Inequalities in child welfare intervention rates: the intersection of deprivation and identity

Child welfare systems internationally exhibit very large inequalities in a variety of dimensions of practice, for example, in rates of child protection plans or registrations and out‐of‐home care. Previous research in the midlands region of England (Bywaters; Bywaters et al.) has detailed key aspects of the relationship between levels of neighbourhood deprivation and intervention rates. This paper reports further evidence from the study examining the intersection of deprivation with aspects of identity: gender, disability, ethnicity and age. Key findings include a decreasing gender gap and a decreasing proportion of children in need reported to be disabled as deprivation increases. The data challenge the perception that black children are more likely than white to be in out‐of‐home care, a finding that only holds if the much higher level of deprivation among black children is not taken into account. Similarly, after controlling for deprivation and age, Asian children were found to be up to six times less likely to be in out‐of‐home care. The study requires replication and extension in order that observed inequalities are tested and explained. Urgent ethical, research, policy and practice issues are raised about child welfare systems.     

Adolescents in Lesbian Families: DSM-Oriented Scale Scores and Stigmatization

The present study focused on the Diagnostic and Statistical Manual of Mental Disorders (DSM)-oriented scale scores from Child Behavior Checklists completed by parents of the 17-year-old offspring in the U.S. National Longitudinal Lesbian Family Study (NLLFS; 39 girls/39 boys). In comparison with the scores of an age-matched normative sample (49 girls/44 boys; MANOVA), no significant differences in scores were found. Within the NLLFS sample, adolescents who reported stigmatization scored higher on affective, anxiety, and conduct problems. Although overall psychological functioning of the NLLFS adolescents fell within the healthy range, stigmatization had a negative impact on the well-being of some adolescents.     

Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form

In this paper we model the Gaussian errors in the standard Gaussian linear state space model as stochastic volatility processes. We show that conventional MCMC algorithms for this class of models are ineffective, but that the problem can be alleviated by reparameterizing the model. Instead of sampling the unobserved variance series directly, we sample in the space of the disturbances, which proves to lower correlation in the sampler and thus increases the quality of the Markov chain.

Using our reparameterized MCMC sampler, it is possible to estimate an unobserved factor model for exchange rates between a group of n countries. The underlying n + 1 country-specific currency strength factors and the n + 1 currency volatility factors can be extracted using the new methodology. With the factors, a more detailed image of the events around the 1992 EMS crisis is obtained.

We assess the fit of competitive models on the panels of exchange rates with an effective particle filter and find that indeed the factor model is strongly preferred by the data.