The digitalisation of work and production has resulted in a profound structural change. For a successful digital transformation of the economy, the training of specialists in STEM (Science, Technology, Engineering, and Mathematics) plays an important role. STEM specialists play a key role in a country's innovative strength, as they contribute in particular to technological progress. In Germany, 83 percent of the 1.3 million people employed in research departments have a STEM qualification. STEM occupations are often the focus of political discussions on the shortage of skilled workers, as digitization is increasing the demand for STEM qualifications through increasingly complex and technically demanding value chains. The current shortage of STEM specialists amounts to about 315,000 jobs, with academic STEM occupations accounting for about one third of this shortage.


Over the past few years, state-funded initiatives have been launched in many countries to secure the next generation of scientists and engineers. The chances of success of such initiatives depend on many factors, e.g. whether the right groups are addressed. Furthermore, it is unclear to what extent people who decide to study STEM fields on the basis of targeted support measures achieve the same labour market success as those graduates who select themselves in STEM subjects because of their comparative advantage. The probability of a STEM training leading to an adequate STEM occupation is of high policy relevance. If there is a mismatch between the skills of graduates and the demands of their profession, the existing STEM skills cannot fully contribute to innovation and growth.


Against this background, this project analyses which determinants influence the choice of vocational training or study in STEM fields. In addition, the factors that determine the choice of a STEM profession will be investigated. The research questions will be examined by microeconometric analyses. Since educational decisions are dynamic and depend on many influencing factors, e.g. individual abilities and preferences, extensive longitudinal studies are suitable for these analyses.

Project duration

01.09.2018 - 30.11.2020

Contact
Departments

Digital Economy

Cooperation partner

Prof. Michael R. Ward, University of Texas at Arlington, Arlington, TX, US