Sven Ehmes

Goethe University Frankfurt

Unemployment can have long-term negative effects on individuals’ careers, commonly referred to as scarring effects. While research has explored these effects, the role of gender remains less understood. This study examines gendered wage scarring through a comparative approach, integrating signalling theory with institutional and gender culture theory. It hypothesises that male breadwinner norms intensify penalties for unemployed men, while inadequate social security systems disproportionately disadvantage unemployed women. In contrast, egalitarian societies with comprehensive social security systems mitigate gender disparities in scarring effects. Using household panel data from the European Union Statistics on Income and Living Conditions (EU-SILC) across 29 countries, combined with country-level data on gender culture and unemployment benefit systems, the study employs multilevel models to analyse wage scars in re-employment. Findings suggest that strong male breadwinner norms amplify wage scarring for men, while lower benefit coverage and restrictive eligibility requirements exacerbate economic disadvantages for women. This research highlights the interaction between cultural norms and welfare state institutions in shaping gendered labour market inequalities, emphasising the need for gender-sensitive policies that address both normative and institutional barriers to gender equality in employment.

Isabel Gebhardt; Timon Drewes; Malte Reichelt

Friedrich-Alexander-Universität Erlangen-Nürnberg

Technological change and digitalization (TC&D) are reshaping labor markets, with rising information and communications technology (ICT) skill demands extending far beyond the IT sector. While some expected these developments to reduce gender inequalities, recent evidence suggests otherwise: progress toward gender equity in employment and earnings has stalled since the 1990s—precisely when digitalization accelerated. This project investigates how, and under what conditions, TC&D influence gendered employment trajectories and wage outcomes.

Building on Relational Inequality Theory (RIT), we examine how technology-driven productivity gains are differentially translated into rewards for men and women within organizations. Our central focus is the development of a novel, longitudinal measure of ICT skill demand at the occupational level. Using advanced machine learning techniques—including NLP, object detection (YOLO), and fine-tuned language models (JobBERT)—we analyze a large corpus of digitized job vacancy advertisements (1975–2024). These data are mapped to the KldB 2010 occupational classification and linked to administrative employment biographies from the German Federal Employment Agency.

This linkage enables us to track wage levels and gender composition within occupations over time, allowing us to examine whether rising ICT demands lead to equal gains for men and women and how occupational gender dynamics shift in response to digitalization. Preliminary findings and a detailed presentation of our measurement approach will be shared. The resulting dataset will be made publicly available, offering a valuable resource for future research on digitalization and inequality.

Johanna Gereke¹; Joshua Hellyer²; Reinhard Schunck³; Emily Hellriegel³; Eva Zschirnt⁴; Susanne Veit⁵

¹ Mannheim Centre for European Social Research, University of Mannheim; ² Uni Mannheim; ³ Uni Wuppertal; ⁴ University of Amsterdam; ⁵ German Center for integration and Migration Research

Ethnic discrimination remains a persistent challenge in European labor markets, limiting employment opportunities for immigrants and their descendants. While extensive research has documented ethnic penalties in hiring, less is known about how physical attractiveness interacts with ethnicity to influence labor market outcomes. Prior studies indicate that attractive applicants often receive preferential treatment, but it is unclear whether this “beauty premium” applies equally across ethnic groups. This study fills this gap through a large-scale field experiment in the German labor market, a setting in which applicants are expected to include photos on their résumés. Exploiting this custom, nearly 4,000 fictitious job applications were submitted, varying applicants’ ethnic background, gender, attractiveness, and information about their prior performance. Our results show evidence of an ethnic hierarchy: Applicants of Turkish descent face significant discrimination, as do men with a Greek background, while Danish-heritage applicants face no discrimination relative to the ethnic majority. While there is a modest beauty premium in overall callback rates, this premium does not close the German-Turkish ethnic gap. This study contributes to the literature on labor market discrimination and status characteristics, illustrating how seemingly advantageous traits can reinforce ethnic stratification rather than mitigating it.

Jule Adriaans¹; Sandra Bohmann²; Ole Brüggemann³; Fabian Kalleitner⁴; Cristóbal Moya²

¹ Bielefeld University; ² DIW Berlin; ³ European University Institute; ⁴ Ludwig-Maximilians-Universität München

Despite persistent gender disparities in labor market outcomes, gender equality policies—particularly affirmative action—remain contentious. Prior research suggests that public opposition to such policies may stem from an underestimation of the gender pay gap. However, evidence shows that informing individuals about the unadjusted gender pay gap has only minor effects on policy preferences, likely due to statistical discrimination, wherein wage disparities are attributed to women’s choices rather than actual discrimination. This study investigates whether different types of gender pay gaps influence support for gender equality policies. We conducted a pre-registered survey experiment in Germany in December 2024 where we exposed participants to information on gender pay gaps that varied by size (high vs. low) and type (unadjusted vs. job-adjusted). We hypothesized that individuals will be more likely to support gender equality policies when presented with a large pay gap (H1) and that a within-job-adjusted pay gap will have a stronger effect than an unadjusted gap (H2). Preliminary results indicate that information can change individual’s perceived gender pay gaps and fairness beliefs, but the impact on policy preferences is limited. The strongest effect is observed when participants receive within-job-adjusted pay gap information, supporting H2 and the idea that respondents might take into account the type of gender pay gap for their policy demand. Combined with additional analyses, these findings suggest that while information can shape fairness perceptions, broader interventions may be needed to drive substantive policy support.