Misreported Schooling, Multiple Measures and Returns to Educational Qualifications
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.
|Date of creation:||Feb 2012|
|Date of revision:|
|Publication status:||published in: Journal of Econometrics, 2014, 181, 136-150|
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- Fruhwirth-Schnatter S., 2001. "Markov Chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 194-209, March.
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