Does unemployment lead to isolation? The consequences of unemployment for social networks

Popular accounts in both social science and society claim that unemployment goes together with social withdrawal. However, empirical support for this conclusion is largely derived from cross-sectional studies or indirect measurements of social contacts. In this study, we argue that consequences of unemployment for personal networks differ across social groups and by length of unemployment. Using longitudinal data from the Swiss Household Panel (1999–2010), we focus on three frequently employed social network statistics: network size, contact frequency, and perceived support by friends, family, neighbors, and acquaintances. We estimate how short (<1 year) and long term (>1 year) unemployment relates to these network characteristics for men and women, people below and above 50 years of age, and lower and higher educated individuals. Our results provide a more-nuanced perspective on the commonly assumed social withdrawal following unemployment.     

A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data

The widely used empirical Bayes (EB) and full Bayes (FB) methods for before–after safety assessment are sometimes limited because of the extensive data needs from additional reference sites. To address this issue, this study proposes a novel before–after safety evaluation methodology based on survival analysis and longitudinal data as an alternative to the EB/FB method. A Bayesian survival analysis (SARE) model with a random effect term to address the unobserved heterogeneity across sites is developed. The proposed survival analysis method is validated through a simulation study before its application. Subsequently, the SARE model is developed in a case study to evaluate the safety effectiveness of a recent red‐light‐running photo enforcement program in New Jersey. As demonstrated in the simulation and the case study, the survival analysis can provide valid estimates using only data from treated sites, and thus its results will not be affected by the selection of defective or insufficient reference sites. In addition, the proposed approach can take into account the censored data generated due to the transition from the before period to the after period, which has not been previously explored in the literature. Using individual crashes as units of analysis, survival analysis can incorporate longitudinal covariates such as the traffic volume and weather variation, and thus can explicitly account for the potential temporal heterogeneity.     

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.     

Modelling of connected processes

The problem of comparing, contrasting and combining information from different sets of data is an enduring one in many practical applications of statistics. A specific problem of combining information from different sources arose in integrating information from three different sets of data generated by three different sampling campaigns at the input stage as well as at the output stage of a grey-water treatment process. For each stage, a common process trend function needs to be estimated to describe the input and output material process behaviours. Once the common input and output process models are established, it is required to estimate the efficiency of the grey-water treatment method. A synthesized tool for modelling different sets of process data is created by assembling and organizing a number of existing techniques: (i) a mixed model of fixed and random effects, extended to allow for a nonlinear fixed effect, (ii) variogram modelling, a geostatistical technique, (iii) a weighted least squares regression embedded in an iterative maximum-likelihood technique to handle linear/nonlinear fixed and random effects and (iv) a formulation of a transfer-function model for the input and output processes together with a corresponding nonlinear maximum-likelihood method for estimation of a transfer function. The synthesized tool is demonstrated, in a new case study, to contrast and combine information from connected process models and to determine the change in one quality characteristic, namely pH, of the input and output materials of a grey-water filtering process.     

陈云与新中国的水利建设

陈云高度重视水利建设对于农业发展和国民经济恢复和增长的作用,强调兴修水利以防治水旱灾害、保障人民的生命财产安全,实现水资源的综合利用和开发。他还提出要节约、保护水资源,防止水资源的污染和破坏。陈云关于水利建设的思想,对于我们总结建国后中国共产党水利建设和水利事业发展的历史、更好地建设中国特色现代化水利具有重要意义。     

农民工子女初中后阶段教育需求及其影响因素分析

利用随机调查学生样本数据,选取一般多元Logit模型对农民工子女初中后阶段教育需求及影响因素进行实证分析。结果表明:农民工子女进城方式和进城上学目的,农民工子女家庭决策者文化水平、月收入、就业地区和职业,农民工子女就读学校教育条件、城里学校老师重视程度等,成为影响农民工初中后阶段教育需求的重要因素。在此基础上提出应提高农民工收入水平,提升农民工文化水平,改善农民工职业流动,加强对农民工子女的关爱和重视等政策建议。     

理工科新生英语自主学习能力的培养

通过对理工科专业大学新生的问卷调查,分析了他们缺乏英语自主学习能力的现状,并从五个方面阐述了培养这类学生英语自主学习能力的途径和方法.     

日据至光复初期台湾博物馆管窥

日据至光复初期台湾的主要博物馆有4个,其中首推台湾总督博物馆,次为台中教育博物馆,台南教育博物馆,嘉义通俗博物馆。台湾总督博物馆,是台湾最早的博物馆,历史可追溯到1899年;1945年台湾光复,改称为台湾省博物馆。此外,当时台湾的部分院校和科研机构,亦设置有博物馆、植物园及小型的陈列馆或标本室。     

高校贫困新生心理障碍探析

本文对以SCL—90(Symptom Checklist 90)筛查出来的82名具有不同程度心理障碍的大学贫困新生进行了面谈和追踪访问,发现经济压力夹杂在各种生活事件中,增加了大学贫困新生适应大学生活的难度,贫困新生的心理障碍主要表现在强迫、人际关系敏感、偏执、抑郁,并且性别之间存在差异,需要加强有针对性的心理健康教育和辅导。     

论檀道鸾的文学“三元(源)”论

在专记东晋中、晚期编年史的《续晋阳秋》中,檀道鸾将文学作为独立学科加以置评。他率先把"独尊"的儒学经典《诗经》与楚辞、前秦百家之言并列,提出了中国文学"体则《诗》《骚》,傍综百家"的"三元"论,并以此"三元"标准评论汉代"赋颂"、建安诗歌和西晋末年诗作。在评价两晋文学时,檀道鸾强调"庄、老"影响下产生的玄学及"玄胜之谈"对两晋道佛文学流变的重要影响。檀道鸾的文学流变史观,显现了南北分裂时期学思活跃、文人反思文化历史、重新界定文学源头的历史现实以及檀氏所历地域文化、家风传统、个人倾向互动等对其文史评论的影响。檀道鸾文学评论的自觉观点,是西晋虞挚的《文章流别论》,与南朝刘勰《文心雕龙》、钟嵘《诗品》等文学评论之间的重要环节。