A statistical framework for ecological and aggregate studies

Inference from studies that make use of data at the level of the area, rather than at the level of the individual, is more difficult for a variety of reasons. Some of these difficulties arise because frequently exposures (including confounders) vary within areas. In the most basic form of ecological study the outcome measure is regressed against a simple area level summary of exposure. In the aggregate data approach a survey of exposures and confounders is taken within each area. An alternative approach is to assume a parametric form for the within-area exposure distribution. We provide a framework within which ecological and aggregate data studies may be viewed, and we review some approaches to inference in such studies, clarifying the assumptions on which they are based. General strategies for analysis are provided including an estimator based on Monte Carlo integration that allows inference in the case of a general risk–exposure model. We also consider the implications of the introduction of random effects, and the existence of confounding and errors in variables.     

FORECASTING VOLATILITY IN THE PRESENCE OF MODEL INSTABILITY

Recent advances in financial econometrics have allowed for the construction of efficient ex post measures of daily volatility. This paper investigates the importance of instability in models of realised volatility and their corresponding forecasts. Testing for model instability is conducted with a subsampling method. We show that removing structurally unstable data of a short duration has a negligible impact on the accuracy of conditional mean forecasts of volatility. In contrast, it does provide a substantial improvement in a model's forecast density of volatility. In addition, the forecasting performance improves, often dramatically, when we evaluate models on structurally stable data.     

When Getting Something Good is Bad: Even Three‐year‐olds React to Inequality

Fairness is central to morality. Previous research has shown that children begin to understand fairness between the ages of four and six, depending on the context and method used. Within distributive contexts, there is little clear evidence that children have a concept of fairness before the age of five. This research, however, has mostly examined children's explicit verbal responses to questions about unequal distributions—a method that often underestimates children's knowledge. In the current study, we instead examined emotional and behavioral signs that children notice and dislike inequality. We distributed an unequal number of rewards (stickers) among pairs of children (the ages of three to five years) and probed their responses to the inequality. Both implicit and explicit measures revealed that children as young as three years old notice and react negatively to an unfair distribution, particularly when they receive less than their partner. The few age trends that were found involved verbal (explicit) responses, providing evidence that although children do not explicitly talk about fairness until the age of five or six, this talk is an effort to explain emotional reactions that emerged earlier in development.     

Is Peer Victimization Associated with Academic Achievement? A Meta‐analytic Review

This paper presents a meta‐analytic review of 33 studies, with a total of 29 552 participants, that examined the concurrent association between peer victimization and academic achievement. The results revealed a small but significant negative correlation between peer victimization and academic achievement under both the random‐effects model (r=?.12, p < .001) and the fixed‐effects model (r=?.10, p < .001). Factors that moderated the strength of this association across studies include the peer victimization informant, the indicator of academic achievement, whether there was shared method variance, and the national setting of the study. An exploratory analysis revealed that the strength of the correlations did not differ for boys and girls. The results help resolve the conflicting findings in the existing literature and suggest the need for further investigation into the association between peer victimization and academic achievement.     

Big data in family firms: A socioemotional wealth perspective

Since about 2010, big data analysis has drastically changed the landscape of information management by becoming a central topic in the academic literature of several fields. Despite the significant contribution of family firms to the economic fabric worldwide and their unique decision-making processes, there is a lack of research investigating big data in family-owned businesses. To address this gap, this article draws on the socioemotional wealth (SEW) perspective and its FIBER model to conceptually investigate its role in family firms’ decision to implement big data. We introduce a set of propositions and a framework linking the FIBER dimensions to the likeliness of implementing big data in family firms. Our research thus contributes to a more fine-grained understanding of the decision-making process in family firms.     

Probability Size Matters: The Effect of Foreground-Only versus Foreground+Background Graphs on Risk Aversion Diminishes with Larger Probabilities

Graphs are increasingly recommended for improving decision-making and promoting risk-avoidant behaviors. Graphs that depict only the number of people affected by a risk (“foreground-only” displays) tend to increase perceived risk and risk aversion (e.g., willingness to get vaccinated), as compared to graphs that also depict the number of people at risk for harm (“foreground+background” displays). However, previous research examining these “foreground-only effects” has focused on relatively low-probability risks (<10%), limiting generalizability to communications about larger risks. In two experiments, we systematically investigated the moderating role of probability size on foreground-only effects, using a wide range of probability sizes (from 0.1% to 40%). Additionally, we examined the moderating role of the size of the risk reduction, that is, the extent to which a protective behavior reduces the risk. Across both experiments, foreground-only effects on perceived risk and risk aversion were weaker for larger probabilities. Experiment 2 also revealed that foreground-only effects were weaker for smaller risk reductions, while foreground-only displays decreased understanding of absolute risk magnitudes independently of probability size. These findings suggest that the greater effectiveness of foreground-only versus foreground+background displays for increasing perceived risk and risk aversion diminishes with larger probability sizes and smaller risk reductions. Moreover, if the goal is to promote understanding of absolute risk magnitudes, foreground+background displays should be used rather than foreground-only displays regardless of probability size. Our findings also help to refine and extend existing theoretical accounts of foreground-only effects to situations involving a wide range of probability sizes.     

Evaluating the Applicability of the Miles and Snow Typology in a Regulated Public Utility Environment

This paper examines and evaluates the applicability of the Miles and Snow organizational strategy, structure and process model to the electricity distribution and supply industry of England and Wales (ESI), and specifically the regional electricity companies (RECs). In the past such generic strategy models have principally been applied to industries that conform to the traditional free-market paradigm, and doubt has been cast upon their applicability in non-traditional environments. This paper argues in contrast that generic strategies are of considerable value in the regulated industries, especially those developing new strategies to address major change, such as the approach of full competition. As a feature of this process, the paper also examines some of the differences and similarities between the ESI and the traditional free-market paradigm. However, having argued for the use of such strategies, it is necessary to make various amendments to enhance the applicability of the model. These amendments and the characteristics of new generic strategies are described.     

Modelling Dependence within Joint Tail Regions

Standard approaches for modelling dependence within joint tail regions are based on extreme value methods which assume max-stability, a particular form of joint tail dependence. We develop joint tail models based on a broader class of dependence structure which provides a natural link between max-stable models and weaker forms of dependence including independence and negative association. This approach overcomes many of the problems that are encountered with standard methods and is the basis for a Poisson process representation that generalizes existing bivariate results. We apply the new techniques to simulated and environmental data, and demonstrate the marked advantage that the new approach offers for joint tail extrapolation.