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Reducing the Need for Heuristic Rules - An Iterative Algorithm for Imputing the Education Variable in SIAB

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  • Christian Hutter
  • Joachim Möller
  • Marion Penninger
Abstract
The article proposes an iterative imputation algorithm based on the EM-Algorithm and employs it to improve the education variable in the Sample of Integrated Labour Market Biographies (SIAB), an administrative panel data set provided by the Institute for Employment Research (IAB). Since the education variable in SIAB is reported for statistical reasons only, it suffers from frequent inconsistent reports and a high and increasing share of missing values. Existing imputation procedures are mainly based on heuristic rules and there is no guidance of which procedure outperforms the others. Our iterative imputation algorithm reduces the role of heuristic decision rules and estimates the most likely educational or vocational status using information based on the employee’s whole employment biography. The resulting imputed education variable does not contain inconsistent reports. Furthermore, the share of missing spells is reduced by 87 percent. After imputation, the education variable shows better congruence to independent survey data (ALWA). The article focuses on the results for a (large) subgroup of SIAB (West German employees born after 1960 with a single main job). However, robustness checks reveal that the final education variable is stable with respect to different samples, termination criteria and control variables. Hence, we conclude that our imputation algorithm can serve as a blueprint for further expansions. / Der vorliegende Artikel nutzt ein iteratives Imputations-Verfahren, das auf dem EMAlgorithmus basiert, zur Korrektur der Bildungsvariable in der Stichprobe der Integrierten Arbeitsmarktbiographien (SIAB), einem administrativen Paneldatensatz des Instituts für Arbeitsmarkt- und Berufsforschung (IAB). Die Bildungsvariable enthält einen großen Anteil an Spells, für die entweder gar kein Bildungsstatus vorliegt oder die als inkonsistent gelten müssen. Bisherige Imputationsverfahren sind größtenteils heuristischer Natur und es ist unklar, welches der Verfahren den anderen vorzuziehen ist. Unser iteratives Imputationsverfahren reduziert den Einsatz von heuristischen Entscheidungsregeln und schätzt den wahrscheinlichsten Bildungsstatus. Grundlage für die Schätzungen sind erklärende Variablen, die während des gesamten Auftretens eines Beschäftigten im Datensatz gesammelt werden können. Die resultierende imputierte Bildungsvariable enthält keine inkonsistenten Bildungsverläufe mehr. Zudem verringert sich die Anzahl der Spells mit fehlenden Bildungsangaben um ca. 87 Prozent. Der Artikel setzt den Fokus auf die Ergebnisse für eine (große) Teilgruppe von SIAB (westdeutsche Beschäftigte, die nach 1960 geboren sind und nicht mehrfachbeschäftigt sind). Da Robustheitschecks eine zuverlässig hohe Stabilität der korrigierten Bildungsvariable bezüglich einer Variation der Stichprobe, der Abbruchkriterien und der Kontrollvariablen zeigen, kann unser vorgeschlagener Imputationsalgorithmus mit wenigen Modifikationen auch auf andere Untergruppen von SIAB ausgedehnt werden.

Suggested Citation

  • Christian Hutter & Joachim Möller & Marion Penninger, 2015. "Reducing the Need for Heuristic Rules - An Iterative Algorithm for Imputing the Education Variable in SIAB," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 135(3), pages 355-388.
    • Handle: RePEc:dah:aeqsjb:v135_y2015_i3_q3_p355-388
    DOI: 10.3790/schm.135.3.355
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    Cited by:

    1. Thomsen, Stephan L. & Trunzer, Johannes, 2020. "Did the Bologna Process Challenge the German Apprenticeship System? Evidence from a Natural Experiment," IZA Discussion Papers 13806, Institute of Labor Economics (IZA).
    2. Wolfgang Dauth & Johann Eppelsheimer, 2020. "Preparing the sample of integrated labour market biographies (SIAB) for scientific analysis: a guide," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 54(1), pages 1-14, December.

    More about this item

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure

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