Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/44457
Authors: 
Dlugosz, Stephan
Year of Publication: 
2011
Series/Report no.: 
ZEW Discussion Papers 11-013
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.
Subjects: 
Measurement error
EM by the method of weights
wage regression
expansion of educational degrees
misclassification
imputation rules
JEL: 
C13
J24
J31
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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