Give missings a chance : combined stochastic and rule-based approach to improve regression models with mismeasured monotonic covariates without side information


Dlugosz, Stephan


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URL: https://ub-madoc.bib.uni-mannheim.de/3141
URN: urn:nbn:de:bsz:180-madoc-31418
Document Type: Working paper
Year of publication: 2011
The title of a journal, publication series: ZEW Discussion Papers
Volume: 11-013
Place of publication: Mannheim
Publication language: English
Institution: Sonstige Einrichtungen > ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung
MADOC publication series: Veröffentlichungen des ZEW (Leibniz-Zentrum für Europäische Wirtschaftsforschung) > ZEW Discussion Papers
Subject: 300 Social sciences, sociology, anthropology
Classification: JEL: J31 J24 C13 ,
Subject headings (SWD): Bildungsniveau , Bildungsabschluss , Lohndifferenzierung , EM-Algorithmus
Keywords (English): measurement error , EM by the method of weights , wage regression , expansion of educational degrees , misclassification , imputation rules
Abstract: Register data are known for their large sample size and good data quality. The measurement accuracy of variables highly depends on their high importance for administrative processes. The education variable in the IAB employment sub-sample is an example for information that is gathered without a clear purpose. It therefore severely suffers from missing values and misclassifications. In this paper, a classical approach to deal with incomplete data is used in combination with rule-based plausibility checks for misclassification to improve the quality of the variable. The developed correction procedure is applied to simple Mincer-type wage regressions. The procedure reveals that the quality of years in education is very important: The German labour market rewards general education less than vocational training. Furthermore, using this method, no indication of an inflation in formal education degrees could be found.
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Dlugosz, Stephan (2011) Give missings a chance : combined stochastic and rule-based approach to improve regression models with mismeasured monotonic covariates without side information. Open Access ZEW Discussion Papers Mannheim 11-013 [Working paper]
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