Model-assisted calibration of online job ads to official vacancy totals yields bias-corrected estimates of employer skill demand in Poland, showing large over-representation of interpersonal and managerial skills in online postings.
Combining Non-probability and Probability Survey Samples Through Mass Imputation
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abstract
This paper presents theoretical results on combining non-probability and probability survey samples through mass imputation, an approach originally proposed by Rivers (2007) as sample matching without rigorous theoretical justification. Under suitable regularity conditions, we establish the consistency of the mass imputation estimator and derive its asymptotic variance formula. Variance estimators are developed using either linearization or bootstrap. Finite sample performances of the mass imputation estimator are investigated through simulation studies and an application to analyzing a non-probability sample collected by the Pew Research Centre.
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Enhancing the Demand for Labour survey by including skills from online job advertisements using model-assisted calibration
Model-assisted calibration of online job ads to official vacancy totals yields bias-corrected estimates of employer skill demand in Poland, showing large over-representation of interpersonal and managerial skills in online postings.