12.12.2019 • 24/2019
Global economy slowly gains momentum – but Germany still stuck in a downturn
In 2020, the global economy is likely to benefit from the recent thaw in trade disputes. Germany’s manufacturing sector, however, will recover only slowly. “In 2020, the German economy will probably grow at a rate of 1.1%, and adjusted for the unusually high number of working days the growth rate will only be 0.7%”, says Oliver Holtemöller, head of the Department Macroeconomics and vice president at Halle Institute for Economic Research (IWH). With an estimated growth rate of 1.3%, production in East Germany will outpace total German production growth.
Oliver Holtemöller
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What Drives the Commodity-Sovereign-Risk-Dependence in Emerging Market Economies?
Hannes Böhm, Stefan Eichler, Stefan Gießler
IWH Discussion Papers,
No. 23,
2019
Abstract
Using daily data for 34 emerging markets in the period 1994-2016, we find robust evidence that higher export commodity prices are associated with higher sovereign bond returns (indicating lower sovereign risk). The economic effect is especially pronounced for heavy commodity exporters. Examining the drivers, we find, first, that commodity-dependence is higher for countries that export large volumes of volatile commodities and that the effect increases in times of recessions, high inflation, and expansionary U.S. monetary policy. Second, the importance of raw material prices for sovereign financing can likely be mitigated if a country improves institutions and tax systems, attracts FDI inflows, invests in manufacturing, machinery and infrastructure, builds up reserve assets and opens capital and trade accounts. Third, the concentration of commodities within a country’s portfolio, its government indebtedness or amount of received development assistance appear to be only of secondary importance for commodity-dependence.
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Employee Treatment and Contracting with Bank Lenders: An Instrumental Approach for Stakeholder Management
Bill Francis, Iftekhar Hasan, Liuling Liu, Haizhi Wang
Journal of Business Ethics,
forthcoming
Abstract
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.
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Pricing Sin Stocks: Ethical Preference vs. Risk Aversion
Stefano Colonnello, Giuliano Curatola, Alessandro Gioffré
European Economic Review,
2019
Abstract
We develop an ethical preference-based model that reproduces the average return and volatility spread between sin and non-sin stocks. Our investors do not necessarily boycott sin companies. Rather, they are open to invest in any company while trading off dividends against ethicalness. When dividends and ethicalness are complementary goods and investors are sufficiently risk averse, the model predicts that the dividend share of sin companies exhibits a positive relation with the future return and volatility spreads. An empirical analysis supports the model’s predictions. Taken together, our results point to the importance of ethical preferences for investors’ portfolio choices and asset prices.
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An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?
Johannes Beutel, Sophia List, Gregor von Schweinitz
IWH Discussion Papers,
No. 2,
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 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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East Germany
East Germany Rearguard Only investments in education will lead to a further catch-up ...
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Reports of the European Forecasting Network (EFN)
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