Beyond Piketty: A new perspective on poverty and inequality in India

The socio-economic literature has focused much on how overall inequality in income distribution (frequently measured by the Gini coefficient) undermines the “trickle down” effect. In other words, the higher the inequality in the income distribution, the lower is the growth elasticity of poverty. However, with the publication of Piketty’s magnum opus (2014), and a subsequent study by Chancel and Piketty (2017) of evolution of income inequality in India since 1922, the focus has shifted to the income disparity between the richest 1% (or 0.01%) and the bottom 50%. Their central argument is that the rapid growth of income at the top end of millionaires and billionaires is a by-product of growth. The present study extends this argument by linking it to poverty indices in India. Based on the India Human Development Survey 2005–12 – a nationwide panel survey-we examine the links between poverty and income inequality, especially in the upper tail relative to the bottom 50%, state affluence (measured in per capita income) and their interaction or their joint effect. Another feature of our research is that we analyse their effects on the FGT class of poverty indices. The results are similar in as much as direction of association is concerned but the elasticities vary with the poverty index. The growth elasticities are negative and significant for all poverty indices. In all three cases, the disparity between the income share of the top 1% and share of the bottom 50% is associated with greater poverty. These elasticities are much higher than the (absolute) income elasticities except in the case of the poverty gap. The largest increase occurs in the poverty gap squared – a 1% greater income disparity is associated with a 1.24% higher value of this index. Thus the consequences of even a small increase in the income disparity are alarming for the poorest.     

高校财务指标体系的建立及其综合评价

讨论了财务指标作为高校综合评价的可能性及高校财务管理的内涵,在此基础上建立了科学、系统、可操作的财务评价指标体系,确定了各项财务指标的权重.     

供应链管理中的信息流网络

本文对供应链管理中的信息流理论进行了探讨 ,指出了传统链式信息流模式的缺陷 ,分析了简单的网络式信息流模型的构造 ;阐述了信息流网络的特征与优势 ;对若干有待深入研究的问题进行了展望。     

企业技术竞争力评价指标体系研究：构建、测评与结论

在分析企业技术竞争力内涵的基础上构建了企业技术竞争力评价指标体系,尝试利用层次分析法对该指标体系进行测评,通过构造判断矩阵并伴随一致性检验,得到了各指标体系的总排序结果。研究表明,从准则层的3项目标来看,企业的技术创新竞争力对总目标的影响权重占有绝对优势;从指标层的29项评价指标来看,影响最大的10项指标在对总目标的影响权重值中占大部分比例,这些指标是提升企业技术竞争力水平的关键所在。     

Predicting body fat using weight–height indices

While body fat is the most accurate measure of obesity, its measurement requires special equipment that can be costly and time consuming to operate. Attention has thus typically focused on the easier to calculate body mass index (BMI). However, the ability of BMI to accurately identify obesity has been increasingly questioned. This paper focuses attention on whether more general body mass indices are appropriate measures of body fat. Using a data set of body fat, height, and weight measurements, general models are estimated which nest a wide variety of weight–height indices as special cases. In the absence of a race and gender categorisation, the conventional BMI was found to be the appropriate index with which to predict body fat. When such a categorisation was made, however, the BMI was never selected as the appropriate index. In general, predicted female body fat was some 10 kg higher than that of a male of identical build and predicted % body fat was over 11 percentage points higher, but age effects were smaller for females. Considerable racial differences in predicted body fat were found for males, but such differences were less marked for females. The implications of this finding for interpreting recent research on the effect of obesity on health, society, and economic factors are considered.     

成熟期医药风险投资项目评价实证研究

作为关系国计民生的特殊产业--医药产业一直是风险投资重要领域,但是针对性相关研究却没有得到应有重视,而专注于医药风险投资重要阶段的成熟期医药风险投资的相关研究更是空白.现有风险投资文献多比较原则,少数套用有关方法、模型改头换面,缺乏实际应用价值.鉴于成熟期医药风险投资特点,本文通过问卷调查和数理统计方法从实证角度揭示成熟期医药风险投资各风险评价指标及重要性和相互关系.通过相关性分析发现成熟期医药风险投资协同效应评价中的负相关性现象.     

一种基于期权的供应商能力预订模型

在供应链中,合作企业实现风险共担,利益共享是供应链管理的基本目标。本文通过引入期权机制,提出了制造商预订上游供应商的生产能力的数学模型,使之能以较低的风险获取外部资源,以保证经营目标的实现。同时,也能使供应商通过期权降低自身的经营风险,增强收益的能力,最终实现供应链利益的整体协调。     

Bayesian adjustment for unidirectional misclassification in ordinal covariates

In this paper, we study the identification of Bayesian regression models, when an ordinal covariate is subject to unidirectional misclassification. Xia and Gustafson [Bayesian regression models adjusting for unidirectional covariate misclassification. Can J Stat. 2016;44(2):198–218] obtained model identifiability for non-binary regression models, when there is a binary covariate subject to unidirectional misclassification. In the current paper, we establish the moment identifiability of regression models for misclassified ordinal covariates with more than two categories, based on forms of observable moments. Computational studies are conducted that confirm the theoretical results. We apply the method to two datasets, one from the Medical Expenditure Panel Survey (MEPS), and the other from Translational Research Investigating Underlying Disparities in Acute Myocardial infarction Patients Health Status (TRIUMPH).     

Bayesian Estimation and Model Choice in Item Response Models

Item response models are essential tools for analyzing results from many educational and psychological tests. Such models are used to quantify the probability of correct response as a function of unobserved examinee ability and other parameters explaining the difficulty and the discriminatory power of the questions in the test. Some of these models also incorporate a threshold parameter for the probability of the correct response to account for the effect of guessing the correct answer in multiple choice type tests. In this article we consider fitting of such models using the Gibbs sampler. A data augmentation method to analyze a normal-ogive model incorporating a threshold guessing parameter is introduced and compared with a Metropolis-Hastings sampling method. The proposed method is an order of magnitude more efficient than the existing method. Another objective of this paper is to develop Bayesian model choice techniques for model discrimination. A predictive approach based on a variant of the Bayes factor is used and compared with another decision theoretic method which minimizes an expected loss function on the predictive space. A classical model choice technique based on a modified likelihood ratio test statistic is shown as one component of the second criterion. As a consequence the Bayesian methods proposed in this paper are contrasted with the classical approach based on the likelihood ratio test. Several examples are given to illustrate the methods.