Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/44457 
Autor:innen: 
Erscheinungsjahr: 
2011
Schriftenreihe/Nr.: 
ZEW Discussion Papers No. 11-013
Verlag: 
Zentrum für Europäische Wirtschaftsforschung (ZEW), Mannheim
Zusammenfassung: 
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.
Schlagwörter: 
Measurement error
EM by the method of weights
wage regression
expansion of educational degrees
misclassification
imputation rules
JEL: 
C13
J24
J31
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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