Skip to content

Archives: IAB-Veranstaltungen

This paper presents first evidence for the opposing effects of imports and exports at the extensive and intensive employment margins. While soaring imports from China are associated with a higher probability of plant closure, exports have the opposite effect. Imports work through the extensive margin of plant closure only, whereas exports have an effect on employment through both margins. Plant closures occur at a lower probability in labor market segments with heightened export opportunities and these plants tend to expand employment. Moreover, we analyze potential interaction effects. Our analysis shows that i) lower domestic competition reduces the impact of both imports and exports on the probability of plant closure, ii) plants with higher productivity are less likely to react to the import shock and iii) a higher routine-task intensity favors the selection of plants due to import competition.

We study whether women and men cope with job loss differently. We use 2006-2017 Dutch administrative monthly microdata and a quasi-experimental design involving job displacement because of firm bankruptcy. We find that displaced women are more likely than displaced men to take up a flexible job with limited working hours and short commutes. However, displaced women experience longer unemployment durations and comparable hourly wage losses. Displaced expectant mothers experience relatively high losses in employment and working hours. Our findings suggest that the costs of job flexibility for displaced female workers come through longer unemployment instead of higher losses in wages.

With rapid advancements in automation technology and artificial intelligence (AI), the question of how technological changes affect work has regained attention in recent decades. Similar to fears in earlier times, policy makers, the public and scientists alike are concerned about technology-driven job losses. While there is little evidence suggesting that predictions of disappearing work will materialize anytime soon, it is also clear that the nature of work is changing rapidly, demanding high degrees of adaptability of workers. We use administrative, individual-level panel data for West Germany from 1990 to 2005 to examine how workers have navigated the labor market in recent decades. To frame our empirical analysis, we construct a simple model of workers' decisions regarding the tasks they perform and occupational mobility in the face of changing task content of production. We find that workers alter the tasks they perform at the workplace and also use occupational mobility to adjust to those changing demands. The results also suggest that resilient workers forgo wage increases but, instead, experience higher future employment stability.

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.

We investigate how negatively reciprocal traits of unemployed individuals interact with “sticks" policies imposing constraints on individual job search effort in the context of the German welfare system. For this we merge survey data of long-term unemployed individuals, containing indicators of reciprocity as a personality trait, to a unique set of register data on all unemployed coached by the same team of caseworkers and their treatments. We find that the combination of a higher negative reciprocity and a stricter regime have a negative interaction effect on search effort exerted by the unemployed. The results are stronger for males than for females. Stricter regimes may therefore drive long-term unemployed males with certain types of social preferences further away from the labor market.

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.

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.

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.

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).