Creative service industries and regional ... - Wiley Online Library

doi:10.1111/pirs.12187

Creative service industries and regional productivity* Rafael Boix-Domenech, Vicent Soler-Marco Departament d’Estructura Econòmica, Facultat d’Economia, Universitat de València, Avda dels Tarongers, s/n, 46022 Valencia, Spain (e-mail: [email protected], [email protected]) Received: 8 January 2015 / Accepted: 27 July 2015

Abstract. This research analyses the effect of creative service industries on labour productivity of the regions. Creative service industries offer services that increase a region’s capacity to generate and combine new ideas, resulting in an increased production of innovations which raise productivity. The paper proposes an analytical framework and compares findings in 250 regions in 24 countries of the European Union in 2008. We find that creative service industries increases labour productivity of the regions and their effects are as important for regional productivity as scientific research or highly qualified human capital. JEL classification: R11, R12, R58 Key words: Creative industries, creative service industries, regional productivity

1 Introduction The differences of productivity across the regions of developed areas are remarkable. For example, in the European Union (EU), Inner London’s labour productivity is more than 300 per cent above the EU average, whereas in regions of Romania and Bulgaria labour productivity is more than 70 per cent below the EU average. The debate about the causes of the differences in labour productivity across regions has highlighted the role of capital and also elements contributing to total factor productivity, such as agglomeration economies, human capital, institutions, social capital, infrastructures, scientific production, and the composition of the structure of activities (Gardiner et al. 2004; Maroto and Cuadrado-Roura 2013). In recent decades we have seen in the economic debate the emergence of a new set of concerns that have served to turn the attention towards business and industries that so far have been neglected. With the relocation of a part of mass production to low cost countries, and with technological advancement having transformed many industries and invented others, the focus has now shifted to those economic activities and businesses that are producing value in developed countries, particularly through the use of brain power intangibles (Fujita 2007). * The authors would like to thank Andrés Maroto, David Bartolini, Enrique Garcilazo, Jesús Peiró, Joaquim Oliveira, María José Murgui, Frank Pyke, Pau Rausell, Raffaele Trapasso, Richard Miller and two anonymous referees for helpful comments to previous versions of the paper. © 2015 The Author(s). Papers in Regional Science © 2015 RSAI Papers in Regional Science, Volume •• Number •• •• 2015.

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R. Boix-Domenech, V. Soler-Marco

In this debate, the creative industries have received increasing attention from scholars and policy-makers (DCMS 1998, 2001; Hawkins 2007; European Commission 2010; UNCTAD 2010). The creative industries can be defined as knowledge-based activities focused on the generation of meaning, content and aesthetic attributes by means of creativity, skill and talent, and with the potential to create wealth from trade and intellectual property rights (DCMS 2001; UNCTAD 2010). Creative service industries (CSI) are those creative industries in the service sector, characterized by an intangible output. Industries such as publishing, audiovisual, radio and TV, software, architecture and engineering, research and development, advertising, design, photography, and arts and entertainment (UNCTAD 2010), are generally considered to be creative service industries. In their study on the differentials of wealth in the European regions, Boix et al. (2013) suggested further research should focus exclusively on creative services because in most of the regions examined activities classified as belonging to creative manufacturing were not in fact engaged in creating but in making. The size of CSI is not negligible: for example, in the European Union amount to more than 6 per cent of the employed workforce (Boix et al. 2013). This paper investigates the effect of CSI on the labour productivity of the European Union’s regions. The question is whether the presence of CSI explains observed differences in labour productivity across the regions. The hypothesis is that the higher the share of the workforce in CSI, the higher the level of productivity. This is because CSI generates spillovers that improve a region’s capacity to innovate, which in turn affects differences in productivity. The objective of this paper is to compare the effects on regional productivity of differences in the proportions of workforces employed in CSI. Despite the recent emphasis scholars have given to the role of creative industries in the development of countries, regions and cities, there have not been contributions specifically focused on explaining and measuring the impact of the creative industries on the productivity of regions. This paper contributes to the debate by introducing an analytical framework along with empirical evidence for a sample covering 250 regions in the European Union. The research findings are relevant to both the scientific debate and to questions of policy, since the evidence that the impact of CSI is such as to result in productivity differentials in European regions implies that a way to raise productivity is by an increased specialization in creative services. The paper is divided into seven parts. Section 2 reviews the literature relating to the creative industries and productivity. Section 3 then develops an analytical model. Section 4 introduces the data. Section 5 explains the variables. Section 6 presents the contribution of CSI to the productivity of regions. Finally, Section 7 is devoted to a discussion of the results.

2 Literature review 2.1 Creative industries The term ‘creative industries’ originates in Australia (DCA 1994), and then its use expanded in the United Kingdom which needed to find new bases for growth for its post-industrial economy (DCMS 1998; O’Connor 2007). One of the reasons for the focus on creative industries was that they had shown high growth rates in the UK during the 1990s (DCMS 2001). In addition, there was the attraction of changing the perception of certain activities such as arts and culture away from being subsidized sectors (Baumol and Bowen 1965) to being generators of wealth (DCMS 1998; UNCTAD 2010) and as contributors to the so called new economy. Papers in Regional Science, Volume •• Number •• •• 2015.

CSI and the productivity of the regions

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The research agenda on the creative industries has hitherto focused on four basic areas: epistemological and taxonomical issues (DCMS 2001; O’Connor 2007; Hesmondhalgh 2008; Flew and Cunningham 2010); geographies (Cooke and Lazzeretti 2008; Lazzeretti et al. 2008; De Propris et al. 2009; Lazzeretti 2012; Boix et al. 2014); policy-making (Garnham 2005; Hesmondhalgh 2008); and economic and social impacts (Potts and Cunningham 2008; Flew and Cunningham 2010; UNCTAD 2010; Rausell et al. 2011; De Miguel et al. 2012; Boix et al. 2013; Marco et al. 2014). 2.2 Creative industries and productivity 2.2.1. Theoretical frameworks

The relationship between the creative industries and productivity clearly belongs to the last area mentioned, economic and social impacts, but it has as yet received little attention. This relationship is intrinsically linked to the debate on the impact of creative industries on economic growth and wealth. A good point of departure to contextualize the problem is Potts and Cunningham (2008), and Potts (2009), who propose four ideal models relating the creative industries to economic performance: the ‘welfare’, ‘competitive’, ‘growth’, and ‘innovation’ models. In the welfare model, the creative industries are deemed to be affected by Baumol’s disease (Baumol and Bowen 1965) and their productivity grows at a slower rate than does the rest of the economy, even if they are subsidized. This is because in this model the creative industries are oriented to the enhancement of welfare. In the competitive model, the creative industries are seen as just another industry and have no more effect on technological change, innovation, or productivity than do any other activities. In the growth model, the creative industries are said to be a growth driver and their impact on the economy is more than proportional. The creative industries have been growing more than other industries simply because they have been in a supply side expansion phase; while there could also be a demand effect in that increasing income causes a proportionate increase in demand for creative industries services. In the innovation model, the creative industries are more notable for their contribution to technological change than they are for their direct effects on production. The creative industries are seen as being a part of a process of economic evolution and their role is to provide evolutionary services to the innovation system, facilitating change of the entire economic system (Potts 2009). The innovation model is supported by the theory of differentiated knowledge bases (Asheim et al. 2011; Asheim and Parrilli 2012). Analytical and synthetic knowledge bases are well-known in economics: the term ‘analytical knowledge base’ refers to the development of new knowledge through the use of the deductive scientific method and scientific laws (e.g. the pharmaceutical industry has an analytical knowledge base), and the term ‘synthetic knowledge base’ refers to the generation of knowledge by an inductive process of testing, experimentation, and practical work (e.g. the mechanical engineering industry develops on a synthetic knowledge base). Of particular interest has been the introduction of a third category, the ‘symbolic knowledge base’. This type is normal for the cultural and creative industries in that it refers to the ‘creation of meaning and desire as well as aesthetic attributes of products, producing designs, images and symbols, and to the economic use of such forms of cultural artefacts’ (Asheim et al. 2011, p. 897). In the case of the symbolic base, knowledge inputs and outputs are aesthetic more than cognitive, and new knowledge is usually developed through a creative process rather than through analytical or problem solving processes. Creative industries provide services to the rest of the productive system in two ways: as inputs to other industries, and also via a horizontal spill-over effect on the perceptions of people, businesses and institutions. Hitherto, contributions explaining the impact of creative industries on productivity and growth have basically referred to the ‘growth’ scenario mentioned above. Thus, for example, Rausell Papers in Regional Science, Volume •• Number •• •• 2015.

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et al. (2011) and Marco et al. (2014) propose a theoretical framework with circular causal effects: an increase in the GDP per capita increases the share of people with high levels of education and income, the percentage of public and private expenditure oriented to creative goods and services, and the stock of cultural capital. The result is an increase in the demand for creative goods and services that then engenders a growth in the share of workers in the creative industries. This has two effects. First, there is an increase in an economy’s overall number of innovations due to, on the one hand, the addition of those innovations produced by the creative industries (supply side), and, on the other hand, a higher propensity to consume innovations by workers employed in the creative industries (demand side). Second, there is an increase in the level of productivity of the economy as a whole, under the assumption that productivity in the creative industries is higher than the average for the economy. Increases in innovation and productivity result in an increase in the GDP per capita, and the process starts over. Sacco and Segre (2006) propose a virtuous circle based on the acquisition of competences, where the notion of competence refers to the effect of the stimulus of cultural, symbolic and identitarian capital. The basic assumption is that the level of competence and capability of consumers (some of which are creative workers) is sufficient enough to guarantee that they would be willing to pay extra for the creative component of a product. In order to meet this demand, firms invest in the skills of creative workers in order to increase the creative component in the enterprise’s goods and services. The result is an increase in a locality’s stock of creative capital. Such changes in local cultural supply and rising social awareness lead to improvements in the competences of noncore creative workers while fostering demand for creative commodities. At this point, a part of the value added generated by the process is then devoted by firms to financing creative activities and by the public sector in investing in creative industries, creating a virtuous cycle. 2.2.2. Empirical evidence

In practice, the empirical evidence on the effects of creative industries on productivity or on related indicators is still poor and unclear, although some works provide positive evidence. Thus, Dolfman et al. (2007) found that in the United States the average wage in the creative industries was 34.9 per cent higher than the national average private sector wage. Potts and Cunningham (2008) provided evidence that in Australia the average incomes of workers in the creative industries were 31 per cent higher than national average incomes, and that the aggregate growth rates of creative industry incomes was higher than incomes for the aggregate economy. Other research takes into account the effects on the overall economy. Florida et al. (2008) found that the presence of occupational groups in the arts and entertainment, architecture and engineering, and research sectors had important impacts on differences in income, productivity and wages in 331 metropolitan regions of the United States. Rausell et al. (2011) for Spain, and De Miguel et al. (2012) and Marco et al. (2014) for the European regions, found that the relative effect of creative industries on the differences in GDP per capita was about between 42 per cent and 44 per cent, whereas in Boix et al. (2013) for CSI this effect moderates to 39 per cent. However, as will be outlined in the following sections, these studies could be overestimating the effect of the creative industries.

3 Creative industries and productivity in an endogeneous growth approach 3.1 A semi-endogenous growth model In this section we employ endogenous growth theory to analyse the relationship between creative industries and regional productivity, as mediated by the generation of innovations. Papers in Regional Science, Volume •• Number •• •• 2015.


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The use of this approach is mainly instrumental in this paper. This intends to link creative industries to a well-defined analytical framework in which creative industries can be separated, and to depart from a theoretical justification for the structural variables included in the model. In this way we try to address: first, the lack of robust analytical modelling in most of the research on the effects of creative industries, and second the biases that theory-free modelling can introduce in the estimates overweighting the impact of the creative industries (e.g. Rausell et al. 2011; De Miguel et al. 2012; Boix et al. 2013). The second wave of endogenous growth theory, generally known as innovation-based growth theory, recognizes that intellectual capital is the source of innovation and technological progress. Contrary to physical and human capital, intellectual capital is not accumulated through savings and schooling but through creativity. The idea of innovation-based growth is generally thought to derive from either of two main conceptual frameworks: Schumpeterian theory (Grossman and Helpman 1991; Aghion and Howitt 1992) and endogenous technological change models (Romer 1990a, 1990b; Jones 1995, 2001), whereas the latter accommodates perfectly the idea of symbolic knowledge. Romer-Jones type models assume that an increase in product variety raises productivity by allowing the spread of intermediate production more thinly across a larger number of activities, each of which is subject to diminishing returns and hence exhibits a higher average product when operated at a lower intensity. Thus, a first sector, the creative sector, creates new varieties (or creations) of intermediate goods and sells production rights to a second sector, one producing intermediate goods. The intermediate goods sector then produces goods based on the creations and sells them to a third, final, sector, which in turn produces final goods and sells them to consumers.1 The implication is that the way to increase productivity levels (and hence, differences in productivity levels) is not by saving, but by dedicating a large fraction of output to creative activities. The Romer-Jones model departs from a multiplicative production function: Y ¼ K ∝ ðALY Þ1∝ ;

(1)

where Y is the output, A is labour-augmenting technology (knowledge stock), K is capital, and α is output elasticity of capital. The key of the model is that working people (L), the source of creativity, can be dedicated to producing ideas (LA) in the creative sector or, alternatively, producing goods and services in other sectors (LY), so that: L ¼ LA þ LY :

(2)

The general solution of Jones (2001) for the simplest version of the model for a path of balanced growth and a moment of time t can be written as:2 1 Notice that in the cases of some creative industries production is more oriented to the final consumer (e.g. the performing arts) than to other sectors. The same is true for analytical innovations, for which the model was originally conceived by Romer. Leaving aside these exceptions, the logic of the model is still valid. 2 The general production function for ideas is A_ ¼ At A0 ¼ δLλA , where δ ¼ δAϕ , δ is the rate of creation of ideas, 0 < λ ≤ 1 measures the existence of scale economies, and ϕ measures the productivity (returns) in the production of the _ can be expressed as ideas. The generation of ideas is A_ ¼ δLλA Aϕ and the growth rate of the generation of ideas (A=A) ϕ λ gA ¼ δLA A =A . Substituting LA by sRL, where sR = LA/L, and solving, the knowledge function equals to A ¼ 1 h i1ϕ . See the complete development of the model in Jones (1995, 2001), including solutions for particular δðsR LÞλ =gA

values, i.e. λ = 1 and ϕ = 0. For all the values of these parameters, the final equation to be estimated is similar. The only difference is the interpretation of the parameters in terms of endogenous growth theory. Papers in Regional Science, Volume •• Number •• •• 2015.

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y¼

sK n þ gA þ d

#b a " δ ðsR L Þλ sY ; gA

(3)

where y is the labour productivity for a year t, sR = LA/L is the share of persons employed in the creative sector, sY is the share of persons employed in the rest of the economy, sK is the intensity of capital per worker, n is the population growth rate, d the rate of depreciation of ∝ 1 and b ¼ 1ϕ . The equation can be linearized using logarithms: capital, a ¼ 1∝ ln y ¼ b lnδ þ bλ lnsR þ lnsy þ a ln sk a lnðn þ gA þ d Þ þ bλ lnL b lngA :

(4)

3.2 Endogenous growth in comparison with other approaches The Romer-Jones model provides an analytical base and structural variables for the estimates, although it also has some limits and shortcomings. More than simply list them, it is more useful a comparison with other lines that are strong in the creative industries literature. First, this model is supply-oriented, whereas researchers coming from a cultural industries background might be more habituated to employing demand-side approaches in their analyses (e.g. when they measure input-output impacts) and policy recommendations (e.g. Garnham (2005), is critical of the fact that in his view a focus on promoting creative industries marks a return to supply-side policies). However, although the model we propose is essentially supply based, it allows for the use of demand indicators in the estimates. A second shortcoming arises from the fact that the Romer-Jones model is a ‘closed-system’, which contrasts with the evolutionary approach to creative industries in Potts (2009). The endogenous model is an equilibrium model, which assumes the existence of representative agents that maximize profits in a context of weak uncertainty in markets, with a gradual transition towards a long-run steady state (is path deterministic), and the way to increase creativity is assuming that the productivity of the workforce in the creative industries increases over time or increasing the size of the creative sector, even if changes in labour force are exogenously determined. By contrast, Potts and Cunningham (2008) and Potts (2009), suggest defining creative industries as social network markets, where these markets are complex open systems. In both frameworks (endogenous growth and evolutionary) creativity determines differences in productivity and growth, although in the evolutionary one, differences in creativity are a consequence of institutions and their systemic interaction. However, at this stage of the research, the semi-endogenous model provides a better defined analytical framework that facilitates the measurement of the effects of creative industries on productivity.

4 Data Our sample comprises 250 NUTS 2 regions in 24 countries of the European Union in 2008: Austria, Belgium, Bulgaria, Czech Republic, Cyprus, Denmark, France, Estonia, Finland, Germany, Hungary, Ireland, Italy, Latvia, Lithuania, Netherlands, Poland, Portugal, Romania, Slovenia, Slovakia, Spain, Sweden, and the United Kingdom. European Union countries for which there is no data, such as Greece, Luxembourg, Malta, French overseas regions, Ceuta and Melilla, are not included. Most data has been drawn from Eurostat’s Structural Business Statistics (SBS), Science and Technology Statistics (STS), and Regional Economic Accounts (ESA) databases for the year 2008. Data for multimodal accessibility come from ESPON (2009), and data of consumption of fixed capital comes from the OECD national accounts at a glance. The choice of 2008 is because that is the first year we can use statistics based Papers in Regional Science, Volume •• Number •• •• 2015.


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on the new NACE Rev.2, and we have information enough to elaborate all the indicators. Contrary to NACE Rev.1, the new NACE Rev.2 is particularly designed to deal with the requirements of the knowledge economy, with the consequence that creative industries are properly captured at the two digits level.3

5 Variables 5.1 Dependent variable The dependent variable is the apparent labour productivity, measured as the regional GDP per person employed. In 2008 average labour productivity for all the regions in the sample is about 52,000 euro. The region with the lowest productivity is Yuzhen Tsentralen in Bulgaria (14,931 euro per person employed), 71 per cent below the average. The highest productivity is found in Inner London (165,957 euro per person employed), 314 per cent above the average. 5.2 Explanatory variables Explanatory variables are directly derived from the model and includes the percentage of persons employed in creative service industries (Table 1), the percentage of persons employed in the rest of the economy, capital intensity, the total number of persons employed, a composite variable n + g + d, and the growth rate of ideas (Tables 2 and 3). sR refers to the share of persons employed in creative service industries. The creative industries have been measured using the taxonomy proposed by UNCTAD (2010). This taxonomy has the advantage of being firmly founded, encompassing both cultural and technological dimensions of creative industries, and is particularly appropriate for cross-country comparisons. It includes both manufacturing and service industries, although the majority of creative industries are in fact services, especially knowledge-intensive services, such as: audiovisual, broadcasting, computer programming, R&D, publishing, architecture and engineering, advertising, design, and arts and entertainment (Table 1). A focus on the service sector is also more consistent with Potts’s (2009) proposal introduced in Section 2, in which creative industries are seen as providing services which engender change in other activities, and with previous empirical evidence found by Boix et al. (2013). sY refers to the share of persons employed in the rest of manufacturing and services industries. In order to avoid perfect collinearity. sY was divided in two parts: manufacturing plus non-creative services, and a residual group which includes agriculture, mining and construction. Only the share of manufacturing plus non-creative services was included in the estimates in order to avoid perfect collinearity. The capital investment rate sK is measured using gross fixed capital formation per worker (e.g. Fischer 2009). The composite term n + gA + d includes the growth rate of population (n), the growth rate of ideas (gA), and the depreciation rate (d). The first part, n, has been measured using the annual growth rate of the working age population (15 to 64 years) between 1992 and 2008 (the largest series available that is not significantly affected by missing data). The depreciation rate (d) has been measured using OECD data of consumption of fixed capital by country (data about depreciation rates are not available for regions). As a simplification, gA is assumed to be equivalent to n in the composite term. Although the assumption that g = n is valid only for 3 In NACE Rev.1 a detail of 3 to 4 digits is required to capture most creative industries, but Eurostat regional data are detailed to not more than 2 digits which makes a proper measurement unfeasible.

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R. Boix-Domenech, V. Soler-Marco Table 1. Creative service industries codes in NACE Rev.2 NACE Rev.2 codes 4779 58 59 60 62 71 72 73 74 90 to 93

Retail sale of second-hand goods in stores Publishing Audiovisual Programming and broadcasting Computer programming Architecture and engineering R&D Advertising Design, photography Arts, entertainment and recreation

Source: Elaborated from UNCTAD (2010).

Table 2. Descriptive statistics. Variables in logarithms. 250 observations

Productivity Percentage of persons employed in creative services Percentage of persons employed in manufacturing and non-creative services Capital investment rate Number of persons employed N + gA + d Specialization Diversity Density Competition Accessibility R&D expenditures on GDP Percentage of people with tertiary education Creative class on active population

Mean

Standard deviation

10.7883 1.7565 4.2902 9.2505 13.4096 –2.5565 1.7579 2.4826 5.0487 –0.1123 4.5350 0.0642 2.9177 3.2779

0.3281 0.6807 0.0975 0.5315 0.7627 0.6138 0.7085 0.2580 1.1548 0.6202 0.4916 0.8631 0.3833 0.2535

an economy close to the steady state, it is an alternative to consider, as many papers do, that g + d is fixed to 0.05 for every region as in the Mankiw-Romer-Weil estimates.4 L is the number of persons employed in the region.

5.3 Modelling the growth rate of ideas (gA) In order to measure the growth rate of ideas (gA), we follow Glaeser et al. (1992), Henderson et al. (1995), and subsequent relevant literature derived from regional and urban economics, which assume that the growth rate of ideas gA is a function of knowledge spillovers generated by dynamic 4 Notice that in the next epigraph the growth rate of ideas is considered different of n and modelled, which introduces a certain contradiction in the applied strategy. Despite this fact, we consider that the operative simplification is valid. Furthermore, using g + d = 0.05 as in the Makiw-Romer-Weil does not produce very different results in the estimates: in both cases the coefficient is very small and statistically non-significant. 5 Glaeser et al. (1992) and Henderson et al. (1995) use the location quotient of each industry because their dependent variable is panelled by industry, which is not so in our case. For non-panelled data, De Miguel et al. (2012) use as a proxy of MAR economies the number of clusters or the percentage of firms in specialized sectors in a region, in both cases identified as sectors with a location quotient above 1. De Miguel et al. indicator was not statistically significant in our controls and, being indeed a constrained measure of variety, was collinear with our indicator of diversity.


Productivity % CSI % manuf+nc services Capital Persons employed n+gA+d Specialization Diversity Density Competition Accessibility R&D expenditures Tertiary education Creative class

1.0000 0.6131 0.2915 0.6302 –0.0137 0.0469 0.0114 0.4687 0.3586 –0.0937 0.5849 0.6243 0.4947 0.6152

1.0000 0.2460 0.4350 0.1255 0.0958 –0.0626 0.5428 0.4037 0.0463 0.4366 0.5436 0.5305 0.4946

1.0000 0.2435 0.1027 –0.0443 –0.2206 0.1978 0.0491 –0.1648 0.3140 0.3479 0.1722 0.3499 1.0000 –0.1308 1.0000 0.0129 0.1477 1.0000 0.0592 –0.2076 0.1331 0.0431 –0.0803 0.0783 0.0318 0.4081 0.0370 0.0972 –0.2553 –0.0219 0.2725 0.2060 0.0304 0.3883 0.2470 0.0349 0.3588 0.0954 –0.0315 0.4249 0.1149 –0.0139 1.0000 0.0463 –0.1050 –0.2197 0.0223 –0.0496 –0.0306 0.0752

1.0000 0.2339 1.0000 0.0178 –0.2443 0.3326 0.5401 0.4654 0.2791 0.3116 0.2526 0.4357 0.3103

1.0000 –0.4282 –0.2633 –0.2138 –0.2225

1.0000 0.5540 0.3554 0.5930

1.0000 0.5292 0.5983

1.0000 0.4768

1.0000

Productivity Percentage Percentage Capital Persons N + gA Specialization Diversity Density Competition Accessibility R&D Tertiary Creative CSI employed + d expenditures education class manuf + nc services

Table 3. Correlation matrix. Variables in logarithms

CSI and the productivity of the regions 9


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externalities. Dynamic external economies are related to the generation and spread of knowledge spillovers and have the ability to produce irreversible changes in the production function. In order to capture the influence of dynamic external economies we draw on three well known conceptual perspectives associated with them in Glaeser et al. (1992). These go by the names: Marshall, Arrow and Romer (MAR) (specialization), Jacobs (diversity) and Porter (competition), These are described below, together with a fourth perspective called network economies (we introduce this one in order to take into account recent advances in system’s dynamics across places). The MAR perspective focuses on knowledge spillovers between firms in the same industry. To measure MAR economies, we calculated the location quotients of firms by sector and selected the highest coefficient for each region: MARi = Max(LQi,j), where LQi;j ¼ F ij =∑Jj¼1 F i;j = ∑Ii¼1 F i;j =∑Jj¼1 ∑Ii¼1 F i;j , F is the number of firms’ local units at two digits of disaggregation, i is the region and j the sector.5 The notion of Jacobs dynamic economies emphasises the variety and diversity of geographically proximate industries as the key determinant of innovation through cross-fertilization processes. The most popular index for Jacobs economies is the Simpson-Hirschman-Herfindahl (SHH) which measures the degree of entropy across sectors region (e.g. Henderson et al. in the 2 F i;j J , where F is the number 1995). The Simpson index can be expressed as: SHH i ¼ 1= ∑j¼1 F i of firms’ local units at two digits of disaggregation, i refers to the region, and j to the sector. The indicator represents the probability of inter-sector encounters within the region as a function of the variety of sectors and the distribution of firms’ local units across sectors. Another usual indicator of Jacobs’s economies (Ciccone and Hall 1996) is the density of jobs per square kilometre, to take into account the fact that density fosters technological spillovers within regions. Porter, like MAR, highlighted the role of knowledge spill-overs in the same industry, although he pointed out that local competition in specialized industries is also necessary for fostering rapid innovation spread. Glaeser et al. (1992) use the location quotient of number of firms per worker in city industry as a proxy for competition economies. In non-pooled data, the index takes the form: C i ¼ ðF i =E i Þ= ∑Ii¼1 F i =∑Ii¼1 E i , where F is the number of firms’ local units and i refers to the region.6 Recently, Trullén et al. (2013) introduced the notion of dynamic network economies. These are networks which facilitate spillovers or network information and knowledge flows among actors located in different cities or regions. To measure Trullén et al.’s (2013) dynamic network external economies we use the ESPON (2009) multimodal accessibility index for 2006, which weights the potential of each region to exchange information through transportation infrastructures. In addition, an estimation of a spatial version of the growth model will provide additional information on network external economies. 6 Contribution of creative service industries to the productivity of European regions 6.1 Direct contribution In 2008, for 250 regions in 24 countries analysed, creative industries generated 7.8 per cent of total production and 7.9 per cent of total employment (Table 4). Labour productivity in creative 6 A larger number of firms per worker is interpreted as a lower concentration in the regional industry (lower tendency to monopoly) and, consequently, higher levels of competition. However, this variable does not capture competitive conditions related to institutions or regulations, and can hide the effect of some large firms on regional markets, in particular when data are not pooled by sector. Notice that this variable is also the inverse of the firm size (employees by firm), which is a usual proxy used in regional studies for scale economies, introducing a certain ambiguity in its interpretation.



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industries was 1.2 per cent lower than the European average, and this was due to the behaviour of creative manufacturing which was 41.1 per cent below the European average (Table 4). This result supports the argument put forward by Boix et al. (2013) that creative manufacturing is basically ‘making’ but not ‘creation’ and that in the case of a wide sample of regions there needs to be a research focus exclusively on services. About 88 per cent of production and 79 per cent of persons employed in creative industries in the 250 regions studied belonged to creative service industries (Table 4). Labour productivity in CSI is 9.1 per cent more than the European average for all industries. This in practice means that doubling the relative contribution of CSI to total employment will raise the average productivity of European regions by about 0.6 per cent, which is a modest contribution. Labour productivity in European regions is highly correlated with the percentage of persons employed in CSI (Pearson correlation PC = 0.61) (Table 3 and see also Figure 1), capital intensity (PC = 0.63), diversity (PC =0.47), and accessibility (PC = 0.58). There is a particularly low correlation between labour productivity manufacturing plus non-creative services (PC = 0.29), the total number of persons in employment (PC = –0.01), n+gA+d (PC = 0.05), specialization (PC = –0.01), and the proxy for competition (PC = –0.09) (Table 3). 6.2 Econometric analysis and total contribution The econometric estimation of the theoretical model (Equation 4) measures the total contribution (direct and indirect) of CSI to the labour productivity of the European regions. The equation is estimated using: a) Ordinary least squares (OLS) for the year 2008, under the assumption that the participation of CSI in the employment is not endogenously determined and the rest of explanatory variables are exogenous. b) Generalized method of moments (GMM) for the year 2008, assuming that CSI is suspected of endogeneity in empirical research. The instruments for CSI are: the regionalized cultural expenditures per capita in 2001 and the number of UNESCO heritage sites in the region in 2001.7 c) To avoid suspicion of simultaneity in the rest of explanatory variables, we performed a third estimation where data on labour productivity is for 2009 and for the remaining variables 2008, which is similar to measuring the output variable at the end of the year and the production factors at the beginning of the year. In Table 5 we present the results of the OLS estimates. Column 1 presents the structural part of the model and column 2 the variables related to the growth rate of technology (gA). Non-significant variables in gA were removed to avoid collinearity problems, and the final results presented in Table 5, columns 4 and 5. As predicted by the theoretical model, the existence of a higher share of people employed in creative service industries has a positive impact on a region’s productivity. A doubling of the percentage of people employed in CSI increases average labour productivity by 5.7 per cent (Table 5, column 4, dependent variable for 2008) to 6 per cent (Table 5, column 5, dependent variable for 2009). If it is assumed that the direct contribution of CSI to labour productivity inferred from Table 4 (about 0.6 per cent) can be compared with the total estimated contribution (5.7 to 6 per cent), then the indirect contribution of CSI providing services to the rest of the economy 7 The regionalization of cultural expenditures is made by dividing the 2001 data by the share of persons employed in culture and heritage in 2001. The equation is identified and Hansen and Anderson tests suggest instruments are exogenous and non-weak. The results are robust to other combination of instruments: for example, cultural expenditures with the share of people with tertiary education in 2001 or with the share of creative class on active population in 2001.


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Table 4. Production, employment and productivity in creative industries for the 250 regions combined. Year 2008 Percentage of production (GDP)

Percentage of persons employed

Productivity (Total =100)

100.0 7.8 1.0 6.8

100.0 7.9 1.6 6.2

100.0 98.8 58.9 109.1

Total - All NACE activities Total creative industries Creative manufacturinga Creative services

Source: Elaborated from Eurostat. Notes: aManufacture of textiles, clothing, leather and related products (NACE Rev.2 13–15), and printing and reproduction of recorded media (NACE Rev.18).

could explain more than 5 per cent of the differences of labour productivity across the European regions (about 90 per cent of the total contribution of CSI). In Table 6 we present the results of the GMM estimates under the assumption that CSI is endogenous. The results are very similar to those obtained using OLS, although in the GMM estimates the effect people employed in CSI on labour productivity increases to 13.9 per cent. However, the Durbin-Wu-Hausman test rejects the existence of strong effects from CSI on the consistency of the model and supports the exogeneity of CSI, particularly for the structural part of the model (p = 0.81). In fact, this is coherent with the Jones’ version of the theoretical model we presented (where the share of workforce in CSI is not endogenously determined) and the empirical findings in Rausell et al. (2011) and Boix et al. (2013). The estimated contribution of CSI to productivity is far less strong than the simple correlation of 61 per cent presented in Table 4, as well as weaker than the estimated coefficients of 39 to 44 per cent in De Miguel et al. (2012), Boix et al. (2013) and Marco et al. (2014) (those authors used as the dependent variable GDP per capita). The explanation for the smaller coefficient in our estimates is the linear form (log-linear) and that the percentage of persons employed in CSI is highly correlated not only with labour productivity but also with capital intensity (Pearson = 0.43), diversity (Pearson = 0.54), and accessibility (Pearson = 0.43), variables that also are highly correlated with productivity (See Lazzeretti et al. 2012; Boix et al. 2014). The influence of capital and accessibility were not considered by previous authors, and as we can see a better specification moderates the size of the estimated coefficient of CSI.

Fig. 1. Scatterplot of labour productivity and percentage of persons employed in creative service industries in each region. 250 regions of the European Union. Year 2008 Papers in Regional Science, Volume •• Number •• •• 2015.

4.01 (0.000) 2.59 (0.107) 1.41 83.33 (0.000) 0.807 (0.369) 103.80 (0.000) 21.27 (0.000) 0.4656 250

8.42 (0.000) 1.19 (0.2748) 1.18 106.81 (0.000) 11.06 (0.001) 113.27 (0.000) 17.53 (0.000) 0.5462 250

Shapiro-Wilk Breusch-Pagan VIF LM-Error LM-Error Robust LM-Lag LM-Lag Robust R2 Observations

7.00 (0.000) 3.59 (0.058) 1.58 45.63 (0.000) 1.10 (0.294) 73.59 (0.000) 29.06 (0.000) 0.6900 250

6.3021 (0.000) 0.0545 (0.048) –0.0556 (0.773) 0.3047 (0.000) –0.0041 (0.803) 0.0017 (0.914) 0.3682 (0.000) 0.2237 (0.000) –0.0143 (0.545) 0.0110 (0.640)

2008

(3)

Notes: Probabilities associated with t-statistics are reported in parentheses. aExplanatory variables for 2008 (Accessibility for 2006).

0.3499 (0.000) 0.3803 (0.000) 0.0326 (0.281) 0.1289 (0.000) 0.0215 (0.290)

8.3199 (0.000)

2008

6.7129 (0.000) 0.2002 (0.000) 0.3080 (0.153) 0.2711 (0.000) –0.0081 (0.626) 0.0050 (0.792)

2008

(2)

Constant Workforce in creative services (%) Workforce in manufacturing and ncs (%) Capital investment rate Number of persons employed N + gA + d Diversity Accessibility Specialization Competition Density

Year for the dependent variable:

(1)

7.00 (0.000) 3.09 (0.078) 1.46 49.56 (0.000) 1.61 (0.205) 76.50 (0.000) 28.55 (0.000) 0.6885 250

6.1560 (0.000) 0.0572 (0.027) –0.0282 (0.884) 0.3056 (0.000) –0.0023 (0.881) –0.0008 (0.956) 0.3673 (0.000) 0.2133 (0.000)

2008

(4)

Table 5. Basic estimates. Variables in logarithms. Dependent variable: Labour productivity. OLS Robust estimates

7.11 (0.000) 4.16 (0.041) 1.46 59.58 (0.000) 3.13 (0.077) 82.40 (0.000) 25.95 (0.000) 0.6696 250

6.3612 (0.000) 0.0600 (0.024) –0.0504 (0.787) 0.3040 (0.000) –0.0012 (0.935) 0.0005 (0.970) 0.3525 (0.000) 0.1895 (0.000)

2009a

(5)



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Table 6. Basic estimates. Variables in logarithms. Dependent variable: labour productivity in 2008. GMM Robust estimatesa

Constant Workforce in creative services (%) Workforce in manufacturing and ncs (%) Capital investment rate Number of persons employed N + gA + d Diversity Accessibility Competition Specialization Density Pagan-Hall 2 Durbin-Wu-Hausman χ Anderson LR Hansen J R2 Observations

(1)

(2)

(3)

6.7805 (0.000) 0.2081 (0.000) 0.2974 (0.156) 0.2677 (0.000) –0.0090 (0.552) 0.0044 (0.816)

6.5864 (0.000) 0.1244 (0.049) 0.0436 (0.799) 0.2830 (0.000) –0.0215 (0.181) 0.0042 (0.791) 0.2117 (0.034) 0.1599 (0.000) 0.0105 (0.639) 0.0014 (0.954) 0.0279 (0.109)

6.7824 (0.000) 0.1390 (0.023) –0.0066 (0.972) 0.2819 (0.000) –0.0112 (0.492) 0.0020 (0.896) 0.2035 (0.033) 0.1768 (0.000)

19.00 (0.004) 0.0560 (0.812) 88.86 (0.000) 0.004 (0.951) 0.5460 250

33.40 (0.000) 2.8648 (0.090) 62.19 (0.000) 2.85 (0.090) 0.6812 250

28.32 (0.000) 3.0829 (0.079) 57.74 (0.000) 2.66 (0.102) 0.6711 250

Notes: Probabilities associated with t-statistics are reported in parentheses. aInstruments for GMM estimates: regionalized cultural expenditures per capita in 2001 and number of UNESCO heritage sites in the region in 2001.

The three variables capital intensity, diversity, and accessibility are statistically significant at 5 per cent (Table 5, columns 4 and 5; Table 6, column 3). Also, the capital investment rate exhibits an elasticity of 30 per cent (quite standard in this type of model), diversity of 36 per cent, and accessibility presents a coefficient of 19 to 21 per cent. 6.3 Sensibility to competing explanations We related the sensibility of the percentage of persons employed in CSI to three competing explanations in the literature: the effect of an influence of science, the presence of highly educated human capital, and the presence of the creative class (See Florida et al. 2008; Maroccu and Paci 2012a, 2012b). These three variables are measured respectively for 2008 according to: expenditures in R&D in relation to GDP, the percentage of people older than 25 years who have a tertiary education (ISCED 5 and 6), and the percentage of people belonging to the creative class relative to the active population. Models have been estimated by OLS and GMM (Table 7).8 Durbin-Wu-Hausman test suggest that the three variables are endogenous and therefore to focus on GMM estimates. For the three cases, in GMM estimates we can accept both CSI and the alternative explanations with a statistical significance of 5 per cent, although the share of variance that the model explains does not change in a significant way. The estimated coefficient of the percentage of persons employed in CSI slightly reduces across each of the three cases, but remains always close to 5 per cent. The estimated coefficients for competing explanations in GMM estimates are: 8 per cent for the percentage of expenditures in R&D in relation to GDP, 3 per cent for the share of people with tertiary education, and 3 per cent for the presence of the creative class. It can be noted that, if treated as exogenous, the coefficient increases to 8 per cent for tertiary 8 The translation of SOC codes for the creative class in Florida (2002) is basically equivalent to codes 1, 2 and 3 of the International Standard Classification of Occupations. At the regional level, the Eurostat website offers detail for ISCO codes 2 and 3 but not for ISCO 1, so that the indicator is slightly biased downward. Maroccu and Paci (2012a) use a more detailed list of codes, separating creative graduates, Bohemians and non-creative graduates.


0.7002 250

6.58 (0.000) 4.31 (0.038) 1.64

7.1586 (0.000) 0.0506 (0.058) –0.0757 (0.700) 0.2776 (0.000) –0.0189 (0.262) 0.0027 (0.862) 0.2996 (0.000) 0.1856 (0.000) 0.0628 (0.012)

4.2358 (0.039) 339.54 (0.000) 0.2450 (0.620) 0.6987 250

29.13 (0.000)

7.4602 (0.000) 0.0547 (0.016) –0.1195 (0.530) 0.2718 (0.000) –0.0245 (0.146) 0.0042 (0.784) 0.2616 (0.000) 0.1728 (0.000) 0.0827 (0.000)

GMM

OLS

0.6944 250

10.8560 (0.000) 471.77 (0.000) 0.1860 (0.666) 0.6920 250

32.74 (0.000)

0.0268 (0.454)

0.0818 (0.014)

6.99 (0.000) 2.75 (0.097) 1.50

6.0646 (0.000) 0.0495 (0.028) –0.0075 (0.967) 0.3004 (0.000) –0.0023 (0.882) 0.0001 (0.994) 0.3645 (0.000) 0.2119 (0.000)

GMM

(4)

0.6944 (0.000) 0.0407 (0.104) –0.0197 (0.917) 0.2946 (0.000) –0.0053 (0.823) 0.0037 (0.741) 0.3563 (0.000) 0.2060 (0.000)

OLS

(3)

0.6933 250

6.92 (0.000) 2.42 (0.120) 1.62

0.1310 (0.049)

6.2671 (0.000) 0.0559 (0.029) –0.0586 (0.769) 0.2871 (0.000) –0.0065 (0.688) 0.0025 (0.874) 0.3307 (0.000) 0.1890 (0.000)

OLS

(5)

4.0105 (0.045) 300.16 (0.000) 0.5760 (0.447) 0.6914 250

32.052 (0.000)

0.0347 (0.674)

6.0487 (0.000) 0.0491 (0.031) –0.0193 (0.920) 0.2974 (0.000) –0.0025 (0.877) –0.0008 (0.957) 0.3842 (0.000) 0.2135 (0.000)

GMM

(6)

Notes: Probabilities associated with t-statistics are reported in parentheses. aInstruments for GMM estimates: R&D expenditures on GDP in 2001 and share of population with tertiary education in 2001. bInstruments for GMM estimates: share of population with tertiary education in 2001 and creative class on active population in 2001. cDurbin-Wu-Hausman on R&D/GDP expenditures, people with tertiary education, or creative class on active population.

Shapiro-Wilk Breusch-Pagan / Pagan-Hall VIF 2 Durbin-Wu-Hausman χ c Anderson LR Hansen J R2 Observations

Constant Workforce in creative services (%) Workforce in manufacturing and ncs (%) Capital investment rate Number of persons employed N + gA + d Diversity Accessibility R&D expenditures on GDPa People with tertiary education (%)b Creative class on active populationb

(2)

(1)

Table 7. Competing explanations. Variables in logarithms. Dependent variable: labour productivity in 2008. Robust estimates



16


education, and 13 per cent for the creative class. The estimates for these competing explanations are in the range of other research that does not include CSI. Since the presence of CSI and the three other explanations are interrelated (people employed in CSI tend to have higher levels of education and work in more creative occupations), it would be appropriate to separate out the effects (See Maroccu and Paci 2012a, 2012b for creative workforce – graduates). However, this separation is not possible for the three dimensions with the data available in the Eurostat website. 6.4 Structural stability and sensibility to spatial effects Previous estimates can be biased if there is structural instability in the sample or if productivity in a region is affected by other regions (see LeSage and Pace 2009). The structural stability of the sample was tested: (i) searching for an endogenous break point in the sample (Berthelemy and Varoudakis 1996); (ii) using dummies related to the economic structure, the degree of urbanization, metro areas, and regions hosting capital cities; (iii), using dummies for EU 15 and fixed effects/dummies by country (representing historical and institutional patterns, national regulations or national economic policy). Only EU 15 dummy and some country dummies produced significant results, although they do not remove the spatial correlation and become non-significant in the spatial models. Spatial tests on the OLS estimates (Table 5) suggest the existence of spatial processes between regions affecting labour productivity. It follows that the spatial effects on the productivity of a region are more likely to be originating in neighbouring regions (See Koch 2008 and Fischer 2009, for theoretical justification of the spatial version of the endogenous growth models, and LeSage and Pace 2009 for empirical justification). The intensity of the effect must depend on the size of the emitting region, so that our matrix of spatial contacts includes the GDP of first-order neighbouring regions (the rest of cells are 0) and is row-standardized. In order to test for competing spatial specifications, we follow LeSage and Pace (2009) and use Bayesian model comparison to jointly compare the different alternatives.9 Posterior Bayesian probabilities for models were: spatial autoregressive model (SAR) = 0.84, spatial error model (SEM) = 0.00, spatial Durbin error model (SDEM) = 0.00, spatial Durbin model (SDM) = 0.15, and spatial exogenous variables model (SLX) = 0.00. The SAR model shows higher probabilities, so that Table 8 (column 2) presents Bayesian estimates for the heteroscedastic SAR model, differentiating the spatial direct effect (within-region), spatial indirect effect (between-regions), and the spatial total effect (the sum of both). Within-region effects of CSI on productivity are 4 per cent, whereas the total effect rises slightly up to 6 per cent, close to the estimates of the non-spatial model, as a consequence of indirect spatial spill-over effects from CSI transmitted through the productivity of neighbouring regions.10

7 Conclusions Given the relevance of the creative industries to the recent debate on economic policy (DCA 1994; DCMS 1998, 2001; Cooke and Lazzeretti 2008; De Propris et al. 2009; Potts 2009; 9 Le Sage and Pace (2009) provide detailed explanation of the procedure. The most relevant advantages of Bayesian comparison are that it compares the entire profile of density (not only at a point as in traditional tests), and allows the integration of all models into a single comparison thereby avoiding the need to make individual comparisons model by model. Bayesian estimates and Bayesian model comparison have been performed using LeSage’s econometric toolbox for Matlab. 10 See in LeSage and Pace (2009) comments about interpreting indirect effects in the SAR model since the rate direct/indirect is fixed for all the exogenous variables. See Gibbons and Overman (2012) for a general criticism on the limitations of the estimations of spatial SAR, although there is no explicit criticism of the Bayesian procedure. 2 SAR also has been estimated using robust GMM with WX, W X as instruments, and the results are quite similar.



17

Table 8. Spatial Bayesian SAR. Dependent variable: labour productivity in 2008. Variables in logarithms Coefficient Constant Workforce in creative services (%) Workforce in manufacturing and ncs (%) Capital investment rate Number of persons employed N + gA + d Diversity Accessibility W*Productivity (ρ) R2 Observations

3.4825 (0.000) 0.0389 (0.011) –0.0045 (0.961) 0.2694 (0.000) 0.0134 (0.202) –0.0053 (0.6825) 0.1586 (0.000) 0.0977 (0.000) 0.3570 (0.000) 0.7490 250

Direct effect

Indirect effect

Total effect

0.0403 (0.012) –0.004 (0.960) 0.2795 (0.000) 0.0139 (0.203) –0.005 (0.682) 0.1646 (0.000) 0.1014 (0.000)

0.0206 (0.035) –0.0031 (0.951) 0.1419 (0.000) 0.0070 (0.227) –0.0028 (0.684) 0.0838 (0.004) 0.0517 (0.001)

0.0610 (0.015) –0.0079 (0.957) 0.4214 (0.000) 0.0210 (0.206) –0.0084 (0.681) 0.2484 (0.000) 0.1532 (0.000)

Notes: Probabilities associated with t-statistics are reported in parentheses. Results for 10,000 replications, using LeSage’s Econometric Toolbox for Matlab (See LeSage and Pace 2009).

European Commission 2010; UNCTAD 2010), it is important to clarify their real effects on regional economies. The main contribution of this study has been an analysis of the effect of creative service industries (CSI) on labour productivity in regions, covering a major gap in the literature. Previous studies measuring the contribution of creative industries have not focused specifically on the relation between creative services and productivity (Dolfman et al. 2007; Florida et al. 2008; Potts and Cunningham 2008), and have provided only partial evidence based on empirical models which bias the results upwards (e.g. Rausell et al. 2011; De Miguel et al. 2012; Boix et al. 2013; Marco et al. 2014). Our hypothesis was that CSI provide services that increase a regional economy’s capacity to generate and combine new ideas. These new ideas are then drawn upon in an increased production of innovations in the form of new products or varieties. And these new products or varieties in turn raise productivity levels. The paper has proposed an analytical framework based on endogenous growth theory which has been applied to a sample covering 250 regions in the European Union in 2008. The empirical results have shown that a doubling of the share of people employed in CSI increases average labour productivity by 6 per cent. Of this, about 4 per cent can be accounted for by factors internal to the region and 2 per cent can be put down to the effect of CSI of neighbouring regions. About 90 per cent of the effect is not explained by the higher productivity of CSI themselves, but, rather, by the fact that the rest of the regional economy experiences higher productivity because it benefits from the spillover effect of CSI services. Differences of labour productivity between the regions of the European Union can also be explained by differences in capital intensity, diversity, and connectivity. Indeed, estimated elasticities for capital intensity (30 to 42 per cent) and diversified specialization (25 to 36 per cent) are much higher than for CSI. The inclusion of these variables, highly correlated with labour productivity and CSI at the same time, moderates the relative effect of CSI. In De Miguel et al. (2012), Boix et al. (2013) and Marco et al. (2014), the elasticity of CSI was about 40 per cent for the European regions, the higher coefficient being due to the misspecification of their empirical models. When our results finding that differences in the presence of CSI explain differences in labour productivity were compared with those consequent upon the application of competing theories, namely, the influence of R&D, tertiary education, and the presence of the creative class, we found that an explanation based on the effect of CSI is compatible with the other explanations (see Lee and Rodríguez-Pose 2014). In fact we found that the four influential factors (including that of the presence of CSI) partially overlap, and that the effects on productivity of all these explanations are more or less similar. Papers in Regional Science, Volume •• Number •• •• 2015.

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From a critical point of view, we can argue that our results indicate that the effects of higher specialization in CSI are more limited than are those of greater or lesser capital intensification, diversity, or accessibility, and that concentrating policies on these factors could lead to higher returns. This might then indirectly lead to greater specialization of the region in creative industries. Indeed, a critical issue is how easy is it to increase regional specialization in CSI in order to raise productivity, and through which mechanisms and in which conditions can it be done (Cooke and Lazzeretti 2008; Rausell et al. 2011; De Miguel et al. 2012) Another issue is whether research must focus on CSI as a whole or on specific parts of the sector. The little evidence on this (Boix et al. 2013) suggests that all sub-sector activities within CSI are correlated positively with GDP per capita, although the correlation is less intense in sectors such as broadcasting, design and photography, and arts, entertainment and recreation. In addition, the possibility of increasing the symbolic base of regions in order to rise their productivity needs to be contextualized in a wider research programme, one that provides evidence about how CSI affect, and are affected by, other economic parameters, such as wealth, employment and unemployment, social inclusiveness, and ecological sustainability.

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