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The Annals of Regional Science

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24.05.2016 | Original Paper

European regional convergence revisited: the role of intangible assets

verfasst von: Jesús Peiró-Palomino

Erschienen in: The Annals of Regional Science | Ausgabe 1/2016

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Abstract

The contributions evaluating convergence across European regions are now numerous and take a wide variety of approaches. However, evidence for the most recent years is still scant, and studies considering the role of intangible assets in the enlarged European Union are virtually nonexistent. This article focuses on the convergence patterns of income per capita in 260 European regions during the period 2000–2011. Following the distribution dynamics approach, the role of a set of intangible assets is considered by introducing several conditioning schemes. In line with previous contributions the results suggest that the distribution of income is bimodal. The regional stocks of the different intangible assets considered have some power for explaining convergence tendencies in the studied period.

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NUTS stands for Nomenclature of Territorial Units for Statistics. NUTS 2 is the level of disaggregation to which the Cohesion Policy objectives are addressed.

Unfortunately, the same updating process that has provided new data for the crisis years means that this information is only available from 2000 onwards, which limits the period of analysis.

Although extending the analysis to the case of total factor productivity (TFP) would be an interesting exercise, the paper focuses only on per capita income (GDP) for reasons of space. The choice of GDP over TFP is justified because the former is more closely related to general welfare, which is closer to the objective of the paper. TFP is more linked to the efficiency of the production factors, and it is actually a vehicle for achieving higher levels of income.

For a comprehensive discussion on social capital theory and its links to economic development, see, for instance, Westlund (2006).

Other alternatives are the triangular, the rectangular or the Epanechkinov kernel. Differences between competing kernel alternatives are slight, whereas of much greater importance is the selection of the bandwidth (h), which determines the amplitude of the bumps (Silverman 1986). When h is too small it produces an excessive number of bumps (undersmoothing), which severely hinders understanding of the data structure. In contrast, when h is too large, some of the features of the data might remain hidden (oversmoothing).

Following Rosenblatt’s (1969) proposal the conditional density can be computed as:

$$\begin{aligned} \hat{{v}}_s (m|y)=\frac{\hat{{f}}_{t,t+s} (y,m)}{\hat{{f}}_t (y)} \end{aligned}$$

where the numerator is the combined density of (y, m) and the denominator is the marginal density of y.

Additional details can be found in the seminal papers (see Quah 1993a, b, 1996a, b, c) and posterior implementations such as Poletti Laurini and Valls Pereira (2009) or Fischer and Stumpner (2008).

The selection of the level of territorial aggregation has become a focus of debate. For instance, Boldrin and Canova (2001) and Basile (2008) criticize the use of NUTS 2 in growth studies, arguing that some NUTS 2 are artificially separated from their hinterland. However, most of the convergence literature in Europe considers these territorial units, since the objectives of the Regional Policy are addressed to NUTS 2 regions.

http://ec.europa.eu/eurostat.

http://europeanvaluesstudy.eu.

In contrast to the rest of the variables in the analysis, aggregated at NUTS 2 level, social capital indicator is constructed at NUTS 1 level because this is the smallest level of disaggregation for some countries. Accordingly, all the regions belonging to the same NUTS 1 area are assumed to have an identical level of social capital. This aggregation level is common in social capital studies (see Beugelsdijk and Van Schaik 2005; Forte et al. 2015).

As explained in Sect. 4.1, it might be relevant the differentiation between pre-crisis years and crisis years. This is the reason why results are reported for the intermediate year 2007, the last of the expansion period.

See Kumar and Russell (2002) for technical details and Murillo-Melchor et al. (2010) or Thieme et al. (2012) for recent applications.

The estimation of a panel data model allowing for the inclusion of both regional fixed effects and temporal effects seems, perhaps, a more suitable strategy. However, the time span considered is too short for a successful implementation of a panel data, since normally in this type of regressions the data are aggregated in periods of 5–10 years.

For a complete description of the different spatial models see, for instance, LeSage and Pace (2009).

In order to save space, results for individual years and for the two subperiods are not included. They can be provided upon request.

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Titel: European regional convergence revisited: the role of intangible assets
verfasst von: Jesús Peiró-Palomino
Publikationsdatum: 24.05.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: The Annals of Regional Science / Ausgabe 1/2016
Print ISSN: 0570-1864
Elektronische ISSN: 1432-0592
DOI: https://doi.org/10.1007/s00168-016-0767-4

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