Foster care assessment: an exploratory study of the placement assessment process in Flanders and the Netherlands

Family foster care placement decision‐making has a weak scientific underpinning. The identification of clusters of foster children (groups of foster children with similar characteristics) can help improve decision quality. In this study, we investigated if foster children could indeed be clustered, which problems were identified at the time of placement and what might be the influence of placement history. Two clusters of foster children were found: (i) young children with coinciding parental child‐rearing incapacity and familial problems, and (ii) older children with child problems. At the time of placement, familial problems were more often identified in younger children with a placement history. These findings stress the importance of approaching foster care assessment as part of a dynamic decision‐making process. It is key to finding the most appropriate situation for the child. At the same time, it must be decided how the desired situation can be realized, wherein placement decisions are based on an appraisal of whether or not a foster placement is an appropriate solution. In conjunction with this, it needs to be decided how the parents can be supported towards reunification, or whether or not long‐term foster care is the best option for the child, and if so what conditions need to be met.     

Top Management Team Heterogeneity,Strategic Change and Operational Performance*

This study examines the role of top management team (TMT) heterogeneity in facilitating strategic change. Based on the upper echelons literature, we argue that heterogeneous management teams are better able to handle the simultaneous and conflicting demands of refocusing the organization strategically and keeping up operational performance. We expect this to be true only for teams that are heterogeneous with respect to factors directly related to job requirements, however. Data were collected from 92 full TMTs of hospitals in Spain that were confronted with institutional pressures that challenged their current strategies. In support of our hypotheses, the results show job‐related TMT heterogeneity moderates the relation between strategic change and operational performance. No moderating effect is found for non‐job‐related TMT heterogeneity.     

Testing Parameters in GMM Without Assuming that They Are Identified

We propose a generalized method of moments (GMM) Lagrange multiplier statistic, i.e., the K statistic, that uses a Jacobian estimator based on the continuous updating estimator that is asymptotically uncorrelated with the sample average of the moments. Its asymptotic χ² distribution therefore holds under a wider set of circumstances, like weak instruments, than the standard full rank case for the expected Jacobian under which the asymptotic χ² distributions of the traditional statistics are valid. The behavior of the K statistic can be spurious around inflection points and maxima of the objective function. This inadequacy is overcome by combining the K statistic with a statistic that tests the validity of the moment equations and by an extension of Moreira's (2003) conditional likelihood ratio statistic toward GMM. We conduct a power comparison to test for the risk aversion parameter in a stochastic discount factor model and construct its confidence set for observed consumption growth and asset return series.     

Why the changing American economy calls for twenty-first century learning: answers to educators' questions

While struggling with the current pressures of educational reform, some educators will ask whether their efforts make economic sense. Questioning the future makeup of the nation's workforce, many wonder how the educational system should be tempered to better prepare today's youth. This chapter answers educators' and parents' questions around the effect of fluctuations in the American economy on the future of education. The authors offer reassurance that good jobs will always be available, but warn that those jobs will require a new level of skills: expert thinking and complex communication. Schools need to go beyond their current curriculum and prepare students to use reading, math, and communication skills to build a deeper and more thoughtful understanding of subject matter. To explain the implications of the nation's changing economy on jobs, technology, and therefore education, the authors address a range of vital questions. Citing occupational distribution data, the chapter explores the supply and range of jobs in the future, as well as why changes in the U.S. job distribution have taken place. As much of the explanation for the shift in job distribution over the past several decades is due to the computerization of the workforce, the authors discuss how computers will affect the future composition of the workforce. The chapter also addresses the consequences of educational improvement on earnings distribution. The authors conclude that beyond workforce preparedness, students need to learn how to be contributing members of a democracy.     

Generalized Method of Moments With Many Weak Moment Conditions

Using many moment conditions can improve efficiency but makes the usual generalized method of moments (GMM) inferences inaccurate. Two‐step GMM is biased. Generalized empirical likelihood (GEL) has smaller bias, but the usual standard errors are too small in instrumental variable settings. In this paper we give a new variance estimator for GEL that addresses this problem. It is consistent under the usual asymptotics and, under many weak moment asymptotics, is larger than usual and is consistent. We also show that the Kleibergen (2005) Lagrange multiplier and conditional likelihood ratio statistics are valid under many weak moments. In addition, we introduce a jackknife GMM estimator, but find that GEL is asymptotically more efficient under many weak moments. In Monte Carlo examples we find that t‐statistics based on the new variance estimator have nearly correct size in a wide range of cases.