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Statistical Matching of Administrative and Survey Data

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  • Anika Rasner
  • Joachim R. Frick
  • Markus M. Grabka

Abstract

Using population representative survey data from the German Socio-Economic Panel (SOEP) and administrative pension records from the Statutory Pension Insurance, the authors compare four statistical matching techniques to complement survey information on net worth with social security wealth (SSW) information from the administrative records. The unique properties of the linked data allow for a straight control of the quality of matches under each technique. Based on various evaluation criteria, Mahalanobis distance matching performs best. Exploiting the advantages of the newly assembled data, the authors include SSW in a wealth inequality analysis. Despite its quantitative relevance, SSW is thus far omitted from such analyses because adequate micro data are lacking. The inclusion of SSW doubles the level of net worth and decreases inequality by almost 25 percent. Moreover, the results reveal striking differences along occupational lines.

Suggested Citation

  • Anika Rasner & Joachim R. Frick & Markus M. Grabka, 2013. "Statistical Matching of Administrative and Survey Data," Sociological Methods & Research, , vol. 42(2), pages 192-224, May.
    • Handle: RePEc:sae:somere:v:42:y:2013:i:2:p:192-224
    DOI: 10.1177/0049124113486622
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    References listed on IDEAS

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    1. Modigliani, Franco, 1988. "The Role of Intergenerational Transfers and Life Cycle Saving in the Accumulation of Wealth," Journal of Economic Perspectives, American Economic Association, vol. 2(2), pages 15-40, Spring.
    2. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    3. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
    4. Rodgers, Willard L, 1984. "An Evaluation of Statistical Matching," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(1), pages 91-102, January.
    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. Stephen P. Jenkins & Lorenzo Cappellari & Peter Lynn & Annette Jäckle & Emanuela Sala, 2006. "Patterns of consent: evidence from a general household survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 701-722, October.
    7. Robert Orlowski & Regina T. Riphahn, 2008. "The East German Wage Structure after Transition," SOEPpapers on Multidisciplinary Panel Data Research 148, DIW Berlin, The German Socio-Economic Panel (SOEP).
    8. Michael R. Elliott & William W. Davis, 2005. "Corrigendum: Obtaining cancer risk factor prevalence estimates in small areas: combining data from two surveys," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(5), pages 958-958, November.
    9. Michael R. Elliott & William W. Davis, 2005. "Obtaining cancer risk factor prevalence estimates in small areas: combining data from two surveys," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 595-609, June.
    10. 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.
    11. Anika Rasner & Joachim R. Frick & Markus M. Grabka, 2011. "Extending the Empirical Basis for Wealth Inequality Research Using Statistical Matching of Administrative and Survey Data," SOEPpapers on Multidisciplinary Panel Data Research 359, DIW Berlin, The German Socio-Economic Panel (SOEP).
    12. Robert Orlowski & Regina T. Riphahn, 2009. "The East German wage structure after transition1," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 17(4), pages 629-659, October.
    13. Joachim R. Frick & Bruce Headey, 2009. "Living Standards in Retirement: Accepted International Comparisons are Misleading," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 129(2), pages 309-319.
    14. Gert G. Wagner & Joachim R. Frick & Jürgen Schupp, 2007. "The German Socio-Economic Panel Study (SOEP) – Scope, Evolution and Enhancements," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 127(1), pages 139-169.
    15. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
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    Cited by:

    1. Beatriz Larraz, 2015. "Decomposing the Gini Inequality Index," Sociological Methods & Research, , vol. 44(3), pages 508-533, August.
    2. Johannes Geyer & Salmai Qari & Hermann Buslei & Peter Haan, 2021. "DySiMo Dokumentation: Version 1.0," Data Documentation 101, DIW Berlin, German Institute for Economic Research.
    3. Lüthen Holger & Schröder Carsten & Grabka Markus M. & Goebel Jan & Penz Hannah & Mika Tatjana & Brüggmann Daniel & Ellert Sebastian, 2022. "SOEP-RV: Linking German Socio-Economic Panel Data to Pension Records," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 242(2), pages 291-307, April.
    4. Markus Knell & Reinhard Koman, 2022. "Pension Entitlements and Net Wealth in Austria (Markus Knell, Reinhard Koman)," Working Papers 238, Oesterreichische Nationalbank (Austrian Central Bank).

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