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Regional economic resilience: the experience of the Italian local labor systems

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Abstract

After defining the concept of resilience and its application to the regional context, the paper presents a preliminary evaluation of regional economic resilience in the case of the Italian regions. In doing so, we follow the approach by Martin (J Econ Geogr 12:1–32, 2012) and Martin and Sunley (2015) who identify three different dimensions to regional economic resilience: (a) resistance, i.e., the degree of sensitivity or depth of reaction of a regional economy to a recessionary shock; (b) recovery, i.e., the speed and magnitude of the recovery; (c) reorientation and renewal, i.e., the ability of a region to adapt in response to the shock and renew its growth path. The analysis is conducted at the local labor systems (LLS) geographical level and focuses, at this stage, only on the first two dimensions of resilience, i.e., resistance and recovery. The recessionary shock (2009–2010) is defined following the Italian National Statistical Institute approach for which a recession implies a decrease in GDP for three consecutive trimesters. The pre-recessionary period is 2007–2008 and the recovery period 2011 (as a new recession started again in Italy at the end of 2011). The results clearly point at very heterogeneous resilience for the Italian LLS.

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Notes

  1. For a more detailed definition of the methodology used, see ISTAT (1997).

  2. The original formula in Martin (2012, p. 16) is actually (\(\Delta {E}_\mathrm{r}/ {E}_\mathrm{r})/ (\Delta {E}_\mathrm{N}/{E}_\mathrm{N})\). However, the formula does not match what Martin himself reports in the same paper in Table 5, p. 22. A ratio between percentage change as the one in the formula above can have both negative and positive value and it is not centered around the value of 1, while the values reported in Table 5 seem to be centered around 1 and are all positive (this is possible, but seems a bit implausible). Hence, we modified the formula to match the “sensitivity index” values reported by Martin (2012) in Table 5.

  3. Summary statistics on the explanatory variables are in Appendix, Table 3.

  4. While deep lags would be ideal, because of the shifting LLS boundaries, this is not possible without intensive manual aggregation of the data to reconstruct consistent areas.

  5. The Hausman test for IIA supports the IIA hypothesis for all categories. Although a variance inflation factor (VIF) cannot be calculated for a nonlinear model, we ran an OLS model and test for multicollinearity of the regressors. None of the regressors was multicollinear—with a maximum VIF of 2.75 (for one of the regional fixed effects) and an average of 1.69.

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Authors and Affiliations

  1. AED Economics Department, The Ohio State University, Columbus, OH, USA

    Alessandra Faggian & Timothy Jaquet

  2. Gran Sasso Science Institute (GSSI), Social Sciences, L’Aquila, Italy

    Alessandra Faggian

  3. Department of Methods and Models for Economics, Territory and Finance, Sapienza University of Rome, Rome, Italy

    Roberta Gemmiti & Isabella Santini

Authors
  1. Alessandra Faggian
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  2. Roberta Gemmiti
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  3. Timothy Jaquet
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  4. Isabella Santini
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Corresponding author

Correspondence to Alessandra Faggian.

Appendix

Appendix

See Table 3.

Table 3 Summary statistics
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Faggian, A., Gemmiti, R., Jaquet, T. et al. Regional economic resilience: the experience of the Italian local labor systems. Ann Reg Sci 60, 393–410 (2018). https://doi.org/10.1007/s00168-017-0822-9

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