农地产权结构变迁视域下土地增值收益的公平分享

农地产权结构是搭建土地增值收益分配与宪法秩序有效链接的重要桥梁.从农地产权结构变迁的视角看,我国传统土地增值收益的国有化机制实际在很大程度上符合1982年宪法所确立的社会主义地租国有原则及地利共享秩序.然而,由于宪法土地条款一开始就被注入了地利分配具有倾向性的思想基因,长此以往便忽视了农民作为整体分享土地增值收益的"另一半宪法秩序".新型城镇化背景下,国家进行了"同地同权"、赋权于"民"的农村土地管理制度改革,其实质是宪法秩序的延续而非替代,我国土地增值收益分配的主要机制仍是征地补偿制度.要实现我国土地增值收益的公平分享,必须回到社会主义"国家—集体"一元论的完整地利共享秩序中来,并遵循实质平等的要求,通过以"人的城镇化"为目标对区片综合地价进行限定、采取倾向于农村及农业发展的"土地财政"政策、打破城乡户籍二元制实现城乡一体化发展等方案,推动农民全过程参与、共享土地利益.     

劳动力转移对城乡居民收入差距的非线性影响研究

借鉴已有相关研究成果,将劳动力转移、农业技术水平与城乡居民收入差距联系起来思考,可以具体地考察劳动力转移对城乡居民收入差距的影响.文章以农业技术水平作为门槛变量,选取2009—2019年中国31个省份的面板数据,利用泰尔指数测定城乡收入差距,以劳动力转移作为核心解释变量构建面板门槛模型,实证得出劳动力转移对城乡居民收入差距的非线性影响.研究发现:劳动力转移可以缩小城乡居民收入差距,且该影响具有单一门槛效应.在农业技术水平较低时,劳动力转移对城乡居民收入差距收敛效应较小;当农业技术水平越过门槛值达到较高水平后,劳动力转移对缩小城乡居民收入差距的效果几乎增加了一倍.此后,通过替换解释变量泰尔指数为城乡人均可支配收入比、替换核心解释变量农业技术水平为农业生产效率重新建立面板门槛模型,发现上述结论仍然成立,其检验结果具有较强的稳健性.文章将农业技术进步、劳动力转移、城乡居民收入差距这三个重要变量纳入同一模型框架中,在加深劳动力流动影响城乡居民收入差距这一视角的研究的同时,对缩小城乡居民收入差距政策的制定提供了重要的参考意义.     

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.     

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.     

A Bayesian Analysis of Abundance,Trend, and Population Viability for Harbor Seals in Iliamna Lake,Alaska

Harbor seals in Iliamna Lake, Alaska, are a small, isolated population, and one of only two freshwater populations of harbor seals in the world, yet little is known about their abundance or risk for extinction. Bayesian hierarchical models were used to estimate abundance and trend of this population. Observational models were developed from aerial survey and harvest data, and they included effects for time of year and time of day on survey counts. Underlying models of abundance and trend were based on a Leslie matrix model that used prior information on vital rates from the literature. We developed three scenarios for variability in the priors and used them as part of a sensitivity analysis. The models were fitted using Markov chain Monte Carlo methods. The population production rate implied by the vital rate estimates was about 5% per year, very similar to the average annual harvest rate. After a period of growth in the 1980s, the population appears to be relatively stable at around 400 individuals. A population viability analysis assessing the risk of quasi‐extinction, defined as any reduction to 50 animals or below in the next 100 years, ranged from 1% to 3%, depending on the prior scenario. Although this is moderately low risk, it does not include genetic or catastrophic environmental events, which may have occurred to the population in the past, so our results should be applied cautiously.     

Modifications of sequential designs in bioequivalence trials

Bioequivalence (BE) studies are designed to show that two formulations of one drug are equivalent and they play an important role in drug development. When in a design stage, it is possible that there is a high degree of uncertainty on variability of the formulations and the actual performance of the test versus reference formulation. Therefore, an interim look may be desirable to stop the study if there is no chance of claiming BE at the end (futility), or claim BE if evidence is sufficient (efficacy), or adjust the sample size. Sequential design approaches specially for BE studies have been proposed previously in publications. We applied modification to the existing methods focusing on simplified multiplicity adjustment and futility stopping. We name our method modified sequential design for BE studies (MSDBE). Simulation results demonstrate comparable performance between MSDBE and the original published methods while MSDBE offers more transparency and better applicability. The R package MSDBE is available at https://sites.google.com/site/modsdbe/ . Copyright © 2015 John Wiley & Sons, Ltd.