Emotional Climate in Families Experiencing Homelessness: Associations with Child Affect and Socioemotional Adjustment in School

This study examined associations among family‐level risks, emotional climate, and child adjustment in families experiencing homelessness. Emotional climate, an indirect aspect of emotion socialization, was indexed by parents’ expressed emotion while describing their children. Sociodemographic risk and parent internalizing distress were hypothesized to predict more negativity and less warmth in the emotional climate. Emotional climate was expected to predict observer‐rated child affect and teacher‐reported socioemotional adjustment, mediating effects of risk. Participants were 138 homeless parents (64 percent African‐American) and their four‐ to six‐year‐old children (43.5 percent male). During semi‐structured interviews, parents reported demographic risks and internalizing distress and completed a Five Minute Speech Sample about their child, later rated for warmth and negativity. Children's positive and negative affect were coded from videotapes of structured parent‐child interaction tasks. Socioemotional adjustment (externalizing behavior, peer acceptance, and prosocial behavior) was reported by teachers a few months later. Hypotheses were partially supported. Parent internalizing distress was associated with higher parent negativity, which was linked to more negative affect in children, and parent warmth was associated with children's positive affect. Neither emotional climate nor child affect predicted teacher‐reported externalizing behavior or peer acceptance, but parental negativity and male sex predicted lower prosocial behavior in the classroom. Future research directions and clinical implications are discussed.     

Servant Leadership in the Public Sector: Employee Perspective

Public Organization Review - The purpose of this study is to explore how servant leadership affects public sector employee engagement, organisational ethical climate, and public sector reform, of...     

The demography of disability and the effects of immigrant history: Older Asians in the United States

Using data from the 2000 U.S. census, we compare the older Asian population with U.S.-born, non-Hispanic whites with respect to three indicators of disability. Insofar as any Asian "advantage" in health vis-a-vis whites exists among the population aged 65 and over, our evidence suggests that it occurs primarily among the U.S.-born segments of this population. We also investigate how differences in disability levels among Asian immigrant groups are influenced by country of birth and by the combined effects of duration of residence in the United States and life cycle stage at entry. These results highlight the diversity of the older Asian population with respect to the ways in which immigration and origin history are linked to disability outcomes. We conclude that in later life, immigrant status confers few disability advantages among the Asian population in the United States.     

A global count of the extreme poor in 2012: data issues,methodology and initial results

The 2014 release of a new set of purchasing power parity (PPP) conversion factors (PPPs) for 2011 has prompted a revision of the World Bank’s international poverty line. In revising the line, we have sought to minimize changes to the real purchasing power of the earlier $1.25 line (in 2005 PPPs), so as to preserve the integrity of the goalposts for international targets such as the Sustainable Development Goals (SDGs) and the World Bank’s twin goals – which were set with respect to that line. In particular, the new line was obtained by inflating the same fifteen national poverty lines – originally used by Ravallion et al. (World Bank Econ. Rev. 23(2): 163–184 2009) to construct the $1.25 line – to 2011 prices in local currency units, and then converting them to US dollars using 2011 PPP conversion factors. With a small approximation, this procedure yields a new international poverty line of $1.90 per person per day. In combination with other changes described in the paper, this revision leads to relatively small changes in global poverty incidence for 2011: from 14.5 % using the old method to 14.1 % using the new method. In 2012, the new reference year for the global count, we find 12.7 % of the world’s population, or 897 million people, are living in extreme poverty. There are changes in the regional composition of poverty, but they are also relatively small. This paper documents methodological decisions taken in the process of updating both the poverty line and the consumption and income distributions at the country level, including issues of inter-temporal and spatial price adjustments. It also describes various caveats and limitations of the approach taken.     

Small-Sample Improved Seasonal Unit Root Tests for Trending and Breaking Series

In this article three unit root tests that allow for a break in both the seasonal mean and linear trend of the data are proposed. The tests, which can be seen as small-sample corrected versions of already known asymptotic tests, are shown to perform very well in simulations, and much better than their asymptotic counterparts.     

NGO Failure: A Theoretical Synthesis

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - An extensive literature identifies conditions under which markets and states work efficiently and effectively toward their...     

Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials

Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization and Monte Carlo methods for solving hybrid Bayesian networks. Any probability density function (PDF) can be approximated by an MTE potential, which can always be marginalized in closed form. This allows propagation to be done exactly using the Shenoy-Shafer architecture for computing marginals, with no restrictions on the construction of a join tree. This paper presents MTE potentials that approximate standard PDF’s and applications of these potentials for solving inference problems in hybrid Bayesian networks. These approximations will extend the types of inference problems that can be modelled with Bayesian networks, as demonstrated using three examples.