Publication details
Using online job postings to predict key labour market indicators
| Authors | |
|---|---|
| Year of publication | 2023 |
| Type | Article in Periodical |
| Magazine / Source | Social Science Computer Review |
| MU Faculty or unit | |
| Citation | |
| Web | https://journals.sagepub.com/doi/full/10.1177/08944393221085705 |
| Doi | http://dx.doi.org/10.1177/08944393221085705 |
| Keywords | vacancy statistics; online data; time series; predictive modelling; unemployment; employment |
| Attached files | |
| 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. |