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Measuring resilience to economic shocks: an application to Spain

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Abstract

In this paper, we evaluate Spanish regions’ resistance to the economic crisis under three main resilience notions: “adaptative,” “engineering” and “ecological.” “Adaptative” resilience is measured through a traditional shift-share approach applied to employment, whereas “engineering” and “ecological” resilience pay attention to growth path and total employment level, in the pre- and post-crisis period. The paper presents an application of the different notion of resilience to the case of Spanish provinces in the last years. We find that provinces with sectoral structure and location advantages, or those with locational advantages in the post-crisis period (according to the “adaptative” resilience measure), exhibit a significantly lower “drop” in growth (according to the “engineering” and “ecological” resilience measure). Furthermore, we conclude that the probability of presenting a better behavior (lower “drop” in growth than the average) increases for those regions specialized in the service sector before the crisis. As expected, the worse behavior has correspond to those regions specialized in the pre-crisis period in the construction sector.

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Fig. 1

Source: Martin (2012)

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Notes

  1. “Ecological” resilience is related to evolutionary theories, which consider the region as the result of social interactions subject to a process of transition, and time is considered a flow.

  2. Nevertheless, our counterfactual predictions are obtained through a different process that is proposed in Fingleton and Palombi (2013).

  3. They are represented as dotted lines in graphs in Fig. 1.

  4. The demeaning equation is obtained by taking each variable in the regression equation in deviation from its average over time, for instance for the endogenous variable: \(y_{\mathrm{rt}}^*=y_{\mathrm{rt}} -\frac{\sum \nolimits _{t=1}^T {y_{\mathrm{rt}}}}{T}\).

  5. We have excluded the Spanish autonomous cities of Ceuta and Melilla.

  6. Such a breakpoint has also been confirmed through the calculation of successive F-Chow tests applied to the proposed models for all possible time periods.

  7. In fact, the correlation coefficient among periods for each sector is the following: (1) agriculture: 0.9644; (2) industry: 0.8984; (3) construction: 0.4307; and (4) services: 0.9561.

  8. Several studies have analyzed the consequences of sparser/denser matrix (see for instance Smith (2009)). Moreover, our results are consistent with other W matrices specified on the basis of the inverse of the distance between centroids in the provinces or the inverse of the square of such distances. Matrices based on the contiguity criteria have been discarded since we assume that (following Zeilstra and Elhorst (2014)), interaction in employment markets is determined, mainly, by distance.

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Acknowledgements

The authors are grateful for the financial support of the Spanish Government’s Ministry of Economy and Competitiveness (ECO2015-65758-P), as well as for the financial support of the Department of Industry and Innovation of Aragon Government and the European Fund of Regional Development through the GAEC group.

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Authors and Affiliations

  1. Universidad de Zaragoza, Gran Vía 2, 50005, Zaragoza, Spain

    A. M. Angulo, J. Mur & F. J. Trívez

Authors
  1. A. M. Angulo
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  2. J. Mur
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  3. F. J. Trívez
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Correspondence to A. M. Angulo.

Appendix

Appendix

See Figs. 9 and 10.

Fig. 9
figure 9

Mapping results shown in Fig. 4 (overall industrial mix and regional share and province classification for employment growth)

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Fig. 10
figure 10

Mapping of distributional effects (DE) shown in Fig. 6

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Angulo, A.M., Mur, J. & Trívez, F.J. Measuring resilience to economic shocks: an application to Spain. Ann Reg Sci 60, 349–373 (2018). https://doi.org/10.1007/s00168-017-0815-8

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