The paper attempts to determine regional unemployment rates for NUTS 2 regions based on data available on a regular basis from Eurostat and ILO. In the first part it is shown how the evolution of regional unemployment rates is determined by national unemployment rates by industry and the...

In this paper, we compare two popular statistical learning techniques, logistic regression and random forest, with respect to their ability to classify jobseekers by their likelihood to become long-term unemployed. We study the performance of the two methods before the COVID-19 pandemic as well...

This paper studies the predictability of long-term unemployment (LTU) and analyzes its main determinants using rich administrative data in Sweden. Compared to using standard socio-demographic variables, the predictive power more than doubles when leveraging the rich data environment. The largest...

Unemployment is notoriously difficult to predict. In previous studies, once country and year fixed effects are added to panel estimates, few variables predict changes in unemployment rates. Using panel data for 29 European countries collected by the European Commission over 444 months between...

In an era characterized by population aging and economic challenges in welfare states across the world, sustaining these welfare systems requires a large workforce. Many individuals outside the labor market aspire to work but encounter a labyrinth of obstacles. While Public Employment Services...

This paper analyzes a setting in which a manufacturer (he) and a retailer (she) face uncertain demand, but the retailer has an information advantage in the form of a private demand forecast. Such information asymmetry causes the manufacturer to incur a hidden information cost. The results show...

We collect rich establishment-level data about advance layoff notices filed under the Worker Adjustment and Retraining Notification (WARN) Act since January 1990. We present in-sample evidence that the number of workers affected by WARN notices leads state-level initial unemployment insurance...

Many large and small companies in the tech and startup sector have been laying off an unusually high number of workers in 2022 and 2023. We are interested in predicting when this period of layoffs might end, without resorting to economic forecasts. We observe that a sample of layoffs up to March...