A Cross‐National Look at How Welfare States Reduce Inequality

A major shift in welfare state research occurred at the turn of the century as researchers moved from explaining the development of welfare states and variations in spending across welfare states to a focus on welfare state outcomes. One of the key outcomes examined in the literature is inequality. While much of the early literature examined overall spending, followed by analysis of specific taxes and transfers related to old age, unemployment, disability, health, and families, more recent research has included a broader range of welfare state policies including work‐family policies and flexicurity. This essay highlights some important developments in the research on welfare states and inequality.     

Bringing Collaborative Teaching into Doctoral Programs: Faculty and Graduate Student Co-teaching as Experiential Training

We discuss faculty-graduate student collaborative teaching as a model to enhance programmatic graduate teacher training efforts. Co-teaching can improve graduate training through intensive support and engagement, while also enhancing the teaching experience of faculty and the learning experience of undergraduates. This form of classroom collaboration between faculty mentors and graduate students provides important hands-on teacher training, emphasizes pedagogical reflexivity, and offers support within a mentorship relationship. Faculty can use co-teaching with graduate students to explore classroom techniques and reassess their teaching. We conclude with strategies for implementing effective collaborative teaching models.     

Selecting the Best Exponential Population When the Scale Parameters are Bounded

Consider k (≥ 2) independent exponential populations with different location and scale parameters. Call a population associated with largest of unknown location parameters as the best population. For the goal of selecting the best population, it is established that if the scale parameters are completely unknown, then the indifference-zone probability requirement can not be guaranteed by any single sample decision rule which is just and translation invariant. Under the assumption that the scale parameters are bounded above by a known constant, a single sample selection procedure is proposed for which the indifference-zone probability requirement can be guaranteed. Under the same assumption, 100P*% simultaneous upper confidence intervals for all distances from the largest location parameter are also obtained.