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. ZusammenfassungDer vorliegende Artikel nutzt ein iteratives Imputations-Verfahren, das auf dem EM-Algorithmus 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 itera-Schmollers Jahrbuch 135 (2015), 355 -388 Duncker & Humblot, Berlin Schmollers Jahrbuch 135 (2015) 3 * We are grateful to Wolfgang Biersack and Roland Weigand for helpful suggestions and valuable input. Special thanks to Johannes Ludsteck for his extraordinary help with the ALWA data set.
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