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A new approach for disclosure control in the IAB Establishment Panel : multiple imputation for a better data access

Author

Listed:
  • Drechsler, Jörg
  • Dundler, Agnes
  • Bender, Stefan
  • Rässler, Susanne
  • Zwick, Thomas

Abstract

"For micro-datasets considered for release as scientific or public use files, statistical agencies have to face the dilemma of guaranteeing the confidentiality of survey respondents on the one hand and offering sufficiently detailed data on the other hand. For that reason a variety of methods to guarantee disclosure control is discussed in the literature. In this paper, we present an application of Rubin's (1993) idea to generate synthetic datasets from existing confidential survey data for public release. We use a set of variables from the 1997 wave of the German IAB Establishment Panel and evaluate the quality of the approach by comparing results from an analysis by Zwick (2005) with the original data with the results we achieve for the same analysis run on the dataset after the imputation procedure. The comparison shows that valid inferences can be obtained using the synthetic datasets in this context, while confidentiality is guaranteed for the survey participants." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Drechsler, Jörg & Dundler, Agnes & Bender, Stefan & Rässler, Susanne & Zwick, Thomas, 2007. "A new approach for disclosure control in the IAB Establishment Panel : multiple imputation for a better data access," IAB-Discussion Paper 200711, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    • Handle: RePEc:iab:iabdpa:200711
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    File URL: https://doku.iab.de/discussionpapers/2007/dp1107.pdf
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    Citations

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    Cited by:

    1. Jörg Drechsler, 2015. "Multiple Imputation of Multilevel Missing Data—Rigor Versus Simplicity," Journal of Educational and Behavioral Statistics, , vol. 40(1), pages 69-95, February.
    2. Andrés F. Barrientos & Alexander Bolton & Tom Balmat & Jerome P. Reiter & John M. de Figueiredo & Ashwin Machanavajjhala & Yan Chen & Charles Kneifel & Mark DeLong, 2017. "A Framework for Sharing Confidential Research Data, Applied to Investigating Differential Pay by Race in the U. S. Government," NBER Working Papers 23534, National Bureau of Economic Research, Inc.
    3. Maurice Brandt & Dirk Oberschachtsiek & Ramona Pohl, 2008. "Neue Datenangebote in den Forschungsdatenzentren – Betriebs- und Unternehmensdaten im Längsschnitt –," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 2(3), pages 193-207, October.
    4. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.
    5. Reiter, Jerome P. & Drechsler, Jörg, 2007. "Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality," IAB-Discussion Paper 200720, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    6. Loong Bronwyn & Rubin Donald B., 2017. "Multiply-Imputed Synthetic Data: Advice to the Imputer," Journal of Official Statistics, Sciendo, vol. 33(4), pages 1005-1019, December.
    7. Jerome P. Reiter, 2009. "Using Multiple Imputation to Integrate and Disseminate Confidential Microdata," International Statistical Review, International Statistical Institute, vol. 77(2), pages 179-195, August.
    8. Ulrich Kaiser & Joachim Wagner, 2008. "Neue Möglichkeiten zur Nutzung vertraulicher amtlicher Personen‐ und Firmendaten," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 9(3), pages 329-349, August.
    9. Stefan Liebig, 2009. "Organizational Data," RatSWD Working Papers 67, German Data Forum (RatSWD).
    10. Jahangir Alam M. & Dostie Benoit & Drechsler Jörg & Vilhuber Lars, 2020. "Applying data synthesis for longitudinal business data across three countries," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 212-236, August.
    11. Drechsler, Jörg, 2011. "Methodenreport: Synthetische Scientific-Use-Files der Welle 2007 des IAB-Betriebspanels," FDZ Methodenreport 201101_de, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    12. Gerd Ronning, 2014. "Vertraulichkeit und Verfügbarkeit von Mikrodaten," IAW Discussion Papers 101, Institut für Angewandte Wirtschaftsforschung (IAW).
    13. Jörg Höhne, 2008. "Anonymisierungsverfahren für Paneldaten," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 2(3), pages 259-275, October.
    14. repec:iab:iabfme:200702(de is not listed on IDEAS
    15. Jan Pablo Burgard & Jan-Philipp Kolb & Hariolf Merkle & Ralf Münnich, 2017. "Synthetic data for open and reproducible methodological research in social sciences and official statistics," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(3), pages 233-244, December.

    More about this item

    Keywords

    Datenaufbereitung ; Datenschutz ; Datensicherheit ; IAB-Betriebspanel ; Datenanonymisierung ; Imputationsverfahren;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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