Driving outcomes: learning to drive,resilience and young people living in residential care

There are continuing concerns about the experiences offered to older adolescents being looked after (‘in care’) in the UK and, especially, to care leavers. Questions are asked about the limitations of State care compared with normal family life. This paper reports on an initiative to provide driving lessons to a group of six young men living in residential homes in one city. It links with resilience theory – how individuals can have relatively good outcomes despite early adversity. A qualitative study was undertaken to explore the effects of the initiative, including individual interviews with young men, heads of homes in which they lived and children's services managers. The overall results indicated that the initiative was very worthwhile. The lessons were a significant part of young people's lives. Possible effects on young people were divided into personal, instrumental and social. Benefits were reported from all parties concerning young people's self‐esteem and self‐confidence, as well as in forging close relationships with supportive adults. Driving would not be seen as a panacea for complex personal histories and structural problems, yet this small experiment suggests that driving lessons could be of disproportionate benefit and there is a moral obligation to provide them in any case.     

Making Sense of After‐School Care Dilemmas in Mothers' Stories of After‐School Care in Finland and Sweden

The study focuses on dilemmas in storied experiences of everyday after‐school care arrangements among Swedish and Finnish mothers. Finland and Sweden, which share a history of strong labour market attachment among women, arrange institutional after‐school care in similar ways. The data consist of interviews with three Swedish and six Finnish mothers. A positioning analysis of four stories shows how decisions related to children's after‐school hours were allocated among different actors. Two reoccurring dilemmas, Competent‐dependent child stories and Unburdened‐deficient mother stories, emerged from the data analysis as related to prevailing moral discourses on childhood and motherhood.     

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.     

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.     

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.     

盈余管理、公司治理与可转债绩效滑坡

选择可转债融资为切入点,以2000年至2008年为研究期间,以中国发行可转债的上市公司为初始样本,采用PSM方法对初始样本进行配对以控制样本自选择问题,对再融资后的绩效进行实证检验,进一步探讨引致可转债再融资后绩效下滑的原因。实证检验结果表明,总体而言,发行可转债的公司绩效显著差于配对公司;在可转债转股高峰期,发行可转债的公司通过盈余管理向上调整当年利润,即存在基于转股目的的盈余管理行为;虽然发行前盈余管理反转是可转债发行后绩效下滑更多的原因之一,但是公司治理却能够有效地制约盈余管理反转效应对可转债发行后绩效滑坡的影响;在保持对可转债发行前和转股高峰期的盈余管理予以重点监控外,加强公司治理的建设更是防止发行后业绩下滑的重要举措。     

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.     

Improved estimators for the selected location parameters

_i , i = 1, 2, ..., k be k independent exponential populations with different unknown location parameters θ _i , i = 1, 2, ..., k and common known scale parameter σ. Let Y _i denote the smallest observation based on a random sample of size n from the i-th population. Suppose a subset of the given k population is selected using the subset selection procedure according to which the population π _i is selected iff Y _i ≥Y ₍₁₎−d, where Y ₍₁₎ is the largest of the Y _i 's and d is some suitable constant. The estimation of the location parameters associated with the selected populations is considered for the squared error loss. It is observed that the natural estimator dominates the unbiased estimator. It is also shown that the natural estimator itself is inadmissible and a class of improved estimators that dominate the natural estimator is obtained. The improved estimators are consistent and their risks are shown to be O(kn ⁻²). As a special case, we obtain the coresponding results for the estimation of θ₍₁₎, the parameter associated with Y ₍₁₎. Received: January 6, 1998; revised version: July 11, 2000     

罪后情节的定罪功能探究——以2009年国家司法考试一道试题为例展开

以2009年国家司法考试一道刑法试题为例,介绍了情节和量刑情节的概念及其分类,并对罪后情节的功能进行了分析。认为作为一种量刑情节,罪后情节原则上只具有影响量刑的功能,但我国刑法典和司法解释却在8处规定了能影响犯罪成立与否的罪后情节,赋予了罪后情节以定罪的功能。这些规定有其刑事政策上的考量,同时符合刑法谦抑性的要求,具有合理性。以此为理论基础,对受贿罪的司法解释进行了研究。