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Which factors safeguard employment? : An analysis with misclassified German register data


  • Wichert, Laura
  • Wilke, Ralf A.


"We analyse the main determinants for job separation with transition to unemployment using individual administrative data from Germany. While the sample size is large and the information in target variables is often highly accurate, non-target variables are subject to considerable measurement error due to a lack of relevance for the data generating process. We show that the high degree of misclassification can even persist after comprehensive logical editing and imputation rules were applied. We find that the measurement error has a sizable effect on our estimation results. Long tenure rather than a higher educational qualification appears to be the key ingredient for a safe job in Germany." (Author's abstract, IAB-Doku) ((en)) Additional Information Appendix for the FDZ-Methodenreport No. 11/2010: Programs

Suggested Citation

  • Wichert, Laura & Wilke, Ralf A., 2010. "Which factors safeguard employment? : An analysis with misclassified German register data," FDZ Methodenreport 201011_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  • Handle: RePEc:iab:iabfme:201011_en

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    Cited by:

    1. Rothe, Thomas & Giannelli, Gianna C. & Jaenichen, Ursula, 2013. "Doing well in reforming the labour market? Recent trends in job stability and wages in Germany," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79932, Verein für Socialpolitik / German Economic Association.
    2. Kohlbrecher, Britta & Merkl, Christian & Nordmeier, Daniela, 2016. "Revisiting the matching function," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 350-374.
    3. Lo, Simon M.S. & Stephan, Gesine & Wilke, Ralf, 2012. "Estimating the Latent Effect of Unemployment Benefits on Unemployment Duration," IZA Discussion Papers 6650, Institute for the Study of Labor (IZA).
    4. Jan Marcus, 2014. "Does Job Loss Make You Smoke and Gain Weight?," Economica, London School of Economics and Political Science, vol. 81(324), pages 626-648, October.
    5. repec:bla:obuest:v:79:y:2017:i:5:p:689-716 is not listed on IDEAS
    6. Melanie Arntz & Stephan Dlugosz & Ralf A. Wilke, 2017. "The Sorting of Female Careers after First Birth: A Competing Risks Analysis of Maternity Leave Duration," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(5), pages 689-716, October.
    7. repec:spr:jlabrs:v:50:y:2017:i:1:d:10.1007_s12651-017-0219-3 is not listed on IDEAS
    8. Kruppe, Thomas & Matthes, Britta & Unger, Stefanie, 2014. "Effectiveness of data correction rules in process-produced data : the case of educational attainment," IAB Discussion Paper 201415, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    9. Dlugosz, Stephan & Mammen, Enno & Wilke, Ralf A., 2017. "Generalized partially linear regression with misclassified data and an application to labour market transitions," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 145-159.
    10. Mona Groß & Annika Herr & Martin Hower & Alexander Kuhlmann & Jörg Mahlich & Matthias Stoll, 2016. "Unemployment, health, and education of HIV-infected males in Germany," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 61(5), pages 593-602, June.
    11. Dlugosz, Stephan, 2011. "Give missings a chance: Combined stochastic and rule-based approach to improve regression models with mismeasured monotonic covariates without side information," ZEW Discussion Papers 11-013, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    12. Nicole Gürtzgen & André Nolte, 2017. "Imputation rules for the implementation of the pre-unification education variable in the BASiD Data Set
      [Imputationsregeln für die Generierung der Bildungsvariable in den BASiD-Daten vor der Wieder
      ," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 50(1), pages 45-65, August.
    13. Stephan Humpert, 2012. "Age and Gender Differences in Job Opportunities," Working Paper Series in Economics 235, University of Lüneburg, Institute of Economics.
    14. Michael Neumann, 2015. "Earnings Responses to Social Security Contributions," Discussion Papers of DIW Berlin 1489, DIW Berlin, German Institute for Economic Research.

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    Beschäftigungsdauer; Beschäftigungssicherung - Determinanten; Datengewinnung; Datenaufbereitung; Datenqualität;

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