An evaluation of the childhood family structure measures from the sixth wave of the British Household Panel Survey

Summary.  The paper performs an evaluation of the data that were collected in the sixth wave of the British Household Panel Survey (BHPS) on childhood family structure. After comparing such data with a large number of studies by using external sources, we find that the BHPS data overestimate the proportion of people who report an experience of life in a non-intact family during childhood by about 10%. Although an explanation based on recall error that deteriorates with the age of the BHPS respondents is possible, the overestimation is likely to be accounted for by non-ignorable attrition that may affect most of the comparison studies based on longitudinal data. Conversely, comparisons with other independent measurements from the BHPS itself reveal that the wave 6 data underestimate the proportion of young people who have lived at least part of their childhood in a non-intact family by about 8%. The probability of disagreement between these two sets of measures is strongly associated with poor interview characteristics, which may affect the comparison measure more than the wave 6 measure. Despite such differences, there is therefore a substantial degree of similarity between the family structure information that was collected in the sixth wave of the BHPS and the host of highly diverse records against which it has been compared.     

新时代中华优秀传统文化进教材的理性审思

中华优秀传统文化是中华民族的精神命脉。推动中华优秀传统文化融入教材,对于建设培根铸魂、启智增慧的新时代中国特色高质量教材体系,培养具有高度文化自信的社会主义建设者与接班人具有重要意义。中华传统文化教材建设百年来经历了从“沉寂”到“复兴”的曲折发展历程,反映出中华优秀传统文化顽强的生命力。在中国特色社会主义新时代,中华优秀传统文化“进教材”既是一种注重文化传承与精神引领的知识建构,也是一种凝聚文化教育合力的协同发展。新时代中华优秀传统文化教材建设,应以习近平新时代中国特色社会主义思想为指导,坚持创造性转化与创新性发展的基本方向,探索教材基于新时代语境的现代化诠释、基于全球化语境的本土化编写的发展向度,塑造中华民族优秀文化形象,开发数字教材资源,让中华优秀传统文化渗透、融入教材。新时代中华优秀传统文化教材建设也应强化规范意识,积极规避“人文关怀式微”的价值危机、“学科视野窄化”的认识误区、“教材泛德育化”的理念偏差以及“教育逻辑缺失”的路向偏移等失范问题,增强责任之心,勇于担当作为,落实立德树人根本任务,在固本与创新中肩负起推动中华优秀传统文化传承和发展的历史使命。     

A Multivariate Model for Repeated Failure Time Measurements

A parametric multivariate failure time distribution is derived from a frailty-type model with a particular frailty distribution. It covers as special cases certain distributions which have been used for multivariate survival data in recent years. Some properties of the distribution are derived: its marginal and conditional distributions lie within the parametric family, and association between the component variates can be positive or, to a limited extent, negative. The simple closed form of the survivor function is useful for right-censored data, as occur commonly in survival analysis, and for calculating uniform residuals. Also featured is the distribution of ratios of paired failure times. The model is applied to data from the literature     

中华菜名比喻艺术管窥

中华菜名中的比喻精巧优美、形神兼备,既有语言精炼、结构紧凑的暗喻,又有惟妙惟肖、独具特色的借喻,其民俗喻体熔铸、凸显了中华文化神韵,包含有取形寓意、雅致高洁的植物类喻体,动静融合、风情万种的动物类喻体,源远流长、底蕴涌动的神异类喻体等,它们既能提升菜肴的审美情趣,提高菜名的艺术品位,又能激发食用者的心理想像,表现汉民族的具象思维特色,从而产生了彰显人文魅力,取得多重效应的功能。     

后WTO时期外资银行对中资银行影响分析及其路径选择

后WTO时期,我国金融业开放程度不断加深,外资银行正以前所未有的深度融入我国金融体系。外资银行的进入,将从总体上提升我国银行系统效率,但同时也不可避免地带来客户资源、银行业务、服务手段、人力资源等方面的冲击,为此,应通过完善金融体系、提高整体实力、培育新生力量及加强合作等策略增强中资银行抗击外资银行的实力,引导中资银行健康发展。     

内生溢出效应、技术能力与企业技术联盟方式选择

溢出效应是影响企业技术联盟稳定运行的重要决定因素,是企业技术联盟方式的重要影响因素。技术能力是溢出效应的主要影响因素,基于技术能力的内生溢出效应能够有效地反映企业的资源和能力状况。通过A-J模型的拓展,对技术能力的互补性进行深入的探讨,分析内生溢出效应对技术联盟运作方式的影响,结果显示:企业的技术能力决定着溢出效应,决定着企业参加技术联盟的创新结果,当企业具有同质的技术能力时,成本共享型联盟方式显得更有效率;当企业的技术能力较高并互补时,企业倾向于组建技术共享联盟;当企业的技术能力比较低并互补时,企业倾向于组建成本共享型联盟。     

网络环境下平台企业的运营策略研究

平台企业在网络环境下发生了深刻的变革,选取恰当的视角研究它的运营策略是个重要的科学问题.以平台生态圈的实际规模、期望规模及时间为参数,构建平台生态圈生命周期曲线,通过识别关键节点将曲线分为若干发展阶段,平台生态圈的规模、网络效应、发展模式在各阶段中体现出不同的特征,这些特征为平台企业制定运营策略提供了依据.总体上讲,当平台企业无法充分利用网络效应时,应依托各种“非网络效应诱因”吸引用户进入生态圈,不断激发网络效应;反之,则应充分利用网络效应,迅速扩张规模,同时解决成长中暴露出的问题,优化成长方式.构建了平台企业运营策略的理论框架,深化了平台生态圈生命周期理论,对网络环境下的平台企业进行自我诊断、制定运营策略具有指导意义.     

基于中国上市公司的资本结构、社会责任与企业绩效分析

以2009～2013年间我国A股上市公司的数据为样本,探讨了资本结构、社会责任和企业绩效之间的关系.通过构建面板数据回归模型,并采用广义最小二乘法对面板模型进行估计.研究表明,企业社会责任水平与资本结构负相关,社会责任水平的高低对资本结构与企业绩效的关系有调节作用:当企业社会责任水平较高时,资本结构与企业绩效负相关;当企业社会责任水平较低时,资本结构与企业绩效正相关,资本结构对企业绩效的影响效果随着社会责任水平的变化而变化.     

我国住宅市场财富效应研究

通过对我国总体数据的实证分析发现,预期收入增长对总消费具有显著正的影响;预期住宅价格对总消费也有显著正的影响,即我国住宅市场财富效应是显著存在的。这与最近关于OECD国家住宅市场研究中的结论是相吻合的。     

Program Evaluation and Causal Inference With High‐Dimensional Data

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data‐rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function‐valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced‐form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post‐regularization and post‐selection inference that are uniformly valid (honest) across a wide range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced‐form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment‐condition framework, which arises from structural equation models in econometrics. Here, too, the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function‐valued) parameters within this general framework where any high‐quality, machine learning methods (e.g., boosted trees, deep neural networks, random forest, and their aggregated and hybrid versions) can be used to learn the nonparametric/high‐dimensional components of the model. These include a number of supporting auxiliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity‐based estimation of regression functions for function‐valued outcomes.