1. ISSUE AND ARGUMENT
The objective of this article is to estimate the potential development of the creative economy through the application of the Development Potential Index of Creative Economy (DPICE) in the 26 state capitals and the Federal District of Brazil. DPICE is an index composed of secondary data that will be applied to the 26 capitals of the Brazilian states and the Federal District, whose purpose is to identify the determinants of the spatial pattern of the creative economy. We started the research with the use of the locational quotient (LQ) (Isard, 1960) to identify the level of productive specialization of the 27 capitals in creative economy; then, we selected a set of variables to identify the determinants of the spatial pattern; finally, we calculated the DPICE as a composite index with greater potential for explanation than the variables alone.

2. REFERENCES
The growing interest in the relations between culture, creativity, economics and development has promoted the broadening of the scope of analysis of the economy of culture beyond the goods defined as cultural, so that “cultural goods and services can be seen as a sub-set of a wider category of goods that can be called creative goods and services. The later are simply products that require some reasonably significant level of creativity in their manufacture, without necessarily satisfying other criteria that would enable them to be labelled ‘cultural’” (Throsby, 2010, p. 16-17).
There are many definitions for creative industries, but the one from the Department for Culture, Media and Sport (DCMS) has become widespread and defines them as “those that are based on individual creativity, skill and talent. They also have the potential to create wealth and jobs through developing and exploiting intellectual property”.
When we analyze the territorial dynamics of the creative economy, it is possible to verify the tendency of its industries to cluster spatially (Scott, 2005; Lazzeretti, Boix and Capone, 2013). Globally, creative industries tend to materialize in the form of dense agglomerations of firms and workers in the landscape of the cities that occupy the highest positions in the urban hierarchy (Scott, 2005; 2010).
The measurement of the spatial concentration of the creative industries can be carried out by applying the LQ, an indicator of the productive agglomerations of a given industry in a given territorial area (Isard, 1960), however, explanations for the spatial concentration of the creative industries still require theoretical and analytical efforts.
The mainstream of explanation of the spatial concentration of the creative industries deals with agglomeration economies, which can be defined as “advantages in costs or quality due to the spacial concentration of productive resources and actors (population, firms, institutions and other collective agents)” (Lazzeretti, Boix and Capone, 2013, p. 46). To Scott (2005), agglomerations allow firms to save on spatial connections, achieve the advantages of the concentrated labor market, and insert themselves in the flows of information and innovation that exist in any place where different complementary producers congregate, evidencing the strong functional interdependence and externalities that transform these units into a whole.
Florida (2002) emphasizes the specific relevance of the "creative class" in the urban economy of the twenty-first century, pointing to the ability to attract and retain creative individuals as the fundamental force in the development of cities. In the model proposed by the author, known as 3Ts (talent, tolerance and technology), the human factor stands out as a fundamental component of the explanation, as well as the environment in which these people want to live. Storper (2013), on the other hand, argues that people in general follow jobs. In his explanation, the forces of agglomeration stem from the productive specialization of cities, which in the long run is dependent on institutions.
In view of these explanations, econometric models were proposed to identify the determinants of spatial concentration in creative industries (Lazzeretti, Boix e Capone, 2013; Machado; Simões and Diniz, 2013) and indexes were created as ways to estimate the potential for the development of the creative economy in a territory (Bowen; Moesen e Sleuwaegen, 2008; Correia and Costa, 2014).

3. METHODOLOGY
In our methodological procedures, we use the LQ of the creative industries as a proxy for the development of the creative economy (dependent variable), as proposed by Lazzeretti, Boix and Capone (2013). To calculate the LQ, the definition given by FIRJAN (2016) of the activities that are part of the core of the creative economy was used.
To build DPICE, we follow the steps proposed by Bowen, Moesen and Sleuwaegen (2008): scope (selection of primitive data); normalization (definition of a common scale); and aggregation (definition of the weights). The hypothesis of the research is that the DPICE is able to explain the LQ better than the variables alone, so that we can confirm it as an index capable of estimating the potential for development of the creative economy in the capitals of the Brazilian states and in the Federal District.
The selection of the variables was based on the identification in the literature of those that would help us to explain three dimensions considered important for the development of the creative industry in the territories (human capabilities; attractiveness and spatial connectivity; cultural environment and creative entrepreneurship).
In the "Human capabilities" dimension, we consider variables that are related to theoretical explanations about the effects of education on the development of the creative economy and the importance of skilled workers. In the dimension "Attractiveness and spatial connectivity" we selected variables that dialogue with the arguments that point to creative cities as spaces of great attractiveness and spatial connectivity, resulting in economic dynamism and social and cultural diversity of the territories. Finally, in the dimension "Cultural environment and creative entrepreneurship" we considered variables that are related to the explanations about the importance of cultural activities, entrepreneurship and institutions for the development of the creative economy.
Regarding data normalization, we opted for the Min-Max normalisation method (OECD and European Commission, 2008). Finally, in to define the weights of each dimension in the construction of the IDPEC, we used the technique of endogenous weights (Bowen; Moesen and Sleuwaegen, 2008). The calculation based on the technique of endogenous weights aims to reveal the best performances of the capitals, assigning to each variable and then to each dimension of a specific city, the weights that most value its performance.

4. TAKEAWAY AND RESULTS
The results confirmed that DPICE can be used as an index capable of estimating the potential of the selected cities to develop through the creative economy. The correlation of the DPICE with the LQ was 0.76, a result higher than that of the dimensions alone, so that we can confirm its ability to estimate the potential of cities to develop through the creative economy. The technique of endogenous weights did not significantly alter the ability of DPICE to explain the results of the LQ of the creative industries, but it is a way of respecting local specificities and valuing the variables of best performance in each city. In view of this, we present below, in figure 1, the direct relationship between the DPICE calculated by endogenous weights and the dependent variable, LQ of the creative industries.
It is hoped that this work will contribute to the understanding of the forces that promote the development of the creative economy, enabling better public policies to be carried out. Finally, we believe that DPICE needs to be tested in cities other than state capitals, as well as evaluated in its ability to explain the spatial dynamics of the creative economy at the state level.

References
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Castro-Higueiras, A., and De Aguilera, M. (2016). El Índice de Potencialidad de Las Industrias Culturales y Creativas. Fonseca, Journal of Communication 13: 129–146. http://dx.doi.org/10.14201/fjc201613129146
Correia, C. M., and Costa, J. S. (2014). Measuring Creativity in the EU Member States.
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Federação das Indústrias do estado do Rio de Janeiro (FIRJAN). (2016). Mapeamento da indústria criativa no Brasil. Rio de Janeiro: FIRJAN. Accessed 15 January 2017. http://www.firjan.com.br/EconomiaCriativa/pages/download.aspx.
Florida, R. (2002). The Rise of the Creative Class: And How it’s Transforming Work, Leisure, Community, & Everyday Life. New York: Basic Books.
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Lazzeretti, L., Boix, R. and Capone, F. (2013). “Why do Creative Industries Cluster?” In Creative Industries and Innovation in Europe: Concepts, Measures and Comparative Case Studies, edited by L. Lazzeretti, 45–64. New York: Routledge.
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Storper, M. (2013). Keys to the City: How Economics, Institutions, Social Interaction and Politcs Shape Development. Princeton University Press.
Throsby. D. (2010). The economics of cultural policy. London: Cambridge University Press.