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Specialisation changes in European regions: the role played by externalities across regions

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Abstract

This paper seeks to determine the factors underpinning changes in regional specialisation patterns in the European Union between 1991 and 2002. First, we consider a set of determinants previously identified in the regional literature, including agglomeration effects and other specific regional factors (business cycle, amount of investment, etc.). However, we then also take into account the fact that the evolution in a region’s specialisation pattern may be affected by the specialisation behaviour of other regions. Thus, not only do we consider the pattern of evolution in a region’s most proximate neighbours but we also examine that of their regional peers, i.e., regions with a similar specialisation pattern independent of their location. Notwithstanding, our empirical evidence indicates that physical distance still plays a very significant, and even more influential, role than similarity in specialisation.

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Notes

  1. In models of regional economic growth, the spatial lag parameter of the endogenous variable is usually interpreted as the impact of a change in growth in region j on growth in other regions. Spatial correlation in the error term, on the other hand, can be interpreted as reflecting regions’ common reaction to shocks because of omitted variables that are spatially correlated (Anselin 1988). In case of developing a cross-section analysis, the strategy to discriminate between one and the other is generally based on the estimation of both alternatives, the spatial lag and the spatial error model, and then run a battery of diagnostic tests to discriminate between them. However, such diagnostics are not available for a panel structure. This is why, since the interpretation is much clearer in the first model, we follow the strategy of estimating the spatial lag model based on theoretical and interpretational reasons (such reasoning is discussed in Fingleton and López-Bazo 2006).

  2. We estimate this spatial lag model with two weighted matrices and employ a maximum likelihood estimation. We are indebted to Paul Elhorst for providing us with his Matlab code for estimating a two-regime model with panel data (Elhorst and Fréret 2009) which we have adapted to our specific case.

  3. We included a square term to account for a possible non-linear effect for the levels of compensations per employee.

  4. We are indebted to Salvador Barrios for providing preliminary computations from the European Labour Force Survey. Indeed, we obtained the percentage values of people in each region attaining low, medium or high education. We expanded the data in order to cover the whole period based on average growth rates. These data consider educational endowments once the effects of mobility have been noted.

  5. We use the common formula for market potential data for each region j: \( {\text{MP}}_{j,t} = \sum\nolimits_{j \ne i} {{{{\text{GDP}}_{i,t} } \mathord{\left/ {\vphantom {{{\text{GDP}}_{i,t} } {d_{ji} }}} \right. \kern-\nulldelimiterspace} {d_{ji} }}} \) in which GDP represents the level of Gross Domestic Product. Likewise, d ji denotes the distance between two capital cities of regions i and j. We also consider non-linear effects in market potential through its square term.

  6. We thank Raffaele Paci and Stefano Usai from CRENOS for providing the number of patents at the regional level. Although the use of patents as a proxy for innovation is not without criticism, we consider it as this has been the proxy most widely used in the literature on innovation. Patents represent the outcome of the inventive and innovative process even though there may be inventions which are never patented, as well as patents which are never developed into innovations. However, the patenting procedures require innovations to have novelty and usability features and imply relevant costs for the proponent. This in turn implies that patented innovations, especially those extended to foreign countries, are expected to have economic, although highly heterogeneous, value (see Griliches 1990 for a broader discussion on the topic).

  7. On this idea of the importance of proximity in regional interactions, Le Gallo and Ertur (2003) and Ezcurra et al. (2006) use a spatial weight matrix that takes into account the interactions between the ten nearest neighbours.

  8. As is well known, consistent estimation of the individual fixed effects is not possible when N→∞, due to the incidental parameter problem. As pointed out by Anselin et al. (2008) “since spatial models rely on the asymptotics in the cross-sectional dimension to obtain consistency and asymptotic normality of estimators, this would preclude the fixed effects model from being extended with a spatial lag”. However, the same authors point out that when we are mainly interested in obtaining consistent estimates of the β coefficients, using the demeaned spatial regressions may be appropriate, such as through the use of the maximum likelihood estimation approach given by Elhorst (2003). One complication with this is that the variance covariance matrix of the demeaned error term differs from the usual matrix. However, since a more careful elaboration of this alternative approach is part of the ongoing research, we estimate our spatial lag fixed effect model using the approach outline by Elhorst.

  9. The Krugman index is obtained by means of the following expression: \( K_{ja} = \sum {| {s_{ij}^{S} - s_{ia}^{S} } |} \) where \( s_{ij}^{S} \) represents the share of employment in industry i in region j in total employment of region j. Meanwhile, \( s_{ia}^{S} \) denotes the characteristics of the average region (a) computed for all other regions distinct to region j (k ≠ j). On the one hand, the higher values for the Krugman index indicate that region j shows a distinct specialisation pattern with respect to the average level. On the other hand, the greater values for the dissimilarity index show maximum specialisation levels for the region under review.

  10. The data on the ICON index are for 2001, the index only being available for that year.

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Acknowledgments

Toni Mora gratefully acknowledges the financial support of the Spanish Ministry of Science and Technology given under grant SEJ2006-01161/ECON & 2009SGR102. (Generalitat of Catalonia); and Rosina Moreno acknowledges the financial support of the Spanish Ministry of Science and Technology given under grant SEJ2005-07814/ECON. We would also like to thank E. Bode, B. Johansson, E. López-Bazo, A. Páez and G. Piras for comments received.

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  1. School of Economics and Social Sciences, Universitat Internacional de Catalunya & Barcelona Institute of Economics (IEB), Immaculada, 22, 08017, Barcelona, Spain

    Toni Mora

  2. Research Group AQR-IREA, University of Barcelona, Av. Diagonal 690, 08034, Barcelona, Spain

    Rosina Moreno

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Correspondence to Toni Mora.

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Mora, T., Moreno, R. Specialisation changes in European regions: the role played by externalities across regions. J Geogr Syst 12, 311–334 (2010). https://doi.org/10.1007/s10109-009-0098-4

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