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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.

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.

Thousands of students leave higher education without graduating, and worry about the negative consequences of dropping out on labour market success. However, research on how employers evaluate higher education dropouts is lacking. And while studies on school-to-work transitions are plentiful, most of them focus on the consequences of successfully attained educational qualifications – and ignore the consequences of unsuccessfully attempted qualifications.

Drawing on human capital, signalling, and credentialism theories, we conducted a series of factorial survey experiments with random samples of employers (N = 1350) to answer the following research questions: First, what is the causal effects of a dropout on the hiring prospects for different types of positions? Second, which factors facilitate labor market entry for dropouts?

Our findings indicate that employment chances depend heavily on the type of job dropouts compete for, and on the mode and duration of the study episode.

In the light of global megatrends such as ageing, globalisation, technological transformation and climate change, the 2019 ESDE is dedicated to sustainability.

One of the major sustainability challenges is sluggish productivity growth despite accelerating technological change and the increasing qualification levels of the EU labour force. We explore the preconditions for sustained economic growth, based on region-level and firm-level data analysis, focusing on complementarities between efficiency, innovation, human capital, job quality, fairness and working conditions. We identify policies that could boost productivity without increasing inequality.

We examine the impact of climate action on the economy and on employment, income and skills. In the light of EU welfare losses from climate inaction, we examine the sectors in which employment and value generation are taking place in the EU economy, estimate the overall impact of climate action in EU Member States, following a full implementation of the Paris agreement, on GDP and employment, as well as its potential impact on job polarisation.

Our main conclusion is that tackling climate change and preserving growth go hand in hand. We highlight a number of policy options to preserve the EU's competitiveness, sustain growth and spread its benefits to the entire EU population, while pursuing an ambitious transition to a climate-neutral economy.

Recent studies have proposed causal machine learning (CML) methods to estimate conditional average treatment effects (CATEs). In this study, I investigate whether CML methods add value compared to conventional CATE estimators by re-evaluating Connecticut’s Jobs First welfare experiment. This experiment entails a mix of positive and negative work incentives. Previous studies show that it is hard to tackle the effect heterogeneity of Jobs First by means of CATEs. I report evidence that CML methods can provide support for the theoretical labor supply predictions. Furthermore, I document reasons why some conventional CATE estimators fail and discuss the limitations of CML methods.

The presentation is about the nature and how to clean errors in occupational coding in order to measure patterns of occupational mobility (US, UK and Canada). Furthermore it is shed light on how occupational mobility matters for cyclical earnings inequality (based on Carrillo-Tudela, Visschers and Wiczer, 2019), unemployment and its duration distribution (based on Carrillo-Tudela and Visschers, 2019) and cleansing and sullying effects of the business cycle (based on Carrillo-Tudela, Sumerfield and Visschers, 2019).

Starting with a comparison between the life-course approach and Bourdieu, the study focuses the relation between social origin and habitus on typical patterns of education- and employment trajectories. Therefore, it tries to provide a test of the social reproduction theory of Pierre Bourdieu using a subsample of longitudinal data from the adult cohort of the German National Educational Panel Study (NEPS). Theoretically, we assume that the social class of one’s origin-family defines the process of socialization and hence the habitus of its members and is cumulative predictive for the generalizable patterns of educational- and employment sequences starting with school entry up to age 30. The individual or class-specific habitus as a “whole set of practices (or those of a whole set of agents produced by similar conditions)” (Bourdieu 1984:170) should hence correspond to differences in successful sequence-patterns, measured personality-traits and attitudes suggesting a stable class-specific realization of the habitus.

This talk will summarize two studies, which respectively study the role of caseworkers and public employment services for the labor market outcomes of unemployment benefit recipients. A first study asks whether and how much caseworkers matter for the outcomes of unemployed individuals. It exploits exogenous variation in unplanned absences among Swiss unemployment insurance caseworkers. A second study evaluates a large-scale policy change in which the public employment service of one Swiss canton changed its strategy by removing restrictions on job search and granting increased autonomy to job seekers.

We estimate heterogeneous returns to a STEM education in Switzerland based on individual-level data, exploiting the regional distribution of relative distances to technical and cantonal universities as a cost factor driving college major choice.

Overall, individuals strongly gain in terms of earnings by graduating from a STEM major, with equally large effects for men and women. Ascending Marginal Treatment Effect curves suggest heterogeneous returns while inverse selection on gains implies that individuals with a higher resistance for a STEM education gain the most, where the latter emerges stronger for men. Eventually, we utilize the recent formation of the University of Lucerne, changing relative distances, to estimate the policy-relevant treatment effect for a counterfactual scenario that this university had been established as a technical one: people shifted into a STEM education significantly gain in terms of earnings, with stronger effects for men.