Socio-political,cultural and economic preferences and behaviour of the social and cultural specialists and the technocrats. Social class or education?

Do the social and cultural specialists differ from the technocrats and other social classes with respect to their socio-political, cultural and economic preferences and behaviour? If they do, is this attributable to their level and field of education? The social and cultural specialists are assumed to form ?new’ social class and the technocrats ?old’ social class. A typical social class should differentiate itself with respect to moral attitudes, socio-political behaviour and lifestyle forms. To reveal whether these assumed classes are indeed typical classes, we have studied a variety of socio-political, cultural and economic preferences and behaviour of members of these social classes using the Family Survey of the Dutch Population (FSDP) 1992, 1998, 2000 and 2003. To unravel the net differences between the social and cultural specialists and the technocrats, we controlled for the field of study, the level of education and background factors. Taking these control factors into consideration, the social and cultural specialists differentiate themselves substantially from the technocrats in their socio-political and especially economic preferences and behaviour. With respect to their cultural preferences and behaviour, they do not differ significantly from the technocrats.     

Führungspositionen im Ehrenamt – ein weiterer Bereich der Benachteiligung von Frauen?Management Positions in Volunteering – Another Field of Discrimination of Women?

Based on data of the 2009 German Volunteer Survey (“Freiwilligensurvey”) the paper analyses whether there is evidence for a discrimination of women in filling management positions in the volunteer sector. It turns out that women have a significant lower propensity to take up an unpaid volunteer management position. This correlation is also robust when we control for self-selection bias as well as certain important socio-demographic, socio-economic and volunteer-specific determinants. Further on we can show that this kind of gender-specific discrimination can be found particularly in leisure-time clubs and religious organisations. These results indicate that structural and organisational parameters play an important role to explain gender specific differences in filling volunteer management positions.     

Parental Speech and Gesture Input to Girls Versus Boys in Singletons and Twins

Journal of Nonverbal Behavior - Children show sex differences in early speech development, with girls producing a greater number and variety of words at an earlier age than boys (Berglund et al. in...     

Partial covariate adjusted regression

Covariate adjusted regression (CAR) is a recently proposed adjustment method for regression analysis where both the response and predictors are not directly observed [?entürk, D., Müller, H.G., 2005. Covariate adjusted regression. Biometrika 92, 75–89]. The available data have been distorted by unknown functions of an observable confounding covariate. CAR provides consistent estimators for the coefficients of the regression between the variables of interest, adjusted for the confounder. We develop a broader class of partial covariate adjusted regression (PCAR) models to accommodate both distorted and undistorted (adjusted/unadjusted) predictors. The PCAR model allows for unadjusted predictors, such as age, gender and demographic variables, which are common in the analysis of biomedical and epidemiological data. The available estimation and inference procedures for CAR are shown to be invalid for the proposed PCAR model. We propose new estimators and develop new inference tools for the more general PCAR setting. In particular, we establish the asymptotic normality of the proposed estimators and propose consistent estimators of their asymptotic variances. Finite sample properties of the proposed estimators are investigated using simulation studies and the method is also illustrated with a Pima Indians diabetes data set.     

Mechanisms to Induce Buyer Forecasting: Do Suppliers Always Benefit from Better Forecasting?

In this study, we consider a supplier's contract offerings to a buyer who may obtain improved forecasts for her demand over time. We investigate how the supplier can take advantage of the buyer's better forecasts and what kind of contracts he should offer to the buyer in order to maximize his profits. We model a natural forecast evolution where the buyer can obtain a more accurate forecast closer to the selling season. We assume there is information asymmetry between the buyer and the supplier at all times in that the buyer understands her demand better than the supplier. Three types of contracts that the supplier can offer are considered: (1) one where a contract is offered before the buyer has a chance to obtain improved forecasts, (2) one where a contract is offered after the buyer has obtained improved forecasts, and (3) a contingent (dynamic) contract which offers an initial contract to the buyer before she obtains improved forecasts, followed by a later contract (contingent on the initial contract) offered after improved forecasts have been obtained. We consider two scenarios: (1) where the supplier is certain that the buyer can obtain more accurate forecasts over time, and (2) where the supplier is uncertain about the buyer's forecasting capability (or forecasting cost). In the first scenario, we show that among the three types of contracts, the contingent contract is always the most profitable for the supplier. Furthermore, using the contingent contract, the supplier always benefits from higher accuracy of the buyer's demand forecasts. In the second scenario, we explicitly model the supplier's level of certainty about the buyer's capability of obtaining better forecasts, and explore how the supplier can design contracts to induce the buyer to obtain better forecasts when she is capable.     

Linking Metatraits of the Big Five to Well-Being and Ill-Being: Do Basic Psychological Needs Matter?

There is considerable evidence that two higher order factors underlie the Big-Five dimensions and that these two factors provide a parsimonious taxonomy. However, not much empirical evidence has been documented as to the extent to which these traits relate to certain psychological constructs. In this study, we tested a structural model to investigate the individual differences in well-being and ill-being by examining the mediating effects of autonomy, relatedness, and competence on the extent to which two higher order factors of personality, namely Stability and Plasticity, are linked to life satisfaction and depression. In testing the model, we controlled for the effects of current affect. A large community sample participated and responded to self-measures of The Big-Five personality, basic psychological needs satisfaction, satisfaction with life, depressive symptoms, and positive and negative affect. The results revealed that satisfaction of basic psychological needs fully mediated the relationship between Plasticity and life satisfaction, as well as depression. It also fully mediated the relationship between Stability and life satisfaction, and partially mediated the relationship between Stability and depression.     

Robust parameter estimation of regression model with AR(p) error terms

In this article, we consider a linear regression model with AR(p) error terms with the assumption that the error terms have a t distribution as a heavy-tailed alternative to the normal distribution. We obtain the estimators for the model parameters by using the conditional maximum likelihood (CML) method. We conduct an iteratively reweighting algorithm (IRA) to find the estimates for the parameters of interest. We provide a simulation study and three real data examples to illustrate the performance of the proposed robust estimators based on t distribution.     

Lost in data translation: A critical review of datasets on refugees

Quantitative data are the backbone of the international refugee regime. Academic researchers and international organizations have been drawing attention to the problems of refugee data that obscure refugee vulnerabilities. Despite these efforts, the limitations of quantitative refugee data, particularly with regard to gendered aspects of refugee vulnerabilities, have often been overlooked. This article aims to address this issue by examining the major limitations of quantitative refugee data. It focuses on the question of “What are the major limitations of quantitative refugee data regarding gendered aspects of refugee vulnerabilities?” By conducting a systematic review of 67 datasets from international organizations, nation-states, and independent data collectors, it argues that there are five fundamental problems with the data: (1) lack of disaggregation, (2) lack of cross-tabulation and inability to harmonize datasets, (3) inconsistent periodization frequency, (4) lack of sex and gender diversity measurement, and (5) survivor bias. This article highlights the importance of critically analysing the data that informs theories and decisions regarding refugee vulnerabilities.