Technological coherence and the adaptive resilience of regional economies

ABSTRACT

This paper explores the effect of different regional technological profiles on the resilience of regional economies to exogenous shocks. It presents an empirical examination of the determinants of resilience through panel analyses of UK NUTS-III-level data for the period 2004–12. The results indicate that regions endowed with technologically coherent – and not simply diversified – knowledge bases are better prepared to face an unforeseen downturn and display adaptive resilience. Moreover, local economies tend to be more adaptable if they innovate in sectors with the strongest growth opportunities, even though firms’ entry does not appear to contribute significantly towards resilience.

KEYWORDS:

• resilience
• adaptation
• innovation
• technological variety
• financial crisis

JEL:

• O30
• O33
• R11

ACKNOWLEDGEMENTS

The authors thank Dieter Kogler, Alessandra Faggian, Andrea Morrison, the CBR team in Cambridge, and the faculty and doctoral students at the Department of Economics and Statistics ‘Cognetti de Martiis’, Turin. They are grateful to the editor and two anonymous referees for comments, criticisms and suggestions. The paper also benefited greatly from feedback received from the participants and discussants at the 56th ERSA Congress, Eurkind GCW 2016 Conference, the DRUID 2017 Conference, and at research seminars at the University of Turin, University of Birmingham and University College Dublin.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. For an in-depth discussion of the notions of resilience and equilibrium in dynamic economic systems, see Reggiani et al. (2002).

2. There are no systematic quantitative data on all these factors, and this underlines the importance of complementary qualitative accounts of resilience generated through a case-study approach to resilience processes in specific local contexts.

3. Van Praag and Versloot (2007), Fritsch (2013) and Doran et al. (2016) provide extensive reviews of this research stream.

4. In the PATSTAT database, patent applications are counted according to the year in which they are filed. Moreover, they are assigned to a country/region on the basis of the inventor’s place of residence, using fractional counting if there are multiple inventors for a single patent. We downloaded the data from EUROSTAT regional statistics in autumn 2015.

5. Source: EUROSTAT, High-tech patent applications to the European patent office (EPO) by priority year (http://ec.europa.eu/eurostat/web/products-datasets/-/tsc00010). Information jointly produced by the patent offices with arguably the strictest scrutiny (EPO, JPO and USPTO) teases out of the ‘noisy’ population of patent data more precise indications of patents of higher quality and its use is well established in the literature (Nagaoka, Motohashi, & Goto, 2010; Picard & van Pottelsberghe de la Potterie, 2011).

6. Since Hidalgo, Klinger, Barabasi, and Hausmann’s (2007) seminal work, the evolutionary economic geography literature has used different data sets and methods based on co-occurrence matrices to calculate relatedness (e.g., Kogler et al., 2017; Neffke, Henning, & Boschma, 2011). As the present paper focuses on how the technological profile of regions shapes resilience, we constructed our co-occurrence matrix using patent data. This makes it possible to capture the degree of technological proximity underlying regional industrial structures following the method indicated by Jaffe (1986) and Breschi, Lissoni, and Malerba (2003).

7. See note 4.

8. Using inverse measures reduces the risk of multicollinearity in the estimation while capturing the relative effects of high and low absorptive capacities of regional labour markets.

9. Robustness checks performed on our econometric model by excluding the year 2012 from the sample do not change the results.

10. The results of robust estimations (shown in Table A1 in Appendix A in the supplemental data online) are entirely consistent.

11. We ran an additional set of estimations with a measure of coherence based on three-digit IPC classes: these results, available from the authors upon request, are also fully consistent.

12. Results are reported in Table A3 in Appendix A in the supplemental data online.

Additional information

Funding

The authors gratefully acknowledge funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme [grant agreement number 715631, TechEvo].