Different degrees of skill obsolescence across hard and soft skills and the role of lifelong learning for labor market outcomes

This paper examines the role of lifelong learning in counteracting skill depreciation and obsolescence. We differentiate between occupations with more hard skills versus more soft skills and draw on representative job advertisement data that contain machine-learning categorized skill requirements and cover the Swiss job market in great detail across occupations (from 1950 to 2019). We examine lifelong learning effects for “harder” versus “softer” occupations, thereby analyzing the role of training in counteracting skill depreciation in occupations that are differently affected by skill depreciation. Our results reveal novel empirical patterns regarding the benefits of lifelong learning, which are consistent with theoretical explanations based on structurally different skill depreciation rates: In harder occupations, with large shares of fast-depreciating hard skills, the role of lifelong learning is primarily as a hedge against unemployment risks rather than a boost to wages. By contrast, in softer occupations, in which workers build on more value-stable soft-skill foundations, the role of lifelong learning instead lies mostly in acting as a boost for upward career mobility and leads to larger wage gains

Education expansion and high-skill job opportunities for workers: Does a rising tide lift all boats?

We examine how education expansions affect the job opportunities for workers with and without the new education. To identify causal effects, we exploit a quasi-random establishment of Universities of Applied Sciences (UASs), bachelor-granting three-year colleges that teach and conduct applied research. By applying machine-learning methods to job advertisement data, we analyze job content before and after the education expansion. We find that, in regions with the newly established UASs, not only job descriptions of the new UAS graduates but also job descriptions of workers without this degree (i.e., middle-skilled workers with vocational training) contain more high-skill job content. This upskilling in job content is driven by an increase in high-skill R&D-related tasks and linked to employment and wage gains. The task spillovers likely occur because UAS graduates with applied research skills build a bridge between middle-skilled workers and traditional university graduates, facilitating the integration of the former into R&D-related tasks

Does updating education curricula accelerate technology adoption in the workplace? Evidence from dual vocational education and training curricula in Switzerland

In an environment of accelerating technological change and increasing digitalization, firms need to adopt new technologies faster than ever before to stay competitive. This paper examines whether updates of education curricula help to bring new technologies faster into firms’ workplaces. We study technology changes and curriculum updates from an early wave of digitalization (i.e., computer-numerically controlled machinery, computer-aided design, and desktop publishing software). We take a text-as-data approach and tap into two novel data sources to measure change in educational content and the use of technology at the workplace: first, vocational education curricula and, second, firms’ job advertisements. To examine the causal effects of adding new technology skills to curricula on the diffusion of these technologies in firms’ workplaces (measured by job advertisements), we use an event study design. Our results show that curriculum updates substantially shorten the time it takes for new technologies to arrive in firms’ workplaces, especially for mainstream firms

Text zoning for job advertisements with bidirectional LSTMs

We present an approach to text zoning for job advertisements with neural networks. Text zoning refers to segmenting texts into eight classes differing from each other regarding content. It aims at capturing text parts dedicated to particular subjects, e.g. the publishing company or qualifications wanted, and hence facilitates subsequent information extraction. We use BiLSTMs, a class of neural networks particularly suited for sequence labeling. Our best approach,vwith task-specific word embeddings and ensemble technique, reaches token-level accuracy of 89.8% and outperforms previous approaches with CRFs