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

Maciej Ber\k{e}sewicz, Greta Bia{\l}kowska, Krzysztof Marcinkowski, Magdalena Ma\'slak, Piotr Opiela, Robert Pater and Katarzyna Zadroga

Papers from arXiv.org

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

Date: 2019-08
New Economics Papers: this item is included in nep-big and nep-tra
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in 2021

Downloads: (external link)
http://arxiv.org/pdf/1908.06731 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1908.06731

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().