Child Care “Packaging” Among TANF Recipients: Implications for Social Work

This article reports the results of a study that examined the ways in which current and recent TANF recipients assembled or “packaged” their child care arrangements among formal and informal providers. The findings are drawn from in-person, in-depth interviews with current and former welfare recipients. The study found that most of the respondents used multiple providers within the category of informal child care for reasons including availability, the use of a network of social acquaintances as a problem-solving resource, the need to accommodate irregular work hours, and personal trauma that contributed to a distrust of strangers in formal settings. The findings can affect the efficacy of practice with TANF recipients by contributing to social workers’ understanding of the context of decision-making regarding child care.     

Toward a model of school inspections in a polycentric system

Many education systems are developing towards more lateral structures where schools collaborate in networks to improve and provide (inclusive) education. These structures call for bottom-up models of network evaluation and accountability instead of the current hierarchical arrangements where single schools are evaluated by a central agency. This paper builds on available research about network effectiveness to present evolving models of network evaluation. Network effectiveness can be defined as the achievement of positive network level outcomes that cannot be attained by individual organizational participants acting alone. Models of network evaluation need to take into account the relations between network members, the structure of the network, its processes and its internal mechanism to enforce norms in order to understand the achievement and outcomes of the network and how these may evolve over time. A range of suitable evaluation models are presented in this paper, as well as a tentative school inspection framework which is inspired by these models. The final section will present examples from Inspectorates of Education in Northern Ireland and Scotland who have developed newer inspection models to evaluate the effectiveness of a range of different networks.     

Two-Group Classification Using Neural Networks*

Artificial neural networks are new methods for classification. We investigate two important issues in building neural network models; network architecture and size of training samples. Experiments were designed and carried out on two-group classification problems to find answers to these model building questions. The first experiment deals with selection of architecture and sample size for different classification problems. Results show that choice of architecture and choice of sample size depend on the objective: to maximize the classification rate of training samples, or to maximize the generalizability of neural networks. The second experiment compares neural network models with classical models such as linear discriminant analysis and quadratic discriminant analysis, and nonparametric methods such as k-nearest-neighbor and linear programming. Results show that neural networks are comparable to, if not better than, these other methods in terms of classification rates in the training samples but not in the test samples.     

The Role of Interorganizational and Organizational Factors on the Decision Mode for Adoption of Interorganizational Systems*

The dramatic growth of interorganizational systems (IOS) has changed the way organizations conduct their business, and has resulted in significant tangible and intangible benefits being realized by participating firms. However, the implementation of these IOS requires the cooperation and commitment of all the participating members. These members may have complex economic and business relationships among themselves that can result in a number of social, political, and economic factors influencing the adoption and implementation of IOS. This study examines the role of interorganizational and organizational factors on the decision mode for adoption of IOS, in the specific context of electronic data interchange (EDI). Four interorganizational factors, based on the socio-political framework derived from research in marketing, and five organizational factors based on research in IS were used in the study. The data for the study were collected through a large scale field survey. Two respondents, the sales/purchase manager and the IS manager, from 201 firms responded to the survey. The results of discriminant analysis of the data reveal that two interorganizational variables, competitive pressure and exercised power, and two organizational variables, internal need and top management support, are important variables to differentiate firms with proactive decision mode from firms with reactive decision mode. The study also evaluates the differences between proactive and reactive firms on three implementation outcomes. Proactive firms are found to have greater extent of adaptation, more external connectivity with trading partners, and better integration of EDI information in their internal IS applications.     

Bringing Japanese Continuous Improvement Approaches to U.S. Manufacturing: The Roles of Process Orientation and Communications*

Continuous improvement (CI) has played a key role in Japan's quality management. U.S. companies have begun to adopt CI in recent years. This paper studies the implementation of CI in seven U.S. manufacturing companies. We view values as the core concept of culture that impacts CI effectiveness. Using both qualitative and quantitative data with a sample of seven companies, we observe empirically a relationship between process-oriented values and CI effectiveness. We also find that communications involving workers is strongly associated with CI effectiveness. Finally, we find an association between process orientation and communication frequency, and argue that communications act as an intervening variable between process orientation and effectiveness.     

论全球生产的网络化及其推动力量

在产业转移的浪潮中,世界经济局势发生了很大的变化。在经济全球化的过程中,市场经济规则在世界范围内被广泛接受。受惠于科技革命特别交通和信息技术的巨大进步,跨国公司得以在世界范围内寻找战略资源满足其趋利性动机,形成了全球生产网络。     

我国A股市场复杂网络结构及稳定性研究

本文运用复杂网络分析方法,从一个全新的角度分析中国A股市场个股的相关性,以2014年至2015年间的股票市场交易数据为基础,以上证180指数成分股股票为样本,构建我国股票市场的复杂网络系统。通过计算分析股票网络的拓扑特征统计量,并与美股成熟市场进行比较,研究我国A股市场个股联动的网络结构;同时,在我国金融市场进一步开放的大背景下,本文通过研究样本股票网络在不同情形攻击下网络结构的变化,探索A股市场不同公司股价波动的联动性规律,为监管者稳定证券市场,特别是在“股灾”等极端情况下维持证券市场的流动性功能提供新的决策参考,以利于中国证券市场更好地为经济转型提供金融支持,保护中小投资者利益。     

FEPA-金融时间序列自适应组合预测模型

本文报告一种金融时间序列预测的信号分析、信息融合与智能计算组合模型,简称FEPA,由针对金融时间序列（FTS）信号分析的经验模态分解（EMD）、用于数据降维的主成分分析（PCA）和用于非线性建模的人工神经网络（ANN）三部分组成。该模型首先应用滑动窗口截取原始金融时间序列最近期数据集,应用EMD分解算法把数据集分解成不同尺度的本征模态函数（IMF）,然后通过主成分分析将分解后的数据降维,提取最有信息量的特征;然后将这些特征输入到神经网络进行组合预测。本文提出的组合预测模型FEPA是基于分解-提优-合成的信息融合思想,有效提高了预测可靠性。其创新点在于：1）首次给出了EMD算法的结构化表达,提供了今后融合更多信息的算法接口;2）通过多步长预测输出深入研究EMD分解的有效信息结构;3）通过切换到更细时间框架来处理EMD的端点效应,并探索了两级时间框架下的预测效果;4）给出了金融时间序列组合预测模型的一般性架构,具有可升级性和可扩展性。并且通过滑动窗口EMD使得实证更能切近实际。通过在沪深300股指和澳大利亚股指上的实证,结果表明FEPA预测模型在沪深300股指日线和15分钟线上的预测命中率高达78%和82%,在澳大利亚股指日线上也达到了74%的命中率,经比较,明显高于文献中常见的5种模型。     

Nonmarket networks among migrants: evidence from metropolitan Bangkok,Thailand

This paper examines the nonmarket interactions among migrants in the urban labor market of Bangkok, Thailand. We test whether the population size and the labor-market performance of previous migrants have externalities to new migrants who have moved from the same province of origin. Our empirical results, which control origin fixed effects, time fixed effects, and origin- and year-specific correlated shocks, show that (1) the relative population size of previous migrants in the market decreases the employment probability of new migrants (substitution effect), (2) the employment probability of previous migrants increases that of new migrants (positive externalities), and (3) when the employment probability of previous migrants is high, however, the scale effect becomes positive, which demonstrates a threshold in the informational scale economies. The results imply that positive informational scale effect dominates negative substitution effect when the efficiency of previous migrants is sufficiently high in the destination labor market.

A framework for the comparison of maximum pseudo-likelihood and maximum likelihood estimation of exponential family random graph models

The statistical modeling of social network data is difficult due to the complex dependence structure of the tie variables. Statistical exponential families of distributions provide a flexible way to model such dependence. They enable the statistical characteristics of the network to be encapsulated within an exponential family random graph (ERG) model. For a long time, however, likelihood-based estimation was only feasible for ERG models assuming dyad independence. For more realistic and complex models inference has been based on the pseudo-likelihood. Recent advances in computational methods have made likelihood-based inference practical, and comparison of the different estimators possible.