Why me? The frog-pond effect,relative deprivation and individual outcomes in the face of budget cuts

Conservation of resources (COR) theory proposes that both individual characteristics and environmental factors predict stress reactions, but does not consider interactions between individual variables and the immediate social context. Using theories regarding the frog-pond effect, which relates to how an individual perceives themselves in relation to that of those around them, and relative deprivation, we tested the hypothesis that employees who were personally more affected by a potential stressor, yet were in an organizational unit less affected by that stressor, would report more negative outcomes than employees who were subjected to the same stress but were in more affected units. Utilizing multilevel modelling and departmental- and individual-level data from a university experiencing severe budget cuts, it was found that both individual perceived budget cut impacts and departmental impacts were related to less job satisfaction, reduced affective commitment, greater perceptions of psychological contract breach and increased turnover intentions, supporting COR theory. Interestingly, as predicted by the frog-pond effect and relative deprivation theory, affected academic staff within departments that were less affected by the cuts had more negative responses than equally impacted individuals in more affected departments. We concluded that the frog-pond effect is relevant to work stress research and practice.     

Local linear kernel estimation for discontinuous nonparametric regression functions

In this paper, we consider using a local linear (LL) smoothing method to estimate a class of discontinuous regression functions. We establish the asymptotic normality of the integrated square error (ISE) of a LL-type estimator and show that the ISE has an asymptotic rate of convergence as good as for smooth functions, and the asymptotic rate of convergence of the ISE of the LL estimator is better than that of the Nadaraya-Watson (NW) and the Gasser-Miiller (GM) estimators.     

Efficient Posterior Simulation for Cointegrated Models with Priors on the Cointegration Space

A message coming out of the recent Bayesian literature on cointegration is that it is important to elicit a prior on the space spanned by the cointegrating vectors (as opposed to a particular identified choice for these vectors). In previous work, such priors have been found to greatly complicate computation. In this article, we develop algorithms to carry out efficient posterior simulation in cointegration models. In particular, we develop a collapsed Gibbs sampling algorithm which can be used with just-identifed models and demonstrate that it has very large computational advantages relative to existing approaches. For over-identifed models, we develop a parameter-augmented Gibbs sampling algorithm and demonstrate that it also has attractive computational properties.     

Urbanization and fertility: An event-history analysis of Coastal Ghana

In this article, we undertake an event-history analysis of fertility in Ghana. We exploit detailed life history calendar data to conduct a more refined and definitive analysis of the relationship among personal traits, urban residence, and fertility. Although urbanization is generally associated with lower fertility in developing countries, inferences in most studies have been hampered by a lack of information about the timing of residence in relationship to childbearing. We find that the effect of urbanization itself is strong, evident, and complex, and persists after we control for the effects of age, cohort, union status, and education. Our discrete-time event-history analysis shows that urban women exhibit fertility rates that are, on average, 11% lower than those of rural women, but the effects vary by parity. Differences in urban population traits would augment the effects of urban adaptation itself. Extensions of the analysis point to the operation of a selection effect in rural-to-urban mobility but provide limited evidence for disruption effects. The possibility of further selection of urbanward migrants on unmeasured traits remains. The analysis also demonstrates the utility of an annual life history calendar for collecting such data in the field.     

AN INVESTIGATION OF MOTIVATIONAL BIAS IN SUBJECTIVE PREDICTIVE PROBABILITY DISTRIBUTIONS*

In this paper the problem of conscious motivational bias in the assessment of subjective predictive probability distributions is explored. The primary purpose of this research is to discover how subjects express their bias. Results of an experiment are reported in which subjective distributions were assessed by subjects, first in the absence of a biasing incentive and then in the presence of an incentive to bias upward coupled with an incentive to be “credible.” In the biased task, subjects reported manipulating up to five characteristics of their distribution, such as location, shape, and dispersion. Furthermore, subjects reported using combinations of the characteristics strategically. Dominant strategies included shifting the mode upward and redistributing probability to the left, shifting the mode upward a small amount and redistributing probability to the right, and shifting the mode upward and tightening the distribution. The location of the mode was mentioned first by nearly all subjects, suggesting that they first locate their biased distribution and then manipulate other characteristics to express their bias, need for credibility, and other concerns.     

THE INFLUENCE OF CLASS POSITION ON THE FORMAL AND INFORMAL SANCTIONING OF WHITE-COLLAR OFFENDERS

Although researchers have investigated formal legal reactions to white-collar crime, few data exist on informal reactions to white-collar lawbreakers and how these reactions influence sentencing decisions. Even so, commentators often assert that whitecollar offenders receive lenient criminal penalties because judges sympathize with such offenders due to the losses they incur through informal sanctions. In this light, a causal model is used to explore the influence of class position on an important informal sanction—loss of job—and the influence of loss of job on sentence severity. Class position is found to determine the likelihood of loss of job but not that of incarceration. Further, offenders who commit large-scale offenses are less likely to suffer loss of job than those who commit small-scale crimes. Social reactions to white-collar crime are inconsistent, and class position more strongly influences informal, nongovernmental social control than social control through law.