Springe zum Inhalt

Veranstaltungsformat: Online

The Covid-19 pandemic has changed higher education in numerous ways. Not only do faculties face new challenges, but the pandemic also affected various aspects of students' academic and social life. Among these are:

  • University entrance: Study decisions may have changed under pandemic conditions with respect to both take-up of university studies and subject choice. Moreover, starting at the university may involve particular obstacles during a pandemic.
  • Social and academic integration: During times of distance learning, students have fewer opportunities to interact face to face with fellow students and academic staff. The more demanding communication and lack of exchange may hamper integration.
  • Study processes: The participation in courses and learning have become more isolated, which may impede many learning formats such as study groups. Students’ and universities’ strategies to meet these challenges – and their success – are of particular interest.
  • Students’ employment and financial situation: Students were among the first to lose their jobs at the beginning of the pandemic. As many students rely on a job to finance their education, they may suffer consequences for their grades and ultimately for study success.
  • Career counselling: The pandemic may have affected not only career guidance before starting university but also counselling services at universities. Both the administration and format of counselling as well as its contents may have been adjusted.
  • Labour market entry: The prospects for entering the labour market after graduation may have changed. The pandemic may have increased employers’ demand for certain subjects and decreased the demand for others, thus affecting graduates’ labour market prospects.

Moreover, the conference offers sessions with a more general perspective on “Higher Education and the Labour Market”, for example on returns to tertiary education, university dropout, graduates’ placement on the labour market, and regional mobility of graduates.

Die digitale Transformation stellt nicht nur Geschäftsmodelle von Unternehmen und ihre Produktionsweisen auf den Prüfstand, sondern auch die sozialen Beziehungen im Betrieb. Die betrieblichen Akteure müssen die Beziehungen zu den Stakeholdern neu bewerten und gegebenenfalls neu aufbauen. Denn digitale Kommunikationsmedien versprechen instantane Erreichbarkeit und steigern die Erwartungen an Transparenz des Handelns. Betriebsräte als institutionelle Akteure der Mitbestimmung im Betrieb sind hiervon besonders betroffen. Im Vortrag werden die Ergebnisse eines interdisziplinären Forschungsprojekts präsentiert, die nahe legen, dass im Zuge der digitalen Transformation die Institution Betriebsrat selbst strukturell verändert wird.

We quantify the effects of wage bargaining shocks on macroeconomic aggregates using a structural vector auto-regression model for Germany. We identify exogenous variation in bargaining power from episodes of minimum wage introduction and industrial disputes. This narrative information disciplines the impulse responses to a wage bargaining shock of unemployment and output, and sharpens inference on the behaviour of other variables. The implied transmission mechanism is in line with the theoretical predictions of a large class of search and matching models. We also find that wage bargaining shocks explain a sizeable share of aggregate fluctuations in unemployment and inflation, that their pass-through to prices is very close to being full, and that they imply plausible dynamics for the vacancy rate, firms' profits, and the labour share.

We examine the properties of a recommender system we developed at the Public Employment Service (PES) in France, prior to its implementation in the field. The algorithm uses past matches and a very large set of covariates to produce, for each job seeker, a ranking of the available offers and score each pair jobseeker-offer. Using a calibration step that takes advantage of the observation of application sequences, it gives a predicted "matching probability" for each pair.  After a theoretical discussion about the possible strategies to design a recommender system, we compare this new machine learning (ML) algorithm with another matching tool, mimicking the one currently used at the PES, based on a score measuring the "closeness" between the jobseeker's search criteria or preferences and the characteristics of the offer. We quantify the trade-off between the matching probability and the later "preference score" when switching from one system to the other. Next, we examine the issue of congestion.  We show that, on the one hand the ML algorithm based on past matches tends to increase congestion and on the other hand that this strongly reduces its performance. Finally, we show that the use of optimal transport to derive recommendations from the matching probability matrix significantly alleviates this problem. The main lesson at this stage is that an algorithm ignoring preferences and competition in the labor market would have very limited performances but that tweaking the algorithm to fit these dimensions substantially improves its properties, at least "in the lab".

Wie kann im Rahmen der Internationalisierungsstrategien von Universitäten und außeruniversitären Forschungseinrichtungen die Gleichstellung der Geschlechter angemessen berücksichtigt werden? Wie können potenzialreiche internationale Wissenschaftlerinnen nach Deutschland geholt und gefördert werden? Wie können gleichzeitig inländische Wissenschaftlerinnen auf den internationalen Markt vorbereitet werden?Das Barcamp hinterfragt die entwickelten Maßnahmen zur Internationalisierung an deutschen Forschungsinstitutionen auf ihre gleichstellungspolitischen Chancen und Risiken hin.

Bei der Fachtagung „Wissenschaft trifft Praxis“ zum Thema „Betriebliche Herausforderungen vor, während und nach der COVID-19-Krise“ diskutieren Expertinnen und Experten aus Wissenschaft und Wirtschaft mit Vertreterinnen und Vertretern der Politik, den Sozialpartnern und der Öffentlichkeit über Potenziale und Schwierigkeiten, denen sich Betriebe – auch angesichts der anhaltenden COVID-19-Krise – in unserer Arbeitswelt gegenübersehen. Die Veranstaltung möchte auf Basis aktueller Forschungsergebnisse und Erfahrungen aus der Praxis den gegenwärtigen Zustand der Betriebe in Deutschland reflektieren, um daraus ableitbare Handlungsoptionen für die Politik zur Diskussion zu stellen.

The Covid crisis revived the interest in the topic of short-time work (sometimes also known as furlough schemes or work sharing). In many countries, the schemes were utilised in unprecendented ways. The Institute for Employment Research organises a one-day online workshop on May 13, 2022 that focuses on current research on short-time work. Contributions may address the Covid crisis or previous economic crises. Both theoretical and applied papers with both micro- and macroeconomic approaches are welcome.

The workshop provides the opportunity for timely exchange on cutting-edge research on a specific topic. Presentations and discussions should spur the debate on usage, effects and design of a crucial labour market instrument.

COVID-19 drove a mass social experiment in working from home (WFH). We survey more than 30,000 Americans over multiple waves to investigate whether WFH will stick, and why. Our data say that 20 percent of full workdays will be supplied from home after the pandemic ends, compared with just 5 percent before. We develop evidence on five reasons for this large shift: better-than-expected WFH experiences, new investments in physical and human capital that enable WFH, greatly diminished stigma associated with WFH, lingering concerns about crowds and contagion risks, and a pandemic-driven surge in technological innovations that support WFH. We also use our survey data to project three consequences: First, employees will enjoy large benefits from greater remote work, especially those with higher earnings. Second, the shift to WFH will directly reduce spending in major city centers by at least 5-10 percent relative to the pre-pandemic situation. Third, our data on employer plans and the relative productivity of WFH imply a 5 percent productivity boost in the post-pandemic economy due to re-optimized working arrangements. Only one-fifth of this productivity gain will show up in conventional productivity measures, because they do not capture the time savings from less commuting.

We investigate the role of information frictions in the US labor market using a new nationally representative panel dataset on individuals' labor market expectations and realizations. We find that expectations about future job offers are, on average, highly predictive of actual outcomes. Despite their predictive power, however, deviations of ex post realizations from ex ante expectations are often sizable. The panel aspect of the data allows us to study how individuals update their labor market expectations in response to such shocks. We find a strong response: an individual who receives a job offer one dollar above her expectation subsequently adjusts her expectations upward by $0.47. We embed the empirical evidence on expectations and learning into a model of search on- and off- the job with learning, and show that it is far better able to fit the data on reservation wages relative to a model that assumes complete information. We use the framework to gauge the welfare costs of information frictions which arise because individuals make uninformed job acceptance decisions and find that the costs due to information frictions are sizable, but mitigated by the presence of learning.

Social science research demonstrates that dispersal policies and restrictions on the freedom of residence have inhibited refugees’ socio-economic integration, presumably because such policies prevent refugees from moving to places where they can employ their skills most fruitfully. However, studies of refugees’ actual residential choices provide little evidence that good economic prospects attract refugees, and some even suggest that refugees often move to deprived cities with frail labor markets. The combination of negative effects of residence restrictions and emerging evidence of disadvantaging secondary migration forms what we call the ‘refugee mobility puzzle’. In this study, we aim at unpacking this puzzle by analyzing the inner-German migration patterns of recent refugees. Specifically, we ask: What attracts refugees to deprived areas, and can their seemingly unfortunate residential choices be understood as moves to opportunity and increased prospects of labor market integration after all? Empirically, we draw on the IAB-BAMF-SOEP Survey of Refugees and track the location of more than 2,000 refugee respondents who were exogenously allocated a place of residence and subsequently became free to move. Based on linear-probability discrete choice models across all German counties and postcodes, we confirm that refugees tend to move to areas with high unemployment. We show that major attractors like housing availability, co-ethnic networks, and service-oriented labor markets are clustered in areas with high unemployment. Taken together, our results complicate recent critiques of dispersal policies and restrictions. On the one hand, our findings show that seemingly disadvantaging relocations into high unemployment areas can conceal potentially improved economic perspectives in relevant labor markets. On the other hand, refugees’ search for affordable housing may turn into an unintended lock-in factor in the mid- and long-run.