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TASKS V: Robotics, Artificial Intelligence and the Future of Work

Technological advances in the fields of robotics and artificial intelligence are increasingly making it possible for machines to perform tasks that previously could only be done by humans. This development has sparked scientific and public debates on the future of work, often dealing with automation and the substitution of labor. The transformation of the working environment goes hand in hand with a reorganization of company structures, occupational and workplace-related content and skill requirements. New inequality paths are emerging and labor market participants are being confronted differently with these changes. In addition, educational and other institutional frameworks keep influencing the labor market. The aim of this conference is to bring together economists, sociologists and researchers from related fields to discuss frontier research on labor market effects of automation and digitization. Special focus is on the following questions:

How do new technologies affect the level and structure of employment?
How are new technologies changing work content?
What are the (non-)monetary returns to work content?
How do new technologies shape skill demands and which role do social skills play?
How does technology affect overall inequality and also inequality between firms and workers?
How does the role of educational and labor market institutions change?
How do firms and workers adapt to changing requirements?
How can policy help firms and workers who are struggling to adapt to digital transformation?

The scientific committee encourages theoretical, empirical, and policy-oriented contributions from all areas of labor economics, labor sociology and related fields.

Perspectives on (Un-)Employment

Talent Hoarding in Organizations

Most organizations rely on managers to identify talented workers. However, because managers are evaluated on team performance, they have an incentive to hoard talented workers, thus jeopardizing the efficient allocation of talent within firms. This study documents talent hoarding using the universe of application and hiring decisions at a large manufacturing firm. When managers rotate to a new position and temporarily stop hoarding talent, workers' applications for promotions increase by 128%. Marginal applicants, who would not have applied in the absence of manager rotations, are three times as likely average applicants to land a promotion, and perform well in higher-level positions. By reducing the quality and performance of promoted workers, talent hoarding causes misallocation of talent within the firm. Female workers react more to managerial talent hoarding than their male counterparts, meaning that talent hoarding perpetuates gender inequality in representation and pay at the firm.

Dokumentenechtheitsprüfung am Beispiel der Stempelerkennung mittels Open Source basierten Algorithmen mit Fokus auf Erklärbarkeit der KI

Im Rahmen der Kernprozesse in der öffentlichen Verwaltung steht dokumentenbasierte Sachbearbeitung weiterhin im Vordergrund. Die Transformation in Richtung einer informationsbasierten Sachbearbeitung sieht IBM als obligatorisch an mit Blick auf IT- und Prozesskosten sowie Datenschutz. Neben der am Markt etablierten Textextraktion und -klassifikation ist insbesondere im Bereich der Dokumentenvalidierung das Thema Bilderkennung zentral. IBM hat in Projekten eine offene Dokumentenverarbeitungsplattform entwickelt, die es erlaubt, auch diese Bildverarbeitung vorzunehmen. Der exemplarisch vorgestellte Usecase betrachtet die Dokumentenechtheitsprüfung am Beispiel der Stempelerkennung mittels Open Source basierten Algorithmen und mit Fokus auf Erklärbarkeit der KI.

Employment-maximizing minimum wages

Motivated by a reduced-form evaluation of the impacts of the German nationally uniform minimum wage on labour, goods and housing markets, we develop a quantitative spatial general equilibrium model with monopsonistic competition and monopsonistic labour markets. The model predicts that the employment effect of a minimum wage is a bell-shaped function of the minimum wage level. Consistent with the model prediction, we find the largest positive employment effects in regions where the minimum wage correspond to 46\% of the pre-policy median wage and negative employment effects in regions where the minimum exceeds 80\% of the pre-policy median wage. After estimating the structural parameters and inverting the structural fundamentals, we use the quantified model to derive minimum wage schedules that maximize employment or welfare.

The Intergenerational Effects of Requiring Unemployment Benefit Recipients to Engage in Non-Search Activities

We use a quasi-experimental design and national administrative data to analyze the intergenerational effects of introducing non-search activity requirements for long-term unemployment benefit recipients aged 18-34. The young adults we study were in early adolescence in 1999 when the requirements were introduced. Using a regression discontinuity approach, we find that young adults whose fathers were subject to the requirements have a lower incidence of unemployment benefit receipt compared to those whose fathers were not. More detailed investigation suggests that completion of the mandated activities, role modeling, changes in attitudes, improved health, and greater support and stability are potential channels.

Minimum Wages and Low Wage Policies

Ursachen und Konsequenzen abgebrochener Bildungsverläufe

Der massive Anstieg der Studierenden- und Promovierendenzahlen in den letzen Jahrzehnten hat zur Folge, dass auch die Anzahl der Studien- bzw. Promotionsabbrüche - bei relativ konstant bleibenden Abbruchquoten - deutlich zunimmt. Was sind die individuellen Folgen solcher Abbrüche? Welche Personengruppen sind besonders „gefährdet“ - und warum? Im Vortrag werden drei aktuelle Studien vorgestellt, die sich mit verschiedenen Facetten abgebrochener Bildungsverläufe befassen.
In der ersten Studie wird ein experimenteller Ansatz zur Abschätzung der individuellen Arbeitsmarktfolgen eines Studienabbruchs vorgestellt. Zwei Fragen stehen im Mittelpunkt: 1. Was sind die (kausalen) Folgen eines Studienabbruchs bei Bewerbungen auf verschiedene Stellen? 2. Welche Faktoren begünstigen im Falle eines Studienabbruchs die Einladungswahrscheinlichkeit zu einem Vorstellungsgespräch? Zur Beantwortung dieser Fragen wurden über 1.000 Arbeitgeber und Arbeitgeberinnen im Rahmen von faktoriellen Surveyexperimenten befragt.

In der zweiten Studie geht es um Studien(miss)erfolge von Studierenden mit Migrationshintergrund. Während eine wachsende Zahl an Studien die Ursachen ambitionierter Bildungsentscheidungen von Migrant:innen diskutiert, liegen bislang kaum Studien zu den Konsequenzen dieser Entscheidungen vor. Wir argumentieren, dass ambitionierte Bildungsentscheidungen am Übergang in die Hochschule eine Schattenseite haben, da sie häufig Studienmisserfolge nach sich ziehen. Zur Überprüfung der Vorhersagen dienen Daten des Nationalen Bildungspanels (NEPS).

Die dritte Studie befasst sich mit Promotionsabbrüchen, einem in Deutschland empirisch kaum erforschten Thema, das nicht zuletzt im Zuge der #ichbinHanna Debatte an Brisanz gewinnt. Auf Basis deutschlandweiter Paneldaten des DZHW werden Zusammenhänge verschiedener individueller und institutioneller Merkmale mit der Promotionsabbruchwahrscheinlichkeit analysiert und diskutiert.

Labour and technology at the time of Covid-19. Can artificial intelligence mitigate the need for proximity?

Social distancing has become worldwide the key public policy to be implemented during the COVID-19 epidemic and reducing the degree of proximity among workers turned out to be an important dimension. An emerging literature looks at the role of automation in supporting the work of humans but the potential of Artificial Intelligence (AI) to influence the need for physical proximity on the workplace has been left largely unexplored. By using a unique and innovative dataset that combines data on advancements of AI at the occupational level with information on the required proximity in the job-place and administrative employer-employee data on job flows, our results show that AI and proximity stand in an inverse U-shape relationship at the sectoral level, with high advancements in AI that are negatively associated with proximity. We detect this pattern among sectors that were closed due to the lockdown measures as well as among sectors that remained open. We argue that, apart from the expected gains in productivity and competitiveness, preserving jobs and economic activities in a situation of high contagion may be the additional benefits of a policy favouring digitization.

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