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What Is the Effect of Population Ageing in Regional Labour Market Fluctuations of Germany? A SVAR with Zero-Sign Restrictions Approach

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Modelling Aging and Migration Effects on Spatial Labor Markets

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

This study analyses the effect of population ageing in the ten federal states (Länder) of West Germany during the period ranging from 1970 to 2014. To that aim, we estimate a Bayesian VAR model including wages, labour force participation rates, unemployment rates and the share of population above 65 years. Structural regional labour market shocks are identified employing a combination of zero and sign restrictions. Our findings suggest that shocks to population ageing on impact, increase the participation rate, the unemployment rate and decrease wages. However, the above effects on participation and unemployment are short-lasting and after a few periods their response is inverted. Our variance decomposition analysis reveals that the most relevant channel of impact on the economy implied by ageing is that of wages.

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Notes

  1. 1.

    Notice that an important difference with respect other regional labor market studies is that we do not include the net migration in the model. This is because of data was not available. The potential negative effects on the quality and reliability of the estimates implied by the omission of this variable are discussed in Sect. 3.2.

  2. 2.

    Often, Bayesian analysis tries to avoid situations where the conclusions depend heavily on subjective prior information by relying on diffuse or non-informative prior distributions. As parameters governing the prior distributions such as the prior variance increase, the prior distributions become more vague or diffuse. Non-informative priors are obtained in this context by setting \(\tilde {V}_{0}^{-1} =0.001\), v 0 = 0.001 and \(\bar {S}=0.001\). Note that given the diffuse prior specification employed, the reduced-form estimates obtained are very close to those that one would derived if the model was estimated by ordinary least squares or maximum likelihood procedures.

  3. 3.

    Notice that the combination of zero and sign restrictions is not straightforward given that multiplying A 0 by a randomly drawn Q violates might violate the zero restrictions embodied in A 0. Thus, the main contribution of this algorithm is a method for drawing Q matrices without violating the zero restrictions we aim to impose. For details see pp 23–24 of Rubio-Ramirez et al. (2014).

  4. 4.

    The HP filter finds the series μ t that minimizes: \(\sum ^{T}_{t=1} \left (y_{t}-\mu _{t} \right )^{2} + \lambda \sum ^{T-1}_{t=2} \left [ \left (\mu _{t+1}-\mu _{t}\right )\right . \left . -\left (\mu _{t}-\mu _{t-1}\right ) \right ]^{2}\) where λ = 100 is the corresponding optimal annual smoothing parameter value. We use this filter instead of other possible alternatives such as the Bo as it is the most common filter employed in the macroeconomics literature.

  5. 5.

    For the shake of brevity, we only report the p-values of these tests for West Germany. However, the results for regions under consideration provide strong evidence for the VAR time-series system in cyclical-fluctuations to be stationary.

  6. 6.

    See Fry and Pagan (2011) for a detailed discussion on the specifities of interpreting sign-restrictions analyses.

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Acknowledgements

The research presented in this chapter is part of the project “Population Ageing and Regional Labour Market Development”, funded by the Institute for Employment Research (IAB), Germany, which the authors gratefully acknowledge. The research has also benefited from the financial support of the Spanish Ministry of Economy and Competitiveness (Project ECO2016-76681-R) and the Excellence Grants Program of the Fundación Bancaria Caja Navarra.

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Authors and Affiliations

  1. Department of Economics, Universidad Pública de Navarra, Pamplona, Spain

    Vicente Rios

  2. Department of Economics, Universitá di Bologna, Bologna, Italy

    Roberto Patuelli

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  1. Vicente Rios
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  2. Roberto Patuelli
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Correspondence to Vicente Rios .

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Editors and Affiliations

  1. Schar School of Policy and Government, Arlington, VA, USA

    Roger R. Stough

  2. KTH Royal Institute of Technology, Stockholm, Sweden

    Karima Kourtit

  3. JADS (Jheronimus Academy of Data Science), ’s-Hertogenbosch, The Netherlands

    Peter Nijkamp

  4. Institute for Employment Research (IAB), Nuremberg, Germany

    Uwe Blien

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Rios, V., Patuelli, R. (2018). What Is the Effect of Population Ageing in Regional Labour Market Fluctuations of Germany? A SVAR with Zero-Sign Restrictions Approach. In: R. Stough, R., Kourtit, K., Nijkamp, P., Blien, U. (eds) Modelling Aging and Migration Effects on Spatial Labor Markets. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-68563-2_12

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