IDEAS home Printed from https://ideas.repec.org/p/iab/iabdpa/200711.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://doku.iab.de/discussionpapers/2007/dp1107.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Gerd Ronning, 2014. "Vertraulichkeit und Verfügbarkeit von Mikrodaten," IAW Discussion Papers 101, Institut für Angewandte Wirtschaftsforschung (IAW).
    5. 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.
    6. 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.
    7. 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.
    8. 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].
    9. 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.
    10. 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.
    11. 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.
    12. repec:iab:iabfme:200702(de is not listed on IDEAS
    13. Stefan Liebig, 2009. "Organizational Data," RatSWD Working Papers 67, German Data Forum (RatSWD).
    14. 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.
    15. 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].

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iab:iabdpa:200711. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: IAB, Geschäftsbereich Wissenschaftliche Fachinformation und Bibliothek (email available below). General contact details of provider: https://edirc.repec.org/data/iabbbde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.