Abstract
In the first part of this book, close attention has been paid to the importance of a regional analysis of globalization trends since these are expected to be spatially differentiated, and to have diverse impacts not only at the national, but especially at the regional level. This chapter takes up the challenge of describing the spatial patterns exhibited by globalization trends over recent years, the aim being to highlight which European regions are most affected.
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Notes
- 1.
For a description of the FDIRegio database, see Annex 2.1 to Chap. 2.
- 2.
This database has been kindly made available to the authors by IGEAT, University of Brussels. For a description of the regional sectoral gross value-added dataset, see Annex 4.1 to Chap. 4.
- 3.
Since large regions collect, in general, more firms than small regions, data on FDI have been size-adjusted. The following maps plot the number of new foreign firms per millions of inhabitants.
- 4.
The econometric evidence discussed in Chap. 7 will shed more light on these issues.
- 5.
Urbanization externalities occur because of the proximity of various economic activities. This encompasses market size effects, diversity of services, intermediate and final products, and the presence of social capital (infrastructure, innovation potential and social capability). See Fujita and Thisse 2002, for an in-depth analysis of different kinds of agglomeration economies.
- 6.
- 7.
- 8.
This necessarily holds for extra-European FDI, which enters the definition of global regions, but it needs to be verified for intra-European FDI.
- 9.
Simple descriptive statistics techniques (ANOVA) were used to explore this issue. Readers not familiar with this methodology can find useful explanations in Rabe-Hesketh and Everitt 2004.
- 10.
In particular, the distribution of FDI differs between globalized and non-globalized regions taken together, while no statistically significant differences have been detected between regional and local players.
- 11.
The presence of regional specific assets is only relevant in regional development processes if these assets complement the strategic needs of global producers (Coe et al. 2004).
- 12.
According to principal component analysis, common factors explain 85% of the sample variance, while factors specific to either intra- or extra-European FDI account for another 11% of variance. Another 3% can be explained by differences between manufacturing and service FDI. The complete results are set out in Annex 5.1 to this chapter.
- 13.
In 1979, Erikson et al. propose merging “higher-grade professionals, administrators and officials, managers in industrial establishments” to form a high-level profession category.
- 14.
For the specifications of the professional types, see Annex 5.2 to this chapter.
- 15.
The tiny size of the sample precludes reliable results for individual regions.
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Appendices
Annex 5.1 FDI Location Patterns: A Principal Component Analysis
Annex 5.2 Typology of Professions
Annex 5.3 Functional Specialization and Regional Type: A Correspondence Analysis
Categories | Mass | Overall quality | % intertia | Dimension 1 | Dimension 2 | Dimension 3 | |||
|---|---|---|---|---|---|---|---|---|---|
Coord | Sqcoord | Contrib | Coord | Sqcoord | Contrib | ||||
Types | |||||||||
Local Players – Old 15 | 0.203 | 0.969 | 0.103 | 0.235 | 0.484 | 0.059 | 0.375 | 0.485 | 0.383 |
Regional Players – Old 15 | 0.288 | 0.854 | 0.021 | −0.066 | 0.270 | 0.007 | 0.155 | 0.584 | 0.093 |
Global Players – Old 15 | 0.326 | 0.996 | 0.350 | −0.481 | 0.954 | 0.397 | −0.160 | 0.042 | 0.112 |
Local Players – New 12 | 0.031 | 0.968 | 0.113 | 0.872 | 0.912 | 0.123 | −0.347 | 0.057 | 0.049 |
Regional Players – New 12 | 0.111 | 0.989 | 0.357 | 0.830 | 0.950 | 0.403 | −0.267 | 0.039 | 0.106 |
Global Players – New 12 | 0.042 | 0.764 | 0.055 | 0.219 | 0.162 | 0.011 | −0.675 | 0.602 | 0.256 |
Professional status | |||||||||
11. Public managers | 0.007 | 0.412 | 0.004 | −0.070 | 0.037 | 0.000 | 0.355 | 0.375 | 0.012 |
12. Corporate managers | 0.073 | 0.985 | 0.123 | −0.580 | 0.887 | 0.130 | −0.308 | 0.098 | 0.093 |
13. SMEs managers | 0.055 | 0.820 | 0.031 | −0.108 | 0.092 | 0.003 | 0.484 | 0.728 | 0.172 |
21. Scientists | 0.047 | 0.990 | 0.141 | −0.769 | 0.888 | 0.148 | −0.416 | 0.102 | 0.110 |
231. University lecturers | 0.008 | 0.901 | 0.008 | −0.436 | 0.867 | 0.008 | −0.138 | 0.034 | 0.002 |
241. Business professionals | 0.020 | 0.908 | 0.064 | −0.238 | 0.080 | 0.006 | −1.222 | 0.827 | 0.406 |
41. Office workers | 0.151 | 0.987 | 0.191 | −0.511 | 0.913 | 0.207 | 0.232 | 0.074 | 0.109 |
42. Customer service workers | 0.032 | 0.990 | 0.027 | −0.428 | 0.966 | 0.031 | 0.106 | 0.023 | 0.005 |
5. Retail workers | 0.219 | 0.567 | 0.008 | 0.002 | 0.000 | 0.000 | 0.106 | 0.567 | 0.033 |
7. Craft workers | 0.241 | 0.992 | 0.207 | 0.437 | 0.991 | 0.244 | 0.011 | 0.000 | 0.000 |
8. Blue collars | 0.146 | 0.988 | 0.197 | 0.536 | 0.950 | 0.222 | −0.172 | 0.038 | 0.058 |
Annex 5.4 Growth of Intertwined Functions by Regional Type: A Principal Component Analysis
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Capello, R., Fratesi, U., Resmini, L. (2011). Spatial Patterns of Globalization Trends. In: Globalization and Regional Growth in Europe. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19251-7_5
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