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In the article we describe an enhancement to the Demand for Labour (DL)survey conducted by Statistics Poland, which involves the inclusion of skillsobtained from online job advertisements. The main goal is to provide estimatesof the demand for skills (competences), which is missing in the DL survey. Toachieve this, we apply a data integration approach combining traditionalcalibration with the LASSO-assisted approach to correct representation error inthe online data. Faced with the lack of access to unit-level data from the DLsurvey, we use estimated population totals and propose a~bootstrap approachthat accounts for the uncertainty of totals reported by Statistics Poland. Weshow that the calibration estimator assisted with LASSO outperforms traditionalcalibration in terms of standard errors and reduces representation bias inskills observed in online job ads. Our empirical results show that online datasignificantly overestimate interpersonal, managerial and self-organizationskills while underestimating technical and physical skills. This is mainly dueto the under-representation of occupations categorised as Craft and RelatedTrades Workers and Plant and Machine Operators and Assemblers.

Enhancing the Demand for Labour survey by including skills from online job advertisements using model-assisted calibration