IDEAS home Printed from https://ideas.repec.org/p/xrs/sfbmaa/07-08.html
   My bibliography  Save this paper

The German SAVE survey: documentation and methodology

Author

Listed:
  • Schunk, Daniel

    (University of Zürich Institute for Empirical Research in Economics)

Abstract

The purpose of this document is to describe methodological details of the German SAVE survey and to provide users of SAVE with all necessary information for working with the publicly available SAVE dataset.

Suggested Citation

  • Schunk, Daniel, 2007. "The German SAVE survey: documentation and methodology," Sonderforschungsbereich 504 Publications 07-08, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
  • Handle: RePEc:xrs:sfbmaa:07-08
    Note: Financial support from the Deutsche Forschungsgemeinschaft, SFB 504, at the University of Mannheim, is gratefully acknowledged.
    as

    Download full text from publisher

    File URL: http://www.sfb504.uni-mannheim.de/publications/dp07-08.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Scheuren, Fritz, 1988. "Missing-Data Adjustments in Large Surveys: Comment," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 298-299, July.
    2. Lothar Essig & Joachim K. Winter, 2009. "Item Non-Response to Financial Questions in Household Surveys: An Experimental Study of Interviewer and Mode Effects," Fiscal Studies, Institute for Fiscal Studies, vol. 30(Special I), pages 367-390, December.
    3. Regina Riphahn & Oliver Serfling, 2005. "Item non-response on income and wealth questions," Empirical Economics, Springer, vol. 30(2), pages 521-538, September.
    4. F. Thomas Juster & James P. Smith, 2004. "Improving the Quality of Economic Data: Lessons from the HRS and AHEAD," Labor and Demography 0402010, University Library of Munich, Germany.
    5. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    6. Hilary W. Hoynes & Michael D. Hurd & Harish Chand, 1998. "Household Wealth of the Elderly under Alternative Imputation Procedures," NBER Chapters, in: Inquiries in the Economics of Aging, pages 229-257, National Bureau of Economic Research, Inc.
    7. David A. Wise, 2005. "Analyses in the Economics of Aging," NBER Books, National Bureau of Economic Research, Inc, number wise05-1.
    8. Axel H. Boersch-Supan & Lothar Essig, 2005. "Household Saving in Germany: Results of the First SAVE Study," NBER Chapters, in: Analyses in the Economics of Aging, pages 317-356, National Bureau of Economic Research, Inc.
    9. Daniel Schunk, 2008. "A Markov chain Monte Carlo algorithm for multiple imputation in large surveys," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 101-114, February.
    10. Essig, Lothar, 2005. "Methodological aspects of the SAVE data set," Papers 05-17, Sonderforschungsbreich 504.
    11. Sande, I G, 1988. "Missing-Data Adjustments in Large Surveys: Comment," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 296-297, July.
    12. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 300-301, July.
    13. Lothar Essig, 2005. "Methodological aspects of the SAVE data set," MEA discussion paper series 05080, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Börsch-Supan, Axel & Reil-Held, Anette & Schunk, Daniel, 2007. "The savings behaviour of German households: First Experiences with state promoted private pensions," MEA discussion paper series 07136, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    2. Börsch-Supan, Axel & Coppola, Michela & Reil-Held, Anette, 1970. "Riester Pensions in Germany: Design, Dynamics, Targetting Success and Crowding-In," MEA discussion paper series 201220, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    3. Ziegelmeyer, Michael, 2009. "Documentation of the logical imputation using the panel structure of the 2003-2008 German SAVE Survey," Sonderforschungsbereich 504 Publications 08-41, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    4. Luik, Marc-André & Berlemann, Michael, 2014. "Institutional Reform and Depositors’ Portfolio Choice: Evidence from Censored Quantile Regressions," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100291, Verein für Socialpolitik / German Economic Association.
    5. Schunk, Daniel, 2007. "A Markov Chain Monte Carlo Multiple Imputation Procedure for Dealing with Item Nonresponse in the German SAVE Survey," Sonderforschungsbereich 504 Publications 07-06, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    6. Coppola, Michela & Börsch-Supan, Axel, 2011. "The German SAVE Study: Design, selected results and future developments," VfS Annual Conference 2011 (Frankfurt, Main): The Order of the World Economy - Lessons from the Crisis 48733, Verein für Socialpolitik / German Economic Association.
    7. Ivonne Honekamp, 2012. "Financial Literacy and Retirement Savings in Germany," NFI Working Papers 2012-WP-03, Indiana State University, Scott College of Business, Networks Financial Institute.
    8. Pahnke, Luise & Honekamp, Ivonne, 2010. "Different Effects of Financial Literacy and Financial Education in Germany," MPRA Paper 22900, University Library of Munich, Germany.
    9. Dummann, Kathrin, 2008. "Retirement saving and attitude towards financial intermediaries: Evidence for Germany," Thuenen-Series of Applied Economic Theory 99, University of Rostock, Institute of Economics.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniel Schunk, 2007. "A Markov Chain Monte Carlo Multiple Imputation Procedure for Dealing with Item Nonresponse in the German SAVE Survey," MEA discussion paper series 07121, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    2. Börsch-Supan, Axel & Coppola, Michela & Reil-Held, Anette, 1970. "Riester Pensions in Germany: Design, Dynamics, Targetting Success and Crowding-In," MEA discussion paper series 201220, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    3. Börsch-Supan, Axel & Reil-Held, Anette & Schunk, Daniel, 2007. "The savings behaviour of German households: First Experiences with state promoted private pensions," MEA discussion paper series 07136, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    4. Schunk Daniel, 2009. "What Determines Household Saving Behavior: An Examination of Saving Motives and Saving Decisions 06.01.2009," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 229(4), pages 467-491, August.
    5. Martin, Eisele & Zhu, Junyi, 2013. "Multiple imputation in a complex household survey - the German Panel on Household Finances (PHF): challenges and solutions," MPRA Paper 57666, University Library of Munich, Germany.
    6. Westermeier, Christian & Grabka, Markus M., 2016. "Longitudinal Wealth Data and Multiple Imputation: An Evaluation Study," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 10(3), pages 237-252.
    7. Schunk, Daniel, 2007. "What determines the saving behavior of German households? : an examination of saving motives and saving decisions," Papers 07-10, Sonderforschungsbreich 504.
    8. Daniel Schunk, 2008. "A Markov chain Monte Carlo algorithm for multiple imputation in large surveys," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 101-114, February.
    9. Adel Bosch & Steven F. Koch, 2021. "Individual and Household Debt: Does Imputation Choice Matter?," Working Papers 202141, University of Pretoria, Department of Economics.
    10. Christian Aßmann & Ariane Würbach & Solange Goßmann & Ferdinand Geissler & Anika Bela, 2017. "Nonparametric Multiple Imputation for Questionnaires with Individual Skip Patterns and Constraints: The Case of Income Imputation in the National Educational Panel Study," Sociological Methods & Research, , vol. 46(4), pages 864-897, November.
    11. Joost Ginkel & Pieter Kroonenberg, 2014. "Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 242-269, July.
    12. Lothar Essig & Joachim K. Winter, 2009. "Item Non-Response to Financial Questions in Household Surveys: An Experimental Study of Interviewer and Mode Effects," Fiscal Studies, Institute for Fiscal Studies, vol. 30(Special I), pages 367-390, December.
    13. Verbeek, M.J.C.M. & Nijman, T.E., 1992. "Incomplete panels and selection bias : A survey," Discussion Paper 1992-7, Tilburg University, Center for Economic Research.
    14. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    15. Essig, Lothar, 2005. "Household saving in Germany : results from SAVE 2001 - 2003," Papers 05-23, Sonderforschungsbreich 504.
    16. Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.
    17. Dang, Hai-Anh H & Carletto, Calogero, 2022. "Recall Bias Revisited: Measure Farm Labor Using Mixed-Mode Surveys and Multiple Imputation," IZA Discussion Papers 14997, Institute of Labor Economics (IZA).
    18. Brownstone, David, 1997. "Multiple Imputation Methodology for Missing Data, Non-Random Response, and Panel Attrition," University of California Transportation Center, Working Papers qt2zd6w6hh, University of California Transportation Center.
    19. Essig, Lothar, 2005. "Measures for savings and saving rates in the German SAVE data set," Papers 05-20, Sonderforschungsbreich 504.
    20. Zachary H. Seeskin, 2016. "Evaluating the Use of Commercial Data to Improve Survey Estimates of Property Taxes," CARRA Working Papers 2016-06, Center for Economic Studies, U.S. Census Bureau.

    More about this item

    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:xrs:sfbmaa:07-08. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Carsten Schmidt (email available below). General contact details of provider: https://edirc.repec.org/data/sfmande.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.