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Misreported Schooling, Multiple Measures and Returns to Educational Qualifications

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  • Battistin, Erich

    () (Queen Mary, University of London)

  • De Nadai, Michele

    () (University of Padova)

  • Sianesi, Barbara

    () (Institute for Fiscal Studies, London)

Abstract

We provide a number of contributions of policy, practical and methodological interest to the study of the returns to educational qualifications in the presence of misreporting. First, we provide the first reliable estimates 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. Second, we provide the academic and policy community with estimates of the accuracy and misclassification patterns of commonly used types of data on educational attainment: administrative files, self-reported information close to the date of completion of the qualification, and recall information ten years after completion. We are in the unique position to assess the temporal patterns of misreporting errors across survey waves, and to decompose misreporting errors into a systematic component linked to individuals' persistent behaviour and into a transitory part reflecting random survey errors. Third, by using the unique nature of our data, we assess how the biases from measurement error and from omitted ability and family background variables interact in the estimation of returns. On the methodological front, we propose a semi-parametric estimation approach based on balancing scores and mixture models, in particular allowing for arbitrarily heterogeneous individual returns.

Suggested Citation

  • Battistin, Erich & De Nadai, Michele & Sianesi, Barbara, 2012. "Misreported Schooling, Multiple Measures and Returns to Educational Qualifications," IZA Discussion Papers 6337, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp6337
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    Cited by:

    1. Battistin, Erich & Chesher, Andrew, 2014. "Treatment effect estimation with covariate measurement error," Journal of Econometrics, Elsevier, vol. 178(2), pages 707-715.
    2. 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.
    3. Freier, Ronny & Schumann, Mathias & Siedler, Thomas, 2015. "The earnings returns to graduating with honors — Evidence from law graduates," Labour Economics, Elsevier, vol. 34(C), pages 39-50.
    4. Erich Battistin & Michele De Nadai & Daniela Vuri, 2014. "Counting Rotten Apples: Student Achievement and Score Manipulation in Italian Elementary Schools," FBK-IRVAPP Working Papers 2014-05, Research Institute for the Evaluation of Public Policies (IRVAPP), Bruno Kessler Foundation.
    5. YANAGI, Takahide, 2017. "Inference on Local Average Treatment Effects for Misclassified Treatment," Discussion Papers 2017-02, Graduate School of Economics, Hitotsubashi University.
    6. Millimet, Daniel L., 2015. "Covariate measurement and endogeneity," Economics Letters, Elsevier, vol. 136(C), pages 59-63.
    7. 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.
    8. Schumann, Mathias & Freier, Ronny & Siedler, Thomas, 2014. "The Economic Returns to Graduating with Honors - Evidence from Law Graduates," Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100338, Verein für Socialpolitik / German Economic Association.
    9. 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.
    10. repec:eee:econom:v:200:y:2017:i:2:p:344-362 is not listed on IDEAS

    More about this item

    Keywords

    mixture models; misclassification; returns to educational qualifications; treatment effects;

    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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