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Secure Access Data Center (CASD) / IAB Conference

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.

23rd Conference “Theories and Methods in Macroeconomics” (T2M)

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)

School-to-Work Transitions and Consequences for Individual Life Courses

This Anglo-German network is being set up to explore the dynamics of the school-to-work transition and its consequences across the life course in the context of educational expansion and technological change. Educational expansion, in particular of higher education, changes school-to-work transition patterns and individual career development in the labour market. At the same time, technological change affects the school-to-work transition by altering the process of individual skill acquisition and the fields of training and study available. The implications of these macro developments for social and educational inequality remain unclear.

The benefit of an Anglo-German comparison in this context is the different timing and degree of educational expansion and technological change in their respective labour markets. Aligning research strategies using German and British data will enable us to generalise our findings given the different institutional settings. The first meeting of the network will focus on innovative empirical designs and on longitudinal data to provide new insights to these broad themes.

Participants in the network should be close to completing a PhD or be within seven years of receipt of a PhD. They should be quantitative education or labour market researchers (economists, sociologists, psychologists, or a related social science discipline) and carry out research on the school-to-work transition and its consequences in the UK or Germany using (quasi-)experimental designs or longitudinal data. Ten will be based in Germany and 10 in the UK.

A successful academic career relies on building strong international networks; however, opportunities for early career researchers to do this are limited. At the same time, there is uncertainty about how Brexit will affect the research funding landscape, particularly for international collaborations. The United Kingdom and Germany have strong research institutions and excellent sources of longitudinal data that can be used to answer questions about education, skills, and life outcomes.

A follow-up workshop will take place at University College London in the autumn.

Senior academics

Prof. Michael Gebel (University of Bamberg)
Prof. Sandra McNally (University of Surrey and London School of Economics)

The Gender Wage Gap in Europe: What Can We Learn Using Linked Employer-Employee Data?

There is growing interest in the gender wage gap (GWG) in Germany and elsewhere in Europe. Recent policy initiatives have tried to increase pressure on employers to ensure their policies and practices do not discriminate, either directly or indirectly, against women. In Germany and the UK, for instance, there are new requirements for large employers to report their GWG.

These initiatives come after a period in which the GWG has been falling, albeit slowly. The GWG remains large, despite the fact that women have overtaken men in terms of academic attainment and have been closing the work experience gap. Compared to a few decades ago, human capital variables explain relatively little of the GWG. The question arises: how do we account for the remaining GWG?
One issue that remains poorly understood is the role of the employer. This seems ironic in light of popular conceptions about where the GWG originates and in light of policy initiatives targeting employers. It arises because most of the analysis of the GWG undertaken by economists and other academics is not based on linked employer-employee data (LEED). Consequently, we only know a limited amount about the role played by employer heterogeneity and worker-firm matches in accounting for the GWG. There are theoretical grounds for thinking that worker sorting and segregation across workplaces and firms could play a sizeable role in accounting for the GWG, and that there may be substantial across-employer heterogeneity in terms of women’s earnings progression.
Some papers have been written using LEED to understand the GWG but, as yet, there is little consensus about the role of workplaces and firms in helping to explain the GWG.

The purpose of the workshop is four-fold, namely to:

Promote understanding of the role employers play in accounting for the GWG;
Establish the size of the GWG across countries and how the gap varies when accounting for the identity of the employer;
Identify mechanisms, which help explain the size of the GWG, e.g. discrimination, worker sorting, worker segmentation, monopsony employer power, rent-sharing, compensating wage differentials;
Discuss methodological challenges and avenues for future research for academics using LEED to investigate the GWG.

Labor Market Challenges in Times of Globalization, Technological, and Demographic Change

Technological progress, especially recent changes through automation and digitalization, international trade, and demographic developments have far-reaching consequences for the way we work and study. In a one-day workshop, we want to discuss the challenges to the labor market and the educational and vocational system in a globalized and digitalized world facing demographic change and migration. The special focus lies on how these developments affect firms and workers (e. g., employment, skill demand and supply, task requirements, wages, working conditions, and workload). Moreover, we want to examine the political sphere, and draw conclusions which policies are effective to foster the benefits and limit the negative consequences for the society. We invite researchers to submit empirical and theoretical contributions on this topic from all areas of economics and social sciences.

Talent Hoarding in Organizations

Most organizations rely on managers to identify talented workers. However, because managers are evaluated on team performance, they have an incentive to hoard talented workers, thus jeopardizing the efficient allocation of talent within firms. This study documents talent hoarding using the universe of application and hiring decisions at a large manufacturing firm. When managers rotate to a new position and temporarily stop hoarding talent, workers' applications for promotions increase by 128%. Marginal applicants, who would not have applied in the absence of manager rotations, are three times as likely average applicants to land a promotion, and perform well in higher-level positions. By reducing the quality and performance of promoted workers, talent hoarding causes misallocation of talent within the firm. Female workers react more to managerial talent hoarding than their male counterparts, meaning that talent hoarding perpetuates gender inequality in representation and pay at the firm.

Labour and technology at the time of Covid-19. Can artificial intelligence mitigate the need for proximity?

Social distancing has become worldwide the key public policy to be implemented during the COVID-19 epidemic and reducing the degree of proximity among workers turned out to be an important dimension. An emerging literature looks at the role of automation in supporting the work of humans but the potential of Artificial Intelligence (AI) to influence the need for physical proximity on the workplace has been left largely unexplored. By using a unique and innovative dataset that combines data on advancements of AI at the occupational level with information on the required proximity in the job-place and administrative employer-employee data on job flows, our results show that AI and proximity stand in an inverse U-shape relationship at the sectoral level, with high advancements in AI that are negatively associated with proximity. We detect this pattern among sectors that were closed due to the lockdown measures as well as among sectors that remained open. We argue that, apart from the expected gains in productivity and competitiveness, preserving jobs and economic activities in a situation of high contagion may be the additional benefits of a policy favouring digitization.