A hierarchy of lorenz curves based on the generalized tukey's lambda distribution

A hierarchy of Lorenz curves based on the generalized Tukey's Lambda distribution is proposed. Representations of the corresponding distribution and density function are also provided, together with popular inequality measures. Estimation methods are suggested. Finally, a comparison with other parametric families of Lorenz curves is established.     

政府宣传对现代燃煤电厂公众支持度的影响——以平江燃煤电厂为例

近年来,中国发生了多起针对燃煤电厂建设的公众抵制事件,影响了现代燃煤发电项目的正常实施。以“平江燃煤电厂事件”为例,分析公众在政府宣传前后对煤电项目的态度变化,应用SIR传染病模型对公众支持率的变化进行系统仿真,通过计算基尼系数对政府宣传效果进行评价。结果表明:传染病模型可以较好地反映公众支持态度的动态变化过程,政府宣传促进了公众支持态度的确立,政府宣传行为的基尼系数值G=0.375<0.4,虽在合理范围内但仍存在优化空间。根据以上研究结果,从大型火电项目决策模式变革和宣传策略实施两个方面提出了政策建议。     

金融普惠和京津冀家庭收入差距——来自CHFS数据的证据

改革开放近40年,家庭收入水平逐年增加,然而家庭收入差距也呈扩大趋势,京津冀地区金融普惠家庭比非金融普惠家庭的收入差距高出全国21.531元,家庭收入差距问题更为突出,严重影响京津冀地区协同发展和家庭幸福感,探求金融普惠和京津冀家庭收入差距之间的关系具有现实意义.文章基于2015年CHFS京津冀样本数据,在社区层面估计发现,京津冀社区金融普惠提高10个百分点,社区家庭收入基尼系数显著下降2.42%.在北京和天津地区,社区金融普惠对家庭收入差距没有显著的影响,在河北地区,社区金融普惠比例提高10个百分点,社区家庭收入基尼系数显著下降2.37%.进一步发现,京津冀金融普惠对各分位点家庭收入的影响随着分位点的提高而呈递减趋势,这是金融普惠降低京津冀家庭收入差距的内在原因.文章的政策含义是在京津冀地区,普及金融普惠能够缩小家庭收入差距,增强家庭金融发展协调性,增进家庭福祉,促进京津冀共同繁荣.     

Inference Tests for Gini-Based Tax Progressivity Indexes

Distribution-free statistical inference procedures for changes in Lorenz- and Gini-based indexes of tax progressivity are developed and applied. Related but distinct tests for the Reynolds–Smolensky index of residual progression and the Kakwani index of liability progression are provided. The inference procedures are illustrated by applying them to Luxembourg Income Study microdata for Sweden, the United Kingdom, and the United States before and after periods of tax reform. In each country a finding of significant change depends on the choice among progressivity indexes. No single index exhibits a consistent pattern of significant change in all countries across time.     

Standards of Probability Sampling for Legal Evidence

Brief recognition is given to bicentennials of the death of James Stirling (1692–1770) and birth of Ferdinand Rudolph Hassler (1770–1843); to sesquicentennials of the publication of the 3rd definitive edition of Laplace's Théorie analytique des probabilités, and of the births of William Chauvenet (1820–1870) and Isaac Todhunter (1820–1884); to centennials of the births of Louis Bachelier (1870–1946) and Jean Perrin (1870–1942); and to the semicentennial of the initial publication (1920) of Metron, an International Review of Statistics.     

Parametric Versus Nonparametric Inference on Zenga Index of Inequality: Issues and Evidence From Survey Data

Recent growing disparities suggests to move from inequality measures based on comparing the incomes of the less fortunate with the overall mean, as the Gini, to the new Zenga index, which instead contrasts the means of the less and the more wealthy subpopulations. After providing a thorough analysis of the theoretical and practical aspects of obtaining parametric and non-parametric confidence intervals for the Zenga inequality measure, we develop a cross-regional study based on the Swiss Income and Consumption Survey, wave 2005. Results show that coverage accuracy and average length of confidence intervals improve when the parametric model offers a good fit to the data.     

贵阳市居民收入差距的基尼系数分析

基于基尼系数指标对贵阳市居民收入差距状况进行测量,研究结果表明:贵阳市居民收入差距基尼系数为0.399。完善社会保障体系对于消减收入差距扩大带来的影响具有积极的意义。     

Estimation of a probability density function using interval aggregated data

ABSTRACT

In economics and government statistics, aggregated data instead of individual level data are usually reported for data confidentiality and for simplicity. In this paper we develop a method of flexibly estimating the probability density function of the population using aggregated data obtained as group averages when individual level data are grouped according to quantile limits. The kernel density estimator has been commonly applied to such data without taking into account the data aggregation process and has been shown to perform poorly. Our method models the quantile function as an integral of the exponential of a spline function and deduces the density function from the quantile function. We match the aggregated data to their theoretical counterpart using least squares, and regularize the estimation by using the squared second derivatives of the density function as the penalty function. A computational algorithm is developed to implement the method. Application to simulated data and US household income survey data show that our penalized spline estimator can accurately recover the density function of the underlying population while the common use of kernel density estimation is severely biased. The method is applied to study the dynamic of China's urban income distribution using published interval aggregated data of 1985–2010.     

A concentration study of principal components

Influence functions are commonly used as diagnostic tools in order to investigate sensitivity aspects in principal component analysis. This paper suggests a practical alternative for the eigenvalues by introducing a sensitivity measure derived from the classical Lorenz curve and associated Gini index. The results are illustrated by analysing an example.     

Some Sampling Properties of a Model for Income Distribution

A recently proposed model for describing the distribution of income over a population, based on the Burr distribution, has been shown to fit better than the commonly used lognormal or gamma distributions. The current article extends that analysis by deriving the large-sample properties of the maximum likelihood estimates for this three-parameter model. Consequently, resulting confidence intervals for some measures of income inequality (including the Gini index) are used to further test the model's validity, as well as to examine apparent trends in inequality over time. Since these properties depend on the way the income data are grouped and censored, implications for choosing data-report intervals can be analyzed. Specifically, a choice between two common methods of reporting the data is shown to have an important impact on Gini index estimates.