07.04.2022 • 7/2022
IWH-Insolvenztrend: Zahl der Insolvenzen steigt weiter, Industriejobs stärker betroffen
Die Zahl der Insolvenzen von Personen- und Kapitalgesellschaften ist im März erneut gestiegen, zeigt die aktuelle Analyse des Leibniz-Instituts für Wirtschaftsforschung Halle (IWH). Auch für die nächsten Monate ist eher mit steigenden Insolvenzzahlen zu rechnen. Vor allem in der Industrie sind seit Jahresbeginn ungewöhnlich viele Jobs betroffen.
Steffen Müller
Read press release
Losing Work, Moving Away? Regional Mobility After Job Loss
Daniel Fackler, Lisa Rippe
LABOUR: Review of Labour Economics and Industrial Relations,
No. 4,
2017
Abstract
Using German survey data, we investigate the relationship between involuntary job loss and regional mobility. Our results show that job loss has a strong positive effect on the propensity to relocate. We also analyse whether displaced workers who relocate to a different region after job loss are better able to catch up with non-displaced workers in terms of labour market performance than those staying in the same region. Our findings do not support this conjecture as we find substantial long-lasting earnings losses for movers and stayers and even slightly but not significantly higher losses for movers.
Read article
Losing Work, Moving Away? Regional Mobility After Job Loss
Daniel Fackler, Lisa Rippe
Abstract
Using German survey data, we investigate the relationship between involuntary job loss and regional mobility. Our results show that job loss has a strong positive effect on the propensity to relocate. We also analyze whether the high and persistent earnings losses of displaced workers can in part be explained by limited regional mobility. Our findings do not support this conjecture as we find substantial long lasting earnings losses for both movers and stayers. In the short run, movers even face slightly higher losses, but the differences between the two groups of displaced workers are never statistically significant. This challenges whether migration is a beneficial strategy in case of involuntary job loss.
Read article
Protest! Die Rolle kultureller Prägung im Volkswagenskandal
Felix Noth, Lena Tonzer
Wirtschaft im Wandel,
No. 3,
2020
Abstract
Die Aufdeckung manipulierter Abgaswerte bei Dieselautos des Herstellers Volkswagen (VW) durch die amerikanischen Behörden im Jahr 2015 brachte einen der größten Unternehmensskandale Deutschlands zutage. Dieser Skandal blieb nicht ohne Konsequenzen. Martin Winterkorn trat von seinem Amt als Vorstandsvorsitzender und Michael Horn als Chef von Volkswagen in den USA zurück. Viele VW-Kunden klagten gegen den Konzern, und in deutschen Großstädten wurde über Dieselfahrverbote diskutiert. Doch gab es auch eine Reaktion auf Konsumentenseite, also seitens der Autokäufer? Und wenn ja, spielen hier gesellschaftskulturelle Unterschiede wie zum Beispiel religiöse Prägung eine Rolle? Diesen Fragen geht ein im letzten Jahr erschienenes Arbeitspapier des IWH nach. Die empirische Analyse beschäftigt sich mit der Frage, ob Konsumenten nach dem VW-Skandal ihr Kaufverhalten stärker anpassen, wenn das gesellschaftliche Umfeld protestantisch geprägt ist. In der wissenschaftlichen Literatur zeigt sich, dass Protestanten mehr Wert auf eine Überwachung und Durchsetzung von Regeln legen, weshalb die Autoren von dieser Religionsgruppe eine ausgeprägtere Reaktion auf den VW-Skandal erwarten. Das Hauptergebnis der Studie legt dann genau diesen Schluss nahe: In den deutschen Regionen, in denen die Mehrheit der Bevölkerung dem protestantischen Glauben angehört, kam es zu signifikant höheren Rückgängen bei VW-Neuzulassungen infolge des VW-Skandals. Der Effekt ist umso stärker, je länger die Region durch protestantische Werte geprägt ist. Offenbar können bestimmte gesellschaftskulturelle Ausprägungen wie Religion und deren Normen ein Korrektiv für Verfehlungen von Unternehmen darstellen und somit verzögerte oder ausbleibende Maßnahmen von Politikern und Regulierern zum Teil ersetzen.
Read article
An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Abstract
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance measure, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly effciently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
Read article
Does Machine Learning Help us Predict Banking Crises?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Journal of Financial Stability,
December
2019
Abstract
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance metric, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly efficiently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
Read article
26.04.2022 • 10/2022
Regional effects of a recession in Germany triggered by an import stop for Russian gas
A halt in Russian gas deliveries would lead to a recession in the German economy. Not all regions would be equally affected: The Halle Institute for Economic Research (IWH) expects a significantly stronger slump in economic output in regions where the manufacturing sector has a large weight than elsewhere.
Oliver Holtemöller
Read press release