Spatial Dimension of Czech Enterprise Support Policy: Where are Public Expenditures Allocated?

Novosák, Jiří; Hájek, Oldřich; Novosáková, Jana; Koleňák, Jiří Article Spatial Dimension of Czech Enterprise Support Policy: Where are Public Expenditures Allocated? Review of Economic Perspectives Provided in Cooperation with: Masaryk University, Faculty of Economics and Administration Suggested Citation: Novosák, Jiří; Hájek, Oldřich; Novosáková, Jana; Koleňák, Jiří (2018) : Spatial Dimension of Czech Enterprise Support Policy: Where are Public Expenditures Allocated?, Review of Economic Perspectives, ISSN 1804-1663, De Gruyter, Warsaw, Vol. 18, Iss. 4, pp. 333-351, https://doi.org/10.2478/revecp-2018-0017 This Version is available at: https://hdl.handle.net/10419/194199 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/3.0/ Review of Economic Perspectives – Národohospodářský obzor Vol. 18, Issue 4, 2018, pp. 333–351, DOI: 10.2478/revecp-2018-0017 © 2018 by the authors; licensee Review of Economic Perspectives / Národohospodářský obzor, Masaryk University, Faculty of Economics and Administration, Brno, Czech Republic. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 3.0 license, Attribution – Non Commercial – No Derivatives. Spatial Dimension of Czech Enterprise Support Policy: Where Are Public Expenditures Allocated? Jiří Novosák 1 , Jana Novosáková 2 , Oldřich Hájek 3 , Jiří Koleňák 4 Abstract: The purpose of the present paper is to find whether the spatial distribution of enterprise support policy funds meet the spatial objectives stated in Czech strategic documents related to enterprise support policy. Are more funds allocated in lagging regions, and does enterprise support policy contribute more to the convergence objective, or are more funds allocated in core regions, and does enterprise support policy contribute more to the competitiveness objective? These questions are answered by evaluating the Structural (and Cohesion) Fund (SF) expenditures that were allocated on operations categorised as part of enterprise support policy (2007-2013). The dependent variable relates to 206 regions, and SF expenditures are calculated for every inhabitant of a region. Moreover, two types of SF operation are distinguished: (a) innovationoriented operations; and (b) other enterprise support operations. Three explanatory variables are defined using Principal Components Analysis (PCA), and these components are understood as: (1) the social disadvantage of regions; (2) the innovation environment of regions; and (3) the quality of regional entrepreneurial environments. The associations between the dependent and explanatory variables are subsequently evaluated by methods of correlation and regression analysis. The findings provide some evidence for both the convergence and competitiveness objectives. Nevertheless, this evidence is rather limited due to a low spatial concentration of SF allocation, and the compensatory effect between the two thematic types of SF operations. Hence, while the quality of their innovation environment has a positive influence on regional SF allocation regardless of the thematic focus of SF operations, socially disadvantaged regions received more funds for SF operations which are not innovation-oriented. The capacity of potential beneficiaries to prepare and submit many project proposals for SF co-financing is the main reason for high or low SF allocation. Key words: enterprise support policy, cohesion policy, the Czech Republic, regional disparities JEL Classification: R12, O18, R58, O22 Received: 16 November 2017 / Accepted: 1 August 2018 / Sent for Publication: 28 November 2018 1 Tomas Bata University, Faculty of Management and Economics, Mostní 5139, 76301 Zlín, Czechia; email: [email protected], n[email protected] 2 Newton College, Václavské náměstí 11, 110 00 Prague, Czechia 3 Tomas Bata University, Faculty of Management and Economics, Mostní 5139, 76301 Zlín, Czechia 4 Newton College, Václavské náměstí 11, 110 00 Prague, Czechia Review of Economic Perspectives 334 Introduction Enterprise support policies are quite high on political agendas of many states. The main motivation of these policies is to influence economic growth, competitiveness and employment through enterprise development (see, e.g., Arshed, Carter and Mason, 2014; Vega and Chiasson, 2015). Generally, two types of enterprise support policies may be distinguished: (a) entrepreneurship policies focusing on new enterprise formation; and (b) SME policies focusing on the competitiveness of existing firms (see, e.g., Storey, 2008). In this regard, Storey (2008), Henry, Hill and Leitch (2003), and also Acs et al. (2016) explain the demand for these policies within the imperfect market framework. Enterprise support policies serve to compensate for information and knowledge imperfections, as compensation for asymmetries in access to finance, and also as compensation for the divergence in private and societal benefits. Acs and Szerb (2007), Huggins and Williams (2009) add the importance of entrepreneurial climate as another argument for enterprise support policies. Enterprise support policies also have a spatial dimension that relates to specific regional conditions. Smallbone, Baldock and North (2003) expound on spatial market imperfections that cause disadvantages in peripheral regions. Acs et al. (2016) additionally point out the importance of spatial externalities, such as tacit knowledge or information spillovers, typically concentrated in core regions. Accordingly, spatial objectives of enterprise support policies may follow either economic or social goals (see, e.g., Dennis, 2011). While economic goals favour more competitive core regions, social goals call for the support of lagging regions. These ideas also constitute the rationale of EU cohesion policy in the programming period 2007-2013. Hence the Treaty establishing the European Community provides that: “in order to strengthen its economic and social cohesion, the Community is to aim at reducing disparities between the levels of development of the various regions (…) – article 158 of the Treaty”. However, “cohesion policy should also contribute to increasing growth, competitiveness and employment, (…) and actions for convergence, competitiveness and employment should therefore be increased throughout the Community” (EC, 2006a). This is also reflected in the first two objectives of cohesion policy (EC, 2006a):  The Convergence objective, which is aimed at speeding up the convergence of the least-developed Member States and regions (…),  The Regional competitiveness and employment objective, which shall, outside the least-developed regions, be aimed at strengthening regions’ competitiveness and attractiveness as well as employment (…). Moreover, the Community’s strategic guidelines on cohesion attach particular importance to entrepreneurship and SME development (EC, 2006b). Consequently, a link forms between spatial objectives of cohesion policy and enterprise support policy. It is worth noting that these spatial objectives were also mentioned in relevant Czech strategic documents for the period 2007-2013. Hence, MIT CR (2006), MRD CR (2006), MRD CR (2007), and MoE CR (2010) emphasise the role of SMEs in alleviating regional disparities – this is the convergence objective. Concurrently, MRD CR (2006), and MRD CR (2007) uphold the importance of innovative enterprises in core regions – Volume 18, Issue 4, 2018 335 the competitiveness objective. Additionally, the cohesion policy was the main source for financing Czech public policies in the period 2007-2013, including enterprise support policy (see, e.g., Wokoun, 2007). The present article deals with the spatial dimension of enterprise support policy, specifically in regard to the question of where enterprise support policy funds are allocated. In answering the question, the aim of this paper is to find whether the spatial distribution of enterprise support policy funds meets the spatial objectives stated in Czech strategic documents related to enterprise support policy – the convergence and competitiveness objectives. In this regard, we evaluate the Structural (and Cohesion) Fund (hereafter referred to as SF) expenditures from the Convergence objective and from the Regional competitiveness and employment objective, categorised as part of enterprise support policy. The article is structured as follows: the first section provides a literature review. The second section presents data and research methods. The third section summarises empirical results that are subsequently discussed in the following section. The last section presents conclusions. Literature review Socioeconomic development is a spatially uneven process (see, e.g., Stillwell et al., 2010). Some regions are quite successful in their socioeconomic development while other regions lag behind. Such regional disparities motivate the interest of many states to conduct regional policies. Two spatially-oriented objectives are typically defined: (a) the convergence objective (or the objective of equity); and (b) the competitiveness objective (or the objective of efficiency). The relationship between these two objectives has been constantly changing. The spatially-oriented objectives of convergence and competitiveness were primarily perceived as complementary to each other (see, e.g., Fratesi, 2008). The neoclassical growth model and the principle of decreasing returns to scale explain this relationship (see, e.g., Solow, 1956; Henley, 2005). It is suggested that spatial concentration of resources in lagging regions can achieve both regional convergence and competitiveness (see, e.g., Boldrin and Canova, 2001; Wu and Gopinath, 2008, Ezcurra, Pascual and Rapún, 2007). However, the theoretical rationale of this relationship has been questioned by several more recent theoretical concepts based on increasing returns to scale. Examples of this are endogenous growth theories (see, e.g., Romer, 1986) and new economic geography (see, e.g., Krugman, 1991). In this case, the objective of competitiveness may be violated when allocating resources in lagging regions (see, e.g., Nijkamp, 2009; De Propris, 2007). There have been changes in the theoretical background of regional policies, and these changes have been reflected in practical implementation. The traditional mechanism of redistribution of financial resources to lagging regions has been complemented by the ideas of endogenous growth theories, new economic geography and also by the ideas of institutional theories of regional development. It is argued that the support of core regions may increase the coherence between the convergence and competitiveness objectives of regional policies (see, e.g., Bentley and Pugalis, 2014). Additionally, all regions have developmental potential and it is also desirable to consider the presence of spatial market imperfections and spatial externalities (see, e.g., Garretsen et al., 2013; Barca, Review of Economic Perspectives 336 McCann and Rodríguez-Pose, 2012; Audretsch, 2015). Finally, the thematic focus of regional policy is of importance. Thus, Kaufmann and Wagner (2005), Crescenzi (2009), Novosák et al. (2017a) point out the low absorption capacity of lagging regions, particularly in the case of innovations and more progressive thematic areas. The above mentioned discussion is also relevant for enterprise support policies. Huggins and Williams (2011) claim that enterprises crucially influence regional development and that they form the basis for regional competitiveness. Similarly, Armington and Acs (2002), and also Tamásy and Le Heron (2008) point out the positive relationship between high entrepreneurship rates and regional concentration of resources. Therefore, enterprise support policies may be understood as an appropriate strategy to achieve both the convergence and competitiveness objectives of regional policies. It is worth noting that these objectives were included in several strategic documents of the Czech Republic in the period 2007-2013, particularly:  new firm formation and growth, SME development and new job creation in lagging regions included in MIT CR (2006), MRD CR (2006), MRD CR (2007), and MoE CR (2010),  support of innovation-oriented enterprises in core regions included in MRD CR (2006), and MRD CR (2007),  the coherence between regional and thematic policies included in MIT CR (2006), and MRD CR (2007). Various policy instruments were used to achieve these objectives. The means also included financial instruments, especially SF expenditures for enterprise development. The question is whether the spatial distribution of these expenditures meets the spatial objectives stated above. Novosák et al. (2017b) discussed this question for different clusters of regions and they identified evidence for the competitiveness objective, but little support was found for the convergence objective. In this article, a different methodology, suggested by Crescenzi (2009) and Crescenzi, De Fillipis and Pierangeli (2015), is applied. The main ideas of this approach rest on the assumption that financial allocation of enterprise support policies ought to compensate for socioeconomic disadvantages of regions in order to achieve the convergence objective of regional policies. Also, that innovation-oriented intervention is expected to be of crucial importance in core regions. The coherence of spatial objectives of enterprise support policy is achieved in this way. Empirically, these ideas are verified on the basis of regression modelling that explains relative financial allocation in regions (see, e.g., Crescenzi, 2009; Crescenzi, De Fillipis and Pierangeli, 2015; Dellmuth and Stoffel, 2012; Schraff, 2014; Camaioni et al., 2013 for this approach). A number of studies using this evaluating approach of various public policies have provided mixed results, depending on the spatial level of evaluation, on the ex-ante or ex-post nature of evaluation, on the eligibility of regions for financing, and also on other factors. Typically, ex-ante evaluation of SF allocation at the NUTS1 and NUTS2 levels indicates higher SF allocation in disadvantaged regions (see, e.g., Crescenzi, 2009; Crescenzi, De Fillipis and Pierangeli, 2015; Bouvet and Dall’Erba, 2010; Kemmerling and Bodenstein, 2006). However, these findings are not surprising because the more Volume 18, Issue 4, 2018 337 developed regions are not eligible for SF financing under the most generous Convergence Objective. Results are quite ambiguous for ex-post evaluations at a lower spatial level that are based on competition among regions (see, e.g., Novosák et al., 2015; Camaioni, et al., 2013; Blažek and Macešková, 2010). Moreover, thematic focus of policy interventions is of crucial importance as shown e.g. by Novosák et al. (2017a). The idea of the so called “innovation paradox” should be particularly mentioned. It claims that innovation-oriented interventions do not target lagging regions due to their low absorption capacity (see, e.g., Kaufmann and Wagner, 2005; Klímová and Žítek, 2015). There are also other factors that may influence the spatial pattern of enterprise support policy expenditures. The concept of absorption capacity is of particular importance. Jurevičienė and Pileckaitė (2013), Milio (2007), understand this concept as being the capacity of states to spend earmarked funds effectively and efficiently. The demand and supply sides of the concept are clearly distinguished (see, e.g., Popescu, 2015; Tosun, 2014; Cace et al., 2009). While the demand side relates to institutional aspects of public policy, the supply side relates to the capacity of actors to prepare and submit projects acceptable for funding. Concerning the spatial dimension of the absorption capacity concept; Jaliu and Radulescu (2013), Tosun (2014) point out a potentially disadvantageous position of lagging regions due to their lack of human capital, lack of cofinancing funds, due to their relatively weak lobbying power and also due to problems with searching for project partners. Hájek et al. (2017) explain that this disadvantage may be reflected in three areas: (a) in a lower number of project proposals; (b) in a smaller project size; and (c) in a lower rate of project approval. There are two other factors that may influence spatial patterns of enterprise support policy expenditures. Firstly, political interests may be a strong predictor of the spatial pattern of enterprise support policy expenditures. Hence, politicians, i.e. decisionmakers, may favour certain regions over others, for various reasons. These include the strategy to “reward loyalty” (see, e.g., Bouvet and Dall’erba, 2010; Dellmuth and Stoffel, 2012; Schraff, 2014), and the strategy to “win elections in marginal districts” (see, e.g., Schraff, 2014; Dellmuth and Stoffel, 2012). Secondly, spatial interactions influence the spatial pattern of enterprise support policy expenditures (see, e.g., Camaioni et al., 2013; Schraff, 2014). These involve cooperation between neighbouring regions which results in positive associations, and also involves competition between neighbouring regions which results in negative associations. Methodology The methodology of the present paper is based on evaluating associations between the regional pattern of enterprise support policy expenditures and the variables related to socioeconomic disadvantages of regions. We evaluate SF expenditures from the Convergence objective and from the Regional competitiveness and employment objective (2007-2013) that were allocated on operations that were categorised as part of enterprise support policies. Table 1 gives operational programmes (hereafter referred to as OPs) and their priority axes from which operations were included in subsequent analyses. All the variables relate to 206 regions which correspond to the so-called administrative districts of municipalities with extended powers, and also the capital city of Prague. Review of Economic Perspectives 338 Table 1. OPs and priority axes from which operations were included in analyses Operational Programme Priority axes Type OP Enterprise and Innovation (1) Establishment of Firms; (2) Development of Firms; (3) Effective Energy; (4) Environment for Enterprise and Innovation; (5) Business Development Services Other (1) Innovation; (2)Environment for Enterprise and Innovation Innovationoriented Human Resources and Employment OP* (1) Adaptability Other OP Research and Development for Innovations* (1) European Centres of Excellence; (2) Regional R&D Centres; (3) Commercialisation and Popularisation of R&D Innovationoriented OP Environment* (1) The Limiting of Industrial Pollution and Environmental Risks Other ROP Prague-Competitiveness* (1) Innovations and Enterprise Innovationoriented ROP Prague-Adaptability* (1) Support to Development of KnowledgeBased Economy Other Regional OPs* (7 OPs) Priority axes related to tourism and enterprise development Other * Only SF operations carried out by private-sector beneficiaries were included in this analysis Source: own elaboration Dependent variables The dependent variable was defined as SF expenditures (in June 2016) per inhabitant of a region and the variable was log-transformed to improve presentation and statistical validity. The official data published by the Ministry of Regional Development of the Czech Republic (hereafter referred to as the MRD CR), the Ministry of Industry and Trade of the Czech Republic (hereafter referred to as the MIT CR) and the Ministry of Labour and Social Affairs of the Czech Republic (hereafter referred to as the MLSA CR) were the sources of information. Apart from the SF expenditures, two other types of dependent variables were used to reflect the thematic focus of SF operations: (a) innovation-oriented operations; and (b) other enterprise support operations (hereafter referred to as other operations). The OPs and priority axes of SF operations determined them being categorised into the defined types (see table 1 for details). Explanatory variables The explanatory variables relate to socioeconomic disadvantages of regions and are dated at the beginning of the period 2007-2013 in order to minimize the problem of endogeneity. In this regard, three variables were defined using Principal Components Analysis (PCA). The first variable, understood as social disadvantages of regions (SOCIAL_DIS), was constructed as the principal component derived from two indicators: (a) unemployment rate, i.e. the annual proportion of unemployed people for the population aged 15-64 in the years 2005-2007; and (b) annual migration change per 1,000 inhabitants in the years 2000-2007. Data was taken from the Czech Statistical Office (hereafter referred to as the CSO) and the PCA scores were used in subsequent analyses. Volume 18, Issue 4, 2018 339 The remaining two variables were constructed from six indicators related to entrepreneurial and innovation environment, which included:  population density (POPULATION_DENSITY), i.e. the number of inhabitants per area (2007; the CSO as the source of information; log-transformed to improve normality);  the nature of the industrial structure (INDUSTRIAL_STRUCTURE) measured as the distance of employment shares in 11 industries in each region from the corresponding employment shares of the capital city of Prague (the mean from the years 2001 and 2011; the CSO as the source of information);  patent activity (PATENT) defined as the number of patents and utility models per 100,000 inhabitants with a double-weight value for patents (2002-2007; the Industrial Patent Office – hereafter referred to as the IPO – as the source of information; log-transformed to improve normality);  the stock of human capital (HUMAN_CAPITAL) measured as the share of tertiary educated people in the population, more than 15 years of age (the mean from the years 2001 and 2011; the CSO as the source of information);  the stock of entrepreneurs (ENTREPRENEUR), i.e. the share of employers and self-employed people in the economically active population (the mean from the years 2001 and 2011; the CSO as the source of information);  entrepreneurial dynamics (ENTREP_DYNAMICS) defined as a composite index composed of three variables: (a) the number of newly created businesses as a percentage of the population aged 15-64 (2002-2007; the CSO as the source of information); (b) the number of newly created businesses registered to VAT in the first three years after their establishment, for the population aged 15-64 (2002-2007; the CSO and the Ministry of Finance of the Czech Republic – hereafter referred to as MF CR – as the source of information); and (c) the number of fast-growing newly created businesses, i.e. businesses with at least 20 employees in the first three years of their establishment, for the population aged 15-64 (2002-2007; the CSO as the source of information; log-transformed to improve normality). Principal component analysis was used as the method for data treatment to establish general relationships among the six indicators, and also as a data reduction tool, so that a more meaningful regression may be carried out. Therefore, only two components with an eigenvalue larger than one (Kaiser’s criterion) were retained for further analyses. Table 2 reproduces the rotated component matrix of the PCA, providing information about the factor loadings of each indicator on the two constructed components and thus enabling interpretation of both of these components. The first component is loaded positively and highly on the indicators related to population density, human capital, and also industrial structure and patents; while the second component is loaded positively and highly on the indicators related to the stock of entrepreneurs and entrepreneurial dynamics. The former group of indicators consists of the factors characteristic of innovation environments (INNOV_ENVIRON) – e.g., patenting, stock of knowledge, innovation spillovers, and agglomeration externalities (see, e.g., Fotopoulos, 2014; Qian, Acs and Stough, 2013; Bishop, 2012), while the latter group of indicators relate to the quality of entrepreneurial environments (ENTREP_ENVIRON; see, e.g., Foreman-Peck and Zhou, 2013). Note that when defining the two components, Review of Economic Perspectives 340 the two defined types of SF operations were taken into account, and that the PCA scores for both components were used in subsequent analyses. Table 2. PCA – rotated component matrix (Varimax rotation with Kaiser Normalization) Indicator Component Innovation environment Entrepreneurial environment POPULATION_DENSITY 0.909 -0.163 INDUSTRIAL_STRUCTURE 0.653 0.455 PATENT 0.595 0.236 HUMAN_CAPITAL 0.740 0.508 ENTREPRENEUR -0.017 0.931 ENTREP_DYNAMICS 0.408 0.761 Eigenvalue 3.132 1.183 Total variance explained (cumulative) 52.2 % 71.9 % Source: own elaboration based on the CSO, the IPO, and the MF CR Control variables Control variables relate to the absorption capacity concept, they relate also to political interests and also to spatial interactions. Firstly, absorption capacity was operationalized using three variables: (a) the number of project applications submitted for SF cofinancing per 10,000 inhabitants (PROJECT_NUMBER); (b) the average size of a project submitted for SF co-financing which was measured by the amount of SF required in project applications (PROJECT_SIZE); and (c) the rate of project acceptance (PROJECT_ACCEPT). This defines the capacity of actors to prepare and submit projects acceptable for SF (see, e.g., Hájek et al., 2017). Political interests were grasped in terms of the “reward loyalty” strategy. Hence, a dummy variable was defined that takes the value of ‘1’ if government parties won more than 50% of votes in the two Parliamentary elections in 2006 and 2010 and the value of ‘0’ otherwise (GOVERNMENT). Spatial interactions were included in subsequent analyses through a variable related to the administrative division of the Czech Republic into eight cohesion regions (NUTS 2). Therefore, seven dummy variables were defined, using the Moravia-Silesia cohesion region as a reference category. The choice of this cohesion region was because its regions were, on average, close to the mean value of all Czech regions. Note also the Prague dummy variable controls for all specific features of Prague (e.g., ineligibility for the Convergence objective, capital-city status). Methods The associations between the regional pattern of enterprise support policy expenditures and the variables related to socioeconomic disadvantages of regions were evaluated, using the following methods. Moran’s I, the most common measure of spatial autocorrelation, was first calculated. We followed the notion that low Moran’s I values indicate high spatial dispersion of both SF allocation and socioeconomic disadvantages of regions (see, e.g., Crescenzi, 2009). Secondly, correlation coefficients between the de- Volume 18, Issue 4, 2018 347 References ACS, Z.J. et al. (2016). Public policy to promote entrepreneurship: a call to arms. Small Business Economics, 47(1), 35-51. DOI: 10.1007/s11187-016-9712-2. ACS, Z.J., SZERB, L. (2007). Entrepreneurship, economic growth and public policy. Small Business Economics, 28(2-3), 109-122. DOI: 10.1007/s11187-006-9012-3. ARMINGTON, C., ACS, Z.J. (2002). The determinants of regional variation in new firm formation. Regional Studies. 36(1), 33-45. DOI: 10.1080/00343400120099843. ARSHED, N., CARTER, S., MASON, C. (2014). The ineffectiveness of entrepreneurship policy: is policy formulation to blame? Small Business Economics, 43(3), 639-659. DOI: 10.1007/s11187-014-9554-8. AUDRETSCH, D.B. (2015). The strategic management of place. In Audretsch, D.B, Link, A.N, Lindestein Walshok, M., The Oxford Handbook of Local Competitiveness. Oxford: Oxford University Press. BARCA, F., MCCANN, P., RODRÍGUEZ-POSE, A. (2012). The case for regional development intervention: place‐based versus place‐neutral approaches. Journal of Regional Science, 52(1), 134-152. DOI: 10.1111/j.1467-9787.2011.00756.x. BENTLEY, G., PUGALIS, L. (2014). Shifting paradigms: people-centred models, active regional development, space-blind policies and place-based approaches. Local Economy, 29(4-5), 283-294. DOI: 10.1177/0269094214541355. BISHOP, P. (2012). Knowledge, diversity and entrepreneurship: a spatial analysis of new firm formation in Great Britain. Entrepreneurship & Regional Development. 24(78), 641-660. DOI: 10.1080/08985626.2011.617786. BLAŽEK, J., MACEŠKOVÁ, M. (2010). Regional analysis of public capital expenditure: to which regions are public capital expenditure channelled – to “rich” or to “poor” ones? Regional Studies, 44(6), 679-696. DOI: 10.1080/00343400903002713. BOLDRIN, M., CANOVA, F. (2001). Inequality and convergence in Europe’s regions: reconsidering European regional policies. Economic Policy, 16(32), 207-253. DOI: 10.1111/1468-0327.00074. BOUVET, F., DALL‘ERBA, S. (2010). European regional structural funds: how large is the influence of politics on the allocation process? Journal of Common Market Studies, 48(3), 501-528. DOI: 10.1111/j.1468-5965.2010.02062.x. CACE, C., CACE, S., IOVA, C., NICOLAESCU, V. (2009). Absorption capacity of the structural funds. Integrating perspectives. Revista de cercetare şi intervenţie socială, 27, 7-28. CAMAIONI, B. et al. (2013). How rural is the EU RDP? An analysis through spatial fund allocation. Bio-based and Applied Economics, 2(3), 277-300. DOI: 10.13128/BAE-13092. CRESCENZI, R. (2009). Undermining the principle of concentration? European Union regional policy and the socio-economic disadvantage of European regions. Regional Studies, 43(1), 111-133. DOI: 10.1080/00343400801932276. Review of Economic Perspectives 348 CRESCENZI, R., DE FILLIPIS, F., PIERANGELI, F. (2015) In tandem for cohesion? Synergies and conflicts between regional and agricultural policies of the European Union. Regional Studies, 49(4), 681-704. DOI: 10.1080/00343404.2014.946401. DELLMUTH, L.M., STOFFEL, M.F. (2012). Distributive politics and intergovernmental transfers: the local allocation of European Union structural funds. European Union Politics, 13(3), 413-433. DOI: 10.1177/1465116512440511. DE PROPRIS, L. (2007). Reconciling cohesion and competitiveness through EU cluster policies? Policy Studies, 28(4), 327-345. DOI: 10.1080/01442870701640690. DENNIS, W.J. (2011). Entrepreneurship, small business and public policy levers. Journal of Small Business Management, 49(2), 149-162. DOI: 10.1111/j.1540627X.2011.00319.x. EC (2006a). Council Regulation (EC) No 1083/2006 of 11 July 2006 laying down general provisions on the European Regional Development Fund, the European Social Fund and the Cohesion Fund and repealing Regulation (EC) No 1260/1999. Official Journal of the European Union, L210, 25-78. EC (2006b). Council Decision of 6 October 2006 on Community strategic guidelines on cohesion (2006/702/EC). Official Journal of the European Union, L291, 11-32. EU (2013). Regulation (EU) No 1303/2013 of the European Parliament and of the Council of 17 December 2013 laying down common provisions on the European Regional Development Fund, the European Social Fund, the Cohesion Fund, the European Agricultural Fund for Rural Development and the European Maritime and Fisheries Fund and laying down general provisions on the European Regional Development Fund, the European Social Fund, the Cohesion Fund and the European Maritime and Fisheries Fund and repealing Council Regulation (EC) No 1083/2006. Official Journal of the European Union, L347, 320-469. EZCURRA, R., PASCUAL, P., RAPÚN, M. (2007). The dynamics of regional disparities in Central and Eastern Europe during transition. European Planning Studies, 15(10), 1397-1421. DOI: 10.1080/09654310701550850. FOREMAN-PECK, J., ZHOU, P. (2013). The strength and persistence of entrepreneurial cultures. Journal of Evolutionary Economics, 23(1), 163-187. DOI: 10.1007/s00191011-0239-z. FOTOPOULOS, G. (2014). On the spatial stickiness of UK new firm formation rates. Journal of Economic Geography, 14(3), 651-679. DOI: 10.1093/jeg/lbt011. FRATESI, U. (2008). Regional policy from a supra-regional perspective. The Annals of Regional Science, 42(3), 681-703. DOI: 10.1007/s00168-007-0167-x. GARRETSEN, H. et al. (2013). The future of regional policy. Cambridge Journal of Regions, Economy and Society, 6(2), 179-186. DOI: 10.1093/cjres/rst013. HÁJEK, O. et al. (2017). Absorpční kapacita strukturálních fondů (2007-2013): typologie českých mikroregionů [Absorption capacity of structural funds (2007-2013): typology of Czech microregions]. Scientific Papers of the University of Pardubice. Series D, Faculty of Economics & Administration, 24(39), 28-38. Volume 18, Issue 4, 2018 349 HENLEY, A. (2005). On regional growth convergence in Great Britain. Regional Studies, 39(9), 1245-1260. DOI: 10.1080/00343400500390123. HENRY, C., HILL, F., LEITCH, C. (2003). Developing a coherent enterprise support policy: a new challenge for governments. Environment and Planning C: Government and Policy, 21(1), 3-19. DOI: 10.1068/c0220. HUGGINS, R., WILLIAMS, N. (2011). Entrepreneurship and regional competitiveness: The role and progression of policy. Entrepreneurship & Regional Development, 23(910), 907-932. DOI: 10.1080/08985626.2011.577818. JALIU, D., RADULESCU, C. (2013). Six years in managing structural funds in Romania. Lessons learned. Transylvanian Review of Administrative Science, 9(38), 79-95. JUREVIČIENĖ, D., PILECKAITĖ, J. (2013). The impact of EU structural fund support and problems of its absorption. Business, Management and Education, 11(1), 1-18. DOI: 10.3846/bme.2013.01. KAUFMANN, A., WAGNER, P. (2005). EU regional policy and the stimulation of innovation: the role of the European Regional Development Fund in the Objective 1 Region Burgenland. European Planning Studies, 13(4), 581-599. DOI: 10.1080/09654310500107274. KEMMERLING, A., BODENSTEIN, T. (2006). Partisan politics in regional redistribution. Do parties affect the distribution of EU structural funds across regions? European Union Politics, 7(3), 373-392. DOI: 10.1177/1465116506066264. KLÍMOVÁ, V., ŽÍTEK, V. (2015). Inovační paradox v Česku: ekonomická teorie a politická realita [Innovation paradox in the Czech Republic: Economic theory and political reality]. Politická ekonomie, 63(2), 147-166. DOI: 10.18267/j.polek.994. KRUGMAN, P. (1991). Increasing returns and economic geography. Journal of Political Economy, 99(3), 483-499. DOI: 10.1086/261763. MILIO, S. (2007). Can administrative capacity explain differences in regional performances? Evidence from structural funds implementation in Southern Italy. Regional Studies, 41(4), 429-442. DOI: 10.1080/00343400601120213. MIT CR (2006). Koncepce rozvoje malého a středního podnikání na období 2007-2013 [Strategy of SME Development for the period 2007-2013]. Prague: Ministry of Industry and Trade of the Czech Republic. MIT CR (2012). Koncepce podpory malých a středních podnikatelů na období let 20142020 [Strategy of SME Development for the period 2014-2020]. Prague: Ministry of Industry and Trade of the Czech Republic. MRD CR (2006). Strategie regionálního rozvoje České republiky na roky 2007-2013 [Strategy of Regional Development of the Czech Republic for the Years 2007-2013]. Prague: Ministry of Regional Development of the Czech Republic. MRD CR (2007). Národní strategický referenční rámec ČR 2007-2013 [National Strategic Reference Framework of the Czech Republic 2007-2013]. Prague: Ministry of Regional Development of the Czech Republic. Review of Economic Perspectives 350 MRD CR (2014). Dohoda o partnerství pro programové období 2014-2020 [Partnership Agreement for the Programming Period 2014-2020]. Prague: Ministry of Regional Development of the Czech Republic. MoE CR (2010). Strategický rámec udržitelného rozvoje ČR [Strategic Framework of Sustainable Development of the Czech Republic]. Prague: Ministry of Environment of the Czech Republic. NIJKAMP, P. (2009). Regional development as self-organized converging growth. In Kochendörfer-Lucius, G., Pleskovic, B., Spatial Disparities and Development Policy. Washington: The International Bank for Reconstruction and Development, World, Bank. NOVOSÁK, J., et al. (2015). Territorial cohesion and the geography of EU cohesion policy funding in the Czech Republic. Transformation in Business & Economics, 14(3C), 419-432. NOVOSÁK, J., et al. (2017a). Structural funding and intrastate regional disparities in post-communist countries. Transylvanian Review of Administrative Sciences, 13(51), 53-69. DOI: 10.24193/tras.51E.4. NOVOSÁK, J., et al. (2017b). Enterprise support policy and territorial cohesion: the Czech Republic (2007-2013). Journal of Urban and Regional Analysis, 9(2), 141-157. POPESCU, A.S. (2015). The absorption capacity of European funds – concepts. AnnalsEconomy Series, 18(3), 119-125. QIAN, H., ACS, Z.J., STOUGH, R.R. (2013). Regional systems of entrepreneurship: the nexus of human capital, knowledge and new firm formation. Journal of Economic Geography, 13(4), 559-587. DOI: 10.1093/jeg/lbs009. ROMER, P.M. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94(5), 1002-1037. DOI: 10.1086/261420. SCHRAFF, D. (2014). Buying turnout or rewarding loyalists? Electoral mobilization and EU structural funding in the German Länder. European Union Politics, 15(2), 277288. DOI: 10.1177/1465116513509308. SMALLBONE, D., BALDOCK, R., NORTH, D. (2003). Policy support for small firms in rural areas: the English experience. Environment and Planning C: Government and Policy, 21(6), 825-841. SOLOW, R.M. (1956). A contribution to the theory of economic growth. The Quarterly Journal of Economics, 70(1), 65-94. DOI 10.2307/1884513. STILLWELL, J. et al. (2010). Spatial and social disparities. In Stillwell, J. et al., Spatial and Social Disparities. Understanding Population. Berlin: Springer. STOREY, D.J. (2008). Entrepreneurship and SME Policy. Warwick: Warwick Business School. TAMÁSY, C., LE HERON, R. (2008). The geography of firm formation in New Zealand. Tijdschrift voor Economische en Sociale Geografie, 99(1), 37-52. DOI: 10.1111/j.1467-9663.2008.00438.x. Volume 18, Issue 4, 2018 351 TOSUN, J. (2014). Absorption of regional funds: a comparative analysis. Journal of Common Market Studies, 52(2), 371-387. DOI: 10.1111/jcms.12088. VEGA, A., CHIASSON, M. (2015). Towards a comprehensive framework for the evaluation of small and medium enterprise policy. Evaluation, 21(3), 359-375. DOI: 10.1177/1356389015593357. WOKOUN, R. (2007). Regionální a strukturální politika (politika soudržnosti) Evropské unie v programovém období 2007-2013 [Regional and structural policy (cohesion policy) of the European Union in the programming period 2007-2013]. Urbanismus a územní rozvoj, 10(1), 3-7. WU, J., GOPINATH, M. (2008). What causes spatial variations in economic development in the United States? American Journal of Agricultural Economics, 90(2), 392-408. DOI: 10.1111/j.1467-8276.2007.01126.x.