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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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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Innovation and Top Income Inequality
Philippe Aghion, Ufuk Akcigit, Antonin Bergeaud, Richard Blundell, David Hemous
Review of Economic Studies,
No. 1,
2019
Abstract
In this article, we use cross-state panel and cross-U.S. commuting-zone data to look at the relationship between innovation, top income inequality and social mobility. We find positive correlations between measures of innovation and top income inequality. We also show that the correlations between innovation and broad measures of inequality are not significant. Next, using instrumental variable analysis, we argue that these correlations at least partly reflect a causality from innovation to top income shares. Finally, we show that innovation, particularly by new entrants, is positively associated with social mobility, but less so in local areas with more intense lobbying activities.
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Zu den rentenpolitischen Plänen im Koalitionsvertrag 2018 von CDU, CSU und SPD: Konsequenzen, Finanzierungsoptionen und Reformbedarf
Oliver Holtemöller, Christoph Schult, Götz Zeddies
Zeitschrift für Wirtschaftspolitik,
No. 3,
2018
Abstract
In the coalition agreement from February 7, 2018, the new German federal government drafts its public pension policy, which has to be evaluated against the background of demographic dynamics in Germany. In this paper, the consequences of public pensions related policy measures for the German public pension insurance are illustrated using a simulation model. In the long run, the intended extensions of benefits would lead to an increase in the contribution rate to the German public pension insurance of about two and a half percentage points. Referring to pension systems of other countries, we discuss measures in order to limit this increase in the contribution rate.
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A Market-based Measure for Currency Risk in Managed Exchange Rate Regimes
Stefan Eichler, Ingmar Roevekamp
Journal of International Financial Markets, Institutions and Money,
November
2018
Abstract
We introduce a novel currency risk measure based on American Depositary Receipts (ADRs). Using an augmented ADR pricing model, we exploit investors’ exposure to potential devaluation losses to derive an indicator of currency risk. Using weekly data for a sample of 807 ADRs located in 21 emerging markets over the 1994–2014 period, we find that a deterioration in the fiscal balance and higher inflation increase currency risk. Interaction models reveal that the fiscal balance and inflation drive the determination of currency risk for countries with poor sovereign rating, low foreign reserves, low capital account openness and managed float regimes.
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Measuring the Impact of Household Innovation using Administrative Data
Javier Miranda, Nikolas Zolas
NBER Working Paper,
No. 25259,
2018
Abstract
We link USPTO patent data to U.S. Census Bureau administrative records on individuals and firms. The combined dataset provides us with a directory of patenting household inventors as well as a time-series directory of self-employed businesses tied to household innovations. We describe the characteristics of household inventors by race, age, gender and U.S. origin, as well as the types of patented innovations pursued by these inventors. Business data allows us to highlight how patents shape the early life-cycle dynamics of nonemployer businesses. We find household innovators are disproportionately U.S. born, white and their age distribution has thicker tails relative to business innovators. Data shows there is a deficit of female and black inventors. Household inventors tend to work in consumer product areas compared to traditional business patents. While patented household innovations do not have the same impact of business innovations their uniqueness and impact remains surprisingly high. Back of the envelope calculations suggest patented household innovations granted between 2000 and 2011 might generate $5.0B in revenue (2000 dollars).
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Information Feedback in Temporal Networks as a Predictor of Market Crashes
Stjepan Begušić, Zvonko Kostanjčar, Dejan Kovač, Boris Podobnik, H. Eugene Stanley
Complexity,
September
2018
Abstract
In complex systems, statistical dependencies between individual components are often considered one of the key mechanisms which drive the system dynamics observed on a macroscopic level. In this paper, we study cross-sectional time-lagged dependencies in financial markets, quantified by nonparametric measures from information theory, and estimate directed temporal dependency networks in financial markets. We examine the emergence of strongly connected feedback components in the estimated networks, and hypothesize that the existence of information feedback in financial networks induces strong spatiotemporal spillover effects and thus indicates systemic risk. We obtain empirical results by applying our methodology on stock market and real estate data, and demonstrate that the estimated networks exhibit strongly connected components around periods of high volatility in the markets. To further study this phenomenon, we construct a systemic risk indicator based on the proposed approach, and show that it can be used to predict future market distress. Results from both the stock market and real estate data suggest that our approach can be useful in obtaining early-warning signals for crashes in financial markets.
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Urban Occupational Structures as Information Networks: The Effect on Network Density of Increasing Number of Occupations
Shade T. Shutters, José Lobo, Rachata Muneepeerakul, Deborah Strumsky, Charlotta Mellander, Matthias Brachert, Teresa Farinha, Luis M. A. Bettencourt
Plos One,
forthcoming
Abstract
Urban economies are composed of diverse activities, embodied in labor occupations, which depend on one another to produce goods and services. Yet little is known about how the nature and intensity of these interdependences change as cities increase in population size and economic complexity. Understanding the relationship between occupational interdependencies and the number of occupations defining an urban economy is relevant because interdependence within a networked system has implications for system resilience and for how easily can the structure of the network be modified. Here, we represent the interdependencies among occupations in a city as a non-spatial information network, where the strengths of interdependence between pairs of occupations determine the strengths of the links in the network. Using those quantified link strengths we calculate a single metric of interdependence–or connectedness–which is equivalent to the density of a city’s weighted occupational network. We then examine urban systems in six industrialized countries, analyzing how the density of urban occupational networks changes with network size, measured as the number of unique occupations present in an urban workforce. We find that in all six countries, density, or economic interdependence, increases superlinearly with the number of distinct occupations. Because connections among occupations represent flows of information, we provide evidence that connectivity scales superlinearly with network size in information networks.
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