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Competitive Scottish Cities?
Placing Scotland's cities in the UK and European context

Chapter 4 Technical Note

Introduction

4.1 The dynamics and processes that contribute to the performance of cities remain complex and often confused. There is a substantial academic and consultancy literature dedicated to a better understanding of the successful city. Place competitiveness cannot be simply or easily quantified, nevertheless it is possible to work with quantitative data to identify the key elements of competitive cities.

4.2 As part of the European Competitive Cities project we developed a set of common indicators that focus on the key assets of the competitive city. These headline indicators were used to place the performance of the English Core Cities in the wider European context. This work has now been updated to incorporate the six Scottish cities: Edinburgh, Glasgow, Inverness, Stirling, Aberdeen and Dundee.

4.3 The indicators were developed to provide a starting point from which to quantify the assets of the competitive city and benchmark the performance of the selected cities. These indicators come with a health warning. Throughout this process we have had to make choices and balance what is desirable with what is possible. Quantitative data can only ever give part of the picture and all the datasets come with caveats, as long as the limitations are transparent and understood by the end user the data can be used effectively.

4.4 The methodology used to develop these indicators, the challenges faced and the solutions adopted are outlined in this technical note, which reviews:

• the key features of competitive cities
• who else is measuring it and how
• defining the city - where's in and where's out
• data availability
• the Competitive European Cities' Indicators
• data sources and references - replicating the study

Key features of competitive cities

4.5 Across studies exploring the nature of urban competitiveness there is a general consensus that emphasises the importance of a number of key factors to the competitive city, these include: a quality labour force; favourable industrial structure; and connectedness.

4.6 Our work for the Competitive European Cities (Parkinson et al 2004) identified six key characteristics of urban competitiveness:

• economic diversity
• skilled workforce
• connectivity - internal and external
• strategic capacity to mobilise and implement long term development strategies
• innovation in firms and organisations
• quality of life - social, cultural and environmental

4.7 James Simmie (2001) has described innovative cities as those that enable local strengths to respond to complex, global opportunities. As he states, "this requires organisational responses that combine power of corporate capital with the opportunism of small business; manufacturing technology and service expertise; entrepreneurial forms of freedom with effective public regulation and support; new ideas encouraged by a stable established institution and physical infrastructure; and a capacity for trail and error through support for risk-taking". Innovative cities reflect:

• Experiences of national innovation system and the city's position in the urban hierarchy - the higher up these scales cities are the more likely local environments facilitate innovation
• Long term historical development - the role of large firms and corporate strategies with urban regional dimension

4.8 For Simmie, the key urban assets of an innovative city are:

• a highly qualified workforce
• fixed infrastructure and telecommunications capacity

4.9 In 'What makes Euro regions prosper?' Business Strategies Ltd state that "prosperity is a function of two constituent parts - employment rates and productivity". Their report 'Long term outlook' (2000) identified the two most important drivers of successful regions as:

• industrial structure; and
• human capital

With the additional drivers of:

• labour supply
• infrastructure
• population density
• ethnicity

4.10 Cheshire and Carbonaro's 1996 study provides an analysis of disparities in the growth of GDP per capita between 118 Functional Urban Regions surrounding major cities. This work identified the following as determinants of regional growth:

• industrial structure
• regional population
• R&D establishments (measured per million population)
• growth of neighbouring regions (close proximity to fast growing region can have a detrimental effect on a region)
• national performance

4.11 In 'State of Urban Britain' Robert Huggins has developed a competitiveness index using Storper's ⁷ definition of place competitiveness as "the capability of an economy to attract and maintain firms with stable or rising market shares in an activity while maintain stale or increasing standards of living for those who participate in it". Huggins' model has three facets:

4.12 In 'The State of England's Cities' Robson et al (2000) identified the key features of the 'urban asset base' as:

• Location
• Age
• Favourable economic structure
• Company characteristics
• Skills, learning and innovation
• Communications
• Quality environment and service
• Alert local Governance

4.13 The EIUA's (1992) study for Lothian and Edinburgh Enterprise Ltd identified four key characteristics of a competitive city:

• A diverse economic base in a range of service and manufacturing sectors, particularly the high value added sectors
• The knowledge based institutions to develop a flow of human capital and skilled workers for the high value added sectors of the economy
• Good economic, institutional, physical and telecommunication links with the most dynamic areas of the European economy
• The local institutional capacity to identify a development strategy for the city and generate the political financial and personnel resources needed for successful implementation

4.14 For Lever (1999) "Competitive success reflects visionary civic leadership, flexibility in the labour force, a responsive public sector, effective public-private partnerships and an entrepreneurial milieu".

4.15 Whilst the precise focus of these studies varies, the attributes they assign to the successful city remain relatively constant, with economic structure; human capital; productivity; connectedness; and innovation featuring as the key determinants of success.

Measuring competitiveness

4.16 An initial review of comparative studies of city performance highlights the lack of robust city level data that is comparable on a trans-national basis and many studies have relied on regional or city-region data.

4.17 The Innovative Functional Urban Areas in North West Europe project (IAURIF, 2001) focused on two 'input' and two 'output' indicators of innovation after finding that very few statistical indicators are available at the regional level.

Input indicators
Inventory of financial resources associated with R&D expenditure.
HR employed in R&D (full time equivalents).

Output indicators
Scientific production (statistics on publications from a US database and Office for Science Technology in France).
Number of patents registered.

4.18 Robert Huggins' 'Global Index of Knowledge Economies' ⁸ includes the following indicators of regional competitiveness:

• Economic Activity - employment/unemployment
• Employment - key sectors Biotechnology, Computing, Automotive and Mechanical, Electrical and instrument, Computer services
• Number of managers
• R&D expenditure by Government
• R&D expenditure by Businesses
• Patents
• GDP
• Labour productivity
• Earnings
• Elementary education
• Higher education
• Secure servers
• Internet hosts

This report also included a set of sub regional indicators for the UK, covering:

• Economic activity
• ILO unemployment
• GDP
• earnings
• Businesses per capital
• Knowledge based companies
• Knowledge based workers
• R&D and HE workers
• Productivity - GDP per employee
• Educational attainment (A level AS level points scored)

4.19 Barclays' 'Competing with the World' ⁹ report provides a detailed profile of 18 regions from across the world, identifying the leading industrial sectors and development issues for each. It also includes a set of economic performance indicators:

• Population - % under 16, % over retirement
• GDP
• Employment
• Unemployment
• Educational attainment
• Patent applications

4.20 In "UK City Competitiveness Index" ¹⁰ Robert Huggins reviews the relative competitiveness of the UK and presents data at 'city level' as defined by local authority boundaries. The final competitiveness index included:

• Productivity - economic output per worker
• GDP per capita
• Average full time earnings
• Business density (number of companies per capita)
• Knowledge based firms as a % of all firms
• Economic Activity rates
• Unemployment

4.21 Business Strategies Limited 'What Makes Euro Regions Prosper' starts from the assertion that the number of people working and the productivity of each employee determine the prosperity of a region (income), this report measures regional prosperity in terms of:

• GDP per head of working age population adjusted for commuting at purchasing power standard
• Employment rates (full time equivalent employment divided by working age population adjusted for commuting)
• Productivity (GDP per head of working age population adjusted for commuting divided by full time equivalent employment)

4.22 Explanatory variables of prosperity include:

• industrial structure
• workplace employment in 1991
• Objective 1 status
• airport - average travel time to airports and number of passengers using them
• qualifications - at three standard levels: higher, medium, lower

4.23 Many of the studies outlined above use very similar indicators of competitiveness. Sometimes this is because these are the best possible measures, but in some cases this is because as researchers we have to balance what's desirable with what is possible and use the best available data. Consequently many of the factors most pertinent to competitiveness - including innovation, governance and connectedness - remain little quantified on a comparative basis.

Defining the City

4.24 Cities vary in scope and scale and these differences are in part due their boundaries. "Under-bounding" occurs when the official delineation of a city does not correspond with its true reach and influence; others are "over-bounded" and incorporate large areas of rural or semi-rural land along with the urban area. The cut off point for city boundaries can have a significant impact on socio-economic indicators ¹¹.

4.25 English Cities tend to have high levels of deprivation concentrated in the inner-city areas with wealthier suburbs towards the edge of town. Here tightly drawn boundaries can exclude successful areas from city-wide averages. The opposite can be seen in many French and Italian and Scottish cities where deprivation is often concentrated on peripheral housing estates, here tightly drawn boundaries exclude less successful areas.

4.26 Ideally city boundaries should reflect the true reach and influence of each city. As many UK cities are under-bounded their significance, scope and structure can only be fully understood with reference to the wider city-region. However, data is not readily available for 'ideal' boundaries. To ensure true comparability when evaluating city performance across Europe city boundaries the data would need to be standardised, ideally on a basis that reflected the functional reach of the urban area rather than administrative boundaries. As more data becomes available at the very local level and with advances in ICT/GIS it will be possible to use local data as the building blocks to construct boundaries for urban areas on a consistent basis and so standardise the spatial units of 'the city' across Europe.

4.27 Given the time and budget available the data presented here is for standard geographies - most usually the cities as defined by their current administrative boundaries. This has three advantages. First, this is the level of political accountability; second this is the functional level of most service delivery. And finally, this is the spatial level at which most readily available secondary data is published. However, it is equally important to understand how the city at the centre of each of these regions is performing. Data relating to the central city provides a valuable starting point for exploring wider relationships between city and region.

Comparable Data?

4.28 The limited quantity of robust comparable city-level data available on a trans-national basis presents a major challenge for this work. The measures of competitiveness included here have been influenced by data availability and informed by our experience conducting the research for the Competitive European Cities report (Parkinson et al 2004).

4.29 The GLA's recent working paper ¹² highlights some of the data issues encountered when collecting data on a trans-national basis. They found that data from several reputable sources gave different figures for basic measures such as total population. All the data was robust, however it was not measuring exactly the same thing for exactly the same city boundaries, and this resulted in different figures.

4.30 Wherever possible trans-national data sources have been used, these provide data collected on a consistent basis across national boundaries, for example data from the Airports Council International ¹³. Unfortunately there are relatively few of these sources available.

4.31 There are a number of international classification systems that can be applied to local data to increase comparability across national basis, these include:

• The OECD's International Standard Classification of Education (ISCED) ¹⁴
• GDP/GVA as defined by the European System of accounts ¹⁵
• European standard classification of economic activity NACE Rev.1 (Nomenclature Européenne des Activités)
• International Labour Organisation's standardised definition of unemployment

4.32 There are domains where data comparability is particularly limited. For example, there is significant variation in definitions, recording methods and reporting rates of crime and without standardisation it is not possible to draw meaningful conclusions from the recorded crime data for different countries.

4.33 Even when working with a relatively small number of headline indicators significant gaps in the data remain. Basic contextual measures such as total population tend to be readily available, however data relating to innovation, connectivity and qualifications remains limited at the city level, in some cases regional data has been used to fill these gaps. Data availability and robustness tends to be highest in the Northern European countries, with the Scandinavian countries having the most accessible and systematically presented data at both national and city level. Consequently there is more data for Copenhagen and Helsinki than for Turin and Milan.

4.34 To provide a more comprehensive picture of recent developments in the UK this project has incorporated additional data from UK sources these are explored in section 2 of the main report.

The indicators

4.35 Despite the challenges presented by boundary definition and limited data availability, indicators describing socio-economic conditions cities remain a valuable tool for policy makers. The indicators can be used effectively and responsibly to identify trends and benchmark cities as long as the associated limitations and caveats of the data are acknowledged and understood.

4.36 The indicators detailed below have been selected as they are:

• focused and limited in number - all the indicators relate to the drivers of urban competitiveness;
• outcome rather than input or output based;
• measurable - the data required to inform the indicator is readily available;
• robust and intelligible;
• timely and ideally available for more than one point in time to identify change over time.

4.37 This set of indicators have been developed to identify the extent to which each of the study cities possess characteristics identifies as contributing to urban competitiveness and to place the performance of selected Scottish and English cities in the European context.

4.38 We have adopted a simple approach to address the two key challenges presented by theses indicators - the issue of city boundaries and the lack of comparative data at the sub-regional level. We have worked with existing administrative boundaries as this is the spatial level at which most data is available whilst noting the limitations of this approach. We have also called on the support of individual cities to access data.

Table 4.1: The Indicators

The Data

Population

4.39 Population figures are amongst the most robust data available, although differences in the size of different cities may in part be due to differences in how city boundaries are defined. In terms of population the study cities vary significantly from Barcelona - home to 1.5 million residents - to Stirling with just over 40,000 residents. The pattern of population change experience varies across the study cities. All the Scandinavian cities have experienced population growth over the last five years, whilst many of the UK cities, including the Scottish cities, have seen their residential population decrease. The dataset for Munich is inconsistent due to changes in statistical methods between 1996 and 2001.

Figure 4.1: Residential population 2001

Source: UK Cities ONS Mid-Year Population Estimates 2001; city sources

Figure 4.2: Percentage change in residential population 1996-2001

Source: UK Cities ONS Mid-Year Population Estimates 2001; various city sources

Productivity

4.40 The European System of Accounts standardises the calculation of Gross Domestic Product across all EU countries. However, GDP figures are not readily available at the city level. In the UK the ONS publishes sub-regional Gross Value Added figures for 1995-2001. A recent study by Barclays Bank Private Clients calculated GDP figures for many European cities. This data is available on a consistent basis from a single source, making it useful for drawing comparisons, however the Barclays' figure for city GDP can differ from figures published by individual cities.

Figure 4.3: GDP per capita 2001 (Barclays)

Source: Barclays Private Clients 2003

4.41 The Urban Audit 2004 provides an alternative source of GDP per capita data. This source does not cover as many of the study cities as the Barclays report, and the actual GDP per capita figures vary significantly between the two studies, however, both studies show a very similar ranking of the study cities.

Figure 4.4: GDP per capita 2001 (Urban Audit 2004)

Source: European Communities Urban Audit 2004
*Data for UK cities updated from 1998 to 2001 using ONS sub-regional GVA data, Manchester data is for Greater Manchester South, Newcastle data is for Tyneside

4.42 In terms of productivity the study cities out perform their national economies. The only European study cities to have GDP below the national rate are Rotterdam and Lille. Whilst in the UK Liverpool, Sheffield, Newcastle, Dundee and Inverness all lag behind the national average GVA ¹⁶.

Figure 4.5: GDP per head 2001 City: National Performance

Source: City Data Barclays Bank Private Clients 2002. National Data Eurostat

Figure 4.6: GVA per head 2001 City: UK

Source: ONS. *Nairn & Moray, Badenoch & Strathspey are also included with Inverness in the relevant NUTS3 Area
**Aberdeenshire and NE Moray are also included with Aberdeen City in the relevant NUTS 3 area

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