Vertical Grants and Local Public Efficiency
Ivo Bischoff, Peter Bönisch, Peter Haug, Annette Illy
Public Finance Review,
No. 3,
2019
Abstract
The existing empirical literature on the impact of vertical grants on local public-sector efficiency yields mixed results. Given the fact that vertical financial equalization systems often reduce differences in fiscal capacity, we argue that empirical studies based on cross-sectional data may yield a positive relationship between grants and efficiency of public service production even when the underlying causal effect is not. We provide a simple illustrative theoretical model to show the logic of our argument and illustrate its relevance by an empirical case study for the German state of Saxony-Anhalt. We show that our main argument of an inference-disturbing effect applies to those existing studies that are more optimistic about the impact of vertical grants. Finally, we argue that it may disturb the inference drawn from studies in a number of other countries where vertical grants—intended or not—concentrate in fiscally weak municipalities.
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Resolving the Missing Deflation Puzzle
Jesper Lindé, Mathias Trabandt
Abstract
We propose a resolution of the missing deflation puzzle. Our resolution stresses the importance of nonlinearities in price- and wage-setting when the economy is exposed to large shocks. We show that a nonlinear macroeconomic model with real rigidities resolves the missing deflation puzzle, while a linearized version of the same underlying nonlinear model fails to do so. In addition, our nonlinear model reproduces the skewness of inflation and other macroeconomic variables observed in post-war U.S. data. All told, our results caution against the common practice of using linearized models to study inflation and output dynamics.
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A Factor-model Approach for Correlation Scenarios and Correlation Stress Testing
Natalie Packham, Fabian Wöbbeking
Journal of Banking and Finance,
April
2019
Abstract
In 2012, JPMorgan accumulated a USD 6.2 billion loss on a credit derivatives portfolio, the so-called “London Whale”, partly as a consequence of de-correlations of non-perfectly correlated positions that were supposed to hedge each other. Motivated by this case, we devise a factor model for correlations that allows for scenario-based stress testing of correlations. We derive a number of analytical results related to a portfolio of homogeneous assets. Using the concept of Mahalanobis distance, we show how to identify adverse scenarios of correlation risk. In addition, we demonstrate how correlation and volatility stress tests can be combined. As an example, we apply the factor-model approach to the “London Whale” portfolio and determine the value-at-risk impact from correlation changes. Since our findings are particularly relevant for large portfolios, where even small correlation changes can have a large impact, a further application would be to stress test portfolios of central counterparties, which are of systemically relevant size.
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National Culture and Risk-taking: Evidence from the Insurance Industry
Chrysovalantis Gaganis, Iftekhar Hasan, Panagiota Papadimitri, Menelaos Tasiou
Journal of Business Research,
April
2019
Abstract
The gravity of insurance within the financial sector is constantly increasing. Reasonably, after the events of the recent financial turmoil, the domain of research that examines the factors driving the risk-taking of this industry has been signified. The purpose of the present study is to investigate the interplay between national culture and risk of insurance firms. We quantify the cultural overtones, measuring national culture considering the dimensions outlined by the Hofstede model and risk-taking using the ‘Z-score’. In a sample consisting of 801 life and non-life insurance firms operating across 42 countries over the period 2007–2016, we find a strong and significant relationship among insurance firms' risk-taking and cultural characteristics, such as individualism, uncertainty avoidance and power distance. Results remain robust to a variety of firm and country-specific controls, alternative measures of risk, sample specifications and tests designed to alleviate endogeneity.
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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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Expectation Formation, Financial Frictions, and Forecasting Performance of Dynamic Stochastic General Equilibrium Models
Oliver Holtemöller, Christoph Schult
Historical Social Research,
Special Issue: Governing by Numbers
2019
Abstract
In this paper, we document the forecasting performance of estimated basic dynamic stochastic general equilibrium (DSGE) models and compare this to extended versions which consider alternative expectation formation assumptions and financial frictions. We also show how standard model features, such as price and wage rigidities, contribute to forecasting performance. It turns out that neither alternative expectation formation behaviour nor financial frictions can systematically increase the forecasting performance of basic DSGE models. Financial frictions improve forecasts only during periods of financial crises. However, traditional price and wage rigidities systematically help to increase the forecasting performance.
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flexpaneldid: A Stata Command for Causal Analysis with Varying Treatment Time and Duration
Eva Dettmann, Alexander Giebler, Antje Weyh
Abstract
>>A completely revised version of this paper has been published as: Dettmann, Eva; Giebler, Alexander; Weyh, Antje: flexpaneldid. A Stata Toolbox for Causal Analysis with Varying Treatment Time and Duration. IWH Discussion Paper 3/2020. Halle (Saale) 2020.<<
The paper presents a modification of the matching and difference-in-differences approach of Heckman et al. (1998) and its Stata implementation, the command flexpaneldid. The approach is particularly useful for causal analysis of treatments with varying start dates and varying treatment durations (like investment grants or other subsidy schemes). Introducing more flexibility enables the user to consider individual treatment and outcome periods for the treated observations. The flexpaneldid command for panel data implements the developed flexible difference-in-differences approach and commonly used alternatives like CEM Matching and difference-in-differences models. The novelty of this tool is an extensive data preprocessing to include time information into the matching approach and the treatment effect estimation. The core of the paper gives two comprehensive examples to explain the use of flexpaneldid and its options on the basis of a publicly accessible data set.
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15.01.2019 • 1/2019
Schneller Braunkohleausstieg hat deutliche Folgen in betroffenen Regionen
Wenn Deutschland die Braunkohleverstromung vorzeitig beendet, wird die Gesamtwirtschaft kaum beeinträchtigt. Allerdings zeigen Berechnungen des Leibniz-Instituts für Wirtschaftsforschung Halle (IWH) spürbare Effekte auf die Arbeitsmärkte des Rheinlands, Mitteldeutschlands und der Lausitz. Eine Region ist besonders betroffen von Arbeitslosigkeit und Abwanderung.
Oliver Holtemöller
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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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