Jobs and Matches: Quits, Replacement Hiring, and Vacancy Chains
Yusuf Mercan, Benjamin Schoefer
American Economic Review: Insights,
Nr. 1,
2020
Abstract
In the canonical DMP model of job openings, all job openings stem from new job creation. Jobs denote worker-firm matches, which are destroyed following worker quits. Yet, employers classify 56 percent of vacancies as quit-driven replacement hiring into old jobs, which evidently outlived their previous matches. Accordingly, aggregate and firm-level hiring tightly track quits. We augment the DMP model with longer-lived jobs arising from sunk job creation costs and replacement hiring. Quits trigger vacancies, which beget vacancies through replacement hiring. This vacancy chain can raise total job openings and net employment. The procyclicality of quits can thereby amplify business cycles.
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Financial Linkages and Sectoral Business Cycle Synchronisation: Evidence from Europe
Hannes Böhm, Julia Schaumburg, Lena Tonzer
Abstract
We analyse whether financial integration between countries leads to converging or diverging business cycles using a dynamic spatial model. Our model allows for contemporaneous spillovers of shocks to GDP growth between countries that are financially integrated and delivers a scalar measure of the spillover intensity at each point in time. For a financial network of ten European countries from 1996-2017, we find that the spillover effects are positive on average but much larger during periods of financial stress, pointing towards stronger business cycle synchronisation. Dismantling GDP growth into value added growth of ten major industries, we observe that some sectors are strongly affected by positive spillovers (wholesale & retail trade, industrial production), others only to a weaker degree (agriculture, construction, finance), while more nationally influenced industries show no evidence for significant spillover effects (public administration, arts & entertainment, real estate).
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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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Flight from Safety: How a Change to the Deposit Insurance Limit Affects Households‘ Portfolio Allocation
H. Evren Damar, Reint E. Gropp, Adi Mordel
IWH Discussion Papers,
Nr. 19,
2019
Abstract
We study how an increase to the deposit insurance limit affects households‘ portfolio allocation by exogenously reducing uninsured deposit balances. Using unique data that identifies insured versus uninsured deposits, along with detailed information on Canadian households‘ portfolio holdings, we show that households respond by drawing down deposits and shifting towards mutual funds and stocks. These outflows amount to 2.8% of outstanding bank deposits. The empirical evidence, consistent with a standard portfolio choice model that is modified to accommodate uninsured deposits, indicates that more generous deposit insurance coverage results in nontrivial adjustments to household portfolios.
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Do Conventional Monetary Policy Instruments Matter in Unconventional Times?
Manuel Buchholz, Kirsten Schmidt, Lena Tonzer
Abstract
This paper investigates how declines in the deposit facility rate set by the ECB affect euro area banks’ incentives to hold reserves at the central bank. We find that, in the face of lower deposit rates, banks with a more interest-sensitive business model are more likely to reduce reserve holdings and allocate freed-up liquidity to loans. The result is driven by well-capitalized banks in the non-GIIPS countries of the euro area. This reveals that conventional monetary policy instruments have limited effects in restoring monetary policy transmission during times of crisis.
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The Economic Impact of Changes in Local Bank Presence
Iftekhar Hasan, Krzysztof Jackowicz, Oskar Kowalewski, Łukasz Kozłowski
Regional Studies,
Nr. 5,
2019
Abstract
This study analyzes the economic consequences of changes in the local bank presence. Using a unique data set of banks, firms and counties in Poland over the period 2009–14, it is shown that changes strengthening the relationship banking model are associated with local labour market improvements and easier small and medium-sized enterprise access to bank debt. However, only the appearance of new, more aggressive owners of large commercial banks stimulates new firm creation.
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Lock‐in Effects in Relationship Lending: Evidence from DIP Loans
Iftekhar Hasan, Gabriel G. Ramírez, Gaiyan Zhang
Journal of Money, Credit and Banking,
Nr. 4,
2019
Abstract
Do prior lending relationships result in pass‐through savings (lower interest rates) for borrowers, or do they lock in higher costs for borrowers? Theoretical models suggest that when borrowers experience greater information asymmetry, higher switching costs, and limited access to capital markets, they become locked into higher costs from their existing lenders. Firms in Chapter 11 seeking debtor‐in‐possession (DIP) financing often fit this profile. We investigate the presence of lock‐in effects using a sample of 348 DIP loans. We account for endogeneity using the instrument variable (IV) approach and the Heckman selection model and find consistent evidence that prior lending relationship is associated with higher interest costs and the effect is more severe for stronger existing relationships. Our study provides direct evidence that prior lending relationships do create a lock‐in effect under certain circumstances, such as DIP financing.
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On the Effect of Business and Economic University Education on Political Ideology: An Empirical Note
Manthos D. Delis, Iftekhar Hasan, Maria Iosifidi
Journal of Business Ethics,
2019
Abstract
We empirically test the hypothesis that a major in economics, management, business administration or accounting (for simplicity referred to as Business/Economics) leads to more-conservative (right-wing) political views. We use a panel dataset of individuals (repeated observations for the same individuals over time) living in the Netherlands, drawing data from the Longitudinal Internet Studies for the Social Sciences from 2008 through 2013. Our results show that when using a simple fixed effects model, which fully controls for individuals’ time-invariant traits, any statistically and quantitatively significant effect of a major in Business/Economics on the Political Ideology of these individuals disappears. We posit that, at least in our sample, there is no evidence for a causal effect of a major in Business/Economics on individuals’ Political Ideology.
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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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Central Bank Transparency and the Volatility of Exchange Rates
Stefan Eichler, Helge Littke
Journal of International Money and Finance,
2018
Abstract
We analyze the effect of monetary policy transparency on bilateral exchange rate volatility. We test the theoretical predictions of a stylized model using panel data for 62 currencies from 1998 to 2010. We find strong evidence that an increase in the availability of information about monetary policy objectives decreases exchange rate volatility. Using interaction models, we find that this effect is more pronounced for countries with a lower flexibility of goods prices, a lower level of central bank conservatism, and a higher interest rate sensitivity of money demand.
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