Fiscal Spending Multiplier Calculations based on Input-Output Tables – with an Application to EU Members
Toralf Pusch, A. Rannberg
Abstract
Fiscal spending multiplier calculations have been revived in the aftermath of the
global financial crisis. Much of the current literature is based on VAR estimation
methods and DSGE models. The aim of this paper is not a further deepening of
this literature but rather to implement a calculation method of multipliers which is
suitable for open economies like EU member states. To this end, Input-Output tables are used as by this means the import intake of domestic demand components can be isolated in order to get an appropriate base for the calculation of the relevant import quotas. The difference of this method is substantial – on average the calculated multipliers are 15% higher than the conventional GDP fiscal spending multiplier for EU members. Multipliers for specific spending categories are comparably high, ranging between 1.4 and 1.8 for many members of the EU. GDP drops due to budget consolidation might therefore be substantial if monetary policy is not able to react in an expansionary manner.
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Police Reorganization and Crime: Evidence from Police Station Closures
Sebastian Blesse, André Diegmann
Abstract
Does the administrative organization of police affect crime? In answering this question, we focus on the reorganization of local police agencies. Specifically, we study the effects police force reallocation via station closures has on local crime. We do this by exploiting a quasi-experiment where a reform substantially reduced the number of police stations. Combining a matching strategy with an event-study design, we find no effects on total theft. Police station closures, however, open up tempting opportunities for criminals in car theft and burglary in residential properties. We can rule out that our effects arise from incapacitation, crime displacement, or changes in employment of local police forces. Our results suggest that criminals are less deterred after police station closures and use the opportunity to steal more costly goods.
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Losing Work, Moving Away? Regional Mobility After Job Loss
Daniel Fackler, Lisa Rippe
LABOUR: Review of Labour Economics and Industrial Relations,
Nr. 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.
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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.
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Protest! Die Rolle kultureller Prägung im Volkswagenskandal
Felix Noth, Lena Tonzer
Wirtschaft im Wandel,
Nr. 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.
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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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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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Did the Swiss Exchange Rate Shock Shock the Market?
Manuel Buchholz, Gregor von Schweinitz, Lena Tonzer
Abstract
The Swiss National Bank abolished the exchange rate floor versus the Euro in January 2015. Based on a synthetic matching framework, we analyse the impact of this unexpected (and therefore exogenous) shock on the stock market. The results reveal a significant level shift (decline) in asset prices in Switzerland following the discontinuation of the minimum exchange rate. While adjustments in stock market returns were most pronounced directly after the news announcement, the variance was elevated for some weeks, indicating signs of increased uncertainty and potentially negative consequences for the real economy.
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The Macroeconomics of Testing and Quarantining
Martin S. Eichenbaum, Sergio Rebelo, Mathias Trabandt
Journal of Economic Dynamics and Control,
May
2022
Abstract
We develop a SIR-based macroeconomic model to study the impact of testing/ quarantining and social distancing/mask use on health and economic outcomes. These policies can dramatically reduce the costs of an epidemic. Absent testing/quarantining, the main effect of social distancing and mask use on health outcomes is to delay, rather than reduce, epidemic-related deaths. Social distancing and mask use reduce the severity of the epidemic-related recession but prolong its duration. There is an important synergy between social distancing and mask use and testing/quarantining. Social distancing and mask use buy time for testing and quarantining to come to the rescue. The benefits of testing/quarantining are even larger when people can get reinfected, either because the virus mutates or immunity is temporary.
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Involuntary Unemployment and the Business Cycle
Lawrence J. Christiano, Mathias Trabandt, Karl Walentin
Review of Economic Dynamics,
January
2021
Abstract
Can a model with limited labor market insurance explain standard macro and labor market data jointly? We construct a monetary model in which: i) the unemployed are worse off than the employed, i.e. unemployment is involuntary and ii) the labor force participation rate varies with the business cycle. To illustrate key features of our model, we start with the simplest possible framework. We then integrate the model into a medium-sized DSGE model and show that the resulting model does as well as existing models at accounting for the response of standard macroeconomic variables to monetary policy shocks and two technology shocks. In addition, the model does well at accounting for the response of the labor force and unemployment rate to these three shocks.
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