Abstract
The previous chapter has examined club convergence in the context of the EU-27 regions, thus providing an alternative perspective on the issue of regional convergence in an enlarged Europe. While previous studies on European regions claim that convergence is slow, the empirical tests reported on Chap. 6 establish that convergence is a property that characterises the regions of the ‘old’ member-states of the European Union together with a selected set of regions located in new member-states.
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Notes
- 1.
Heteroscedasticity occurs when the disturbance variance is not constant and arises due to measurement problems, inadequate specification or omitted variables.
- 2.
An alternative is to include a spatial moving average error, \( \varepsilon = \lambda {\mathbf{W}}\nu + \nu \), with, \( \nu \sim N(0,{{\sigma }^2}{\mathbf{I}}) \) or a spatial error component model, \( \varepsilon = {\mathbf{W}}\nu + \psi \), with two independent error components, one associated with the ‘region’ (weighted average of neighbour’s error), and one which is location-specific (Acosta 2010).
- 3.
For example, entire countries (e.g. Denmark, Cyprus, Latvia, Lithuania and Slovenia) are treated by EUROSTAT as NUTS2 regions.
- 4.
If the obtained p-value is less that 0.10, 0.05 and 0.01 then the \( {{H}_O} \) hypothesis is rejected and the alternative \( {{H}_a} \) is accepted at 10 %, 5 % and 1 % level of significance, respectively.
- 5.
The presence of spatial autocorrelation makes the R2 an unreliable measure of the goodness of fit and so is not reported.
- 6.
As a rule of thumb, the best fitting model is the one that yields the smallest values for the AIC or the SBC criterion. The SBC has superior properties and is asymptotically consistent, whereas the AIC is biased towards selecting an overparameterized model (Enders 1995).
- 7.
This production function is similar to Barro and Sala-i-Martin (1997) in which technology adoption/diffusion is approximated by the quantity of non-durable inputs (\( {{X}_j} \)), modelled as a separate element in a production function, i.e. \( {{Y}_i} = {{A}_i}L_i^{{1 - \alpha }}\sum\limits_{{j = 1}}^N {{{{({{X}_j})}}^{\alpha }}} \).
- 8.
- 9.
This point is aptly summarised by Rosenberg (1982) when he suggests that: ‘It may be seriously argued that, historically, European receptivity to new technologies, and the capacity to assimilate them whatever their origin, has been as important as inventiveness itself’ (p. 245).
- 10.
- 11.
For a more detailed analysis see Appendix II.
- 12.
As Funke and Niebuhr (2005) claim: ‘[…] current R&D should affect future GDP.’ (p. 149).
- 13.
Richardson (1973b), for example notes: ‘Innovations and technical progress do not spread evenly and rapidly over space but frequently cluster in a prosperous region; for instance, technical progress may be a function of the levels of R and D expenditures which are higher in high-income regions.’(p. 56) while Hirschman (1962) argues along similar lines. More recently, Mulas-Granados and Sanz (2008) report evidence of a strong relationship between the distribution or technology indicators and the distribution of regional income in Europe.
- 14.
In an empirical study for OECD economies Verspagen (1995) assumes that the initial level of per capita GDP takes into account the effect of knowledge spillovers.
- 15.
Marjit and Beladi (1998) make a distinction between product and process patents.
- 16.
EUROSTAT is the main source for the data used in the empirical analysis in this chapter.
- 17.
Jaffe et al. (1993) argue that knowledge spillovers as evidenced in spatial patterns of patent citations are strongly localized.
- 18.
Andonelli (1990) and Alderman and Fisher (1992) use a similar approach in identifying sectors that are able to adopt technological innovations, although in a context other than of regional convergence.
- 19.
This is region UKJ1 (Berkshire, Bucks and Oxfordshire). The choice is made for two reasons. First, this region has retained its leading position throughout the examined period. In 1995 the share of employment in high-tech manufacturing and knowledge-intensive high-technology services (‘innovative employment’) in the total labour force of this region was 9.77 % and 11.44 % in 2006. Second, this region is an illustrative example of local empowerment can create possibilities of invention to overcome local difficulties, and enhance the likelihood of increased localisation of the geographic scope of spillovers between knowledge creation and production (Smith 2000, p. 88).
- 20.
Richardson (1973c) notes that the relevant empirical work relies heavily on demographic data and, consequently, growth is associated with an increase in a locality’s population.
- 21.
- 22.
- 23.
- 24.
Empirical tests were conducted also using location quotients and results were very similar.
- 25.
Typical examples are the regions FR52 and PL33, highly specialised in food/beverage processing and mining and quarrying, respectively. Highly specialised regions in activities related to wood, pulp and paper products can be found in the Baltic and Nordic forested areas.
- 26.
Regions UKD2, BE21, BE31, DEE1, DE71, DEA3, DEE2 and DEB3, for example, can be characterised as highly specialised in chemical products, DE26 in machinery equipments, DE21 in R&D and UKJ1 in computer activities.
- 27.
- 28.
At the firm level Griffith et al. (2009) present evidence that establishments further behind the industry frontier experience faster rates of productivity growth.
- 29.
More specifically, for any regression equation with \( k \) independent variables, it is possible to calculate a VIF for every dependent variable running an OLS regression for each variable as a function of all the other explanatory variables. Then a VIF is calculated for each \( {{\hat{\beta }}_i} \): \( VIF({{\hat{\beta }}_i}) = \frac{1}{{1 - R_i^2}},\quad \forall i = 1,\; \ldots, \;k \), where \( R_i^2 \) is the multiple correlation coefficient. As a rule of thumb, if \( VIF({{\hat{\beta }}_i}) > 5 \), or \( VIF({{\hat{\beta }}_i}) > 10 \)according to Neter et al. (1990), then multicollinearity is high.
- 30.
Gripaios et al. (2000) using actual percentages of employment in similar sectors for the UK counties, estimate a negative coefficient. In this case a negative coefficient can be interpreted as a source of convergence, if employment in these sectors is located mainly in rich regions. In this case, a high percentage of employment in such sectors is associated with low rates of growth, thus, promoting convergence between rich and poor regions. Experimenting with the proxy by Gripaios et al. (2000) the resulting coefficient was positive, which can be considered an indication of diverging tendencies (Alexiadis 2010a). However, the technological gap variable is chosen because of its ability to embody two concepts, namely the extent of the potential for technology adoption and the appropriateness of infrastructure conditions to take advantage of this potential.
- 31.
The null hypothesis associated with the Ramsey RESET test is accepted indicating that the particular model is well specified. Furthermore, the probability associated with the F-statistic for overall significance of the regression rejects the null hypothesis of zero coefficients.
- 32.
The variable employed to approximate localisation in this study does not distinguish between dynamic and non-dynamic sectors. That is, it does not distinguish between the different sectors in which regions are specialised, some of which grow faster than others. Dynamic sectors could be defined as promoting exports; in which case a location quotient would be a more suitable proxy. However, using such a proxy gives similar results in econometric terms.
- 33.
This outcome can be interpreted also as evidence that negative externalities are present in highly diversified regions.
- 34.
Some evidence using time-series data is provided in Appendix III.
- 35.
The existing observations are ordered in increasing order based on a control variable and then the split that minimises the residual variance is identified. Durlauf and Johnson (1995) propose two methods. The first identifies the number of splitting in an arbitrary way, based exclusively on one variable (usually per-capita income). The second implements a branching approach. The entire sample is divided into two sub-samples based on the variable that produces the best fit and this procedure is repeated for each of the resulting sub-samples, until the degrees of freedom become too small or the split into sub-samples becomes insignificant.
- 36.
Stated in alternative terms, Eq. 7.26 and 7.27 imply that low-level equilibria arise not because the ratio between the poor and the rich regions is below some critical value, but due to the fact that the poor regions have not managed to cross a threshold level in their initial technological and agglomerative conditions.
- 37.
‘Technologically lagging’ regions are defined as regions with employment shares in technological advanced sectors less than 75 % of the EU-27 average.
- 38.
The European Commission (1999) argues that a low innovative capacity, combined with an ‘unfavourable’ sectoral structure and disparities in transport and telecommunications infrastructure, reduces competitiveness. Similar factors were identified by Fatás (1997), Beine and Hecq (1998), Paci and Pigliaru (1999a,b), Martin (1998), Dyson (2000), Marginson and Sisson (2002).
- 39.
Such findings are in accordance with Fothergill and Gudgin (1982) who, in an examination of the growth performance of the UK regions over the period 1952–1979, find that those regions with heavy concentration in urban areas suffer from slow growth. They also detect divergence in terms of the growth rates of regional manufacturing employment.
- 40.
Huggins and Johnston (2009) identify a series of characteristics ‘unfavourable’ to regional competitiveness, such as a limited number of knowledge-intensive firms and organizations and a ‘thin’ institutional structure. Several other elements were proposed The relevant literature (e.g. Doloreux and Dionne 2008; Malecki 2007; Tödtling and Trippl 2005) put emphasis on the lack of an innovation-driven public sector, high dependence on Small-Medium Enterprises (SMEs) with low-growth trajectories (a typical characteristic of the regions in the Southern Member-States), fragmented connection to external sources of knowledge, etc.
- 41.
These are: LU, BE10, DE60, FR10, NL11, BE21 and DE71. The choice was made on the basis that GVA per-worker in these regions exceeds 4 (in natural logarithms). Moreover, the time-series tests in Appendix III imply absence of convergence towards these regions.
- 42.
Excluding these regions yields a rate of absolute convergence about 1 %.
- 43.
Interestingly, the value of the initial level of productivity implied by the spatial-lag version of the extended club convergence model excludes regions CZ04 and CZ06 from the interim club.
- 44.
This similarity between the ‘northern’ regions of the EU-15 is pointed out by several studies. Neven and Gouyette (1995), for example, prove that the ‘northern’ regions are more homogenous in terms of output per-head than the ‘southern’ regions. This pattern is attributed, mainly, to two factors. First, the regions of the ‘northern’ countries exhibited a better degree of adjustment to policy changes during the mid 1980s (i.e. implementation of the internal market programme) and second the response of population of the southern regions to wages and unemployment differences is slow, relative to the ‘northern’ regions. Cardoso (1993) uses the Dutch regions during the period 1984–1988, as an example of how migration contributes to a fall in regional inequalities and concludes that human resource management and spatial mobility (e.g. spatial redistribution of civil servants) are more effective in promoting regional development than financial transfers. Similarly, Boldrin and Canova (1995) argue that regional and structural policies in the EU are of a redistributive character and have limited success in fostering economic growth. On the premise that tests for regional convergence can be considered as an indirect evaluation of the effectiveness of regional policy, this argument receives further support given the relatively low rates of regional convergence reported in this study.
- 45.
Rodríguez-Pose (1999a) reports an analogous pattern. This pattern can be attributed to specific characteristics of these countries. Puga (2002) points out that the industrial structures of the UK, France, Italy and Germany are relatively similar, but different from Greece and Portugal. In these countries, for example, several regions are characterised by a very large agricultural sector. Consequently a substantial amount of funds were transferred to these countries. Between 2000 and 2006, for example, cohesion programmes boosted Greece’s GDP by 2.8 % and Portugal’s by 2 %. In Portugal there was a successful use of these funds. On the other hand, in Greece (a country with unfavourable investment climate due to unstable macroeconomic policies, characterized by the presence of a substantial ‘black’ economy, about 29–35 % of total employment), there are several difficulties by authorities to implement European regional development programmes and regional policies lack an overall strategy, a programming approach and co-ordination.
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Alexiadis, S. (2012). ‘Club Convergence’: Geography, Externalities and Technology. In: Convergence Clubs and Spatial Externalities. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31626-5_7
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