Archiv: IAB-Veranstaltungen
The Long-Run Labor Market Effects of the Canada-U.S. Free Trade Agreement
Victims of Crime
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.
The Impact of the Federal Pandemic Unemployment Compensation on Job Search and Vacancy Creation
Digitale Potenziale nutzen und gestalten
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.
Perspectives on (Un-)Employment
Methodische Herausforderungen beim Übergang von persönlichen zu web-basierten Interviews in einer laufenden Panelstudie
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.
Different Paths to Success – Habitus, Career-patterns and the Reproduction of Social Inequality
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.
Patterns of occupational mobility – cyclical earnings inequality, unemployment and its duration distribution
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).
What is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?
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.