Measuring the Indirect Effects of Adverse Employer Behavior on Worker Productivity – A Field Experiment
The Economic Journal,
12.03.2020 • 4/2020
Global economy under the spell of the coronavirus epidemic
The epidemic is obstructing the economic recovery in Germany. Foreign demand is falling, private households forgo domestic consumption if it comes with infection risk, and investments are postponed. Assuming that the spread of the disease can be contained in short time, GDP growth in 2020 is expected to be 0.6% according to IWH spring economic forecast. Growth in East Germany is expected to be 0.9% and thus higher than in West Germany. If the number of new infections cannot be decreased in short time, we expect a recession in Germany.
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Employee Treatment and Contracting with Bank Lenders: An Instrumental Approach for Stakeholder Management
Journal of Business Ethics,
Adopting an instrumental approach for stakeholder management, we focus on two primary stakeholder groups (employees and creditors) to investigate the relationship between employee treatment and loan contracts with banks. We find strong evidence that fair employee treatment reduces loan price and limits the use of financial covenants. In addition, we document that relationship bank lenders price both the levels and changes in the quality of employee treatment, whereas first-time bank lenders only care about the levels of fair employee treatment. Taking a contingency perspective, we find that industry competition and firm asset intangibility moderate the relationship between good human resource management and bank loan costs. The cost reduction effect of fair employee treatment is stronger for firms operating in a more competitive industry and having higher levels of intangible assets.
Gender Equality & Anti-Discrimination
Equal Opportunities at IWH ...
Transformation tables for administrative borders in Germany
Transformation tables for administrative borders in Germany The state has the ability...
An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?
IWH Discussion Papers,
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.
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