Contributions published at Empirical Research in Business, Industrial Relations and Human Resource Management (Uschi Backes-Gellner)

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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.

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Purpose: The purpose of this paper is to investigate self-competence—the ability to act responsibly on one's own—and likely nonlinear wage returns across different levels of self-competence as part of training curricula. Design/methodology/approach: The authors identify the teaching of self-competence at the occupational level by applying machine-learning methods to the texts of occupational training curricula. Defining three levels of self-competence (high, medium, and low) and using individual labor market data, the authors examine nonlinearities in wage returns to different levels of self-competence. Findings: The authors find nonlinear returns to teaching self-competence: a medium level of self-competence taught in an occupation has the largest wage returns compared to low or high levels. However, in occupations with a high cognitive requirement profile, a high level of self-competence generates positive wage returns. Originality/value: This paper first adds to research on the importance of teaching noncognitive skills for economic outcomes, which recently—in addition to personality traits research—has primarily focused on social skills by introducing self-competence as another largely unexplored but important noncognitive skill. Second, the paper studies not only average but also nonlinear wage returns, showing that the right level of self-competence is crucial, i.e. neither teaching too little nor too much self-competence provides favorable returns because of trade-offs with other skills (e.g. technical or professional skills). Third, the paper also examines complementarities between cognitive skills and noncognitive skills, again pointing toward nonlinear returns, i.e. only in occupations with a high cognitive requirement profile, high levels of self-competence generate positive wage returns.

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Länder mit einer starken Betonung der beruflichen Bildung in ihren Bildungs- und Innovationssystemen, beispielsweise Deutschland und die Schweiz, zählen seit Jahren zu den innovativsten der Welt. Gleichzeitig konstatiert internationale Innovationsforschung, dass ein hoher Akademisierungsgrad (und damit gerade nicht die berufliche Bildung) für starke Innovationsleistungen unabdinglich ist. Wie lässt sich dieser Widerspruch erklären? In diesem Überblicksartikel zeigen Uschi Backes-Gellner und Patrick Lehnert anhand einer Aufarbeitung neuer Forschungserkenntnisse, warum die deutschsprachigen Länder nicht trotz, sondern gerade auch wegen ihres Berufsbildungssystems hochinnovativ sind. Zu den erforderlichen institutionellen Rahmenbedingungen für diesen Effekt gehören dabei die zukunftsorientierte Gestaltung und Aktualisierung von Berufsausbildungscurricula, die Vermittlung von breiten beruflichen (statt engen betriebsspezifischen) Kompetenzen in dualen Berufsausbildungsprogrammen, ein durchlässiges Bildungssystem mit attraktiven Karrierepfaden sowie eine Verknüpfung von tertiärer beruflicher Bildung und angewandter Forschung.

This paper develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. We demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications.

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