Effectiveness of data correction rules in process-produced data: the case of educational attainment

Thomas Kruppe, Britta Matthes and Stefanie Unger
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Britta Matthes: Institute for Employment Research (IAB), Nuremberg, Germany
Stefanie Unger: Institute for Employment Research (IAB), Nuremberg, Germany

No 201415, IAB-Discussion Paper from Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany]

Abstract: "The use of process-produced data plays a large and growing role in empirical labor market research. To address data problems, previous research have developed deductive correction rules that make use of within-person information. We test data reliability and the effectiveness of different correction rules for information about educational degrees as reported in German register data. Therefore we use the unique dataset ALWA-ADIAB, which combines interview data and process-produced data from exactly the same individuals. This approach enables us to assess how effective the existing correction rules are and whether they manage to eliminate structural biases. In sum, we can state that simple editing rules based on logic assumptions are suitable for improving the quality of process-produced data, but they are not able to correct for structural biases." (Author's abstract, IAB-Doku) ((en))

Keywords: Bundesrepublik Deutschland; Datenaufbereitung; Datenqualität; IAB-Datensatz Arbeiten und Lernen; Imputationsverfahren; Interview; prozessproduzierte Daten (search for similar items in EconPapers)
JEL-codes: C80 I20 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2014
New Economics Papers: this item is included in nep-edu
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:iab:iabdpa:201415

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