Reliability of creative composite indicators with territorial specification in the EU

sustainability Article Reliability of Creative Composite Indicators with Territorial Specification in the EU Iván Boal-San Miguel * and Luis César Herrero-Prieto Research Group on Cultural Economics and Department of Applied Economics, Faculty of Commerce, University of Valladolid, 47011 Valladolid, Spain; herrer[email protected] *Correspondence: [email protected] Received: 10 March 2020; Accepted: 9 April 2020; Published: 11 April 2020   Abstract: There is wide consensus regarding the capacity of the creative economy as a factor in economic development and sustainability in regions in the medium term, and it is an issue that has aroused interest in measuring the creative potential of areas by estimating composite indicators. Most works posit indicators by countries or cities and normally employ a single aggregation method. This work proposes the construction of a synthetic indicator of creativity with regional disaggregation for a wide sample of European countries, comparing different aggregation methods under the hypothesis of the benefit of the doubt (BoD). We use Eurostat data and also analyse spatial dependence processes. Findings point to an inequality in the regional distribution of creativity in Europe and this reveals creative gap models in areas, suggesting that creativity triggers greater spatial disparities wherever it is found. The principal creative clusters are located in regions in central and northern Europe, particularly in regions with national capitals. The regional distribution of the indicator evidences positive spatial dependence, thereby revealing the existence of spatial externalities that provide feedback for the concentration process and, therefore, a widening of the disparities. Keywords: creativity; European regions; composite indicators; creative cluster; distance measures; data envelopment analysis; principal component analysis; spatial analysis 1. Introduction By creativity, we understand a process of formulating new ideas and applying them to produce original works of art and cultural products, functional creations, scientific inventions and technological innovations as well as their conversion into market goods and services [ 1 ]. Thus defined, they would seem to have made a substantial contribution in recent times to economic and urban development and now provide a new area of competitiveness for territories. An excellent review of what contribution creativity makes to the economy may be found in the works of Correa-Quezada et al. [ 2 ] and Rodrigues and Franco [ 3 ]. Creative potential has also triggered a new competitiveness profile for regions and cities. In this regard, see the delimitation of the concept of creative cities provided by Scott [ 4 ] and the new definition of competitive advantages for regions in Sleuwaegen and Ramboer [ 5 ]. In the academic sphere, there are two main approaches to measuring the scale of the creative economy: the sectorial or the occupational perspective. The former was the first to appear and focuses on identifying creative industries in a given area. It has yielded abundant literature to define the sector, with more or less widespread agreement concerningtheUNCTAD catalogueof branches[ 1 ]. Thisapproachhas alsoled to a number of studies aimed at measuring the sector’s dimension and the territorial impact of the creative industry, with numerous applied exercises, many of which may be found for the European Union [ 5 , 6 ] as well as for the rest of the world [ 7 – 9 ]. The latter, which appeared later, and which is the so-called occupational approach, is essentially based on Richard Florida’s work [ 10 ] addressing the concept of the creative class, a group of professionals dedicated to activities related to more stylised cultural creation Sustainability 2020,12, 3070; doi:10.3390/su12083070 www.mdpi.com/journal/sustainability Sustainability 2020,12, 3070 2 of 27 but that also embraces all of those specialising in designing and forging creative activities geared towards the market and that display a certain level of talent and skill and who tend to concentrate in specific areas that exhibit certain conditions and lifestyles [ 11 ]. There is also the stream that has given rise to numerous studies aimed at measuring the scale of the territorial agglomeration of creative classes, which is generally measured through the employment variable in creative occupations [ 2 , 12 , 13 ]. Based on the two approaches, many works have appeared that strive to develop a theoretical framework explaining the link between creativity and economic development [ 14 , 15 ] and to show how creativity can shape an area’s competitive advantage [ 5 , 16 ], and which also provide ample margin to design and implement new regional policies [ 3 , 17 ]. In general terms, there is consensus visà -vis accepting the accumulation of human capital, the capacity for innovation and the current state of technology as drivers that encourage the development of creative industries and/or classes, coupled with conditions of tolerance and openness towards society, as originally posited in the works of Florida [ 10 ] but which has also been shown in other more recent studies such as Boschma and Fritsch [ 12 ] and Sleugwaegen and Ramboer [ 5 ]. One aspect which is being seen as increasingly important is the pre-existence of cultural amenities and an intense level of cultural characterisation in the area in question as highlighted in the works of Cerisola [ 14 ] and Backman and Nilsson [ 18 ]. The spatial dimension thus proves fundamental in creative sectors and is the context in which cooperation networks are also present that enable knowledge to be disseminated and creative processes to be generated, as pointed out by Rodrigues and Franco [ 3 ]. The creation of networks and alliances that are characteristic of the organisational models of creative activities, and the retention of talent, are strongly influenced by the local context [ 19 ], which impacts the capacity to create synergies between creative classes who seek to benefit from economies of agglomeration. In this way, European regional policy highlights the creative sector as being key towards fostering intelligent, sustainable and integrating growth, where creativity plays a vital role in regional policy strategy [13,20]. The implications creativity has for economic development and the sustainability of regions requires constant diagnosis and assessment, which can then aid decision making and help to gauge the effectiveness of regional policies. This is the main motivation underlying our research, wherein we aim to build an indicator of creative potential at a disaggregated territorial scale for a broad sample of countries and regions in an effort to show operational utility in current understanding of territorial disparities and what the implications might be for regional policy. Nevertheless, measuring creativity with territorial scope proves complex, given the difficulty in defining and delimiting the area of culture and the various components that make up creativity, as pointed out by Ortega-Villa and Ley-Garc í a [ 21 ], Kemeny et al. [ 13 ] and Rodrigues and Franco [ 22 ], and because of the myriad of aspects involved and the enormous heterogeneity of the actors concerned. In addition, data that are appropriate, sufficient and comparable between the various demarcations are required if effective measurements of territorial creative potential are to be carried out. Although the last ten years have witnessed a proliferation of studies involving composite creativity indicators [ 3 , 23 , 24 ], there remains a lack of studies which at the same time consider a wide sample of countries and significant territorial disaggregation at a regional scale. Moreover, few indices extensively embrace primary indicators of cultural activity (endowment and participation), which are shown to be a key basis of creativity [21,25]. Based on these premises, the present work specifically aims to construct a composite indicator for creativity with a specification for European regions and which we term the European Indicator of Regional Creativity (EIRC). To achieve this, and after considering several dimensions of creativity based on a wide sample of primary variables related to these dimensions, we compare various statistical aggregation methods that determine the importance of each underlying component of creativity, which allows us to examine the robustness and reliability of the indicator estimated. In addition, we aim to identify spatial patterns of creativity in European regions and to estimate the effects of spatial autocorrelation in an effort to verify the existence of economies of agglomeration and creative clusters. We therefore define creative clusters based on spatial interaction processes between territories, considering significant dependence relations of the creativity indicator, along the line of the works by Sustainability 2020,12, 3070 3 of 27 Bertacchini and Borrione [ 26 ] and He and Gebhardt [ 9 ]. The empirical application is carried out on a wide sample of EU countries for which we have been able to compile a reasonable and homogeneous number of variables that are representative of creativity at the NUTS2 regional level. Considerable effort has also been made to compile different Eurostat databases so as to include primary variables from the cultural and creative sector. The technique involves applying three aggregation methods based on the principle of the benefit of the doubt (BoD). This allows us to select the weights inside the data: Principal Component Analysis (PCA), Data Envelopment Analysis (DEA) and Measure of Distance P 2 (DP2). Descriptive techniques based on Geographical Information System (GIS mapping) are used for territorial analysis, and spatial econometric tools are applied to spatial dependence phenomena and territorial clusters. In sum, the principal contributions our study makes in relation to the comparative literature on the matter are mainly grounded on the construction of an indicator of creative potential with regional specification for a broad sample of European countries, whereas studies thus far have tended to be conducted at a country level or to take smaller regional samples. A relative comparison of what this effort has meant visà -vis the work carried out to date may be seen in the review of creativity indicators provided in the following section. We also evidence the reliability of composite indicators by applying different aggregation methods based on endogenous weights. This is an important methodological issue when building composite indicators given that, depending on the aggregation method applied, the final indicator might prove to be totally different. In this regard, in contrast to methods based on discretional interventions with previously established or agreed weightings [ 25 ], we opted for aggregation criteria where the weightings stem from the actual underlying structure of the data (BoD criteria) since we believe that an indicator built in this way provides a better reflection of the reality explained. Finally, as regards the results, we evidence a new source of regional disparities since we demonstrate that creativity behaves along the lines of technology gap models, creating greater spatial imbalances wherever these are most apparent. We also determined creative clusters after analysing spatial dependence on creativity indicator results. The work is, therefore, eminently instrumental and empirical and seeks to extend current knowledge concerning how to measure creative potential at a territorial level and to ascertain the spatial logic which is specifically evident at a European scale. All of these results prove useful to both the scientific community and policymakers alike. Based on the above, the work is structured in five sections. In addition to this introduction, Section 2deals with the conceptual framework of creativity and provides a review of the literature on composite indicators of culture and creativity and the analysis of underlying dimensions. Section 3 addresses the data and the sources used in the work, together with the methodological stages involved in constructing the indicator and the various aggregation methods employed. Section 4then presents the empirical application, with the results of the composite indicators for creativity and their spatial distribution in European regions, as well as spatial dependence analysis. The work finishes with the conclusions section. 2. Review of the Literature on Indicators and Components of Creativity Creativity is a complex and multidimensional phenomenon [ 22 ] involving multiple components (skills, talent, cultural support, innovative capacity, etc.), which makes measuring it difficult. There is no single definition for this term and creativity is studied from disciplines as far and wide as psychology, the arts and sociology and is a concept that is subject to a myriad of interpretations [ 27 ]. Yet when speaking of the creative economy, we are assuming that the end result of the innovation process gives rise to ideas or results that have economic implications, which may be subject to valuation in the market [ 1 ]. When generating these creative processes, the importance of the social and territorial factor should be highlighted, since creativity stems from the interaction between these components over a given area, with the role played by spatial demarcations proving particularly crucial in the geography of creativity. The existence of a favourable environment coupled with the driving forces behind creative processes can provide a given region with greater competitive advantage compared Sustainability 2020,12, 3070 4 of 27 to others [ 22 ], and it can also generate a virtuous circle because the creative classes in turn demand creative goods and services and tend to concentrate where these are to be found [ 12 ]. As pointed out by Cerisola [ 14 ], only when there is cross fertilization of different types of talent does creativity give rise to sustained local economic development processes, such that creativity should be approached from three perspectives: artistic (a cultural background of the environment), technological (capacity to innovate and the current state of technology) and economic (a capacity for entrepreneurship and the ability to create new products). Even though efforts to gather and analyse cultural indicators are by no means new [ 21 , 28 ], there have been a number of works in recent years focusing particularly on constructing creativity and cultural activity indices, in an attempt to evidence what contribution these factors make to the economic development and sustainability of regions, as is shown, for example, through the works of Correa-Quezada et al. [ 2 ] for the case of Ecuador, and Sleuwaegen and Ramboer [ 5 ] for Europe. This line of work has also been boosted by the standardisation and proliferation of cultural statistics both at an international [ 29 ] as well as a European [ 30 ] level. In an effort to be as thorough as possible, Table 1provides an overview of the principal composite indicators for the creative economy together with the main underlying dimensions included, the case study and the aggregation methods employed. Nevertheless, conceptual models of cultural and creative indicators and compilations of existing indicators may also be consulted in Stano and Weziak-Bialowolska [ 31 ], Ortega-Villa and Ley-García [21] and Rodrigues and Franco [22]. Table 1. Overview of composite cultural and creative indicators. Indicators Dimensions Application Weighting Criteria Florida’s Creative Index [10]Talent, Technology and Tolerance Metropolitan areas Equal weights Silicon Valley´s Creative Community Index [32] Talent, Openness, Culture, Technology, Regulation, Employment, Entrepreneurship Silicon Valley Analysis by dimensions Cultural Life Index [33]Cultural resources, Cultural participation and Cultural production Without application Theoretical proposal of indicators Euro-Creativity Index [11]Talent, Technology and Tolerance 14 European countries and US Sum of the dimension scores divided by the maximum possible score Hong Kong Creativity Index [34] Social, Human, Cultural, Structural and Institutional Capital Hong Kong Multivariate analysis Composite Index of the Creative Economy [35] Innovation, Entrepreneurship and Openness Nine regions Endogenous weighting-DEA European Creativity Index [36] Human capital, Openness and diversity, Cultural environment, Technology, Institutional environment, Creative outputs European countries, without empirical application Theoretical proposal of indicators Design, Creativity and Innovation Scoreboard [37] Creative education, Self-expression, Openness, Creative sector, Creativity in R&D, Design activities, Competitiveness in design European countries Equal weights unweighted average Czech Creative Index [38]Talent, Technology and Tolerance Czech regions Fuzzy method. Weights depending on the distance to the best region’s score Creative Grid [39] Creative class, Culture, Talent, Government, Creative and Culture industries Proposal for the UK Only a proposal of key themes for the creative economy Creative City Index [40] Political frameworks, Diversity, Openness, Trust, Accessibility, Entrepreneurship. Innovation, Talent, Connectivity and networking, Liveability and well-being, Communication 20 Cities External weighting by experts Sustainability 2020,12, 3070 5 of 27 Table 1. Cont. Indicators Dimensions Application Weighting Criteria Creative City Index [41] Creative industries, Microproductivity and microproduction, Economy of attention, Participation and expenditure, Public support, Human capital, Global integration, Openness, Tolerance Six Cities Equal weights Creative Space Index [23] Talent, Openness, Culture and Tourism, Technology and Innovation, Industry, Regulation and Incentives, Entrepreneurship, Accessibility, Liveability 26 European countries Endogenous weighting-DEA Global Creativity Index [42]Talent, Technology and Tolerance 139 countries Equal weights Creative Economy Index [43] Talent, Research systems, Finance and support, Investments, Entrepreneurship, Intellectual assets, Innovators, Economic effects 34 European countries Multivariate analysis European 3T Creativity Index [24] 3T with Creative Class, Human Capital, Scientific Talent, Innovation, High tech innovation, R&D, Attitudes index, Values index, Self-expression index 28 European countries Equal weights Creativity Index for Portuguese Cities [3] Places of Culture, Cultural Participation, Creative Industries, Research & Development, Innovation, Human Capital, Openness, Connections, Governance Portuguese cities Multivariate analysis Cultural and Creative Cities Index [25] Cultural Vibrancy, Creative Economy, Human Capital, Openness, Connections and Quality of governance European Cities Budget allocation method Source: Authors´ own. Various creative indicators have been constructed for the European context, although measurement thereof for the regional territorial level and in the context of several countries remains scarce, with the sample of regions included tending to be small. The most recent indicators applied at a country level (Global Creativity Index [ 42 ], Creative Space Index [ 23 ], Creative Economy Index [ 43 ]) have increased the number of latent dimensions and countries. Recent studies into creative indicators have sought to explore the network of cities in a given country (Creativity Index for Portuguese Cities [ 3 ]) or for large European cities (Cultural and Creative Cities Index [ 25 ]). By estimating the EIRC in our work, we aim to cover one of the more underexplored areas, namely European regional analysis for an ample number of countries, and to compare various aggregation methods based on BoD principles, which in turn enables us to analyse the reliability and robustness of the results. In doing so, we seek to examine the current situation and trends of European spatial disparities in terms of creative capacity. Based on the review of the existing composite indicators in the literature (Table 1) and subject to the availability of data for the regional level considered in this work, we identify six operational dimensions underlying the creative economy and which constitute the reference for compiling primary variables for comparing and constructing partial indicators for each dimension. As a result, we are not actually defining creativity itself but are rather exploring and explaining which components feed said concept, based on tested processes related to the accumulation of talent, the degree of openness and the innovative capacity of the environment and which we have furthered by taking into account a broad sample of variables that are representative of cultural participation and of the creative industry, conscious of the fact that these are key to creativity [ 21 ]. These dimensions are shown in Figure 1and are described in detail below, whilst the primary variables that make up each dimension are displayed in Table 2. Sustainability 2020,12, 3070 6 of 27 Table 2. Dimensions, indicators, variables and descriptive statistics of the European Indicator of Regional Creativity (EIRC). Dimension Description of the Variables Source Period of Reference Obs. Minimum Maximum Mean SD Formulae Talent TA1: Number of bachelor degrees per capita Eurostat: Regions 2015 171 11.600 54.100 28.918 8.285 I_TAi=xTAi max(xTAi), i =1,2,3 PI_TA =n qQn i=1I_TAi, n =3 TA2: Number of people in creative occupations per capita Eurostat: Regions 2015 171 0.008 0.105 0.037 0.016 TA3: Number of people in arts occupations per capita Eurostat: Regions 2015 171 0.006 0.047 0.021 0.007 Openness and Tolerance OP1: Share of non-nationals among residents Eurostat: Regions 2015 171 0.001 0.309 0.051 0.048 I_OPi=xOPi max(xOPi), i =1,2,3 PI_OP =n qQn i=1I_OPi, n =3 OP2: Share of non-national bachelor degrees among non-nationals Eurostat: Regions 2011 171 0.107 4.810 0.603 0.593 OP3: Employment rate of foreigners Eurostat: Regions 2011 171 38.900 84.600 62.106 8.719 Culture CU1: Number of cinema seats per capita Eurostat: Cities 2015 171 228.085 18,500.359 5,489.519 3,506.160 I_CUi=xCUi max(xCUi), i =1,2,3,4,5 PI_CU =n qQn i=1I_CUi, n =5 CU2: Number of theatres per capita Eurostat: Cities 2015 171 0.000 87.830 8.879 11.191 CU3: Number of public libraries per capita Eurostat: Cities 2015 171 1.486 156.515 32.141 28.762 CU4: Cinema attendance per capita Eurostat: Cities 2015 171 0.003 4.528 0.975 0.694 CU5: Number of museum visitors per capita Eurostat: Cities 2015 171 0.013 4.670 0.596 0.649 Technology and Innovation TE1: R&D expenditure per capita Eurostat: Regions 2015 171 5.800 3,737.300 538.922 630.827 I_TEi=xTEi max(xTEi), i =1,2,3,4,5 PI_TE =n qQn i=1I_TEi, n =5 TE2: Share of R&D personnel and researchers Eurostat: Regions 2015 171 0.099 4.304 1.117 0.814 TE3: Number of patents registered per million inhabitants Eurostat: Regions 2012 171 0.233 468.132 90.432 109.277 TE4: Percentage of human resources in science and technology Eurostat: Regions 2015 171 11.800 53.700 31.468 7.720 TE5: European Union trademarks per million inhabitants Eurostat: Regions 2015 171 3.969 610.396 140.430 118.461 Cultural and Creative Industries CR1: Number of creative enterprises per capita Eurostat: Regions 2015 171 0.001 0.090 0.017 0.013 I_CRi=xCRi max(xCRi), i =1,2,3,4, PI_CR =n qQn i=1I_CRi, n =4 CR2: Share of employment in creative industries Eurostat: Regions 2015 171 0.002 0.179 0.077 0.027 CR3: Gross fixed capital formation of the creative industries per million inhabitants Eurostat: Regions 2015 171 19.866 5,559.190 859.489 899.914 CR4: Gross value added at basic prices Eurostat: Regions 2015 171 53.667 2,249.637 747.132 490.857 Liveability LI1: Household income per capita Eurostat: Regions 2015 171 2,100.000 34,100.000 16,491.81 8,534.376 I_LIi=xLIi max(xLIi), i =1,2,3,4,5 PI_LI =n qQn i=1I_LIi, n =5 LI2: Employment rate Eurostat: Regions 2015 171 48.800 81.800 66.329 6.961 LI3: Number of years of life expectancy Eurostat: Regions 2015 171 73.500 84.500 79.936 2.665 LI4: Available rent per capita Eurostat: Regions 2015 171 2,610.790 26,065.78 14,596.67 6,751.579 LI5: Percentage of the young population from 5 to 34 years over the total population Eurostat: Regions 2015 171 0.260 0.415 0.343 0.030 Source: authors´ own based on Eurostat. Sustainability 2020,12, 3070 7 of 27 Sustainability 2020, 12, x FOR PEER REVIEW 6 of 26 Figure 1. Creative economy dimensions. Source: Authors´ own. 1. Talent (TA): we understand talent, on the one hand, to be the accumulation and level of human capital, which constitutes a widely recognised factor as a determinant of economic development and the capacity to innovate [44,45]. Yet also, and specifically, we see it as the degree of concentration of those engaged in artistic and creative activities and that therefore determine the scale of the regional creative sector and its dynamism in the medium term, given the possibility of generating economies of agglomeration and attracting creative classes [10]. It is worth highlighting that a creative environment is characterised by being able to nourish, foster, promote, attract and retain talent [40]. In this dimension, we therefore include basic variables of human capital together with others that are indicative of the number of people employed in creative and artistic jobs in the regions. 2. Openness and Tolerance (OP): social diversity and the exchange of ideas are deemed to be a source of innovation and play a key role in generating creative environments [46]. A tolerant atmosphere and one that is open to diversity is able to attract people of different cultures as well as overseas talent and can enhance an area’s creative potential [47]. According to Florida [10] the nucleus of people who make up the so-called creative class tend to settle and concentrate in places that offer an atmosphere of diversity, authenticity or tolerance that is in accordance with their lifestyle. In this dimension, we therefore include variables related to the degree of foreign diversity of a region’s population, overseas talent and the capacity to integrate this into the labour market. 3. Culture (CU): a region’s cultural capital is made up of the set of tangible and intangible elements that are the expression of ingenuity, history or identity [48]. There is a positive relation between the cultural environment and academic performance [49], such that it also acts as a factor which attracts talent and generates human capital [18,50]. In addition, the cultural stock and synergies between the various talents in a region foster creativity and innovative processes, thereby boosting local economic development [14,51]. Few creativity indicators cover the dimensions that are most specifically related to the cultural environment [25], probably due to the statistical difficulties involved in compiling data. In this regard, and as we said above, we have striven to gather variables concerning cultural endowment and participation with regional disaggregation, which constitutes one of the principal novelties of this work. 4. Technology and Innovation (TE): research and an area’s level of technological development shape its capacity to innovate and generate creative processes, as well as Creativity Talent Openness and Tolerance Technology and Innovation Cultural and Creative Industries Liveability Culture Figure 1. Creative economy dimensions. Source: Authors´ own. 1. Talent (TA): we understand talent, on the one hand, to be the accumulation and level of human capital, which constitutes a widely recognised factor as a determinant of economic development and the capacity to innovate [ 44 , 45 ]. Yet also, and specifically, we see it as the degree of concentration of those engaged in artistic and creative activities and that therefore determine the scale of the regional creative sector and its dynamism in the medium term, given the possibility of generatingeconomies ofagglomeration andattracting creative classes[ 10 ]. Itis worthhighlighting that a creative environment is characterised by being able to nourish, foster, promote, attract and retain talent [ 40 ]. In this dimension, we therefore include basic variables of human capital together with others that are indicative of the number of people employed in creative and artistic jobs in the regions. 2. Openness and Tolerance (OP): social diversity and the exchange of ideas are deemed to be a source of innovation and play a key role in generating creative environments [ 46 ]. A tolerant atmosphere and one that is open to diversity is able to attract people of different cultures as well as overseas talent and can enhance an area’s creative potential [ 47 ]. According to Florida [ 10 ] the nucleus of people who make up the so-called creative class tend to settle and concentrate in places that offer an atmosphere of diversity, authenticity or tolerance that is in accordance with their lifestyle. In this dimension, we therefore include variables related to the degree of foreign diversity of a region’s population, overseas talent and the capacity to integrate this into the labour market. 3. Culture (CU): a region’s cultural capital is made up of the set of tangible and intangible elements that are the expression of ingenuity, history or identity [ 48 ]. There is a positive relation between the cultural environment and academic performance [ 49 ], such that it also acts as a factor which attracts talent and generates human capital [ 18 , 50 ]. In addition, the cultural stock and synergies between the various talents in a region foster creativity and innovative processes, thereby boosting local economic development [ 14 , 51 ]. Few creativity indicators cover the dimensions that are most specifically related to the cultural environment [ 25 ], probably due to the statistical difficulties involved in compiling data. In this regard, and as we said above, we have striven to gather variables concerning cultural endowment and participation with regional disaggregation, which constitutes one of the principal novelties of this work. 4. Technology and Innovation (TE): research and an area’s level of technological development shape its capacity to innovate and generate creative processes, as well as their dissemination in society [ 22 ]. The ability to turn technological and innovative effort into economic results is also a factor of territorial competitiveness [ 9 ]. In this way, we consider the scope of the R&D sector in terms of spending and employment as well as the capacity for innovation measured in the number of patents and creation of trade marks to be the representative variables of this dimension, as is also used in Porter et al. [27]. 5. Cultural and Creative Industries (CCI): formed by industries that use culture as a resource, although the outputs may be functional and target mass consumption, embracing creative and innovative Sustainability 2020,12, 3070 8 of 27 elements in wider-ranging processes. They are aimed at producing and commercialising goods and services with a high degree of cultural, artistic or heritage content and which enable creativity to be expressed, materialised and spread. They display positive externalities, since firms who have production links with this sector are able to include a higher level of innovation in their products [ 52 ]. In order to reflect the scale of this sector in European regions, in this dimension we include variables concerning the number of firms, employment, gross aggregate value and gross formation of capital in the cultural and creative sector. 6. Liveability (LI): this notion focuses on the quality of life and wellbeing a given place offers for living, which encourage the location and generation of talent and creative processes. It is one of the dimensions included in the most recent works dealing with creativity indicators [ 46 ] and follows the line of OECD wellbeing indicators [ 53 ]. Here are included variables addressing job opportunities and level of rent, youth index and population life expectancy. As mentioned above, the variables finally chosen, and which are representative of each dimension, are shown in attached Table 2, where the source and reference year, in addition to descriptive statistics, are also indicated. 3. Methodological Approach and Case Study 3.1. Constructing Composite Indicators Given that creativity is a multidimensional phenomenon, estimating composite indicators allows us to measure regional creative potential based on a synthetic measure that merges the different underlying dimensions. Composite indicators are a mathematical combination of partial indicators that represent the different profiles of a concept being measured [ 54 ] and constitute an extremely useful analytical tool for evaluating notions in comparative terms through space and time. One of the most hotly debated issues involves determining which aggregation method to use and which enables the information from the partial indicators to be integrated and summed up [ 55 ]. This therefore requires specifying how the weights will be assigned to the original variables, which may ultimately determine the reliability of the outcomes [ 22 , 56 ]. Our methodological approach thus follows two stages. First, we compile the greatest number of primary variables that are representative of each of the underlying dimensions of creativity (See Table 2), and we construct the partial indicators that are representative of each of them. To do this, we employ the method widely recognised and used when constructing the Human Development Index—HDI [ 57 ] and which consists of aggregating the variables through multiplicative weighting by geometric mean, thus avoiding the assumption of total compensation [ 54 ] and allowing the extreme values to be smoothed [ 58 ]. Previously, and in order to facilitate comparative analysis between regions, the scale effect of the primary variables has been removed in the form of rates, percentages or calculations in terms of population (per capita), and the initial values have been normalised through the quotient of each region’s value and the maximum value of the sample [ 59 ]. The method used to obtain the partial indicators of each dimension is also shown in Table 2. Second, we construct the synthetic indicator for regional creativity (EIRC), adding the partial indicators obtained in the previous stage. A wide range of aggregation methods are available [ 54 , 55 ], and in this work we consider methods based on the benefit of the doubt (BoD) approach, and which obtain the weights endogenously to the data. These are used when, a priori, prior weighting schemes of the partial indicators are not known, and they help with objectivity when constructing the synthetic index [ 60 ]. In our research, we use three noncompensatory aggregation methods: Principal Component Analysis (PCA), Data Envelopment Analysis (DEA) and Distance P2 (DP2), which will enable us, on the one hand, to compare the different results, and on the other, to gauge the robustness and reliability of our composite indicator. Various advantages, disadvantages as well as the differences of these aggregation methods can be seen in greater detail [61,62]. PCA has been used by numerous authors when constructing social wellbeing indicators [ 63 , 64 ] as well as for indicators of talent and creativity [ 3 ]. It is a multivariate technique designed to reduce the Sustainability 2020,12, 3070 9 of 27 original size of a set of variables so that the weight of each variable is proportional to the part of its variance explained by the factor associated to it, whilst each factor is weighted by its contribution to the part of the variance explained in the data set [65]. Through PCA, the indicator is obtained as: EIRC_PCAj=Pr i=1Zrj ·√λr Pr i=1√λr (1) where Zrj is the score of the component r-th for region j-th and λrthe self-value for said component. For its part, the DEA method is a nonparametric linear programming technique, which is widely used to measure the efficiency of a set of production units based on optimising the production function. When applied to constructing composite indicators, it is an endogenous aggregation method, since it involves maximising the output values (in our case, dimensions of creativity) subject to the restriction of a hypothetical unitary input. Therefore, the weights are given naturally depending on the initial data range. It is becoming increasingly common to find applications of the DEA method for obtaining composite indicators in the literature, for example in the tourist sector [ 66 ] and for wellbeing indices [ 58 , 67 ]. Some creativity indicators also use this technique, as is the case in Bowen et al. [ 35 ] for a specific sample of nine creative regions, Correia and Costa [ 23 ] for all EU member countries and more recently De Jorge-Moreno and De Jorge-Huertas [ 68 ], who estimate a creative composite index for European cities with DEA. The indicator is expressed as follows: EIRC_DEAj=maxwc,i m P i=1wr,i∗yr,i subject to : m P i=1wr,i∗yj,i≤1∀j{1,. . . n} wr,i≥0∀i{1,. . . m} (2) where nrepresents the number of regions, mthe number of partial indicators that are representative of creative dimensions, while y r,i represents the value of the partial indicator iin region r, with w r,i being the respective weights. The DP2 distance based method, defined by [ 69 ], avoids some of the usual problems encountered when constructing synthetic indicators such as the aggregation of variables expressed in different measurement units, arbitrary and compensatory weighting and information duplicity, and it allows interspatial and intertemporal comparisons to be carried out. It calculates each region’s distance from a reference region which achieves the worst values in the partial indicators, such that a higher value of the distance measured indicates a greater level of creative potential. It has been used in works addressing social indicators [ 62 ] and economic indicators [ 70 ], although there are very few applications to the domain of creative indicators. It is defined as: EIRC DP2= n X i=1( di σi!1−R2i,i−1,...,1)(3) with R2 1= 0;where di=di(r∗)=|xri −x∗i| and the reference base is X∗= (x∗1 , x∗2 , . . . , x∗n ), where mis the number of regions, nis the number of variables, xij is the value of variable iin region j, σi is the standard deviation of the variable i, R2i,i−1,...,1 is the coefficient of determination in the regression of Xi over Xi−1 , Xi−2 , . . . , X1 . The input order of the partial indicators, which establishes the relative weight of each variable, is determined through a convergence algorithm and only the new information contributed by each partial indicator is retained, thereby avoiding duplicated information. Having constructed the EIRC using these three methods, we subsequently assess the reliability and variability of the results. In order to test the indicator’s internal validity, we calculate two correlation coefficients of nonparametric ranges: the rho Spearman correlation coefficient and Kendall’s tau-b correlation coefficient. The former is a nonparametric version of the Pearson correlation coefficient, Sustainability 2020,12, 3070 16 of 27 Sustainability 2020, 12, x FOR PEER REVIEW 8 of 26 Figure 3. European Indicator of Regional Creativity by Principal Component Analysis (EIRC_PCA). Source: authors´ own. Figure 4. European Indicator of Regional Creativity by DEA (EIRC_DEA). Source: authors´ own. Figure 5. European Indicator of Regional Creativity by DP2 (EIRC_DP2). Source: authors´own. Figure 5. European Indicator of Regional Creativity by DP2 (EIRC_DP2). Source: authors´own. The maps provide a more accurate picture of the distribution of creative potential in Europe, where the worst levels of creativity are seen in regions in southern and western Spain together with Portugal and regions in countries in eastern Europe. In contrast, the highest levels of creativity are concentrated particularly in central-east European regions, specifically Belgium and Germany, together with the Nordic countries such as Denmark, Sweden and Finland, as well as certain regions of the Czech Republic and Slovakia, northern Spain, as well as many regions in France and southern Ireland. Prominent once again are the regions which are home to national capital cities [ 25 ] and, in more general terms, regions in the south of Germany and Sweden. Likewise, the maps of the indicator in terms of the PCA and DP2 method are more similar [ 62 ], although the results visà -vis concentration and distribution also hold with regard to EIRC_DEA. This concentration pattern shown by our indicator is characteristic of creative activities at different territorial levels and when applying different forms of measuring, as also shown by Boix et al. [ 6 ] in a comparative analysis between European countries, Bertacchini and Borrione [ 26 ] in urban areas and Italian regions and Mart í n et al. [ 16 ] for regions in the United States. Moreover, several works such as those of Boix et al. [ 78 ] and Boal and Herrero [ 71 ] have reported high concentration coefficients in the cultural and creative sector in specific regions of Italy and Spain. In light of the results, it is worth asking whether these spatial patterns of creativity give rise to territorial dependence and economies of agglomeration. Spatial dependence points to the existence of spatial autocorrelation amongst regions; in other words, a region’s creative performance may be linked to thecreativity ofits neighbours, inline withother works[ 6 , 26 , 71 ]. To verifythis, we estimatedMoran’s I for the three composite indicators of creativity and its dimensions (Table 6). These showed positive and significant results, pointing to the existence of spatial dependence regardless of the aggregation method used. We can thus confirm that creativity is not distributed randomly amongst European regions and that high and low values of the indicator are grouped spatially. Geographic proximity does therefore prove to be a crucial factor, with the spatial externalities of creativity feeding the creative potential of European regions that are close to one another, as has also been evidenced in Dominicis et al. [ 79 ] thus highlighting the importance of economies of agglomeration in this sector [ 71 , 80 , 81 ]. Figure 6shows the spatial dispersion diagram of the EIRC_PCA, where it can be seen that most regions are located in quadrants displaying positive spatial autocorrelation. Quadrants I and II reflect positive spatial autocorrelation, while quadrants II and IV reflect negative spatial autocorrelation. Identical spatial dependence results may be found for the underlying dimensions of the composite indicator, with the exception of the cultural dimension, whose result gives a nonsignificant value. It is conceivable that regions’ cultural capital, measured as a part of the supply (cinema, theatres and libraries) and demand Sustainability 2020,12, 3070 17 of 27 (cinema and museums) of culture, plays a part in regional creative potential but that it is distributed randomly and displays no established spatial pattern. Table 6. Spatial autocorrelation analysis. Indicators Moran´s I Z-score EIRC_PCA 0.5594 10.41 *** EIRC_DEA 0.8089 14.76 *** EIRC_DP2 0.4468 8.29 *** Dimensions Moran´s I Z(I) Talent 0.4027 7.72 *** Openness and Tolerance 0.6851 12.78 *** Culture 0.0617 1.25 Technology and Innovation 0.5118 9.53 *** Cultural and Creative Industries 0.3200 5.98 *** Liveability 0.8907 16.77 *** *** p-value =0.001. Source: authors´own. Sustainability 2020, 12, x FOR PEER REVIEW 10 of 26 Figure 6. Moran scatter plot. Source: authors´own. Figure 7 shows the spatial patterns of the EIRC_PCA indicator and its statistically significant hotspots (spatial clusters with high values) and cold spots (spatial clusters with low values), based on the Getis-Ord Gi* statistic. These are spatial groupings formed by those regions displaying similar values (high or low) in terms of the territorial clusters and which are identified thanks to their geographical proximity and the existence of spatial autocorrelation between regions, based on the creativity indicator. This is a different approach for determining spatial clusters to the one used by Rodrigues and Franco [82], who employ hierarchical clustering for this purpose. Figure 7. Regional creativity clusters. Source: authors´ own. Regions shaded in red evidence a strong likelihood of spatial dependence in the indicator’s high value; in other words, better performance in the creative economy, and they are mainly located in Germany, Belgium, Denmark, northern France and the south of the Nordic countries. In contrast, regions shaded in blue display significant spatial interaction in high values, with poorer levels of creativity, located in eastern Europe (Bulgaria, Croatia, Slovakia, Hungary, Poland, the Czech Republic and Romania). This distribution of spatial clusters of the multidimensional synthetic indicator is similar to the results obtained by Boix et al. [80] for Europe, identifying creative clusters using geo-statistical algorithm and firm data. 5. Discussion and Conclusions Figure 6. Moran scatter plot. Source: authors´own. Figure 7shows the spatial patterns of the EIRC_PCA indicator and its statistically significant hotspots (spatial clusters with high values) and cold spots (spatial clusters with low values), based on the Getis-Ord Gi* statistic. These are spatial groupings formed by those regions displaying similar values (high or low) in terms of the territorial clusters and which are identified thanks to their geographical proximity and the existence of spatial autocorrelation between regions, based on the creativity indicator. This is a different approach for determining spatial clusters to the one used by Rodrigues and Franco [82], who employ hierarchical clustering for this purpose. Sustainability 2020,12, 3070 18 of 27 Sustainability 2020, 12, x FOR PEER REVIEW 10 of 26 Figure 6. Moran scatter plot. Source: authors´own. Figure 7 shows the spatial patterns of the EIRC_PCA indicator and its statistically significant hotspots (spatial clusters with high values) and cold spots (spatial clusters with low values), based on the Getis-Ord Gi* statistic. These are spatial groupings formed by those regions displaying similar values (high or low) in terms of the territorial clusters and which are identified thanks to their geographical proximity and the existence of spatial autocorrelation between regions, based on the creativity indicator. This is a different approach for determining spatial clusters to the one used by Rodrigues and Franco [82], who employ hierarchical clustering for this purpose. Figure 7. Regional creativity clusters. Source: authors´ own. Regions shaded in red evidence a strong likelihood of spatial dependence in the indicator’s high value; in other words, better performance in the creative economy, and they are mainly located in Germany, Belgium, Denmark, northern France and the south of the Nordic countries. In contrast, regions shaded in blue display significant spatial interaction in high values, with poorer levels of creativity, located in eastern Europe (Bulgaria, Croatia, Slovakia, Hungary, Poland, the Czech Republic and Romania). This distribution of spatial clusters of the multidimensional synthetic indicator is similar to the results obtained by Boix et al. [80] for Europe, identifying creative clusters using geo-statistical algorithm and firm data. 5. Discussion and Conclusions Figure 7. Regional creativity clusters. Source: authors´ own. Regions shaded in red evidence a strong likelihood of spatial dependence in the indicator’s high value; in other words, better performance in the creative economy, and they are mainly located in Germany, Belgium, Denmark, northern France and the south of the Nordic countries. In contrast, regions shaded in blue display significant spatial interaction in high values, with poorer levels of creativity, located in eastern Europe (Bulgaria, Croatia, Slovakia, Hungary, Poland, the Czech Republic and Romania). This distribution of spatial clusters of the multidimensional synthetic indicator is similar to the results obtained by Boix et al. [80] for Europe, identifying creative clusters using geo-statistical algorithm and firm data. 5. Discussion and Conclusions The importance of the creative economy in regions’ economic development has aroused interest in measuring its scope by estimating composite indicators of creativity. The main existing cultural and creative indicators are applied to countries or to large urban areas, whereas works that consider the regional level and a broad sample of countries remain few and far between. As a result, in this work we have built composite indicators of creativity at a (NUTS 2) regional disaggregation level for a sample of 20 EU countries, in an effort to enhance measurement of creative potential and to gain an insight into territorial behaviour. Considering the principal underlying dimensions of creativity pinpointed subsequent to a review of the literature on creative indicators, we construct the index based on six creativity vectors, focusing particularly on compiling participation and cultural endowment indicators which are in turn grounded on a wide compilation of primary variables. We use different aggregation methods in an effort to gauge their robustness and reliability, and we assess the indicator’s distribution and spatial patterns. These are three of the main methodological contributions our research makes. One of the most widely debated issues when constructing composite indicators is deciding what weight should be assigned to each partial indicator since, depending on the aggregation method used, the final indicator may differ. As a result, we use three aggregation methods based on the BoD hypothesis, which means that the weightings stem from the actual underlying structure of data, thereby allowing data intensity to speak for itself. The rank correlation coefficients for the classifications obtained using the three methods were positive and significant, such that the choice of one aggregation method or another has little impact on our indicator, therefore making the result robust and reliable. In the three methods, the dimension concerning the scope of the cultural and creative industries, together with the accumulation of talent and the technological level, seem to carry greater weight in terms of the regional creativity indicator. Sustainability 2020,12, 3070 19 of 27 As regards the empirical results, the indicator evidences an unequal distribution in European regions, with different territorial patterns and substantial spatial disparity emerging, and in which strong polarisation between regions in central and northern Europe on the one hand, and regions in eastern and southern Europe, on the other, can be seen. In turn, we observe major disparities between regions in the same country, with these being more acute the greater the country’s creative performance in overall terms. This shows that creativity functions as a technological gap; in other words, creativity triggers greater regional imbalances wherever it appears more intensely. This concentration of creative potential and the gap it leads to when compared to other regions is particularly evident in areas that are home to national capitals, which emerge as the extreme points of the indicator’s regional distribution in all countries, with the exception of Germany. We have also been able to verify that the European distribution of the creativity indicator coincides with regional economic disparities and exhibits a high correlation with per capita GDP and the index of regional competitiveness drawn up by the European Commission, thereby also confirming the external validity of our indicator. Although an analysis of causal relations lies outside the scope of this work, these results strengthen the belief posited in the literature regarding the importance of the creative economy in regions’ economic development and sustainability. Nevertheless, it would also appear that creativity may prove to be a new source of spatial disparities given its high tendency to concentrate and the gap it generates when compared to spaces that offer less creative potential. A further empirical contribution this work makes involves considering geographic proximity and the interactions between regions in their creative performance. Applying spatial autocorrelation techniques has enabled us to pinpoint the existence of positive spatial dependence in the regional creativity indicator. We are therefore in a position to state that spatial externalities feed the creative economy of adjacent regions and that the likelihood of one region obtaining a higher value in the indicator is not independent from the values of its neighbouring regions, thereby triggering the formation of spatial clusters of creativity. Clusters displaying the greatest creative performance are mainly to be found in northern and central Europe (Germany, Belgium, northern France, Denmark and Sweden), whereas the clusters with the lowest creative performance are located in eastern European regions (Bulgaria, Slovakia, Hungary, Poland and Romania). Spatial and geographic proximity thus emerge as crucial and determinant factors of regional creative stock. The practical utilities of a research which is basically instrumental, such as our study, therefore lie both in the contribution made to current knowledge concerning how regional creative potential is distributed over a broad sample of European countries as well as the methodological design of composite indicators of creativity over a wide base of primary variables and partial dimensions, applying endogenous weighting criteria. As regards regional policy implications, the results of this work may prove useful visà -vis assessing the dynamics of the creative economy in European regions. Creative indicators provide a useful tool for monitoring the planning of regional policies and for gauging their effectiveness. As a comparative measuring tool, they help pinpoint examples of good practices that serve as a reference for political decision making at the regional scale. Faced with the challenge of revitalising regional development through creative and innovative processes, as well as of reducing territorial differences in an effort to achieve greater regional convergence, the results obtained provide vital information for policymakers to evaluate the outcomes of the policies implemented and to focus regional policy strategy. In this regard, and given that the importance of the dimensions of talent, innovation, and scope of the cultural and creative industry when constructing the composite indicator of creativity have also been shown, the most efficient regional policies should be grounded on three complementary vertices: promoting training and attracting human capital; encouraging entrepreneurship and the innovation capacity; and, finally, fostering environmental cultural assets, not only in terms of upstream cultural amenities but also when developing new culturally and downstream based creative market products. Nevertheless, certain limitations which are put forward as future challenges to be improved do emerge. On the one hand, improving data availability at a regional scale would enable a greater Sustainability 2020,12, 3070 20 of 27 number of variables and dimensions to be included. There is also a need to extend the number of EU countries. In addition, it would prove enlightening to explore which determinants drive the location of cultural and creative industries, which would therefore provide further complementary information to be included in the indicators when fostering creative environments. Author Contributions: All authors contributed equally to this work. Both authors have read and approved the final manuscript. Funding: This study was supported by the Regional Ministry of Education at the Regional Government of Castile and Leon (Spain) (project no VA012G19) and has benefitted from a research stay at the School of Technology and Management of the Polytechnic Institute of Braganza, funded by the University of Valladolid under the 2019 Teaching StaffMobility Programme. Acknowledgments: The authors would like to thank participants at the 9 th European Workshop on Applied Cultural Economics, Copenhagen (2019), and the XXX Portuguese-Spanish Conference on Scientific Management, Braganza, Portugal (2020), for comments and discussion on a preliminary version of the paper. The usual disclaimer applies. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Table A1. Scores of EIRC indicators in European regions NUTS 2. EIRC_PCA EIRC_DEA EIRC_DP2 BE Belgium BE10 Région de Bruxelles-Capitale 0.608 100.000 12.307 BE21 Prov. Antwerpen 0.412 85.797 6.184 BE22 Prov. Limburg 0.355 82.723 5.198 BE23 Prov. Oost-Vlaanderen 0.394 87.946 5.471 BE24 Prov. Vlaams-Brabant 0.470 91.547 6.741 BE25 Prov. West-Vlaanderen 0.359 85.439 5.092 BE31 Prov. Brabant Wallon 0.550 88.611 8.755 BE32 Prov. Hainaut 0.292 74.782 3.825 BE33 Prov. Liège 0.344 77.600 4.878 BE34 Prov. Luxembourg 0.321 81.646 4.475 BE35 Prov. Namur 0.321 80.021 4.288 BG Bulgaria BG31 Severozapaden 0.131 31.916 0.601 BG32 Severen tsentralen 0.142 34.417 1.024 BG33 Severoiztochen 0.180 43.589 1.480 BG34 Yugoiztochen 0.152 35.156 1.049 BG41 Yugozapaden 0.272 62.541 3.179 BG42 Yuzhen tsentralen 0.146 35.587 0.888 HR Croatia HR03 Jadranska Hrvatska 0.230 81.299 3.475 HR04 Kontinentalna Hrvatska 0.217 53.839 2.842 CZ Czech Republic CZ01 Praha 0.517 90.115 10.208 CZ02 StredníCechy 0.264 59.825 3.013 CZ03 Jihozápad 0.236 57.123 2.810 CZ04 Severozápad 0.211 53.435 2.227 CZ05 Severovýchod 0.222 56.337 2.371 CZ06 Jihovýchod 0.272 57.541 3.443 CZ07 StredníMorava 0.222 55.108 2.346 CZ08 Moravskoslezsko 0.226 54.311 2.537 Sustainability 2020,12, 3070 21 of 27 Table A1. Cont. EIRC_PCA EIRC_DEA EIRC_DP2 DK Denmark DK01 Hovedstaden 0.605 97.716 9.836 DK02 Sjælland 0.376 98.555 6.446 DK03 Syddanmark 0.372 88.098 4.844 DK04 Midtjylland 0.426 90.745 5.948 DK05 Nordjylland 0.361 87.888 4.980 EE Estonia EE00 Eesti 0.329 99.294 5.325 FI Finland FI19 Länsi-Suomi 0.388 83.551 4.928 FI1B Helsinki-Uusimaa 0.587 94.757 8.938 FI1C Etelä-Suomi 0.372 82.587 4.654 FI1D Pohjois- ja Itä-Suomi 0.341 80.553 3.950 FI20 Åland 0.348 94.475 3.836 FR France FR10 Île de France 0.604 100.000 11.330 FR21 Champagne-Ardenne 0.289 79.074 3.822 FR22 Picardie 0.308 80.812 4.043 FR23 Haute-Normandie 0.320 82.090 4.461 FR24 Centre 0.328 81.611 4.492 FR25 Basse-Normandie 0.310 80,432 4.002 FR26 Bourgogne 0.305 80.759 3.978 FR30 Nord - Pas-de-Calais 0.308 77.519 4.521 FR41 Lorraine 0.309 80.186 4.001 FR42 Alsace 0.384 84.697 5.785 FR43 Franche-Comté0.317 82.024 4.351 FR51 Pays de la Loire 0.343 82.892 4.723 FR52 Bretagne 0.342 81,828 4.771 FR53 Poitou-Charentes 0.317 79.823 4.327 FR61 Aquitaine 0.355 81.490 5.121 FR62 Midi-Pyrénées 0.398 82,297 5.768 FR63 Limousin 0.303 79.113 4.233 FR71 Rhône-Alpes 0.408 86.199 6.034 FR72 Auvergne 0.321 81.069 4.054 FR81 Languedoc-Roussillon 0.343 76.641 4.988 FR82 Provence-Alpes-Côte d’Azur 0.402 81.663 6.414 FR83 Corse 0.287 76.923 4.485 DE Germany DE11 Stuttgart 0.517 95.807 7.440 DE12 Karlsruhe 0.480 92,525 7.081 DE13 Freiburg 0.427 93.207 5.948 DE14 Tübingen 0.451 94.434 6.310 DE21 Oberbayern 0.595 100.000 9.469 DE22 Niederbayern 0.357 90.361 4.886 DE23 Oberpfalz 0.395 90.060 5.266 DE24 Oberfranken 0.387 88,811 5.348 DE25 Mittelfranken 0.477 92.460 7.363 Sustainability 2020,12, 3070 22 of 27 Table A1. Cont. EIRC_PCA EIRC_DEA EIRC_DP2 DE26 Unterfranken 0.407 90.653 5.575 DE27 Schwaben 0.407 92.768 5.818 DE30 Berlin 0.556 90.976 9.799 DE40 Brandenburg 0.351 79.346 4.626 DE50 Bremen 0.456 86.453 7.580 DE60 Hamburg 0.602 95.338 10.805 DE71 Darmstadt 0.512 92.687 8.014 DE72 Gießen 0.386 87.666 5.255 DE73 Kassel 0.361 85.439 4.977 DE80 Mecklenburg-Vorpommern 0.340 76.534 4.714 DE91 Braunschweig 0.424 86.381 5.844 DE92 Hannover 0.414 86.052 6.027 DE93 Lüneburg 0.347 87.665 4.461 DE94 Weser-Ems 0.343 86.183 4.524 DEA1 Düsseldorf 0.434 87.117 6.749 DEA2 Köln 0.472 89.112 7.404 DEA3 Münster 0.370 86.564 5.227 DEA4 Detmold 0.394 89.374 5.574 DEA5 Arnsberg 0.375 85.886 5.311 DEB1 Koblenz 0.349 88.718 4.704 DEB2 Trier 0.374 89.857 5.120 DEB3 Rheinhessen-Pfalz 0.429 89.833 6.134 DEC0 Saarland 0.344 83.176 4.520 DED2 Dresden 0.391 79.714 5.646 DED4 Chemnitz 0.316 77.279 3.938 DED5 Leipzig 0.395 80.391 5.863 DEE0 Sachsen-Anhalt 0.318 76.428 4.027 DEF0 Schleswig-Holstein 0.382 87.810 5.260 DEG0 Thüringen 0.332 78.158 4.220 HU Hungary HU10 Közép-Magyarország 0.381 77.342 6.924 HU21 Közép-Dunántúl 0.212 50,120 2,440 HU22 Nyugat-Dunántúl 0.214 50.268 2.558 HU23 Dél-Dunántúl 0.195 45.984 2.258 HU31 Észak-Magyarország0.181 44.963 1.890 HU32 Észak-Alföld 0.182 45.819 1.928 HU33 Dél-Alföld 0.196 47.852 2.277 IE Ireland IE01 Border, Midland and Western 0.319 84.669 3.871 IE02 Southern and Eastern 0.485 97.565 8.303 LV Latvia LV00 Latvija 0.273 83.498 3.984 LT Lithuania LT00 Lietuva 0.277 65.572 3.834 PL Poland PL11 Lódzkie 0.217 51.974 2.145 PL12 Mazowieckie 0.305 61.002 4.243 PL21 Malopolskie 0.224 52.603 2.551 PL22 Slaskie 0.208 53.546 2.147 PL31 Lubelskie 0.178 48.890 1.513 Sustainability 2020,12, 3070 23 of 27 Table A1. Cont. EIRC_PCA EIRC_DEA EIRC_DP2 PL32 Podkarpackie 0.171 47.350 1.178 PL33 Swietokrzyskie 0.176 48.643 1.258 PL34 Podlaskie 0.174 49.092 1.227 PL41 Wielkopolskie 0.203 54.715 2.032 PL42 Zachodniopomorskie 0.193 50.994 1.975 PL43 Lubuskie 0.196 49.846 1.836 PL51 Dolnoslaskie 0.237 53.618 2.955 PL52 Opolskie 0.185 49.770 1.590 PL61 Kujawsko-Pomorskie 0.181 49.672 1.739 PL62 Warminsko-Mazurskie 0.176 48.566 1.576 PL63 Pomorskie 0.229 52.880 2.592 PT Portugal PT11 Norte 0.268 59.723 3.813 PT15 Algarve 0.287 77.643 4.007 PT16 Centro 0.254 66.313 3.235 PT17 Á.Metropolitana de Lisboa 0.403 80.671 7.275 PT18 Alentejo 0.240 61.803 3.405 PT20 R.Autónoma dos Açores 0.249 64.084 3.166 PT30 R.Autónoma da Madeira 0.271 67.012 4.270 RO Romania RO11 Nord-Vest 0.159 44.933 1.300 RO12 Centru 0.155 43.996 1.305 RO21 Nord-Est 0.126 40.102 0.594 RO22 Sud-Est 0.130 41.446 0.592 RO31 Sud - Muntenia 0.135 39.972 0.484 RO32 Bucuresti - Ilfov 0.339 67.238 5.223 RO41 Sud-Vest Oltenia 0.118 39.302 0.375 RO42 Vest 0.163 46.970 1.211 SK Slovakia SK01 Bratislavskýkraj 0.477 86.864 9.240 SK02 ZápadnéSlovensko 0.202 56.497 1.928 SK03 StrednéSlovensko 0.198 56.304 2.013 SK04 VýchodnéSlovensko 0.190 52.881 1.758 SI Slovenia SI03 Vzhodna Slovenija 0.244 61.569 2.701 SI04 Zahodna Slovenija 0.360 65.742 5.572 ES Spain ES11 Galicia 0.301 64.085 3.912 ES12 Principado de Asturias 0.313 64.439 4.233 ES13 Cantabria 0.317 66.591 4.386 ES21 País Vasco 0.408 76.436 5.927 ES22 Comunidad Foral de Navarra 0.375 77.087 5.255 ES23 La Rioja 0.330 71.345 4.554 ES24 Aragón 0.338 71.434 4.751 ES30 Comunidad de Madrid 0.505 90.726 8.805 ES41 Castilla y León 0.298 66.682 4.049 ES42 Castilla-la Mancha 0.262 63.200 3.364 ES43 Extremadura 0.225 58.906 2.392 ES51 Cataluña 0.414 77.857 6.402 ES52 Comunidad Valenciana 0.328 73.770 4.788 Sustainability 2020,12, 3070 24 of 27 Table A1. Cont. EIRC_PCA EIRC_DEA EIRC_DP2 ES53 Illes Balears 0.379 86.120 6.075 ES61 Andalucía 0.284 60.588 3.797 ES62 Región de Murcia 0.290 63.352 3.954 ES70 Canarias 0.308 81.459 4.583 SE Sweden SE11 Stockholm 0.725 100.000 13.140 SE12 Östra Mellansverige 0.462 89.237 7.159 SE21 Småland med öarna 0.391 88.804 5.565 SE22 Sydsverige 0.497 89.758 7.626 SE23 Västsverige 0.499 92.291 7.659 SE31 Norra Mellansverige 0.379 85.771 5.732 SE32 Mellersta Norrland 0.393 86.623 6.216 SE33 Övre Norrland 0.425 88.517 6.580 References 1. UNCTAD. Special Unit for South-South Cooperation- Creative Economy Report 2008, The Challenge of Assessing the Creative Economy: Towards Informed Policy-making; UNCTAD: Geneva, Switzerland, 2008. 2. Correa-Quezada, R.; Á lvarez-Garc í a, J.; Del R í o-Rama, M.D.C.; Maldonado-Erazo, C.P. Role of Creative Industries as a Regional Growth Factor. Sustainability 2018,10, 1649. [CrossRef] 3. Rodrigues, M.; Franco, M. 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