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

  • Dlugosz, Stephan

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.

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Paper provided by ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research in its series ZEW Discussion Papers with number 11-013.

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Date of creation: 2011
Date of revision:
Handle: RePEc:zbw:zewdip:11013
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  1. Ichino, Andrea & Winter-Ebmer, Rudolf, 1999. "Lower and upper bounds of returns to schooling: An exercise in IV estimation with different instruments," European Economic Review, Elsevier, vol. 43(4-6), pages 889-901, April.
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  5. 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.
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  7. Behrman, Jere R. & Rosenzweig, Mark R., 1999. ""Ability" biases in schooling returns and twins: a test and new estimates," Economics of Education Review, Elsevier, vol. 18(2), pages 159-167, April.
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  10. Laura Wichert & Ralf A. Wilke, 2012. "Which factors safeguard employment?: an analysis with misclassified German register data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(1), pages 135-151, 01.
  11. Katz, Lawrence F. & Autor, David H., 1999. "Changes in the wage structure and earnings inequality," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 26, pages 1463-1555 Elsevier.
  12. Thomas J. Kane & Cecilia Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," Working Papers 798, Princeton University, Department of Economics, Industrial Relations Section..
  13. Alan S. Blinder, 1973. "Wage Discrimination: Reduced Form and Structural Estimates," Journal of Human Resources, University of Wisconsin Press, vol. 8(4), pages 436-455.
  14. Jeffrey M. Wooldridge, 2004. "Estimating average partial effects under conditional moment independence assumptions," CeMMAP working papers CWP03/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  15. Per Johansson & Per Skedinger, 2009. "Misreporting in register data on disability status: evidence from the Swedish Public Employment Service," Empirical Economics, Springer, vol. 37(2), pages 411-434, October.
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