Rent-Sharing und Energiekosten: In welchem Umfang geben Industrieunternehmen Gewinne und Verluste an ihre Beschäftigten weiter?
Matthias Mertens, Steffen Müller, Georg Neuschäffer
Wirtschaft im Wandel,
No. 2,
2024
Abstract
Diese Studie untersucht, wie die betrieblichen Erträge zwischen deutschen Industrieunternehmen und ihren Beschäftigten aufgeteilt werden. Dafür werden Energiepreisänderungen auf Unternehmensebene und die daraus resultierenden Veränderungen im Unternehmensertrag betrachtet. Wir finden heraus, dass höhere Energiepreise die Löhne drücken und dass ein Rückgang bei den Erträgen um 10% zu einem Rückgang der Löhne um 2% führt. Dieser Zusammenhang ist asymmetrisch, was bedeutet, dass die Löhne nicht von Senkungen der Energiepreise profitieren, aber durch Energiepreiserhöhungen sinken. Kleine Unternehmen geben Schwankungen im Ertrag stärker an die Beschäftigten weiter als Großunternehmen.
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Media Response November 2024 Reint Gropp: Nach US-Wahl: Ökonom rechnet nicht mehr mit Intel-Ansiedlung in: DIE WELT, 08.11.2024 IWH: Zombiesterben in: Frankfurter Allgemeine…
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What Explains International Interest Rate Co-Movement?
Annika Camehl, Gregor von Schweinitz
IWH Discussion Papers,
No. 3,
2023
Abstract
We show that global supply and demand shocks are important drivers of interest rate co-movement across seven advanced economies. Beyond that, local structural shocks transmit internationally via aggregate demand channels, and central banks react predominantly to domestic macroeconomic developments: unexpected monetary policy tightening decreases most foreign interest rates, while expansionary local supply and demand shocks increase them. To disentangle determinants of international interest rate co-movement, we use a Bayesian structural panel vector autoregressive model accounting for latent global supply and demand shocks. We identify country-specific structural shocks via informative prior distributions based on a standard theoretical multi-country open economy model.
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Identifying Rent-sharing Using Firms‘ Energy Input Mix
Matthias Mertens, Steffen Müller, Georg Neuschäffer
IWH Discussion Papers,
No. 19,
2022
Abstract
We present causal evidence on the rent-sharing elasticity of German manufacturing firms. We develop a new firm-level Bartik instrument for firm rents that combines the firms‘ predetermined energy input mix with national energy carrier price changes. Reduced-form evidence shows that higher energy prices depress wages. Instrumental variable estimation yields a rent-sharing elasticity of approximately 0.20. Rent-sharing induced by energy price variation is asymmetric and driven by energy price increases, implying that workers do not benefit from energy price reductions but are harmed by price increases. The rent-sharing elasticity is substantially larger in small (0.26) than in large (0.17) firms.
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On Modeling IPO Failure Risk
Gonul Colak, Mengchuan Fu, Iftekhar Hasan
Economic Modelling,
April
2022
Abstract
This paper offers a novel framework, combining firm operational risk, IPO pricing risk, and market risk, to model IPO failure risk. By analyzing nearly a thousand variables, we observe that prior IPO failure risk models have suffered from a major missing-variable problem. Evidence reveals several key new firm-level determinants, e.g., the volatility operating performance, the size of its accounts payable, pretax income to common equity, total short-term debt, and a few macroeconomic variables such as treasury bill rate, and book-to-market of the DJIA index. These findings have major economic implications. The total value loss from not predicting the imminent failure of an IPO is significantly lower with this proposed model compared to other established models. The IPO investors could have saved around $18billion over the period between 1994 and 2016 by using this model.
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Real Estate Transaction Taxes and Credit Supply
Michael Koetter, Philipp Marek, Antonios Mavropoulos
Deutsche Bundesbank Discussion Paper,
No. 4,
2021
Abstract
We exploit staggered real estate transaction tax (RETT) hikes across German states to identify the effect of house price changes on mortgage credit supply. Based on approximately 33 million real estate online listings, we construct a quarterly hedonic house price index (HPI) between 2008:q1 and 2017:q4, which we instrument with state-speci c RETT changes to isolate the effect on mortgage credit supply by all local German banks. First, a RETT hike by one percentage point reduces HPI by 1.2%. This effect is driven by listings in rural regions. Second, a 1% contraction of HPI induced by an increase in the RETT leads to a 1.4% decline in mortgage lending. This transmission of fiscal policy to mortgage credit supply is effective across almost the entire bank capitalization distribution.
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Lending Effects of the ECB’s Asset Purchases
Michael Koetter
Journal of Monetary Economics,
December
2020
Abstract
Between 2010 and 2012, the European Central Bank absorbed €218 billion worth of government securities from five EMU countries under the Securities Markets Programme (SMP). Detailed security holdings data at the bank level affirms an effective lending stimulus due to the SMP. Exposed banks contract household lending, but increase commercial lending substantially. Holding non-SMP securities from stressed EMU countries amplifies the commercial lending response. The SMP also improved liquidity buffers and profitability without compromising credit quality.
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Why are some Chinese Firms Failing in the US Capital Markets? A Machine Learning Approach
Gonul Colak, Mengchuan Fu, Iftekhar Hasan
Pacific-Basin Finance Journal,
June
2020
Abstract
We study the market performance of Chinese companies listed in the U.S. stock exchanges using machine learning methods. Predicting the market performance of U.S. listed Chinese firms is a challenging task due to the scarcity of data and the large set of unknown predictors involved in the process. We examine the market performance from three different angles: the underpricing (or short-term market phenomena), the post-issuance stock underperformance (or long-term market phenomena), and the regulatory delistings (IPO failure risk). Using machine learning techniques that can better handle various data problems, we improve on the predictive power of traditional estimations, such as OLS and logit. Our predictive model highlights some novel findings: failed Chinese companies have chosen unreliable U.S. intermediaries when going public, and they tend to suffer from more severe owners-related agency problems.
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