Creative Indexes: Economic Space Matters?

CREATIVE INDEXES: ECONOMIC SPACE MATTERS? by Carlos Miguel de Oliveira Correia Master degree dissertation in Economics Area of specialisation in Economic Analysis Supervisor José da Silva Costa 2012 ii Biography Carlos Miguel de Oliveira Correia was born in Vila Nova de Famalicão on 9 June of 1988. He did his primary and secondary education studies in his hometown where he lived until moving to Oporto in 2011. In 2006 he entered the University of Porto School of Economics and Business (FEP - Faculdade de Economia da Universidade do Porto) where he graduated in 2010 in Economics. In 2010, at that same institution, he joined the Masters of Science (MSc) in Economics specialising in Economic Analysis. iii Acknowledgements I would like to thank all the persons who, directly or indirectly, have contributed to the development and success of this dissertation. Special thanks to my supervisor, José Costa, for his guidance, support and all the hours spent dicussing this work. I am very grateful to all my family, specially my father Manuel, my mother Maria and my sister Carla, who provided all the conditions and support necessary for the completion of this dissertation. Thanks to all my friends for their patience and encouragement, specially to José Gaspar for his comments and advices on this dissertation. iv Abstract Creativity is now seen as the new currency in a global world. It is considered the best intangible resource and, more or less, it is inherent to everyone. While in the previous era, the requirement to attain success was learning how to use, integrate and work with new technologies, nowadays the ability to generate new ideas and to transform them into innovation is the new key to succeed personally, professionally, corporately and socially. Many studies have assessed the importance of creativity as a key driver for economic growth and development. The interest in the estimation of indexes of creativity has been increasing in the last years, not only for territorial marketing purposes, but also, because they provide analytical tools to assess the economic impact of the creative economy and are useful to measure the effectiveness of political decisions. This dissertation draws attention to the spatial analysis which we consider that has been overlooked by the existing studies on creativity and, consequently, by creativity indexes. It presents a review of creativity literature and a critical review of the creativity indexes industry by selecting the most relevant references among the existing ones. Then it proposes a new index which seeks to fill the gaps and amend the weaknesses of its predecessors. This new indicator is used to measure the creativity on EU-27 states and on Portuguese cities, enabling the spatial analysis at country and city level. Finally, it is proposed a new typology of cities based on their creative performance and their proximity to creative centres which is applied to Portuguese cities using the new index results. Keywords: creative economy, creativity indexes, spatial economics JEL Codes: O31, R12, Z1 v Resumo A criatividade é atualmente vista como a nova moeda de um mundo global. É considerada o melhor recurso intangível e, em maior ou menor escala, está inerente a todos os indivíduos. Enquanto que na era anterior o segredo para se ser bem sucedido residia na capacidade de aprender a usar, integrar e trabalhar com as novas tecnologias, nos dias de hoje a capacidade de gerar novas ideias e de convertê-las em inovação é o novo fator de sucesso pessoal, profissional, social e empresarial. Vários estudos analisaram a importância de criatividade como motor de crescimento e desenvolvimento económico. O interesse na estimação de índices de criatividade tem aumentado nos últimos anos, não só devido a objetivos relacionados com marketing territorial, mas também, devido ao facto de serem instrumentos analíticos que medem o impacto económico da economia criativa e são úteis para avaliar a eficácia de decisões políticas. Esta dissertação realça a análise espacial que nós consideramos ser um assunto negligenciado pelos estudos existentes e, consequentemente, pelos índices de criatividade. Apresenta uma revisão da literatura sobre criatividade e uma análise crítica da indústria de índices de criativade selecionando os mais relevantes entre os existentes. Depois é proposto um novo índice que procura corrigir os pontos fracos e lacunas dos seus antecessores. Este novo indicador é usado para estimar a criatividade dos países da UE-27 e das cidades Portuguesas, permitindo a análise espacial a nível nacional e urbano. Por último, é proposta uma nova tipologia de cidades baseada nas suas performances criativas e na proximidade de centros criativos, a qual é aplicada às cidades Portuguesas usando os resultados obtidos com o novo índice. Palavras-chave: economica criativa, índices de criatividade, economia geográfica Códigos JEL: O31, R12, Z1 vi Index of Contents 1. Introduction ............................................................................................................... 1 2. Creative Indexes: a literature review ......................................................................... 5 2.1. Concepts and definitions .................................................................................... 5 2.2. The state of the art on creative indexes ............................................................ 12 2.3. Indexes comparison .......................................................................................... 22 2.4. Conclusions of the indexes review ................................................................... 24 3. A proposal of a creativity index .............................................................................. 25 3.1. Theory .............................................................................................................. 25 3.2. The Creative Space Index ................................................................................ 28 3.3. Why space matters? .......................................................................................... 31 4. CSI empirical application: EU member states ......................................................... 34 4.1. CSI with endogenous weigthing ...................................................................... 36 4.2. CSI and Florida’s Euro-Creativity Index ......................................................... 38 4.3. Creativity and Economic Performance ............................................................ 39 4.4. Creativity and space ......................................................................................... 39 5. CSI empirical application: Portuguese cities ........................................................... 46 5.1. Creativity and space at city level ..................................................................... 51 6. A proposal for a new typology of cities .................................................................. 53 7. Conclusion ............................................................................................................... 56 References ....................................................................................................................... 59 Attachements .................................................................................................................. 62 vii Index of Tables TABLE 1 - AGES OF ECONOMIC DEVELOPMENT ............................................................................................ 1 TABLE 2 - CREATIVE ECONOMY SPECIFIC CHARACTERISTICS ....................................................................... 8 TABLE 3 - CREATIVE INDUSTRY SECTORS .................................................................................................. 10 TABLE 4 - CREATIVE CLASS OCCUPATIONS ................................................................................................ 13 TABLE 5 - CREATIVITY INDEX COMPONENTS ............................................................................................. 13 TABLE 6 - F-ECI DIMENSIONS AND INDICATORS ......................................................................................... 15 TABLE 7 - HKCI DIMENSIONS ..................................................................................................................... 16 TABLE 8 - J-CCI CATEGORIES AND DIMENSIONS ......................................................................................... 18 TABLE 9 - ECI DIMENSIONS ....................................................................................................................... 19 TABLE 10 - L-CCI DIMENSIONS ................................................................................................................. 20 TABLE 11 - CCI-CCI DIMENSIONS .............................................................................................................. 21 TABLE 12 - CHECKLIST OF INDEXES DIMENSIONS AND INDICATORS .......................................................... 22 TABLE 13 - INDEXES STRENGTHS AND WEAKNESSES .................................................................................. 23 TABLE 14 – EUROPEAN CSI - DIMENSIONS, INDICATORS AND DESCRIPTION .............................................. 28 TABLE 15 - EUROPEAN CREATIVE SPACE INDEX ........................................................................................ 34 TABLE 16 - COMPARISON OF CSI WITH AND WITHOUT ENDOGENOUS WEIGTHING ...................................... 38 TABLE 17 - COMPARISON BETWEEN CSI AND F-ECI .................................................................................. 38 TABLE 18 - CORRELATION BETWEEN CREATIVITY, SPATIAL VARIABLES AND GDP .................................... 44 TABLE 19 - REGRESSION RESULTS OF CREATIVITY IN EU-27 ...................................................................... 45 TABLE 20 - PORTUGUESE CSI DIMENSIONS, INDICATORS AND DESCRIPTION .............................................. 46 TABLE 21 - PORTUGUESE CREATIVE SPACE INDEX ..................................................................................... 47 TABLE 22 - MUNICIPALITIES OF GRANDE LISBOA AND GRANDE PORTO ..................................................... 49 TABLE 23 - PORTUGUESE CSI WITH GREAT LISBON AND GREAT OPORTO ................................................. 49 TABLE 24 - CORRELATION BETWEEN CREATIVITY AND SPATIAL VARIABLES .............................................. 51 TABLE 25 - REGRESSION RESULTS OF CREATIVITY IN PORTUGUESE CITIES ................................................. 52 TABLE 26 - CCM TYPES AND DESCRIPTION ................................................................................................ 54 TABLE 27 - CCM APPLICATION: PORTUGUESE CITIES ................................................................................. 55 viii Index of Figures FIGURE 1 - CREATIVE CONCEPTS RELATIONSHIP .......................................................................................... 5 FIGURE 2 - THE CREATIVE ECONOMY........................................................................................................... 8 FIGURE 3 - CSI SPATIAL FRAMEWORK ........................................................................................................ 33 FIGURE 4 - CREATIVE SPACE INDEX IN EU MEMBER STATES ...................................................................... 35 FIGURE 5 - TOP 6 CSI SCORES BY DIMENSION ............................................................................................. 36 FIGURE 6 - CORRELATION BETWEEN CREATIVITY AND GDP PER CAPITA.................................................... 39 FIGURE 7 - CORRELATION BETWEEN CREATIVITY AND POPULATION .......................................................... 40 FIGURE 8 - CORRELATION BETWEEN CREATIVITY AND DENSITY ................................................................. 40 FIGURE 9 - CORRELATION BETWEEN CREATIVITY AND URBAN HIERARCHY ................................................ 41 FIGURE 10 - CORRELATION BETWEEN CREATIVITY AND THE DEGREE OF POLYCENTRICITY ........................ 41 FIGURE 11 - CORRELATION BETWEEN CREATIVITY AND AGGLOMERATION ................................................ 42 FIGURE 12 - CORRELATION BETWEEN CREATIVITY AND INTERACTION ....................................................... 44 FIGURE 13 - PORTUGUESE CSI MAP ............................................................................................................ 50 FIGURE 14 - CREATIVE CITY MATRIX ......................................................................................................... 53 1 1. INTRODUCTION Creativity and its importance to economic development is, now more than ever, a subject of debate and research both by academic and political institutions. The creative economy is developing fast as it integrates and influences the rest of the economy. The value of world exports of creative goods and services reached $592 billion in 2008, growing at an annual rate of 14 per cent between 2003 and 2008, according to UNCTAD 1 . In the early 1990s, the Nomura Research Institute of Japan already predicted that the “Information Age” would be followed by a fourth era of economic activity, calling it “Creation Intensification” 2 . Daniel Pink (2005) in “A Whole New Mind” endorses the previous classification and defines four Ages of Economic Development as shown in Table 1. Table 1 - Ages of Economic Development 1 Agriculture Age (farmers) 2 Industrial Age (factory workers) 3 Information Age (knowledge workers) 4 Creative Age (creators and empathizers) Source: Pink (2005) Nowadays creativity is seen as the new currency in a global world. It is our best intangible resource and, more or less, it is inherent to everyone. The ability to generate new ideas and to transform them into innovation is the new key to succeed personally, professionally, corporatively and socially, while in the previous era, the requirement to attain success was learning how to use, integrate and work with new technologies. 1 Source: Creative Economy Report 2010: A Feasible Development Option, UNCTAD, 2010. 2 Source: Sozo no Senryaku [Strategy for Creation], Nomura Research Institute, 1990. 8 Secondly, he takes an industry approach which defines the creative economy as a combination of industries classified by their form of intellectual property: Copyright, Patent, Trademark and Design industries. Table 2 - Creative Economy specific characteristics Intangible assets Infinite resources (ideas) Competition with low barriers to entry Market driven by demand Increasing returns The “currency” is creativity and intellectual property Increasing marginal utility Source: (Howkins, 2001) Another approach was adopted by Richard Florida (2002) taking an “Occupational” perspective. This option faces dificulties on data availability for analysis but highlights the problem that arises with the “Industry” approach: creative industries employ many workers whose work doesn’t involve creative tasks or requires creativity; whereas, “Creative Occupations” capture many creative workers, considered individually, who are not assigned to any creative industry. The New England Foundation for the Arts has combined both “Industry” and “Occupational” approaches into a new framework and adds a third dimension which refers to spatial matters (NEFA, 2007). Figure 2 - The Creative Economy Source: NEFA (2007) Businesses and Organizations Creative Enterprises Places Creative Communities People Creative Workforce 9 The main hypothesis of this model is that a relatively higher concentration of creative enterprises and creative workers in a geographic area yields a competitive edge by elevating the area’s quality of life and improving its ability to attract economic activity. The UNCTAD definition of creative economy is: “The creative economy is an evolving concept based on creative assets potentially generating economic growth and development. It can foster income generation, job creation and export earnings while promoting social inclusion, cultural diversity and human development. It embraces economic, cultural and social aspects interacting with technology, intellectual property and tourism objectives. It is a set of knowledge-based economic activities with a development dimension and crosscutting linkages at macro and micro levels to the overall economy. It is a feasible development option calling for innovative, multidisciplinary policy responses and interministerial action. At the heart of the creative economy are the creative industries.” The definition of creative economy that is going to be adopted in this dissertation is a combination of all the previous approaches and the DCMS (1998) definition of creative industries. Thus, the Creative Economy is an evolving concept, based on creative assets potentially generating economic growth and development (UNCTAD, 2010), that consists of all those activities which have their origin in individual creativity, skill and talent, and which have a potential for wealth and job creation through the generation and exploitation of intellectual property (DCMS, 1998). These activities result from the action or interaction of enterprises, organisations and individuals in a creative place (NEFA, 2007), and can be delineated according to their type of intellectual property: Copyright, Patent, Trademark and Design (Howkins, 2001). D. Creative Industries The origin of this term dates back to 1994 with the launch of the report “Creative Nation” in Australia. But only a few years later has it gained wider popularity with the creation of the “Creative Industries Task Force”, in 1997, under Tony Blair’s administration in the United Kingdom. The definition advocated by this organism considers that creative industries are “‘those activities which have their origin in individual creativity, skill and talent, and which have a potential for wealth and job 10 creation through the generation and exploitation of intellectual property” (DCMS, 1998). The thirteen sectors identified within this framework are listed in Table 3. Table 3 - Creative Industry Sectors Advertising Architecture Art and antiques market Crafts Design Designer fashion Film Interactive leisure software Music Performing arts Publishing Software Television and radio Source: DCMS(1998) This has been the most used definition in studies related to creative industries. The reason behind its popularity is, not only, the high credibility that DCMS has earned over the years, but also, for being part of a document that challenged the traditional economic perspective on creative industries and that has regarded them as an important economic sector which should be a subject of governments’ attention way beyond a simple market failure analysis. The “creative industries” designation marks a historical shift in approach to potential commercial activities that until recently were regarded purely or predominantly in non-economic terms (UNCTAD, 2004). E. Creative Millieu and Creative City Creativity is fostered by creative people and by organisations that are attracted to places with specific characteristics and, when all of these three elements come together in one area, they constitute a creative milieu. Depending on the scale of analysis, the Creative Millieu can be a building, a street, a campus, a city, a region or a cluster, among others. Historically, the city has proved to be the most creative space by nature. It has both hard and soft infrastructures needed to nurture creativity which is stimulated by urbanity 11 itself – critical mass, density, diversity and interaction. This concept has gained popularity after Landry’s (2000) book. According to this author, the Creative City is one that produces cultural goods and services; attracts innovative high-technology enterprises; has networks for exchanging information and knowledge between individuals, enterprises and public sector; organises activities including creative ones in clusters, offers a variety of public spaces of quality and opportunities for leisure, entertainment and self-development; has an effective transport infrastructure and fosters participation and involvement both by inhabitants and tourists. Many cities call themselves “creative city”, but only a few are comprehensively creative, i.e., cities with a global reputation over a long time period and where creativity dominates the urban scene, e.g., New York, Los Angeles, Amsterdam, London, Milan and Tokyo (Landry, 2007). Not all cities are creative, but every city can increase its creativity. Creative policies must be adapted to the specificities of each city and should be planned and executed within a multidisciplinary and cross-sectorial framework in order to avoid biasing the creative sector itself 3 . That is, a city would benefit more from taking measures considering different policy domains all together (social, cultural, economic and geographic, among others) than if they are taken individually. 3 “Unlocking the potential of cultural and creative industries”, European Commission Green Paper (2010) 12 2.2. The state of the art on creative indexes There is a fierce competition going on between cities which has been fostered by globalisation. Nowadays, when one thinks of migrating or travelling is more likely to think of cities attractiveness than of countries characteristics. This competition exists at a global scale, and cities are aware of their competitors at both intranational and international level. Therefore, most cities try to emphasise their attractive features and distinctiveness, and a good way to achieve this is to position themselves on the top of rankings. A good score in an index is a competitive advantage and a great way of attracting people and enterprises. Thus, the demand for creative indexes by policymakers has been increasing in the last few years, not only for territorial marketing purposes but also because they provide analytical tools to assess the economic impact of the creative economy and are useful to measure the effectiveness of political decisions. The development of creativity indexes emerges from the combination of the need to assess the relationship between creativity and the economy with the lessons learned from many other kinds of indexes – art-based, culture-based, of regions liveability, of global cities and ICT. So, although city performance indexes are not recent, only during the past decade did appear the first studies and the development of creativity-based indexes. Among the many existing indexes we have selected eleven we consider being the more relevant and indubitable references in creativity indexes literature. They will be presented by chronological order for a better understanding of their evolution over the time. 2.2.1. Florida’s Creativity Index (FCI) In the book “The Rise of The Creative Class” Richard Florida (2002) has pointed out to the importance of the creative economy and has presented the concept of Creative Class in an occupational point of view, defined in two major sub-components: Super-Creative Core and Creative Professionals. 13 Table 4 - Creative Class Occupations Super-Creative Core occupations Creative Professionals occupations Computer and mathematical Management Architecture and engineering Business and financial operations Life, physical and social science Legal occupations Education, training and library Healthcare practitioners and technical Arts, design, entertainment, sports and media High-end sales and sales management Source: Florida (2002) He argues that policymakers should focus on a People Climate rather than on a Business Climate, that is, instead of investing on attracting firms and capital, a city should invest on its attractiveness to creative people. According to Florida, the creative class is a key factor in economic development and those cities capable of attracting creative people are more likely to succeed because this class includes those who are more innovative, more entrepreneurial and attract creative enterprises. He explains the geographical distribution of the creative class based on a 3T Model: Talent, Tolerance and Technology. His Creativity Index is based on those three dimensions and is a synthetic index composed by the sub-indexes of Talent, Tolerance and Technology. The indicators of each sub-index are listed on Table 5. Table 5 - Creativity Index Components Source: Florida (2002) Florida’s work has gained popularity due to its pioneering and radical vision. Also, many times it was considered controversial due to the usage of Gay and Bohemian subindexes as drivers of economic development. Although we find the number of dimensions and indicators very limited to effectively assess regions creativity, this Talent Tolerance Technology Human Capital Foreign-born Innovation Creative Class Diversity Index High-Tech Innovation Researchers Gay Index High-Tech Industry Bohemian Index 14 index was taken into account on the construction of the index proposed by this dissertation. 2.2.2. Silicon Valley’s Creative Community Index (SV-CCI) The Creative Community Index stems from a collaborative project between the Knight Foundation, Americans for the Arts, the City of San José Office of Cultural Affairs and Cultural Initiatives Silicon Valley. This project aimed to develop a tool capable of providing an objective source of information about the artistic, creative and cultural life of Silicon Valley (CISV, 2002). The index was built within a conceptual framework based on a causal theory of the impact of creativity on a community: “(…) various “levers” are available for influencing the dynamics of the arts and culture in Silicon Valley. As these “levers” are exercised (e.g., a local city government establishes an ordinance to support the acquisition of public works of art), they generate “assets” (e.g. sculptures, fountains or murals). These assets, in turn, provide a basis for public “participation” in the arts and culture (e.g. enjoying a piece of sculpture in the midst of a shopping district). Finally, the accumulated results of this participation are measurable “outcomes”, such as increased feelings of connectedness to neighbours or heightened sense of community identification as a result of living in an aesthetically inspiring environment” (CISV, 2002) . According to this guiding framework, the SV-CCI organises its indicators into four categories: Outcomes: the desired outcomes of a healthy cultural life, broad-based creativity, social connectedness among diverse people and contribution to the quality of life in Silicon Valley. Participation: residents’ participation in arts and cultural activities, including the extent to which diverse people participate together. Assets: the mix of cultural assets present in the community, including talent in the creative sector (non-profit, public and private), venues and facilities, and the aesthetic quality of our environment. 15 Levers: the extent to which we leverage and build our cultural assets and encourage people’s interaction with them through arts education, leadership, investment, and policies. Although this project mentions artistic, creative and cultural concepts, the latter was mostly used comprising the first two. For a better and objective comparison between indexes, the “cultural” term mentioned above should be interpreted as “creative” according to the definitions adopted in this dissertation. 2.2.3. Euro-Creativity Index (F-ECI) A few years later, Florida, in a joint work with Irene Tinagli, tailored his model to fit European reality (Florida and Tinagli, 2004). The main changes were made in the Tolerance sub-index which was built based on a completely different set of indicators with a more subjective nature. Nevertheless, it keeps the main hypothesis of Florida’s Creative Capital Theory whose relevance is proven empirically in European regions. Table 6 - F-ECI dimensions and indicators Index Sub-Indexes Description Talent Creative Class Employed in creative occupations as percentage of total employment. Human Capital Percentage of population 25-64 with a bachelor degree or above. Scientific Talent Number of researchers in scientific disciplines per thousand workforce. Technology Innovation Index Patents applications to the US Patent Office per million population. Technology Innovation Index High-Tech Patents per million population (US Patent Office). R&D Index R&D expenditure as percentage of GDP. Tolerance Attitudes Index Percentage of population that express tolerant attitudes toward minorities. Values Index Degree to which a country is based on traditional values versus more rational/secular values. Self Expression Index Degree to which a country recognises and accepts self expression values. Source: Florida and Tinagli (2004) 16 2.2.4. Hong Kong Creativity Index (HKCI) This index was developed by the Centre for Cultural Policy Research of the University of Hong Kong and commissioned by Home Affairs Bureau, The Hong Kong Special Administrative Region Government. The HKCI was built from the combination of several theories, including the Creative Capital Theory (Florida, 2002), human, social and cultural capital. The framework behind the HKCI is that any creative act can be analysed by applying the concept of a Cycle of Creative Activity – “creativity is a social process continuously shaped and constrained by the values, norms, practices and structures of “Social Capital”, “Cultural Capital” as well as the development of “Human Capital”. While the ability to create is embedded in the contexts of three forms of capital, its articulation would be promoted or constrained by the availability and accessibility of facilities, institutions, market and social enablers, or in short the “Structural/Institutional Capital”. The accumulated effects and interplay of these different forms of capital are the “Outcomes of Creativity” which could be measured in terms of economic outputs, incentive activities and any other forms of creative goods, services and achievements” (HKSAR, 2004). Therefore, the HKCI framework builds on a 5C’s Model. Table 7 - HKCI dimensions 1 Creativity Outcomes 2 Structural/Institutional Capital 3 Human Capital 4 Social Capital 5 Cultural Capital Source: (HKSAR, 2004) The HKCI comprises 88 indicators that are way more than the number of indicators used in Florida’s indexes. This option increases the difficulty of collecting data and analysing it but, on the other hand, results in a more complete and effective assessment of a region’s creativity and allows to extend the scope of indicators to other important dimensions. 17 2.2.5. Czech Creativity Index (CZCI) The CZCI was developed by Kloudova and Stehlikova, in 2007, based on Florida’s model and its index dimensions: Talent, Technology and Tolerance. This approach has a peculiarity relevant for this dissertation: their main concern was to analyse the creativity overall and individual scores of Czech regions in terms of regional similarities and geographic location (Kloudova and Stehlikova, 2010). The main conclusions of this study were: i. Creative regions tend to cluster. ii. It has been proved that there is a spatial autocorrelation between creative regions, where individual regions affect one another and the neighbouring regions are similar. iii. The hypothesis about the formation of a creative core or centre in Czech Republic has been rejected. The hypotheses were only tested in Czech regions but this study has done an interesting analysis on spatial matters. 2.2.6. Composite Index of the Creative Economy (CICE) The CICE was developed to measure the creative capacity and capability of the Flanders District of Creativity regions (Bowen, Moesen, and Sleuwaegen, 2008). Nevertheless, it was designed to be used in any other region. This index has three key dimensions: Innovation, Entrepreneurship and Openness. These categories are clearly inspired in Florida’s theory but the CICE extends the selected indicators to new aspects such as business activity and ICT infrastructure. This index stands out from the others by proposing an innovative method to determine the weight that each indicator has on the index global value. Normally, in order to ease the index calculation, it is adopted a simple aggregation procedure, which consists of assigning equal weights to each indicator. In many cases, this may give a wrong perception that each indicator has the same importance when it is not true. Unequal weights can be determined, based on the opinion of experts, but, this is an expensive 24 2.4. Conclusions of the indexes review Although creativity indexes only started being developed in the last decade, there is already a considerable number of indexes created to measure creativity at country, regional and city level. We only reviewed eleven we consider the most important and the most relevant for this dissertation, but there are a few others. Much has been done since Florida presented the first one in 2002. New frameworks have been assumed, different methodologies were adopted and many dimensions and respective indicators have been proposed to better assess creative performances. Other indexes were mere adaptations of Florida’s work to a particular region’s reality. The SV-CCI has emphasised the importance of culture. The CICE has presented the innovative endogenous weigthing method. The J-CCI has enabled a better analysis in an evolutionary perspective by separating flow and stock indicators. The CZCI and the BCI highlighted some spatial aspects of creativity. The latest index created, the CCICCI, is, in our point of view, the most complete and developed one. It stems from the lessons learned from the past, gathering the best from the existing indexes. However, it also has a few gaps that we will seek to fill such as not including an entrepreneurship dimension and the fact that some of its indicators data does not have a commom source. This forces to collect data from different sources which makes the process more difficult and also compromises the comparability of the results. 25 3. A PROPOSAL OF A CREATIVITY INDEX An index can be a great tool to stimulate dialogue about the importance of creativity as well as to improve policymaking. There are already many indexes; however, we think they have weaknesses that need to be fixed. Therefore, we propose a new index seeking to fill the existing gaps. Due to the nature of this dissertation we called it Creative Space Index (CSI). Inspired by the lessons learned from the past, it aims to be a superior index by gathering the best aspects of the existing ones and complementing them with additional features. The index was developed according to the following principles:  Universal – it should be able to analyse different realities and to enable comparisons across the globe.  Flexible – it should be adaptable to work with different scopes – country level, regional level and city level – and with different data sources.  Efficient – it should cover as many aspects as possible of the creative phenomenon, keeping the data collection easy and simple.  Unbiased – creativity does not depend on a single dimension and it is important for the index to be wide ranging and properly weighted for a better policymaking. 3.1. Theory There is no unique recipe of a creative country, region or city. It is not something entirely plannable and controllable because there is a lot of informality and spontaneity involved in the creative process. Each place has to find its own particularities and its mission is to potentiate the existing resources. A good creativity index should reveal what a region is doing well or wrong, so it can optimise its policies and decisions. Measuring creativity, both at the individual and at the collective perspective, is not an easy task due to its complex nature. There is neither an established framework nor a generally accepted methodology. We decided that the best way to design a solid index 26 was to define its dimensions based on the categories used to group indicators in the indexes comparison because they are in conformity with the principles stated above and cover all relevant aspects of creativity. D1. Talent A creative place should nurture, foster, promote and reward all talents (Landry, 2010). It is a place that offers a wide range of learning options, enabling people to find their right vocation. These are provided by institutions such as universities as well as by a more informal interaction between individuals, organisations and places. Economists agree that skilled and educated people, normally referred as human capital, play a role in economic progress. The Creative Class has an equally important role as well (Florida, 2002). D2. Openness A creative place should be open minded and tolerant in order to welcome people with different backgrounds and cultures (Florida, 2002; Landry, 2010). An environment of diversity increases the generation and the flow of ideas. It eases the interaction communication and it attracts talent. D3. Cultural Environment and Tourism Cultural life is a key element in a region’s quality of life and the participation in cultural activities increases people connections to each other and to place (CISV, 2002). So, the cultural offering must include a variety of experiences and ways for the community to express itself. Tourism is the best way to promote and potentiate the cultural assets that a region has to offer and culture is what most motivates tourists to visit a specific place. D4. Technology and Innovation Technology and innovation simultaneously foster and depend on creativity. People’s creativity is the motor of technological progress and innovation (CISV, 2002, Florida, 2002; HKSAR, 2004; Landry, 2010). The latter are the indicators of how well is taken advantage of the first. 27 D5. Industry A high share of creative industries is a good indicator of good creative performance. However, a region should also have a diversified business structure with international reach in order to maximise positive externalities and spillovers. D6. Regulation and Incentives Both creative individuals and businesses play an important role, but they need a favourable environment to create. A place should ensure good conditions for creativity to develop, whether with public support or with a fair regulatory system (CISV, 2002). D7. Entrepreneurship Without entrepreneurship, creativity is not likely to lead to economic growth as ideas are not translated to the market. On the other hand, the economic success of a creative individual or organisation depends very much on the level of easiness of doing business combined with the financial resources available. D8. Accessibility A creative place is well connected internally and externally (Landry, 2010). So, it should have a good transport system and infrastructure. Proximity to other creative regions increases the creative potential of the place, but only if it is accessible. D9. Liveability A region should be able not only to attract creative talent but also retaining it (Florida, 2002). Therefore, a creative place must have a good quality of life and should offer local amenities that make it a place where people like to live and work. D10. Notoriety A creative place should be distinct and have a clear identity (Landry, 2010). It can result either from historical and natural reasons or from the dynamism of its culture. Now more than ever, it is usual to see creativity being used for territorial marketing purposes. 28 3.2. The Creative Space Index The CSI comprises a wide variety of quantitative and qualitative indicators to estimate creative performance at country, region and city level. In order to capture different aspects of creativity, the indicators are grouped into dimensions as explained in Table 14. Creativity is a complex concept and, therefore, in order to build an index that addresses its characteristics as efficiently and logically as possible, each dimension is composed by indicators that, if applicable, measure both inputs and outputs, both demand and supply, both investments and results, both hard and soft characteristics, both people and business climate, both stock and flow factors. Table 14 – European CSI - Dimensions, Indicators and Description Dimension Indicator Description D1 - Talent Human capital Nr of graduates per capita Creative class Nr of persons in creative occupations per capita Education Nr of universities in THEWUR per million inhabitants D2 - Openness Diversity Share of non-nationals among residents Discrimination FRA's multiple discrimination index Foreign talent Share of tertiary foreign students D3 - Cultural Environment and Tourism Cultural offering Nr of museums and cinemas per million inhabitants Cultural participation Nr of visitors per museum Cultural values Degree of personal importance of culture Cultural expend. Share of household expenditure on culture Tourism capacity Nr of bed-places per capita Tourism occupancy Tourism establishments occupancy rate D4 - Technology and Innovation R&D R&D expenditure as percentage of GDP HRST Percentage of human resources in science and technology Internet access Share of households with internet access at home Patents Nr of patents registered per million of inhabitants D5 - Industry Creative industries Nr of creative enterprises per capita Creative employment Share of employment in creative industries Creative diversity Shannon's diversity index Internationalisation Exportation of cultural goods Value added VA of creative industries as percentage of GDP Turnover Turnover in creative industries per capita 29 Source: Author The main data source is Eurostat providing 75% of the indicators data (30/40 at country level). For the remaining indicators data is obtained from World Bank, International Labour Organization, European Group of Museum Statistics, KEA European Affairs, International Confederation of Societies of Authors and Composers, among others. Data is not always available for all countries neither is it always referring to the same year for all elements. So, the selection procedure involves getting data of the most recent year available and when the data from the main common source is missing for some country it is obtained from the relevant national institutes or organisations. If there is still no data available, the remaining values are imputed using the immediately above hierarchical level, e.g, if the index is being estimated at city level and there is missing data for any element, the value is imputed using the value of the NUTSIII region to Dimension Indicator Description D6 – Regulation and Incentives Public incentive Direct public expenditure on culture per capita Royalties Author's Royalties Collected per capita Property rights Score in the International Property Rights Index D7 - Entrepreneurship Startups Newly established enterprises per 1000 inhab. Venture capital Venture capital per capita Business angels Business Angels funding per capita Beasiness Level of easiness of starting a business D8 - Accessibility Air Nr of airports per capita Road Length of motorway per area Rail Length of railway per area D9 - Liveability Purchase power National price level indices (EU27=100) Crime Nr of recorded crimes per thousand Health Care Nr of health care facilities per capita Leisure and recreation Share of land in recreational and leisure use Well-being Experienced well-being score in Happy Planet Index D10 - Notoriety Capitals of culture Nr of UNESCO capitals of culture World Heritage Nr of buildings in UNESCO Wordl Heritage list Gastronomy Nr of Michelin stars per capita 30 which it belongs. The same happens for NUTSIII regions, this time using values from NUTSII. In order to remove the scale effect from the index and to make the scores directly comparable between all elements, when necessary, the indicators were relativised using auxiliary indicators such as Population, GDP and Area. The type of number and the nature of each indicator are well explained in its description. A structure analysis has been done aiming to study the overall structure of the index and to check if there are any indicators that are statistically similar, i.e, that provide the same information and, therefore, at least one is redundant. Using a correlation matrix of all indicators we have checked that all of them are relevant and their presence in the index is advantageous. Only a few indicators presented high values of correlation: Air, Road and Rail, used to measure the dimension Accessibility, are highly correlated but all of them are important otherwise the exclusion of any of them would bias the analysis. For all indicators data is transformed using the Min-Max normalisation method. This process transforms data from its original units to a value between 0 and 1. The normalised value for country, region or city i is defined as: () ( ) ( ) i i i i i i i i X MIN X NMAX X MIN X     (3.1) The maximum normalised score is equal to 1 and the minimum normalised score is equal to 0. In all composite indicators, aggregation is an important step of their construction and should not be taken lightly. Any modification in the weightings will change the overall score of the index and, consequently, the rankings. Normally, in order to ease the indexes calculation, it is adopted a simple aggregation method which consists of assigning equal weights to each dimension. This may give the wrong perception that each dimension has the same importance, which may not be true. Unequal weights can be determined based on the opinion of experts, but, this is an expensive procedure, not 31 to mention that is a subjective judgement and, as such, probably will result in several divergent opinions. In CSI we first use equal weights and then we also apply an endogenous weighting technique which will be explained in section 1 of chapter 4. 3.3. Why space matters? Spatial economics was left out of mainstream economics during most of the twentieth century. The fact that it has produced only literary ideas and has been followed by almost no mathematisation, added to the fact that it was mostly discussed in German, explains why spatial theories and models were overlooked by the Anglo-Saxon mainstream. In the end of the twentieth century the spatial economics has gained higher significance with the arising of a new line of theory - New Economic Geography (NEG). The globalisation increased the importance of a spatial perspective in the analysis of the economic phenomena with the growth of international trade and migration, the opening up of markets, the emergence of regional blocks and the impact on world political strategies. The NEG provided a new set of tools for spatial analysis. Economists have resisted the analysis of creativity under a spatial perspective because the creative process is usually more heterogeneous and complex when compared to the rest of the economic goods and activities. Most of the literature is focused on clustering theories since it is the most visible phenomenon. It is commonly accepted that creative businesses and people tend to cluster due to economies of scale and positive externalities, but little has been done on other spatial aspects. We will try to fill this gap by addressing the relationship between some key spatial characteristics and creativity based on the results of our index. 3.3.1. Spatial approaches of existing indexes After a thorough search we find only a couple of studies that have already approached creativity indexes in a spatial perspective which, by this reason, where included in the literature review, namely BCI and CZCI. 32 Acs and Megyesi (2009) presented a case study of Baltimore and they refer to spatial matters by two separate ways. On the one hand, by adding Territory as a new dimension to Florida’s index. The underlying theory is that a city with territorial amenities will attract creative talent. This could be infrastructures, higher wages and house affordability, among others. On the other hand, they say that Baltimore has a huge creative potential due to its proximity with Washington, DC which is a recognised creative core and considered the largest reservoir of creative talent in the USA. The creative performance of Baltimore might be highly dependent on the region’s ability to absorb the talent from the surrounding area. The matter of proximity seems to be extremely important to study creativity and we will address it in a further chapter. Kloudova (2010) searched for spatial similarities in the results of CZCI. First, the author tested czech regions for cluster formation and concluded that creativity tends to cluster, with Prague isolated as the biggest one. Second, the author proceeded with a spatial autocorrelation analysis, proving that a region affects the surrounding area and that neighbouring regions are more similar in terms of creativity than those more distant. Again, proximity raises interest in researchers. Third, the author tested the hypothesis of concentration into creative centres also known as creative cores, which has not been confirmed. 3.3.2. The CSI spatial framework The assessment of spatial matters by the CSI is twofold. In section 4 of chapter 4 we will analyse how the CSI results relate to a region’s spatial structure. On the other hand, some CSI indicators were selected according to an underpinning spatial theory inspired in gravity models. Every place can be creative in its own way, but it has necessarily to be attractive and interactive. To be attractive it must have an open minded and tolerant community, a diverse cultural offering, amenities that make it a desirable place to live and a distinctive identity. To be interactive it must reduce the distance decay effect by investing in its accessibility, either physical or virtual. The CSI spatial framework and its respective indicators are presented in Figure 3. 33 Figure 3 - CSI spatial framework Creative Space Attractive D2 - Openness Diversity Discrimination Foreign Talent D3 - Cultural Environment Cultural Offering D9 - Liveability Purchase Power Crime Health Care Leisure and Recreation Well-Being D10 - Notoriety Capitals of Culture World Heritage Gastronomy Interactive Virtually D4 - Technology and Innovation Internet Access Physically D8 - Accessibility Air Road Rail Desirable characteristics Dimensions Indicators 40 4.4.1. Scale By looking at Figure 7 it makes clear that there is not a minimum size required to be a creative leader. The Netherlands has about one third of the population size of the other two countries that share the CSI podium. There is a small correlation between creativity and the population size of the EU-27 countries, however it is not statistically significant. Figure 7 - Correlation between creativity and population 4.4.2. Density The geographic concentration of people powers the interaction, exchange and spillovers that are crucial to creativity. Figure 8 supports this idea showing a positive correlation between creativity and population density which is statistically significant at the 0.05 level. Although not visible in EU-27 countries, it is expectable that there is a limit beyond which population density will be negatively related with creativity due to the negative effects of overpopulated areas. Figure 8 - Correlation between creativity and density 41 4.4.3. Urban Hierarchy We questioned if a country’s creative performance is related to the position of its cities in European urban hierarchy. We rated cities between 1 and 12 according to GaWC typology 7 and then we summed the scores for each country. The results presented in Figure 9 reveal a positive correlation statistically significant at the 0.01 level, between creativity and how well countries’ cities rank in the urban hierarchy. Figure 9 - Correlation between creativity and urban hierarchy 4.4.4. Polycentricity We computed the correlation between level of polycentricity and creativity. The measure used was the morphological polycentricity according to ESPON (2007). The results show that the two variables are uncorrelated. A further research should also analyse functional polycentricity which may reveal a different relationship with creativity. Figure 10 - Correlation between creativity and the degree of polycentricity 7 http://www.lboro.ac.uk/gawc/world2010t.html 42 4.4.5. Agglomeration In order to compute the relationship between creativity and agglomeration, we have adapted a Von Böventer’s (1975) agglomeration model. Given a country’s number of urban centres, n, their dimension, z, and the average distance between them, dm, agglomeration can be written 1 1n n n i z n Adm      (4.4) where β and γ capture, respectivelly, the economies of agglomeration’s sensibility to urban centres dimension and the distance between them. The distance matrix 8 comprises travel-time distances by car between urban centres. Relative space was analysed instead of absolute space because it captures more characteristics of territorial dynamics. The results show a positive correlation between the two variables, which is statistically significant at the 0.05 level. Figure 11 - Correlation between creativity and agglomeration 4.4.6. Interaction The interaction between regions maximise each region’s creative potential. Therefore, it is expectable for a more interactive country to have a better creative performance. We measured the interaction between countries with a spatial interaction model. 8 The distance matrix was computed using Matlab and Google Distance Matrix API. 43 Given regions i and j, the interaction between them can be written: () ij ij b ij PP Ig d  (4.5) where the interaction, ij I , depends on the capacity of the origin to generate flows, i P , the capacity of the destination to attract flows, j P , and the distance between them, ij d . The variables g and b are, respectively, a scale factor and a parameter that measures the resistance caused by distance. The concept of Potential consists of the interaction between city i and all other cities, including itself by making 1 ii d , as shown in equation (4.6). 12 1 12 12 1 ... ... ... ... ( ) ( ) ( ) ( ) ( ) () n ij i i ii ij in j ij i i i i i n b b b b b i i ii ij in nij b jij I I I I I I PP PP PP PP PP g g g g g d d d d d PP gd                    (4.6) One simplifying solution of measuring Potential is using statistical data of traffic flows (Dentinho, 2011). Since traffic flows between i and j can be written () ij ij b ij PP Tg d  (4.7) one may deduce that region i's Potential, as a measure of accessibility, can be written 1 . n i ij n Pot T   (4.8) We used air traffic from European airports and the results support our expectations. There is a positive correlation between creativity and a country’s level of interaction, as shown in Figure 12. 44 Figure 12 - Correlation between creativity and interaction 4.4.7. Multivariate analysis The relationship between creativity and each of the analysed variables provides useful information, but it may be misleading. It is important to study how they relate to each other and how they affect creativity when analysed simultaneously. Table 18 resumes the correlation between creativity and spatial characteristics and also economic performance of EU-27 countries. All the analysed variables are positively correlated with creativity. Except for Polycentricity and Population, all spatial variables are statistically significant. The GDP per capita results are in accordance with the idea that creativity is an important economic motor, but oddly its correlation with the spatial variables are not statistically significant. Table 18 - Correlation between creativity, spatial variables and GDP Score Agglomeration Density Urban Hierarchy Potential Polycentricity Population GDP per capita Score 1 Agglomeration 0.406* 1 Density 0.486* 0.270 1 Urban Hierarchy 0.534** 0.926** 0.433* 1 Potential 0.491* 0.863** 0.331 0.911** 1 Polycentricity 0.133 0.471* 0.403* 0.465* 0.381 1 Population 0.373 0.982** 0.305 0.903** 0.869** 0.538** 1 GDP per capita 0.734** 0.061 0.338 0.178 0.144 -0.233 0.026 1 *. Correlation is significant at the 0.05 level. **. Correlation is significant at the 0.01 level. 45 Four OLS regressions were run. Starting from one with all spatial variables and GDP as independent variables and then removing highly correlated variables to improve the quality of the regression. The OLS results reported in Table 19 show that when analysing all variables simultaneously, there is only statistical evidence to affirm that GDP has a positive impact in creativity. After running different combinations of variables, only Model 4 presented all variables with statistically significant coeficients at the 0.01 level. Although Model 4 only has one spatial variable, Potential, it captures many of the other spatial variables dynamics. As already reported in Table 18, Potential is highly correlated with Population, Agglomeration and Urban Hierarchy. It is a concept that captures the effects of scale, accessibility, traffic flows, interaction and centrality of a region. According to Model 4 which accounts for 66 percent of the variance in the dependent variable, CSI Score, both Potential and GDP per capita have a positive impact in creativity. Table 19 - Regression results of creativity in EU-27 Model 1 Model 2 Model 3 Model 4 Constant 1.276369 (0.1181) 1.838807 (0.0000) 1.881215 (0.0000) 1.989729 (0.0000) Agglomeration 0.016599 (0.5118) -0.002322 (0.8005) Population Density 0.001170 (0.5619) 0.001270 (0.4611) 0.001819 (0.2288) Urban Hierarchy 0.007355 (0.8649) 0.026775 (0.4741) Potential 1.30E-08 (0.3670) 5.82E-09 (0.6297) 1.41E-08 (0.0092) 1.60E-08 (0.0028) Morphological Polycentricity 0.013661 (0.3043) Population -3.94E-08 (0.3883) GDP per capita 5.10E-05 (0.0003) 4.79E-05 (0.0001) 4.84E-05 (0.0000) 5.21E-05 (0.0000) N 26 26 26 26 Adjusted R2 0.638003 0.649811 0.670476 0.662852 Lengend: Coeficient (p-value) 46 5. CSI EMPIRICAL APPLICATION: PORTUGUESE CITIES The analysis of creativity at city level offers a very different perspective and complements the conclusions drawn at country level. Cities can be defined morphologically, functionally or administratively. We have applied the CSI at city level in Portugal using the administrative concept. Hereinafter, one should be aware that we will be using the term “city”, but in fact we will be referring to Portuguese municipalities. This choice is mainly justified by the availability of data with this spatial unit and the ease of collecting and using it. We have selected the largest Portuguese mainland cities with more than 100,000 inhabitants and cities with universities with more than 50,000 inhabitants, which have data that enable us to compute the index and represent about 50% of the country population. Nevertheless, we still had to reduce the index to 25 indicators and adapt some of them, as presented in Table 20. Due to the elimination of part of the original indicators, the dimensions have gained a disproportional weight in the overall score. Therefore, the Portuguese CSI aggregation is done by giving equal weights to each indicator instead of having the original equally weighted dimensions. Table 20 - Portuguese CSI dimensions, indicators and description Dimension Indicator Description D1 - Talent Human capital Nr of graduates per capita Creative class Share of persons employed in creative enterprises Education Nr higher education establishements per thousand inhab. D2 - Openness Diversity Share of non-nationals among residents Tolerance Share of marriages between individuals of same gender D3 - Cultural Environment and Tourism Cultural offering Nr event facilities, museums and art galleries per 1000 inhab. Cultural participation Nr of visitors per live show, museum and art gallery Tourism capacity Nr of bed-places per capita Tourism occupancy Tourism establishments occupancy rate D4 - Technology and Innovation R&D R&D expenditure as percentage of GDP HRST Percentage of human resources in science and technology Internet access Nr of computers with internet access at schools per capita Patents Nr of patents registered per thousand inhabitants 47 Dimension Indicator Description D5 - Industry Creative industries Share of creative enterprises Value added Value Added of creative industries per thousand inhab. Turnover Turnover in creative industries per thousand inhab. D6 – Regulation and Incentives Public incentive Direct public expenditure on culture per capita D7 - Entrepreneurship Startups Birth rate of enterprises D8 - Accessibility Road Length of motorway per area Rail Length of railway per area D9 - Liveability Purchase power Purchase power (Portugal=100) Crime Nr of recorded crimes per thousand inhabitants Health Care Nr of physicians per thousand inhabitants D10 - Notoriety Capitals of culture Nr of times elected UNESCO capital of culture Heritage Nr of monuments and other cultural properties By looking at Table 21, it is clear that Portugal has two main creative cores: Lisbon and Oporto, showing the scores of 7.99 and 6.35, respectively. These cities are two unquestionable creative leaders, followed then by Cascais, Oeiras, Portimão, Coimbra, Loulé and Mafra with scores between 3 and 5. There are eight cities with scores below 1 and the last place is taken by Marco de Canaveses. The amplitude of the Portuguese CSI scores is much bigger when compared to European CSI (1.51-5.36). This means that creativity is even more heterogeneous when assessed at city level in Portugal than at country level in Europe. Table 21 - Portuguese Creative Space Index City Score Rank D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 Lisboa 7.99 1 0.64 0.87 1.00 0.79 0.66 1.00 0.83 0.88 0.87 0.76 Porto 6.35 2 0.78 0.45 0.80 0.51 0.46 0.81 0.45 0.81 0.80 0.61 Cascais 4.32 3 0.36 0.69 0.31 0.86 1.00 0.63 0.83 0.32 0.36 0.05 Oeiras 3.51 4 0.22 0.54 0.10 0.93 0.59 0.61 0.83 0.55 0.39 0.01 Coimbra 3.16 5 0.70 0.27 0.28 0.59 0.16 0.72 0.41 0.25 0.62 0.05 Guimarães 2.76 6 0.05 0.07 0.08 0.34 0.03 0.35 0.24 0.17 0.09 0.55 Évora 2.75 7 0.35 0.30 0.44 0.36 0.22 0.81 0.50 0.17 0.23 0.08 Faro 2.75 8 0.50 0.84 0.29 0.23 0.18 0.57 0.68 0.15 0.44 0.02 Almada 2.41 9 0.29 0.70 0.09 0.38 0.22 0.44 1.00 0.14 0.26 0.01 Amadora 2.38 10 0.10 0.56 0.03 0.74 0.20 0.14 0.83 0.53 0.27 0.01 Setúbal 2.32 11 0.22 0.47 0.15 0.29 0.20 0.43 1.00 0.37 0.24 0.03 48 City Score Rank D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 Sintra 2.24 12 0.11 0.51 0.02 0.65 0.17 0.25 0.83 0.26 0.10 0.07 Aveiro 2.23 13 0.41 0.25 0.23 0.66 0.15 0.00 0.31 0.32 0.36 0.01 Vila Franca de Xira 2.13 14 0.08 0.31 0.04 0.82 0.17 0.27 0.83 0.20 0.10 0.02 Matosinhos 2.11 15 0.15 0.12 0.08 0.43 0.16 0.77 0.45 0.63 0.29 0.02 Loures 2.09 16 0.09 0.44 0.01 0.76 0.15 0.11 0.83 0.22 0.25 0.02 Odivelas 2.06 17 0.13 0.37 0.01 0.95 0.13 0.11 0.83 0.00 0.10 0.01 Braga 2.04 18 0.25 0.15 0.14 0.53 0.09 0.65 0.36 0.17 0.22 0.05 Seixal 1.96 19 0.14 0.26 0.02 0.38 0.22 0.53 1.00 0.22 0.11 0.01 Viseu 1.92 20 0.36 0.11 0.17 0.23 0.20 0.95 0.15 0.18 0.15 0.03 Covilhã 1.77 21 0.30 0.15 0.18 0.35 0.16 0.66 0.33 0.08 0.07 0.01 Vila Nova de Gaia 1.77 22 0.14 0.20 0.08 0.34 0.17 0.73 0.45 0.31 0.14 0.01 Maia 1.76 23 0.11 0.08 0.06 0.55 0.08 0.59 0.45 0.43 0.19 0.00 Santarém 1.73 24 0.29 0.15 0.10 0.24 0.16 0.46 0.56 0.12 0.19 0.04 Leiria 1.35 25 0.22 0.19 0.16 0.35 0.10 0.41 0.00 0.13 0.13 0.01 Gondomar 1.16 26 0.10 0.02 0.01 0.38 0.10 0.26 0.45 0.20 0.08 0.00 Vila Nova de Famalicão 1.10 27 0.09 0.02 0.05 0.34 0.07 0.35 0.24 0.29 0.05 0.01 Barcelos 0.95 28 0.06 0.01 0.02 0.29 0.00 0.39 0.36 0.19 0.00 0.02 Santa Maria da Feira 0.78 29 0.07 0.02 0.06 0.32 0.04 0.15 0.06 0.15 0.07 0.01 Paredes 0.70 30 0.13 0.00 0.01 0.10 0.05 0.33 0.17 0.16 0.03 0.01 Source: Author The Portuguese municipalities have very different structures and in some cases the administrative boundaries do not generate comparable spatial units. This problem is particularly more relevant when comparing Lisbon or Oporto. Many of the analysed municipalities are part of their functional urban zone and together they act as a single urban unit. To overcome this problem, we decided to use the statistical units known as Greater Lisbon (Grande Lisboa) and Greater Oporto (Grande Porto) 9 . They comprise several municipalities which are centred around the cities of Lisbon and Oporto, and share functions and resources with the core cities. The municipalities that compose the Greater Lisbon zone were all part of the selection presented in Table 21, which listed only seven from the nine municipalities of Greater Oporto. 9 Defined according to Statistics Portugal (INE), the used source for collecting indicators data. 49 Table 22 - Municipalities of Grande Lisboa and Grande Porto Grande Lisboa Grande Porto Amadora Espinho* Cascais Gondomar Lisboa Maia Loures Matosinhos Mafra Porto Odivelas Póvoa de Varzim Oeiras Valongo Sintra Vila do Conde* Vila Franca de Xira Vila Nova de Gaia *below 50000 inhabitants and not included in the previous index The results presented in Table 23 reveal some differences in CSI scores caused by the inclusion of Greater Lisbon and Greater Oporto units. Lisbon remains the index leader but its score has decreased from 7.99 to 6.59. Oporto also decreased from 6.35 to 4.62. One may conclude that Lisbon and Oporto creativity is relatively higher in the core and it dilutes when adressing its creative performance at the larger functional urban level. Greater Lisbon comprises municipalities that also score relatively better in the CSI, that is why when analysed at its larger urban functinal unit, Lisbon still ranks first. Table 23 - Portuguese CSI with Great Lisbon and Great Oporto City Score Rank D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 Grande Lisboa 6.59 1 0.42 0.64 0.29 0.66 0.56 0.47 0.83 0.36 0.36 1.00 Grande Porto 4.62 2 0.43 0.19 0.19 0.36 0.35 0.64 0.45 0.46 0.26 0.69 Coimbra 3.41 3 0.83 0.27 0.28 0.69 0.19 0.72 0.41 0.26 0.62 0.04 Faro 2.95 4 0.64 0.84 0.29 0.28 0.21 0.57 0.68 0.15 0.44 0.01 Évora 2.87 5 0.35 0.30 0.43 0.48 0.24 0.81 0.50 0.16 0.23 0.07 Guimarães 2.81 6 0.11 0.07 0.07 0.35 0.04 0.35 0.24 0.17 0.09 0.54 Almada 2.65 7 0.48 0.70 0.09 0.43 0.25 0.44 1.00 0.12 0.26 0.00 Setúbal 2.55 8 0.37 0.47 0.15 0.35 0.23 0.43 1.00 0.38 0.24 0.02 Aveiro 2.50 9 0.52 0.25 0.23 0.80 0.18 0.00 0.31 0.34 0.36 0.01 Seixal 2.20 10 0.33 0.26 0.02 0.42 0.25 0.53 1.00 0.21 0.11 0.00 Viseu 2.17 11 0.55 0.11 0.16 0.24 0.29 0.95 0.15 0.17 0.15 0.02 Braga 2.16 12 0.35 0.15 0.13 0.56 0.11 0.65 0.36 0.17 0.22 0.04 Covilhã 1.92 13 0.44 0.15 0.18 0.37 0.18 0.66 0.33 0.06 0.07 0.01 56 7. CONCLUSION The interest in the estimation of indexes of creativity has been increasing in the last years, not only for territorial marketing purposes, but also, because they provide analytical tools to assess the economic impact of the creative economy and are useful to measure the effectiveness of political decisions. However, there is still not an established index accepted by the majority and widely used. The literature review on creativity indexes presented in Chapter 2 highlighted their main gaps and weaknesses which served as a basis for designing our own index. In the last decade it has been produced a considerable amount of literature on creativity, but it still remains a subject that resists economic analysis due to the ambiguity inherent to the subject itself and the difficulty of measuring such a complex and subjective phenomenon. There are several different approaches to the analysis of creativity but all agree on one thing: creativity is the new motor of economic development. Our calculations also support that idea. According to the results obtained at country level by testing the CSI in EU-27 member states, there is a statistically significant positive correlation between creativity and economic performance. However, there is no evidence wether higher creativity is the cause or the consequence of better economic performance, and vice versa; or if they mutually feed each other. One of the main aims of this dissertation was to draw attention to the spatial analysis which has been overlooked by the existing studies on creativity and, consequently, by creativity indexes. First, we designed the CSI with an underlying spatial theory inspired by gravitational models. Second, we analysed the relationship of the CSI scores with spatial variables individually and simultaneously. The main conclusion is that the level of interactivity is an important leverage of creativity, both at country and city level. For measuring interactivity we used the concept of Potential which is highly correlated with other variables, such as scale, accessibility, traffic flows, proximity and centrality; therefore, capturing many of their dynamics. We also conlude that while in European countries creativity is higher in Central Europe and it dilutes when moving to the periphery, in Portuguese cities there is no clear pattern of centrality. Lastly, we 57 proposed a new tool which aims to complement creativity indexes and to support the policy and decision making process: the Creative City Matrix. It defines the type of city according to its creative performance and its distance to creative centres of excellence. By testing it in Portuguese cities using the CSI scores obtained in Chapter 5, we concluded that Portugal has two creative Cores: Lisbon and Oporto. These two cities are surrounded by Sponges and Slugs. While the former take advantage of their proximity to the Cores, the latter seem to be unable to absorb the creativity from the near Core and maximise existing potential spillovers. Three cities located far from the Cores - Coimbra, Évora and Faro - still rank relatively well in the CSI, enabling us to conclude that it is possible to be creative even far from the Cores. Albeit, we must be aware that the results and conclusions are based on testing a sample that only represents part of the Portuguese cities. There are several options to extend this dissertation in further research:  Refinement of the index methodology;  Update of the index on a time basis for trend analysis;  Extension of the European analysis to regions and cities;  Extension of the Portuguese analysis to all cities;  Analysis of the relationship of creativity with other spatial variables using more advanced statistical and econometric methods. However, most of these possibilites are limited to the same challenges faced by this dissertation, most of them concerning data availability, collection and manipulation. Since there is still no commonly accepted framework on creativity, there is no database for creativity and data has to be collected from different sources individually and then compiled altogether. The indicators are not always available for the same territorial unit and for the same year. This problem is even bigger when doing the analysis at city level, which has proved to be the best and natural environment of creativity. At European level there is already a database of cities’ vitality, the Urban Audit, that already holds some creativity indicators and which could be extended to address this phenomenon. In Portugal, there is a database for Cultural Statistics but it also needs to extend its indicators to other creative fields and provide data at city level. 58 Despite the limitations of this dissertation, we believe that it has produced a relevant contribution in the context of the Creative Economy and, particularly, of the creativity indexes. 59 REFERENCES Acs, Z., and Megyesi, M. (2009). Creativity and industrial cities: A case study of Baltimore. Entrepreneurship & Regional Development, Vol.1, No. 4, 421-439. Affairs, K. E. (2009). The impact of culture on creativity. Retrieved April 2012, from http://www.keanet.eu/docs/impactculturecreativityfull.pdf ARC-CCI. (2012). The CCI Creative City Index 2012. Cultural Science Journal, Vol.5, No. 1. Bowen, H., Moesen, W., and Sleuwaegen, L. (2008). A Composite Index of the Creative Economy. Review of Business and Economics, v.54, iss.4, 375-397. CISV. (2002). Creative Community Index. San José: Cultural Initiatives Silicon Valley. Consortium, F. B. (2008). Comparison of Cities (Creative City Index). Retrieved April 2012, from http://www.internationalregions.org/docs/InnovativeRegion_Nagase.pdf Csíkszentmihályi, M. (1999). Implications of a systems perspective for the study of creativity. In R. J. Sternberg, Handbook of Creativity. Cambridge University Press. DCMS. (1998). Creative Industries Mapping Document. London: DCMS. DCMS. (2001). Creative Industries Mapping Document. London: DCMS. DCMS. (2004). The Contribution of Culture to Regeneration in the UK: a Review of Evidence. London: DCMS. Dentinho, T. (2011). Modelos Gravitacionais. In Compêndio de Economia Regional, Vol.II (pp. 610-640). Cascais: Principia. Doyle, J. (2010). Why culture attracts and resists economic analysis. Journal of Cultural Economics, 4, 245-259. 60 ESPON. (2007). ESPON project 1.4.3: Study on Urban Functions. Florida, R. (2002). The Rise of the Creative Class. New York: Basic Books. Florida, R. (2005). Cities and Creative Class. Routledge: Oxon. Florida, R., and Tinagli, I. (2004). Europe in the Creative Age. DEMOS. HKSAR. (2004). A Study on Creativity Index. Hong Kong: HKSAR. Howkins, J. (2001). The Creative Economy: How people make money from ideas. London: Penguin Books. Howkins, J. (2009). Creative Ecologies: Where Thinking is a Proper Job. UQP. KEA. (2006). The Economy of Culture in Europe. Bruxelles: KEA. Kloudova, J., and Stehlikova, B. (2010). Creativity Index for the Czech Republc in Terms of Regional Similarities and Geographic Location. Economics and Management, 100-109. Landry, C. (2000). The Creative City: A Toolkit for Urban Innovators. London: Earthscan. Landry, C. (2007). Creativity and the City: thinking trough the steps. COMEDIA. Landry, C. (2010). The Creative City Index: Measuring the creative pulse of your city. Retrieved April 2012, from http://www.vwec2010.be/notulen/VWEC2010_Landry_Creative%20City%20In dex.pdf NEFA. (2007). The Creative Economy: A New Definition. Retrieved May 2012, from http://www.nefa.org/sites/default/files/ResearchCreativeEconReport2007.pdf Pink, D. (2005). A Whole New Mind: Why Right-Brainers Will Rule the Future. Riverhead Books. Potts, J., Cunningham, S., Hartley, J., e Ormerod, P. (2008). Social network markets: A new definition of creative industries. Journal of Cultural Economics, 32, 17685. 61 Romer, P. (1990). Endogenous Technological Change. Journal of Political Economy, Part 2, v. 98, iss. 5, 71-102. Rubenson, D., and Runco, M. (1992). The psychoeconomic approach to creativity. New Ideas in Psychology 10 (2), 131-147. Schumpeter, J. (1942). Capitalism, Socialism and Democracy. New English Weekly. Throsby, D. (2008). Culture and development - making the connection. London: Commonwealth Foundation. UNCTAD. (2004). Creative Industries and Development. Geneva: United Nations. UNCTAD. (2010). Creative Economy Report 2010. Geneva: United Nations. UNESCO. (2000). World Culture Report. Paris: UNESCO Publishing. Von Böventer, E. (1975). Regional Growth Theory. Urban Studies, vol. 12, 1-29. 62 ATTACHEMENTS Country Agglomeration Density Urban Hierarchy Potential Polycentrism Population GDP pc Belgium 3.62 358.7 15 15442618 69.6 10951266 32600 Bulgaria 4.11 69.1 6 4645378 73.2 7504868 4800 Czech Republic 9.00 136.2 8 8441703 74.2 10532770 14200 Denmark 0.81 128.7 9 15477167 56.6 5560628 42500 Germany 169.38 229.0 64 83406962 93.4 81751602 30300 Estonia 0.12 30.9 0 1089926 45.6 1340194 10700 Ireland 0.70 65.4 9 19983202 45.8 4480858 34900 Greece 3.73 86.4 8 21281235 38.6 11309885 20100 Spain 75.17 91.8 29 94055445 77.6 46152926 22800 France 120.18 102.5 27 51955175 62.4 65048412 29900 Italy 86.46 200.7 26 58395624 86.2 60626442 25700 Cyprus 0.00 87.2 6 5662642 51.4 804435 20600 Latvia 0.19 36.0 5 3395120 32.2 2229641 8600 Lithuania 0.47 52.4 4 1978996 72 3244601 8400 Luxembourg 0.00 196.0 7 1261800 30 511840 79500 Hungary 5.47 107.5 7 6266701 54.8 9985722 9700 Netherlands 13.20 492.2 18 28028933 90.8 16655799 35400 Austria 2.25 101.8 10 15553224 56.8 8404252 34100 Poland 56.82 122.1 15 13319110 85.6 38200037 9300 Portugal 2.66 115.4 12 17674285 54.4 10636979 16200 Romania 13.01 93.2 7 6968124 77.2 21413815 5800 Slovenia 0.09 101.7 3 773423 56.8 2050189 17300 Slovakia 1.87 110.7 6 1506496 74.8 5435273 12100 Finland 0.80 17.6 6 8887886 63.6 5375276 33500 Sweden 3.95 22.9 13 15664912 71.8 9415570 37200 United Kingdom 116.61 254.2 53 107553330 63.8 62498612 27500 Attachement 1 – Spatial variables of EU-27 63 City Score Area Population Density Potential Almada 2.65 70.2 165758 2361.225 65085 Aveiro 2.50 197.6 72601 367.414 26239 Barcelos 0.97 378.9 124395 328.3056 18539 Braga 2.16 183.4 177940 970.229 35752 Coimbra 3.41 319.4 131446 411.5404 43082 Covilhã 1.92 555.6 51145 92.05364 5277 Évora 2.87 1307.1 54111 41.39775 7106 Faro 2.95 201.8 58625 290.5104 16947 Guimarães 2.81 241 162313 673.4979 28846 Leiria 1.48 565.1 129745 229.5965 21449 Lisboa 6.25 1376.7 2036181 1479.03 468104 Paredes 0.75 156.8 87632 558.8776 20398 Porto 4.34 814.7 1286111 1578.631 199356 Santa Maria da Feira 0.82 215.9 149337 691.6952 29654 Santarém 1.84 560.2 63149 112.7258 12741 Seixal 2.20 95.5 180741 1892.576 57021 Setúbal 2.55 230.3 126013 547.1689 30480 Vila Nova de Famalicão 1.17 108.5 135959 1253.078 29023 Viseu 2.17 507.1 99737 196.6811 11638 Attachement 2 – Spatial variables of Portuguese cities