Re‐framing the Analysis: A 3‐dimensional Perspective of Prisoners’ Children's Well‐being

This article highlights three dimensions to understanding children's well‐being during and after parental imprisonment which have not been fully explored in current research. A consideration of ‘time’ reveals the importance of children's past experiences and their anticipated futures. A focus on ‘space’ highlights the impact of new or altered environmental dynamics. A study of ‘agency’ illuminates how children cope within structural, material and social confines which intensify vulnerability and dependency. This integrated perspective reveals important differences in individual children's experiences and commonalities in broader systemic and social constraints on prisoners’ children. The paper analyses data from a prospective longitudinal study of 35 prisoners’ children during and after their (step) father's imprisonment to illustrate the arguments.     

Pathways from workplace flexibility to turnover intention: Role of work–family conflict,family–work conflict,and job satisfaction

This study examined the prevalence of workplace flexibility and the mechanisms that allow workplace flexibility to influence turnover intentions through work–family and family–work conflicts and job satisfaction among low‐wage workers in South Korea. Participants included 250 low‐wage workers whose monthly salary was less than 2 million Korean won (approx. $1,900). The study results indicate that low‐wage workers have limited access to workplace flexibility and that workplace flexibility plays a significant protective role in reducing their turnover intention, indirectly by decreasing work–family conflicts and enhancing job satisfaction. This article also discusses the implications of these findings for labor policy and social work practice.     

Employers' perspectives on benefit conditionality in the UK and Denmark

This article examines the under-explored demand-side of active labour market policies (ALMPs). Based on interview data from a comparative study of the UK and Denmark, the paper analyses employers' perspectives and experiences of ALMPs. In both countries, employers were favourably disposed towards employing unemployed jobseekers but held negative views on conditionality. First, benefit conditionality led to employers receiving large numbers of unsuitable and unfiltered job applications, with associated negative resource impacts. Second, employers perceived this as a product of ‘box ticking' and compliance targets. Finally, employers criticised policy and media rhetoric for focusing solely on the supply-side and for problematizing unemployed candidates. The paper argues that these crucial, but neglected, employer perspectives demonstrate that the current benefit conditionality regime in the UK risks irrevocably ‘tarnishing' candidates, which undermines, rather than enhances, their chances of securing employment through ALMPs. This unique dataset provides further evidence that the current direction of policy requires urgent and radical re-thinking.     

Determinants of social assistance caseloads for employable single adults without dependants in Canada

Government officials like the idea of just a small number of households in their respective jurisdiction receiving social assistance. A large number is seen as costly to the public treasury, and declining caseloads are generally viewed as a mark of success for both the economy and the government of the day. But what factors account for the size of a Canadian province's social assistance caseload? This article aims to shed light on this question, with a focus on single adults without dependants (and without serious disabilities) during the 1989–2017 period. One important finding is that when the value of social assistance benefit levels for this group increases by 1% in a province, the social assistance caseload for this demographic rises by 0.457%. Put differently, there is indeed an important behavior response associated with higher benefit levels. In response, we propose that provincial officials budget for higher take up levels when they increase benefit levels for this household group.     

市场经济下政府与市场的关系

在市场经济中，市场必须在资源配置中发挥基础性或决定性的作用，但政府在市场失灵的地方又必须积极地发挥作用。本文分别从早期市场经济中政府的作用、现代市场经济中政府的作用等五个方面对这一问题进行论述。     

以市场为导向的高职院校家政专业发展研究

市场经济下社会发展为家政行业的发展带来了巨大的潜力空间,但高职业素养的家政从业人员缺乏逐渐成为行业发展的瓶颈。以市场为导向设置专业是高职院校的重要法则,但因受师资、生源、就业等问题的制约,高职院校家政专业发展出现了诸多问题。可采取以下措施促进高职院校的家政专业良性发展:依据市场导向,立足自身特色,精准定位培养；以家政专业内涵发展及就业前景为宣传突破口破解招生难题；积极“引才”、“育才”构建专业化的师资队伍；推进产学研深度融合助推学生就业等。     

Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.     

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.     

Design selection for strong orthogonal arrays

Strong orthogonal arrays (SOAs) were recently introduced and studied as a class of space‐filling designs for computer experiments. An important problem that has not been addressed in the literature is that of design selection for such arrays. In this article, we conduct a systematic investigation into this problem, and we focus on the most useful SOA(n,m,4,2 + )s and SOA(n,m,4,2)s. This article first addresses the problem of design selection for SOAs of strength 2+ by examining their three‐dimensional projections. Both theoretical and computational results are presented. When SOAs of strength 2+ do not exist, we formulate a general framework for the selection of SOAs of strength 2 by looking at their two‐dimensional projections. The approach is fruitful, as it is applicable when SOAs of strength 2+ do not exist and it gives rise to them when they do. The Canadian Journal of Statistics 47: 302–314; 2019 © 2019 Statistical Society of Canada     

Properties of the weighted log‐rank test in the design of confirmatory studies with delayed effects

Proportional hazards are a common assumption when designing confirmatory clinical trials in oncology. This assumption not only affects the analysis part but also the sample size calculation. The presence of delayed effects causes a change in the hazard ratio while the trial is ongoing since at the beginning we do not observe any difference between treatment arms, and after some unknown time point, the differences between treatment arms will start to appear. Hence, the proportional hazards assumption no longer holds, and both sample size calculation and analysis methods to be used should be reconsidered. The weighted log‐rank test allows a weighting for early, middle, and late differences through the Fleming and Harrington class of weights and is proven to be more efficient when the proportional hazards assumption does not hold. The Fleming and Harrington class of weights, along with the estimated delay, can be incorporated into the sample size calculation in order to maintain the desired power once the treatment arm differences start to appear. In this article, we explore the impact of delayed effects in group sequential and adaptive group sequential designs and make an empirical evaluation in terms of power and type‐I error rate of the of the weighted log‐rank test in a simulated scenario with fixed values of the Fleming and Harrington class of weights. We also give some practical recommendations regarding which methodology should be used in the presence of delayed effects depending on certain characteristics of the trial.