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We are very pleased to announce the first CASD / IAB Conference on the advances in social sciences using administrative and survey data. The CASD and the IAB have recently established cross-country access to confidential administrative data in France and Germany. The aim is to foster and facilitate the use of rich register datasets – which can be partly combined with detailed survey data – from both countries and to improve the data sources for comparative research in social sciences.

The conference aims to bring together researchers using confidential administrative data in France and Germany and also welcomes researchers using similar data from other countries.

For the T2M 2019 conference, the program committee has selected special sessions organized by leading researchers in these fields:

  • “Expectations in macroeconomics” by Ruediger Bachmann (University of Notre Dame)
  • “Safe assets and the macroeconomy” by Kenza Benhima (Université de Lausanne)
  • “IAB data and macro applications” by Britta Gehrke (IAB and FAU)
  • “Vacancies and recruitment” by Leo Kaas (Goethe-Universität Frankfurt)
  • “Real exchange rate dynamics” by Gernot Müller (Universität Tübingen)

Technological advances in the fields of robotics and artificial intelligence are increasingly making it possible for machines to perform tasks that previously could only be done by humans. This development has sparked scientific and public debates on the future of work, often dealing with automation and the substitution of labor. The transformation of the working environment goes hand in hand with a reorganization of company structures, occupational and workplace-related content and skill requirements. New inequality paths are emerging and labor market participants are being confronted differently with these changes. In addition, educational and other institutional frameworks keep influencing the labor market. The aim of this conference is to bring together economists, sociologists and researchers from related fields to discuss frontier research on labor market effects of automation and digitization. Special focus is on the following questions:

  • How do new technologies affect the level and structure of employment?
  • How are new technologies changing work content?
  • What are the (non-)monetary returns to work content?
  • How do new technologies shape skill demands and which role do social skills play?
  • How does technology affect overall inequality and also inequality between firms and workers?
  • How does the role of educational and labor market institutions change?
  • How do firms and workers adapt to changing requirements?
  • How can policy help firms and workers who are struggling to adapt to digital transformation?

The scientific committee encourages theoretical, empirical, and policy-oriented contributions from all areas of labor economics, labor sociology and related fields.

Die Digitalisierung ist Treiber eines Strukturwandels, der das Wirtschafts- und Arbeitsleben dynamisch verändert. Auf der Konferenz soll analysiert und diskutiert werden, wie sich digitale Potenziale nutzen und gestalten lassen.

Das Beziehungs- und Familienpanel pairfam steht vor tiefgreifenden Veränderungen: Im Zuge der Fusion mit dem Generations and Gender Program (GGP) zur gemeinsamen Forschungsinfrastruktur FReDA (Family Research and Demographic Analysis) wird auch das Erhebungsdesign der Panelstudie umgestellt. Die bisherigen Face-to-face-Interviews wird ab 2021 eine Mixed-mode-Befragung ersetzen, in der die Befragten zwischen einem web-basierten Interview und einem Papierfragebogen entscheiden können. Diese Entscheidung zieht weitere Veränderungen im Design nach sich, etwa hinsichtlich Frageprogramm und Filterführung, Verwendung von Preloads und Event-History-Calendar bis hin zum zeitlichen Verlauf der Studie. Gerade in einer laufenden Studie ergibt sich hierdurch das Problem, dass Moduseffekte Längsschnittanalysen verzerren können. In diesem Vortrag werden methodische Herausforderungen eines derartigen Moduswechsels in einer laufenden Panelstudie sowie unsere Vorbereitungen und methodische Begleitung des Moduswechsels dargestellt.

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).

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.

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.