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

Stephan Dlugosz

No 11-013, ZEW Discussion Papers from ZEW - Leibniz Centre for European Economic Research

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.

Keywords: Measurement error; EM by the method of weights; wage regression; expansion of educational degrees; misclassification; imputation rules (search for similar items in EconPapers)
JEL-codes: C13 J24 J31 (search for similar items in EconPapers)
Date: 2011
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:zewdip:11013

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