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Archives: IAB-Veranstaltungen

Technological progress, especially recent changes through automation and digitalization, international trade, and demographic developments have far-reaching consequences for the way we work and study. In a one-day workshop, we want to discuss the challenges to the labor market and the educational and vocational system in a globalized and digitalized world facing demographic change and migration. The special focus lies on how these developments affect firms and workers (e. g., employment, skill demand and supply, task requirements, wages, working conditions, and workload). Moreover, we want to examine the political sphere, and draw conclusions which policies are effective to foster the benefits and limit the negative consequences for the society. We invite researchers to submit empirical and theoretical contributions on this topic from all areas of economics and social sciences.

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.

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.

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.

Germany has a strong skill development system. The country’s 15 year old students performed above the OECD average in the last (2018) edition of the Programme for International Student Assessment (PISA), continuing a trend of significant improvement since PISA’s first edition in 2000. Its adult population also has above average literacy and numeracy skills, according to the OECD Survey of Adult Skills (PIAAC). A strong and well-respected vocational education and training system is seen as one of the success factors behind these achievements. However, participation in learning beyond initial education lags behind other high-performing OECD countries and varies considerably across different groups of the population. This is problematic in a rapidly changing labour market, where participation in continuing education and training is a precondition for individuals, enterprises and economies to harness the benefits of these changes. This report assesses the current state of the German continuing education and training (CET) system. It examines how effectively the system prepares people and enterprises for the changes occurring in the world of work, and identifies what changes are necessary to make the CET system more future ready. The report makes recommendations for the further development of the CET system based on international good practice.

Ms. Meierkord plans to give the lecture in German.

Online delivery of higher education has taken center stage but is fraught with issues of student self-organization. We conducted an RCT to study the effects of remote peer mentoring at a German university that switched to online teaching due to the COVID-19 pandemic. Mentors and mentees met one-on-one online and discussed topics like self-organization and study techniques. We find positive impacts on motivation, studying behavior, and exam registrations. The intervention did not shift earned credits on average, but we demonstrate strong positive effects on the most able students. In contrast to prior research, effects were more pronounced for male students.

During the COVID-19 crisis the U.S. increased UI benefits substantially, leading to earnings replacement rates above 100% for many workers. In this paper, we use the universe of micro records on UI claims from the state of California going back over 15 years to study the impact of UI benefits on labor supply and job outcomes during the COVID-19 crisis, and contrast it with the variation of effects in booms and recessions before the crisis. Our main estimation strategy exploits the fact that UI benefits rise linearly with earnings up to a maximum, leading to a sharp kink that allows us to implement a Regression Kink Design (RKD) to estimate the effect of UI benefit changes on a range of outcomes. We also analyze the effect of sharp changes in UI benefits during the COVID-19 crisis. Preliminary estimates suggest that increase in UI benefits during the COVID-19 crisis raised unemployment durations for affected workers. These estimates do not imply increases in unemployment or reduction in hiring rates because they may be offset by workers not covered by UI.