Abstract
This paper investigates the extent of regional cohesion in the European Union by taking into account the notion of knowledge-based economy. To pursue this task, we present results produced by estimating different regional models on an extensive regional data set. The empirical results suggest a development gap across the EU-27 regions. Possible ways to overcome this gap and certain areas of intervention are also suggested.
The findings, interpretations and conclusions are entirely those of the authors and do not necessarily represent the official position, policies or views of the Ministry of Rural Development and Foods and/or the Greek Government.
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Notes
- 1.
Recent empirical research has produced clear evidence that new and small firms can be a key contributor to job creation but this contribution varies spatially.
- 2.
Innovation is an iterative process, building upon the results of R&D activities and in turn informing and being informed by new research and innovations in product and processes.
- 3.
A more detailed elaboration of this model can be found in Alexiadis (2010).
- 4.
Regional policy can take several forms, e.g. transfer payments, public investment, provision of incentives to encourage private investment, etc.
- 5.
Just as an example, Pigliaru (2003) uses the ‘propensity to innovate’, which can be measured in terms of the number of patents per-capita in each region. Empirical applications can be found in Alexiadis (2010b), Alexiadis and Korres (2010).
- 6.
These two variables are in accordance with the notion of ‘smart growth’, as formulated in the strategy ‘Europe 2020’, i.e. strengthening knowledge and innovation as drivers of future growth. The structural funds are the most important financial instruments for supporting the renewed Lisbon strategy and in some countries were able to increase their GDP by almost 4 %. This strategy is monitored by a set of indicators, covering the domain of employment, innovation, research, economic reforms, social cohesion, overall economic and environmental background. In 2004, the European Commission suggested a ‘short list’ of 14 structural indicators, allowing for a “concise presentation and a better assessment of achievement over time vis-à-vis the Lisbon agenda”. These indicators include for example, gross domestic product per-capita and per-worker, employment rate, gross domestic expenditure on R&D, long-run unemployment rate, etc. Of these indicators only the ‘dispersion of employment rates’ has, by definition, an explicit spatial dimension.
- 7.
Each EU country has a different way of dividing its territory into administrative units. For the purposes of managing programmes and comparing statistics, the EU devised the NUTS system – dividing each country into statistical units (NUTS regions). The NUTS (Nomenclature des Unités Territorial les Statistiques) regions are not the same with the so-called ‘Euro-regions’, which are associations without a precise legal status, dating back to the period after World War II when local politicians in border regions tried to promote common interests on both sides of the borders.
- 8.
This slow process of regional convergence can, possibly, be explained by the low degree of labour mobility that characterises the European regions, due to linguistic and cultural barriers. As Boldrin and Canova (2001, p. 243) state ‘while capital is moving around Europe, labour is definitely not’. Obstfeld and Peri (1998) report that labour mobility in Germany, Italy and the UK over the period 1970–1995 was only about one-third of the US level.
- 9.
Alexiadis (2010a) applies this method in the case of the Greek regions.
- 10.
These regions have more possibilities to innovate if they are connected to central regions; a view put forward by Rodríguez-Pose and Crescenzi (2008).
- 11.
A target set is the EU as whole to reach R&D intensity above 3 % βυ 2010, corresponding to the new world-wide division of labour and globalisation. This target is set by the Barcelona Council in 2002 and maintained in the EUROPE 2020 strategy. Only 10 % of the EU regions were able to reach this target. In 2007, only 19 out of 287 NUTS-2 regions corresponding to only (6.6 %) were able to meet the target of 3 %. In particular, three regions in Finland (Pohjois-Suomi, Länsi-Suomi and Etelä-Suomi), four in Sweden (Stockholm, Östra Mellansverige, Västsverige and Sydsverige), seven in Germany (Dresden, Oberbayern, Darmstadt, Karlsruhe, Unterfranken, Stuttgart and Berlin), two in France (Île-de-France and Midi-Pyrénées), two in Austria (Wien and Steiermark) and one in the Netherlands (Noord-Brabant). In some of these regions, capital-cities are located (e.g. Paris, Vienna, Berlin, Stockholm and Helsinki).
- 12.
Overall, there is a tendency for R&D expenditure to be higher in urban parts of Europe. R&D spending in Europe is below 2 %, compared to 2.6 % in the US and 3.4 % in Japan, mainly due to low levels of private investment. It would take more than 50 years for Europe to reach the US level of innovation performance.
- 13.
Over the periods 1998–2000 and 2005–2007, GDP per-capita in these regions was above 75 % of the EU average; a threshold, which is a key criterion for being eligible to support from the Structural Funds. In the remaining regions GDP per-capita is still below the threshold.
- 14.
The best educated labour force is located in the urbanised regions of. There is a tendency for the best educated labour force to concentrate in or around capital cities; a pattern obvious in Northern Europe and particularly in countries with a low overall proportion of HRST.
- 15.
Only few EU-27 NUTS-2 regions (e.g. Ireland, Malta and Cyprus) appear to be in a relatively favourable position. An inspection, however, at the NUTS-3 level might reveal a different picture.
- 16.
Nevertheless, a rural character of a region is not always a disadvantage. Several rural regions, for example, attract retirees, which provide a source of income and future growth.
- 17.
In 2008, regions with the highest unemployment rates (above 10 %) are mainly located in Southern Spain, Southern Italy, Greece, Eastern part of Germany, Poland, Hungary and Slovakia. The lowest levels can be found in the United Kingdom, Belgium, and the Netherlands as well as in capital city-regions of Eastern Europe.
- 18.
Population density is defined as the ratio of the population of a territory to its size (inhabitants per km2). A ratio between 60 and 120 is estimated for the diverging group. In EU-27, the capital city-regions are among the most densely populated, located in central areas of Europe especially around Brussels. In this context, some remarks by Krugman (1991) are highly pertinent: ‘It has often been noted that night-time satellite photos of Europe reveal little of political boundaries but clearly suggest a centre-periphery pattern whose hub is somewhere in or near Belgium.’ (p. 484) [Emphasis added]
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Alexiadis, S., Ladias, C., Milionis, S. (2013). Competitiveness and Cohesion in the European Union: A Dilemma?. In: Karasavvoglou, A., Polychronidou, P. (eds) Balkan and Eastern European Countries in the Midst of the Global Economic Crisis. Contributions to Economics. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2873-3_8
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