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Imputation rules for the implementation of the pre-unication education variable in the BASiD data set

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  • Gürtzgen, Nicole
  • Nolte, André

Abstract

Using combined data from the German Pension Insurance and the Federal Employment Agency (BASiD), this study proposes different procedures for imputing the pre-unification education variable in the BASiD data. To do so, we exploit information on education-related periods that are creditable for the Pension Insurance. Combining these periods with information on the educational system in the former GDR, we propose three different imputation procedures, which we validate using external GDR census data for selected age groups in 1981. A common result from all procedures is that they tend to underpredict (overpredict) the share of high-skilled (low-skilled) for the oldest age groups. Comparing our imputed education variable with information on educational attainment from the Integrated Employment Biographies (IEB) reveals that the best match is obtained for the vocational training degree. Although regressions show that misclassification with respect to IEB information is clearly related to observables, we do not find any systematic pattern across skill groups.

Suggested Citation

  • Gürtzgen, Nicole & Nolte, André, 2016. "Imputation rules for the implementation of the pre-unication education variable in the BASiD data set," ZEW Discussion Papers 16-020, ZEW - Leibniz Centre for European Economic Research.
    • Handle: RePEc:zbw:zewdip:16020
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    7. Büttner, Thomas & Rässler, Susanne, 2008. "Multiple imputation of right-censored wages in the German IAB Employment Sample considering heteroscedasticity," IAB-Discussion Paper 200844, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
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    9. Antonia Manzari, 2004. "Combining editing and imputation methods: an experimental application on population census data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 295-307, May.
    10. Bernd Fitzenberger & Aderonke Osikominu & Robert Völter, 2006. "Imputation Rules to Improve the Education Variable in the IAB Employment Subsample," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 126(3), pages 405-436.
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    Cited by:

    1. Pfister, Mona & Lorenz, Svenja & Zwick, Thomas, 2018. "Calculation of pension entitlements in the sample of integrated labour market biographies (SIAB)," FDZ Methodenreport 201801_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    2. Nicole Gürtzgen & André Diegmann (né Nolte), 2020. "Does low‐pay persist across different regimes? Evidence from German Unification," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 28(3), pages 413-440, July.
    3. repec:iab:iabfme:201801(en is not listed on IDEAS
    4. Lorenz, Svenja & Pfister, Mona & Zwick, Thomas, 2018. "Identification of the statutory retirement dates in the Sample of Integrated Labour Market Biographies (SIAB)," FDZ Methodenreport 201808_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    5. Nolte, Andre & Gürtzgen, Nicole, 2015. "Changing Fortunes during Economic Transition - Low-Wage Persistence before and after German Unification," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112828, Verein für Socialpolitik / German Economic Association.
    6. repec:iab:iabfme:201808(en is not listed on IDEAS

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    More about this item

    Keywords

    Imputation Rules; Administrative Data; East Germany; Education; Institutions;
    All these keywords.

    JEL classification:

    • I2 - Health, Education, and Welfare - - Education
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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