Urban Oppurtunities for Growth a Description of Swedish Urban Areas and Their Opportunities for Growth

Report 2011:08

Urban oppurtunities for growth A description of Swedish urban areas and their opportunities for growth

The purpose of this report is to devise an analytical structure to be able to study and analyse urban areas, the intraregional differences between urban and rural areas, and different types of urban areas.

Reg. No. 2011/053 Swedish Agency for Growth Policy Analysis Studentplan 3, SE-831 40 Östersund, Sweden Telephone: +46 (0)10 447 44 00 Fax: +46 (0)10 447 44 01 E-mail [email protected] www.growthanalysis.se

For further information, please contact Gustav Hansson Telephone +46(0)10 447 44 40 E-mail [email protected]

URBAN OPPORTUNITIES FOR GROWTH

Foreword

Östersund, Sweden, December 2011

Dan Hjalmarsson

URBAN OPPORTUNITIES FOR GROWTH

Table of Contents

Summary ...... 7 Sammanfattning ...... 9 1 Introduction ...... 10 2 Regional growth ...... 12 2.1 Interdependencies between urban and rural areas ...... 12 2.2 Urban areas as engines of growth ...... 13 2.3 Industry’s localisation and focus ...... 14 2.4 Summarising conclusions ...... 16 3 Defining urban areas – Can it be done? ...... 18 3.1 What constitutes a town or city? ...... 18 3.2 A definition of localities, urban areas and their networks ...... 22 3.2.1 How are urban areas defined? ...... 22 3.2.2 Micro-level: Localities ...... 24 3.2.3 Meso-level: Urban areas ...... 26 3.2.4 Macro-level: Networks ...... 28 4 Urban areas and their opportunities for growth and development ...... 37 4.1 Population ...... 37 4.1.1 Localities ...... 37 4.1.2 Urban areas ...... 38 4.2 Economy ...... 48 4.2.1 Labour productivity and income per capita ...... 48 4.2.2 Daytime and night-time population ...... 52 4.2.3 Structure of trade and industry ...... 56 4.3 Human capital ...... 61 4.3.1 Formal education ...... 61 4.3.2 University towns ...... 62 4.3.3 The mobility of the labour force and urban areas’ attractiveness ...... 64 4.4 Mobility ...... 66 4.4.1 The importance of mobility for growth ...... 66 4.4.2 Indicators of mobility ...... 67 4.4.3 Accessibility between urban areas ...... 67 4.4.4 Global mobility ...... 75 4.4.5 Airport accessibility ...... 76 4.4.6 Virtual mobility ...... 78 4.5 A classification of urban areas ...... 80 4.5.1 The different roles and functions of urban areas ...... 80 4.5.2 A description of different urban areas ...... 80 4.5.3 A comparison of different types of urban areas ...... 83 5 Summarising conclusions ...... 89 References ...... 91 Appendix ...... 95

URBAN OPPORTUNITIES FOR GROWTH

Summary

Urban development has come to be an increasingly important element in growth policy. For example, the European Commission’s Cohesion Policy clearly recognizes urban areas as growth engines and has called for an urban policy agenda. Urban areas, however, can have vastly different characteristics, opportunities and needs. So-called “one-size-fits-all” policies are in many cases not appropriate. Consequently, there is a growing demand for “place-based” policies and therefore a better understanding of different kinds of urban areas. Previous research has often focused on metropolises and megacities and paid little attention to small and medium-sized urban areas. Sweden, like many other countries, has few megacities but many small and medium-sized towns and cities. With respect to land area, Sweden is the third largest county in the EU but has the second lowest population density. Many urban areas in Sweden can therefore be characterized as “isolated” because they are a long way from neighbouring urban areas. It is therefore very important to get a deeper understanding of urban areas in Sweden and their functions as engines of growth. The Swedish Agency for Growth Analysis has been commissioned by the Swedish Government to analyse how different types of urban areas can contribute to national growth. The present report, Urban opportunities for growth - a description of Swedish urban areas and their opportunities for growth, has three objectives: first, construct a definition of urban areas; second, measure the characteristics of different urban areas; and third, gain an understanding of the opportunities for growth in different kinds of urban areas. The report consists of three main parts. The first part consists of a brief literature review and discusses the interrelationships between cities and rural areas, as well as the role of urban areas’ as regional and national growth engines. Urban areas can be described as centres of commerce, culture and learning, which in effect give agglomeration economies. This also has the consequence that certain industry sectors only develop, or develop better, in towns or cities. An important message from this part of the report is also that it is not only big cities that innovate, but rather that the characteristics of innovation vary between large and small urban areas. The second part of the report creates an analytical structure to analyse urban areas. The analytical structure makes it possible to study intraregional differences between urban and rural areas, as well as variations between different urban areas. The analytical structure is based on the idea that urban areas constitute a centre for a surrounding rural area. The definition of urban areas is based on a functional approach and consists of three main levels: localities (tätorter), urban areas (tätortsområden) and urban networks (nätverk). The definition has been constructed with the help of a Geographic Information System (GIS) and calculations of geographical accessibility, as well as population statistics geographically linked to a nationwide grid of 500x500metre squares. The report also attempts to classify urban areas according to their different functions. The different types of urban areas that are discussed are: urban areas according to population size, college towns, commuter towns, and single-industry cities. The report also presents a novel attempt to classify urban areas according to an urban hierarchy. The classification is

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constructed based on the degree of connectedness and the relative population size between urban areas. According to Growth Analysis’ definition Sweden has 221 towns and cities (urban areas). In 2009, 81% of the population lived in an urban area. Like many other countries, Sweden has many small towns but few large cities. Larger, more densely populated cities are generally associated with a higher labour productivity than smaller, less densely populated towns. However, the smaller towns exhibit a large variation in labour productivity and some of the smallest towns have some of the highest labour productivity values. Large cities in general have a more specialized service sector, a larger diversification of industry sectors and a workforce with a higher level of formal education than smaller urban areas. These results imply that some industry sectors only develop, or develop better, in large urban areas. This is especially evident in the service sector. In order for the service sector to grow and develop successfully, it is important not to prevent agglomeration. The development of the service sector in big cities would not be at the expense of its development in small towns or rural areas, since it is not possible for specialized services to be developed in these areas. The results in the report are in accordance with previous research and imply that it is important not to prevent agglomerations and that urban areas should be encouraged to take advantage of their comparative advantage. However, an increased specialization could, in especially small urban areas, also imply an enhanced risk and vulnerability. An important question is therefore how to manage the risks associated with specialization.

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Sammanfattning

Tillväxtanalys har fått regeringens uppdrag att ”analysera hur olika typer av städer på bästa sätt kan bidra till landets hållbara tillväxt.” Denna rapport syftar till att skapa en struktur för att studera och analysera städer, samt att med hjälp av ett urval av indikatorer och mått beskriva städers olika förutsättningar för tillväxt. Rapporten består av tre huvudsakliga delar: Den första delen baseras på tidigare studier och innehåller en beskrivning av beroendeförhållandet mellan stad och land, stadens roll som centrum och tillväxtmotor, samt koncentrationsfördelarnas betydelse för näringslivets val av lokalisering och inriktning. Det huvudsakliga budskapet i detta avsnitt är att staden i sin roll som mötesplats för handel, kultur och lärande ger upphov till viktiga agglomerationsfördelar. Detta betyder i sin tur att vissa branscher endast utvecklas, eller utvecklas bättre, i städer och framför allt i stora städer. Ett viktigt budskap från detta avsnitt är även att både stora och små städer kan vara innovativa, men att de troligen är innovativa på olika sätt. I rapportens andra del skapas en analytiskt användbar definition av staden, vilket gör det möjligt att studera skillnader mellan stad och land samt mellan olika städer. Definitionen tar sin utgångspunkt i att staden utgör ett centrum för en omgivande landsbygd och bygger på ett funktionellt synsätt där staden analyseras i olika nivåer. Definitionen är framtagen med hjälp av tillgänglighetsanalyser och befolkningsstatistik som geografiskt knutits till ett landstäckande rutsystem med en upplösning av 500x500 meter. Den tredje delen av rapporten innehåller en beskrivning av städerna och deras olika förutsättningar för tillväxt. Beskrivningen är gjord med hjälp av ett urval av indikatorer baserade på tidigare studier och syftet är att dessa indikatorer skall kunna användas för en löpande uppföljning av städers tillstånd och utveckling. Avsnittet innehåller även ett försök att identifiera och klassificera olika typer av städer, med syftet att få en bättre förståelse för olika städers roller och funktioner. Enligt Tillväxtanalys definition finns det 221 tätortsområden i Sverige. Dessa tätortsområden utgör den analytiska definition av städer som används i studien. År 2009 bodde 81 procent av Sveriges befolkning i en stad. Sverige består, likt många andra länder, av många små men få stora städer. Större och tätare städer har generellt en högre arbetsproduktivitet, än mindre och glesare bebodda städer. För de mindre städerna är variationen i arbetsproduktivitet stor, viket innebär att även en del mindre städer återfinns bland de med högst arbetsproduktivitet. Näringslivet i större städer kännetecknas dessutom av att vara mer kunskapsintensivt, att ha en högre branschdiversifiering, samt att vara mer specialiserat inom tjänstesektorn. Detta resultat tyder på att vissa branscher utvecklas bäst i stora städer, medan andra branscher utvecklas bäst i mindre städer och på landsbygden. En gynnsam regional och nationell tillväxt handlar således om att främja och ta vara på koncentrationsfördelarna i städerna, samt att stad och land, och olika typer av städer, identifierar sina komparativa fördelar och utvecklas inom dessa. En ökad specialisering ger flera fördelar, men kan i främst mindre städer leda till en ökad sårbarhet. En svår utmaning för politiken är således hur den ska främja en specialisering och samtidigt hantera städers och regioners sårbarhet.

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1 Introduction

Urban development has come to be an increasingly important element in growth policy. The European Commission’s Cohesion Policy clearly recognizes urban areas as important growth engines and has called for a specific urban policy agenda. Sweden’s regional growth policy is directed towards a placed-based policy, which means the regions are to develop on the basis of their own local prerequisites. As regional centres, urban areas are in a position to play a decisive role in regional and national growth, and so there is a need for a better understanding of different kinds of urban areas and their importance for growth. Growth Analysis has been commissioned by the Swedish Government to “analyse how different kinds of urban areas can best contribute to the country’s sustainable growth.” Growth Analysis’ assignment is to create an understanding of different kinds of urban areas and their opportunities for growth. An important aspect of the assignment is to also analyse the interdependency between urban and rural areas and discuss how this interdependency can be strengthened. The report aims to (i) devise an analytically useful definition of urban areas, (ii) achieve a better understanding of the difference between urban areas’, and (iii) describe their varying prerequisites for growth and development. The report thus has a descriptive purpose to give a general understanding of different urban areas and their opportunities for growth. The report is limited to the economic dimension of sustainable growth, which means that no indicators of environmental or social aspects are presented. The environmental and social aspects are on the other hand taken into account in the discussion. The report is further limited to describing urban areas’ opportunities for growth based on earlier studies and thus does not purport to explain for example the per capita variation in income or its growth. The report consists of three main parts. The first is presented in Chapter 2 and contains a general description of urban areas’ importance for growth based on earlier research. The starting point of the chapter is to describe how urban and rural areas complement and are dependent on each other, before moving on to describe urban areas’ importance for growth. The second part is presented in Chapter 3 and contains a description of how urban areas can be defined and described. Sweden is characterised by a comparatively sparse population structure with few large cities, which means that international studies of urban development are rarely adapted to Swedish conditions. Nor does Sweden have a generally accepted definition of urban areas and it is therefore unclear as to what actually constitutes an urban area, town or city. A major challenge has therefore been to create a structure to study and analyse urban areas. The analytical structure is based on a functional approach where an urban area can be analysed at different levels and takes its starting point in the fact that an urban area constitutes a centre for the surrounding rural area. The third part of the report is presented in Chapter 4 and contains a description of urban areas’ opportunities for growth. The description has been composed with the help of a selection of indicators and measures based on earlier studies and has a similar structure to the report “Regional tillväxt 2011” (Growth Analysis, 2011b). The chapter also contains a

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suggestion on how urban areas can be typologised and classified. The final and concluding chapter, Chapter 5, summarises the conclusions from the study.

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2 Regional growth

This chapter deals with the subject of regional growth, the importance of urban areas for growth, and the interaction between urban and rural areas. One important issue as regards regional growth is how urban areas can strengthen rural areas and how rural areas can strengthen urban areas. The chapter therefore begins by first discussing the different roles that urban and rural areas have and how urban and rural areas interact. The chapter then continues by describing the role of urban areas’ as growth engines and the location of industry.

2.1 Interdependencies between urban and rural areas The relationship between urban and rural areas is a kind of interdependency where town and city exchange goods with each other, see Figure 2–1 below. The rural area supplies the urban areas with goods mainly by producing food, raw materials and energy. The urban area supplies the rural areas with goods mainly by acting as a trading and meeting place. The urban areas also offer a substantial supply of culture, public and commercial service and act as a centre of knowledge and learning. The rural area is therefore dependent on the urban areas to be able to sell its goods but also for buying, for example, specialised services.

Figure 2-1 Interdependencies between urban and rural areas

Urban Rural

• Centre for trade, culture • Food, raw materials and knowledge and energy • Public and commercial • Labour service • Recreation • Labour market (natural resources) • Recreation (cultural activities)

Source: Growth Analysis.

There is also an exchange of labour between urban and rural areas. This flow is principally from rural to urban areas, but may also take place in the opposite direction. Another flow is recreation and tourism. People in urban areas want to have access to the forests and the countryside while people in the rural areas want to have access to cultural activities in the urban areas.

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Urban and rural areas can thus be described as playing different roles in the development of the region. The urban area acts as a meeting place for trade, culture and knowledge while the rural area is a producer of raw materials and energy. This picture is naturally a very generalised one; rural areas also have significant cultural treasures, for example. It is also important to remember that this line of discussion does not attempt to assign values, i.e. that the one is better than the other; urban and rural areas have different roles and complement and are dependent upon each other. It should also be emphasised that urban areas in Sweden do not necessarily need to buy their raw materials and their energy from Swedish rural areas and vice versa. This means that even though an interdependency exists between urban and rural areas, it is uncertain how strong this interdependency is between Swedish urban areas and Swedish rural areas.

2.2 Urban areas as engines of growth When economic activity is concentrated it gives rise to increased productivity. This usually manifests itself in the form of salaries and wages in the major cities being higher, i.e. that there is an urban wage premium. Since companies do not pay higher wages out of the goodness of their heart, this would seem to indicate that employees are more productive in large urban areas, partly because more productive employees tend to move to the city but also because employees become more productive in the big city. That an urban wage premium exists is thus an indication of agglomeration economies. The mechanisms that give rise to agglomeration economies usually have their origins in sharing, matching, and learning. Sharing means that several people can share the cost of various facilities, for example libraries and laboratories. It also means that it is profitable for similar companies to locate close to each other and “share” the same infrastructure, labour market, suppliers and so on. This in turn promotes a cheaper, faster, and more varied and specialised supply of input goods. Agglomeration also gives rise to matching between employees and employers, buyers and sellers of goods and services, or between potential business partners. The improvement occurs in the form of faster and/or better quality matching. Agglomeration also gives rise to increased learning. In a densely populated urban area, it is easier to both create and disseminate ideas, since the flow of knowledge and the possibility to interact are that much greater than in small, sparsely populated places or areas. Agglomeration economies can be divided into localisation and urbanisation economies. Localisation economies arise when companies in the same sector interact. Urbanisation economies arise when companies in different sectors interact, which enables so-called cross-pollination (Growth Analysis, 2010a). Another way of illustrating this is to describe the companies as having forward, backward and horizontal linkages. Forward, a company has linkages to the market and its consumers, backward to suppliers and horizontally to the companies in the same sector and/or in different sectors. Companies strive as a rule to have close forward, backward and horizontal linkages, since they want to take advantage of sharing, matching and learning. The urban area’s role as a meeting place for trade, culture and learning means that it acts as a growth engine for its region and the surrounding rural area. The positive advantages that result from agglomeration do not however need to be limited to urban areas. Theoretical studies have shown that increased agglomeration stimulates innovative activity, which can benefit the whole economy (Fujita and Thisse, 2003, quoted in Growth Analysis, 2010a). A similar argument can be found in Gunnar Myrdal’s theory of spread and backwash

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effects. The increased productivity in the urban areas has an ability to spread to the surrounding rural area, giving rise to a spread effect. It also causes knowledge, capital and labour to be concentrated to the urban areas. This means that the increased productivity in urban areas first gives rise to spread effects and then that parts of these effects are “washed back” in a so-called backwash effect. According to Myrdal, both these effects cancel each other out only occasionally, meaning that the differences between urban and rural areas increase (Myrdal 1955, quoted in Hagget, 2001). This may lead to some antagonism between urban and rural areas, but probably does not constitute an obstacle to national growth. For regional and national growth it is important to create an understanding of how urban and rural areas can complement and benefit from each other, i.e. take advantage of the spread effects and reduce some of the backwash effects (but without sheltering regions or creating a dependency culture). The links between urban and rural areas can be strengthened by creating opportunities for interaction between the two, i.e. providing incentives to interact, and a physical and virtual accessibility that makes interaction possible. An even more important aspect is to create an understanding of urban and rural areas’ different roles, how urban and rural areas can complement each other, and thereby identify and further develop their comparative advantages. This is the subject of the next section.

2.3 Industry’s localisation and focus According to Krugman (1991), the location of industry is principally determined by two facts: economies of scale and transportation costs. Economy of scale in production means that companies choose to establish themselves in a few places, which, due to transportation costs, will be close to the company’s customers. That companies choose to establish themselves close to their customers is then assumed to mean that individuals will move to where the companies are. In this theory, the transportation costs are the key that starts up the localisation process, in which the initial conditions, where people live, largely determine the location. According to Midelfart-Knarvik and Overman (2002), industry’s localisation is mainly determined by two factors; industries’ concentration advantages and the mobility of input factors. This theory gives an understanding of how different industries choose to locate. Some industries have small or no concentration advantages, others primarily have concentration advantages with companies in the same industry, while a third group principally has concentration advantages with companies in other industries. In some industries, the input factors are mobile (e.g. telemarketing) while in others they are more stationary (e.g. agriculture). The two factors, industries’ concentration advantages and the input factors’ mobility, give rise to six possible outcomes, see Figure 2-2. If the concentration advantages and the input factors’ mobility are small, the companies will be geographically spread over the whole country depending on where the input factors are situated (outcome 1). Agriculture is an obvious example. If, on the other hand, the factors are mobile and there are significant concentration advantages within an industry, that industry will be concentrated to certain places. This has been compared to industry- specific “black holes”, referring to the black holes in space that suck in anything that happens to be close by (outcome 4). If instead the input factors are mobile and concentration advantages exist both within and between different industries, companies are assumed to locate in one and the same place, i.e. there is one “black hole” (outcome 6).

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The industry-specific “black hole” gives rise to co-location advantages and can for example be found in industry clusters or specialised urban areas. The “one black hole” is on the hand an expression of urbanisation economies and can be found in large diversified urban areas. Where concentration advantages exist, and capital is highly mobile at the same time as labour mobility is low, the companies and capital owners will be more inclined to move than the employees. This can lead to an economic polarisation between those who live in the centre and those who live on the periphery (outcome 5). Since the concentration advantages are substantial while the factors’ mobility is limited, the region is assumed to specialise in certain individual industries depending on the region’s assets (outcome 3). If, conversely, the concentration advantages are small and the factors are highly mobile, the input factors are assumed to move to regions where they give the greatest return, which also leads to regional specialisation (outcome 2).

Figure 2-2 Scenario of industry’s localisation Concentration advantages Small Large Large (within industry) (within and between industries) Small (1) Geographical (2) Regional specialisation depending on assets

spread Employees: Small (3) Regional (5) Possible polarisation Capital: Large specialisation (4) Industry-specific

mobility depending on return Large “black holes” (6) One “black hole” The factors’

Source: Midelfart-Knarvik and Overman (2002), SOU (2007:25).

Midelfart-Knarvik and Overman’s explanation of industry’s localisation can also be seen in a historical perspective. Historically, employment was mainly in the agrarian industries. The factors’ mobility was therefore limited and the concentration advantages small, which meant a geographical spread of labour over the whole of Sweden. The industrial revolution changed all this and production came to be characterised by greater and greater concentration advantages and the input factors no longer being restricted. Over the past 30 years, industry has also come to be characterised as more service- and knowledge-based, which has meant that the concentration advantages have become of even greater importance. All in all, this has led to increased regional specialisation and the occurrence of so-called “black holes”. In the discussion above, concentration advantages were compared to the “black holes” in space that suck in all matter within a certain distance from them. Concentration advantages have thus been compared to natural forces. Another comparison often made in the literature is that the existence of concentration advantages makes it like “standing on a slippery surface”. Towns, cities and regions that want to resist these natural forces must thus master the art of being “sticky places on a slippery space” (Markusen, 1996). Towns and regions are “sticky” when the mobility of the main input factors is restricted. Åre has for example developed into a popular skiing resort thanks to its mountain location.

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And thanks to their climate, Arjeplog and Arvidsjaur have developed into winter test centres for the automotive industry. Location-specific assets, for example nature and culture, are not things that can easily be relocated to other parts of the country. Not all places, however, have a high mountain or a cold climate. A place can also be “sticky” through enterprises in industries with low concentration advantages, for example that an enterprise is focused on more standardised production. Vernon (1960, quoted in McCann, 2001) states that the companies’ location depends on where in the production cycle the company is. Early in the production cycle, a company is focused on information-intensive activities such as research and development. These are more readily carried on in large urban areas. When the product is fully developed and the production process has attained a certain level of maturity and is characterised as being more standardised, it is an advantage to locate production to the periphery, where costs are lower. Activities with significant concentration advantages should therefore be located to the major cities, while activities with small concentration advantages, e.g. standardised production, should be located where they are most cost effective. Some businesses are characterised as having both small concentration advantages and mobile input factors, e.g. telemarketing. These types of businesses can be established in both urban and rural areas. Since the input factors are mobile and the concentration advantages small, it is however difficult to make these operations “sticky”. In the Europe 2020 strategy, the focus is on both smart growth and smart specialisation. Smart growth means that the European Union must improve its results in education, research, innovation and the digital society. Smart specialisation means that regions must identify their comparative advantages and focus on a few important priorities instead of spreading the investments over different areas and sectors. These priorities must not come from the European, national or regional level, but should come from the companies, centres of research, universities and so on. The role of the politicians should instead be to support the entrepreneurs’ role of identifying comparative advantages (European Commission, 2010; McCann and Ortega-Argilés). Towns, cities and regions shall thus focus on smart growth and smart specialisation. Towns, cities and regions, however, can be smart in different ways. Capello and Lenzi (2011) identify three main types of regions: (i) Regions focused on research and development, in particular basic research with application in several areas. (ii) Applied research regions, which use existing research to develop new products, and (iii) imitative regions, which imitate and refine products and services that others have developed. This is also in agreement with Orlando and Verba (2005), who by using data on American patents showed that both large and small regions can be innovative, but that they are innovative in different ways. In large regions, patents are registered mainly in new areas and technologies while in smaller regions patents are registered mainly in more mature industries.

2.4 Summarising conclusions Urban and rural areas fulfil different functions, which implies that they complement each other in different ways. Urban areas act as a meeting place for trade, knowledge and culture while the rural areas produce food, raw materials and energy. Those industries that are characterised by large concentration advantages primarily establish themselves in urban areas, especially large ones, while industries with small concentration advantages mainly establish themselves in smaller urban areas. This also means that enterprises

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focused on information and development of products mainly establish themselves in urban areas (metropolitan areas) while more mature production will mainly locate to rural areas (and the smaller urban areas). This also means that some industries only develop, or develop better, in large towns or cities. This development will not take place at the expense of the rural areas or smaller towns, since it is not possible for these industries to develop there. Favourable regional and national growth is therefore a matter of promoting and exploiting concentration advantages in the urban areas and of urban and rural areas, as well as different kinds of urban areas, identifying their comparative advantages. This conclusion is in line with how the cohesion policy is designed. The cohesion policy can be divided into an efficiency objective and an equity or social inclusion objective. The efficiency objective is directed towards taking better advantage of local and regional opportunities for growth. This includes, among other things, promoting and exploiting the concentration advantages in the urban areas and ensuring that there are no obstacles to natural agglomerations. The equity objective implies that all parts of the country shall be given the opportunity to have access to a basic level of service supply within a reasonable distance. This means among other things giving towns, cities and regions the possibility to master the art of “standing in a sticky place on a slippery space” (Barca, 2009; Ministry of Finance, 2011).

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3 Defining urban areas – Can it be done?

3.1 What constitutes a town or city? Sweden has no definition of what constitutes a “town” or “city”. Historically, a place could be granted a town charter, which among other things meant that it received the right to call itself a “stad” (i.e. town or city). Town charters were initiated with the aim of creating a more powerful nation. By bringing together a relatively large population into a common settlement, better preconditions would be created for production and trade, which would give rise to greater riches. The officials therefore focused on a more cohesive settlement where trade and handicrafts would be concentrated to the urban areas by means of legislation and other coercive powers. The urban areas were thus given certain rights and among other things had a monopolistic position in practising trade and handicraft. They also had certain obligations, for example the administration of justice (SOU 1967:58). During the 1800s, the town charter system gradually lost its significance and the concept of rural and urban municipalities, which had a special form of government, was introduced in the Local Government Act of 1862. As part of the municipal reform of 1971, this was changed again; all municipalities were given equal status and were to be governed in the same way (Nationalencyklopedin). The official concept of urban areas thereby ceased to exist. On the other hand, a number of municipalities have chosen to call themselves cities in different contexts1. They are, however, still municipalities in a legal sense. (For more information about those municipalities that had earlier received town privileges, see Fact Box 3-11 below.) Defining a city can also be a linguistic question. In Sweden, a difference is made between “by” (village) and “stad” (town or city), and sometimes also between “småstad” (small “stad”) and “storstad” (large or metropolitan “stad”). This can be compared to the United Kingdom where a difference is made between village, town and city, where a town is larger than a village, and a city is larger than a town. In Spanish there is a similar division (pueblo, villa, and ciudad). This usage does not exist in Sweden and the English terms “town” and “city” are both translated as “stad” in Swedish. This is also true of German where stadt is translated as both town and city (Google translate). Studying how urban areas are defined in other countries can possibly provide insight into the construction of a Swedish definition of “stad”. A starting point for an international comparison is that a “stad” can be compared to a town. It is thus the lower limit we are interested in to decide when a place becomes a “stad” (town). In an international comparison it is clear that there are great differences as regards what constitutes a town. This fact quite naturally demonstrates the difficulty in defining towns and cities. It is usual for the size of the population to be part of the definition but the actual limit varies widely. In France and Portugal, the limit for a town is approximately 3,000 inhabitants, while in Greece and Spain it is approximately 10,000, and in Austria and Germany approximately 20,000 (ESPON, 2006). In some countries, a town is defined both by the number of inhabitants and the fact that it contains certain central functions. In Portugal, a vilas (town) must have at least 3,000 inhabitants and be able to offer at least

1 The municipalities that have chosen to call themselves ""cities""" are: Borås, Göteborg, Haparanda, Helsingborg, Landskrona, Lidingö, Malmö, Mölndal, Solna, Stockholm, Sundbyberg, Trollhättan, Vaxholm and Västerås. http://www.skl.se/web/Kommuner_som_atertagit_benamningen_stad.aspx

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half of the following functions: health care centre, pharmacy, cultural centre, public transport, post office, shopping centre, hotel, pre-school or bank. For a vilas (town) to be promoted to a cidades (city) it must have at least 8,000 inhabitants and be able to offer at least half of the following functions: hospital, pharmacy, fire service, theatre/cultural centre, museum, library, hotel, elementary school, public transport and public parks (www.povt.gren.pt). A Swedish definition of a town might thus be based on (i) those localities that have historically been granted town privileges, (ii) those localities that have a certain minimum number of inhabitants, or (iii) localities that have a certain minimum number of inhabitants and that offer certain central functions. One important question is what the purpose of a definition of urban areas would be. If there are no legal differences between towns and other places, what would the purpose of a definition of urban areas be? This report does not therefore present a definition of what urban areas are but instead presents a structure for studying urban areas. The following sections describe how this structure has been created.

Fact Box 3-1 Municipalities that have had town privileges. When the municipal reform of 1862 passed into law in 1863, Sweden had 88 “städer” (towns/cities). At the time of the municipal reform of 1952, the number of towns/cities had grown to 133 (Wångmar, 2003). No new towns/cities were formed in 1952, and the municipal division into “stad” (town), “köping” (smaller towns with some market rights) and “landskommun” (rural municipality) was abolished in the municipal reform of 1971 (Statistics Sweden, 1986). One of the reasons why no new towns/cities were formed after 1952 was probably that the legal differences between the various municipal divisions had begun to disappear. A list of the municipalities that were designated “städer” in 1952 can be found by studying the municipality codes from that year. A municipality code, then as now, consists of four digits, of which the first two represent the county to which the municipality belongs and the final two are the municipality’s serial number. When the codes were assigned in 1952, the urban areas were given a number between 80 and 99 where the county seat was number 80. “Köping” and “Landskommun” were given a number between 60 and 79 and 01 and 59 respectively. When the division into “städer”, “köpingar” and “landskommun” was abolished in 1971, no changes were made to the earlier numbering. The municipalities that in 2011 have a municipality code between 80 and 99 are thus the municipalities that were previously called “städer”. Of the original 133 “städer”, 124 remain today, although in a somewhat changed form. The other nine have been absorbed by other municipalities. The table below shows the municipalities that were designated as “stad” according to the municipal reform of 1952. The table also shows details of which municipalities are county seats (CS), the municipalities that have been absorbed by other municipalities (A) and those that were formerly county seats (FCS). The table also shows the year town privileges were granted.

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County and municipality Town since County and municipality Town since Stockholm (01) Halland (13) Stockholm (CS) ca. 1250 Halmstad (CS) 1200s Nynäshamn (FCS) 1946 Laholm unknown Södertälje unknown Falkenberg unknown Nacka 1949 Varberg unknown Sundbyberg 1927 Kungsbacka unknown Solna 1943 Västra Götaland (14) Djursholm (A) 1914 Göteborg (CS) 1619 Lidingö 1926 Mölndal 1922 Vaxholm 1652 Kungälv unknown Norrtälje 1622 Marstrand (A) 1200s Öregrund (A) unknown Lysekil 1903 Sigtuna ca. 1000 Uddevalla 1400s Uppsala (03) Strömstad 1672 Uppsala (CS) ca. 1300 Vänersborg (CS) 1642 Enköping ca. 1300 Trollhättan 1916 Östhammar 1368 Alingsås 1649 Södermanland (04) Borås 1622 Nyköping (CS) 1200s Ulricehamn unknown Oxelösund 1950 Åmål 1646 Flen 1949 Mariestad (CS) 1583 Katrineholm 1917 Lidköping unknown Eskilstuna 1659 Skara unknown Torshälla (A) ca. 1300 Skövde ca. 1400 Strängnäs 1300s Hjo unknown Mariefred (A) 1605 Tidaholm 1940 Trosa unknown Falköping unknown Östergötland (05) Värmland (17) Linköping (CS) 1100s Karlstad (CS) 1584 Norrköping 1300s Kristinehamn 1642 Söderköping 1200s Filipstad 1611 Motala 1881 Hagfors 1950

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Vadstena 1400 Arvika 1911 Skänninge (A) ca. 1200 Säffle 1951 Mjölby 1920 Örebro (18) Jönköping (06) Örebro (CS) 1200s Jönköping (CS) 1200s Kumla 1942 Huskvarna (A) 1911 Askersund 1643 Nässjö 1914 Karlskoga 1940 Värnamo 1920 Nora 1643 Sävsjö 1947 Lindesberg 1643 Vetlanda 1920 Västmanland (19) Eksjö 1400s Västerås (CS) unknown Tranås 1919 Sala 1624 Gränna (A) 1652 Fagersta 1944 Kronobergs län (07) Köping 1400s Växjö (CS) 1300s Arboga 1200s Ljungby 1936 Dalarna (20) Kalmar (08) Falun (CS) 1641 Kalmar (CS) 1100s Borlänge 1944 Nybro 1932 Säter 1642 Oskarshamn 1856 Hedemora 1400s Västervik 1200s Avesta 1919 Vimmerby unknown Ludvika 1919 Borgholm 1816 Gävleborg (21) Gotland (09) Gävle (CS) 1300s Visby (CS) unknown Sandviken 1943 Blekinge (10) Söderhamn 1620 Karlskrona (CS) 1680 Bollnäs 1942 Ronneby 1882 Hudiksvall 1582 Karlshamn 1664 Västernorrland (22) Sölversborg 1400s Härnösand (CS) 1585 Skåne län (12) Sundsvall unknown Kristianstad (CS) 1622 Kramfors 1947 Simrishamn unknown Sollefteå 1917 Ängelholm ca. 1500 Örnsköldsvik 1894

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Hässleholm 1914 Jämtland (23) Malmö (CS) 1200s Östersund (CS) 1786 Lund ca. 1000 Västerbotten (24) Landskrona 1413 Umeå (CS) 1588 Helsingborg unknown Lycksele 1946 Höganäs 1936 Skellefteå 1845 Eslöv 1911 Norrbotten (25) Ystad unknown Luleå (CS) 1621 Trelleborg 1867 Piteå 1621 Skanör med Falsterbo (A) unknown Boden 1919 Haparanda 1842 Kiruna 1948

Source: Statistics Sweden (1986) and Tidens kalender (1970)

Remarks: CS = County Seat, A = Absorbed by another municipality, FCS = Formerly County Seat. Nynäshamn was the county seat of Stockholm County (Stockholm was City of Stockholm), Kristianstad was the county seat of Kristianstad County, Vänersborg of Älvsborg County and Mariestad of Skaraborg County.

3.2 A definition of localities, urban areas and their networks

3.2.1 How are urban areas defined? To be able to perform statistical calculations and analyses of urban areas, the area must be clearly delimited and defined. There are three overarching methods of defining an urban area: administratively, physically or functionally (OECD). The administrative method is based on already existing administrative boundaries. In Sweden’s case this might mean that certain municipalities are referred to as urban municipalities, as they were in the past. In Growth Analysis (2011a), two ways of classifying Swedish municipalities are described. The main advantage of an administrative method is that it is relatively simple to obtain data for the areas and that it corresponds to the political mandate. The drawback of an administrative division is that it does not necessarily reflect “real” boundaries. An urban area defined on the basis of an administrative method can thus comprise both urban and rural areas. The starting point of the physical method is an area’s physical characteristics, e.g. population density or the density of the built-up areas. The physical definition is thus more advanced than the administrative method and gives greater possibilities to measure what are considered to be “real” boundaries. This method, however, is more demanding as regards the availability of data and the tools and systems that are used. Defining localities according to a physical method can also make political control difficult. The functional method is based on the functional behaviour of households and companies. Nutek’s (The Swedish Agency for Economic and Regional Growth) division into FA

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regions (Functional Analysis Regions) is an example of a functional division since it is based on commuting patterns. The physical and functional methods are mainly used in this report. The register-based statistics that Statistics Sweden produces can in most cases be geographically positioned with great accuracy. The geographic positioning is based either on where people live (i.e. individual information such as age, education, income, etc.) or where people work (i.e. work place information such as branch of industry). Through a number of databases, Growth Analysis has at its disposal register-based statistics that have been linked geographically to a nationwide grid with a resolution of 500x500 metres or 250x250 metres. To create the definition of the urban areas, physical boundaries were determined using GIS and were then linked to the data. (For more information about the data and the geographical analyses used, see Fact Box 3-2.) In a similar way to Geyer (2002), an analytical framework that consists of three levels – micro-level, meso-level and macro-level (see Figure 3-1) – is used. The micro-level is the smallest geographical unit and consists of small individual areas (localities). At the meso- level, the areas from the micro-level form common urban areas, while the macro-level joins the different urban areas together to form a network. The three different levels are called localities, urban areas and networks.

Figure 3-1 Framework for analysing urban areas

Macro: Networks

Meso: Urban Areas

Micro: Localities

Source: Growth Analysis based on Geyer (2002)

Fact Box 3-2 Information about geographical analyses in the report The geographical analyses are supported by the GIS platform PiPoS (GIS = Geographical Information System, PiPoS = PinPoint Sweden). The basic aim of a geographical analysis is to identify how different phenomena relate to each other in a geographical perspective. The databases used in PiPoS are therefore geo-coded, i.e. have coordinates assigned. An accessibility analysis is an example of a geographical analysis. One form of accessibility analysis is to make calculations of the shortest distance in time between a starting point and a destination point. In this report, calculations are made of both what

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statistical squares can be reached within a certain driving distance by car from each locality’s outer boundary and the driving time distance between different urban areas’ centre-points. PiPoS distinguishes itself through its ability to make accessibility analyses of detailed input data. The calculations made in this report are based on squares with a resolution of 250 metres and a road network called Nationella Vägdatabasen (The National Road Database) or NVDB for short. The NVDB contains all of Sweden’s roads, right down to forest routes and also contains details of the roads’ speed limits. This means that time can be used as a measure in accessibility calculations. If the road database is of sufficiently high quality, the time measure describes accessibility better than a linear measure. A kilometre of gravel road is not the same as a kilometre of motorway from the point of view of accessibility. To obtain statistics of population, number of people in employment, salary totals, etc., Growth Analysis uses the Individ- och Företagsdatabas (Database of Individuals and Companies), known as the IFDB. The IFDB contains statistics from Statistics Sweden, for example RTB (population), KU (income statements) and LISA (sickness insurance and labour market studies). In the IFDB, the statistics can be linked geographically to a nationwide grid of 500x500metre squares. This means that the accessibility analyses in the report were performed in PiPoS, which uses 250x250metre squares. All statistics of localities and urban areas, however, are taken from the IFDB, which has a resolution of 500x500 metres. Unless stated otherwise, all statistics presented in the report are from 2009. For more information see http://www.tillvaxtanalys.se/sv/statistik/geografisk-analys-pipos/

3.2.2 Micro-level: Localities The first level in the locality system, the micro-level, refers in this report to “localities” and consists of Statistics Sweden’s localities that have a population of 3,000 or more. Statistics Sweden defines a locality as a group of buildings normally not more than 200 metres apart from each other, and must fulfil a minimum criterion of having at least 200 inhabitants (see Fact Box 3-33). This definition has been in use since the 1960s and is with few exceptions common to all the Nordic countries. The former National Rural Development Agency (Glesbygdsverket) used Statistics Sweden’s definition of locality in its divisions of urban areas, rural areas close to urban areas and sparsely populated rural areas. The starting point for the definition of urban area is to separate out Statistics Sweden’s localities with a population of 3,000 inhabitants or more. The 3,000-inhabitant limit was chosen on the assumption that localities of that size have reached a certain critical limit of public and commercial service. A limit of 3,000 inhabitants can also be justified on the basis of the limits in other countries. As described in the previous section, the Swedish word “stad” can be translated as both “town” and “city”. As mentioned earlier, the limit for “town” in France and Portugal is 3,000 inhabitants. Choosing a limit of 3,000 can thus be justified on the basis of previous limits both in Sweden and in other countries. On the assumption that localities of this size have reached a certain critical level of public and commercial service, the locality is assumed to act as a centre and meeting place for a surrounding rural area. This gives the definition a distinct town-country perspective. Setting a relatively low limit is also justified since in this report it is of interest to study several different kinds of locality.

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Fact Box 3-3: Statistics Sweden’s definition of locality Statistics Sweden’s definition of locality has been in use since 1960 and is common to all the Nordic countries. The purpose of the locality statistics is to show where in the country the population is concentrated. The locality delimitation and its statistics are updated every five years, the latest update having been made in 2010. Statistics Sweden defines a densely built-up area as all groups of buildings normally not more than 200 metres apart from each other and fulfilling a minimum criterion of having at least 200 inhabitants. The distance stated can be permitted to exceed 200 metres in the case of groups of buildings within the area of influence of a large population centre. The maximum distance between the buildings can also be set lower than 200 metres in those cases where, in small localities, there is no distinct locality centre and in cases where the boundary between the locality and the rural area is blurred, in other words when the buildings in the locality do not stand out as being considerably more densely concentrated than nearby built-up areas. The delimitation of built-up areas also includes uninhabited houses and buildings used exclusively as workplaces. Farm buildings are not included in a built-up area unless they are fully detached from the main farm property. The question of whether second homes (such as summer homes) are to be included or not is a question left to the individual countries. In Sweden, areas of second homes are not counted as localities. For them to be so, at least half must be permanent residences. Institutions and similar that are situated outside built-up areas are counted as localities if the number of resident staff including their families, but not including patients, totals at least 200. Even if the distance between the buildings exceeds 200 metres, using the area between the buildings for the public good in the form of for example roads, car parks, parks, sports facilities and churchyards is not considered a discontinuation of the built-up area. The same applies to vacant space such as storage areas, railway lines and wharves. The division into densely and sparsely built-up areas is independent of the administrative division. Groups of buildings that constitute a direct continuation of a densely built-up area in a neighbouring municipality are therefore included in the locality (Statistics Sweden, 2005). Statistics Sweden also provides statistics for “smaller localities” and for “concentrations of workplaces outside localities”. Smaller localities are defined as continuous built-up areas with not more than 150 metres between the buildings and having 50-199 inhabitants, and concentrations of workplaces outside localities as continuous areas with not more than 300 metres between the workplaces (buildings) and at least 50 employees. A manual check has been made of each workplace concentration and those that are situated close to the edge of the locality have been included in the locality delimitation (see Statistics Sweden 2007, 2010). An illustration of where localities, smaller localities and concentrations of workplaces outside localities are situated can be found at http://www.gis.scb.se

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3.2.3 Meso-level: Urban areas In its definition of urban areas, the former National Rural Development Agency first used Statistics Sweden’s localities of 3,000 inhabitants or more and then calculated a hinterland of five minutes’ journey by car from the border of the locality and outwards. This method is also used in Growth Analysis (2010b). Placing a hinterland around the localities can be justified on the basis of two aspects: 1) to combine neighbouring localities into the same urban area, in order to 2) create a functional definition. This is based on the assumption that households and companies do not differentiate between neighbouring communities but assume them to be part of the same functional urban area. Adding a hinterland of five minutes to Statistics Sweden’s localities thus gives a more functional division in different urban areas. The meso-level is termed “urban areas” and consists of localities plus a hinterland of five minutes’ journey by car. When a locality’s hinterland is in direct contact with another locality, a common urban area is formed. An urban area can thus contain several localities. In addition, when a locality’s hinterland overlaps part of a Statistics Sweden locality with less than 3,000 inhabitants, the hinterland is adjusted to also encompass that entire locality. The hinterland is calculated from the locality’s boundary and as far as it is possible to travel by car in five minutes at the posted speed. For those urban areas that contain several localities, this means that the localities are situated no further than five minutes by car from each other. Figure 3-2 shows the delimitation of localities and urban areas for Göteborg and Borås. In the figure, the localities (more than 3,000 inhabitants) are shown in black and the urban areas in grey. The dark-grey areas are localities with fewer than 3,000 inhabitants that are enclosed in an urban area. The figure thus shows how neighbouring localities are joined together into common urban areas. Localities with fewer than five minutes between them belong to the same urban area, i.e. if the journey time between locality A and locality B is five minutes they belong to the same urban area. This means that a person who lives five minutes outside locality A, i.e. in the locality’s hinterland, has the same accessibility to locality A as a person in locality B. This line of reasoning is the reason why people who live in the hinterland have also been included in the calculation of the urban area’s population.

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Figure 3-2 Delimitation of localities and urban areas for Göteborg and Borås

Source: Growth Analysis (PiPoS).

Remarks: The localities are shown in black and the urban areas in grey. The dark-grey areas are localities with fewer than 3,000 inhabitants enclosed within the urban areas. The red lines constitute 45 minutes’ accessibility between urban areas (see the next section).

The criterion for belonging to the same urban area is that there must be at least five minutes between the localities’ respective boundaries. This causes “knock-on” effects, i.e. if there are five minutes between localities A and B, and five minutes between localities B and C, but more than five minutes between A and C, localities A, B and C will nonetheless belong to the same urban area. Some localities are situated more than five but less than ten minutes from another locality, which means that the localities’ hinterlands overlap each other. This also manifests itself in Figure 3-2 where Borås’ urban area is detached but nonetheless adjoins Bollebygd to the west and Viskafors to the south. These adjacent urban areas enable the urban areas to be studied on the basis of different levels: localities (the micro-level), urban areas (the meso- level) and adjacent urban areas (more than five minutes but less than ten minutes away). This also illustrates the difficulties in creating a definition for “stad”: how should urban areas be geographically defined?

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3.2.4 Macro-level: Networks Urban areas often have connections with other urban areas. Those that are part of networks can take advantage of each other’s functions. In Chapter 2, it was stated that the advantages of urban areas are the possibilities for sharing, matching and learning. When different urban areas are joined together in a network, sharing different functions (ports, airports, libraries, etc.) is made easier through better matching in the labour market, which is a natural result of a larger labour market, and better opportunities for learning and exchange of knowledge. Urban areas in a network are thus allowed to specialise and thereby complement each other. This argument is also related to the discussion of regional expansion. The various networks can be schematically described as in Figure 3-3 below. A centre consists of one dominating urban area (centre) and several smaller ones (neighbourhoods). A polycentre consists of several urban areas of equal standing that together have several centres. Some urban areas do not have any connections to other urban areas and are thus “isolated”. An isolated urban area has no connections to other urban areas and therefore cannot utilise the advantages of sharing, matching and learning in the same way as other urban areas in the network. Isolated urban areas must thus be “self-supporting” as regards important public functions. An urban area in a network, on the other hand, does not need to be self-supporting to the same extent but can take advantage of the functions of neighbouring urban areas.

Figure 3-3 Schematic image of urban areas and types of networks

Isolated Centre Polycentre

Source: Growth Analysis (2010b).

Urban areas can be said to have networks at different levels. For the purposes of the present report, the lowest level is Statistics Sweden’s localities with more than 3,000 inhabitants. The second level is urban areas, which consist of localities plus a hinterland. Those localities that are part of the same urban area can thus be interpreted as being part of a common network. The Stockholm urban area consists for example of the Stockholm locality and a number of smaller suburbs.

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The urban areas also have connections to other neighbouring urban areas. Stockholm has for example connections out into the Lake Mälaren valley and Göteborg is not far from Borås, Trollhättan and so on. Section 4.4.3 describes how connections between urban areas can be analysed. Two urban areas are considered to be part of the same network if the distance between their centres is 45 minutes or less. Figure 3-4 on pages 30 and forward, show maps of Sweden which display the division into localities, urban areas and networks. The figure initially consists of a map of the whole of Sweden and then of more detailed maps. The more detailed maps allow differences to be studied between the various delimitations between localities and urban areas. The localities are shown in black and the urban areas in grey. The red lines represent networks/connections between urban areas. Urban areas are the level that is of primary interest in the present report and that constitute the analytical definition of “urban areas” and of “town and city”. (The map is also displayed on our website: http://www.tillvaxtanalys.se/sv/statistik/geografisk-analys- pipos/Tatortsomraden/).

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Figure 3-4 Map of Sweden, its localities, urban areas and networks

Source: Growth Analysis (PiPoS).

Remarks: Localities are shown in black and urban areas in grey. Red lines indicate networks/connections within a range of 45 minutes. Dark grey areas are localities with fewer than 3,000 inhabitants inside urban areas.

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32 URBAN OPPORTUNITIES FOR GROWTH

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URBAN OPPORTUNITIES FOR GROWTH

4 Urban areas and their opportunities for growth and development

4.1 Population This section describes the structure of the population in localities, urban areas and rural areas. For the purposes of the present report, all land area that is not urban areas is defined as rural areas. In the same way as urban areas can be divided into different types of urban area, rural areas can also be divided into different types of rural area.

4.1.1 Localities As described in earlier sections, urban areas are made up of one or more localities and their hinterland(s). All in all, Sweden has 221 urban areas and 337 localities, which means that some urban areas comprise more than one locality. Table 4-1 shows the number of urban areas that have one, two or more localities. A clear majority (185) of the urban areas consist of one locality. The population in these urban areas ranges from 3,557 to 122,877 inhabitants. This means that there are both large and small urban areas that contain only one locality. The table also shows that there are 17 urban areas that consist of two localities, 12 that consist of three, and so on. The Stockholm locality consists for example of a total of 24 different localities.

Table 4-1 Number of urban areas containing one, two or several localities, and variation in the urban areas’ population Number of urban areas Number of localities Population (urban areas) Mean value Median Min Max 185 1 13,679 8,941 3,557 122,877 17 2 39,830 23,196 8,710 155,331 13 3 68,987 61,582 21,332 142,640 2 (Borås, Jönköping) 4 106,172 106,172 91,418 120,926 1 (Helsingborg) 6 147,419 147,419 147,419 147,419 1 (Malmö) 19 522,069 522,069 522,069 522,069 1 (Göteborg) 22 782,739 782,739 782,739 782,739 1 (Stockholm) 24 1,808,494 1,808,494 1,808,494 1,808,494 221 337

Source: Statistics Sweden (RTB), Growth Analysis (PiPoS)

In the case of those urban areas that consist of more than one locality, the name of the largest locality is used as the name of the urban area. The urban areas often consist of a dominating locality. If instead two localities are considered to be equally large when the smaller locality is 70% of the size of the larger locality, only four urban areas are included: Sölvesborg (8,405 inhabitants) and Bromölla (7,512), Ursviken (3,905) and Skelleftehamn (3,185), Vaggeryd (4,870) and Skillingaryd (3,887) and Åstorp (9,380) and Bjuv (6,728). A hinterland of only five minutes’ journey by car may seem short, but it means that for all urban areas the area of the hinterland is larger than the area of the locality (or localities).

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The median of the localities’ share of the total area of the urban areas is 9%. The majority of the population in urban areas, however, live in a locality. In eight urban areas, less than 50% of the population live in a locality. This is fully acceptable if we assume that people five minutes from a locality have very good accessibility to the locality and thus should belong to its urban area.

4.1.2 Urban areas Table 4-2 shows the population of urban and rural areas in 2009. All in all, there are 221 different urban areas with a total population of 7.6 million people, which is equivalent to 81% of Sweden’s population. This means that 19% of Sweden’s population live in rural areas. As stated earlier, localities can be divided into localities and hinterlands. 6.7 million people live in the localities, which is equivalent to 71% of Sweden’s population, and 0.9 million live in the hinterlands.

Table 4-2 Population in urban and rural areas in 2009 Population Proportion of Sweden’s population Urban areas (221) 7,577,578 81.12% Urban areas (337) 6,659,509 71.30% Hinterland 918,069 9.83% Rural area 1,763 083 18.86% SWEDEN 9,340,661 100%

Source: Statistics Sweden (RTB), Growth Analysis (PiPoS)

Figure 4-1 shows the proportion of the population in Functional Analysis regions (FA regions) which lives in an urban area or a rural area. (For more information about FA regions, see Growth Analysis, 2010d.) In 52 out of 72 FA regions, more than 50% of the population live in an urban area. Stockholm is the FA region with the largest proportion of residents in urban areas (92%), closely followed by the FA regions Malmö (91%) and Göteborg (88%). 12 FA regions do not have any urban areas at all. These regions have localities, of course, but no Statistics Sweden locality with a population over 3,000. The Gällivare and Kiruna FA regions belong to the regions where a high proportion of the population live in an urban area. These regions might therefore be able to be termed “urban regions”. These regions are at the same time characterised by rural areas with long distances to other urban areas, which shows the difficulty of designating FA regions by whether they are urban or rural regions.

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Figure 4-1 Proportion of the FA regions’ population living in urban or rural areas

Stockholm Malmö Västerås Göteborg Hällefors Karlskoga Borås Östergötland Gällivare Jönköping Riket Gävle Karlstad Eskilstuna Ludvika Kiruna Nyköping Blekinge Luleå Örebro Halmstad Kristianstad Tranås Fagersta Falun/Borlänge Sundsvall Skövde Haparanda Arvidsjaur Avesta Umeå Trollhättan Kalmar Mora Lidköping Älmhult Ljungby Värnamo Filipstad Västervik Vetlanda Örnsköldsvik Malung Söderhamn Oskarshamn Växjö Skellefteå Lycksele Vilhelmina Hagfors Sollefteå Vimmerby Hudiksvall Kramfors Östersund Ljusdal Gotland Torsby Årjäng Bengtsfors Strömstad Jokkmokk Pajala Övertorneå Överkalix Arjeplog Sorsele Åsele Dorotea Storuman Härjedalen Vansbro Eda 0,0% 20,0% 40,0% 60,0% 80,0% 100,0%

Tätortsområde Landsbygd

Source: Statistics Sweden (RTB), Growth Analysis (PiPoS)

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Figure 4-2 shows the proportion of the population of Sweden’s counties which lives in an urban or rural area. Unlike the FA regions, there are urban areas in all counties. In all counties except Gotland and Jämtland, most of the population lives in an urban area.

Figure 4-2 Proportion of the counties’ population living in urban or rural areas

Stockholms län Skåne län Västmanlands län Östergötlands län Västra Götalands län Riket Södermanlands län Örebro län Blekinges län Jönköpings län Hallands län Uppsala län Värmlands län Dalarnas län Norrbottens län Gävleborgs län Västernorrlands län Kalmars län Västerbottens län Kronobergs län Gotlands län Jämtlands län

0 0,2 0,4 0,6 0,8 1

Tätortsområde Landsbygd

Source: Statistics Sweden (RTB), Growth Analysis (PiPoS)

Table 4-3 shows the number of urban areas for different population intervals. Only three urban areas have a population of over half a million (Stockholm, Göteborg and Malmö). These three together have a population of 3.1 million, which is equivalent to 41% of the total population of the urban areas, or 33% of the country’s total population. A third of Sweden’s population thus lives in the metropolitan areas of Stockholm, Göteborg and Malmö.

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Eight urban areas have a population of between 100,000 and 500,000, while ten have a population of between 50,000 and 100,000. Most localities, 100 in all, have between 3,000 and 10,000 inhabitants. Most of the 221 urban areas thus consist of smaller urban areas.

Table 4-3 Number of localities for different population intervals Population interval Number of Total population Proportion of population Proportion of for urban areas urban areas in urban areas Sweden’s population 500,000 – 3 3,113,302 41.1% 33.3% 100,000 – 499,999 8 1,027,242 13.6% 11.0% 50,000 – 99,999 10 748,068 9.9% 8.0% 20,000 – 49,999 38 1,202,517 15.9% 12.9% 10,000 – 19,999 52 752,684 9.9% 8.1% 3,000 – 9,999 110 733,765 9.7% 7.9% Total 221 7,577,578 100.0% 81.1%

Source: Statistics Sweden (RTB), Growth Analysis (PiPoS)

Table 4-4 The 40 urban areas with the greatest number of inhabitants Rank Urban area Population Rank Urban area Population 1 Stockholm 1,808,494 21 Kalmar 52,817 2 Göteborg 782,739 22 Nyköping 48,569 3 Malmö 522,069 23 Borlänge 48,205 4 Uppsala 155,331 24 Karlskrona 47,120 5 Helsingborg 147,419 25 Östersund 47,087 6 Örebro 142,640 26 Kristianstad 46,991 7 Västerås 122,877 27 Skövde 41,868 8 Linköping 121,450 28 Falun 39,850 9 Jönköping 120,926 29 Karlskoga 36,829 10 Gävle 109,657 30 Uddevalla 36,515 11 Norrköping 106,942 31 Landskrona 36,346 12 Borås 91,418 32 Märsta 36,222 13 Umeå 86,552 33 Varberg 35,864 14 Karlstad 86,229 34 Skellefteå 35,556 15 Eskilstuna 84,899 35 Åkersberga 34,419 16 Trollhättan 77,558 36 Åstorp 33,728 17 Sundsvall 73,482 37 Ängelholm 33,714 18 Halmstad 71,621 38 Örnsköldsvik 33,331 19 Växjö 61,910 39 Trelleborg 33,283 20 Luleå 61,582 40 Motala 31,569

Source: Statistics Sweden (RTB), Growth Analysis (PiPoS)

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Table 4-4 shows the 40 urban areas that have the most inhabitants. Table 4-4 shows among other things that Stockholm has the largest population, 1.8 million, which is even larger than the rural population (see Table 4-2). The reason why this list differs from other lists of urban areas’ sizes is probably due to how the urban areas have been defined. The values in Table 4-4 differ from both the municipality’s size and Statistics Sweden’s definition of a locality. In some urban areas, the size of the population is probably underestimated because the urban area is delimited. In order for two localities to belong to the same urban area, they must be five minutes or less apart. This means for example that Östersund and Brunflo are two urban areas and not one. If the limit had instead been set at ten minutes, Malmö, Landskrona and Helsingborg would have belonged to the same urban area, and Stockholm and Uppsala would have belonged to the same urban area. Once again, this shows the difficulty in creating a definition of localities and urban areas. A useful approach, as is adopted in this report, is to consider urban areas as consisting of different levels. Östersund and Brunflo, and also Malmö, Landskrona and Helsingborg, will prove to have connections with each other when we study the next level: the urban areas’ networks or “adjacent urban areas”. It can be seen from Table 4-3 that there are many small but few large urban areas in Sweden. Table 4-4 also shows that Stockholm is more than twice the size of Göteborg and almost three times as big as Malmö. A natural question to ask is therefore whether this heavily skewed distribution of urban area sizes means that Sweden is different to other countries. The answer is no – the distribution is similar in most countries. The fact that Göteborg has fewer than half the number of inhabitants of Stockholm is in agreement with a rule of thumb known as the “Rank-Size” rule. The Rank-Size rule states that the second largest urban area has approximately half the population of the largest urban area, the third largest roughly a third of the population of the largest urban area and so on. The Rank-Size rule can thus be formulated as saying that the ith urban area’s rank (Ri) can be calculated by dividing the population of the largest urban area (B) by the population of the ith largest urban area (Bi). Or alternatively, that the size of the ith urban area’s population can be calculated by dividing the number of inhabitants of the largest urban area (B) by the ith urban area’s rank (Ri), i.e.:

= = . 퐵 퐵 푅푖 ↔ 퐵푖 퐵푖 푅푖 Göteborg should be half the size of Stockholm, and Malmö should be a third of its size and so on. One way of illustrating how well Sweden’s urban areas agree with the Rank-Size rule is to take the (natural) logarithm of population (lnBi) and the rank (lnRi), and plot the values as shown in Figure 4-3. The black dots represent the different urban areas and the dashed line how the relationship would look if the Rank-Size rule were completely fulfilled. The Rank-Size rule is thus in fairly good agreement with Sweden’s urban areas and this rule of thumb is approximately true for most countries. This also means that even when the distribution of the population in Sweden’s urban areas is also skewed, with few large urban areas and many small ones, the distribution agrees with the situation in other countries (Hagget, 2001).

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Figure 4-3 The Rank-Size rule for Sweden’s urban areas 14 12 ln(Population) 10 8

0 2 4 6 ln(Rank)

Source: Statistics Sweden (RTB), Growth Analysis (PiPoS)

Table 4-5 shows the change in population between 2000 and 2009 in urban areas and rural areas. Populations have increased in the urban areas at the same time as they have fallen in rural areas. The urban areas’ share of the total population has also increased over the same period. The calculation was made using the 2010 Statistics Sweden locality delimitation, which means that the table shows how the urban areas of 2010 developed between 2000 and 20092. It is difficult to study the urban areas over time since the delimitation of the urban area is constantly changing. This means that if a similar calculation of urban areas had been made for 2000, this would probably have given other boundaries and possibly a somewhat different picture than the one presented in Table 4-5. Table 4-6 shows the number of urban areas where the population either increased or decreased between 2000 and 2009. In the same way as in Table 4-5 the calculation was made using the 2010 urban area delimitations. In 2000, there were 112 urban areas with between 3,000 and 10,000 inhabitants. In 2009, there were 109 in the 3,000-10,000 interval, while three urban areas had grown and moved to the 10,000-20,000 interval. In 2000, there were 52 urban areas in the 10,000-20,000 interval. In 2009, 49 of them still remained in the 10,000-20,000 interval, while one had moved to the interval below (3,000- 10,000) and two had moved to the next interval (20,000-50,000). Another look at the table shows that the change over time in the number of urban areas in different population intervals is fairly small. Most of the population intervals consisted of roughly the same number of urban areas in 2009 as they did in 2000.

2 Population data from 2009 has been used for consistency with the data available for other variables.

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Table 4-5 Change in population over time Areas Population Proportion of total population 2000 2009 2000 2009 Rural areas 1,803,666 1,763,083 20% 19% Urban area 7,079,082 7,577,578 80% 81% Locality 6,232,404 6,659,509 70% 71% Hinterland 846,678 918,069 10% 9% Total 8,882,748 9,340,661 100% 100%

Source: Statistics Sweden (RTB), Growth Analysis (PiPoS)

Table 4-6 Transition matrix of the number of urban areas in different population intervals (000s) between 1999 and 2009 2009 Total 1999 3 – 10 10 – 20 20 – 50 50 – 100 100 – 500 500 – 1999 3 – 10 109 3 112 10 – 20 1 49 2 52 20 – 50 36 36 50 – 100 10 10 100 – 500 8 1 9 500 – 2 2 Total 2009 110 52 38 10 8 3 221

Source: Statistics Sweden (RTB), Growth Analysis (PiPoS)

Table 4-7 shows the population’s age distribution for urban and rural areas. Of the population in urban areas, 23% are aged between 0 and 19, 56% are aged between 20 and 64 and 21% are aged 65 and over. The population in the rural areas has the same proportion aged between 0 and 19 as the urban areas, while the proportion aged 65 and over is a little higher (approximately four percentage points). The urban areas thus have a larger proportion between the ages of 20 and 64 and a smaller proportion aged 65 and over than the rural areas. A look at the upper part of the table, however, reveals that most people aged 65 and over live in an urban area. Table 4-8 shows the population’s age distribution for urban areas of various sizes. Larger urban areas generally have a higher proportion of people of working age (20-64) and as a rule a somewhat smaller proportion aged 65 and over. The larger urban areas (50,000 inhabitants or more) have the highest proportion of all age groups. This means that even while the smaller urban areas generally have a higher proportion of their population aged 65 and over, most people aged 65 and over can be found in the larger urban areas.

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Table 4-7 Age distribution in urban and rural areas 0-19 20-64 65+ Total Population Rural areas 412,210 982,063 368,791 1,763,064 Urban areas 1,775,768 4,479,786 1,321,954 7,577,508 Total 2,187,978 5,461,849 1,690,745 9,340,572 Proportion of population Rural areas 23% 56% 21% 100% Urban areas 23% 59% 17% 100% Total 23% 58% 18% 100%

Source: Statistics Sweden (RTB), Growth Analysis (PiPoS)

Remarks: The urban areas’ total percentage is not 100 due to rounding.

Table 4-8 Age distribution among urban areas distributed over the urban areas’ population intervals Population interval 0-19 20-64 65+ Total 500,000 - 24% 61% 15% 100% 100-000 - 500,000 23% 60% 17% 100% 50-000 - 100,000 22% 60% 18% 100% 20-000 - 50,000 23% 57% 20% 100% 10-000 - 20,000 23% 56% 21% 100% 3,000 – 10,000 24% 55% 21% 100% Urban areas, total 23% 59% 17% 100% 0-19 20-64 65+ Total 500,000 - 42% 43% 35% 41% 100-000 - 500,000 13% 14% 13% 14% 50-000 - 100,000 9% 10% 10% 10% 20-000 - 50,000 16% 15% 18% 16% 10-000 - 20,000 10% 9% 12% 10% 3,000 – 10,000 10% 9% 12% 10% Urban areas, total 100% 100% 100% 100%

Source: Statistics Sweden (RTB), Growth Analysis (PiPoS)

The number of men and women in urban and rural areas is shown in Table 4-9. The table shows that most people in rural areas are men, while most people in urban areas are women. It should be emphasised, however, that the differences are relatively small. If we instead study the age distribution, we see that more men aged 0-19 and 20-64 live in urban areas than women in the same age groups. Men are also in the majority in rural areas in the same age groups. In the 65+ age group, women are in the majority in both urban areas and rural areas.

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Table 4-9 Number of men and women in urban areas and rural areas Men Women Total Rural areas 906,575 856,489 1,763,064 Urban areas 3,742,473 3,835,035 7,577,508 Whole country 4,649,048 4,691,524 9,340,572 Men Women Total Rural areas 20% 18% 19% Urban area 80% 82% 81% Whole country 100% 100% 100% Men Women Total Rural areas 51% 49% 100% Urban areas 49% 51% 100% Whole country 50% 50% 100%

Source: Statistics Sweden (RTB), Growth Analysis (PiPoS)

Table 4-10 Number of men and women of different ages in urban areas and rural areas 0-19 20-64 65+ Total Men Rural areas 212,132 512,328 182,115 906,575 Urban area 911,546 2,259,435 571,492 3,742,473 Whole country 1,123,678 2,771,763 753,607 4,649,048 Women Rural areas 200,078 469,735 186,676 856,489 Urban area 864,222 2,220,351 750,462 3,835,035 Whole country 1,064,300 2,690,086 937,138 4,691,524

Source: Statistics Sweden (RTB), Growth Analysis (PiPoS)

Most people in an urban area live in a locality, while a clear majority of the land area is allocated to the hinterland. This makes it difficult to describe the population density in urban areas correctly. Probably the most correct way, and the way in which the calculation has been made in the present report, is to calculate the population density for the locality (or localities) that are part of the urban area. The population density of an urban area ranges from 245 to 2,444 people per square kilometre. The median is 835 inhabitants/km2. Stockholm has the highest population density (2,444), closely followed by Uppsala (2,208), Malmö (2,207) and Landskrona (1,911). Figure 4-4 shows the relationship between population density and population size for the 221 urban areas. There is a strong correlation between population density and population size (log, correlation = 0.77). This means that larger urban areas generally have a higher population density than smaller urban areas. Population density in urban areas of the same size also varies widely. As can be seen from the figure, it can vary by as much as 1,000 inhabitants/km2 between two urban areas of roughly the same size.

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Population density not only varies between urban areas but also between localities within the urban areas. In the Stockholm urban area, the locality of Fisksätra has the highest population density with 4,866 inhabitants/km2, while the localities of Stockholm and Bålsta have 3,002 and 868 inhabitants/km2, respectively. In the Stockholm urban area, the locality of Stockholm has the largest population (1.3 million) and the largest area (448 km2). In such a large land area, there may naturally be substantial differences in population density, i.e. there may also be differences in population density within localities, but this has not been investigated.

Figure 4-4 Relationship between population density and size, urban areas 2500 2000 1500 1000 Population density Population 500 0

8 10 12 14 ln(Population)

Source: Statistics Sweden (RTB), Growth Analysis (PiPoS)

Remarks: Population density = population per km2. The urban areas’ populations and population densities have been calculated on the basis of their associated localities, i.e. without their hinterland.

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4.2 Economy This section describes differences between the urban areas as regards labour productivity, income per capita, daytime and night-time populations and the structure and organisation of business and industry.

4.2.1 Labour productivity and income per capita The present report uses two measures to describe the urban areas’ income and production: • Labour productivity • Income per capita Labour productivity is a measure of the production value generated at places of work located in the urban area and is calculated as the daytime population’s income per person employed in the urban area. Income per capita is a measure of the population’s income and opportunities for consumption and is calculated as the night-time population’s income per inhabitant. These measures were earlier used to describe regions’ production and income in the reports “Regional tillväxt” (Growth Analysis, 2009) and “Regionernas tillstånd” (ITPS, 2008). Since the measures are based on income figures that can either be localised to workplaces or individuals’ registered addresses, they can be calculated for the urban areas, which requires statistics with a high geographical resolution. For a more detailed discussion of these measures’ advantages and disadvantages, see ITPS (2008).

Figure 4-5 Relationship between labour productivity and population (left) and population density (right) 350000 350000 300000 300000 Labour productivity Labour productivity 250000 250000 200000 200000 8 10 12 14 0 500 1000 1500 ln(Population) Population density

Source: Statistics Sweden; KU, LISA, Growth Analysis (PiPoS).

Figure 4-5 shows the relationship between the urban areas’ labour productivity and population size and population density. There is a low positive correlation (0.52) between the urban areas’ size and labour productivity. This means that larger urban areas generally have higher labour productivity than smaller urban areas. The smaller urban areas do, however, show significant variation in labour productivity. Some of the smaller urban areas can thus be found among those with the highest labour productivity (see Table 4-11). It should nonetheless be noted that there are no large urban areas in the group with lowest labour productivity.

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Figure 4-5 shows the relationship between labour productivity and population density (daytime population/km2). Urban areas with a high population density generally have high labour productivity (correlation = 0.51). This result is in agreement with earlier studies (see for example Growth Analysis, 2010a). However, it is important to emphasise that Figure 4-5 does not show a correlation but only a covariation between two variables. Explaining why some urban areas have high labour productivity and others low would require a much more extensive analysis which lies outside the scope of the present report. (For a more detailed discussion of the difficulties in interpreting the covariation between population density and labour productivity, see Growth Analysis, 2010a, page 24.)

Table 4-11 The ten urban areas with the highest labour productivity in 2009 Urban area Labour productivity Population size Stockholm 335,921 1,808,494 Finspång 324,182 14,582 Älmhult 322,364 10,518 Ursviken 321,790 8,710 Stenungsund 318,957 6,658 Linköping 315,102 19,884 Kiruna 308,293 121,450 Göteborg 307,967 18,079 Olofström 304,107 782,739

Source: Statistics Sweden; KU, LISA, Growth Analysis (PiPoS).

Figure 4-6 Relationship between labour productivity in 2000 and 2009 (left) and between population size and annual average growth in labour productivity between 2000 and 2009 (right) .04 350000 .03 300000 .02 .01 250000 Labour Productivity 2009 Labour Productivity 0 Growth in labour productivity 2000-2009 inGrowth productivity labour -.01 200000 150000 200000 250000 300000 8 10 12 14 Labour Productivity 2000 ln(Population 2000)

Source: Statistics Sweden; KU, LISA, Growth Analysis (PiPoS).

Figure 4-6 above shows the relationship between labour productivity in 2000 and 2009 (left), and between the urban areas’ size and growth in labour productivity (right). Those urban areas that had a high level of labour productivity in 2000 (fixed prices) generally

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also had a high level of labour productivity in 2009 (correlation = 0.82). The relationship between urban areas over time with regard to labour productivity thus seems to be stable. Figure 4-6 also shows the annual growth rate of labour productivity for urban areas of different population size. All urban areas except two have seen positive growth. There is no covariation between the urban areas’ size and the growth in labour productivity. This means that even when large urban areas generally have a higher level of labour productivity, they also generally have higher growth in labour productivity. Most urban areas have an annual average rate of growth of 1-3%. The greatest variation can be seen in the smaller urban areas, representing both those with the highest and those with the lowest growth. An increase in labour productivity is obtained either by an increase in income or a decrease in employment. Åtvidaberg and Vaxholm are the only urban areas where growth was negative between 2000 and 2009. In these two urban areas, development of daily income was negative at the same time as the number of people in employment decreased. Only 16 urban areas, however, have seen negative growth in daily income and 77 negative growth in employment. Most urban areas have thus seen positive growth in both daily income and the number of people in employment.

Figure 4-7 Relationship between population size and income per capita (urban areas, 2009) 160000 140000 120000 Income capita per 100000 80000 8 10 12 14 ln(Population)

Source: Statistics Sweden; KU, LISA, Growth Analysis (PiPoS).

Figure 4-7 above shows the relationship between the urban areas’ size and income per capita. Just like the relationship between the urban areas’ size and labour productivity, the covariation between the urban areas’ size and income per capita is positive. Another similarity is that the largest variation in income per capita can be found amongst the slightly smaller urban areas. All in all, income per capita ranges from 86,330 to 167,581 SEK per person. The income per capita values are thus lower than the labour productivity values, which can mainly be explained by the fact that the daily income is set in relation to

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population instead of to the number of people employed. The spread in income per capita between urban areas is also considerably lower than the spread in labour productivity3. The ten urban areas with the highest income per capita are shown in Table 4-12. Several of these urban areas are “suburbs” or neighbouring areas to Stockholm. Four urban areas that were also among the areas with the highest labour productivity can also be found here: Stockholm, Kiruna, Älmhult and Stenungsund.

Table 4-12 The ten urban areas with the highest income per capita in 2009 Urban area Income per capita Population size Vaxholm 167,581 9,962 Knivsta 163,535 11,718 Stockholm 161,031 1,808,494 Åkersberga 156,474 34,419 Ljungsbro 150,897 9,217 Stenhamra 150,530 4,318 Kiruna 149,875 18,079 Stenungsund 147,632 19,884 Älmhult 146,371 10,518 Åsa 143,648 8,420

Source: Statistics Sweden; KU, LISA, Growth Analysis (PiPoS).

The next figure, Figure 4-8, shows the relationship between annual average income per capita between 2000 and 2009 and income per capita in 2000. All urban areas except one (Perstorp) have seen a positive growth in income per capita. The figure shows that there exists a negative covariation between initial income per capita and growth in income per capita, which means that that those urban areas that in 2000 had a relatively low income per capita, grow faster than those urban areas that had a relatively high income per capita. Urban areas with a low income per capita thus catch up with urban areas that have a high income per capita (i.e. β-convergence). The spread in income per capita between urban areas has also decreased between 2000 and 2009 (i.e. σ-convergence).4

3 Calculated as the difference in standard deviation and the difference in range (difference between maximum and minimum). 4 The standard deviation and range of log income per capita has been used as a measure of dispersion (variation).

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Figure 4-8 Annual average growth in income per capita between 2000 and 2009 and income per capita in 2000 .03 .02 .01 0

11 11.2 11.4 11.6 11.8 12 ln(income per capita 2000)

Source: Statistics Sweden, Growth Analysis (PiPoS).

4.2.2 Daytime and night-time population In the previous section’s description of labour productivity and income per capita, the terms daytime population and night-time population were used. This section will look more closely at daytime and night-time population to determine which urban areas have a net in- or out-commuting rate. Daytime population is defined as the number of people in employment with their working place in the urban area (which is the same variable that is used to calculate labour productivity). The night-time population is calculated as the number of people in employment who are resident in the urban area (which is not, on the other hand, the same variable that is used to calculate income per capita). The night-time population is limited to population in employment for it to be able to be related to the daytime population. The ratio between daytime and night-time population shows whether the urban area has a net in- or outflow of labour. A ratio above 1 shows that more people commute in to the urban area to work than there are people in employment who live there. A ratio less than 1 similarly shows that the urban area has a net outflow of people in employment. Table 4-13 shows the ratio between the daytime and night-time population in employment in urban and rural areas. The ratio is above 1 for the urban areas and less than 1 for the rural areas, showing that the urban areas have a net in-commuting of labour while the rural areas have a net out-commuting. This indicates that urban areas act as centres for the rural areas.

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Table 4-13 Ratio between daytime and night-time population for urban areas and rural areas Area Daytime/night-time population ratio Rural areas 0.87 Urban areas 1.03 Total 1.00

Source: Statistics Sweden, Growth Analysis (PiPoS).

Remarks: Daytime and night-time population in employment

Figure 4-9 shows the relationship between the urban areas’ size and the ratio between the daytime and night-time population. It is principally small and medium-sized urban areas that have a daytime/night-time ratio of less than 1, i.e. those with a population of less than 36,000 inhabitants (ln[population] below 10.5). It is also among the small and medium- sized urban areas that the spread in daytime/night-time ratio is the greatest. Urban areas with a ratio of less than 1 give an indication of being “commuter towns” or “sleeper towns” where people live at the same time as they commute to work in another urban area. A commuter town is expected to be a smaller town situated close to a larger town or city. Since its inhabitants mainly work somewhere else, a commuter town is expected to have lower industry diversification and a less extensive supply of commercial service. A commuter town has several advantages from being situated so close to a large urban area, such as access to a larger labour market and a greater supply of shops. The greater supply of shops in the larger urban areas means that there is less incentive to open shops etc. in the smaller town. A commuter town thus has both advantages and disadvantages.

Figure 4-9 Relationship between the size of the urban areas’ populations and the ratio between the daytime and night-time population in 2009 1.5 1 .5 daytime/night time population daytime/night time 0

8 10 12 14 ln(Population)

Source: Statistics Sweden, Growth Analysis (PiPoS).

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Few guidelines exist for how to define a commuter town. SKL (2011) defines a suburban municipality as a municipality where more than 50% of the night-time population commute to work in a metropolitan municipality and defines a commuter municipality as a municipality where more than 40% of the night-time population commute to work in another municipality. This definition is based solely on out-commuting and not on net commuting. Table 4-14 shows how many urban areas would be classified as commuter towns if the limit were set to a net out-commuting rate of 50%, 40%, 30% and so on (share of net out-commuting calculated as 1-daytime/night-time ratio). If the delimitation for commuter town is out-commuting of 50% or more, 16 urban areas can be classified as commuter towns. If, on the other hand, the delimitation is 10% or more, 89 urban areas can be classified as commuter towns. The table also shows that urban areas with a high level of out-commuting have comparatively low diversification of trade and industry.

Table 4-14 Number of urban areas and diversification of trade and industry per proportion of net out- commuting Proportion of Number of Diversification of trade and industry out-commuting urban areas Median Min Max (net) (cumulative) 0.50-1.00 16 0.19 0.14 0.44 0.40-1.00 30 0.22 0.14 0.44 0.30-1.00 45 0.22 0.14 0.44 0.20-1.00 60 0.24 0.14 0.46 0.10-1.00 89 0.25 0.14 0.46 0.05-1.00 105 0.27 0.14 0.52 0.001-1.00 119 0.27 0.14 0.52

Source: Statistics Sweden, Growth Analysis (PiPoS).

Remarks: Proportion of net out-commuting calculated as 1 – daytime/night-time ratio

The map in Figure 4-10 shows which urban areas have negative or positive net commuting. Urban areas are thus only divided according to whether net commuting is negative or positive and not how positive or negative. The map thus gives an indication of which towns are commuter towns and which are work towns. It is also clear from the map that the large urban areas have a positive net commuting rate while the closest smaller towns have a negative net commuting rate. Once again, it should be emphasised that commuting not only takes place between urban areas but also between urban and rural areas. Four urban areas with no close connections to other urban areas have negative net commuting: Nynäshamn (0.59), Arvidsjaur (0.89), Gällivare (0.96) and Malung (0.98, daytime/night-time ratio in brackets). This may be partly due to commuting being to another urban area further than 45 minutes away. As stated earlier, calculations are from centre to centre. From Nynäshamn, for example, the distance to Stockholm’s suburbs is less than 45 minutes. Another possible explanation is that people commute to a concentration of workplaces outside localities. A closer study of these urban areas shows that most commuting from Nynäshamn is to Stockholm and that most commuting from Arvidsjaur, Gällivare and Malung is to their associated municipalities. From Gällivare, people probably commute to a mining area and from Malung to the skiing resort of Sälen.

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Figure 4-10 Map of urban areas and their net commuting

Source: Statistics Sweden, Growth Analysis (PiPoS).

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Table 4-15 shows the urban areas with the highest and lowest daytime/night-time ratio. The urban areas with the lowest ratios are mainly smaller communities situated close to larger urban areas. The urban areas with the highest daytime/night-time ratios are both small and medium-sized urban areas. A high daytime/night-time ratio may be a result of in-commuting from other urban areas, but also in-commuting from the surrounding rural area. Urban areas can thus constitute a centre not only for the surrounding rural area, but also for other urban areas.

Table 4-15 Urban areas with highest and lowest ratio between daytime and night-time population (2009) Highest daytime/night- Lowest daytime/night- time ratio time ratio Älmhult 1.56 Storvreta 0.25 Tingsryd 1.47 Vi 0.30 Olofström 1.36 Stenhamra 0.30 Skövde 1.33 Björklinge 0.34 Torsby 1.32 Ljungsbro 0.34 Markaryd 1.31 Åsa 0.36 Laxå 1.30 Viskafors 0.40 Sunne 1.28 Veberöd 0.41 Sundsvall 1.27 Vaxholm 0.44 Kristianstad 1.25 Åkersberga 0.45

Source: Statistics Sweden, Growth Analysis (PiPoS).

4.2.3 Structure of trade and industry Table 4-16 shows the proportion of people employed in different sectors in rural areas, urban areas and in the whole country. The rural areas have a large proportion of people employed in Agriculture, forestry and fishing and Construction than the urban areas have. Employment in urban areas is characterised more by services, in particular Business services (business-to-business), Private household services and Miscellaneous services. On the other hand, there are hardly any differences in the proportion of people employed in Manufacturing and mining, Energy, water and waste, and Public household services (see Table 1 in the Appendix for an explanation of industrial sectors). Most people employed can be found in the urban areas. The only industrial sector with most people employed in the rural areas is Agriculture, Forestry and Fishing. Table 4-17 shows the proportion of people employed in different sectors distributed over urban areas of varying size. Smaller urban areas generally have a higher proportion of people employed in Agriculture, Forestry and Fishing and Manufacturing and mining, while larger urban areas have a higher proportion of people employed in Business services, Private household services and Miscellaneous services. There are small or no differences between Energy, water and waste, Construction or Public household services. The results in Table 4-16 and Table 4-17 are as expected and in accordance with Chapter 2. Urban areas, and in particular larger urban areas, are expected to be more oriented towards the service sector and specialised business services, while rural areas and smaller towns have more agriculture and manufacturing.

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Table 4-16 Employment by industry sector in urban and rural areas, 2009 Industry sector Rural areas Urban areas Total Agriculture, forestry and fishing 7% 1% 2% Manufacturing and mining 16% 14% 15% Energy, water and waste 1% 1% 1% Construction 10% 6% 7% Business services 14% 22% 21% Private household services 11% 16% 15% Public household services 28% 27% 27% Miscellaneous services 7% 13% 12% Unknown 6% 0% 1% Total 100% 100% 100% Total number in employment 734,703 3,556,384 4,291,087

Source: Statistics Sweden, Growth Analysis (PiPoS).

Table 4-17 Employment in different industry sectors distributed over the urban areas’ size intervals Urban area size interval 100,000 500,000+ – 50,000 – 20,000 – 10,000 – 3,000 – Industry sector 499,999 99,999 49,999 19,999 9,999 Total Agriculture, forestry and fishing 0% 1% 1% 1% 1% 3% 1% Manufacturing and mining 10% 14% 13% 17% 24% 25% 14% Energy, water and waste 1% 1% 1% 1% 1% 1% 1% Construction 6% 6% 6% 6% 7% 7% 6% Business services 28% 23% 18% 16% 14% 12% 22% Private household services 17% 15% 16% 15% 14% 14% 16% Public household services 23% 28% 31% 31% 28% 30% 27% Miscellaneous services 14% 13% 14% 13% 11% 9% 13% Total 100% 100% 100% 100% 100% 100% 100%

Source: Statistics Sweden, Growth Analysis (PiPoS).

Larger urban areas are also expected to have a more diversified industry structure. Figure 4-11 shows the relation between the urban areas’ population and a measure of industry diversification. The industry diversification measure is calculated as the number of existing sectors in every urban area in relation to the total number of possible sectors (where the number of sectors is calculated as the number of unique four-digit SNI-codes). An urban area that has a value of 1 thus has all sectors represented, while an urban area that has a value of 0.5 has only half of all possible sectors represented and so on. This measure of industry diversification has previously been used in for example Growth Analysis (2011b). The FA regions show a positive, high correlation between population size and industry

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diversification. From Figure 4-11 we can see that the relationship between population size and industry diversification is also strong (0.97) in urban areas. The industry diversification measure in Figure 4-11 is primarily to be interpreted as a measure of vulnerability, where urban areas with a low level of industry diversification is more sensitive to industry-specific shocks and cyclical fluctuations. Another measure of vulnerability that has also been used, for example in Growth Analysis (2011b), is Concentration of workplaces. Concentration of workplaces is calculated as the number of people employed at the five largest companies in the Manufacturing and mining category in relation to the total number of people in employment in the urban area. Manufacturing and mining is a sector that is to a large extent governed by market forces and is sensitive to external shocks. Counting only the number of people employed at the five largest companies also shows the dependency on individual companies. An urban area with a high concentration of workplaces thus indicates that urban areas are sensitive to external shocks and cyclical fluctuations.

Figure 4-11 Relationship between urban areas’ population size and industry diversification, 2009 1 .8 .6 .4 Industry diversification .2

8 10 12 14 ln(Population)

Source: Statistics Sweden, Growth Analysis (PiPoS).

Figure 4-12 shows the relationship between the size of different urban areas and the concentration of workplaces (left) and the relationship between concentration of workplaces and industry diversification (right). The smaller urban areas show significant variation in workplace concentration. Smaller urban areas can also be found among those that have the highest and the lowest concentration of workplaces. Figure 4-12 also shows that those urban areas that have a high concentration of workplaces also have a low level of industry diversification and should thus be especially vulnerable. There are also a great many urban areas that have both a low level of industry diversification and a low concentration of workplaces. These are thus characterised by a relatively one-sided industry structure. But this could also be explained by the proportion of people in manufacturing and mining not being particularly large, or that there few large workplaces.

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The measures of industry diversification and concentration of workplaces are primarily measures of vulnerability and thus function less well when it comes to assessing the degree of specialisation in a certain industry. The term single-industry cities describes smaller urban areas whose trade and industry is strongly concentrated to a particular branch of industry. Kiruna and Gällivare are examples of single-industry cities, since their trade and industry is strongly focused on mining. Kiruna and Gällivare both have a workplace concentration of 0.2 and an industry diversification of 0.36.

Figure 4-12 Concentration of workplaces in relation to population size (left) and industry diversification (right) .5 .5 .4 .4 .3 .3 .2 .2 Concentration workplaces of Concentration workplaces of .1 .1 0 0

8 10 12 14 .2 .4 .6 .8 1 ln(Population) Industry diversification

Source: Statistics Sweden, Growth Analysis (PiPoS).

High industry diversification is not only positive from a vulnerability point of view but can also contribute to the development of trade and industry. When mining is for example established in a town, it not only creates jobs in the mining industry but it is also expected to have spillover effects into other sectors. The higher the industry diversification, the higher the number of local companies that will benefit from the spillover effects. High industry diversification is thus also an advantage for towns with a more specialised trade and industry (Growth Analysis, 2010f). Industries are dependent on each other, which means that a demand shock can send shock waves to other industries. A high level of industry diversification is thus no guarantee that the industry structure is not one-sided. A single-industry city with high industry diversification probably has a fairly one-sided industry which is sensitive to external shocks. Industries being dependent on each other are not, however, unique to single- industry cities. Increased outsourcing has led to different industries becoming more dependent on each other, with the result that a demand shock in one industry also affects other industries (Svensson, 2010). In the case of small urban areas there are several advantages to being specialised, due to the co-location advantages mentioned in Chapter 2. Smaller urban areas can therefore be described as facing a problem as to whether they should be specialised or diversified. For small urban areas, a specialised industry means a greater chance of returns at the same time as their vulnerability is greater. A small urban area with a more diversified industry structure is therefore less vulnerable but at the same time has a greater difficulty in being

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the market leader. In general a small urban area grows thanks to its specialisation but survives thanks to a diversified industry structure (Growth Analysis, 2010a). Larger urban areas generally gain from being diversified. Entrepreneurial activity can be described as an experimental activity where new ideas are tested and tried out. A larger urban area has therefore a greater demand for differentiated, specialised and new products. Large differentiated urban areas thus act as “experimental workshops” where new ideas are generated, tested and developed. Empirical studies have also shown that large, diversified urban areas stimulate innovation and entrepreneurship (Growth Analysis, 2010a). The Europe 2020 strategy focuses on smart growth and smart specialisation, which means that regions are to specialise in areas where they have a comparative advantage. Because of industries’ interdependency, it is difficult to measure the degree of specialisation and vulnerability. It is also uncertain whether it is the role of government agencies to measure the degree of specialisation. According to the Europe 2020 strategy, the choice of specialisation shall not be decided through a top-down approach, but should instead be developed by those best equipped to do so, i.e. the entrepreneurs. The politicians’ role is to support the entrepreneurs in their role of identifying the comparative advantages (European Commission, 2010; McCann and Ortega-Argilés). It is thus probably of greater importance to measure vulnerability than specialisation.

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4.3 Human capital Human capital is important for economic growth. The purpose of this section is to describe and discuss urban areas’ prerequisites for a functioning matching in the labour market.

4.3.1 Formal education “Competence” refers to all the knowledge, skills and qualities that are desirable in a particular work situation. Competence thus comprises more than formal education. However, it is normally formal education that is used to quantify competence and human capital (see for example Growth Analysis, 2010e). The use of human capital is, however, different in different urban areas. As described in Chapter 2, some urban areas may be more focused on creating ideas, while others are more focused on standardised production. All urban areas are thus not cast in the same mould and therefore have different needs as regards human capital. Statistics of the education level of the population in employment therefore give a picture of urban areas’ different human capital profiles. Table 4-18 shows the education level of the daytime (employed) population between the ages of 20 and 64. The table gives a rough picture of the different competence levels in urban and rural areas. The rural areas have a higher proportion of employed with primary and secondary education than urban areas. Urban areas have a higher proportion of employed with tertiary education (which includes both university and post-secondary school education). There are relatively large differences between urban areas and rural areas. This means that urban areas have a more knowledge-intensive production than rural areas do. In the country as a whole, 39% of the population in employment have tertiary education of some kind. Since employment in the urban areas is so much larger than in the rural areas, the differences between urban areas and the country as a whole are fairly small.

Table 4-18 Education levels in rural areas and urban areas Primary Secondary Tertiary Total Rural areas 17% 58% 25% 100% Urban areas 10% 48% 41% 100% Whole country 11% 49% 39% 100%

Source: Statistics Sweden (LISA), Growth Analysis (PiPoS)

Remarks: Calculation made for people aged 20-64 in employment, 2009

Figure 4-13 shows the relationship between the size of the urban areas (measured as log population) and the proportion of people employed between the ages of 20 and 64 with some form of tertiary education. There is a clear covariation between the urban areas’ size and the proportion of people with tertiary education, which indicates that larger urban areas generally have a higher proportion of people with tertiary education (correlation = 0.72). There is also a large variation among urban areas of roughly the same size. In urban areas with between 10,000 and 20,000 inhabitants (ln[population] = 9.2-9.9), the proportion of people with tertiary education ranges from 24% to 43%. The urban areas with the highest proportions are often college- and/or university towns.

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Figure 4-13 Relationship between (log) population and the proportion of the population with tertiary education .5 .4 .3 .2 Proportion of the population with teritiary education with teritiary population the of Proportion

8 10 12 14 ln(Population)

Source: Statistics Sweden (LISA), Growth Analysis (PiPoS)

Remarks: Proportion of people aged 20-64 with tertiary education in employment, 2009

An urban area that experiences a shortage of a particular competence faces the choice of either training the local workforce or attracting skilled people with the desired competence from elsewhere (for more details, see Growth Analysis, 2010e). University towns have an advantage since they do not need to attract people from other parts of the country, as is probably the case for non-university towns.

4.3.2 University towns University towns are urban areas that comprise a college and/or university (Swe. Högskola and universitet). University towns can sometimes also be defined as towns that are to a large degree influenced by a college and/or university, e.g. that student life makes a mark on the town’s culture. University towns have good opportunities to exploit agglomeration economies; sharing, matching and learning. Sharing through the access to libraries and laboratories, matching through teaching, and learning thanks to research. University towns act as centres for the creation of ideas and the exchange of knowledge. One obstacle for university towns is that they run the risk of being just temporary stops where students come to study and then leave after graduation. A challenge for university towns is therefore to be able to exploit the university’s advantages in the best possible way, for example by creating prerequisites for students to stay once they have completed their studies and create prerequisites for interaction between academia and industry.

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Table 4-19 Urban areas with a college and/or university Ranking in Proportion with County the county tertiary Urban area Population seat County (population) education Stockholm 1,808,494 Yes Stockholm 1 49% Uppsala 155,331 Yes Uppsala 1 52% Eskilstuna 84,899 Södermanland 1 36% Nyköping 48,569 Yes Södermanland 2 33% Linköping 121,450 Yes Östergötland 1 49% Jönköping 120,926 Yes Jönköping 1 40% Växjö 61,910 Yes Kronoberg 1 45% Kalmar 52,817 Yes Kalmar 1 42% Visby 26,078 Yes Gotland 1 37% Karlskrona 47,120 Yes Blekinge 1 45% Malmö 522,069 Yes Skåne 1 48% Kristianstad 46,991 Formerly Skåne 3 40% Halmstad 71,621 Yes Halland 1 39% Göteborg 782,739 Yes Västra Götaland 1 46% Borås 91,418 Västra Götaland 2 36% Trollhättan 77,558 Västra Götaland 3 39% Skövde 41,868 Västra Götaland 4 38% Skara 13,790 Västra Götaland 12 30% Karlstad 86,229 Yes Värmland 1 44% Örebro 142,640 Yes Örebro 1 39% Västerås 122,877 Yes Västmanland 1 42% Borlänge 48,205 Dalarna 1 34% Falun 39,850 Yes Dalarna 2 44% Gävle 109,657 Yes Gävleborg 1 37% Sundsvall 73,482 Västernorrland 1 39% Härnösand 19,644 Yes Västernorrland 3 41% Östersund 47,087 Yes Jämtland 1 43% Umeå 86,552 Yes Västerbotten 1 52% Luleå 61,582 Yes Norrbotten 1 45%

Source: Statistics Sweden (RTB & LISA), Growth Analysis (PiPoS), Statistics Sweden (1986), www.hsv.se

Sweden has 47 colleges and universities, spread over 29 urban areas. This means that some urban areas have more than one university/institute of higher education; Stockholm for example has 22 universities (http://www.hsv.se/). A university may also be situated in several different urban areas; Mid Sweden University for example has campuses in Härnösand, Sundsvall and Östersund. Most urban areas have a university that offers courses and programmes in a wide variety of subjects. In some urban areas, however, the scope is more limited as regards different programmes and courses, for example the

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Nursing programme in Nyköping or studies at the University of Agricultural Sciences in Skara. The fact that the number of different programmes and courses is limited does not necessarily mean that the university lacks importance for the urban area’s and the region’s attractiveness and development. Table 4-19 shows the 29 urban areas that have at least one college or university. Every county has at least one college or university. The university is often situated in the county’s largest urban areas, which is often also the county seat. Universities thus appear to be located in regional centres. The university urban areas also belong to the category of urban areas with the highest proportion of employed with tertiary education.

4.3.3 The mobility of the labour force and urban areas’ attractiveness An important prerequisite for urban areas’ growth and the development of the labour force is access to people with the right competence. The matching process between employer and employees is dependent on the mobility of the labour force. If individuals feel that relocating for a new job involves too many obstacles to overcome, they will choose not to move. An important issue for urban areas is therefore whether there are any obstacles to labour force mobility. An individual’s decision to relocate is probably based on a number of different factors. In economics, it is customary in such contexts to discuss the individual’s total utility from relocating and what factors may conceivably influence that utility. An individual’s utility from relocating can thus be illustrated by the following utility function:

Utility = f(income, costs, other values).

An individual’s utility from relocating in relation to not moving can be described as a function of income, costs and a number of other factors (which for the sake of simplicity have here been termed “other values”). Moving to a metropolitan area may mean an increase in housing and living costs, but moving to a metropolitan area to begin a new job probably also means higher income for the individual. Cost increases naturally have a negative impact on the utility of the individual but a higher income has a positive impact. The higher cost of living in a metropolitan area might thus be equivalent to the increase in income, which might mean that the total utility is unchanged. However, it is not only monetary factors that determine the decision to move. “Other values” that are important in individuals’ decisions to move have to do with both the urban area’s “attractiveness” and its “inconveniences”. An urban area is attractive if for example it can offer a wide selection of cultural activities, shopping and attractive career opportunities. An urban area may also be associated with a large amount of “inconveniences” such as congestion, long commuting distances, exhaust emissions and crime. If the attractiveness of an urban area is sufficiently large, the individual will choose to move even if it means, for example, higher housing and living costs. A study conducted by Nutek (2007) showed that the housing situation in Stockholm is not an obstacle to recruitment. The response rate, however, was low and it would be interesting to probe deeper and study whether the housing situation in metropolitan areas constitutes an obstacle to labour force mobility. High housing prices and “big city inconveniences” are obstacles to labour mobility, but labour mobility is also influenced by several other factors.

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It is, therefore, difficult to determine how high housing prices need to be to constitute an obstacle to labour mobility. Urban areas can thus create better conditions for labour mobility by studying the housing market (se for example BKN, 2008) and by minimising the “inconveniences” often associated with urban areas. According to Florida (2002, quoted in Growth Analysis 2010a) there are three conditions that promote economic growth: technology, talent and tolerance. By strengthening these three conditions, urban areas and regions can attract “the creative class”, i.e. scientists, engineers, authors, etc. An urban area or region must therefore attract the creative class, which in turn will attract business. Florida’s claims are, however, not without criticism. Glaeser (2005, quoted in Growth Analysis 2010a) finds that Florida’s result is driven by the degree of human capital, since human capital is strongly correlated to the “creative class”. Moreover, Niedomysl and Hansen (2010) find studying Swedish migrants that work and career opportunities are considerably more important for the decision to migrate than the presence of amenities (such as cultural facilities and outdoor recreation). The relative importance of jobs versus amenities was especially evident amongst the highly educated migrants.

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4.4 Mobility

4.4.1 The importance of mobility for growth When economic activity is concentrated it gives rise to increased productivity. An improved infrastructure with lower transport costs and shorter journey times provides opportunities for more interaction and thereby opportunities for sharing, matching and learning. Research has shown that face-to-face interaction is important. Business people prefer to meet face-to-face for important meetings and negotiations or to sell their products (Forbes, 2009). Research has also shown that an increase in the number of business trips between countries contributes to increased innovation and productivity (Hovhannisyan and Keller, 2010; Barnebeck Andersen and Dalgaard, 2011). However, it is not always easy to see unambiguously positive effects of investments in infrastructure. Inregia (2006) made a meta-study of research concerning investments in infrastructure and its regional impact. The research can be summarised into three general outcomes: (1) Investments in infrastructure with little or no impact. (2) Investments in infrastructure with positive impact but only redistributive effects. The positive development within a region is matched by negative effects in other regions. (3) Investments in infrastructure with positive impact but only in regions that are already enjoying positive development. This would mean that investments in infrastructure are a necessary, but not sufficient, condition to create positive regional development. Increased mobility can also give rise to negative effects, for example negative effects on health like increased stress and tiredness and less time for recreational activities, physical activity and sleep. Increased mobility also increases the risk of accidents and the risk of respiratory and heart problems due to air pollution (Trafikanalys, 2011). Increased mobility also leads to a greater impact on the environment. Environmental pollution is a residual product of producing something that is requested, for example a journey. Whether the benefit of what is produced outweighs the cost of the environmental pollution is something that must be evaluated from case to case. It is also important that environmentally friendly alternatives exist. In summary, there are several advantages to improved mobility and increased interaction. At the same time, it is not always easy to see unambiguously positive effects of investments in infrastructure. It is difficult to achieve regional-economic effects through investments in infrastructure alone; there must be a reason why point A should have enhanced accessibility to point B. Moreover, transportation should be designed to safeguard both people and the environment.

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4.4.2 Indicators of mobility Four aspects should be considered when creating indicators of mobility:

(i) What type of mobility is to be studied: actual or possible? (ii) What is to be transported: goods or people (business or pleasure)? (iii) Where is the transportation to take place: within regions, between regions, or internationally? (iv) How is the transportation to take place: physically or virtually?

These four aspects constitute a checklist of possible mobility indicators. Actual mobility refers to how something has moved in reality, while possible mobility refers to the possibility to move something. Measuring the number of people who commute to work by contrasting daytime and night-time population is an example of actual mobility, while the distance in minutes between two places is a measure of accessibility and possible mobility. Since the aim of this report is to study urban areas’ opportunities for growth, the primary focus of interest is to study urban areas’ possibilities for interaction. The measures studied in the report are accessibility between urban areas and urban areas’ accessibility to airports, i.e. possible physical mobility. The airports studied were chosen with regard to possible work commuting. Accessibility to airports gives information about the possibility to travel between regions and to travel abroad. The chapter concludes with a discussion of virtual mobility. Growth Analysis’ GIS tool PiPoS is currently undergoing substantial development, which is expected to be completed in the spring of 2012. The new tool will be able to perform more qualified and reliable calculations of accessibility within urban areas, between urban areas and between urban and rural areas.

4.4.3 Accessibility between urban areas Urban areas which have a geographical proximity to other urban areas can take advantage of the functions and facilities that exist there. In Chapter 2, it was stated that the advantages of urban areas are the possibilities for sharing, matching and learning. When different urban areas are joined together in a network, sharing different functions (ports, airports, libraries, etc.) is made easier through better matching of buyers and sellers, and employees and employers, and better opportunities for learning and exchange of knowledge. There are thus several advantages for urban areas being situated geographically close to other urban areas. This provides a first classification of urban areas: those that are situated close to other urban areas and those that are not. Urban areas that are situated close to other urban areas are called “network urban areas” and those that are not situated close to other urban areas are called “isolated urban areas”.

Data To be able to identify “isolated urban areas” and “network urban areas”, it must first be determined what is to be defined as a “close connection between urban areas”. Since the purpose is to identify urban areas’ possibilities, connections between them are measured as

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the possible accessibility between different urban areas. Studies of work commuting show that the optimum journey time is between 20 and 25 minutes and that the limit for acceptable daily commuting is between 45 and 60 minutes (Growth Analysis, 2010c). Even if 45 minutes is a reasonable threshold value for commuting to work, it is not certain whether 45 minutes is also a reasonable threshold for taking advantage of goods and services in nearby urban areas. Randstad is the collective name given to the Dutch cities of Amsterdam, Rotterdam, The Hague and Utrecht and is normally used as an example of a polycentre of joined cities. The distance between the cities that make up Randstad could therefore provide a guideline as to a suitable distance between urban areas that have close connections to each other. It is only Rotterdam and The Hague, however, which are less than 45 minutes apart by car (Google maps). A limit value of 45 minutes thus does not identify the cities in Randstad as network cities. It should, however, be mentioned that means of transport other than the car have not been studied. In Sweden, Linköping and Norrköping have the ambition to become Sweden’s fourth largest urban area, in the spirit of polycentricity. The distance between Linköping and Norrköping is about 27 minutes, which means that they would fall within the 45-minute limit threshold (Growth Analysis (PiPoS)). Enköping markets itself as “Sweden’s nearest city”. The distance by car between Stockholm and Enköping, however, is more than 45 minutes, which means that Stockholm and Enköping are not part of the same network. It might, on the other hand, be the case that Stockholm is such a magnet that a limit value of 45 minutes is too small. The calculation, however, is from centre to centre. The distance to the outskirts of Stockholm, or the distance by train, is probably less than 45 minutes. Setting a limit value for urban areas’ connections entails several difficulties. The generally accepted measure in the literature is 45 minutes, and this is also the threshold used in the present report. The calculation was made with the help Growth Analysis’ GIS tool PiPoS, and the distances were measured from centre to centre; (“centre” was determined by using the centre used in Google maps, which is usually the main square or the railway station). Calculating accessibility to urban areas’ centres can be justified on the basis that it is probably the place of greatest importance. The fact that urban areas may have more than one centre has been ignored for the sake of simplicity. The accessibility calculation is naturally also dependent on the point where the calculation begins. The centre has been considered the most suitable alternative, since the distance from the city centre and outwards should approximately represent the mean distance from any location within the city.

Number of connections Table 4-20 shows how many urban areas have 0, 1 or more connections within a distance of 20, 30 and 45 minutes. Within 20 minutes, 93 of 221 urban areas do not have any connection to another urban area. Within 30 minutes, the same figure has fallen to 28 urban areas and within 45 minutes, only 11 urban areas do not have any connection to another urban area. Longer times mean that not only do more urban areas have a connection to another urban area, but also that the number of connections per urban area increases. Within 20 minutes, there are no urban areas that have five or more connections. When the time is extended to 45 minutes, however, 135 urban areas, i.e. more than half, have connections to five or more urban areas. The number of connections does not appear to be associated with the size of an urban area’s population. The urban areas with most

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connections can be found among both the large and the small urban areas. The map in Figure 3–14 on page 30 and the following pages illustrates which urban areas have connections to each other within a distance of 45 minutes. The urban areas that have no connections to other urban areas within 45 minutes, and that are therefore termed “isolated” are: Kiruna, Gällivare, Arvidsjaur, Lycksele, Vilhelmina, Strömsund, Örnsköldsvik, Malung, Nynäshamn, Strömstad and Visby. The first seven of these are in northern Sweden, as expected. The fact that Strömstad, on the other hand, is identified as an isolated city can be questioned, as it is situated close to the Norwegian border. However, Strömstad has no connections to Swedish towns or cities. The fact that Nynäshamn is classified as isolated may seem unreasonable, but it must be remembered that the calculations were performed from centre to centre, i.e. the distance to the outskirts of Stockholm is shorter.

Table 4-20 Number of urban areas with 0, 1 or more connections within 20, 30 and 45 minutes. Number of 5 or Total number of 0 1 2 3 4 connections more urban areas Within 20 min 93 75 43 9 1 221

Within 30 min 28 54 54 28 29 28 221 Within 45 min 11 14 23 15 23 135 221

Source: Growth Analysis (PiPoS).

There are 14 urban areas that have only one close connection to another urban area within 45 minutes. This group contains three “pairs”: Östersund and Brunflo, Haparanda and Kalix and Skellefteå and Ursviken. These pairs only have a close connection to each other and can thus be regarded as isolated. Similarly, Umeå-Strömsund-Vännäs can also be considered an isolated urban area.

Urban hierarchy For those urban areas that are part of a network, it interesting to investigate their similarities and if they can be classified according to an urban hierarchy. One of the more well-known theories concerning urban hierarchy is the Christaller model. In the Christaller model, it is assumed that a small town produces a less varied supply of goods which are primarily produced for a limited local market. A large urban area is instead assumed to produce a more varied and specialised supply of goods, which are distributed over a larger, regional market. Large and small urban areas are therefore assumed to be located in such a manner that all urban areas can make use of all goods in an efficient manner. Urban areas can thus be placed in an urban hierarchy on the basis of urban areas’ mutual roles and functions (Hagget, 2001). The report presents an attempt to identify urban areas’ mutual hierarchies. The purpose of this is twofold: Firstly, it is important to emphasise that urban areas have close connections to each other and thus can benefit from each other. Secondly, it is of interest to try to obtain an understanding of urban areas’ mutual relationships and thus gain a better understanding of urban areas’ different roles and functions. A region may also have urban areas that are obviously regional centres while others function more like local centres. Göteborg is for example clearly a regional centre in western Sweden. Borås is also a

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regional centre in western Sweden at the same time as Göteborg is a centre for Borås. It would therefore be possible to call Göteborg a regional centre, while Borås is a regional sub-centre. Göteborg and Borås thus have different roles. Göteborg probably has an attraction that Borås is not able to compete with. Borås must therefore reinforce its position as a local centre and at the same time find its role in relation to Göteborg. A definition of an urban hierarchy must be able to identify these different roles. Within a region there will also be urban areas that are of equal size, where it is not possible to decide which is a centre and which a neighbourhood. These urban areas are instead “polycentres”, i.e. an urban network consisting of several centres. A definition of an urban hierarchy must also be able to identify polycentres. A simple and direct approach to identifying an urban hierarchy is to begin with those urban areas that are at least 45 minutes away from another urban area and their relative population size. The classification of the urban hierarchy is done in three steps: In step 1, the bilateral connections between urban areas are classified as either: Centre, Polycentre or Neighbourhood. Two urban areas are termed “polycentres” (of equal size) if the smaller is at least 70% of the size of the greater. If, on the other hand, the smaller urban area has a population that is less than 70% of the larger urban area’s population, the larger one is termed “centre” and the smaller one “neighbourhood”.5 The limit value of 70% has been chosen on the basis of two urban areas being equal if they are of “approximately” the same size. In step 2, the different classifications are summarized for each individual area. An urban area that only constitutes a centre for other neighbouring urban areas is called a “Centre”, while an urban area that is classified as both centre and neighbourhood is termed a “Centre-Neighbourhood” and so on. Göteborg is thus a “Centre” while Borås is a “Centre- Neighbourhood”. The different classifications can be summarised into three overarching classifications: Centre, Sub-centre and Neighbourhood. Isolated urban areas are placed in a category of their own. This means that Göteborg is classified as a “Centre” while Borås is classified as a “Sub-centre”. Similarly, Linköping and Norrköping are classified as a Centre-Polycentre and thus together constitute a regional “Centre” in Östergötland. Figure 4-14 illustrates all the possible classifications. The classification has until now been based on direct connections, i.e. neighbours’ connections have not been taken into account. As can be seen from the map in Figure 3-4, most urban areas in Sweden are linked together when also considering their neighbours’ connections. This means that it is often difficult to determine that an urban area belongs to a specific network, but rather that urban areas often belong to several networks. This is also the reason why the classification of an urban hierarchy is constructed solely on bilateral connections. Including only the direct (bilateral) connections, however, causes a problem for those urban areas that are classified as polycentres. For a polycentre to be able to be classified as a regional centre, none of the polycentric urban areas can be a neighbourhood of another centre. This means that an urban area that is a Centre-Polycentre and whose neighbour of the same size is at the same time a Neighbourhood of another urban area is classified as a Centre-Polycentre-Neighbourhood, and thus goes from being a regional centre to being a regional sub-centre. If an urban area which is a Polycentre has a neighbour which is also a

5 i.e. if 0.7 ≤ town1/town2 ≤ 1/0.7, town1 and town2 are Polycentres. If, on the other hand, town1/town2 < 0.7, town1 is a Neighbourhood and town2 a Centre and vice versa.

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Neighbourhood, those urban areas are classified as a Polycentre-Neighbourhood (see Figure 4-14).

Figure 4-14 Classification of urban hierarchy

Source: Growth Analysis.

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Figure 4-15 Map of a classification of urban areas according to an urban hierarchy

Source: Growth Analysis

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Figure 4-15 presents a map of urban areas classified according to Isolated towns, Centres, Sub-Centres, and Neighbourhoods. The map clearly shows that Stockholm, Göteborg and Malmö are classified as Centres, which are surrounded by Sub-centres and Neighbourhoods. The results of the classification for individual regions can also be seen. In the county of Kalmar; Kalmar, Västervik and Oskarshamn are classified as Centres, Nybro, Vimmerby, Färjestaden, Hultsfred and Emmaboda as Sub-centres and Borgholm and Mönsterås as Neighbourhoods. It is thus not only Kalmar, the largest city, that is classified as a regional centre in the county. Classifying urban areas according to an urban hierarchy can therefore be seen as a method to better understand the different roles of urban areas. Figure 4-16 below presents the distribution of the population in urban areas, according to the classification of urban hierarchies. Out of the 221 urban areas, 11 are classified as isolated. Isolated urban areas do not have the same possibilities as network urban areas to take advantage of sharing, matching and learning in nearby urban areas. Isolated urban areas must thus be self-sufficient and are dependent on other ways to interact with other urban areas, for example by air transport or virtual mobility. An isolated urban area constitutes a regional centre, if not for other urban areas, then for the surrounding rural area.

Figure 4-16 Population size distributed over urban hierarchy (Stockholm, Göteborg and Malmö not included)

Isolated

Centre

Sub-centre

Neighbourhood

0 20000 40000 60000 80000 100000120000140000160000

Source: Statistics Sweden (RTB), Growth Analysis (PiPoS)

Remarks: Stockholm, Göteborg and Malmö are not included. The box-plot shows the minimum, the 25th percentile, the median, the 75th percentile and the maximum. A quarter of the observations are below the 25th percentile (i.e. the first quartile) and three quarters are below the 75th percentile (i.e. the third quartile). These five values divide the population into four equally large parts.

Centres are those urban areas with, in general, the largest population. This is of course expected considering that the definition is based on relative population size. Centres also have the greatest variation in population size; (this is also true when excluding Stockholm, Göteborg and Malmö). This infers that smaller urban areas have also been classified as regional centres, which is desirable since the primary reason for classifying urban areas

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according to an urban hierarchy was to be able to give a more nuanced picture than merely classifying them as large or small. Among the urban areas classified as Centres, there are nine urban areas that can be classified as polycentres: Hudiksvall-Söderhamn-Bollnäs, Falun-Borlänge, Lidköping- Skövde, and Linköping-Norrköping. Polycentres are defined as urban areas of approximately the same size. Polycentric urban areas can specialise in different fields and at the same time benefit from each other. Polycentric urban areas have favourable possibilities to utilize sharing, matching and learning by collaboration and complementing each other. Sub-centres generally consist of considerably smaller urban areas than centres, although the variation in population size is large. The largest urban areas classified as Sub-centres are Helsingborg (147,419), Borås (91,418), Eskilstuna (84,899) and Nyköping (48,569). There is probably an asymmetrical relationship between a Centre and a Sub-centre. A Centre has an attraction that a Sub-centre is unable to compete with. A Sub-centre should therefore secure its position as a local centre and at the same time identify its role in relation to the larger urban area. As expected, urban areas classified as Neighbourhoods are amongst the smallest urban areas. Finally, it is interesting to note that the differences in population size between Centres, Sub-centres, and Neighbourhoods, are so distinct and evident.

Figure 4-17 Box-plot of the ratio of daytime and night-time population distributed over urban hierarchy

Isolated

Centre

Sub-centre

Neighbourhood

0 0,5 1 1,5 2

Source: Statistics Sweden, Growth Analysis (PiPoS).

Data for other variables than population size can also be analysed using our definition of urban hierarchy. Figure 4-17 above shows the ratio between daytime and night-time population distributed over isolated urban areas, Centres, Sub-centres and Neighbourhoods. The Centres all have a daytime/night-time ratio of 1 or higher, i.e. they have a net in-commuting to work. As regards Sub-centres and Neighbourhoods, most urban areas have a daytime/night-time ratio of less than 1, i.e. they have a net out- commuting to work. The variation, however, is large for both these groups. Isolated urban areas are centres for a surrounding rural area and most urban areas in this group have a daytime/night-time ratio greater than 1. These results are to be expected. A few isolated

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urban areas have a daytime/night-time ratio less than 1. This was discussed earlier in section 4.2.2 on page 52. Figure 4-18 shows how labour productivity is distributed according to urban hierarchy. Generally speaking, Centres have higher labour productivity than Sub-centres and Neighbourhoods, even if the variation for Sub-centres and Neighbourhoods is large. Isolated urban areas generally have a labour productivity somewhere between Centre, Sub- centre and Neighbourhood. These results are also according to expectations.

Figure 4-18 Box-plot of labour productivity distributed over urban hierarchy

Isolated

Centre

Sub-centre

Neighbourhood

150000 200000 250000 300000 350000

Source: Statistics Sweden, Growth Analysis (PiPoS).

4.4.4 Global mobility In the previous section a threshold of 45 minutes was used to determine which urban areas have close connections to each other. Urban areas can naturally also be part of networks that cover greater distances than 45 minutes, for example to urban areas in other countries (see for example ESPON). It is therefore important that urban areas also have good accessibility to urban areas a little further away. This can partly be studied by calculating urban areas’ accessibility to an airport (which will be discussed in the next section). Two urban areas that are far apart may have more in common than with urban areas that are closer at hand. It is for example fully possible that it is more important for Stockholm to have good accessibility to London than it is for Stockholm to have good accessibility to Gävle. A simple way to estimate the attraction between urban areas is to use a gravity model. One of the perhaps simplest gravity models consists of calculating the attraction (or flow of people, Fij) between the urban areas i and j, by multiplying their respective populations 2 (PiPj) and then dividing by the distance (squared) between them (Dij ), i.e. = (Hagget, 2001). According to this formula, Göteborg would for example have a greater2 푖푗 푖 푗 푖푗 attraction (a greater flow of people) to Stockholm than to Malmö despite퐹 Malmö푃 푃 being⁄퐷 closer. The gravity model can also be developed to take into account other factors.

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4.4.5 Airport accessibility An urban area’s accessibility depends to a large extent on its accessibility to different transport nodes such as airports, railway stations and ports. This section studies the urban areas’ accessibility to Swedish airports that have regular services to and from Stockholm. Access to and from Stockholm also means access to a system of airports over the whole of Sweden as well as to other countries. There are, however, more airports than those taken up here; these do not have regular air services to and from Stockholm, only a few international flights or a few flights to other places in Sweden. Table 4-21 shows the airports that had regular air services to and from Stockholm Arlanda or Stockholm Bromma in November 2011. In all, there are 30 airports with regular services to Stockholm. 28 have regular services to Arlanda while 13 have regular services to Bromma and 12 of these have regular services to both Arlanda and Bromma. (For more information about different airports’ accessibility to domestic and international destinations, see Growth Analysis, 2011b.)

Figure 4-19 Box-plot of urban areas’ distance in minutes from an airport with regular services to Stockholm

Minutes from airport

0 20 40 60 80 100 120 140

Source: Growth Analysis (PiPoS).

Figure 4-19 shows the variation for distance in minutes between an urban area and its nearest airport. Half of all urban areas have 42 minutes or less to an airport. A quarter of all urban areas, however, have between 70 and 127 minutes to an airport. There is no clear covariation between, for example, population size, labour productivity and distance to an airport. The measure “distance to an airport” can be interpreted as both an input and an output variable. It is an input variable since it shows the possibility to get to an airport. It is an output variable since it shows an outcome of a demand for air traffic and/or that there are no other means of transport.

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Table 4-21 Airports in Sweden with regular services to and from Stockholm Regular services to Regular services to Airports Remarks: Arlanda Bromma Arvidsjaur 1 Gällivare 1 Göteborg-Landvetter 1 1 Hagfors 1 Halmstad 1 1 Hemavan-Tärnaby 1 Jönköping 1 1 Kalmar 1 1 Karlstad 1 Kiruna 1 Kramfors 1 Kristianstad 1 Luleå 1 Lycksele 1 Malmö 1 1 Mora 1 Oskarshamn 1 Ronneby 1 1 Skellefteå 1 Hub for domestic air Stockholm-Arlanda 1 services Hub for domestic air Stockholm-Bromma 1 services Sundsvall Härnösand 1 1 Sveg 1 Torsby 1 Trollhättan 1 Umeå 1 1 Vilhelmina 1 Visby 1 1 Växjö (Småland Airport) 1 1 Åre Östersund 1 1 Ängelholm Helsingborg 1 1 Örnsköldsvik 1

Source: www.arlanda.se, www.brommaairport.se, 2/11/2011

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4.4.6 Virtual mobility People are virtually mobile when they use information and communication technology (ICT) to communicate via for example fixed telephony, mobile telephony, fax, the Internet, etc. ICT thus makes it possible to move virtually (illusively, not in reality) instead of physically (tangibly, in reality). ICT has broadened people’s possibilities to communicate with the world around them, immediately and largely irrespective of spatial distance. The possibility to meet and communicate virtually regardless of location is sometimes described as a victory over spatial distance or “the death of distance”. There is, however, much to indicate that virtual mobility cannot completely replace physical mobility. According to Fischer (1992, quoted in Frändberg et al. 2009) the invention of the telephone complemented rather than replaced other forms of contact and thereby intensified interaction between people. Frändberg et al. (2009) found that the same conclusion can be drawn regarding young people’s use of the Internet. Forbes (2009) concludes that business people prefer to meet “face-to-face” for important meetings and negotiations or to sell their products. The relationships between virtual and physical mobility can be described in terms of substitution, complementarity, generation and modification: Physical mobility can be substituted by virtual mobility, e.g. when a physical meeting is replaced by a web conference. Virtual mobility can complement physical mobility, i.e. when a person calls or e-mails his or her contacts without for that reason needing to meet more seldom. Virtual mobility generates physical mobility, i.e. when virtual mobility stimulates people to travel more, or when virtual mobility generates more contacts which leads to more travelling. Virtual mobility modifies physical mobility when ICT influences when, where and how people travel without affecting the scope of the journey, i.e. when a person seeks information about the traffic situation and decides to travel by a different means of transport at a different time (Mokhatarian, 1997; Hjorthol, 2001, quoted in Frändberg, 2009). For urban areas, the possibility of virtual mobility means two things: On the one hand, it gives urban areas the possibility to enlarge its area of influence. The urban area no longer needs to be the centre or meeting-place for a hinterland; virtual mobility makes it possible to interact without regard to spatial limits. On the other hand, the lack of spatial distances also implies that urban area’s role as a centre and meeting-place is weakened, since it is possible to shop and meet virtually (Hedlund, 2003). However, meeting “face-to-face” is and will probably continue to be of great importance. Table 4-22 shows coverage and broadband speed in densely built-up and sparsely built-up areas in Sweden in 2009. Broadband coverage for households and workplaces is 99.9%. Broadband speeds are higher in densely built-up areas than in sparsely built-up areas. In densely built-up areas, 94% of workplaces have a broadband speed of 10 Mbit/s or higher while in sparsely built-up areas only 48% of workplaces have access to the same speed. It should be added that these figures are based on Statistics Sweden’s definition of localities. If instead we had used the definitions of urban areas and rural areas proposed in the present report, the rural area figures would probably have been higher than those for the sparsely built-up areas. The Government’s target is that 90% of all households and companies shall have access to broadband with a speed of at least 100 Mbit/s by the year 2020 (PTS, 2009).

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Table 4-22 Coverage and broadband speed in Sweden in 2009 1 Mbit/s or faster 10 Mbit/s or faster 50 Mbit/s or faster Whole country Households 99.97% 87.69% 53.15% Workplaces 99.88% 77.75% 41.45% Densely built-up areas Households 100.00% 94.49% 62.46% Workplaces 100.00% 94.12% 61.14% Sparsely built-up areas Households 99.81% 55.49% 9.03% Workplaces 99.65% 47.78% 5.41%

Source: The Swedish Post and Telecom Authority (PTS, 2010)

Remarks: Densely built-up areas are defined as groups of buildings having at least 200 inhabitants provided that the distance between the buildings does not exceed 200 metres. Sparsely populated areas are defined as areas outside densely built-up areas.

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4.5 A classification of urban areas

4.5.1 The different roles and functions of urban areas An urban area is a centre for trade, culture and learning. Thanks to the urban area’s function as a meeting and market place, it provides opportunities for sharing utilities, for a better matching of buyers and sellers, employees and employers, etc., as well as providing better possibilities for learning and exchanging knowledge. It is, however, difficult to generalise about urban areas; a better description is instead that there are several different types of urban areas that all have their own advantages and disadvantages. Urban areas can therefore be described as having different and complementary roles in the regional and national growth process. Those industries that have significant concentration advantages primarily establish themselves in large urban areas, while industries with small concentration advantages mainly establish themselves in smaller urban areas. This also means that enterprises focused on information and the development of products mainly establish themselves in the metropolitan areas, while more mature production will mainly locate to the slightly smaller urban areas. Orlando and Verba (2005) have shown that both large and small urban areas are innovative but that they are innovative in different ways: in large urban areas innovation mainly takes the form of developing a new product, while innovation in smaller urban areas primarily takes place at a more mature production phase where old inventions are refined. A way of classifying the different roles and functions of urban areas is to adapt a classification proposed by Capello and Lenzi (2011):

• Type 1: Urban areas that create knowledge. • Type 2: Urban areas that use existing knowledge to create new products. • Type 3: Urban areas that produce, imitate and refine existing products.

Urban areas of Type 1 are characterised by knowledge creation through research and development. The knowledge creation is characterised by research with application in a range of different fields. Urban areas of Type 2 are characterised by using existing knowledge to develop new products. These urban areas are therefore characterised by a high innovative capability, but it is more oriented towards using and adapting existing knowledge than generating new knowledge. Urban areas of Type 3 produce, imitate and refine existing products. While Type 2 urban areas use existing knowledge to produce new products, urban areas of Type 3 focus on existing products with the aim of refining them and thereby producing new products. Urban areas of Type 3 can consequently also be characterised by a high innovative activity.

4.5.2 A description of different urban areas This section consists of a short description of different urban classifications. The descriptions are based on previous discussions in the report. The different urban classifications that will be discussed are; urban areas with different population sizes, typologies based on urban hierarchy, single-industry cities, university towns, and commuter towns.

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The different classifications are not mutually exclusive but represent different ways of classifying and thereby gaining a deeper understanding of different urban areas. Creating an understanding of different types of urban areas is of importance for (i) gaining a better understanding of what different urban areas exist in a particular region, and (ii) to be able to study differences between urban areas within the same classification.

Urban areas with different population sizes Classifying urban areas according to the size of their population is probably the simplest and most common method to classify and make distinctions between urban areas. Large urban areas generally have a higher level of industry diversification. Smaller urban areas generally have a lower level of industry diversification and are characterised to a greater extent by pursuing more standardised, imitative production. Large to medium-sized urban areas can be expected to be of types 1 and 2, while smaller urban areas are assumed to be primarily of types 2 and 3. The advantage of classifying urban areas by their population size is that it is a fairly simple and straightforward classification. Population size probably works fairly well for measuring critical mass and diversification. The drawback of classifying urban areas by their size is that it is a rather imprecise and sweeping measure. It is for example difficult to make general statements about different population sizes.

Typologies based on urban hierarchy Classifying urban areas based on urban hierarchy is described in Section 4.4.3. The classification is based partly on the notion of the importance of being or not being part of a network of urban areas and partly on the notion of interdependence between urban areas. Based on calculations of urban areas’ accessibility to each other and their relative population size, four types of classifications are defined: Isolated towns, Centres, Sub- centres and Neighbourhoods. The purpose of classifying urban areas according to an urban hierarchy is to depart from the discussion of large and small urban areas, and instead try to obtain a more nuanced picture of urban areas’ different roles and functions. Naturally, this classification also has its disadvantages and cannot identify the interdependence between urban areas with a high degree of accuracy. The classification shall instead be seen as an attempt to identify and thereby investigate urban areas’ different roles and functions. Isolated towns are urban areas which are situated far from other urban areas. Isolated towns do not have the same possibilities as urban areas belonging to a network to take advantage of sharing, matching and learning. Isolated towns must thus be self-sufficient and are dependent on other means of interaction, for example through air transport and virtual mobility. An isolated town constitutes a regional centre, if not for other urban areas, then for a surrounding rural area. Isolated towns are probably mainly of types 2 and 3 but may possibly also be of type 1. A Centre is the largest urban area in a network and constitutes a regional centre. Centres are usually slightly larger urban areas, although there are exceptions (see Figure 4-16 on page 73). Large centres are probably of types 1 and 2, while smaller centres may also be of type 3. A Sub-centre is a local centre for nearby smaller urban areas and a surrounding rural area, but is at the same time a neighbourhood of a larger urban area. There is probably an

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asymmetrical relationship between a Centre and a Sub-centre. A Centre probably has an attraction that a Sub-centre cannot compete with. A Sub-centre must therefore reinforce its position as a local centre and at the same time position itself in relation to the larger urban area. Sub-centres are probably predominantly of type 2 or type 3. Neighbourhoods are the smallest urban areas in a network. Neighbourhoods probably have difficulty competing with the attraction of the largest nearby urban areas. A neighbourhood urban area can still constitute a centre for a surrounding rural area. Neighbourhood urban areas are expected to largely consist of “commuter towns” and are assumed to be of type 2 or 3.

Single-industry cities Single-industry cities are urban areas that are strongly dependent on a particular company and/or branch of industry, and are probably of type 2 or 3. Urban areas which are dependent on a particular industry are more sensitive to external shocks and cyclical fluctuations. Single-industry cities with an apparently high level of industry diversification are probably equally sensitive to external shocks due to industries’ mutual interdependence. In the same way as the dominant industry has spillover effects on other local industries, a demand shock affecting the dominant industry has spillover effects on other local industries. This implies that it is also difficult to define a single-industry city using existing measures of industry diversification. Single-industry cities are therefore not considered in Section 4.5.3 below. According to the Europe 2020 strategy, regions should focus on smart growth and smart specialisation. Single-industry cities are thus an interesting topic for further studies. A high degree of specialisation gives greater growth opportunities while at the same time increasing vulnerability.

University towns University towns are towns that comprise a college and/or university. University towns can sometimes also be defined as towns that are to a large degree influenced by the college and/or university, e.g. that student life makes a mark on the town’s culture. University towns have good opportunities to exploit benefits of agglomeration economies; sharing due to access to libraries and laboratories, matching through teaching, and learning thanks to research. University towns act as centres for the creation and exchange of knowledge and ideas, and are therefore characterised as type 1 or type 2. Universities are situated in the county’s largest urban areas or in the county seat, which means that university towns often are regional centres. University towns have great potential which should not be neglected. A university town runs, for example, the risk of becoming a transit hall for students, which means that the university makes little impact on the region. Decision-makers both in the region and at the university thus have a joint responsibility for the university’s interaction with the surrounding community.

Commuter towns “Commuter towns” or “sleeper towns” are towns where people live at the same time as they commute to work elsewhere. Commuter towns were discussed earlier in Section 4.2.2 and can be identified by comparing the daytime population with the employed night-time population. Few guidelines exist, however, for how to define a commuter town. In later

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tables and figures a commuter town is defined as an urban area that has a net out- commuting of 5% or more (i.e. has a daytime/night-time ratio of 0.95 or less). A commuter town is expected to be a smaller town situated close to a larger urban area. Since the town’s inhabitants mainly work somewhere else, the industry diversification as well as supply of commercial services in the town are expected to be limited only to satisfy the basic requirements. A commuter town has several advantages from being situated close to a large urban area, such as access to a larger labour market and a greater supply of stores and shops. The greater supply of stores and shops in the larger urban areas implies that there is less incentive to open shops etc. in the smaller town. A commuter town thus has both advantages and disadvantages. A commuter town is difficult to define as either a type 1, 2 or 3, since the town is primarily a place where people sleep while they work somewhere else. However, commuter towns are most probably type 3.

Other types of urban areas Urban areas can of course also be classified in other ways, for example as “border towns” and “gateway cities”. A “border town” is an urban area that is situated at a border or close to a country’s border. The urban areas in Sweden that can be classified as border towns are: Strömstad, Haparanda, Malmö and Helsingborg. Border towns may have considerable commuting and cross-border trade. This relationship may be one-sided, which means for example that fluctuations in the exchange rate may have a substantial impact on the border towns. Commuting across national borders also affects regional statistics. A net outflow of labour means for example that the night-time (employed) population is underestimated and the daytime/night-time ratio will be overestimated. A net outflow of labour also means that the night-time income is underestimated, which in turn means that income per capita is underestimated. “Gateway cities” are towns or cities that serve a role as a gateway or link to other regions or other countries. Gateway cities may be border towns, railway metropolises, logistics centres, seaports or urban areas with an airport. Gateway cities have a strategic advantage thanks to their geographical location (e.g. as logistics centres) and/or access to a transport node (e.g. seaports). Gateway cities may often be transit cities, i.e. cities that people merely pass through on their way to somewhere else. Nynäshamn can be regarded as a gateway city since most visitors only pass through town to take the ferry to Gotland.

4.5.3 A comparison of different types of urban areas Classifying urban areas as a certain classification does not mean that it cannot belong to another classification. Table 4-23 shows how urban areas that are classified according to an urban hierarchy can also be classified according to population size if they are a university town and/or commuter town. The table shows that urban areas with a population of more than 500,000 (i.e. Stockholm, Göteborg and Malmö) are all classified as Centres. Among urban areas that have a population of between 100,000 and 500,000, all but one (Helsingborg) are classified as Centres. Urban areas with a population of between 10,000 and 100,000 are classified as both Isolated towns, Centres, Sub-centres and Neighbourhoods. The very smallest urban areas (3,000-10,000), however, are mainly classified as Sub-centres or Neighbourhoods. A university town is defined as an urban area that has a college and/or university (i.e. according to Table 4-19 on page 63). Most university towns (23 of the 29) are also

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classified as Centres. Five university towns are classified as Sub-centres (Borås, Härnösand, Eskilstuna, Nyköping and Skara). One (Visby) is classified as an Isolated town. As discussed earlier in Section 4.4.3, Östersund and Umeå could also be classified as isolated. An Isolated town, however, is also a type of centre. A commuter town has been defined as an urban area that has a net out-commuting of 5% or more (i.e. has a daytime/night-time ratio of 0.95 or less). Most commuter towns are Sub- centres or Neighbourhoods. Two commuter towns (Nynäshamn and Arvidsjaur), however, are classified as Isolated towns. This may be due to that commuting time being in excess of 45 minutes, or because the estimation is performed from city-centre to city-centre. From Nynäshamn, for example, the distance to the suburbs of Stockholm is less than 45 minutes. Another possible explanation is that people commute to a workplace not situated within an urban area (see also the discussion in Section 4.2.2).

Table 4-23 Urban areas belonging to different types of urban areas Population size University Commuter towns towns 10,000 100,000- – 3,000 – 500,000+ 500,000 100,000 10,000 Isolated towns 0 0 5 6 1 2 Centres 3 7 24 1 23 0 Sub-centres 0 1 68 29 5 58 Neighbourhoods 0 0 3 74 0 45 Total 3 8 100 110 29 105

Figure 4-20 to Figure 4-24 show the differences between different types of urban areas for different variables. Figure 4-20 shows differences in (log) population between different urban areas. The (natural) logarithm of the population has been taken in order to remove extreme values and thus give a more even distribution. “Urban areas” shows the spread of the population size for all urban areas. The categories “500,000+” and “100,000 – 500,000” represent urban areas with a population within a certain population range. It is interesting to note the lack of continuity between the largest (500,000+) and the second largest (100,000 – 500,000) urban areas. The distribution in population size according to an urban hierarchy has previously been discussed in Section 4.4.3. Centres have a wide spread, even if most urban areas in this category are larger urban areas. Sub-centres also have a relatively wide spread, even if most urban areas in this category are medium-sized urban areas. Neighbourhoods have the smallest spread and belong to the smallest urban areas. University towns are normally medium-sized to large urban areas. Commuter towns, on the other hand, are normally smaller urban areas. Figure 4-21 shows differences in labour productivity between different types of urban areas. Larger urban areas generally have a higher labour productivity than smaller ones, even if the slightly smaller urban areas show a wide variation. Centres also generally have higher labour productivity than Sub-centres, and Sub-centres generally have higher labour productivity than Neighbourhoods. However, the variation is wide for both Sub-centres and Neighbourhoods. Commuter towns also show a wide variation, which is almost in

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agreement with the distribution of urban areas as a whole. University towns generally have a high labour productivity. They consist to a large extent of Centres and the variation as regards university towns largely follows the variation for Centres for all variables in Figure 4-20 to Figure 4-24. It must be remembered, however, that not all 29 university towns are centres and that not all centres are university towns.

Figure 4-20 Differences in ln(population) between different types of urban areas

Urban areas (total) 500,000 + 100 - 500,000 10 - 100,000 3 - 10,000 Isolated Centre Sub-centre Neighbourhood University towns Commuter towns

8 9 10 11 12 13 14 15

Source: Statistics Sweden and Growth Analysis (PiPoS)

Figure 4-21 Differences in labour productivity between different types of urban areas

Urban areas (total) 500,000 + 100 - 500,000 10 - 100,000 3 - 10,000 Isolated Centre Sub-centre Neighbourhood University towns Commuter towns

150000 200000 250000 300000 350000

Source: Statistics Sweden and Growth Analysis (PiPoS)

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Figure 4-22 Differences in proportion of people with tertiary education between different types of urban areas

Urban areas (total) 500,000 + 100 - 500,000 10 - 100,000 3 - 10,000 Isolated Centre Sub-centre Neighbourhood University towns Commuter towns

0,1 0,2 0,3 0,4 0,5 0,6

Source: Statistics Sweden and Growth Analysis (PiPoS)

Figure 4-23 Differences in daytime/night-time ratio between different types of urban areas

Urban areas (total) 500,000 + 100 - 500,000 10 - 100,000 3 - 10,000 Isolated Centre Sub-centre Neighbourhood University towns Commuter towns

0 0,5 1 1,5 2

Source: Statistics Sweden and Growth Analysis (PiPoS)

Figure 4-22 shows differences in the proportion of the daytime population that has tertiary education. Large urban areas generally have a higher proportion than smaller ones. Centres generally have a higher proportion than Sub-centres, and Sub-centres generally have a higher proportion than Neighbourhoods. Isolated towns and Commuter towns both have a proportion that largely follows the average for urban areas as a whole. University towns, on the other hand, generally have a high proportion. All university towns even have a higher proportion with tertiary education than the median for all urban areas.

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Differences in daytime/night-time ratio for different types of urban areas are shown in Figure 4-23. Urban areas with a ratio higher than 1 have a larger daytime population than employed night-time population, i.e. net commuting is positive. Urban areas with a high daytime/night-time ratio, on the other hand, have a negative net commuting rate, i.e. more people commute out than in. The large and medium-sized urban areas generally have a positive net commuting rate, while the very smallest urban areas generally have a negative net commuting rate. Centres and Isolated towns generally have a positive net commuting rate while it is generally negative for Sub-centres and Neighbourhoods. Commuter towns naturally have a negative net commuting rate. The only classifications where all urban areas have a positive net commuting rate are university towns and urban areas with more than 500,000 inhabitants. (With the exception of Lidköping, all centres have a daytime/night-time ratio higher than 1. Lidköping has a daytime/night-time ratio of 0.99.)

Figure 4-24 Differences in industry diversification between different types of urban areas

Urban areas (total) 500,000 + 100 - 500,000 10 - 100,000 3 - 10,000 Isolated Centre Sub-centre Neighbourhood University towns Commuter towns

0 0,2 0,4 0,6 0,8 1

Source: Statistics Sweden and Growth Analysis (PiPoS)

The last figure, Figure 4-24, shows differences in industry diversification between different classifications of urban areas. The measure of industry diversification was presented in 4.2.3, where it was made clear that this is primarily a measure of vulnerability and that it is strongly correlated to population size. The largest urban areas are therefore naturally the group that has the highest level of industry diversification. Medium-sized urban areas (10,000 – 100,000 and 100,000 – 500,000) have a markedly lower level of industry diversification than the largest urban areas (500,000+). Centres show a wide variation in industry diversification, even if most Centres have a higher industry diversification than urban areas in general. Centres generally have higher industry diversification than Sub-centres, and Sub-centres generally have higher industry diversification than Neighbourhoods. University towns have in general a higher industry diversification than urban areas overall. Commuter towns, on the other hand, in general have a similar spread to urban areas overall.

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This section has thus shown that there are differences between different types of urban areas. Hopefully, the description has given a more nuanced picture of different urban areas, in addition to only classifying them by population size. The different typologies presented here can be used to (i) gain a better understanding of the different roles of urban areas in a particular region, and also to (ii) study differences between urban areas within the same classification. The classifications presented must therefore be seen as a potential framework to classify and thereby gain a deeper understanding of the differences between urban areas. The purpose of this paper has been to classify urban areas in order to determine and analyse differences between them. However, trying to identify differences may lead to identifying differences that do not exist or that are irrelevant. There is therefore a risk involved in classifying urban areas. An important area for further research is therefore to try to gain a deeper understanding of what differences are conducive to the growth and development of urban areas.

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5 Summarising conclusions

The Swedish Agency for Growth Policy Analysis (Growth Analysis) has been commissioned by the Swedish Government to develop an understanding of different kinds of urban areas and their opportunities for growth. The purpose of the report is to develop an analytical framework in order to be able to study differences between urban and rural areas and between different urban areas. An important purpose of the report is also to create a deeper understanding of Swedish urban areas and their opportunities for growth. A primary challenge has been to create an analytical framework to be used to analyse urban areas in Sweden. The analytical framework is based on a functional approach combined with Statistic Sweden’s definition of “localities”. The framework is built on the idea of analysing urban areas at different levels, such as localities, urban areas and urban networks. The principal level studied is called “urban areas” and is thus the analytical definition of towns and cities used in this report. Defining urban areas also implies that all non-urban areas are defined as “rural” areas. Since urban areas can be classified in a multitude of ways, rural areas can also be classified in a multitude of ways. The present report also makes an attempt to classify different urban areas. The purpose is to gain an understanding of the different roles and functions of urban areas in order to gain a deeper understanding of urban areas’ different opportunities for growth and development. The classifications discussed are: urban areas with different population sizes, urban areas’ different roles based on an urban hierarchy, single-industry cities, university towns and commuter towns. According to Growth Analysis’ definition, Sweden has 221 urban areas (towns and cities). In 2009, 81% of Sweden’s population lived in an urban area. The smallest urban area was Laxå with 3,577 inhabitants and the largest was Stockholm with 1.8 million inhabitants. Like many other countries, Sweden has many small urban areas but few large ones. Most urban areas, 162 of a total of 221, had a population of between 3,000 and 20,000. There were only 11 urban areas with a population of more than 100,000 and only three of these (Stockholm, Göteborg and Malmö) had a population of over half a million. This implies that a third of Sweden’s population lives in the urban areas of Stockholm, Göteborg and Malmö. An urban area is a centre for trade, culture and learning. As a centre, the urban area provides opportunities for sharing of different functions, a better matching of buyers and sellers, employees and employers, etc., and increased learning and exchange of knowledge. That an urban area constitutes a centre entails that economic activity is concentrated and thus gives rise to increased productivity. The results in the report show that larger, more densely populated urban areas generally have a higher labour productivity than smaller, less densely populated towns. The smaller urban areas exhibit a large variation in labour productivity, which means that smaller urban areas can also be found among those with the highest labour productivity. No large urban areas are found among those with the lowest labour productivity. Earlier studies have shown that urban and rural areas, as well as different urban areas, have different prerequisites for growth and development. Those industries that are characterised by concentration advantages primarily establish themselves in urban areas, especially large ones, while industries with small concentration advantages mainly establish themselves in

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rural areas or smaller urban areas. This entails that firms focused on information or development of products, mainly establish themselves in large urban areas, while more mature production will mainly locate to smaller urban areas as well as rural areas. Earlier research has shown that both large and small urban areas can be innovative, but that they probably are innovative in different ways. This also entails that some industries only develop, or develop better, in large urban areas. Nor will this development take place at the expense of the rural areas or smaller towns, since it is not possible for these industries to develop there. The results in the report thus show that the industry in larger urban areas is characterised as being more knowledge-intensive, having a higher industry diversification and being more oriented towards the service sector than the industry in rural areas and in smaller urban areas. These results are in line with earlier research and imply that some industries develop best in large towns or cities, while others develop best in smaller towns or in rural areas. Favourable regional and national growth is therefore a matter of promoting and exploiting concentration advantages in the urban areas and of urban and rural areas respecting and valuing their comparative advantages. A high degree of specialisation has several advantages but may lead to a greater vulnerability, particularly in smaller urban areas. A difficult challenge for the politicians is thus how to promote specialisation and at the same time deal with the vulnerability amongst urban and rural areas.

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Appendix

Table A1: Industry groups

Group Name SNI 2002 1 Agriculture, forestry and 01-05 fishing 2 Manufacturing and mining 10-37 3 Energy, water and sanitation 40, 41, 90 4 Construction 45 5 Business services 51, 72-74, 555, 603, 631, 634, 713, 911, 6024, 55102, 61102, 62300, 65210, 65231, 67110, 70110, 70120, 70202, 70203, 70329, 71210-71230, 80425, 93011. 6 Private household services 52, 92, 95, 552-554, 633, 714, 912, 913, 6021-6023, 70201, 70204, 70209, 70321, 93012-93050. 7 Public household services 8532, 75300, 80100-80424, 80426-80429, 85111-85316. 8 Miscellaneous services 50, 64-67 (except 6510, 65231 and 67110), 99, 601, 612, 622, 632, 751, 752, 55101, 55103, 61101, 62100, 70310, 71100. 9 Unknown 0000

95 The Swedish Agency for Growth Policy Analysis (Growth Analysis) is a cross-border organisation with 60 employees. The main office is located in Östersund, Sweden, but activities are also conducted in Stockholm, Brasilia, Brussels, New Delhi, Beijing, Tokyo and Washington, D.C.

Growth Analysis is responsible for growth policy evaluations and analyses and thereby contributes to:

• stronger Swedish competitiveness and the establishment of conditions for job creation in more and growing companies • development capacity throughout Sweden with stronger local and regional competitiveness, sustainable growth and sustainable regional development.

The premise is to form a policy where growth and sustainable development go hand in hand. The primary mission is specified in the Governme- nt directives and appropriations documents. These state that the Agency www.growthanalysis.se shall:

• work with market awareness and policy intelligence and spread knowledge regarding trends and growth policy • conduct analyses and evaluations that contribute to removing barriers to growth • conduct system evaluations that facilitate prioritisation and efficiency enhance- ment of the emphasis and design of growth policy • be responsible for the production, development and distribution of official statistics, facts from databases and accessibility analyses.

About the Report series: Growth Analysis’ main channels for publications.

Other series: Statistics series – continuous statistical production. Working paper/Memorandum series - some examples of publications in the series are method reasoning, interim reports and evidential reports. Svar Direkt [Direct Response] – assignments that are to be presented on short notice.

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