Transmitting Fiscal Covid-19 Counterstrikes Effectively: Mind the Banks!
Reint E. Gropp, Michael Koetter, William McShane
IWH Online,
No. 2,
2020
Abstract
The German government launched an unprecedented range of support programmes to mitigate the economic fallout from the Covid-19 pandemic for employees, self-employed, and firms. Fiscal transfers and guarantees amount to approximately €1.2 billion by now and are supplemented by similarly impressive measures taken at the European level. We argue in this note that the pandemic poses, however, also important challenges to financial stability in general and bank resilience in particular. A stable banking system is, in turn, crucial to ensure that support measures are transmitted to the real economy and that credit markets function seamlessly. Our analysis shows that banks are exposed rather differently to deteriorated business outlooks due to marked differences in their lending specialisation to different economic sectors. Moreover, a number of the banks that were hit hardest by bleak growth prospects of their borrowers were already relatively thinly capitalised at the outset of the pandemic. This coincidence can impair the ability and willingness of selected banks to continue lending to their mostly small and medium sized entrepreneurial customers. Therefore, ensuring financial stability is an important pre-requisite to also ensure the effectiveness of fiscal support measures. We estimate that contracting business prospects during the first quarter of 2020 could lead to an additional volume of non-performing loans (NPL) among the 40 most stressed banks ‒ mostly small, regional relationship lenders ‒ on the order of around €200 million. Given an initial stock of NPL of €650 million, this estimate thus suggests a potential level of NPL at year-end of €1.45 billion for this fairly small group of banks already. We further show that 17 regional banking markets are particularly exposed to an undesirable coincidence of starkly deteriorating borrower prospects and weakly capitalised local banks. Since these regions are home to around 6.8% of total employment in Germany, we argue that ensuring financial stability in the form of healthy bank balance sheets should be an important element of the policy strategy to contain the adverse real economic effects of the pandemic.
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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.
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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,
2019
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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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.
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31.07.2018 • 16/2018
Fairness pays off
When companies arbitrarily cut their wages, the motivation and productivity of the employees decrease – this is clear. Less obvious, however: employees also become less productive even if it is their colleagues who are treated unfairly and not them. This was confirmed by a research group led by Sabrina Jeworrek at the Halle Institute for Economic Research (IWH) – Member of the Leibniz Association.
Sabrina Jeworrek
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Indirekte Effekte von als unfair wahrgenommenem Arbeitgeberverhalten auf die Produktivität von Beschäftigten
Sabrina Jeworrek
Wirtschaft im Wandel,
No. 3,
2018
Abstract
Jede Organisation, die darüber nachdenkt zu restrukturieren, Löhne zu kürzen oder Angestellte zu entlassen, sollte auch über mögliche Reaktionen der persönlich nicht betroffenen Arbeitnehmer nachdenken. Dieser Beitrag präsentiert Ergebnisse eines Feldexperiments. Es offenbart, dass die als unfair wahrgenommene Handlung des Arbeitgebers – in diesem Fall die Entlassung von Arbeitskollegen – die anschließende Produktivität der nicht direkt betroffenen Arbeitskräfte mindert. Als Teil des Experiments antizipierten erfahrene Personalmanager zwar im Durchschnitt erfolgreich die Konsequenzen unfairen Arbeitgeberverhaltens auf nicht betroffene Arbeitnehmer, einzeln lagen sie jedoch oft daneben.
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Measuring Indirect Effects of Unfair Employer Behavior on Worker Productivity – A Field Experiment
Matthias Heinz, Sabrina Jeworrek, Vanessa Mertins, Heiner Schumacher, Matthias Sutter
Abstract
We present a field experiment in which we set up a call-center to study how the productivity of workers is affected if managers treat their co-workers in an unfair way. This question cannot be studied in long-lived organizations since workers may change their career expectations (and hence effort) when managers behave unfairly towards co-workers. In order to rule out such confounds and to measure productivity changes of unaffected workers in a clean way, we create an environment where employees work for two shifts. In one treatment, we lay off parts of the workforce before the second shift. Compared to two different control treatments, we find that, in the layoff treatment, the productivity of the remaining, unaffected workers drops by 12 percent. We show that this result is not driven by peer effects or altered beliefs about the job or the managers’ competence, but rather related to the workers’ perception of unfair behavior of employers towards co-workers. The latter interpretation is confirmed in a survey among professional HR managers. We also show that the effect of unfair behavior on the productivity of unaffected workers is close to the upper bound of the direct effects of wage cuts on the productivity of affected workers. This suggests that the price of an employer’s unfair behavior goes well beyond the potential tit-for-tat of directly affected workers.
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Indirect Effects of Unfair Employer Behaviour on Workplace Performance
Matthias Heinz, Sabrina Jeworrek, Vanessa Mertins, Heiner Schumacher, Matthias Sutter
VOX CEPR's Policy Portal,
2017
Abstract
Any organisation that needs to restructure, cut wages, or make layoffs needs to know how the employees who are not affected will respond. This column presents a field experiment which revealed that the perception that employers are unfair – in this case, as a result of layoffs – reduces the performance of employees who have not been not directly affected. As part of the experiment, experienced HR managers were able to successfully anticipate the consequences of unfair employer behaviour on unaffected workers.
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Taxation, Corruption, and Growth
Philippe Aghion, Ufuk Akcigit, Julia Cagé, William R. Kerr
European Economic Review,
2016
Abstract
We build an endogenous growth model to analyze the relationships between taxation, corruption, and economic growth. Entrepreneurs lie at the center of the model and face disincentive effects from taxation but acquire positive benefits from public infrastructure. Political corruption governs the efficiency with which tax revenues are translated into infrastructure. The model predicts an inverted-U relationship between taxation and growth, with corruption reducing the optimal taxation level. We find evidence consistent with these predictions and the entrepreneurial channel using data from the Longitudinal Business Database of the US Census Bureau. The marginal effect of taxation for growth for a state at the 10th or 25th percentile of corruption is significantly positive; on the other hand, the marginal effects of taxation for growth for a state at the 90th percentile of corruption are much lower across the board. We make progress towards causality through Granger-style tests and by considering periphery counties where effective tax policy is largely driven by bordering states. Finally, we calibrate our model and find that the calibrated taxation rate of 37% is fairly close to the model׳s estimated welfare maximizing taxation rate of 42%. Reducing corruption provides the largest potential impact for welfare gain through its impact on the uses of tax revenues.
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How Selective Are Real Wage Cuts? A Micro-analysis Using Linked Employer–Employee Data
Boris Hirsch, Thomas Zwick
LABOUR: Review of Labour Economics and Industrial Relations,
No. 4,
2015
Abstract
Using linked employer–employee panel data for Germany, we investigate whether firms implement real wage reductions in a selective manner. In line with insider–outsider and several strands of efficiency wage theory, we find strong evidence for selective wage cuts with high-productivity workers being spared even when controlling for permanent differences in firms' wage policies. In contrast to some recent contributions stressing fairness considerations, we also find that wage cuts increase wage dispersion among peers rather than narrowing it. Notably, the same selectivity pattern shows up when restricting our analysis to firms covered by collective agreements or having a works council.
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