中国城乡居民多代际收入流动研究

根据多代际收入流动的理论研究框架,基于CHIP2013和CHIP1995的调查数据,分别使用两样本两阶段最小二乘估计(TS2SLS)和分位数回归方法,估计城乡子代对父代和祖父代的多代际收入弹性,结果发现:城镇父子代际收入弹性大于农村父子,城镇居民的代际收入流动性低于农村居民;在多代际收入流动的理论模型中控制父代收入变量,城乡祖父代对子代收入的回归系数都明显下降;城乡居民的父子代际收入弹性从低收入家庭到高收入家庭都呈现递减的特征;城镇和农村居民不同收入阶层多代际收入流动现象差异明显。     

基于LISREL结构方程模型的社会经济地位继承效应分析

结构方程模型(SEM)是在路径分析基础上发展形成的一种多元统计方法,在经济学、社会学等多学科领域获得了广泛应用.文章在介绍结构方程模型基本原理以及用LISREL方法估计模型参数的基础上,采用中国综合社会调查(CGSS2013)数据研究中国家庭社会经济地位的继承效应.研究发现中国家庭社会经济地位有着较高的代际继承效应,父辈职业、收入、教育程度等家庭因素对子女的社会经济地位获得产生了直接影响,子女教育则是社会经济地位获得的重要中介变量.应消除阻碍社会经济地位代际流动的制度障碍,增强社会代际流动性.     

医疗费用预测的贝叶斯多项式混合效应模型

医疗费用预测是健康保险费率厘定的前提和基础。对于多年期的医疗费用数据,通常使用线性混合效应模型对其进行拟合,但线性混合效应模型对非线性关系的纵向数据建模具有一定的局限性。本文对线性混合效应模型进行扩展,根据医疗费用数据中变量之间的非线性关系,建立了多项式混合效应模型,并将其应用于一组医疗费用数据进行实证研究。结果表明,多项式混合效应模型对住院医疗费用的拟合效果显著优于通常使用的线性混合模型,在医疗费用管理和健康保险的费率厘定中具有重要的应用价值。     

转居农民的职业地位获得影响因素研究——基于地位获得理论视角

基于地位获得理论,利用中国综合社会调查数据建立多元线性回归模型、二元logistic回归模型,考察了父母人力资本与职业代际传递、个人因素、社会交往、经济环境、政策环境对转居农民职业地位获得的影响。研究发现,父母受教育年限、政治身份、自身受教育年限、社交频率、朋友数量、征地补偿水平对转居农民职业地位获得有显著正向影响;居住地与市中心距离对转居农民职业地位获得有显著负向影响。以上结论说明,转居农民的就业困境主要受代际传递资本匮乏、劳动力水平不高、社会网络资源有限、社会经济环境促进就业作用不佳、征地补偿政策不当影响,更是城乡二元社会结构下的资源分配不均导致的。     

不确定性数据的交互分析方法

本文提出利用三维非完备线性等级对数模型,分析不确定数据的方法,主张用迭代法求对数模型的数据解,进一步分析哪一个模型最适合,此模型的交互项就是各变量之间的交互项,交互关系的确立可为排除共线性提供依据.     

列联表分析在市场调查中的深入应用

列联表分析(CROSSTABS,CROSS-TABULATION,交叉分组列表),又称交互分析、交叉分析.它是对一组观察对象分别观察其分类变量的表现,每种分类变量可以有两种或两种以上的观察结果,目的在于研究分类变量之间的关系.目前,许多调查公司列联表分析都只停留在频数和百分比的直观对比上,而没有进一步的检验和变量控制,这样不仅浪费数据,而且可能得出错误结论.笔者在文中从实践角度对列联表分析进行了系统概述,相信不仅有利于引起有关分析人员加深对列联表分析应用,而且对其他的社会调查研究中列联表分析的应用也有一定的借鉴意义.     

贫困代际传递对主观幸福感影响机制分析

鉴于贫困代际传递存在的事实,利用中国综合社会调查(CGSS)2015年数据,结合有序Logit和倾向得分匹配模型,实证检验了家庭贫困代际传递对居民主观幸福感的影响及其影响机制。结果表明,贫困代际传递居民幸福感的Likert值低于非贫困居民,贫困代际传递显著降低了居民主观幸福感,但贫困对居民幸福感的影响存在一定的适应性。对影响机制的研究发现,贫困代际传递可以通过影响居民对社会公平、阶层流动机会和社会信任的感知影响居民主观幸福感。同时,也会通过影响情绪,进一步调节个体对主观幸福感的评价。     

统计分组与虚拟变量在线性模型中的比较应用

一、统计分组的应用及其在线性模型拟合中的优缺点统计分组是根据事物内在的特点和统计研究的目的,将特定的总体按某种划分标准区分为若干个不同的组成部分的一种统计方法。统计分组的应用相当广泛。对数据按一定的标准分组后,可以发现数据内的潜在的规律性,呈现出不同组别之间的联系与差异,便于找出深藏于表面现象之下的内在本质,比如列联表分析。统计分组也可以应用于模型的建立中。在拟合样本数据进行建立模型时,常常会有一些质的因素(比如职业、性别、战争、季节等等)影响被解释变量,这些质的因素可能会使回归模型中的参数发生变化。在这种情况下,如果对样本数据笼统地用一个模型来拟合就会产生很大的误差。相反,如果将样本数据按引起线性模型的截距或斜率变化的标志分组,再针对每一组分别拟合模型,则可以得到拟合程度更高的模型。     

提高灰建模数据列光滑度的一种新方法

本文在对建模数据序列进行一定处理的基础上,提出了经函数cosx变换来提高数据光滑度的方法,理论上证明了这种变换可以有效地提高建模数据列的光滑度,其模型精度优于对数及幂函数变换所建模型的精度;并通过实例表明了该方法的有效性。     

低收入群体代际收入流动性变动趋势

传统的代际收入弹性法由于采用双对数线性回归模型,对低收入样本的代际收入流动性估计存在偏误。因此,采用统计特性优良的代际位序相关性方法,利用1989—2015年CHNS数据,对中国低收入群体代际收入流动性进行了测度,得到的结果在不同偏误条件下更加准确、稳健。研究结果表明,低收入群体代际流动性在1991—2006年呈现先上升后下降的趋势,但在2006年后显著上升,其中底层贫困人群子女收入位序上升更快;不同地区代际流动性存在差异,西部地区高于东、中部。进一步对绝对向上流动性的考察发现,代际流动性更大的贫困人群、西部地区人群,其子女尽管有机会摆脱贫困,但收入地位并没有发生实质性改变,社会给予收入底层居民的上升空间十分有限。阻断低收入代际传递,政府应当加强对低收入家庭子女的教育投资和职业培训。     

A NOTE ON MULTIVARIATE LOGISTIC MODELS FOR CONTINGENCY TABLES

The log-linear model is a tool widely accepted for modelling discrete data given in a contingency table. Although its parameters reflect the interaction structure in the joint distribution of all variables, it does not give information about structures appearing in the margins of the table. This is in contrast to multivariate logistic parameters, recently introduced by Glonek & McCullagh (1995), which have as parameters the highest order log odds ratios derived from the joint table and from each marginal table. Glonek & McCullagh give the link between the cell probabilities and the multivariate logistic parameters, in an algebraic fashion. The present paper focuses on this link, showing that it is derived by general parameter transformations in exponential families. In particular, the connection between the natural, the expectation and the mixed parameterization in exponential families (Barndorff-Nielsen, 1978) is used; this also yields the derivatives of the likelihood equation and shows properties of the Fisher matrix. The paper emphasises the analysis of independence hypotheses in margins of a contingency table.     

Log-linear models for correlated marginal totals of a contingency table

The marginal totals of a contingency table can be rearranged to form a new table. If at least twoof these totals include the same cell of the original table, the new table cannot be treated as anordinary contingency table. An iterative method is proposed to calculate maximum likelihood estimators for the expected cell frequencies of the original table under the assumption that some marginal totals (or more generally, some linear functions) of these expected frequencies satisfy a log-linear model.In some cases, a table of correlated marginal totals is treated as if it was an ordinary contingency table. The effects of ignoring the special structure of the marginal table on thedistributionof the goodness-of-fit test statistics are discussed and illustrated, with special reference to stationary Markov chains.     

The asymptotic covariance structure of log-linear model estimated parameters for the multiple recapture census

This paper presents a matrix formulation for log-linear model analysis of the incomplete contingency table which arises from multiple recapture census data. Explicit matrix product expressions are given for the asymptotic covariance structure of the maximum likelihood estimators of both the log-linear model parameter vector and the predicted value vector for the observed and missing cells. These results are illustrated for data pertaining to a population of children possessing a common congenital anomaly.     

Independence in Contingency Tables Using Simplicial Geometry

Frequently, contingency tables are generated in a multinomial sampling. Multinomial probabilities are then organized in a table assigning probabilities to each cell. A probability table can be viewed as an element in the simplex. The Aitchison geometry of the simplex identifies independent probability tables as a linear subspace. An important consequence is that, given a probability table, the nearest independent table is obtained by orthogonal projection onto the independent subspace. The nearest independent table is identified as that obtained by the product of geometric marginals, which do not coincide with the standard marginals, except in the independent case. The original probability table is decomposed into orthogonal tables, the independent and the interaction tables. The underlying model is log-linear, and a procedure to test independence of a contingency table, based on a multinomial simulation, is developed. Its performance is studied on an illustrative example.     

Empirical bayes estimation in contingency tables

In the estimation of cell probabilities from a two–way contingency table, suppose that a priori the classification variables are believed independent. New empirical Bayes and Bayes estimators are proposed which shrink the observed proportions towards classical estimates under the model of independence. The estimators, based on a Dirichlet mixture class of priors, compare favorably to an estimator of Laird (1978) that is based on a normal prior on terms of a log–linear model. The methods are generalized to three–way tables.     

Partitioning Pearson's chi-squared statistic for a partially ordered three-way contingency table

This paper presents a generalization of the partition of the chi-squared statistic presented in Beh & Davy (1998). For a three-way contingency table with one or two sets of ordered categories, the chi-squared statistic partition is defined using orthogonal polynomials. Using this partition, information about the relationship between the variables can be obtained by identifying important associations in terms of the location (linear), dispersion (quadratic) and higher order components. The paper compares these partitions with log-linear models for ordinal data.     

On bayes-type estimators of linearly or log-linearly constrained multinomial cell probabilities

Two Bayes-type procedures for estimating a multinomial cell probabilities vector, P, in the presence of linear constraints on the parameters are proposed and illustrated by examples from contingency table analysis. Estimation under log-linear constraints is also considered.     

Log-linear model analysis of the semi-symmetric intraclass contingency table

This paper addresses the problem of analyzing a three-way contingency table that is upper-triangular, and a priori symmetric within layers. The log-linear model is modified to handle this kind of table, and maximum likelihood estimation is carried out for the modified log-linear model. This leads to an expression of the maximum likelihood estimates exclusively in terms of the observed cell counts. It is skin this analysis is equivalent to an application of the gone log-linear model to an artificially complete table, obtain. by splitting the off-diagonal cells in half within layers. This analysis is used in analyzing the results of a study done to determine the effect of the sex-linked dwarfing gene in male chickens on resistance to E. coli infection; the conclusion differs from that of a previous analysis of the same data (see Norwood and Hinkelmann 1978). It is found, in fact, that the structure of association among the two allele variables and the disease variable is somewhat more complex than previously proposed. A second example is taken from Ishii (1960). Finally, collapsibility conditions for the modified log-linear model, as well as various other sampling plans and limitations to the testing procedure, are discussed.     

新生代农民工职业转换的影响因素分析——基于陕西省的调查数据

职业转换率是衡量职业流动的主要指标,也是考察社会阶层流动性和社会公平性的重要指标。以陕西省的实地调查数据为基础,运用交叉列联表和Logistic回归方法分析性别、年龄、户籍所在地、受教育程度、职业技能培训等因素对新生代农民工职业流动的影响。研究发现,性别、年龄、户籍所在地等个人因素对职业转换率没有显著影响,而受教育程度和是否接受职业技能培训等人力资本因素对职业转换率有显著的影响。在此基础上,提出增加新生代农民工职业技术教育培训机会的政策建议。     

Bayesian selection of log-linear models

A general methodology is presented for finding suitable Poisson log-linear models with applications to multiway contingency tables. Mixtures of multivariate normal distributions are used to model prior opinion when a subset of the regression vector is believed to be nonzero. This prior distribution is studied for two- and three-way contingency tables, in which the regression coefficients are interpretable in terms of odds ratios in the table. Efficient and accurate schemes are proposed for calculating the posterior model probabilities. The methods are illustrated for a large number of two-way simulated tables and for two three-way tables. These methods appear to be useful in selecting the best log-linear model and in estimating parameters of interest that reflect uncertainty in the true model.