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Thresholds for Employment and Unemployment - a Spatial Analysis of German Regional Labour Markets 1992-2000

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  • Reinhold Kosfeld

    ()

  • Christian Dreger

    ()

Abstract

Changes in production and employment are closely related over the course of the business cycle. However, as exemplified by the laws of Verdoorn (1949, 1993) and Okun (1962, 1970), thresholds seem to be present in the relationship. Due to capacity reserves of the firms, output growth must exceed certain levels for the creation of new jobs or a fall in the unemployment rate. While Verdoorn's law focuses on the growth rate of output sufficient for an increase in employment, in Okun's law, the fall in the unemployment rate becomes the focus of attention. In order to assess the future development of employment and unemployment, these thresholds have to be taken into account. They serve as important guidelines for policymakers. In contrast to previous studies, we present joint estimates for both the employment and unemployment threshold. Due to demographic patterns and institutional settings on the labour market, the two thresholds can differ, implying that minimum output growth needed for a rise in employment may not be sufficient for a simultaneous drop in the unemployment rate. Second, regional information is considered to a large extent. In particular, the analysis is carried out using a sample of 180 German regional labour markets, see Eckey (2001). Since the cross-sections are separated by the flows of job commuters, they correspond to travel-to-work areas. Labour mobility is high within a market, but low among the entities. As the sectoral decomposition of economic activities varies across the regions, the thresholds are founded on a heterogeneous experience, leading to more reliable estimates.The contribution to the literature is twofold. First, to the best of our knowledge, no previous paper has investigated a similar broad regional dataset for the German economy as a whole before. By using a panel dataset, information on the regional distributions around the regression lines as well as theirs positional changes is provided for each year. Second, the methods applied are of new type. They involve a mixture of pooled and spatial econometric techniques. Dependencies across the regions may result from common or idiosyncratic (region specific) shocks. In particular, the eigenfunction decomposition approach suggested by Griffith (1996, 2000) is used to identify spatial and non-spatial components in regression analysis. As the spatial pattern may vary over time, inference is conducted on the base of a spatial SUR model. Due to this setting, efficient estimates of the thresholds are obtained. With the aid of a geographic information system (GIS) variation of the spatial components can be made transparent. With Verdoorn’s and Okun’s law the figures show some significant patterns become obvious over time. In respect to Verdoorn’s law, for instance, a stripe of high values in the north-western part from Schleswig-Holstein via Lower Saxony and North Rhine Westfalia to Rhineland Palatinate is striking in all years but 1994 and 1995. In most periods the spatial component is likewise concentrated in Saxony. Clusters of low values can be found in northern Bavaria and, in some periods, in Thüringen and Mecklenburg-Vorpommern. Other parts of Germany appear to be more fragmented consisting of relative small clusters of low, medium and high values of the spatial component. With Okun’s law some changing spatial patterns arise. In all, spatially filtering provides valuable insights into the spatial dimensions of the laws of Verdoorn and Okun. Threshold employment and unemployment, regional labour markets, spatial filtering techniques, spatial SUR analysis

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Paper provided by European Regional Science Association in its series ERSA conference papers with number ersa05p39.

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Date of creation: Aug 2005
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Handle: RePEc:wiw:wiwrsa:ersa05p39

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  1. Bernard Fingleton, 2001. "Equilibrium and Economic Growth: Spatial Econometric Models and Simulations," Journal of Regional Science, Wiley Blackwell, vol. 41(1), pages 117-147.
  2. Elhorst, J.P., 2000. "Dynamic models in space and time," Research Report 00C16, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
  3. Attfield, Clifford L. F. & Silverstone, Brian, 1998. "Okun's Law, Cointegration and Gap Variables," Journal of Macroeconomics, Elsevier, vol. 20(3), pages 625-637, July.
  4. Stephen G. Cecchetti & Robert W. Rich, 1999. "Structural estimates of the U.S. sacrifice ratio," Staff Reports 71, Federal Reserve Bank of New York.
  5. Lee, Jim, 2000. "The Robustness of Okun's Law: Evidence from OECD Countries," Journal of Macroeconomics, Elsevier, vol. 22(2), pages 331-356, April.
  6. Kaldor, Nicholas, 1975. "Economic Growth and the Verdoorn Law-A Comment on Mr. Rowthorn's Article," Economic Journal, Royal Economic Society, vol. 85(340), pages 891-96, December.
  7. Bernard Fingleton, 2000. "Spatial econometrics, economic geography, dynamics and equilibrium: a 'third way'?," Environment and Planning A, Pion Ltd, London, vol. 32(8), pages 1481-1498, August.
  8. Moosa, Imad A., 1997. "A Cross-Country Comparison of Okun's Coefficient," Journal of Comparative Economics, Elsevier, vol. 24(3), pages 335-356, June.
  9. Prachowny, Martin F J, 1993. "Okun's Law: Theoretical Foundations and Revised Estimates," The Review of Economics and Statistics, MIT Press, vol. 75(2), pages 331-36, May.
  10. Rowthorn, R E, 1979. "A Note on Verdoorn's Law," Economic Journal, Royal Economic Society, vol. 89(353), pages 131-33, March.
  11. Freeman, Donald G, 2001. "Panel Tests of Okun's Law for Ten Industrial Countries," Economic Inquiry, Western Economic Association International, vol. 39(4), pages 511-23, October.
  12. Jordi Pons-Novell & Elisabet Viladecans-Marsal, 1999. "Kaldor's Laws and Spatial Dependence: Evidence for the European Regions," Regional Studies, Taylor & Francis Journals, vol. 33(5), pages 443-451.
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Cited by:
  1. Ulrich Zierahn, 2010. "The Importance of Spatial Autocorrelation for Regional Employment Growth in Germany," MAGKS Papers on Economics 201031, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  2. Herwartz, Helmut & Niebuhr, Annekatrin, 2011. "Regional labor demand and national labor market institutions in the EU15," HWWI Research Papers 112, Hamburg Institute of International Economics (HWWI).
  3. Roberto Patuelli & Norbert Schanne & Daniel A. Griffith & Peter Nijkamp, 2009. "Persistence of Regional Unemployment: Application of a Spatial Filtering Approach to Local Labour Markets in Germany," Working Paper Series 49_09, The Rimini Centre for Economic Analysis, revised Nov 2011.
  4. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2011. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," International Regional Science Review, , vol. 34(2), pages 253-280, April.
  5. Hans-Friedrich Eckey & Reinhold Kosfeld & Matthias Türck, 2007. "Regionale Entwicklung mit und ohne räumliche Spillover-Effekte," Jahrbuch für Regionalwissenschaft, Springer, vol. 27(1), pages 23-42, February.
  6. Nikolay Kaledin, 2013. "Societal geography and regionalization of society," ERSA conference papers ersa13p1334, European Regional Science Association.
  7. Martinho, Vítor João Pereira Domingues, 2012. "What the Keynesian theory said about Portugal?," EconStor Preprints 59007, ZBW - German National Library of Economics.

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