Interpersonal trust and voluntary associations: examining three approaches

The relationship between interpersonal trust and membership in voluntary associations is a persistent research finding in sociology. What is more, the notion of trust has become a central issue in current social science theorizing covering such diverse approaches as transaction costs economics or cognitive sociology. In different ways and for different purposes, these approaches address the role of voluntary organizations, although, as this paper argues, much of this thinking remains sketchy and underdeveloped. Against an empirical portrait of this relationship, the purpose of this paper is to assess such theorizing. We first set out to explicate major approaches to trust in economics, sociology and political science, using the non-profit or voluntary organization as a focal point. We then examine the various approaches in terms of their strengths and weaknesses, and, finally, identify key areas for theoretical development. In particular, we point to the social movement literature, the social psychology of trust, and recent thinking about civil society.     

Changes in Population,Affluence, and Environmental Pressures During Industrialization: The Case of Austria 1830–1995

This paper relates changes in aggregate population, affluence (measured as GDP), and indicators for environmental pressures, the latter being based upon the socioeconomic metabolism concept, for Austria from 1830 to 1995. During this period of time Austria underwent a transition from a predominantly agricultural mode of substistence to an industrial economy. The Austrian population increased by a factor of 2.3, total GDP by a factor of 28.2 and per capita GDP by a factor of 12.2. Environmental indicators change by factors of between 0.85 and over 1000. In general we find that although efficiencies (environmental pressure per unit of GDP) increased dramatically, total environmental pressures increased considerably for most indicators, except for those that are related to an agricultural mode of subsistence. Our results indicate that environmental policies that aim to reduce the environmental pressure per unit of GDP (i.e., increase ecological efficiency) are not likely to be sufficient for sustainable development because efficiency gains are more than compensated for by increases in affluence. Instead, sustainability policy should focus on reducing total environmental pressures.     

The cost of not knowing the radius

Robust Statistics considers the quality of statistical decisions in the presence of deviations from the ideal model, where deviations are modelled by neighborhoods of a certain size about the ideal model. We introduce a new concept of optimality (radius-minimaxity) if this size or radius is not precisely known: for this notion, we determine the increase of the maximum risk over the minimax risk in the case that the optimally robust estimator for the false neighborhood radius is used. The maximum increase of the relative risk is minimized in the case that the radius is known only to belong to some interval [r _l ,r _u ]. We pursue this minmax approach for a number of ideal models and a variety of neighborhoods. Also, the effect of increasing parameter dimension is studied for these models. The minimax increase of relative risk in case the radius is completely unknown, compared with that of the most robust procedure, is 18.1% versus 57.1% and 50.5% versus 172.1% for one-dimensional location and scale, respectively, and less than 1/3 in other typical contamination models. In most models considered so far, the radius needs to be specified only up to a factor , in order to keep the increase of relative risk below 12.5%, provided that the radius–minimax robust estimator is employed. The least favorable radii leading to the radius–minimax estimators turn out small: 5–6% contamination, at sample size 100.      

Order-picking in deep cold--physiological responses of younger and older females. Part 1: heart rate

The sales figures of chilled and frozen food have been rising steadily over the years. Naturally, this has also led to an increase in the number of jobs related to these goods. While these workplaces are becoming more and more important there are, nevertheless, only a few investigations into the effects of working in deep cold on humans. Order-picking in a cold environment represents a high workload. Especially working at -24°C with wearing heavy cold protective clothing leads to explicitly higher strain. Since performance decreases with age, varying physical strain between younger and older employees can hypothetically be expected. In order to quantify the physiological responses to working in the cold, 15 subjects of two female age groups, each, (20- to 35-year-olds and 40- to 65-year-olds) were asked to carry out whole working day tasks in a chill room (+3°) and in a cold store (-24°C). Simultaneously, heart rate and other physiological relevant parameters were measured.     

Problems related to confidence intervals for impulse responses of autoregressive processes

Confidence intervals for impulse responses computed from autoregressive processes are considered. A detailed analysis of the methods in current use shows that they are not very reliable in some cases. In particular, there are theoretical reasons for them to have actual coverage probabilities which deviate considerably from the nominal level in some situations of practical importance. For a simple case alternative bootstrap methods are proposed which provide correct results asymptotically.     

Sensitivity Analysis to Evaluate the Impact of Uncertain Factors in a Scenario Tree Model for Classical Swine Fever Introduction

Introduction of classical swine fever virus (CSFV) is a continuing threat to the pig production sector in the European Union. A scenario tree model was developed to obtain more insight into the main risk factors determining the probability of CSFV introduction (P(CSFV)). As this model contains many uncertain input parameters, sensitivity analysis was used to indicate which of these parameters influence model results most. Group screening combined with the statistical techniques of design of experiments and meta-modeling was applied to detect the most important uncertain input parameters among a total of 257 parameters. The response variable chosen was the annual P(CSFV) into the Netherlands. Only 128 scenario calculations were needed to specify the final meta-model. A consecutive one-at-a-time sensitivity analysis was performed with the main effects of this meta-model to explore their impact on the ranking of risk factors contributing most to the annual P(CSFV). The results indicated that model outcome is most sensitive to the uncertain input parameters concerning the expected number of classical swine fever epidemics in Germany, Belgium, and the United Kingdom and the probability that CSFV survives in an empty livestock truck traveling over a distance of 0-900 km.