Imputation Rules to Improve the Education Variable in the IAB Employment Subsample
The education variable in the IAB employment subsample has two shortcomings : missing values and inconsistencies with the reporting rule. We propose several deductive imputation procedures to improve the variable. They mainly use the multiple education information available in the data because the employees' education is reported at least once a year. We compare the improved data from the different procedures and the original data in typical applications in labor economics: educational composition of employment, wage inequality, and wage regression. We find, that correcting the education variable: (i) shows the educational attainment of the male labor force to be higher than measured with the original data, (ii) gives different values for some measures of wage inequality, and (iii) does not change the estimates in wage regressions much.
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| Date of creation: | 2005 |
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| Handle: | RePEc:zbw:zewdip:2900 |
| Contact details of provider: | Postal: Phone: +49/621/1235-01 Fax: +49/621/1235-224 Web page: http://www.zew.de/ Email: More information through EDIRC
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- Thomas J. Kane & Cecilia Elena Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," NBER Working Papers 7235, National Bureau of Economic Research, Inc.
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"Estimation of Average Treatment Effects With Misclassification,"
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- Arthur Lewbel, 2003. "Estimation of Average Treatment Effects With Misclassification," Boston College Working Papers in Economics 556, Boston College Department of Economics, revised 04 Sep 2006.
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