Contributions published at Department of Business Administration (Egon Franck)

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.

Employees with relevant knowledge and skills for digitalization have become increasingly important for the competitiveness of MNCs. However, the shortage of such digital human capital in many host countries is putting pressure on MNC subsidiaries to prevent these employees from leaving. We theorize that the retention of digital human capital in MNC subsidiaries does not merely depend on salaries but crucially on the learning opportunities that subsidiaries offer. By integrating mechanisms from the literature on subsidiary-specific advantages into theoretical models explaining voluntary mobility constraints of employees, we reason that the opportunities for acquiring new skills in subsidiaries with advanced digital expertise will reduce the odds of losing these valuable employees. We test our theoretical predictions for 11,598 employees with digital human capital working for 866 foreign MNC subsidiaries in Denmark observed between 2002 and 2012. We find that digital expertise helps retaining digital human capital. The effect is stronger if subsidiaries have an internationally diverse workforce and when they possess patented technologies. Both factors provide distinct learning opportunities from digital expertise. The effect is weaker if the subsidiary is located in regional clusters of digital expertise since alternative employers may offer similar learning opportunities.

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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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In this article, Gorodzeisky, Semyonov, Davidov and Schmidt suggest that perceived economic threat, perceived threat to the cultural homogeneity of society, and racial prejudice, although being interrelated, each exerts an independent influence on opposition to immigration. The analysis use data obtained from six representative national samples of the 2014 European Social Survey: Hungary, France, Ireland, Portugal, Sweden and the United Kingdom. The analysis demonstrates that the three sources exert an independent additive effect on opposition to immigration. Moreover, the findings reveal that racial prejudice does not mediate the effect of perceived economic and cultural threats on opposition to immigration.

A key element to understand complex systems is the relationship between the spatial scale of investigation and the structure of the interrelation among its elements. When it comes to economic systems, it is now well-known that the country-product bipartite network exhibits a nested structure, which is the foundation of different algorithms that have been used to scientifically investigate countries' development and forecast national economic growth. Changing the subject from countries to companies, a significantly different scenario emerges. Through the analysis of a unique dataset of Italian firms' exports and a worldwide dataset comprising countries' exports, here we find that, while a globally nested structure is observed at the country level, a local, in-block nested structure emerges at the level of firms. This in-block nestedness is statistically significant with respect to suitable null models and the algorithmic partitions of products into blocks correspond well with the UN-COMTRADE product classification. These findings lay a solid foundation for developing a scientific approach based on the physics of complex systems to the analysis of companies, which has been lacking until now.

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The governance of corruption is increasingly important in a global business environment involving ever more frequent transactions across diverse institutional contexts. Previous scholarship has theorized a fundamental tension between the enforcement of organizational compliance and the achievement of social ends, finding that efforts to remedy policy-practice decoupling in the governance of corruption and other complex global issues can exacerbate means-ends decoupling. However, these studies have tended to apply a rather static lens to a highly evaluative and processual phenomenon, meaning we still lack in-depth understanding of the dynamics underlying the interactive communicative processes of sensemaking and negotiation involved in working out the problems of both means-ends and policy-practice decoupling across different institutional contexts. To address this gap, we present a longitudinal qualitative study of the governance of corruption that identifies the emergence of locally contingent and open-ended sensemaking processes arising from and surrounding problems of decoupling. Specifically, we identify four key sensemaking mechanisms across different contexts and periods that ultimately shifted the focus of the actors away from a compliance-based approach toward a new shared understanding of progress as achievement, i.e., the mechanisms of localized theorizing, leveling, recalibrating, and public criticizing. Based on these findings, we develop a model to explain the role of sensemaking in the governance of corruption and the dynamics of decoupling.

Diversity is a topic that is garnering much attention in society and organizations. We use a paradox lens to suggest a tendency of diversity practices to focus on diversity only, and to essentially neglect unity. We identify two categories of strategies to address diversity: (1) “woke”, and (2) “integrative.” Woke strategies take a single-edged approach to diversity, whereby concerns for diversity are pursued without concern for potentially detrimental effects on unity. In contrast, integrative strategies simultaneously activate the advantages of both diversity and unity, while neutralizing their downsides. In support of this reasoning, we also refer to positive organizational behavior (POB) theory, which would suggest that “woke” strategies run counter to the development of psychological capital on the part of both minority and majority group members. Despite the arguments in this article, it is clear that research is necessary to better understand these alternative approaches to diversity, as well as their effects on individuals, organizations, and society as a whole. Thus, we conclude with a consideration of research needs, as well as practical and policy implications

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.