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Recently, Fuest and Sinn (2018) have demanded a change of rules for the Eurozone’s Target 2 payment system, claiming it would violate the Statutes of the European System of Central Banks and of the European Central Bank. The authors present a stylized model based on a set of macro-economic assumptions, and show that Target 2 may lead to loss sharing among national central banks (NCBs), thus violating the no risk-sharing requirement laid out by the Eurosystem Statutes.
In this note, I present an augmented model that incorporates essential features of the micro- and macroprudential regulatory and supervisory regime that today is hard-wired into Europe’s banking system. The model shows that the original no-risk-sharing principle is not necessarily violated during a financial crisis of a member state. Moreover, it shows that under a banking union regime, financial crisis asset value losses at or below the 99.9th percentile are borne by private investors, not by taxpayers, and particularly not by central banks.
Therefore, policy conclusions from the micro-founded model differ significantly from those suggested by Fuest and Sinn (2018).
We propose a shrinkage and selection methodology specifically designed for network inference using high dimensional data through a regularised linear regression model with Spike-and-Slab prior on the parameters. The approach extends the case where the error terms are heteroscedastic, by adding an ARCH-type equation through an approximate Expectation-Maximisation algorithm. The proposed model accounts for two sets of covariates. The first set contains predetermined variables which are not penalised in the model (i.e., the autoregressive component and common factors) while the second set of variables contains all the (lagged) financial institutions in the system, included with a given probability. The financial linkages are expressed in terms of inclusion probabilities resulting in a weighted directed network where the adjacency matrix is built “row by row". In the empirical application, we estimate the network over time using a rolling window approach on 1248 world financial firms (banks, insurances, brokers and other financial services) both active and dead from 29 December 2000 to 6 October 2017 at a weekly frequency. Findings show that over time the shape of the out degree distribution exhibits the typical behavior of financial stress indicators and represents a significant predictor of market returns at the first lag (one week) and the fourth lag (one month).
Extending the data set used in Beyer (2009) to 2017, we estimate I(1) and I(2) money demand models for euro area M3. After including two broken trends and a few dummies to account for shifts in the variables following the global financial crisis and the ECB's non-standard monetary policy measures, we find that the money demand and the real wealth relations identified in Beyer (2009) have remained remarkably stable throughout the extended sample period. Testing for price homogeneity in the I(2) model we find that the nominal-to-real transformation is not rejected for the money relation whereas the wealth relation cannot be expressed in real terms.
This paper examines how networks of professional contacts contribute to the development of the careers of executives of North American and European companies. We build a dynamic model of career progression in which career moves may both depend upon existing networks and contribute to the development of future networks. We test the theory on an original dataset of nearly 73 000 executives in over 10 000 _rms. In principle professional networks could be relevant both because they are rewarded by the employer and because they facilitate job mobility. Our econometric analysis suggests that, although there is a substantial positive correlation between network size and executive compensation, with an elasticity of around 20%, almost all of this is due to unobserved individual characteristics. The true causal impact of networks on compensation is closer to an elasticity of 1 or 2% on average, all of this due to enhanced probability of moving to a higher-paid job. And there appear to be strongly diminishing returns to network size.
Using a unique confidential contract level dataset merged with firm-level asset price data, we find robust evidence that firms' stock market valuations and employment levels respond more to monetary policy announcements the higher the degree of wage rigidity. Data on the renegotiations of collective bargaining agreements allow us to construct an exogenous measure of wage rigidity. We also find that the amplification induced by wage rigidity is stronger for firms with high labor intensity and low profitability, providing evidence of distributional consequences of monetary policy. We rationalize the evidence through a model in which firms in different sectors feature different degrees of wage rigidity due to staggered renegotiations vis-a-vis unions.
This paper analyzes the effect of financial constraints on firms' corporate social responsibility. Exploiting heterogeneity in firms' exposure to a monetary policy shock in the U.S., which reduced financial constraints for some firms, I find that firms increase their environmental responsibility. I use facility-level data to account for unobservable time-varying influences on pollution and find that toxic emissions decrease when parent companies are more exposed to the monetary policy shock. I further find that these facilities are also more likely to implement pollution abatement activities. Examining within-parent company heterogeneity I find that pollution abatement investments center on facilities at greater risk of facing additional costs due to environmental regulation. The findings are consistent with the idea that a reduction in financial constraints reduces pollution as it allows firms to implement pollution abatement measures.
Households buy life insurance as part of their liquidity management. The option to surrender such a policy can serve as a buffer when a household faces a liquidity need. In this study, we investigate empirically which individual and household specific sociodemographic factors influence the surrender behavior of life insurance policyholders. Based on the Socio-Economic Panel (SOEP), an ongoing wide-ranging representative longitudinal study of around 11,000 private households in Germany, we construct a proxy to identify life insurance surrender in the data. We use this proxy to conduct fixed effect regressions and support the results with survival analyses. We find that life events that possibly impose a liquidity shock to the household, such as birth of a child and divorce increase the likelihood to surrender an existing life insurance policy for an average household in the panel. The acquisition of a dwelling and unemployment are further aspects that can foster life insurance surrender. Our results are robust with respect to different models and hold conditioning on region specific trends; they vary however for different age groups. Our analyses contribute to the existing literature supporting the emergency fund hypothesis. The findings obtained in this study can help life insurers and regulators to detect and understand industry specific challenges of the demographic change.