutional quality are crucial factors in regional development (Bachtler & Begg, 2018). However, as Bentley (2018) notes, the discussion of devolution for the English regions in Westminster policy circles has abated: ‘[w] ith all attention on Brexit, the drive for devolution has waned’ (Bentley, 2018). There is thus a clear need to move beyond the current limited debate on devolution and ‘regional rebalancing’, and actually embrace a new constitutional settlement for the UK (Budd, 2018) with attendant transfer of resourcing decisionmaking ability. Indeed, if the UK is to survive as a political entity post-Brexit, we would argue that this is essential. For us, perceptions of social exclusion, or otherwise ‘being left behind’ – particularly in the ‘Missing Middle’ of the UK – could be related to a lack of suitably devolved governance 89Policy Implications arrangements within England (and Wales, to a degree). As such, Brexit poses a challenge to traditional notions of governance (Stoker, 1998), in that governance ‘is ultimately concerned with creating the conditions for ordered rule and collective action’ (Stoker, 1998, p. 17, emphasis added). Collective action in itself though requires some semblance of community and solidarity in the pursuit of basic shared objectives (Ostrom & Ahn, 2009). Hence, if the inequalities brought into sharp focus by Brexit are to be addressed, then ‘meaningful’ devolution cannot be done in a ‘top-down’ or ‘dirigiste’ manner, but rather, must engage directly with voters in order to overcome a perceived democratic deficit. What would ‘meaningful devolution’ look like, then? Ideally, we would suggest that such a devolution would be one characterised by governance arrangements whereby voters in a given (e.g. English) region directly elect representatives to make decisions on resourcing priorities for their area. Against this we would stress that the English experience with regional devolution has not given cause for optimism in this regard, having noted the failure of a referendum on the creation of an elected regional assembly in the North East of England in late 2004 (Pike & Tomaney, 2009, p. 24).6 Indeed, English identities seem firmly rooted in local orientations, rather than regional ones, as our own focus group studies under the auspices of the ‘CBS Roadshow’ also have demonstrated (De Ruyter et al., Forthcoming).7 As the experience with Metro Mayors has shown, local parochialism is difficult to overcome in trying to consolidate governance mechanisms at the regional level. We are then left with the somewhat problematic thought that some element of top-down imposition is required to enact such structures, rather than relying on the local ‘democratic will’. However, further research is needed in this regard to ascertain what the ideal form of regionally devolved governance in England would be. 90 Regional Success After Brexit However, the level of governance notwithstanding, given the manifest disparities in funding between London, Scotland and the English regions, a necessary reform would be an updated funding formula to equalise per capita funding across the UK at the GOR level of public services and infrastructure (see Forrest et al., 2017, for a discussion of this). Of course, the question remains as to which policies should be devolved to a regional level. For this we need to revisit the debate between ‘people versus place’ policies, in order to arrive at a better understanding of what the optimal scale (national/regional/ local) for policy design and delivery should be. 4.6 MOVING BEYOND ‘PEOPLE VERSUS PLACE’: PUTTING IT TOGETHER IN A POLICY FRAMEWORK The argument over whether policies to help those living in lagging regions should be ‘place based’ or ‘people centred’ has a long academic pedigree (Barca, McCann, & RodríguezPose, 2012). Divergent views are clear internationally, with some arguing of ‘the futility of providing economic incentives for staying and striving in lagging regions’ (Gill, 2010) and that institutions should be ‘space-blind’ (World Bank, 2009). Indeed, the World Bank (2009) goes so far as to argue that economic growth ‘will be unbalanced’ (World Bank, 2009). As such, this view has been criticised on the grounds it ignores sociospatial factors (Murphy, 2011). In contrast to the ‘people-centred’ vision espoused by Gill (2010), others argue in favour of a ‘place-based’ approach to policy (Bentley & Pugalis, 2014). As noted by the latter researchers, in the post-war period, ‘local development policies veered between being targeted interventions intended to improve property (e.g. business accommodation, housing stock, etc.), and those intended to improve people 91Policy Implications (e.g. workforce skills, education, etc.)’ (Bentley & Pugalis, 2014, p. 286). Nevertheless, since the advent of the neoliberal consensus in economic policy from the premiership of Thatcher onwards, policy interventions in the UK have overwhelmingly been people-focussed. In recent years, even industrial policy has become more centralised (Peck, Connolly, Durnin, & Jackson, 2013). Indeed, even many innovations that ostensibly have a regional focus, of which LEPs are probably the best example, have ultimately ended up being delivery vehicles for an increasingly centralised set of policies (Bailey, 2011; Bentley et al., 2010). It is clear from many of the ‘strategic economic plans’ adopted by LEPs that most focus on the same handful of sectors that happen to be ‘in vogue’, almost irrespective of their real local strengths (Swinney, Larkin, & Webber, 2010). Further academic critique has focussed on the ‘missing middle’ in terms of regional policy (Bentley et al., 2010), together with their lack of financial firepower (Bentley et al., 2010) and the absence of statutory ‘teeth’ (Pugalis, Shutt, & Bentley, 2012). It is ironic that the intellectual underpinnings of this consensus stem from traditional economic orthodoxy: the primacy of the neoliberal agenda coincided with economic orthodoxy moving decisively away from many of the intellectual foundations that it formed. This was perhaps most obvious in the development of the new Keynesian school of macroeconomics, which has come to dominate modern DSGE8 modelling (Stiglitz, 2018). In the same vein, there can be no doubt that the ‘new economic geography’ associated with Krugman (1991) and others is now firmly mainstream. Indeed, the tractable mathematical model of imperfect competition pioneered by Dixit and Stiglitz (1977) has become a workhorse of modern economics and is one of the ‘bag of tricks’ used by those economists seeking to understand why economic activities and certain industries ‘cluster’ in certain places (Krugman, 1998). 92 Regional Success After Brexit Our contention is that the debate over ‘people versus place’ has grown stale. What is instead needed is a realistic conceptual framework within which to evaluate where ‘space neutral’ policies are most appropriate, and where such policies can become damagingly ‘spatially-blind’ and engender ‘perverse spatial outcomes’(Bentley & Pugalis, 2014, p. 289). We therefore posit the following policy framework within which to frame the discussion and avoid what Barca et al. (2012, p.139) refer to as ‘explicit spatial effects, many of which will undermine the aims of the policy itself’. Thus, in putting forward a framework, or recommendations, for regional policy in a post-Brexit landscape, we return to the dichotomy between incomes (measured by GDHI per capita), which are ultimately what define living standards, and productivity (measured by GVA per hour worked), which is a fundamental measure of economic performance.9 In essence, to reiterate, regional policy must be characterised by meaningful devolution of powers, as well as resources, from central government. Hence, we capture this by putting forward the following very simple Bigger-Better-Bolder (or B-B-B) framework for devolution: 4.6.1. Bigger and Better Simply put, meaningful devolution means greater sums of money allocated to the regions. This can take place directly via increased UK Government funding of the regions; but also in enabling regions greater powers to secure resources themselves. In terms of central government funding, we propose that the ‘Shared Prosperity Fund’ consist of two key funding tranches: • A ‘social fund’ to replace the current UK allocation of European social fund (ESF) monies (including matched UK government contribution) in order to tackle deprivation. 93Policy Implications This fund would be allocated to areas on the basis of our identified metric of adjusted GDHI per capita at the local authority level. Crucially, this should be done on the basis of measures that account for differences in regional prices. For example, the London borough of Barking and Dagenham should not be penalised simply because it is in a high-cost region. Similarly, Portsmouth and Southampton (alongside large parts of the eastern reaches of the River Thames) are every bit as poor in real terms as more northerly regions in the UK. Finally, using such figures would acknowledge the fact that, in real terms after adjusting for purchasing power, many of the poorest parts of the UK lie in the Midlands rather than the North, and they have a commensurately large need for funding. Whilst GDHI per capita is not perfect (as it still conceals inequalities within a given area), we argue that this would represent a dramatic improvement over doling out such monies on the basis of GVA per capita. As the methodology develops (an item we take up under ‘further research’ in our Conclusion section), we would suggest that the funding mechanism thereof could be further refined to consider household income deciles within a given local authority GDHI per capita average, as a means to ‘prioritise’, or target, locales for funding. • An ‘infrastructure fund’ to replace the current UK European regional development funding (ERDF) allocation (including matched UK government contribution) to the regions. This fund would be allocated to areas on the basis of our estimated regional productivity figures. This infrastructure fund would include funding for infrastructure as broadly defined, which would include both physical transport and digital infrastructure. On this basis the current disparity in transport funding 100 Appendices • Social contributions and social benefits: These include government welfare spending (including state pensions, maternity benefits, disability payment, unemployment benefits and housing benefit amongst others) and private pensions. This category also covers a variety of other minor items and the interested reader is again referred to the ONS’ methodology guide. • Other current transfers: This category lumps together a variety of income sources, including insurance settlements, grants, gifts, etc. From these headings are subtracted: • Primary income paid: This primarily consists of interest payments made by households including mortgage interest payments and the interest on other consumer loans. It does not include the capital repayment portion of any mortgages. • Taxes: These include taxes on income, wealth and other taxes (for example council tax) • Social contributions/benefits paid: This category includes payments by both employees and employers. These include pension contributions and national insurance contributions. • Other current transfers: Once again, this category lumps together a variety of payments, including nonlife insurance premiums and a variety of other smaller payments (including fines, transfers of money abroad etc.) APPENDIX 2: THE EKS METHOD One solution is to choose a base region and construct a price index that evaluates the prices of each other region using the spending structure of the base region. This is a so-called 101Appendices Laspeyres index. The relative price levels depend heavily on which region is chosen as a ‘base’ and there is no reason to choose one region above another. One way around this is to use the spending structure of the UK as a whole as a base. Unfortunately, this method does suffer from certain key weaknesses: it is not scale invariant (approaching a Laspeyres index with the largest region as a base) and is more vulnerable to (stochastic) measurement errors than our preferred method (Dikhanov, 1997). Many (including the authors) would argue that the Fisher Ideal Index is an optimum measure of bilateral relative prices. It is the only homogeneous2 symmetric3 average of the Laspeyres and Paasche indices that is base-invariant4 (Diewert, 2004). As such, in a certain sense it can be seen as dealing optimally with the substitution effect (where the Laspeyres and Paasche indices are extremes). Unfortunately the Fisher index is not transitive: the direct purchasing power parity (PPP) is not necessarily equal to the indirect PPP (derived via a third country). Our preferred method is the Èltetö-Köves-Szulc (EKS) method used by Eurostat and the OECD in cross-country comparisons (OECD, 2012). It provides transitive price levels that are as close as possible to the bilateral Fisher relative price levels. This is the so-called property of charactericity (see OECD, 2012, for further details) and is an important motivator in using the EKS method over alternatives. As noted, no system of comparing regional prices is perfect and unlike some alternatives, the EKS procedure is not additive: PPPs for the constituent components of consumer spending or GDP do not add up to the PPP calculated for spending or GDP as a whole. Moreover, real incomes (or real GDP) do not sum to their nominal counterpart. In other words, the real GDP of each region (deflated by a PPP calculated according to the EKS method) does not add up to the GDP of the UK as a whole. Nevertheless, the authors believe additivity to be a less important attribute than scale invariance. If all quantities in a 102 Appendices given region double but prices remain identical then the relative price levels shouldn’t change. In addition, it is desirable that the index chosen should be base invariant (i.e. it should not matter which region is chosen as Region 1). A further major consideration here is the fact that the ONS RRCPLs that we use as a data source are compiled on this basis. In practical terms, the precise method used does not appear to materially affect our results. The overall differences when using the GK method instead of EKS appear in the order of a single percentage point for most regions. Although this is significant, the overall trend of London being far more expensive than any other region (and northern England and Wales being particularly inexpensive) remains unchanged. In practical terms, we calculate the EKS price levels by calculating Laspeyres, Paasche and (thus) Fisher indices for each region before applying the EKS procedure to the latter and standardising the results. Whilst we lack data on quantities consumed by region, the Living Costs and Food survey does allow us to calculate weights that can be used as an input via a trivial rearrangement as follows: ∑ ∑ ∑ ∑ () () =× × =              weight L aspeyres price quantity price quantity price price*weight region 2/1 region 2r egion1 region 1r egion1 region2 region1 region1 1 ∑ ∑ ∑ ∑ =× × = ×              Paascheprice quantity price quantity weight price priceweight 2/1 region 2r egion2 region 1r egion2 region2 region1 region2 region2 103Appendices The Fisher index is the geometric average of the Laspeyres and Paasche indices and this is made transitive by applying the following formula: ∏             = = Fisher Fisher EKS k Nkj ki K 1 / / 1 2/1 Price levels are standardised by dividing each region’s price level by the geometric average of all price levels. APPENDIX 3: FISIM A further technical issue involves the calculation of the size of the financial services sector. Whilst this is a national issue, it has an exaggerated regional impact because the activities of the financial services sector are spatially concentrated. Regional GVA are calculated by ‘regionalising’ the output of each industry (as reported in the National Accounts).5 Naturally, certain regions specialise in certain industries. Whilst the methodology for regionalisation employed by the ONS Regional Accounts team is robust, if the output of a particular industry is overstated or understated on a national level, any disparities will be magnified on a regional level. This is of particular concern in the case of financial services, both because FISIM6 is known to overstate the output of the financial services sector and because the latter is overwhelmingly concentrated in London. As a result, this artificially inflates the GVA of London at the expense of the rest of the UK. This problem comes about because, unlike most sectors of the economy, the financial services sector does not charge directly for the services that it provides. Instead, money is lent out at higher interest rates than it is borrowed. FISIM therefore works by multiplying the amount lent by the interest rate charged and subtracting the interest charged on borrowings. 104 Appendices The reason that this overstates financial services output is because it takes no account of risk (if a large proportion of debtors default then interest charged needs to be high just to cover these losses). As a result, in times of financial stress it can produce particularly perverse results: official figures suggest that the financial services sector grew by over 16% in 2009 during the nadir of the financial crisis (at a time when banks were reporting enormous losses and receiving state aid). In reality, of course, this result is generated by the fact that the risk premium spiked dramatically, leading to a huge disparity between borrowing and lending rates. The ONS is constrained by international and European conventions in how it calculates FISIM. Whilst attempts have been made to quantify the impact on GDP of adjusting FISIM for risk (Akritidis & Francis, 2017; Haldane, Brennan, & Madouros, 2010), doing so on the components of GVA is much more difficult. Nevertheless, adjustments can be made by users of statistics. One can exclude financial services entirely from regional output and thus estimate the size of the ‘non-financial economy’. Obviously this is a conceptually different output to GVA for the whole economy and in areas with a large financial services sector it will not give an accurate view of the economy as a whole. To calculate regional productivity it is then necessary to estimate the number of workers (and ideally hours worked) in the financial services sector in each region before dividing non-financial output by the size of the non-financial workforce. The only figures available are from the Business Register and Employment Survey, which are not completely comparable to their counterparts in the ONS’ official regional productivity statistics. We also lack any viable measure of the number of hours worked per job in the non-financial sector of the economy. 105Appendices Alternatively, one can seek to use the regional shares calculated by the ONS and input an alternative measure of financial services output, for which the experimental statistics compiled by the ONS are likely to prove extremely useful (Akritidis & Francis, 2017). These show that risk-adjusted FISIM output was around £10–12bn, potentially reducing nominal GDP by up to 0.7% (Akritidis & Francis, 2017). This reduces total financial services output by around onesixth. Ultimately the impact of doing so is rather small reducing London’s productivity advantage over the rest of the country by around one percentage point. NOTES 1. Imputed rent refers to the amount that owner–occupiers would have to pay in order to rent their homes on the open market. Strictly speaking, this is the ‘income’ associated with owning the housing asset. It is discussed in more detail below. 2. Doubling both the Laspeyres and Paasche indices should double the average. 3. Symmetric in the sense of giving both regions equal importance. 4. Meaning that it is irrelevant whether one chooses the West Midlands, North East or Great Britain overall as a ‘home region’. 5. The technical details of how this is done are here: https:// consultations.ons.gov.uk/national-accounts/consultation-onbalanced-estimates-of-regional-gva/supporting_documents/ Development%20of%20a%20balanced%20measure%20of%20 regional%20gross%20value%20added.pdf. When the ONS publish the figures on 13 December, there will hopefully be a fuller methodology document I can reference. 6. Financial Intermediation Services Indirectly Measured. This page intentionally left blank 107 BIBLIOGRAPHY Abreu, M., Oner, O., Brouwer, A., & van Leeuwen, E. (2018). Well-being effects of self-employment: A spatial inquiry. Journal of Business Venturing. doi:https://doi. org/10.1016/j.jbusvent.2018.11.001 Aiton, A., Burnside, R., Campbell, A., Edwards, T., Liddell, G., McIver, I., & McQuillen, A. (2016). SPICe briefing: Election 2016. Retrieved from http://www.parliament. scot/ResearchBriefingsAndFactsheets/S5/SB_16-34_ Election_2016.pdf Akritidis, L., & Francis, P. (2017). Financial intermediation services indirectly measured (FISIM) in the UK revisited. Retrieved from https://www.ons.gov.uk/economy/ grossdomesticproductgdp/articles/financialintermediation servicesindirectlymeasuredfisimintheukrevisited/2017-0424#experimental-estimates-of-fisim-adjusted-for-default-risk Asea, P. K., & Corden, W. M. (1994). The Balassa–Samuelson model: An overview*. Review of International Economics, 2(3), 191–200. doi:10.1111/j.1467-9396.1994.tb00040.x Aten, B., Figueroa, E., Mbu, C., & Vengelen, B. (2017). Real per capita personal income and regional price parities for 2015. Washington, DC: US Department of Commerce, Bureau of Economic Analysis. Bachtler, J. (2017). Brexit and regional development in the UK: What future for regional policy after structural 108 Bibliography funds? In D. Bailey & L. Budd (Eds.), The political economy of Brexit (pp. 129–158). Newcastle upon Tyne: Agenda Publishing Ltd. Bachtler, J., & Begg, I. (2018). Beyond Brexit: Reshaping policies for regional development in Europe. Papers in Regional Science, 97(1), 151–170. doi:10.1111/pirs.12351 Bailey, D. (2011). From RDAs to LEPs in England: Challenges and prospects. Regions Magazine, 284(1), 13–15. doi:10.1080/13673882.2011.9721721 Bailey, D., & De Propris, L. (2017). Brexit and the UK automotive industry. National Institute Economic Review, 242(1), R51–R59. doi:10.1177/002795011724200114 Ball, A., & Fenwick, D. (2004). Relative regional consumer price levels in 2003. Economic Trends, 603, 42–51. Barca, F., McCann, P., & Rodríguez-Pose, A. (2012). The case for regional development intervention: Place-based versus place-neutral approaches. Journal of Regional Science, 52(1), 134–152. doi: 10.1111/j.1467-9787.2011.00756.x Barr, N. (2002). Reforming pensions: Myths, truths, and policy choices. International Social Security Review, 55(2), 3–36. doi:10.1111/1468-246X.00122 BBC. (2017a). EU aid to be replaced by new fund, conservatives pledge. Retrieved from https://www.bbc.co.uk/ news/uk-wales-politics-39965358 BBC. (2017b). Heathrow cost of third runway cut by £2.5bn. Retrieved from https://www.bbc.co.uk/news/ uk-england-london-42399840 BBC. (2018a). Heathrow airport: Cabinet approves new runway plan. Retrieved from https://www.bbc.co.uk/news/ uk-politics-44357580 109Bibliography BBC. (2018b). Heathrow third runway: M25 ‘could be moved to make room’. Retrieved from https://www.bbc. co.uk/news/uk-england-london-42715407 BBC News. (2015). Scottish powers ‘too centralised’, Alexander warns. Retrieved from https://www.bbc.co.uk/ news/uk-scotland-scotland-politics-31018980 Beatty, C., & Fothergill, S. (2017). The impact on welfare and public finances of job loss in industrial Britain. Regional Studies, Regional Science, 4(1), 161–180. doi:10.1080/2168 1376.2017.1346481 Becker, S. O., Egger, P. H., & von Ehrlich, M. (2010). Going NUTS: The effect of EU structural funds on regional performance. Journal of Public Economics, 94(9), 578–590. doi:https://doi.org/10.1016/j.jpubeco.2010.06.006 Becker, S. O., Fetzer, T., & Novy, D. (2017). Who voted for Brexit? A comprehensive district-level analysis. Economic Policy, 32(92), 601–650. doi:10.1093/epolic/eix012 Bell, B., & Machin, S. (2016). Brexit and wage inequality. In R. E. Baldwin (Ed.), Brexit beckons: Thinking ahead by leading economists. London: Centre for Economic Policy Research. Retrieved from https://voxeu.org/content/ brexit-beckons-thinking-ahead-leading-economists Benneworth, P. (2006). The ‘rise’ of the region: The English context to the raging academic debates. In I. Hardill, P. Benneworth, M. Baker, & L. Budd (Eds.), The rise of the English regions? (pp. 44–67). Oxfordshire: Routledge. Bentley, G. (2018). The new industrial strategy: Policy and governance for a place-based approach to regional and local development policy post-Brexit. Retrieved from https:// centreforbrexitstudiesblog.wordpress.com/2018/08/13/ the-new-industrial-strategy-policy-and-governance-for-a- 116 Bibliography Hayes, P. (2005). Estimating UK regional price indices, 1974–96. Regional Studies, 39(3), 11. Hearne, D. (In prep.). Geographies of absurdity: Brexit Britain and the ‘Missing Middle’. CBS Working Paper. Hearne, D. (In prep.). Real regional incomes in the UK. BCU Centre for Brexit Studies Working Papers. Henderson, A., Jeffery, C., Liñeira, R., Scully, R., Wincott, D., & Wyn Jones, R. (2016). England, Englishness and Brexit. The Political Quarterly, 87(2), 187–199. doi:10.1111/1467-923X.12262 HM Government. (2017). Industrial strategy: Building a Britain fit for the future. Retrieved from https://assets. publishing.service.gov.uk/government/uploads/system/ uploads/attachment_data/file/664563/industrial-strategywhite-paper-web-ready-version.pdf. HM Treasury. (2018). Country and regional analysis. Retrieved from https://www.gov.uk/government/statistics/ country-and-regional-analysis-2017 House of Commons Select Committee on Crossrail. (2007). Crossrail: First Special Report. London: HMSO. Retrieved from https://publications.parliament.uk/pa/cm200607/ cmselect/cmcross/235/23514.htm. Huggett, M., & Ventura, G. (2000). Understanding why high income households save more than low income households. Journal of Monetary Economics, 45(2), 361–397. doi: https://doi.org/10.1016/S0304-3932(99)00058-6 Huggins, C. (2018). The future of cohesion policy in England: Local government responses to Brexit and the future of regional funding. Cuadernos Europeos de Deuesto, 58, 131–153. 117Bibliography Huggins, R., & Thompson, P. (2017). Networks and regional economic growth: A spatial analysis of knowledge ties. Environment and Planning A: Economy and Space, 49(6), 1247–1265. doi:10.1177/0308518x17692327 Ivanov, S., & Webster, C. (2007). Measuring the impact of tourism on economic growth. Tourism Economics, 13(3), 379–388. doi:10.5367/000000007781497773 Jenkins, S. (2018, August 9). The HS2 rail project is out of date and out of control. But it can still be halted. The Guardian. Retrieved from https:// www.theguardian.com/commentisfree/2018/aug/09/ hs2-out-of-control-care-homes-childrens-centres Jiang, X., & Li, H. (2006). Smaller real regional income gap than nominal income gap: A price-adjusted study. China & World Economy, 14(3), 38–57. doi:10.1111/j.1749-124X. 2006.00021.x Johnston, R., McKinney, M., & Stark, T. (1996). Regional price level variations and real household incomes in the United Kingdom, 1979/80–1993. Regional Studies, 30(6), 567–578. doi:10.1080/00343409612331349868 Kangasharju, A., Tavera, C., & Nijkamp, P. (2012). Regional growth and unemployment: The validity of Okun’s Law for the Finnish regions. Spatial Economic Analysis, 7(3), 381–395. doi:10.1080/17421772.2012.694141 Kentish, B. (2018, August 5). HS2 requires £43bn of funding for people to make the most it, government’s infrastructure tsar says. The Independent. Retrieved from: https://www.independent.co.uk/news/ uk/politics/hs2-government-funding-benefits-43billion-benefit-tsar-infrastructure-commission-sir-johnarmitt-a8478586.html 118 Bibliography Kim, K., Kratena, K., & Hewings, G. J. D. (2015). The extended econometric input–output model with heterogeneous household demand system. Economic Systems Research, 27(2), 257–285. doi:10.1080/09535314.2014.991778 Kriesi, H. e., & Pappas, T. S. e. (2015). European populism in the shadow of the great recession. Colchester: ECPR Press. Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy, 99(3), 483–499. doi:10.1086/261763 Krugman, P. (1997). The age of diminished expectations: U.S. economic policy in the 1990s (3rd ed.). Cambridge, MA: London: MIT Press. Krugman, P. (1998). What’s new about the new economic geography? Oxford Review of Economic Policy, 14(2), 7–17. doi:10.1093/oxrep/14.2.7 Krugman, P. (2011). The new economic geography, now middle-aged. Regional Studies, 45(1), 1–7. doi:10.1080/0034 3404.2011.537127 Larrimore, J., Dodini, S., & Thomas, L. (2016). Report on the economic well-being of U.S. households in 2015. Retrieved from https://www.federalreserve.gov/2015-reporteconomic-well-being-us-households-201605.pdf Lee, N. (2017). Powerhouse of cards? Understanding the ‘Northern Powerhouse’. Regional Studies, 51(3), 478–489. doi:10.1080/00343404.2016.1196289 Leicester and Leicestershire Local Economic Partnership. (2014). Leicester and Leicestershire Strategic Economic Plan: 2014–2020. Retrieved from: https://www.llep.org.uk/ wp-content/uploads/2015/03/SEP_-_full_document.pdf 119Bibliography Lemoine, F., Poncet, S., & Ünal, D. (2015). Spatial rebalancing and industrial convergence in China. China Economic Review, 34, 39–63. doi:https://doi.org/10.1016/j. chieco.2015.03.007 Ley, E. (2005). Whose inflation? A characterization of the CPI plutocratic gap. Oxford Economic Papers, 57(4), 634–646. doi:10.1093/oep/gpi040 Li, C., & Gibson, J. (2014). Spatial price differences and inequality in the People’s Republic of China: Housing market evidence. Asian Development Review, 31(1), 92–120. doi:10.1162/ADEV_a_00024 Liu, T.-Y., Su, C.-W., Chang, H.-L., & Xiong, D.-P. (2018). Does the law of one price hold? A cross-regional study of China. Economic Research-Ekonomska Istraživanja, 31(1), 592–606. doi:10.1080/1331677X.2018.1429294 Los, B., McCann, P., Springford, J., & Thissen, M. (2017). The mismatch between local voting and the local economic consequences of Brexit. Regional Studies, 51(5), 786–799. doi:10.1080/00343404.2017.1287350 Los, B., Timmer, M. P., & de Vries, G. J. (2016). Tracing Value-added and double counting in gross exports: Comment. American Economic Review, 106(7), 1958–1966. doi:10.1257/aer.20140883 Martin, P., Mayer, T., & Mayneris, F. (2011). Spatial concentration and plant-level productivity in France. Journal of Urban Economics, 69(2), 13. Martin, R., Pike, A., Tyler, P., & Gardiner, B. (2016). Spatially rebalancing the UK economy: Towards a new policy model? Regional Studies, 50(2), 342–357. doi:10.1080/00343404.2015.1118450 120 Bibliography Martin, R., & Tyler, P. (1994). Real wage rigidity at the local level in Great Britain. Regional Studies, 28(8), 833–842. doi: 10.1080/00343409412331348736 McCann, P. (2016). The UK regional–national economic problem: Geography, globalisation and governance. New York, NY: Routledge. Ministry of Housing Communities & Local Government. (2015). English indices of deprivation 2015. Retrieved from https://www.gov.uk/government/statistics/ english-indices-of-deprivation-2015 Monbiot, G. (2018). This disastrous new project will change the face of Britain, yet no debate is allowed. The Guardian. Retrieved from https:// www.theguardian.com/commentisfree/2018/aug/22/ project-britain-debate-oxford-cambridge-expressway Murphy, J. T. (2011). The socio-spatial dynamics of development: Geographical insights beyond the 2009 World Development Report. Cambridge Journal of Regions, Economy and Society, 4(2), 175–188. doi:10.1093/cjres/rsr009 Nenna, M. (2001). Price level convergence among Italian cities: Any role for the Harrod–Balassa–Samuelson hypothesis? Working Paper No. 64. Sapienza University of Rome, CIDEI. North East Local Enterprise Partnership. (2018). Our progress: What do we want to achieve and how are we doing? Retrieved from https://www.nelep.co.uk/the-plan/our-targets/ Northern Ireland Statistics and Research Agency. (2017). Northern Ireland Multiple Deprivation Measure 2017 (NIMDM2017). Retrieved from https://www.nisra.gov.uk/ statistics/deprivation/northern-ireland-multiple-deprivationmeasure-2017-nimdm2017 121Bibliography Noss, J., & Sowerbutts, R. (2012). The implicit subsidy of banks. (Bank of England Financial Stability Paper, No.15). Retrieved from: https://www.bankofengland.co.uk/ financial-stability-paper/2012/the-implicit-subsidy-of-banks. Office for National Statistics (ONS). (2011). UK relative regional consumer price levels for goods and services for 2010. Retrieved from: https://webarchive.nationalarchives. gov.uk/20151014001900/http://www.ons.gov.uk/ons/rel/cpi/ regional-consumer-price-levels/2010/index.html Office for National Statistics (ONS). (2014). Changes to national accounts: Review of the non-profit institutions serving household sector. Retrieved from https://www. ons.gov.uk/ons/guide-method/method-quality/specific/ economy/national-accounts/articles/2011-present/revisedmethodology-and-sources-for-non-profit-institutions-servinghouseholds.pdf Office for National Statistics (ONS). (2016a). Healthy life expectancy at birth and age 65 by upper tier local authority and area deprivation: England, 2012 to 2014. Retrieved from https://www.ons.gov.uk/peoplepopulationandcommunity/ healthandsocialcare/healthandlifeexpectancies/bulletins/ healthylifeexpectancyatbirthandage65byuppertierlocal authorityandareadeprivation/england2012to2014/relateddata Office for National Statistics (ONS). (2016b). User requested data: Annual Survey of Hours and Earnings (ASHE) – Gross annual earnings for the 90 to 99 percentiles of employee jobs by sector in the UK and Regions, 2017 provisional. Retrieved from: https://www.ons.gov.uk/employmentand labourmarket/peopleinwork/ earningsandworkinghours/ adhocs/007818annualsurveyofhoursandearningsashegross annualearningsforthe90to99percentilesofemployeejobsby sectorintheukandregions2017provisional 122 Bibliography Office for National Statistics (ONS). (2017a). Annual Population Survey Jan–Dec 2016. Retrieved from https://www.nomisweb. co.uk/query/construct/summary.asp?mode=construct& version=0&dataset=17. Accessed on August 22, 2018. Office for National Statistics (ONS). (2017b). Annual survey of hours and earnings, 1997–2016. Retrieved from: https://www.ons.gov.uk/employmentandlabourmarket/ peopleinwork/earningsandworkinghours/bulletins/annualsur veyofhoursandearnings/2017provisionaland2016revisedresul ts/relateddata?sortBy=title&query=&size=50 Office for National Statistics (ONS). (2017c). Country and regional public sector finances: Financial year ending March 2016. Retrieved from https://www.ons.gov.uk/economy/ governmentpublicsectorandtaxes/publicsectorfinance/ articles/countryandregionalpublicsectorfinances/2015to2016 Office for National Statistics (ONS). (2017d). Detailed household expenditure by countries and regions, UK: Table A35. Retrieved from https://www.ons.gov.uk/ file?uri=/peoplepopulationandcommunity/personal andhouseholdfinances/expenditure/datasets/detailed householdexpenditurebycountriesandregionsuktablea35/ financialyearending2014tofinancialyearending2016/ a35final201516.xls Office for National Statistics (ONS). (2017e). Input–output supply and use tables. Retrieved from https://www.ons.gov. uk/economy/nationalaccounts/supplyandusetables/datasets/ inputoutputsupplyandusetables Office for National Statistics (ONS). (2017f). Regional gross value added (balanced), UK: 1998 to 2016. Retrieved from https://www.ons.gov.uk/economy/grossvalueaddedgva/ bulletins/regionalgrossvalueaddedbalanceduk/1998to2016 123Bibliography Office for National Statistics (ONS). (2017g). Regional gross value added (income approach) QMI. Retrieved from https://www.ons.gov.uk/economy/grossvalueaddedgva/ methodologies/regionalgrossvalueaddedincomeapproachqmi Office for National Statistics (ONS). (2017h). UK national accounts, The blue book: 2017. Retrieved from https://www. ons.gov.uk/economy/grossdomesticproductgdp/compendium/ unitedkingdomnationalaccountsthebluebook/2017 Office for National Statistics (ONS). (2017i). User requested data: Regional gross fixed capital formation, 2000 to 2014. Retrieved from https://beta.ons.gov.uk/economy/ grossvalueaddedgva/adhocs/006749regionalgrossfixedcapital formation2000to2014 Office for National Statistics (ONS). (2018a). Dataset: Estimates of the population for the UK, England and Wales, Scotland and Northern Ireland. Retrieved from https://www. ons.gov.uk/peoplepopulationandcommunity/population andmigration/populationestimates/datasets/population estimatesforukenglandandwalesscotlandandnorthernireland Office for National Statistics (ONS). (2018b). Economic statistics sector classification: Classification update and forward work plan: January 2018. Retrieved from https://www.ons.gov.uk/economy/nationalaccounts/ uksectoraccounts/articles/economicstatisticssector classificationclassificationupdateandforwardworkplan/ january2018 Office for National Statistics (ONS). (2018c). Index of private housing rental prices, Great Britain: February 2018. Retrieved from https://www.ons. gov.uk/economy/inflationandpriceindices/bulletins/ indexofprivatehousingrentalprices/february2018 124 Bibliography Office for National Statistics (ONS). (2018d). Public sector finances, UK: May 2018. Retrieved from https:// www.ons.gov.uk/economy/governmentpublicsectorandtaxes/ publicsectorfinance/bulletins/publicsectorfinances/may2018 #how-was-debt-in-the-latest-financial-year-accumulated Office for National Statistics (ONS). (2018e). Regional and sub-regional productivity in the UK: February 2018. Retrieved from https://www.ons.gov.uk/employmentand labourmarket/peopleinwork/labourproductivity/articles/ regionalandsubregionalproductivityintheuk/february2018 Office for National Statistics (ONS). (2018f). Regional gross disposable household income, UK: 1997 to 2016. Retrieved from https://www.ons.gov.uk/economy/regionalaccounts/ grossdisposablehouseholdincome/bulletins/regionalgrossdisp osablehouseholdincomegdhi/1997to2016 Office for National Statistics (ONS). (2018g). Relative regional consumer price levels of goods and services, UK: 2016. Retrieved from https://www.ons.gov.uk/economy/ inflationandpriceindices/articles/relativeregionalconsumer pricelevelsuk/2016/pdf Office for National Statistics (ONS). (2018h). UK House Price Index: January 2018. Retrieved https://www.ons.gov.uk/ economy/inflationandpriceindices/bulletins/housepriceindex/ january2018#house-price-index-by-english-region Ostrom, E., & Ahn, T. (2009). The meaning of social capital and its link to collective action. In G. T. Svendsen & G. L. H. Svendsen (Eds.), Handbook of social capital: The Troika of sociology, political science and economics. (pp. 17–35). Cheltenham: Edward Elgar Publishing Limited. Parkinson, J. (2016). Who are the Jams (the ‘just about managing’)? Retrieved from http://www.bbc.co.uk/news/ uk-politics-38049245 125Bibliography Peck, F., Connolly, S., Durnin, J., & Jackson, K. (2013). Prospects for ‘place-based’ industrial policy in England: The role of local enterprise partnerships. Local Economy, 28(7–8), 828–841. doi:10.1177/0269094213498470 Perrons, D., & Dunford, R. (2013). Regional development, equality and gender: Moving towards more inclusive and socially sustainable measures. Economic and Industrial Democracy, 34(3), 483–499. doi:10.1177/0143831x13489044 Pidd, H. (2016). Blackpool’s Brexit voters revel in ‘giving the metropolitan elite a kicking’. The Guardian. Retrieved from https://www.theguardian.com/uk-news/2016/jun/27/ blackpools-brexit-voters-revel-in-giving-the-metropolitanelite-a-kicking Pike, A., & Tomaney, J. (2009). The state and uneven development: The governance of economic development in England in the post-devolution UK. Cambridge Journal of Regions, Economy and Society, 2(1), 13–34. doi:10.1093/ cjres/rsn025 Plimmer, G., & Ford, J. (2018). Who will pay for Heathrow airport’s £14bn third runway? Financial Times. Retrieved from https://www.ft.com/content/98e6b128-7533-11e8aa31-31da4279a601 Pont, M. (2008). Improvements to the measurement of government output in the National Accounts. Economic & Labour Market Review, 2(2), 17–22. Pugalis, L., Shutt, J., & Bentley, G. (2012). Local enterprise partnerships: Living up to the hype? Critical Issues in Economic Development. 4, 1–10. Retrieved from: http://nrl. northumbria.ac.uk/6516/ Retail Prices Index Advisory Committee. (1971). Proposals for retail prices indices for regions. London: HMSO. 132 Index Department for Business Energy & Industrial Strategy (2017), 42 Department for Communities and Local Government (DCLG), 57 Devolution, 76, 87–88 Digital infrastructure, 93–94 Dividends, 37n3 Domestic basis, 56 concept, 49 regional policy, 88 tourism, 73n8 Dynamic Stochastic General Equilibrium, 97n8 Econometric model, 18 Economic geography, 6, 15 justification, 87 performance, 92 policy, 91 theory, 75 Èltetö-Köves-Szulc method (EKS method), 29, 45–46, 100–103 English identity, 6 Enterprise zones, 88 European regional development funds, 79–80 European Structural and Investment Funds (ESIF), 84 European Union (EU), 2, 19, 76 policy, 39 regional funding schemes, 85 Eurostat-OECD methodology, 40 Family Resources Survey (FRS), 27–28, 30, 56 Financial services, 79–80 sector, 41 Fisher Ideal Index, 101–103 FISIM, 41, 103–105 Gross capital consumption, 52–54, 58–59, 66 Gross disposable household income (GDHI), 9, 19–21, 27, 32, 50, 84–85, 99 ONS in, 34 ‘operating surplus’ portion of, 29 per capita, 15–18, 20 Gross domestic product (GDP), 9–10, 40–41, 49 apportioning regional, 54–56 France’s GDP per capita, 45 GVA to, 47–49 Gross value added (GVA), 2, 4–5, 9–11, 14, 39–40, 77, 104 to GDP, 47–49 133 GVA-based upper bound, 51 per capita, 12, 14–15 Per Worker, 15–18 real, 44 Heathrow’s third runway, 81 High-speed 2 (HS2), 79, 81–82, 87 House of Commons Select Committee on Crossrail (2007), 82–83 Household final consumption expenditure (HHFCE), 40, 49, 64–65 Household sector, 49–51, 56–57 Human development index, 9 Imputed rents, 41, 105n1 income, 68–69 Income(s) (see also Gross disposable household income (GDHI)), 13, 43, 77, 92 implicit, 21 mixed, 99 nominal, 50 non-wage, 33 primary, 34, 100 property, 99 real, 34, 101 real estate rental, 69 Index of Multiple Deprivation (IMD), 19, 34–35 Industrial strategy, 42 Inequality, 32–34 Infrastructure fund, 93–94 Intellectual property, 52, 58 Intra-area inequality, 35 Labour market, 12–15 Laspeyres index, 101 ‘Law of One Price’, 18 Living costs and food survey (LCF survey), 26–27, 51 Local development policies, 90–91 Local Enterprise Partnerships (LEPs), 42, 88 London and South East, 80 crossrail, 82–84 Heathrow’s third runway, 81 HS2, 81–82 London-centric approaches, 79–80 price-adjusted productivity, 42 London–Cambridge— Oxford ‘golden triangle’, 87 Lower bound, 39, 64–66, 69 absolute, 47, 68–70 calculation, 44 VAT-based, 51 Index 134 Index Management consultancies, 79 Median full-time public sector salaries, 62 ‘Midlands Engine’ brands, 88 Modified Eurostat-OECD EKS procedure, 38n4 National Infrastructure Commission and Highways Agency, 87 National operating surplus, 27 Net exports, 63 ‘New economic geography’, 8, 91 Nominal and real regional GDP, 67–68 Nominal regional GDHI, 30 Non-financial economy, 104 Non-profit institutions serving households (NPISH), 41, 51–52, 58 Northern Powerhouse, 88 NUTS1 regions, 8, 68, 76, 94 NUTS2 level, 18n1 OECD, 29, 45, 63, 101 Office for National Statistics (ONS), 3, 9, 11, 17–20, 24, 27–28, 40, 104–105 methodology guide, 99–100 RRCPLs, 102 Paasche index, 38n4, 101 People vs. place policy, 90–95 ‘Perverse spatial outcomes’ policy, 92 Physical transport, 93–94 Policy interventions, 91 makers, 75 ramifications, 15 Policy framework, 90 B-B-B framework, 92–95 LEPs, 91 traditional economic orthodoxy, 91 Policy implications, 75 Brexit overhang, 84–87 case for ‘meaningful devolution’, 87–90 current spending bias towards London and South East, 80–84 people vs. place, 90–95 recap, 76–80 Post-Brexit environment, 39, 79, 92 Price (see also Regional prices) index, 23 levels, 21–27, 40, 44–46, 103 Primary income, 34, 100 Private rents, 27 Private sector, 56 Property, 27 of charactericity, 101 income, 99 135Index Public sector, 60, 81 Public transport services, 83 Purchasing power parity (PPP), 39, 65, 79, 101 Real estate rental income in GVA, 69 Real GDHI (see also Gross disposable household income (GDHI)) per capita, 86 regional GDHI, 30–32 Real GVA, 44 Real incomes, 34, 101 Real labour productivity estimating real regional GDP and productivity, 67–69 FISIM and imputed rents, 41 GVA, 40 price levels, 44–46 regional PPPs, 63–67 regional prices, 56–63 regionalisation, 47–56 time to re-evaluate regional success, 69–72 Real living standards GDHI, 19–21 index of multiple deprivation, 34–35 inequality, 32–34 operating surplus, 27–29 price levels, 21–27 real regional GDHI, 30–32 results, 29–30 Real regional GDP and productivity estimation, 67 absolute minimum, 68 central scenarios, 69 conservative estimate, 68–69 Regional Accounts, 43–44, 57, 62 Regional Development Agencies, 88 Regional differences across UK economy, 5–8 Regional GDHI, 20 Regional GVA, 103 Regional indicators, 43–44 Regional living standards, 19, 21 Regional policy, 39, 92 makers, 34 Regional PPPs, 63 lower bound, 64–66 realistic estimates, 66–67 Regional prices, 56, 74n11 government expenditure, 59–63 gross capital consumption, 58–59 household sector, 56–57 indices, 23 net exports, 63 NPISH, 58 relative, 25 136 Index Regional productivity, 77, 104 relative regional productivity in UK, 71 Regionalisation, 6, 29, 47, 103 apportioning regional GDP, 54–56 government expenditure, 53–54 gross capital consumption, 52–53 from GVA to GDP, 47–49 household sector, 49–51 NPISH, 51–52 Relative Regional Consumer Price Level (RRCPL), 64–65 Residence-based GVA, 11 Retail Prices Index Advisory Committee, 23 Scottish Government, 51, 54, 87–88 Shared Prosperity Fund, 80, 85, 92 Social benefits, 100 capital networks, 80 contributions, 100 exclusion, 95 fund, 92–93 rents, 27 Social justification, 87 ‘Space neutral’ policies, 92 Spatial Adjustment Factors, 23 ‘Spatially-blind’ policy, 92 Taxes, 100 on production, 47 Traditional economic orthodoxy, 91 Travel to Work Area (TTWA), 6–7 UK Government expenditure, 53–54, 59–63 funding, 92 Industrial Strategy Green Paper, 10, 77, 42 sector, 60 welfare, 100 UK Government Office Region (GORs), 78 Unemployment rates, 14 Valuation Office Agency, 33, 57 Value-added tax (VAT), 41, 48 threshold, 73n5 VAT-based lower bound, 51 West Midlands Combined Authority (WMCA), 10