Housing hardship and energy insecurity among native-born and immigrant low-income families with children in the United States

The costs for rent and utilities account for the largest share of living expenses, yet these two critical dimensions of material hardship have seldom been examined concurrently in population-based studies. This paper employs multivariate statistical analysis using American Community Survey data to demonstrate the relative risk ratio of low-income renter-occupied households with children experiencing ‘rent burden', ‘energy insecurity', or a ‘double burden’ as opposed to no burden. Findings suggest that low-income households are more likely to experience these economic hardships in general but that specific groups are disproportionately burdened in different ways. For instance, whereas immigrants are more likely to experience rental burden, they are less likely to experience energy insecurity and are also spared from the double burden. In contrast, native-born African Americans are more likely than all other groups to experience the double burden. These results may be driven by the housing stock available to certain groups due to racial residential segregation, decisions regarding the quality of housing low-income householders are able to afford, as well as home-country values, such as modest living and energy conservation practices, among immigrant families. This paper also points to important policy gaps in safety net benefits related to housing and energy targeting low-income households.     

A general approximation to quantiles

For many continuous distributions, a closed-form expression for their quantiles does not exist. Numerical approximations for their quantiles are developed on a distribution-by-distribution basis. This work develops a general approximation for quantiles using the Taylor expansion. Our method only requires that the distribution has a continuous probability density function and its derivatives can be derived to a certain order (usually 3 or 4). We demonstrate our unified approach by approximating the quantiles of the normal, exponential, and chi-square distributions. The approximation works well for these distributions.     

FamilyLive: Parental Skill Building for Caregivers with Interpersonal Trauma Exposures

Mental health treatments for emotionally traumatized children incorporate family and caregiver-child therapy sessions to promote child recovery and minimize developmental disruption. Such sessions require that caregivers regulate their emotions to remain productively engaged in the therapeutic process. However, caregivers with histories of unresolved interpersonal trauma have difficulty with emotional regulation. Interpersonal trauma also negatively affects the ability to reflect on one’s own and others’ feelings and intentions. This limitation interferes with caregiver engagement in psychotherapy relationships aimed at supporting child trauma work. FamilyLive is an innovative caregiver-focused family therapy model that uses a one-way mirror, a specially trained reflecting team, structured routines and individualized verbalizations to address this complex clinical phenomenon. Guided by the literature on attachment and trauma, FamilyLive has yielded anecdotal successes and positive pilot results. FamilyLive is a viable approach to engaging caregivers with histories of interpersonal trauma in trauma-focused child and family therapy relationships.     

Bayesian adaptive dose‐escalation designs for simultaneously estimating the optimal and maximum safe dose based on safety and efficacy

The main purpose of dose‐escalation trials is to identify the dose(s) that is/are safe and efficacious for further investigations in later studies. In this paper, we introduce dose‐escalation designs that incorporate both the dose‐limiting events and dose‐limiting toxicities (DLTs) and indicative responses of efficacy into the procedure. A flexible nonparametric model is used for modelling the continuous efficacy responses while a logistic model is used for the binary DLTs. Escalation decisions are based on the combination of the probabilities of DLTs and expected efficacy through a gain function. On the basis of this setup, we then introduce 2 types of Bayesian adaptive dose‐escalation strategies. The first type of procedures, called “single objective,” aims to identify and recommend a single dose, either the maximum tolerated dose, the highest dose that is considered as safe, or the optimal dose, a safe dose that gives optimum benefit risk. The second type, called “dual objective,” aims to jointly estimate both the maximum tolerated dose and the optimal dose accurately. The recommended doses obtained under these dose‐escalation procedures provide information about the safety and efficacy profile of the novel drug to facilitate later studies. We evaluate different strategies via simulations based on an example constructed from a real trial on patients with type 2 diabetes, and the use of stopping rules is assessed. We find that the nonparametric model estimates the efficacy responses well for different underlying true shapes. The dual‐objective designs give better results in terms of identifying the 2 real target doses compared to the single‐objective designs.     

Activation and social inclusion: challenges and possibilities

The last two decades have seen an increasing focus on the activation of individuals receiving welfare benefits. This has entailed an attempt to transform employment assistance and social security systems to make them more employment‐friendly through the development of explicit linkages between social protection policies, labour market participation and labour market programs. At the same time there has been a growing interest in the concept of social inclusion, and social inclusion has been a commonly stated goal of activation programs. However, despite some synergies between these two approaches, in other respects it is not clear that they are always compatible. This paper explores the extent to which activation programs are central to the social inclusion approach and the similarities and differences between these approaches. It then briefly examines the evidence regarding the ability of activation programs to enhance social inclusion.     

The Distribution of Returns to Education for People with Disabilities

This article takes a first look at the distribution of returns to education for people with disabilities, a particularly disadvantaged group whose labor market performances have not been well studied or documented. Using a nonparametric approach, we uncover significant heterogeneity in the returns to education for these workers, which is drastically masked by conventional parametric methods. Based on these estimates, we construct the Sharpe ratio of human capital investment (taking into account its substantial risk), and our results corroborate the claimed importance of human capital in improving these workers’ wages. Our stochastic dominance tests show that the returns to education for workers with disabilities, as a group, may have been affected more adversely in the most recent recession, relative to their non-disabled counterparts.     

The Multi-dimensional Characteristics of Wellbeing: How Different Aspects of Wellbeing Interact and Do Not Interact with Each Other

Because wellbeing is a multi-varied and dynamic phenomenon, social scientists need to better understand how different aspects of people’s wellbeing are tied together and how these ties differ between individuals at one point in time and within individuals over time. The paper outlines a general model that considers the multidimensional structure of wellbeing. We utilize the potential of latent variable modelling on a unique Swedish database: the Panel Survey of Ageing and the Elderly (PSAE). An inclusive and flexible model that considers the interactions between semi-autonomous domains (material resources, health, psychosocial factors, lifestyle, etc.) is developed. Our empirical study is based on cross-sectional PSAE data and focuses on people 55 years or older (N = 5,374). The analysis takes advantage of recent developments in statistical theory in the field of latent variable modelling to bring about a more dynamic and theory confirming analysis of a multifaceted phenomenon such as wellbeing.     

Deferred life annuities: on the combined effects of stochastic mortality and interest rates

The paper analyzes the systematic risk which is inherent in a portfolio of deferred life annuities. We take into account stochastic mortality as well as stochastic interest rates. For the specification of the mortality rate dynamics, we consider a pure diffusion model as well as a compound Poisson jump model. The interest rate dynamics are given by a one-factor Hull–White model. All models, interest rate and mortality rate, are calibrated to financial market as well as demographic data. We use Monte Carlo simulations to approximate the variance of the discounted cash flow and its decomposition into a pooling and a non-pooling risk part. We also consider pricing effects using the principle of zero expected utility and the quantile principle. The estimated risk premiums are benchmarked to the equivalence premium. Finally, we focus on solvency requirements which are based on the investment decisions and the associated shortfall probability of the annuity provider.