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Archives: IAB-Veranstaltungen

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

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