中国人口年龄结构与居民医疗保健消费

本文借助中国2001—2010年的省级面板数据,运用动态面板GMM估计方法,考察了中国人口年龄结构(少儿和老年抚养系数)变化对居民医疗保健消费支出的影响。结果发现,中国少儿抚养系数对居民医疗保健消费支出的影响不显著;老年抚养系数对居民医疗保健消费支出有正影响,即老年抚养系数的上升会带动居民人均医疗保健消费支出的增加。此外,本文还发现,医疗保健消费支出的滞后一期系数都为正,系统GMM两步估计系数为0.51,表明居民医疗保健消费支出存在较强的惯性;人均收入增长对居民医疗保健消费支出有正向的显著影响。     

城镇家庭收入差异演变及其原因解析——基于北京市城镇住户调查数据

基于北京市城镇住户调查数据,采用分位数回归与分解技术,结合核密度估计方法,定量考察了性别、年龄、受教育年限、工作经验等因素对城镇家庭收入差异及其演变的贡献。研究表明：不同收入群体并没有均等地分享经济社会发展的成果;城镇家庭收入差异是＂回报效应＂和＂变量效应＂共同作用的结果,不同历史时期两种效应的重要性有所不同;教育机会不均等是导致城镇家庭收入差异的关键因素;就业性质的差异助长了城镇家庭收入差距;不同收入群体间自身素质的差异是影响城镇家庭收入差异的重要因素;工作年限、工作经验、年龄、性别及家庭规模等因素影响力不容忽视。     

Design and Evaluation of Prophylactic Interventions Using Infectious Disease Incidence Data from Close Contact Groups

Summary.  Prophylaxis of contacts of infectious cases such as household members and treatment of infectious cases are methods to prevent the spread of infectious diseases. We develop a method based on maximum likelihood to estimate the efficacy of such interventions and the transmission probabilities. We consider both the design with prospective follow-up of close contact groups and the design with ascertainment of close contact groups by an index case as well as randomization by groups and by individuals. We compare the designs by using simulations. We estimate the efficacy of the influenza antiviral agent oseltamivir in reducing susceptibility and infectiousness in two case-ascertained household trials.     

Inference for Epidemics with Three Levels of Mixing: Methodology and Application to a Measles Outbreak

Abstract. A stochastic epidemic model is defined in which each individual belongs to a household, a secondary grouping (typically school or workplace) and also the community as a whole. Moreover, infectious contacts take place in these three settings according to potentially different rates. For this model, we consider how different kinds of data can be used to estimate the infection rate parameters with a view to understanding what can and cannot be inferred. Among other things we find that temporal data can be of considerable inferential benefit compared with final size data, that the degree of heterogeneity in the data can have a considerable effect on inference for non‐household transmission, and that inferences can be materially different from those obtained from a model with only two levels of mixing. We illustrate our findings by analysing a highly detailed dataset concerning a measles outbreak in Hagelloch, Germany.     

谁进入了体制内部门就业——教育与家庭背景的作用分析

在劳动力市场分割的情况下,体制内部门的优势吸引了大量高素质劳动者的竞争。体制内部门就业受到劳动者个人人力资本和家庭背景等因素的影响。使用CHNS的数据,采用logit模型说明了教育和家庭背景在进入体制内就业过程中所起的作用。从家庭背景来看,家庭收入、父亲或母亲在体制内部门就业及母亲的户口类型都明显增加了劳动者个人进入体制内部门就业的机会,其影响远远超过了教育的作用。家庭背景在就业过程中的巨大作用形成了职业的代际传递,损害了机会平等原则。教育可以在一定程度上削弱代际职业传递效应。     

Efficient survey sampling of households via Gaussian quadrature

Summary.  The collection of data through surveys is a costly and time-consuming process, particularly when complex economic data are involved. The paper presents an efficient approach, based on Gaussian quadrature, to survey sampling when some information is available about the target population. Using household data from Mozambique, we demonstrate that Gaussian quadrature subsamples, based on relatively easy to observe household characteristics such as size and educational attainment of members, generate better estimates of the moments of household expenditure than random samples of equal size.     

Simulation of close-to-reality population data for household surveys with application to EU-SILC

Statistical simulation in survey statistics is usually based on repeatedly drawing samples from population data. Furthermore, population data may be used in courses on survey statistics to explain issues regarding, e.g., sampling designs. Since the availability of real population data is in general very limited, it is necessary to generate synthetic data for such applications. The simulated data need to be as realistic as possible, while at the same time ensuring data confidentiality. This paper proposes a method for generating close-to-reality population data for complex household surveys. The procedure consists of four steps for setting up the household structure, simulating categorical variables, simulating continuous variables and splitting continuous variables into different components. It is not required to perform all four steps so that the framework is applicable to a broad class of surveys. In addition, the proposed method is evaluated in an application to the European Union Statistics on Income and Living Conditions (EU-SILC).     

On future household structure

Summary.  We develop a method for computing probabilistic household forecasts which quantifies uncertainty in the future number of households of various types in a country. A probabilistic household forecast helps policy makers, planners and other forecast users in the fields of housing, energy, social security etc. in taking appropriate decisions, because some household variables are more uncertain than others. Deterministic forecasts traditionally do not quantify uncertainty. We apply the method to data from Norway. We find that predictions of future numbers of married couples, cohabiting couples and one-person households are more certain than those of lone parents and other private households. Our method builds on an existing method for computing probabilistic population forecasts, combining such a forecast with a random breakdown of the population according to household position (single, cohabiting, living with a spouse, living alone etc.). In this application, uncertainty in the total numbers of households of different types derives primarily from random shares, rather than uncertain future population size. A similar method could be applied to obtain probabilistic forecasts for other divisions of the population, such as household size, health or disability status, region of residence and labour market status.     

Sampling within households in household surveys

Summary.  The number of people to select within selected households has significant consequences for the conduct and output of household surveys. The operational and data quality implications of this choice are carefully considered in many surveys, but the effect on statistical efficiency is not well understood. The usual approach is to select all people in each selected household, where operational and data quality concerns make this feasible. If not, one person is usually selected from each selected household. We find that this strategy is not always justified, and we develop intermediate designs between these two extremes. Current practices were developed when household survey field procedures needed to be simple and robust; however, more complex designs are now feasible owing to the increasing use of computer-assisted interviewing. We develop more flexible designs by optimizing survey cost, based on a simple cost model, subject to a required variance for an estimator of population total. The innovation lies in the fact that household sample sizes are small integers, which creates challenges in both design and estimation. The new methods are evaluated empirically by using census and health survey data, showing considerable improvement over existing methods in some cases.     

社会保障对农民工家庭城市生活消费的影响--以举家迁移农民工家庭为例

基于辽宁省农民工调研数据,采用OLS回归、Heckman两步法和处理效应模型,分析了社会保障对举家迁移农民工家庭城市生活消费的影响,并采用分位数回归法进一步考察了社会保障在不同消费水平上对家庭消费决策机制产生的影响。研究表明,社会保障对农民工家庭生活消费有显著的正向影响;有社会保障家庭的消费决策明显区别于无社会保障家庭,这主要表现为人均收入、人均耕地面积、户主年龄和人均受教育年限等家庭特征对有社会保障家庭和无社会保障家庭生活消费的影响存在明显差异。     

Multiple imputation: an alternative to top coding for statistical disclosure control

Summary.  Top coding of extreme values of variables like income is a common method of statistical disclosure control, but it creates problems for the data analyst. The paper proposes two alternative methods to top coding for statistical disclosure control that are based on multiple imputation. We show in simulation studies that the multiple-imputation methods provide better inferences of the publicly released data than top coding, using straightforward multiple-imputation methods of analysis, while maintaining good statistical disclosure control properties. We illustrate the methods on data from the 1995 Chinese household income project.     

Small area estimation of expenditure means and ratios under a unit-level bivariate linear mixed model

Under a unit-level bivariate linear mixed model, this paper introduces small area predictors of expenditure means and ratios, and derives approximations and estimators of the corresponding mean squared errors. For the considered model, the REML estimation method is implemented. Several simulation experiments, designed to analyze the behavior of the introduced fitting algorithm, predictors and mean squared error estimators, are carried out. An application to real data from the Spanish household budget survey illustrates the behavior of the proposed statistical methodology. The target is the estimation of means of food and non-food household annual expenditures and of ratios of food household expenditures by Spanish provinces.     

Multilevel modelling of refusal and non-contact in household surveys: evidence from six UK Government surveys

Summary.  We analyse household unit non-response in six major UK Government surveys by using a multilevel multinomial modelling approach. The models are guided by current conceptual frameworks and theories of survey participation. One key feature of the analysis is the investigation of the extent to which effects of household characteristics are survey specific. The analysis is based on the 2001 UK Census Link Study, which is a unique source of data containing an unusually rich set of auxiliary variables. The study contains the response outcome of six surveys, linked to census data and interviewer observations for both respondents and non-respondents.     

District-level poverty estimation: a proposed method

This paper develops a method of estimating micro-level poverty in cases where data are scarce. The method is applied to estimate district-level poverty using the household level Indian national sample survey data for two states, viz., West Bengal and Madhya Pradesh. The method involves estimation of state-level poverty indices from the data formed by pooling data of all the districts (each time excluding one district) and multiplying this poverty vector with a known weight matrix to obtain the unknown district-level poverty vector. The proposed method is expected to yield reliable estimates at the district level, because the district-level estimate is now based on a much larger sample size obtained by pooling data of several districts. This method can be an alternative to the “small area estimation technique” for estimating poverty at sub-state levels in developing countries.     

Imputation of Household Survey Data Using Linear Mixed Models

Mixed models are regularly used in the analysis of clustered data, but are only recently being used for imputation of missing data. In household surveys where multiple people are selected from each household, imputation of missing values should preserve the structure pertaining to people within households and should not artificially change the apparent intracluster correlation (ICC). This paper focuses on the use of multilevel models for imputation of missing data in household surveys. In particular, the performance of a best linear unbiased predictor for both stochastic and deterministic imputation using a linear mixed model is compared to imputation based on a single level linear model, both with and without information about household respondents. In this paper an evaluation is carried out in the context of imputing hourly wage rate in the Household, Income and Labour Dynamics of Australia Survey. Nonresponse is generated under various assumptions about the missingness mechanism for persons and households, and with low, moderate and high intra‐household correlation to assess the benefits of the multilevel imputation model under different conditions. The mixed model and single level model with information about the household respondent lead to clear improvements when the ICC is moderate or high, and when there is informative missingness.     

Semiparametric Spatial Autoregressive Models With Endogenous Regressors: With an Application to Crime Data

This study considers semiparametric spatial autoregressive models that allow for endogenous regressors, as well as the heterogenous effects of these regressors across spatial units. For the model estimation, we propose a semiparametric series generalized method of moments estimator. We establish that the proposed estimator is both consistent and asymptotically normal. As an empirical illustration, we apply the proposed model and method to Tokyo crime data to estimate how the existence of a neighborhood police substation (NPS) affects the household burglary rate. The results indicate that the presence of an NPS helps reduce household burglaries, and that the effects of some variables are heterogenous with respect to residential distribution patterns. Furthermore, we show that using a model that does not adjust for the endogeneity of NPS does not allow us to observe the significant relationship between NPS and the household burglary rate. Supplementary materials for this article are available online.     

收入风险、金融教育与家庭金融市场参与

运用中国城市居民消费金融调查数据,基于因果推断的分析框架,采用倾向分值匹配法研究了收入风险、金融教育对家庭金融市场参与的影响。研究发现,收入越稳定,家庭开展金融教育的概率越高;金融教育显著提高了家庭的金融市场参与;按收入风险将样本分组后,上述结论依然稳健,且金融教育对不同家庭金融市场参与的影响具有非对称性。在对家庭收入稳定性分类的基础上,有针对性地增加金融教育的供给,提升家庭整体的金融教育水平,有利于提高家庭金融市场参与度,优化家庭金融资产配置,进而改善家庭金融福利。     

Private households according to socioeconomic classification in national accounting: the model of the Federal Statistical Office

The author notes that "for calculating income distribution and expenditure by groups of households, annual averages are required concerning the number of private households by household groups and...the structure of the household members in a socio-economic classification." Problems of adjusting such household data so that they are compatible with data from other sources such as employment statistics are discussed, and a procedure for adjusting annual micro-census data on households in the Federal Republic of Germany is described. "Results for the year 1982 are presented, showing the main development trends as compared with the year 1972. Finally, the quality of forecasting with the adjustment procedure is studied." (summary in ENG)     

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.     

Empirical Likelihood-Based Inference for Poverty Measures with Relative Poverty Lines

In this article, we propose an empirical likelihood-based method of inference for decomposable poverty measures utilizing poverty lines which are some fraction of the median of the underlying income distribution. Specifically, we focus on making poverty comparisons between two subgroups of the population which share the same poverty line. Our proposed method is assessed using a Monte Carlo simulation and is applied to some Canadian household income data.