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Gauging two sides of regional economic resilience in Western Germany—Why sensitivity and recovery should not be lumped together

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Abstract

The paper empirically investigates the economic resilience of Western German regions in the wake of the Great Recession of 2008/2009. In particular, the focus is laid on the influence of regional agglomeration economies (arising from specialisation, related and unrelated variety) and the explicit subdivision of short-term resilience into sensitivity and recovery. The necessity to distinguish between different factors and phases is well documented by means of the spatial lag and OLS regression results as all three types of agglomeration economies reveal varying, if not opposing directions of influences across the sensitivity and recovery phase. A pregnant example refers to regional specialisation. Not only does it increase sensitivity while exerting a positive influence during the recovery phase, but it is also mediated by the regional share in manufacturing workforce. This workforce reveals opposing phase-specific effects itself. Hence, ignoring the two-component structure of short-term resilience entails the risk of imprecise, if not false conclusions on the driving mechanisms stabilising and/or destabilising regional economies in times of crisis.

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Notes

  1. In our empirical models, we use indices for regional specialisation and diversification as regressors assuming that their effects on regional economic resilience are channeled through the respective externalities (see Sect. 3.2).

  2. Unfortunately, the database of this article (ending in 2012) does not allow for the examination of a long-term perspective which is why we must abstain from testable hypotheses on the renewal and re-orientation period.

  3. Both independent variables are computed using data from the regional databases of the German Statistical Federal Office and the Statistical State Offices of the German “Länder”, respectively. As the original data on nominal GDP are only available at the level of administrative districts and cities, we needed to aggregate them at the level of labour market regions (LMR) according to the reference scheme of the Joint Task for the Improvement of Regional Economic Structure (see Sect. 3.4).

  4. We use LMR as units of analysis (for further details see Sect. 3.4).

  5. All three industry indicators (specialisation, unrelated variety, and related variety) are computed using data on employees who are within the scope of national insurance and thus registered in the Federal Employment Agency. As data on employees are available on a quarterly basis, the reporting date March 31st, 2008 is chosen to depict the economic situation immediately prior to the shock. Furthermore, all three indicators are calculated for both non-knowledge-intensive and knowledge-intensive sectors according to the “NIW/ISI/ZEW”-list by Gehrke et al. (2010).

  6. This method was chosen to face the problem that some regions exhibit no employment in specific two-digit industries. Thus, if vacant sectors occur the arithmetic mean of their ordinal ranks can be assigned to the particular \(R_{g}\).

  7. Appendix Tables 5, 6 and 7 contains full information on operationalisation and data sources of all dependent and independent variables (including controls).

  8. In a previous version of this paper (see Pudelko and Hundt 2017), we solely performed OLS regressions. The reason for this was that our former dependent variable (“growth rate(s) of real GDP”) was not affected by spatial autocorrelation, neither in the sensitivity nor in the recovery models.

  9. The LM approach has been often criticised in the literature because the underlying tests compare models with only one spatial dependence parameter and thus neglect potential combinations of spatial dependent processes. Nevertheless, we employ LM diagnostics as a subordinate robustness test (LeSage and Pace 2009; Elhorst 2010).

  10. Data for the control variables is retrieved from the INKAR database and the BBSR. [INKAR is the abbreviation for “INdikatoren und KArten zur Raum- und Stadtentwicklung” which can be translated as “indicators and maps on spatial and urban development”. BBSR is the abbreviation for “Bundesinstitut für Bau‑, Stadt- und Raumforschung” which is “The Federal Institute for Research on Building, Urban Affairs and Spatial Development”.].

  11. For the avoidance of doubt, however, we re-estimate the OLS models by means of the spatial error method which produces robust results.

  12. A second difference refers to including regional “sensitivity” (see Sect. 3.1) as an additional control variable to be able to relate the regions’ recovery to the extent of their preceding decline.

  13. The definition of LMR primarily bases on commuter data for administrative districts and cities. For details see, for example, Binder and Schwengler (2006), Eckey et al. (2007) and Milbert (2014).

  14. Below- (Above-) average sensitivity indicates a greater (smaller) decrease in GDP than the Western German average and therefore a higher (lower) degree of “sensitivity”. Below- (Above-) average recovery denotes a smaller (greater) increase in GDP than the Western German average. Also, see Appendix Table 10 for descriptive statistics on all types of regions as displayed in Fig. 2.

  15. See Appendix Table 9 for the respective correlation table. The negative sign of the Pearson coefficient—thus making it a negative correlation—results from the negative values we obtain for the sensitivity indicator.

  16. Total impact (TO) = Direct impact (DI) [effect of a change in a region’s zth covariate on own-region growth] + Indirect impact (IN) [effect of a change in a neighbouring region’s zth covariate on own-region growth] (LeSage and Pace 2009).

  17. In other studies, \(\rho\) mostly takes on values of around 0.5 when administrative regions form the base for regional analyses and Queen weighting schemes are used in spatial lag models.

  18. As a test for mediation, we successfully perform the four-step-procedure as proposed by Baron and Kenny (1986) and confirm the results by means of the Sobel test (1982) for both the sensitivity and recovery period.

  19. Further evidence for this interpretation is provided by the positive and significant correlation between the degree of regional specialisation as computed by means of (3a) and the regional share of manufacturing employment.

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Authors and Affiliations

  1. Geography Department, Economic Geography and Location Research, Philipps University Marburg, Deutschhausstraße 10, 35032, Marburg, Germany

    Franziska Pudelko

  2. Thünen Institute of Rural Studies, Johann Heinrich von Thünen Institute, Bundesallee 64, 38116, Braunschweig, Germany

    Christian Hundt

  3. Geography Department, Urban and Regional Economics, Ruhr University Bochum, Universitätsstraße 150, 44780, Bochum, Germany

    Christian Hundt & Linus Holtermann

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  1. Franziska Pudelko
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Correspondence to Christian Hundt.

Appendix

Appendix

 

Fig. 5
figure 5

Development of GDP in the LMR of Hildesheim, 2007–2012. (Source: own depiction based on table 426-71-4 of the “Regionaldatenbank Deutschland”, state of calculation: August 2014)

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Fig. 6
figure 6

Development of GDP in the LMR of Heide, 2007–2012. (Source: own depiction based on table 426-71-4 of the “Regionaldatenbank Deutschland”, state of calculation: August 2014)

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Table 5 Operationalisation and data sources of the dependent variables
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Table 6 Operationalisation and data sources of the independent variables
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Table 7 Operationalisation and data sources of the control variables
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Fig. 7
figure 7

Selection tree for the “sensitivity” model (Manski 1993; Anselin et al. 1996; Elhorst 2010, 2014)

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Fig. 8
figure 8

Selection tree for the “recovery” model (Manski 1993; Anselin et al. 1996; Elhorst 2010, 2014)

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Table 8 Correlation table for the sensitivity models
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Table 9 Correlation table for the recovery models
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Table 10 Descriptive statistics for specific types of regions
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Table 11 Spatial lag results: Overall resilience models with “specialisation”
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Table 12 Spatial lag results: Overall resilience models with “unrelated” and “related variety”
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Table 13 OLS regression results: Overall resilience models with “specialisation”
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Table 14 OLS regression results: Overall resilience models with “unrelated” and “related variety”
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Pudelko, F., Hundt, C. & Holtermann, L. Gauging two sides of regional economic resilience in Western Germany—Why sensitivity and recovery should not be lumped together. Rev Reg Res 38, 141–189 (2018). https://doi.org/10.1007/s10037-018-0124-4

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