The effect of fast and slow decisions on risk taking

We experimentally compare fast and slow decisions in a series of experiments on financial risk taking in three countries involving over 1700 subjects. To manipulate fast and slow decisions, subjects were randomly allocated to responding within 7 seconds (time pressure) or waiting for at least 7 or 20 seconds (time delay) before responding. To control for different effects of time pressure and time delay on measurement noise, we estimate separate parameters for noise and risk preferences within a random utility framework. We find that time pressure increases risk aversion for gains and risk taking for losses compared to time delay, implying that time pressure increases the reflection effect of Prospect Theory. The results for gains are weaker and less robust than the results for losses. We find no significant difference between time pressure and time delay for loss aversion (tested in only one of the experiments). Time delay also leads to less measurement noise than time pressure and unconstrained decisions, and appears to be an effective way of decreasing noise in experiments.     

The Human Capital Characteristics and Household Living Standards of Returning International Migrants in Eastern and Southern Africa

Africa’s experience with return migration is not new. However, few empirical studies have examined the social and economic characteristics of returning migrants within the continent. In this study, the human capital endowments and household living standards of returning migrants in Uganda and South Africa are examined using recently available data. The study compares returnees in both countries with immigrants as well as the native‐born population with no international migration experience. It also investigates how factors such as previous country of residence, year of arrival, and other demographic factors predict levels of education and living standards among returning migrants. In Uganda, the results show that recently arrived returning migrants had better educational endowments than both immigrants and non‐migrants. Migrants who returned to Uganda following the fall of Idi Amin’s regime had the lowest educational levels and lowest living standards compared to other returnees. Furthermore, the results indicate that previous residence in countries in the West was associated with four additional years of schooling while returning migrants arriving from other African countries had the lowest levels of schooling among returning migrants. In South Africa, the study finds that returnees arriving almost immediately following the end of Apartheid had the highest levels of education compared to either immigrants or non‐migrants. Returnees on average also had the highest household living standards in South Africa. Among South African immigrants, the results indicate that those arriving towards the end of the century had lower educational endowments compared to immigrants who arrived in the country two to four years after the end of Apartheid.     

Warum und wodurch Coaching wirkt

The contribution reviews the state oft the art of the research on general success factors in the coaching process. Research in the field of psychotherapy on basic variables of client centered counseling and general therapeutic change mechanisms set an example. Results of first studies on these and modified factors show that the coaching outcome depends on observed esteem and emotional support by the coach, resource activation and support of transfer into practice. Process analysis of nonverbal interactions between coach and client demonstrate that goal attainment influence goal attainment. Finally, the paper focuses on limitations, of the studies, scientific and practical conclusions as well as future perspectives.     

An Additive–Multiplicative Restricted Mean Residual Life Model

The mean residual life measures the expected remaining life of a subject who has survived up to a particular time. When survival time distribution is highly skewed or heavy tailed, the restricted mean residual life must be considered. In this paper, we propose an additive–multiplicative restricted mean residual life model to study the association between the restricted mean residual life function and potential regression covariates in the presence of right censoring. This model extends the proportional mean residual life model using an additive model as its covariate dependent baseline. For the suggested model, some covariate effects are allowed to be time‐varying. To estimate the model parameters, martingale estimating equations are developed, and the large sample properties of the resulting estimators are established. In addition, to assess the adequacy of the model, we investigate a goodness of fit test that is asymptotically justified. The proposed methodology is evaluated via simulation studies and further applied to a kidney cancer data set collected from a clinical trial.     

An Integrated Risk Assessment Model of Township‐Scaled Land Subsidence Based on an Evidential Reasoning Algorithm and Fuzzy Set Theory

Land subsidence risk assessment (LSRA) is a multi‐attribute decision analysis (MADA) problem and is often characterized by both quantitative and qualitative attributes with various types of uncertainty. Therefore, the problem needs to be modeled and analyzed using methods that can handle uncertainty. In this article, we propose an integrated assessment model based on the evidential reasoning (ER) algorithm and fuzzy set theory. The assessment model is structured as a hierarchical framework that regards land subsidence risk as a composite of two key factors: hazard and vulnerability. These factors can be described by a set of basic indicators defined by assessment grades with attributes for transforming both numerical data and subjective judgments into a belief structure. The factor‐level attributes of hazard and vulnerability are combined using the ER algorithm, which is based on the information from a belief structure calculated by the Dempster‐Shafer (D‐S) theory, and a distributed fuzzy belief structure calculated by fuzzy set theory. The results from the combined algorithms yield distributed assessment grade matrices. The application of the model to the Xixi‐Chengnan area, China, illustrates its usefulness and validity for LSRA. The model utilizes a combination of all types of evidence, including all assessment information—quantitative or qualitative, complete or incomplete, and precise or imprecise—to provide assessment grades that define risk assessment on the basis of hazard and vulnerability. The results will enable risk managers to apply different risk prevention measures and mitigation planning based on the calculated risk states.     

The Box–Cox Transformation and Non‐Iterative Estimation Methods for Ordinal Log‐Linear Models

Recently Beh and Farver investigated and evaluated three non‐iterative procedures for estimating the linear‐by‐linear parameter of an ordinal log‐linear model. The study demonstrated that these non‐iterative techniques provide estimates that are, for most types of contingency tables, statistically indistinguishable from estimates from Newton's unidimensional algorithm. Here we show how two of these techniques are related using the Box–Cox transformation. We also show that by using this transformation, accurate non‐iterative estimates are achievable even when a contingency table contains sampling zeros.