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On the Stability of the German Beveridge Curve. A Spatial Econometric Perspective

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  • Christian Dreger

    ()

  • Reinhold Kosfeld

    ()

Abstract

In this paper, we use the Beveridge relationship to address the effectiveness of the matching process, that brings workers searching for jobs together with employers searching for workers. For a fixed matching technology, the curve yields a negative relation between the unemployment rate and the rate of vacancies. Movements along a curve reflect adjustments over the business cycle. In a recession vacancies are closed, and workers enter the unemployed. Shifts of the curve are more important here, as they point to structural change. For example, an outward shift of the curve indicates an in-creased mismatch, perhaps due to a deterioration in human capital of the unemployed or changes in the unemployment benefit system, which affects the willingness of the un-employed to fill out vacancies. Empirical estimates rely on panel data. A sample of 180 regional labour markets is em-ployed, and the sample period runs from 1993 to 2004. The regional labour markets are seperated on the base of flows of the job commuters and correspond to travel-to-work areas. Due to common or idiosyncratic shocks, however, the cross sections are not inde-pendent. Instead, they are tied together to some extent, and the spillovers account for spatial effects. As these patterns can have an impact on the correlation between unem-ployment and vacancy rates, the results of OLS regressions are eventually biased. Thus the Beveridge curve is efficiently estimated by a spatial procedure, where regional de-pendencies are taken into account. No previous paper has investigated a similar broad regional dataset so far. The eigenfunction decomposition approach suggested by Griffith (1996, 2000) is used to identify spatial and non-spatial components in regression analysis. As the spatial pat-tern may vary over time, inference is conducted on the base of a spatial seemingly unre-lated regressions (spatial SUR) model. Due to this setup, efficient estimates for the Beveridge relationship are obtained. Time dummies are used to control for shifts in the curve. The empirical results provide some indication that the degree of job mismatch has increased over the recent periods.

Suggested Citation

  • Christian Dreger & Reinhold Kosfeld, 2006. "On the Stability of the German Beveridge Curve. A Spatial Econometric Perspective," ERSA conference papers ersa06p396, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa06p396
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Klinger, Sabine & Weber, Enzo, 2014. "Decomposing Beveridge curve dynamics by correlated unobserved components: The impact of labour market reforms in Germany," Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100499, Verein für Socialpolitik / German Economic Association.
    2. Cristina Lincaru, 2010. "Reginal Mismatch Tendencies In Romania-Evidence From Beveridge Curve," Romanian Journal of Regional Science, Romanian Regional Science Association, vol. 4(2), pages 32-61, DECEMBER.
    3. Sabine Klinger & Enzo Weber, 2016. "Decomposing Beveridge Curve Dynamics By Correlated Unobserved Components," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(6), pages 877-894, December.
    4. Kosfeld Reinhold, 2007. "Regional Spillovers and Spatial Heterogeneity in Matching Workers and Employers in Germany," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 227(3), pages 236-253, June.
    5. Kosfeld, Reinhold, 2006. "Regional spillovers and spatial heterogeneity in matching workers and employers in Germany," Volkswirtschaftliche Diskussionsbeiträge 89, University of Kassel, Faculty of Economics and Management.
    6. Richard Dutu & Mark J. Holmes & Brian Silverstone, 2009. "Modelling a Regime-Shifting New Zealand Beveridge Curve," Working Papers in Economics 09/13, University of Waikato.

    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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