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Unemployment duration and the length of entitlement periods for unemployment benefits: do the IAB employment subsample and the German Socio-Economic Panel yield the same results?

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Author Info

  • Martin Biewen

    ()

  • Ralf Wilke

    ()

Abstract

We compare information on the length of unemployment spells contained in the IAB employment subsample (IABS) and in the German Socio-Economic Panel (GSOEP). Due to the lack of information on registered unemployment in the IABS, we use two proxies of unemployment in the IABS as introduced by Fitzenberger/Wilke (2004). The first proxy comprises all periods of nonemployment after an employment spell which contain at least one period with unemployment compensation transfers. The second proxy includes all episodes between two employment spells during which an individual continuously received unemployment benefits. Estimation of standard duration models indicates that conclusions drawn from the IABS and the GSOEP differ in many cases. While the GSOEP suggests that the hazard rate has a maximum at about 12 months of nemployment, the IABS results suggest that this maximum is at about 20 months. Contrary to our GSOEP results and contrary to many results based on the GSOEP found in the literature, we find a statistically significant association between longer maximum entitlement periods of unemployment benefits (?Arbeitslosengeld?) and longer unemployment durations for men in the IABS. The results for women do not show such clear patterns. The large sample size of the IABS also allows one to trace out statistically significant effects of characteristics such as regional and industry indicators, which is generally not possible in the relatively small GSOEP. --

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File URL: http://hdl.handle.net/10.1007/s10182-005-0201-1
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Bibliographic Info

Article provided by Springer in its journal Allgemeines Statistisches Archiv.

Volume (Year): 89 (2005)
Issue (Month): 2 (June)
Pages: 209-236

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Handle: RePEc:spr:alstar:v:89:y:2005:i:2:p:209-236

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Related research

Keywords: unemployment duration; duration analysis; unemployment insurance; administrative data; JEL C24; J64; J65;

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Cited by:
  1. Lee, Sokbae & Wilke, Ralf A., 2005. "Reform of Unemployment Compensation in Germany: A Nonparametric Bounds Analysis Using Register Data," ZEW Discussion Papers 05-29, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  2. Matthias S. Hertweck & Oliver Sigrist, 2013. "The Aggregate Effects of the Hartz Reforms in Germany," SOEPpapers on Multidisciplinary Panel Data Research 532, DIW Berlin, The German Socio-Economic Panel (SOEP).
  3. Wichert, Laura & Wilke, Ralf A., 2007. "Simple nonparametric estimators for unemployment duration analysis," FDZ Methodenreport 200709_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  4. Wilke, Ralf A. & Wichert, Laura, 2005. "Application of a simple nonparametric conditional quantile function estimator in unemployment duration analysis," ZEW Discussion Papers 05-67 [rev.], ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  5. Bernd Fitzenberger & Ralf Wilke, 2006. "Using quantile regression for duration analysis," AStA Advances in Statistical Analysis, Springer, vol. 90(1), pages 105-120, March.
  6. Müller, Eva & Wilke, Ralf A. & Zahn, Philipp, 2006. "Beschäftigung und Arbeitslosigkeit älterer Arbeitnehmer: Eine mikroökonometrische Evaluation der Arbeitslosengeldreform von 1997," ZEW Discussion Papers 06-21, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  7. Andrew Chesher, 2002. "Local identification in nonseparable models," CeMMAP working papers CWP05/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  8. John P. Haisken-DeNew & Matthias Vorell, 2009. "Killing them with Kindness: Negative Distributional Externalities of Increasing UI Benefits," Ruhr Economic Papers 0121, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  9. Bachmann, Ronald & Schaffner, Sandra, 2009. "Biases in the measurement of labour market dynamics," Technical Reports 2009,12, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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