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dc.contributor.authorGrabowski, Wojciech
dc.contributor.authorKorczak, Karol
dc.date.accessioned2020-09-23T08:17:55Z
dc.date.available2020-09-23T08:17:55Z
dc.date.issued2020
dc.identifier.citationE+M. Ekonomie a Management = Economics and Management. 2020, roč. 23, č. 3, s. 4-22.cs
dc.identifier.issn2336-5604 (Online)
dc.identifier.issn1212-3609 (Print)
dc.identifier.urihttp://hdl.handle.net/11025/39768
dc.format19 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherTechnická univerzita v Libercics
dc.rightsCC BY-NC 4.0en
dc.subjectLFScs
dc.subjectSEScs
dc.subjectprůzkum pracovních silcs
dc.subjectStruktura průzkumu výdělkůcs
dc.subjectmikroekonometriecs
dc.subjectrežim smíšených efektůcs
dc.subjectmezery v datechcs
dc.titleComplementing data gaps on wages in the labour force survey data set: evidence from Polanden
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedDue to the low level of quality of the Labour Force Survey (LFS) data set, studies devoted to matching the LFS data with data from alternative sources are frequent. In this paper, we propose a novel method of complementing data gaps on wages in the Labour Force Survey data set. The method is based on estimataing the parameters of the multilevel model explaining wages on the basis of the Structure of Earnings Survey (SES) data set. In such a way, we identify the impact of individual characteristics and enterprise-level features on wages. We also find evidence of random differences between the wages of workers from different professional groups. The relative importance of consecutive groups of variables is evaluated on the basis of the estimates of the parameters of the full model and reduced models. The results of the estimation of the parameters are in line with expectations. The estimates of parameters and predictions of random effects are used in order to calculate the theoretical wages of individuals who do not report wages in the Labour Force Survey. When the predicted wages are compared with the observed ones, some discrepancies are observed. Rationales for these discrepancies are provided. Therefore, the use of a correction factor is proposed. Correction factors are provided for different features of workers and different features of enterprises. The use of the microeconometric multilevel model, as well as the correction factor, leads to reasonable wage estimates of workers not reporting them in the Labour Force Survey. The proposed method may be used in order to complement data gaps on wages for other EU countries.en
dc.subject.translatedLFSen
dc.subject.translatedSESen
dc.subject.translatedLabour Force Surveyen
dc.subject.translatedStructure of Earnings Surveyen
dc.subject.translatedmicroeconometricsen
dc.subject.translateddata gapsen
dc.subject.translatedmixed-effects modelen
dc.identifier.doihttps://doi.org/10.15240/tul/001/2020-3-001
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Číslo 3 (2020)
Číslo 3 (2020)

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Ekonomie a management
  5. 2020
  6. Číslo 3 (2020)