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Veranstaltungsformat: Online

Is corona the great leveller? Rich or poor, everyone can get sick from the virus. The measures to deal with the pandemic affect everyone equally: We all wear masks and the lockdown banishes us all to our homes. Or is corona an amplifier of existing and a cause for new inequalities? Important social and economic resources for coping with burdens, economic risks and availability of support by the welfare state are unequally distributed.

At the same time, new and old social divides are breaking open: Parents, especially working parents, face a particular burden in view of the closures of schools and childcare facilities and must often take over the schooling and care of children themselves. Also, people in large cities might be more affected by the crisis than people outside metropolitan areas. But the crisis also contributes to inequalities directly in the labor market: Many of the workers affected by the closures are found in the food service and personal services industries. But those particularly affected also include already disadvantaged groups such as temporary and marginal workers, who are more often in danger of losing their jobs and have less access to social protection. Low-income earners and people living in poverty may suffer particularly from the restrictions, as they have significantly fewer resources to cope with stress or deal with new challenges like home schooling. They may even be hit more often directly by the virus if they have to economize on personal protective measures. Similarly, self-employed face also specific challenges as they have often limited funding and assistance programs were not tailored to this group. At the same time, international comparisons reveal differences – not only are countries affected by the pandemic to varying degrees, but the economic and social consequences are also uneven. This raises the question of the role of social security systems and the labor market and economic policy responses.

Shortly after the pandemic, many researchers turned their attention to such and similar questions, and initial results were available in a short time. After a year of research, however, it is also clear that the observed effects of the crisis are not always uniform, but can differ significantly by the dimensions of inequality under study, by country, and also among different groups of people. In addition, aspects of data collection or measurement and the resulting possibilities for analysis are also likely to play a role. Against this background, this seminar series aims to bring together empirically rigorous contributions from the fields of sociology, economics and related fields on issues of social policy, social ad economic inequality following the Corona Crisis.

Job retention schemes (Kurzarbeit) have been a key policy tools to contain the employment and social fallout of the COVID-19 crisis in a number of OECD countries. By May 2020, job retention schemes supported about 50 million jobs across the OECD, about ten times as many as during the global financial crisis of 2008-09.

The schemes prevented a surge in unemployment, mitigated financial hardship and stabilized demand. However, as the COVID-19 crisis moved into its second wave, deeper structural changes are becoming more likely. Job retention schemes should respond to this new situation, become more targeted and attention should shift towards supporting workers, rather than their jobs.

Based on an OECD policy brief, this online Seminar will give an overview on the use of job retention schemes in OECD countries and discuss in detail the schemes in France, Germany and the Netherlands.

The Institute of Employment Research (IAB), the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), and the Labour and Socio-Economic Research Center (LASER) are pleased to announce a workshop on machine learning in economics. Empirical research in economics typically focuses on the unbiased estimation of causal effects. In contrast, statistics and computer science place more value on prediction (especially out-of-sample) and data-driven selection of models and variables. So far, only few studies apply these methods in empirical economic research, but their importance is growing. This holds in particular with the increasing availability of big data for economic research. The two-day workshop seeks to bring together researchers who apply machine learning methods in the following fields: Labor economics, economics of education and health economics.

We estimate heterogeneous returns to a STEM education in Switzerland based on individual-level data, exploiting the regional distribution of relative distances to technical and cantonal universities as a cost factor driving college major choice.

Overall, individuals strongly gain in terms of earnings by graduating from a STEM major, with equally large effects for men and women. Ascending Marginal Treatment Effect curves suggest heterogeneous returns while inverse selection on gains implies that individuals with a higher resistance for a STEM education gain the most, where the latter emerges stronger for men. Eventually, we utilize the recent formation of the University of Lucerne, changing relative distances, to estimate the policy-relevant treatment effect for a counterfactual scenario that this university had been established as a technical one: people shifted into a STEM education significantly gain in terms of earnings, with stronger effects for men.

This paper studies the adoption of local preferences and norms by refugees over time. Exploiting plausibly exogenous variation in the allocation of refugees across German regions between 2013 and 2018, we examine the path of their convergence towards local culture in the short-run. We assemble a novel data set on values, habits, and preferences for 8,000 refugees, and combine it with information on more than 34,000 locals. We find strong evidence that refugees converge to local culture, closing the gap by 5% every year. This effect is stronger for regions whose culture is more distinct from the national one and more internally homogeneous. We also provide evidence that refugees' cultural convergence is faster where support for anti-immigrant parties is stronger, where there are more hate-crimes against refugees, and where locals are less open to diversity - patterns consistent with what we label the ``threat hypothesis''. Despite the positive effect of a threatening environment on the pace of refugees' cultural convergence, we document that the former slows down their economic integration.

The California Policy Lab (CPL) is part of a growing number of research centers in the United States that do applied economic research in partnership with local or state government agencies. The goals of such long-term research partnerships is to work on problems that are directly policy relevant, help implement relevant findings, and integrate administrative data that otherwise would be difficult to access. The presentation reviews CPL’s approach to government partnerships and reviews examples of joint research projects, including nudge experiments, predictive work on homelessness, COVID-19 related projects, with particular focus on studies of unemployment insurance benefits

Employment and Social Developments in Europe (ESDE) 2020: “Leaving no one behind and striving for more: fairness and solidarity in the European social market economy”

The review provides evidence-based analysis on how to achieve greater fairness across the EU in the face of crises such as the COVID-19 pandemic and of long-term challenges arising from structural change due to demographic ageing, climate change and digitalisation.

The COVID-19 pandemic is having profound health, economic, employment and social effects, hitting society’s most vulnerable disproportionately hard and threatening much of the progress that the EU had achieved previously in labour markets and social outcomes. Against this background, this year’s ESDE analyses the state of play of and challenges to social fairness and inclusivity of growth in the EU. It also explores specific policies and tools that can improve the prospects of greater social fairness and enhanced solidarity in the future. ESDE provides evidence-based groundwork for the reflection on how policy can help achieve recovery and further normalisation while meeting Europeans’ expectations regarding fairness and solidarity.

We analyze workers' risk preferences and training investments. Our conceptual framework differentiates between the investment risk and insurance mechanisms underpinning training decisions. Investment risk leads risk-averse workers to train less; they undertake more training if it insures them against future losses. We use the German Socioeconomic Panel (SOEP) to demonstrate that risk-affinity is associated with more training, implying that, on average, investment risks dominate the insurance benefits of training. Crucially, this relationship is evident only for general training; there is no relationship between risk attitudes and specific training. Thus, as expected, risk preferences matter more when skills are transferable - and workers have a vested interest in training outcomes - than when they are not. Finally, we provide evidence that the insurance benefits of training are concentrated among workers with uncertain employment relationships or limited access to public insurance schemes.