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Imputation Rules to Improve the Education Variable in the IAB Employment Subsample

Author

Listed:
  • Bernd Fitzenberger
  • Aderonke Osikominu
  • Robert Völter

Abstract

The education variable in the IAB employment subsample has two shortcomings: missing values and inconsistencies in 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 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 and wage inequality. We find that correcting the education variable shows the educational attainment of the male labor force to be higher than measured with the original data and changes some estimates of wage inequality. Our analysis does not provide a definite rule on how to choose among the different imputation procedures discussed, but we recommend correcting the original education variable.

Suggested Citation

  • Bernd Fitzenberger & Aderonke Osikominu & Robert Völter, 2006. "Imputation Rules to Improve the Education Variable in the IAB Employment Subsample," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 126(3), pages 405-436.
  • Handle: RePEc:aeq:aeqsjb:v126_y2006_i3_q3_p405-436
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    References listed on IDEAS

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    4. Thomas J. Kane & Cecilia E. Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," Working Papers 798, Princeton University, Department of Economics, Industrial Relations Section..
    5. Molinari, Francesca, 2008. "Partial identification of probability distributions with misclassified data," Journal of Econometrics, Elsevier, vol. 144(1), pages 81-117, May.
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    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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