3_Zlenka.indd 239Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257.DOI: 10.15201/hungeobull.67.3.3 Hungarian Geographical Bulletin 67 2018 (3) 239–257. Introduction There are several reasons why creative indus- tries (CI) have been standing in the spotlight of economic and urban geographers. Positive effects of these industries on urban regenera- tion and stimulation of productivity growth and innovation performance in other sectors of the economy have been reported (Stam, E. et al. 2008; Müller, K. et al. 2009). Over-repre- sentation of CI in large urban areas (Lazzeretti, F. et al. 2008, 565) may contribute to spatially uneven development and an increasing gap in economic performance between metropoli- tan and non-metropolitan areas (Rodríguez- Pose, A. and Fitjar, R.D. 2013). City size and status – inherited, slowly evolving and hardly changeable factors in a short time period – are among the key drivers of CI localisation (Musterd, S. et al. 2007; Musterd, S. and Gritsai, O. 2010). Most importantly, propensity of firms in CI to cluster into dense hubs suggests the key importance of local amenities and geo- graphical proximity for their productivity and growth. Therefore, urbanization and localisa- tion economies are most frequently mentioned as the key drives for clustering of CI in and around large cities (Lazzeretti, F. et al. 2008). Despite consensus on the key role of ag- glomeration economies some principal ques- tions remain unanswered (Gong, H. and Hassink, R. 2017). Do varieties in national in- stitutional frameworks lead to distinct spatial patterns of CI at the regional level? How are How do various types of regions attract creative industries? Comparison of metropolitan, old industrial and rural regions in Czechia Jan ŽENKA1 and Ondřej SLACH1 Abstract In this paper we aim to describe and explain current spatial distribution of creative industries in Czechia. We ask to what extent are localisation patterns of creative industries influenced by specific local contexts. Therefore, we employed a typology of regions (close to local labour areas) that may explain spatial distribution of creative industries: metropolitan cores, metropolitan hinterlands, old industrial regions, urban and rural regions. We use general linear models combining the regional typology as a fixed factor and four covariates – employment density, density of cultural industries, mean firm size in creative industries and diversity of economic activities. We employed horizontal localisation quotient of regional employment in creative industries for the year 2014 as a dependent variable. Our main finding is that the regional typology explains a higher share of variability of the dependent variable (relative specialisation in creative industries) than any other explanatory variable. However, after exclusion of metropolitan cores, the model lost a significant amount of its explanatory power. Urban size/density and position in urban hierarchy are the key explanatory variables. We found only limited empirical evidence that regional contexts affect localisation of CI – regions of similar population/economic size do not differ significantly in spatial concentration of CI. Keywords: creative industries, localisation, spatial distribution, metropolitan regions, old industrial regions, rural regions, Czechia 1 Department of Human Geography and Regional Development, Faculty of Science, University of Ostrava, Chittussiho 10, 710 00 Ostrava. E-mails: jan.zenka@osu.cz, ondrej.slach@osu.cz Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257.240 current geographies of CI rooted in their his- torical development? To what extent can we explain the spatial patterns of CI by the urban hierarchy and what is the importance of the local contextual factors? What do we know about localisation of firms in CI that focus rather on standardized routine activities and are positioned in lower tiers of the global pro- duction networks? Most importantly, while there have been many studies focusing on the effects of various factors on the localisation of CI (e.g. urbanization and localisation econo- mies, related variety, cultural heritage or crea- tive class – Lazzeretti, F. et al. 2012), we still lack the theoretical framework and empiri- cal evidence on how these effects interact in various local contexts and in various types of regions such as metropolitan, old industrial or rural (Tödtling, F. and Trippl, M. 2005). In this paper we aim to fill the gaps and answer at least partly the above mentioned questions. Our primary goal is to describe and explain current spatial distribution of CI em- ployment at inter-urban level (municipalities with extended competences – microregions roughly corresponding to local labour areas). Our primary research question is to what ex- tent can we explain spatial distribution of CI in Czechia by the position and function of re- gions in national settlement system and their economic structure. We ask how and why do metropolitan cores, metropolitan hinterlands, urban regions, old industrial and peripheral regions differ in their ability to attract CI. In addition, we also examine potential colloca- tion between creative and cultural industries (for definition and comparison see Tomczak, P. and Stachowiak, K. 2015) and collocation between CI, other knowledge-intensive busi- ness services and manufacturing industries. The second section provides a theoreti- cal discussion of the localisation factors of CI, while in the third section we summarize briefly the geographical and institutional context of Czech regions and previous em- pirical findings concerning the spatial distri- bution of CI. Fourth question is focused on the data and methods. Fifth section describes current spatial distribution of CI at microre- gional level, while the sixth explains it using several regression models. Theoretical framework Spatial patterns and localisation factors of CI have been empirically documented and tested elsewhere (see e.g. Lazzeretti, F. et al. 2008, 2012; Polese, M. 2012; Rehák, Š. and Cho- vanec, M. 2012; Bertacchini, E.E. and Bor- rione, P. 2013; Slach, O. et al. 2013; Cruz, S.S. and Teixeira, A.A.C. 2015; Escalona-Orcao, A.I. et al. 2016; Danko, L. et al. 2017; Cerisola, S. 2018). There is a general consensus that CI tend to cluster in four types of locations: large urban agglomerations (Boix, R. et al. 2015; van Winden, W. and Carvalho, L. 2016) and their centres or inner cities (Spencer, G.M. 2015; Wood, S. and Dovey, K. 2015), metropolitan hinterlands (Felton, E. et al. 2010; Gregory, J. and Rogerson, C. 2018), smaller towns con- centrating cultural heritage (Lazzeretti, F. et al. 2012), touristic centres/environmentally and residentially attractive regions including some rural and peripheral areas (Cruz, S.S. and Teixeira, A.A.C. 2015; Escalona-Orcao, A.I. et al. 2016). The authors mostly agree on the key role of urbanization economies re- lated to diversity of industries, labour, infra- structure and institutions (Lorenzen, M. and Frederiksen, L. 2008), localisation economies resulting from specialisation, allowing for re- duction of production/transaction costs, in- creased efficiency of factors of production and increased dynamic efficiency (Brazanti, C. 2015), cultural heritage and concentration of cultural industries (Lazzeretti, F. et al. 2008) and soft factors like local atmosphere, toler- ance and amenities (Escalona-Orcao, A.I. et al. 2016) that may attract creative workforce and foster development of another key locali- sation factor: human capital endowments.2 2 Some other factors have been tested: telecommuni- cation infrastructure, settlement factors (proximity to an urban marker or demographic status) and economic performance of the municipality (Escalo- na-Orcao, A.I. et al. 2016), the role of related variety (Lazzeretti, F. et al. 2008). 241Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257. Our primary goal is not to test the effects of above mentioned localisation factors per se (this has been done by Slach, O. et al. 2013). We try to determine how urbanization/locali- sation economies and other explanatory vari- ables affect spatial patterns of CI in different types of regions and to what extent local con- textual factors such as historical specialisation, institutional framework and current industrial structure matter. To answer these questions, we will first discuss how various types of (non) metropolitan regions may theoretically affect localisation pattern of CI. In the section 5 we provide empirical tests of these theoretical as- sumptions that are listed in Table 1. Metropolitan cores provide generally the most favourable conditions for incubation, growth and clustering of CI. Combination of high population/firm density, large mar- ket, diversity of industries, labour and insti- tutions (Lorenzen, M. and Frederiksen, L. 2008) stimulates localisation factors of CI both at the demand and supply side. Metropolitan cores are large enough to provide urbaniza- tion economies (Rodríguez-Pose, A. and Fitjar, R.D. 2013; Puga, D. 2010) and locali- sation economies resulting from diversified specialization (Farhauer, O. and Kröll, A. 2012). Intersection of morphological, func- tional and social diversity in some parts of inner cities can lead into the development of the so-called creative field (Scott, A.J. 2010; Wood, S. and Dovey, K. 2015), characteristic by clustering of creative firms with symbolic knowledge base that require local buzz or noise (Grabher, G. 2002) and unique local atmosphere conducive for dissemination of knowledge. In addition, large (capital) cit- ies often have a gateway function, providing access to knowledge transmitted through trans-local knowledge pipelines (Keeble, D. and Nachum, L. 2002). Metropolitan cores concentrate all service industries that are the key customers for CI and other knowledge- intensive business services (Ciarli, T. et al. 2012). CI tend to require geographical prox- imity to their principal customers – corporate, headquarters, public institutions and firms in various (knowledge-intensive) business services that are disproportionately concen- trated in the largest urban agglomerations (Keeble, D. and Nachum, L. 2002; Gallego, J. and Maroto, A. 2015; Ženka, J. et al. 2017a). Metropolitan hinterlands may attract CI by the combination of urbanization econo- mies available thanks to the proximity of metropolitan cores (effect of borrowed size – Meijers, E.J. and Burger, M.J. 2017) and lower diseconomies of agglomeration (Jacobs, W. et al. 2014). Lower rents, proxim- ity to the place of residence, less congestion and less stressful lifestyle are among the key advantages of those areas (Felton, E. et al. 2010; Grodach, C. et al. 2014; Murphy, E. et al. 2015). Economic activities with synthetic and analytical knowledge base are general- ly more prone to move to hinterlands than activities with a symbolic knowledge-base Table 1. Expected CI in various types of regions Type of region Expected CI Metropolitan cores High concentration of all kinds of CI and knowledge-intensive business services; over-representation of CI with purely symbolic knowledge-base (publishing, media and advertising); high diversity of CI. Metropolitan hinterlands Higher specialisation in CI with partly synthetic knowledge base – printing and reproduction of recorded media, architecture, technical testing and other profes- sional services. Urban regions Similar industrial structure as in metropolitan cores; lower representation of CI, higher share of CI with synthetic knowledge base. Old industrial regions Limited presence of CI; specialization in technically related CI (printing and re- production of recorded media; architecture and technical analyses and testing). Peripheral/rural regions Minor presence of CI. Source: Compiled by the authors. Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257.242 (van Winden, W. and Carvalho, L. 2016). At the same time, routine and standardized lower value-added functions are expected to concentrate in hinterlands rather than skilled jobs and high value-added functions requiring face-to-face contacts with custom- ers or suppliers (Merino, F. and Rubalcaba, L. 2013). Nevertheless, in some hinterlands creative jobs may flourish (Gregory, J. and Rogerson, C. 2018) and “the geography of creative industries is more complex than sim- ple concentric-circle models – in which inner cities are the hub of creative industries ac- tivity, and in which that activity diminishes with distance from the inner core – suppose” (Felton, E. et al. 2010, 67). Because urban density and land rents in Czech metropolitan cores are significantly lower than in Western Europe (Ženka, J. et al. 2017b), we expect significantly smaller con- centration of CI into metropolitan hinterlands compared to metropolitan cores. In addition, we expect that various types of regions will differ in their industrial structure of CI – high- er share of CI with purely symbolic knowl- edge base in metropolitan cores (publishing, media and advertising) and higher specialisa- tion of hinterlands in CI with a partly synthet- ic knowledge base – printing and reproduc- tion of recorded media, architecture, technical testing and other professional services. Urban regions represent a residual and rela- tively heterogeneous category that is “some- where between” the metropolitan cores and rural regions. Larger urban regions con- centrate some metropolitan functions and should attract CI by similar mechanisms and localisation factors as metropolitan cores. However, smaller population size/density, higher rate of specialization (often on man- ufacturing industries) and limited presence of knowledge-intensive business services reduce the amount and intensity of CI clus- tering driven by urbanization economies (Ženka, J . et al. 2017b). Smaller urban regions are expected to show very limited concen- tration of CI. They are often highly special- ized (could be in manufacturing, transport, tourism or public services) and rarely create a favourable business environment for clus- tering of market-oriented CI, although they may succeed in attraction of cultural indus- tries (Lazzeretti, F. et al. 2008; Cruz, S.S. and Teixeira, A.A.C. 2015). Polese, M. (2012) ar- gued that smaller blue collar industrial cities dominated by large manufacturing firms are less oriented towards the arts, which is prob- ably relevant for market-oriented CI as well. Cities in old industrial regions (COIR) are gen- erally less expected to attract and develop CI in comparison with metropolitan cities of similar population size (Rumpel, P. et al. 2010; Mossig, I. 2011). COIR are generally characteristic by lower diversity of economic activities and less developed generic assets, which are crucial for incubation of new firms and ideas (Boschma, R.A. and Lambooy, J.G. 1999). Births of firms in CI may also be hindered by concentrated firm structure (higher share of large firms), lower entrepreneurial activity, inadequate skill structure (magnified by outflows of high- ly skilled workforce – Martinez-Fernandez, C. et al. 2012) and traditional specialisation in heavy manufacturing industries that mostly supply industrial products to other companies and do not need creative inputs. On the other hand, CI may emerge in COIR through diversification of manufac- turing industries into technologically related knowledge-intensive business services (e.g. software, technical testing and analysis or de- sign activities, see Birch, K. et al. 2010) that form a part of broadly defined market-orient- ed CI or their potential customers. However, probably the most important scenario3 of CI growth in COIR is an implantation from other regions through offshore outsourcing or cap- tive offshoring (Slach, O. et al. 2018). These investment flows are often motivated by the reduction of rents and wages (Hardy, J. et al. 2011), leading into the development of rath- er routine, standardized, lower skilled and lower value-added economic activities that are often represented by relatively large firms or subsidiaries. Combination of lower rents, morphological, functional and social diver- 3 See Martin, R. and Sunley, P. (2006) for theoretical discussion of various scenarios of regional delocking. 243Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257. sity, attractive industrial premises (Hutton, T.A. 2004; Martinát, S. et al. 2018) and pres- ence of universities can foster clustering of creative firms and workers in inner cities (Slach, O. et al. 2015) of COIR. To sum up, we expect smaller presence of CI in COIR, more concentrated firm structure and higher share of technically related CI – NACE 18 and 71. There is a rich empirical evidence that CI develop and cluster also in some rural and pe- ripheral regions (e.g. Escalona-Orcao, A.I. et al. 2016; Townsend, L. et al. 2017). Creative work- force can be attracted by a plethora of localisa- tion factors including amenities, proximity to the place of residence, local cultural heritage including craft tradition or tourism incomes. Nevertheless, these localisation factors are rel- evant rather for cultural, artisan and artistic subjects than for purely market-oriented CI and for rural regions close to the metropoli- tan cores rather than for more distant areas. Therefore, rural regions are expected to con- centrate minor share of total CI employment. As already suggested, CI are heterogenous in their spatial organization, because (among oth- ers) they vary significantly in their prevailing knowledge base – see Plum, O. and Hassink, R. (2011) for discussion of the concept, the au- thors distinguish between analytical, synthetic and symbolic knowledge base. Knowledge bases differ in the character of innovation pro- cess, importance of face-to-face communication for knowledge sourcing and the importance of codified/tacit knowledge. The majority of CI (publishing, media, advertising) are charac- terised by a purely symbolic knowledge base: they require geographical proximity or their customers/suppliers in order to capitalize on local buzz and face-to-face communication. Therefore, they are expected to cluster in the cores and inner cities of the largest metropoli- tan regions. Technically related CI with a pre- dominantly synthetic knowledge base (print- ing and reproduction of recorded media, ar- chitecture and technical analyses/testing) rely on knowledge sourcing and communication inside the value chains that are not usually lo- cal. Thus, this kind of CI are expected to show more dispersed spatial patterns. Another important source of theoretical ar- guments was the concept of path dependence that is intended to capture the way in which small, historically contingent events can set-off self-rein- forcing mechanisms and processes that ‘lock-in’ particular structures and pathways of development (Martin, R. and Sunley, P. 2006, 5). Current spatial concentrations of CI do not arise ‘from scratch’, but are rooted in a long-term devel- opment trajectory of the region, its historical industrial specialization and institutional con- text, infrastructural projects, political and busi- ness decisions and various other events in the past. Regions that were traditionally highly specialized in mining and manufacturing are generally less likely to develop a strong spe- cialization in CI than diversified regions with high share of services (Slach, O. et al. 2018). Data and methods Empirical analysis of the spatial distribution of CI is based on the datasets from the Czech Statistical Office (CSO 2009, 2014). The data- sets cover firm-level data aggregated into 2-digital industries (NACE rev. 2.0) and 206 spatial units – municipalities with extended competences (microregions). Localisation of CI was measured by the employment, which was available for the years 2009 and 2014, therefore for the (post)crisis period. The data cover roughly two thirds of total national employment, they are not available for several industries: mining and quarry- ing; energy, water distribution, sewerage and waste management; wholesale and re- tail trade, repair of motor vehicles and public services. Regional shares of CI are thus not related to the total employment of the region, but to the sum of employment in industries covered by microregional level data: agricul- ture, manufacturing and business services (NACE 49-53; 55-56; 58-64; 66; 68-75; 77-82). With the exception of mining and energy, the industries not covered by the datases show relatively even spatial distribution. Other in- dicators employed in our analysis come from public databases. Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257.244 In order to ensure the compatibility of the results with our previous study mapping the spatial distribution of CI in Czechia for 2009 (Slach, O. et al. 2013) we employ the same definition and delimitation of market- oriented CI as we used in the former paper. CI are defined as economic activities … con- cerned with the creation and provision of market- able outputs (goods, services and activities) that depend on creative and cultural inputs for their value (Power, D. 2011, 32). Delimitation of CI is based on the sectoral approach (Gibson, C. and Kong, L. 2005), selection of particular industries departs from modified approach of Power, D. (2011). The group of CI includes NACE industries with a strong symbolic content: publishing activities (58), motion picture, video and television programme production, sound recording and music publishing activities (59), programming and broadcasting activities (60), architectural and engineering activities, technical testing and analysis (71), advertising and market research (73) and other professional, scien- tist and technical activities (74). Following Power, D. (2011) we also added printing and reproduction of the recorded media (18). This industry is tightly connected to the de- mand of CI firms, but it is more technically oriented: we can’t thus expect different lo- calisation patterns in comparison with above mentioned CIs. We tested also the effects of education (85) or cultural industries (90, 91, 93) on localisation of CI. However, spatial distribution of education and cultural indus- tries was measured only by the number of economic subjects due to the unavailability of other indicators. Spatial distribution of CI in Czechia was evaluated by the horizontal location quotient (HLQ) – for the definiton and construction see Fingleton, B. et al. (2004, 779–780). This indicator is an improved version of the lo- calisation quotient, which takes into account the employment size of local/regional econ- omy. It is defined as the number of jobs in the local industry that exceeds an expected number. The expected number equals to the number of jobs in local industry that would be present if the share of the local industry in regional employment is the same as the share in the national economy, therefore if the localisation quotient is equal to 1. The HLQ is calculated from the standard locali- sation quotient: LQ = Eij/Ein Ej/En In the second step Eij is replaced by Eij_HLQ, computed as: LQ = Eij_HLQ/Ein = 1, Ej/En where Eij_HLQ is the number of jobs making LQ = 1. Finally, HLQ is calculated: HLQ = Eij – Ei_HLQ, HLQ was used also as a dependent variable in regression models. However, share of CI in re- gional employment yielded better results, so it was employed as the main dependent variable and HLQ as a supplementary variable. The most important explanatory variable (fixed factor) used in all regression models was a nominal variable Type of region, distin- guishing the metropolitan cores, metropolitan hinterlands, urban regions with metropolitan functions, metropolitan old-industrial regions, non-metropolitan old industrial regions. Although these groups of regions are rela- tively internally homogeneous, their ability to attract CI still varies. To capture these in- ternal differences, we tested also the effects of selected covariates that may contribute to bet- ter explanation of inter-regional differences. After several pre-tests and model calibrations we decided to use employment density per a hectare of built-up area and diversity of local industrial structure as proxies for urbaniza- tion economies. While the latter reflects diver- sity of economic structure as the essence of urbanization economies (Parr, J.B. 2002), the former captures the effects urban size/density that should increase productivity (Ciccone, A. and Hall, R.E. 1996) and innovation perfor- (1) (2) (3) 245Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257. mance, stimulate the local buzz (Storper, M. and Venables, A.J. 2004) and efficiency of local labour markets (Puga, D. 2010). Moreover, this indicator explained more variability than pop- ulation size or sophisticated indicators of the position in urban hierarchy, calculated from the population/economic size and accessibil- ity (Ženka, J. et al. 2017a). We employed also two indicators of localisation economies – av- erage size of firms in creative industries and the density of firms in education and cultural industries to capture potential knowledge spillovers and other positive effects related to the existence of local cultural milieu. We ran a general linear regression model in order to explain current spatial distribution of CI in Czechia and its post-crisis development. Dependent variable was the share of CI in re- gional employment, explanatory variables (Table 2) included the type of region (TYPE), employment density (DENS), number of firms in education and culture industries per a hec- tare of built-up land and average firm size in CI (SIZE). The dependent variable and all covari- ates were transformed by natural logarithmic transformations. Despite tendency of CI to cluster in and around large urban regions the diagnostic tests did not find a significant auto- correlation, so it was not necessary to employ spatial lag or spatial regression models. Types of regions in Czechia were delimited according to Ženka, J. and Slach, O. (2018) (Figure 1). Prague and Brno were marked as metropolitan regions (based on the approach of OECD 2012). Ostrava is also a metropoli- tan core, but we classified both Ostrava and Ústí nad Labem as the cores of old industrial regions Ostravsko and Ústecko. Rural regions were defined by the index of rurality (inspired by Novotný, L. et al. 2015), which is based on three criteria: dispersion of the settlement, low population density and low spatial productiv- ity, which suggests higher share of agricul- ture and limited presence of high value-added knowledge-intensive economic activities (see Ženka, J. et al. 2017c for details). Dispersion of the settlement was expressed by the share of municipalities with less than 3,000 inhabitants. Population density was calculated using population per one hectare of built-up area, spatial productivity by value added per one hectare of built-up area. Table 2. Variables employed in regression models Variable Proxy indicator and year Abbreviation Source of data Share of CI Share of CI in regional employment in %, 2014 CI CSO (2014) Importance of CI Horizontal localisation quotient of CI, 2009, 2014 HLQCI CSO (2014) Growth of CI Index of employment growth in CI, 2009–2014 (2009 = 100) GRCI CSO (2009), CSO (2014) Type of region Type of region according to Ženka, J. et al. 2017c TYPE Ženka, J. et al. (2017c) Employment density Number of jobs in CI per one hectare of built-up area, 2014 DENS CSO (2014), CSO (2018a) Economic diversity Herfindahl-Hirschmann index of local employ- ment, 2014 (inverse values) DIVERS CSO (2014) Cultural industries Number of firms in education (85) and cultural in- dustries (90, 91, 93) per one hectare of built-up land CULT CSO (2018b) Firm size structure Herfindahl-Hirschmann index calculated from employment size categories in CI, 2014 SIZE CSO (2018c) Source: Compiled by the authors. Index of rurality = settlement dispersion + 2 * population density + 2 * spatial productivity 5 Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257.246 Fi g. 1 . M et ro po lit an c or es a nd h in te rl an ds , o ld in du st ri al , u rb an a nd ru ra l r eg io ns in C ze ch ia . S ou rc e: Že nk a, J. a nd S la ch , O . ( 20 18 ). 247Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257. Residual category of urban regions in- cludes larger regional cities with metro- politan functions (Plzeň, České Budějovice, Olomouc, Liberec etc.), smaller industrial regions dominated by a single large manu- facturing firm (e.g. Mladá Boleslav, Jihlava), transport hubs (Děčín, Česká Třebová) or regions specialized in capital-intensive in- dustries apart from old industrial regions (Sokolov, Valašské Meziříčí etc.) Results CI jobs in Czechia are heavily concentrated in metropolitan cores of Prague (41.4%) and Brno (9.8%). If we sum all three metropolitan regions (including Ostravsko as metropolitan OIR), we get more than 55 per cent share in national employment in CI. Since 2009 there has been relatively significant increase in geo- graphic concentration of CI – in 2009 three largest units accounted for 49.8 per cent in national CI employment (Slach, O. et al. 2013; Slach, O. and Ženka, J. 2017). Increasing con- centration was, however, fuelled only by the relatively growing share of Prague in national CI employment, while the position of Brno and Ostrava slightly deteriorated.4 Metropoli- 4 In the post-crisis period 2009–2014 absolute CI em- ployment at national level decreased by 5 per cent, in urban and rural regions fell by 9 per cent, metropolitan hinterlands grew by 6 per cent, Prague and COIR tan regions of Prague and Brno experienced a deconcentration of jobs from the cores to- wards hinterlands, although the numbers are relatively modest. In 2014 metropolitan hin- terlands concentrated only 6.2 per cent of jobs in CI, although their relative specialization is above national average (Table 3). The lat- ter contrasts with a dynamic socio-economic development of Czech metropolitan hinter- lands in the last two decades (Maier, K. and Franke, D. 2005; Šimon, M. 2017). Empirical results of Slach, O. et al. (2018) did not support theoretical assumption that COIR should in the post-crisis period at least partly reorient from traditional mining and manufac- turing industries towards CI. While employ- ment in traditional mining and manufacturing industries declined in the period 2009–2014, COIR experienced a process of reindustriali- zation that was driven by an expansion of the automotive industry and some related services – transport, warehousing, employment activi- ties or office administrative and business sup- porting activities (Slach, O. et al. 2018). Ranking of microregions according to their CI employment is primarily driven by their position in urban hierarchy (Figure 2), which almost perfectly corresponds to population size. Only 18 per cent of all microregions show higher share in national CI employment Ostravsko stagnated. COIR Ústecko experienced a sharp decline in CI employment by 25 per cent (827 jobs were lost) – see also Slach, O. and Ženka, J. (2017). Table 3. CI in metropolitan, urban, old industrial and rural regions, 2014 Regions CI employment, persons Specialisation in CI, % CI employment Total employment Number of firms in education and cultural industries % share in Czechia Metropolitan cores Metrop. hinterlands Urban regions Metropolitan OIR Non-metrop. OIR Rural regions Czechia 55,576 6,733 18,087 8,116 3,188 16,766 108,465 10.0 5.5 4.3 4.1 3.4 2.3 5.1 51.2 6.2 16.7 7.5 2.9 15.5 100.0 26.0 5.7 19.9 9.3 4.4 34.6 100.0 20.5 8.2 10.5 8.1 2.9 49.9 100.0 Source: CSO 2014. Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257.248 Fi g. 2 . S pa tia l d is tr ib ut io n of C I e m pl oy m en t i n C ze ch ia (2 01 4) S ou rc e: C SO 2 01 4. 249Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257. compared to their share in population – most of them are located in Prague metropolitan region. On the other hand, Brno and Ostrava show the largest gap in comparison to their population weight (despite high values of hor- izontal location quotient), the same holds for majority of urban regions with metropolitan functions and also for old industrial regions. As already noted by Slach, O. et al. (2013), it is possible to distinguish between two ma- jor groups of CI (Figure 3). The first group includes printing, architectural and engineer- ing activities and other professional, scien- tific and technical activities (NACE 18, 71, 74), while publishing, media and advertis- ing (NACE 58, 59, 60, 73) belong to the sec- ond group. While the former industries are characteristic by a mix of knowledge bases (symbolic and synthetic) and show more dis- persed patterns, the latter have almost purely symbolic knowledge base and are heavily concentrated into the metropolitan cores. The higher share of activities and knowledge with synthetic knowledge base, the higher rate of spatial concentration of employment. Printing and reproduction of recorded media (industries with significant portion of manufacturing production and technical activities) are by far the most dispersed and significantly represented in metropolitan hin- terlands (Beroun, Pohořelice) and some old industrial (Český Těšín) and urban regions (Plzeň, Zlín, Olomouc etc.). Media form the second extreme industries heavily concen- Fig. 3. Spatial distribution of employment in selected CI (2014). Source: CSO 2014. Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257.250 trated in Prague, while other professional, scientific and technical activities are some- where between these two extremes (Figure 4). As we have expected, analysed types of regions differ relatively significantly in the industrial structure of CI. There are two com- mon features – high share of architectural and testing activities (NACE 71) and comparable shares of other professional, scientific and technical activities in employment. Printing is over-represented especially in metropolitan hinterlands and also in rural and non-met- ropolitan old industrial regions, which are characteristized by a high specialisation in industries with (partly) synthetic knowledge base. Metropolitan cores, on the other hand, are distinct by higher representation of pub- lishing and media, although even in Prague and Brno the first group of CI (NACE 18, 71, 74) clearly dominate in terms of employment. We employed four general linear models in order to explain spatial distribution of CI. While the first two models aim to test the effects of selected explanatory variables on regional specialisation in CI as a dependent variable, the third model explains localisation patterns of advertising and market research representing industry with purely symbolic knowledge base, the fourth focuses on architecture and testing as an industry with the mix of symbolic and synthetic knowledge-base. Specialisation in CI is measured by the horizontal localisation quo- tient, so the size of local economic base matters. Therefore, in the first model we include all 206 microregions, while in the second we exclude metropolitan cores. Explanatory variables are the type of regions, employment density, eco- nomic diversity, density of cultural industries and mean size of a firm in CI. The first model explained 75.5 per cent of variability in CI specialization (Table 4). Employment density, cultural industries, firm size in CI and a dummy variable marking the metropolitan cores showed statistically signifi- cant (p < 0.001) positive relationship. Economic diversity, on the other hand, was not signifi- cant. This does not mean that diversity has no relevance for localisation of CI. Diversity is re- lated to urban size, density and corresponds also with the typology of regions, so its effects Fig. 4. Industrial structure of CI employment in various types of regions (2014). Source: CSO 2014. 10 20 30 40 50 0 60 18 Metro.cores Metro. hinterlands Urban regions Metropolitan OIR Non-metro. OIR Rural regions Czechia 58 59 60 71 73 74 251Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257. Table 4. Correlates of regional specialisation in CI, 2014 Dependent variable HLQ of CI employment HLQ of CI employment except for metropolitan cores Source Type III sum of squares B St. error p Type III sum of squares B St. error p Corrected model 14.040a – .000 831.497a – Intercept 40.73 7.435 191 .000 273.957 651.41 164.637 .000 ln_empl_dens 0.62 –.153 030 .000 441.334 –129.32 26.210 .000 ln_divers 0.00 .012 028 .672 67.885 46.81 24.191 .054 ln_kult 0.50 .098 021 .000 237.866 67.92 18.752 .000 ln_AVG_CI 0.37 .055 014 .000 221.546 42.60 12.187 .001 Type_region 11.43 – .000 76.375 – 12.187 .381 Error 4.55 – 3,535.275 –Corrected total 18.59 4,366.773 region_core – 2.562 0.121 .000 – region_hinter –0.048 0.047 .309 .673 41.498 .673 reg_OIR_Ostr –0.082 0.059 .166 .385 51.948 .385 reg_OIR_Ust –0.076 0.051 .137 .706 44.593 .706 region_rural –0.093 0.034 .007 .072 29.805 .072 R2 0.755 0.190 Source: CSO 2014, compiled by the authors. are probably obscured by other explanatory variables. Dummies of all other types of re- gions had negative effects, but only the effect of rural regions was significant (p < 0.01) due to their small economic base and low density. Therefore, urban scale and concentration of metropolitan functions seem to be the most important factors of CI localisation, while differences among metropolitan hinterlands, urban and old industrial regions do not affect spatial patterns of CI significantly. This finding is supported also by the sec- ond regression model (Table 5) that tested the same explanatory variables after exclusion of metropolitan cores. Results are in some aspects similar to the former model (signifi- cant positive effects of employment density, cultural industries, firm size structure: p < 0.01; economic diversity: p < 0.1), but there are two major differences – type of region did not show significant effects (except for rural regions) and R2 fell rapidly: this model explained only 19 per cent of total variabil- ity. Employment density, cultural industries, economic diversity and CI firm size ex- plained much more than the type of region. Therefore, the effects of urbanization and localisation economies matter for the spatial distribution of CI in urban and non-metro- politan regions. On the other hand, when we exclude metropolitan cores, regional contexts cease to be important for CI localisation. Third model tested spatial distribution of pub- lishing. Maybe surprisingly, share of explained variability is lower (52.2%) compared to mod- els that tested regional specialisation in CI as a whole. Type of region is the key explanatory variable. Cultural industries showed no signifi- cant effect, while the firm size was the second most important explanatory variable. When we turned to regional specialisation in architecture and testing as dependent vari- able (fourth model), we found results that are very similar to the findings of the first model. This may be explained by high share of archi- tecture and testing in total CI employment. Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257.252 Discussion Empirical results showed an excessive and in- creasing spatial concentration of CI into the two largest metropolitan cores – Praha and Brno. Localisation patterns of CI (especially CI with with symbolic knowledge base) reflect to a certain degree a process of metropoliza- tion, understood as “selective concentration of research-intensive industries and knowledge- intensive services on metropolitan regions and major urban agglomerations“ (Krätke, S. 2007, 1). High transaction intensity of CI firms (Growe, A. 2012) is one of the reasons why these industries tend to concentrate heavily in the largest cities. Therefore, large and increasing spatial concentration of CI in Czechia corresponds with the intensification of metropolization, a tendency discussed and documented also by other authors (Hampl, M. and Marada, M. 2015; Viturka, M. et al. 2017). Nevertheless, it is necessary to distin- guish between two basic types of metropoli- zation. The first is based on the difference in urban size/density between metropolitan and non-metropolitan regions, the second refers to the differences among metropolitan regions. The dominant position of Praha is not sur- prising, although its increase in total CI em- ployment does not correspond to the overall economic development in the post-crisis pe- riod (Ženka, J. et al. 2017c). However, con- sidering strong position of the capital city in other knowledge-intensive services (Blažek, J. and Bečicová, I. 2016; Sucháček, J. et al. 2017) and concentration of corporate head- quarters (Dostál, P. and Hampl, M. 1994; Sucháček, J. and Baránaek, P. 2013) we argue that Praha has been moving from the sectoral to the functional specialization (Duranton, G. and Puga, D. 2005), at least within Czechia. Although urban size/density has been identi- fied as a key explanatory variable, individual comparisons among selected microregions indicate some ambiguity. Significance of ur- ban size is well illustrated by the difference Table 5. Correlates of regional specialisation in publishing (58) and architecture and testing (71) Dependent variable HLQ of employment in publishing (58) HLQ of employment in architecture and testing (71) Source Type III sum of squares B St. error p Type III sum of squares B St. error p Corrected model 692.146a – .000 144.357a – .000 Intercept 3.925 3.287 2.587 .296 .103 –497 .756 .604 ln_empl_dens 26.611 1.217 .446 .007 11.106 .648 .120 .000 ln_divers 18.703 .884 .386 .023 7.023 .476 .111 000 ln_kult 187 .073 .318 .819 3.153 .246 .086 .005 ln_AVG_CI 84.398 1.086 .223 .000 7.790 .253 .056 .000 Type_region 96.485 – .000 11.754 – .000 Error 571.220 – 75.104 – Corrected total 1,263.367 219.461 region_core – –2.263 1.515 .137 – .942 .490 .056 region_hinter –1.242 .657 .060 .203 .191 .288 reg_OIR_Ostr –854 .754 .259 –047 .239 .843 reg_OIR_Ust –523 .657 .427 .031 .205 .879 region_rural –946 .506 .064 –446 .137 .001 R2 0.548 0.658 Source: CSO 2014, compiled by the authors. 253Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257. in concentration of CI between metropolitan OIR (Ostravsko) and non-metropolitan (Ústí nad Labem). On the other hand, metropolitan region Ostravsko has approximately 2.5 times lower concentration of market-oriented CI and also significantly lower representation of cul- tural industries (Ivan, I. et al. 2015) than met- ropolitan region of Brno, which is comparable in terms of urban size. Existing disproportions can be at least partly explained by a different regional context in terms of positive and nega- tive path dependency (Henning, M. et al. 2013), or between the “good” inheritance of Brno and “bad” inheritance of Ostrava (paraphrase of Storper, M. 2013; for empirical illustration see Ženka, J. et al. 2017a). The influence of path dependency can also explain the mismatch be- tween CI size/concentration in urban regions, namely relatively higher concentration of CI into Olomouc (university city) in comparison to larger and economically better performing Plzeň, traditionally specialized in engineering. The concept of path dependency (partly co-evolution) can also be used to explain re- gional differentiation of industrial structure of CI (Berg, S.H. and Hassink, R. 2014). High share of architecture and testing (NACE 71) in employment of urban regions and COIR (to some extent also to rural and peripheral regions) results from traditional specialisation in manufacturing industries (architecture is of minor importance, technical testing and anal- yses clearly dominate – Ivan, I. et al. 2015). Path-dependence is relevant also for metro- politan hinterlands. Low employment in CI in these regions is in direct contradiction with their dynamic economic and demographic growth (Maier, K. and Franke, D. 2015). The first explanation is relatively weak impor- tance of agglomeration disadvantages for the spatial distribution of printing. The second reason can be seen in the fact that in Czechia the process of metropolization was “delayed” (Musil, J. 1993; Hampl, M. 2005) in compari- son with Western European economies due to the centrally planned economy. For this reason, these regions are not yet able to offer adequate infrastructure and environment for more intensive localization of CI, which is not the case for less knowledge-intensive services (Sýkora, L. and Ouředníček, M. 2007). Conclusions In this paper we aimed to describe and ex- plain spatial distribution of CI in Czechia. More specifically, we tried to determine to what extent localisation patterns can be ex- plained primarily by traditional factors such as the position in urban hierarchy, urbaniza- tion and localisation economies and to what extent do regional contexts (metropolitan cores and hinterlands, old industrial, urban and rural regions) matter. We tested the ef- fects of regional contexts (types of regions) together with traditional factors: employ- ment density and economic diversity as proxies for urbanization economies, CI firm size structure and density of cultural indus- tries representing localisation economies. Regression model testing the effects of these explanatory variables explained more than 70 per cent of the total variability of the dependent variable, which was represented by horizontal location quotient of CI. Types of regions showed stronger effect than tradi- tional explanatory variables. However, only two types were significant – positive effect of metropolitan cores and negative effect of rural regions. After exclusion of metropolitan cores the model significantly lost its explana- tory power. Position in urban size/density and position in urban hierarchy seem to be the key explanatory variables. Differences among regions with similar size and density are of minor importance. Despite several theoreti- cal arguments supporting assumptions that regional contexts should affect spatial concen- tration of CI, we found only limited empirical evidence to prove this statement – above men- tioned comparisons of Plzeň and Olomouc or explanations for high share of architecture and testing in urban regions and COIR. Minor dif- ferences were found between spatial patterns of publishing, architecture-testing, advertise- ment and market research. Industries with a mix of symbolic and synthetic knowledge base Ženka, J. and Slach, O. Hungarian Geographical Bulletin 67 (2018) (3) 239–257.254 showed more dispersed localisation patterns, while purely symbolic industries were heavily concentrated into metropolitan cores. Therefore, above mentioned types of (non) metropolitan regions differ significantly in their industrial structure of CI employment. Metropolitan cores are characterised by high- er shares of purely symbolic industries, for which urban amenities, centrality and local buzz (see Polese, M. 2012, 1813) are of key importance. CI employment in metropolitan hinterlands, on the other hand, is dominated by printing, architecture and testing. The same applies to lesser degree also for urban, old industrial and rural regions, where ar- chitecture and testing accounts for (almost) more than half of the jobs in CI. To summarize previous findings, locali- sation patterns of CI reflect existing spatial differentiation of social and economic phe- nomena in Czechia. 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