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Knowledge, innovation, agglomeration and regional convergence in the EU: motivating place-based regional intervention

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Abstract

The accumulation of knowledge, human capital and agglomeration are indicated as prominent sources of externalities. Therefore, this study examines their contributions to the economic growth of regions in Europe, while accounting for non-linear and threshold effects as well as spatial dependence. The results highlight differentiated growth patterns for less and more developed regions with the effect of knowledge being considerable only in the latter group. The findings suggest that there is the potential for innovation and agglomeration in many less developed regions located in both the new member states (NMS) and the old member states (OMS). However, to reach sustained growth, structural change is necessary in these regions. We conclude that the existing gaps in the economic structure are deemed responsible for the persistence of income disparities. This reinforces the call for specific policy actions in catching-up regions, thus strengthening the arguments in favour of a place-based approach to regional policy.

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Notes

  1. An extensive discussion regarding the different implications of theoretical growth models based on the convergence hypothesis is found in Galor (1996).

  2. For more information on the territorial classification adopted by the OECD, please refer to the following: http://www.oecd.org/dataoecd/60/35/42392313.pdf.

  3. Millions of euros at 2000 prices.

  4. This measure of agglomeration does not consider the extent to which the industry mix in the region is responsible for agglomeration externalities. It only examines the total employment in the region. Notably, there are different views regarding the conceptualisation of agglomeration economies, as reflected in a number of surveys and meta-studies (Melo et al. 2009; Beaudry and Schiffauerova 2009; De Groot et al. 2009), which oppose specialisation to diversity and interest as well as the notion of related variety.

  5. See the Appendix for a description of the contiguity matrix used for the Moran test.

  6. See http://ec.europa.eu/regional_policy/atlas2007/index_en.htm for more information.

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Acknowledgments

The research was conducted while Gianni Guastella was PhD student at the Università Cattolica, Milan, and the financial support from the institution is gratefully acknowledged. The paper benefits of the valuable comments received by the anonymous reviewers. An earlier version of the paper was presented at the RSAI 2012 world congress. Any error and omission should be considered responsibility of the authors.

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  1. Università Cattolica, Via Emilia Parmense 84, 29122, Piacenza (PC), Italy

    Gianni Guastella & Francesco Timpano

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  1. Gianni Guastella
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Correspondence to Gianni Guastella.

Appendix

Appendix

1.1 Contiguity matrix

The generic element \({{w}_{ij}}\) of the contiguity matrix \(W\) is defined using the critical cut-off distance criterion with \({{d}^{*}}\) being the minimum distance such that at least each region has one neighbouring region. The squared inverse distance between each pair of contiguous regions is used in place of the simple binary option and the elements are row-standardised.

$${{w}_{ij}}=\left\{ \begin{matrix} d_{ij}^{-2}/\sum\limits_{j}{d_{ij}^{-2}}\text{ if }d<{{d}^{*}} \\ 0\text{ otherwise } \\\end{matrix} \right.$$

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Guastella, G., Timpano, F. Knowledge, innovation, agglomeration and regional convergence in the EU: motivating place-based regional intervention. Rev Reg Res 36, 121–143 (2016). https://doi.org/10.1007/s10037-015-0104-x

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