Flood Risk Assessment in the Netherlands: A Case Study for Dike Ring South Holland

Large parts of the Netherlands are below sea level. Therefore, it is important to have insight into the possible consequences and risks of flooding. In this article, an analysis of the risks due to flooding of the dike ring area South Holland in the Netherlands is presented. For different flood scenarios the potential number of fatalities is estimated. Results indicate that a flood event in this area can expose large and densely populated areas and result in hundreds to thousands of fatalities. Evacuation of South Holland before a coastal flood will be difficult due to the large amount of time required for evacuation and the limited time available. By combination with available information regarding the probability of occurrence of different flood scenarios, the flood risks have been quantified. The probability of death for a person in South Holland due to flooding, the so‐called individual risk, is small. The probability of a flood disaster with many fatalities, the so‐called societal risk, is relatively large in comparison with the societal risks in other sectors in the Netherlands, such as the chemical sector and aviation. The societal risk of flooding appears to be unacceptable according to some of the existing risk limits that have been proposed in literature. These results indicate the necessity of a further societal discussion on the acceptable level of flood risk in the Netherlands and the need for additional risk reducing measures.     

Non-parametric regression for spatially dependent data with wavelets

We study non-parametric regression estimates for random fields. The data satisfies certain strong mixing conditions and is defined on the regular N-dimensional lattice structure. We show consistency and obtain rates of convergence. The rates are optimal modulo a logarithmic factor in some cases. As an application, we estimate the regression function with multidimensional wavelets which are not necessarily isotropic. We simulate random fields on planar graphs with the concept of concliques (cf. [Kaiser MS, Lahiri SN, Nordman DJ. Goodness of fit tests for a class of markov random field models. Ann Statist. 2012;40:104–130]) in numerical examples of the estimation procedure.     

Happiness and altruism within the extended family

We propose a direct measure of altruism between parents and adult children, using survey data on happiness from the German Socio-Economic Panel for the years 2000–2004. The question of altruism within families has policy relevance, for example, to understand whether public transfers crowd out private ones. Previous empirical evidence, based on observed transfer behavior, has failed to establish a clear consensus. Using various cross section, panel data, and instrumental variable estimators, we find a robust association between the happiness of parents and that of their adult children. A 1 standard deviation increase in a child’s happiness is associated with the same increase in own happiness as that of a 20–45% increase in household income, depending on specification.     

Ausbildungssupervision per E-Mail — Möglichkeiten und Grenzen

Internet-based supervision for trainees — possibilities and limitations This paper presents an internet-based supervision concept for trainees in development aid projects, that has been designed at the KFH NW / University of Applied Sciences in Aachen (Germany). Based on a phase model, the article highlights requirements of email supervision in content and method. Furthermore, it reports results from a study with former participants of email supervision. Possibilities and limitations of internet-based supervision are discussed. Conclusion: Although email-supervision cannot be considered as an equivalent alternative to face-to-face consultation, it could be a useful methodical variation of supervision and coaching.     

Affective reactions influence investment decisions: evidence from a laboratory experiment with taxation

We experimentally investigate the effect of taxation of gains and losses on investment behavior. Based on the insights of economic research and psychological concepts, we expect subjects to react to taxation with behavioral and affective changes. Our main results are threefold: first, we show that taxation on gains and the possibility to deduct losses bias investment behavior, but in different directions. Since net payoffs are identical across all tax scenarios and therefore the same investment behavior is to be expected, these differences are in contrast to what a standard theory would predict. Second, we observe that different tax regulations have different effects on the affective and cognitive perception of our subjects. Third, with respect to possible connections of the affective and cognitive ratings, tax regulations, and investment decisions, we are able to show that arousal and risk perception fail to influence the decision making of participants, while there is a highly significant influence of valence perception on choice patterns.     

Estimating network properties from snowball sampled data

This article addresses the estimation of topological network parameters from data obtained with a snowball sampling design. An approximate expression for the probability of a vertex to be included in the sample is derived. Based on this sampling distribution, estimators for the mean degree, the degree correlation, and the clustering coefficient are proposed. The performance of these estimators and their sensitivity with respect to the response rate are validated through Monte Carlo simulations on several test networks. Our approach has no complex computational requirements and is straightforward to apply to real-world survey data. In a snowball sample design, each respondent is typically enquired only once. Different from the widely used estimator for Respondent-Driven Sampling (RDS), which assumes sampling with replacement, the proposed approach relies on sampling without replacement and is thus also applicable for large sample fractions. From the simulation experiments, we conclude that the estimation quality decreases with increasing variance of the network degree distribution. Yet, if the degree distribution is not to broad, our approach results in good estimates for the mean degree and the clustering coefficient, which, moreover, are almost independent from the response rate. The estimates for the degree correlation are of moderated quality.     

Living Well in Times of Threat: The Importance of Adjustment Processes to Explain Functional Adaptation to Uncertain Security in Expatriates Deployed in the Sudan

The present study investigated expatriate humanitarian aid workers’ perceptions and responses to uncertain security while deployed in the Sudan. Interviews conducted in Khartoum (n = 7) and Darfur (n = 17) focused on risk perception, concern for personal security, and strategies used to function well in an insecure environment. Despite a high perceived general risk, as well as broad knowledge and experience with security incidents, participants often expressed low concern. General adjustment processes were drawn on to explain this finding, while different constellations of processes resulted in different patterns of adjustment. Functional adjustment, resulting in adequate risk perception, protective behavior, protection, and low concern, was characterized by a constellation of complementary activation of accommodation and assimilation processes.     

Das Zürcher Ressourcen Modell im Change-Management

This article demonstrates the reasons for reactance in enterprise change processes. To understand the personal reasons for the resistance, one has to look at the personality of a person. The PSI theory explains the functional systems responsible for how one perceives the world, how one thinks, feels and acts. The knowledge gained from this theory can be realized into good self-management using the ZRM method, which incorporates all the needs, preferences, abilities and values of a person.     

Estimation Bias in the First-Order Autoregressive Model and Its Impact on Predictions and Prediction Intervals

The least squares estimate of the autoregressive coefficient in the AR(1) model is known to be biased towards zero, especially for parameters close to the stationarity boundary. Several methods for correcting the autoregressive parameter estimate for the bias have been suggested. Using simulations, we study the bias and the mean square error of the least squares estimate and the bias-corrections proposed by Kendall and Quenouille.

We also study the mean square forecast error and the coverage of the 95% prediction interval when using the biased least squares estimate or one of its bias-corrected versions. We find that the estimation bias matters little for point forecasts, but that it affects the coverage of the prediction intervals. Prediction intervals for forecasts more than one step ahead, when calculated with the biased least squares estimate, are too narrow.