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Misreported schooling, multiple measures and returns to educational qualifications

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  • Battistin, Erich
  • De Nadai, Michele
  • Sianesi, Barbara

Abstract

We consider the identification and estimation of the average wage return to attaining educational qualifications when attainment is potentially measured with error. By exploiting two independent measures of qualifications, we identify the extent of misclassification in administrative and self-reported data on educational attainment. The availability of multiple self-reported educational measures additionally allows us to identify the temporal patterns of individual misreporting errors across survey waves. We provide the first reliable estimate of a highly policy relevant parameter for the UK, namely the return from attaining any academic qualification compared to leaving school at the minimum age without any formal qualification. We identify returns to qualifications under two alternative settings: a strong ignorability assumption and an exclusion restriction. All these results are obtained by casting the identification problem in terms of a mixture model, and using a semi-parametric estimation approach based on balancing scores, which allows for arbitrarily heterogeneous individual returns.

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  • Battistin, Erich & De Nadai, Michele & Sianesi, Barbara, 2014. "Misreported schooling, multiple measures and returns to educational qualifications," Journal of Econometrics, Elsevier, vol. 181(2), pages 136-150.
  • Handle: RePEc:eee:econom:v:181:y:2014:i:2:p:136-150
    DOI: 10.1016/j.jeconom.2014.03.002
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    Cited by:

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    3. Erich Battistin & Barbara Sianesi, 2006. "Misreported schooling and returns to education: evidence from the UK," CeMMAP working papers CWP07/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Santiago Acerenza & Kyunghoon Ban & D'esir'e K'edagni, 2021. "Marginal Treatment Effects with Misclassified Treatment," Papers 2105.00358, arXiv.org, revised Jan 2022.
    5. Chen, Linkun & Clarke, Philip M. & Petrie, Dennis J. & Staub, Kevin E., 2021. "The effects of self-assessed health: Dealing with and understanding misclassification bias," Journal of Health Economics, Elsevier, vol. 78(C).
    6. Schumann, Mathias & Freier, Ronny & Siedler, Thomas, 2014. "The Economic Returns to Graduating with Honors - Evidence from Law Graduates," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100338, Verein für Socialpolitik / German Economic Association.
    7. Battistin, Erich & De Nadai, Michele & Vuri, Daniela, 2017. "Counting rotten apples: Student achievement and score manipulation in Italian elementary Schools," Journal of Econometrics, Elsevier, vol. 200(2), pages 344-362.
    8. Burgess, Simon & Metcalfe, Robert & Sadoff, Sally, 2021. "Understanding the response to financial and non-financial incentives in education: Field experimental evidence using high-stakes assessments," Economics of Education Review, Elsevier, vol. 85(C).
    9. Marilia Nepomuceno & Cássio M. Turra, 2020. "Assessing the quality of education reporting in Brazilian censuses," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 42(15), pages 441-460.
    10. Francis DiTraglia & Camilo Garcia-Jimeno, 2015. "On Mis-measured Binary Regressors: New Results And Some Comments on the Literature, Third Version," PIER Working Paper Archive 15-040, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 24 Nov 2015.
    11. DiTraglia, Francis J. & García-Jimeno, Camilo, 2019. "Identifying the effect of a mis-classified, binary, endogenous regressor," Journal of Econometrics, Elsevier, vol. 209(2), pages 376-390.
    12. Denni Tommasi & Arthur Lewbel & Rossella Calvi, 2017. "LATE with Mismeasured or Misspecified Treatment: An application to Women's Empowerment in India," Working Papers ECARES ECARES 2017-27, ULB -- Universite Libre de Bruxelles.
    13. Steven J. Haider & Melvin Stephens Jr., 2020. "Correcting for Misclassified Binary Regressors Using Instrumental Variables," NBER Working Papers 27797, National Bureau of Economic Research, Inc.
    14. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    15. Battistin, Erich & Chesher, Andrew, 2014. "Treatment effect estimation with covariate measurement error," Journal of Econometrics, Elsevier, vol. 178(2), pages 707-715.
    16. Takahide Yanagi, 2019. "Inference on local average treatment effects for misclassified treatment," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 938-960, September.
    17. Francis J. DiTraglia & Camilo García-Jimeno, 2017. "Mis-classified, Binary, Endogenous Regressors: Identification and Inference," NBER Working Papers 23814, National Bureau of Economic Research, Inc.
    18. Joseph Friedman & Nicholas Graetz & Emmanuela Gakidou, 2018. "Improving the estimation of educational attainment: New methods for assessing average years of schooling from binned data," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-15, November.
    19. Wang, Jun & Li, Bo, 2020. "Does employer learning with statistical discrimination exist in China? Evidence from Chinese Micro Survey Data," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 319-333.
    20. Hiroyuki Kasahara & Katsumi Shimotsu, 2019. "Identification of Regression Models with a Misclassified and Endogenous Binary Regressor," Papers 1904.11143, arXiv.org, revised Aug 2021.
    21. Millimet, Daniel L., 2015. "Covariate measurement and endogeneity," Economics Letters, Elsevier, vol. 136(C), pages 59-63.
    22. Marilia R. Nepomuceno & Cássio M. Turra, 2019. "Assessing the quality of self-reported education in Brazil with intercensal survivorship ratios," MPIDR Working Papers WP-2019-022, Max Planck Institute for Demographic Research, Rostock, Germany.

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    More about this item

    Keywords

    Misclassification; Mixture models; Returns to educational qualifications; Treatment effects;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • I20 - Health, Education, and Welfare - - Education - - - General
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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