Localization of Business Services in European Regions: Large Urban Areas Stand Out

Abstract

The recent increase in business services in terms of both value added and employment has had a positive effect on regional economies, helping them to create a comparative advantage and contributing to the general development of regional productive systems. This heterogeneous set of activities is characterized, among others, by its uneven distribution in the territory. In this paper we aim to identify the key factors that influence the location of business services within the NUTS 2 regions of the European Union. Using a multivariate analytical approach (combining a principal components factor analysis and a multiple linear regression by means of ordinary least squares), four key factors have been identified. In terms of their influence on the location of business services, we can pinpoint the following factors: urban dimension, competitiveness, accessibility and economic dynamism. It was found that business services tend to locate in urban, densely populated and dynamic areas with high levels of competitiveness and easy access.

Notes

1. Business services are defined in the appendix.

2. Compiled by Melikhova (2010), modified.

3. All business service activities form part of the so-called knowledge-intensive service sector (see the appendix).

4. See Puga (2010) for a review of the literature on the economics of agglomeration.

5. NACE is the acronym for ‘Nomenclature statistique des activités économiques dans la Communauté européenne.

6. The non-financial business economy includes the following sectors (European Union, 2010): Mining and Quarrying (C); Manufacturing (D); Electricity, Gas and Water Supply (E); Construction (F); Wholesale and Retail Trades (G); Hotels and Restaurants (H); Transport, Storage and Communication (I); and Real Estate, Renting and Business Activities (K). It excludes agricultural, forestry and fishing activities. It also excludes public administration and non-market services such as education and health. Financial services (NACE section J) are also not included.

7. HRST includes those who have completed a tertiary level of education and/or are employed in a science and technology occupation where a tertiary level of education is normally required.

8. RCI is based on three groups of indicators: (a) Basic pillars: Institutions, macro-economic stability, infrastructure, health and quality of primary and secondary education. (b) Efficiency pillars: Higher education, training and lifelong learning, labour market efficiency and market size. (c) Innovation pillars: Technological readiness, business sophistication and innovation.

9. In order to estimate the convenience of the application of the PCFA, three attempts were carried out. Thus, the determinant of the correlation matrix is 0, which demonstrates the existence of the linear dependence between the variables. Moreover, both the Kayser–Meyer–Olkin measure and the Barlett's test of sphericity indicate that the reduced set of variables is appropriate for the factor analysis (Hair et al., 1999). Therefore, these results point out that there is need to reduce the initial number of variables to remove redundant information.

10. To interpret the factor matrix, the factor rotation is used. It provides a new and easier way to interpret the matrix. To achieve the rotation, we used the Varimax method. Its purpose is to maximize the variation of factors. To achieve this, some factors should have high loads while others should have loads close to zero.

11. All factors are significant at 1%.