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Veranstaltungsreihe: IAB-Colloquium (en)

The discussion series “Labour Market and Occupational Research (IAB-Colloquium zur Arbeitsmarkt- und Berufsforschung)” is a forum where primarily external researchers present the results of their work and discuss these with experts from IAB. Practitioners from the political, administrative and business fields are naturally also welcome.

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.

We analyse who falls victim of crime, the immediate and long term consequences of victimization for victims and their families and the heterogeneity in these effects using rare administrative data on the population of reported victims in Denmark. Victims are more likely to grow up in disadvantaged environments and to be of lower SES. Moreover, victimization has long-lasting negative effects on the labor market outcomes of victims, and these effects are larger and more persistent for female than for male victims. 

I will talk a bit on how we use machine learning in general in the area of labour market policy in DK, and how we relate this to our core business of producing results on employment and education.
As a specific example of our work, I will illustrate our statistical profiling of newly unemployed, both the technical/methodological side as well as the practical implementation and general experiences in this area, and some thoughts on further development.
Finally I will talk a bit on other more recent areas of developing datadriven solutions in the field of labour market policy, drawing perspectives to new possibilities deriving from machine-learning and modern Technology

Unemployment insurance systems in modern labor markets are riddled with a multitude of rules and regulations governing job seekers' economic situation and their incentives to search for employment. These include, for instance, detailed regulations specifying individuals' benefit level and potential benefit duration, job search requirements, conditions for avoiding benefit sanctions, possibilities for earning extra income or additional benefit entitlements by working in part-time or short-term jobs, etc. The complexity of UI systems makes it challenging for job seekers to understand the prevailing rules, their built-in incentives, and the resulting consequences for their personal economic situation. This is potentially problematic, as a lack of understanding may distort individuals' job search incentives and employment prospects.

In this paper, we report the results from a randomized controlled trial among the universe of registered Danish job seekers that studies how reducing complexity affects individuals' understanding of UI benefit rules and labor market behavior. Our intervention exploits an online information tool that provides individuals with continuously updated, personalized information on their remaining UI benefit period, their accumulated working time that can be used to prolong the potential benefit duration, as well as information on essential rules regarding job seekers' benefit duration and benefit sanctions. We match the data from our experiment with data from an online survey and rich information from administrative records to evaluate the causal effects of our intervention on individuals' understanding of the prevailing labor market rules, their job search behavior, and resulting labor market outcomes.