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Review of Regional Research
Erschienen in: Review of Regional Research 2/2018

08.08.2018 | Original Paper

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

verfasst von: Franziska Pudelko, Christian Hundt, Linus Holtermann

Erschienen in: Review of Regional Research | Ausgabe 2/2018

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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.
Vorheriger Artikel The detection of natural cities in the Netherlands—Nocturnal satellite imagery and Zipf’s law
Nächster Artikel Evaluation of the Brazilian regional development funds

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Fußnoten
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.
 
Literatur
Anselin L (1988) Spatial Econometrics: methods and models. Kluwer Academic Publisher, DordrechtCrossRef
Anselin L, Bera AK, Florax R, Yoon MJ (1996) Simple diagnostic tests for spatial dependence. Reg Sci Urban Econ 26:77–104CrossRef
Arrow KJ (1962) The economic implications of learning by doing. Rev Econ Stud 29(3):155–173CrossRef
Augustine N, Wolman H, Wial H, McMillen M (2013) Regional Economic Capacity, Economic Shocks and Economic Resilience (MacArthur Foundation Network on Building Resilient Regions. Working Paper)
Baron RM, Kenny DA (1986) The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 51:1173–1182CrossRef
Binder J, Schwengler B (2006) Neuer Gebietszuschnitt der Arbeitsmarktregionen im Raum Berlin und Brandenburg. Kritische Überprüfung der bisher gültigen Arbeitsmarktregionen und Vorschläge für einen Neuzuschnitt. IAB (Institut für Arbeitsmarkt- und Berufsforschung), Nuremberg (Forschungsbericht Nr. 4/2006)
Bishop P, Gripaios P (2010) Spatial externalities, relatedness and sector employment growth in Great Britain. Reg Stud 44(4):443–454CrossRef
Boschma R (2014) Towards an evolutionary perspective on regional resilience. Reg Stud 49(5):733–751CrossRef
Boschma R, Frenken K (2011) Technological relatedness and regional branching. In: Bathelt H, Feldman MP, Kogler DF (eds) Beyond territory: dynamic geographies of knowledge creation, diffusion, and innovation. Routledge, Abingdon, pp 64–81
Boschma R, Iammarino S (2009) Related variety, trade linkages, and regional growth in Italy. Econ Geogr 85:289–311CrossRef
Boschma R, Minondo A, Navarro M (2012) Related variety and regional growth in Spain. Pap Reg Sci 91(2):241–256
Brachert M, Titze M (2012) Wirtschaftsstruktur und Regionalentwicklung: Zur Bedeutung von Headquartern und verbundenen Wirtschaftszweigen. Wirtsch Wandel 18(7):209–216
Cerra V, Panizza U, Saxena SC (2013) International evidence on recovery from recessions. Contemp Econ Policy 31(2):424–439CrossRef
Coleman JS (1991) Handlungen und Handlungssysteme. Grundlagen der Sozialtheorie, vol 1. Scientia Nova, Munich
Conley TG, Ligon E (2002) Economic distance, spillovers and cross country comparisons. J Econ Growth (Boston) 7(2):157–187CrossRef
Duschl M (2016) Firm dynamics and regional resilience: an empirical evolutionary perspective. Ind Corp Change 25(59):867–883CrossRef
Elhorst JP (2010) Applied spatial econometrics: raising the bar. Spat Econ Anal 5(1):9–28CrossRef
Elhorst JP (2014) Spatial econometrics: from cross-sectional data to spatial panels. Springerbriefs in regional science. Springer, Berlin, HeidelbergCrossRef
Ezcurra R, Rios V (2015) Volatility and regional growth in europe: does space matter? Spat Econ Anal 10(3):344–368CrossRef
Farhauer O, Kröll A (2013) Standorttheorien. Regional- und Stadtökonomik in Theorie und Praxis. Springer, Wiesbaden
Fingleton B, Palombi S (2013) Spatial panel data estimation, counterfactual predictions, and local economic resilience among British towns in the Victorian era. Reg Sci Urban Econ 43(4):649–660CrossRef
Frenken K, van Oort F, Verburg T, Boschma RA (2004) Variety, employment and regional development in the Netherlands. 44th ERSA Conference, Porto, 25–29 August 2004
Friedman M (1993) The plucking model of business fluctuations revisited. Econ Enq 31:171–177CrossRef
Glaeser EL, Kallal HD, Scheinkman JA, Schleifer A (1992) Learning in cities. J Urban Econ 46:254–277CrossRef
Hartog M, Boschma R, Sotarauta M (2012) The impact of related variety on regional employment growth in Finland 1993–2006: high-tech versus medium/low-tech. Ind Innov 19(6):459–447CrossRef
Hundt C, Sternberg R (2014) How did the economic crisis influence new firm creation? Jahrb Natl Okon Stat 234(6):722–756
Hundt C, Sternberg R (2016) Explaining new firm creation in Europe from a spatial and time perspective: A multilevel analysis based upon data of individuals, regions and countries. Pap Reg Sci 95(2):223–258CrossRef
Jacobs J (1969) The economy of cities. Random House, New York
Jakubowski P, Lackmann G, Zarth M (2013) Zur Resilienz regionaler Arbeitsmärkte—theoretische Überlegungen und empirische Befunde. Inf Raumentwickl 40(4):351–370
Knoben J, Arikan AT, van Oort F, Raspe O (2016) Agglomeration and firm performance: One firm’s medicine is another firm’s poison. Environ Plan A 48(1):132–153CrossRef
LeSage JP, Fischer MM (2008) Spatial growth regressions: model specification, estimation and interpretation. Spat Econ Anal 3(3):275–304CrossRef
LeSage JP, Pace RK (2009) Introduction to spatial econometrics. Statistics: a series of textbooks and monographs. Chapman and Hall, CambridgeCrossRef
Lintz G, Schmude K (2005) Germany: tackling the east-west divide. In: Mueller B, Finka M, Lintz G (eds) Rise and decline of industry in central and eastern europe. Springer, Berlin, Heidelberg, pp 81–110CrossRef
Manski CF (1993) Identification of endogenous social effects: the reflection problem. Rev Econ Stud 60(3):531–542CrossRef
Marshall A (1890) Principles of economics. Macmillan, London
Martin R (2012) Regional economic resilience, Hysteresis and recessionary shocks. J Econ Geogr 12:1–32CrossRef
Martin R, Sunley P (2015) On the notion of regional economic resilience: conceptualisation and explanation. J Econ Geogr 15(1):1–42CrossRef
Martin R, Sunley P, Gardiner B, Tyler P (2016) How regions react to recessions: resilience and the role of economic structure. Reg Stud 50(4):561–585CrossRef
Otto A, Nedelkoska L, Neffke F (2014) Skill-relatedness und Resilienz: Fallbeispiel Saarland. Raumforsch Raumordn 72:133–151CrossRef
Pudelko F, Hundt C (2017) Gauging two sides of regional economic resilience in Western Germany—Why resistance and recovery should not be lumped together. Working Papers on Innovation and Space, vol #01.17. Philipps University Marburg, Marburg
Reinhart CM, Rogoff KS (2014) Recessions and recoveries—recovery from financial crises: evidence from 100 episodes. Am Econ Rev 104(5):50–55CrossRef
Robinson W (1950) Ecological correlations and behaviour of individuals. Am Sociol Rev 15:351–357CrossRef
Romer PM (1986) Increasing returns and long-run growth. J Polit Econ 94(5):1002–1037CrossRef
Schmidt-Seiwert V (2011) Finanz- und Wirtschaftskrise—das Europa der unterschiedlichen Betroffenheiten. Inf Raumentwickl 38(2):113–119
Schwengler B, Hecht H (2011) Regionale Arbeitsmärkte in der Wirtschaftskrise. Inf Raumentwickl 38(2):121–133
Shiskin J (1974) The Changing Business Cycle. New York Times (1st December 1974):222
Sobel ME (1982) Asymptotic intervals for indirect effects in structural equations models. In: Leinhart S (ed) Sociological methodology. Jossey-Bass, San Francisco, pp 290–312
Van Oort F, De Geus S, Dogaru T (2015) Related variety and economic growth in a cross-section of European Urban regions. Eur Plan Stud 23(6):1110–1127CrossRef
Wink R (2011) Evolution regionaler Resilienz: theoretischer Rahmen und Messkonzepte. In: Dreger C, Kosfeld R, Türck M (eds) Empirische Regionalforschung heute. Gabler, Wiesbaden, pp 111–124CrossRef
Wink R (2015) Regionale wirtschaftliche Resilienz und die Finanzierung von Innovationen. In: Krüger J, Parthey H, Wink R (eds) Wissenschaft und Innovation: Wissenschaftsforschung. Jahrbuch 2014. Wissenschaftlicher Verlag, Berlin, pp 57–72
Zarth M (2011) Zur Entwicklung der deutschen Regionen in den langfristigen Konjunkturzyklen. Inf Raumentwickl 38(2):99–112
Zimmermann K, Hundt C, Jütte C, Pofalla C (2017) Dortmund, Duisburg und die Rezession von 2008/09—Warum zwei Standorte derselben Region ökonomisch unterschiedlich resilient sind. Raumforsch Raumordn 75(5):439–453CrossRef
Metadaten
Titel
Gauging two sides of regional economic resilience in Western Germany—Why sensitivity and recovery should not be lumped together
verfasst von
Franziska Pudelko
Christian Hundt
Linus Holtermann
Publikationsdatum
08.08.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Review of Regional Research / Ausgabe 2/2018
Print ISSN: 0173-7600
Elektronische ISSN: 1613-9836
DOI
https://doi.org/10.1007/s10037-018-0124-4

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