Using online job postings to predict key labour market indicators

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Using online job postings to predict key labour market indicators

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Using online job postings to predict key labour market indicators

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Social Science Computer Review

ISSN

0894-4393

e-ISSN

1552-8286

Svazek periodika

41

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

1630-1649

Kód UT WoS článku

000799700900001

EID výsledku v databázi Scopus

2-s2.0-85130618439