Using online job postings to predict key labour market indicators
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F23%3A00132300" target="_blank" >RIV/00216224:14560/23:00132300 - isvavai.cz</a>
Result on the web
<a href="https://journals.sagepub.com/doi/full/10.1177/08944393221085705" target="_blank" >https://journals.sagepub.com/doi/full/10.1177/08944393221085705</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1177/08944393221085705" target="_blank" >10.1177/08944393221085705</a>
Alternative languages
Result language
angličtina
Original language name
Using online job postings to predict key labour market indicators
Original language description
We explore data collected as an administrative by-product of an online job advertisement portal with dominant market coverage in Slovakia. Specifically, we process information on the aggregate quarterly registered number of online job vacancies. We assess the potential of this information in predicting official vacancy, employment and unemployment statistics. We compare the characteristics of the online job posting data with those reported in comparable studies conducted for the Netherlands and Italy. Several differences are identified; most notably, our data are more persistent and stationary around a linear time trend. Additionally, we assess the predictive potential of the online job posting data by comparing in- and out-of-sample estimates of three regression models that predict job vacancy statistics and employment and unemployment levels one to four quarters ahead. Irrespective of the predictive horizon and labour market indicator, the online job posting data always provide a statistically significant predictor. These results are further solidified in an out-of-sample study that shows that forecast errors are lowest for predictions generated by models incorporating online job posting data. In general, the usefulness of the data seems best for longer forecast horizons.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50202 - Applied Economics, Econometrics
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Social Science Computer Review
ISSN
0894-4393
e-ISSN
1552-8286
Volume of the periodical
41
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
1630-1649
UT code for WoS article
000799700900001
EID of the result in the Scopus database
2-s2.0-85130618439