对内蒙古农村弱势群体收入水平现状的思考

党的十六大提出，发展农村经济，增加农民收入是全面建设小康社会的重大任务。扶助农村弱势群体，尽快使他们摆脱贫困，提高生活水平，是今后农村经济工作的重要任务，也是解决小康水平低，不全面，尤其是不平衡问题，实现全面建设小康社会的需要。一、农村弱势群体产生和存在的原因农村弱势群体产生的原因比较复     

创新思路 全面建设农村小康社会

全面建设小康社会,是以全面建设农村小康社会为基础的,没有农村的稳定和全面进步,就不可能有整个社会的稳定和全面进步,没有农民的小康就不会有全国人民的小康。我们必须从战略高度充分认识到,农村全面建设小康社会是我国全面建设小康社会的关键。一、坚持以经济建设为中心,不断提高农民收入水平推进农村小康建设,必须以经济建设为中心,以发展为主题,以农业和农村经济结构战略性调整为主线,以增加农民收入为核心,努力提高农业和农村经济的发展水平。在第一产业方面,要狠抓农业结构调整工作:一是把农产品的质量调“高”。要从抓品种和投入品…     

农村剩余劳动力测算方法研究

一、引言农村剩余劳动力转移是加快实现全面建设小康社会的前提。改革开放以来 ,农村经济取得了巨大发展 ,农民收入大幅度提高 ,农民生活明显改善 ,农村贫困人口迅速大量减少。但是与全面建设小康社会目标相比 ,还有较大差距。全面建设小康社会 ,重点和难点都在农村。长期以来     

全面小康社会的评价指标体系

一、基本依据和原则 (一)全面建设小康社会的内涵.一个指标体系的确定与所依据的具体理论密切相关,全面建设小康社会的定义与实质这是其评价指标体系构建的基本依据.全面建设小康社会是与总体小康相对应的,概括来讲,全面建设小康社会就是要建立水平较高、较全面的小康社会.     

我国住房金融创新的思路与建议

一、我国住房金融的发展趋势与现存问题 1.全面建设小康社会和全面对外开放为房地产行业提供了新机遇 提高城市化水平是全面建设小康社会的要求之一,随着经济的发展和技术水平的提高,更多的农村劳动力从土地上解放出来并进入城市是社会进步的必然趋势.     

县域民营企业发展中的问题及对策

县级层面是国家经济、社会最具综合性的基本单元,是中央和地方、宏观和微观、工业和农业、城市和农村的结合部.党的十六大把壮大县域经济作为全面建设小康社会的重大任务予以强调,充分说明了加快县域经济发展的极端重要性.     

提高农民科技文化素质的意义及对策

党的十六大提出了全面建设小康社会的奋斗目标,实现这一目标,关键在农村,重点在农村,难点也在农村.农民科技文化素质的高低将直接决定和影响农村经济的发展水平和全面建设农村小康社会的进程.     

我国农村全面建设小康社会迈上新台阶

党的十六大提出了全面建设小康社会的宏伟目标。全面建设小康社会最艰巨、最繁重的任务在农村。为了科学地反映农村全面建设小康社会的现状,为党和政府制定政策提供依据,国家统计局每年对农村全面建设小康社会进程进行监测和评价。     

全面建设小康社会中经济结构战略调整的重要意义

全面建设小康社会是新的社会发展观。全面小康不只是经济问题,将是物质文明、政治文明和精神文明的协调发展,是全面而深刻的社会变革。全面建设小康社会必须推动经济结构的战略性调整,实现产业结构、地区结构、城乡结构、就业结构的系统调整,第三产业将会大发展,成为经济增长的第一推动力。     

实施工业强省战略 促进四川经济发展新跨越

实施工业强省战略是时代的选择全面建设小康社会的重要基础党的“十六大”提出全面建设小康社会的奋斗目标,其经济内涵是:到2020年使国内生产总值在2000年的基础上翻两番,并相应完善市场经济体制、加快城市化进程和缩小收入差距,使人民的小康生活更加富足。而要实现这一鼓舞人心的奋斗目标,其经济基础就是基本实现工业化。工业化是社会经济发展中由以农业经济为主过渡到以工业经济为主的一个特定历史阶段和发展过程。工业化不仅意味着大量转移农村富余劳动力,以及大幅度增加工业在国内生产总值中的比重,而且意味着将工业的社会化大生产方式…     

价格波动与资源配置效率研究

 文章从理论和经验两方面研究了价格波动对资源配置效率的影响。理论模型显示：由于调整成本的存在,面对较低水平价格波动的企业可能并不会立刻调整要素投入比例和生产计划,这一“保守策略”意味着价格波动可能带来企业配置效率损失的增加。但是随着价格波动幅度进一步增加,越来越多的企业将会主动的对生产计划进行调整,以便抵消较大幅度波动的不利影响。“保守策略”向“调整策略”的转变意味着价格波动引起的资源的配置效率损失可能存在门阀效应。利用1978-2007年省级面板数据和基于极大似然估计的随机前沿方法,文章实证研究证实了价格波动对资源配置效率损失的非线性影响,认为传统的研究可能低估价格波动对经济的负面影响。     

Parental partnership and joblessness in childhood and their influence on young people's outcomes

Summary.  The paper estimates the relationship between several outcomes in early adulthood (education, inactivity, early birth, distress and smoking) and experiences of life in a single-parent family and with jobless parent(s) during childhood. The analysis is performed using a sample of young adults, who are selected from the first nine waves of the British Household Panel Survey (1991–1999) and can be matched with at least one parent and one sibling over the same period. This sample allows us to estimate the relationship of interest by using sibling differences. We also use another sample of young adults from the British Household Panel Survey, matched to at least one parent, to estimate more conventional level models and to compute nonparametric bounds and point estimates. The estimates based on sibling differences require weaker assumptions (compared with the assumptions that are imposed by nonparametric estimators under conditional independence and level estimators) for the identification of the effects of family structure and parental joblessness on the outcomes under analysis. We find that experiences of life in a single-parent family and with jobless parents during childhood are usually associated with disadvantageous outcomes for young adults, the effect of family structure is in general significantly greater (in absolute value) than the effect of parental worklessness and most of the unfavourable outcomes are linked to an early family disruption, when the child was aged 0–5 years, whereas the timing of parental joblessness during childhood has more complex effects, with different outcomes being more strongly influenced by parental worklessness at different ages of the child.     

Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research

Researchers are increasingly using the standardized difference to compare the distribution of baseline covariates between treatment groups in observational studies. Standardized differences were initially developed in the context of comparing the mean of continuous variables between two groups. However, in medical research, many baseline covariates are dichotomous. In this article, we explore the utility and interpretation of the standardized difference for comparing the prevalence of dichotomous variables between two groups. We examined the relationship between the standardized difference, and the maximal difference in the prevalence of the binary variable between two groups, the relative risk relating the prevalence of the binary variable in one group compared to the prevalence in the other group, and the phi coefficient for measuring correlation between the treatment group and the binary variable. We found that a standardized difference of 10% (or 0.1) is equivalent to having a phi coefficient of 0.05 (indicating negligible correlation) for the correlation between treatment group and the binary variable.     

Interval estimation of the mean in a two-stage nested model

The problem of constructing confidence intervals to estimate the mean in a two-stage nested model is considered. Several approximate intervals, which are based on both linear and nonlinear estimators of the mean are investigated. In particular, the method of bootstrap is used to correct the bias in the ‘usual’ variance of the nonlinear estimators. It is found that the intervals based on the nonlinear estimators did not achieve the nominal confidence coefficient for designs involving a small number of groups. Further, it turns out that the intervals are generally conservative, especially at small values of the intraclass correlation coefficient, and that the intervals based on the nonlinear estimators are more conservative than those based on the linear estimators. Compared with the others, the intervals based on the unweighted mean of the group means performed well in terms of coverage and length. For small values of the intraclass correlation coefficient, the ANOVA estimators of the variance components are recommended, otherwise the unweighted means estimator of the between groups variance component should be used. If one is fortunate enough to have control over the design, he is advised to increase the number of groups, as opposed to increasing group sizes, while avoiding groups of size one or two.     

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)     

Linear and non-linear canonical correlation analysis:an exploratory tool for the analysis of group-structured data

Confronted with multivariate group-structured data, one is in fact always interested in describing differences between groups. In this paper, canonical correlation analysis (CCA) is used as an exploratory data analysis tool to detect and describe differences between groups of objects. CCA allows for the construction of Gabriel biplots, relating representations of objects and variables in the plane that best represents the distinction of the groups of object points. In the case of non-linear CCA, transformations of the original variables are suggested to achieve a better group separation compared with that obtained by linear CCA. One can detect which (transformed) variables are responsible for this separation. The separation itself might be due to several characteristics of the data (eg. distances between the centres of gravity of the original or transformed groups of object points, or differences in the structure of the original groups). Four case studies give an overview of an exploration of the possibilities offered by linear and non-linear CCA.     

A Note on the Variances of the Tests of Kendall,Jonckheere, and Terpstra

Recently, some papers have been appearing that have noticed some mistakes in the calculations in the statistical software in simple and classical tests (Wilcoxon–Mann-Whitney and Log-Rank). This article examines the variance formulas for the Kendall test, the Jonckheere test, and the Terpstra test. We show in this article that these variances (in spite of what is said in the literature and in the statistical software), in the presence of ties and r ≥ 3 groups, are only equal approximately and are exactly equal for two groups and ties as we prove in the Appendix.     

On the estimators and tests for the semiparametric hazards regression model

In the accelerated hazards regression model with censored data, estimation of the covariance matrices of the regression parameters is difficult, since it involves the unknown baseline hazard function and its derivative. This paper provides simple but reliable procedures that yield asymptotically normal estimators whose covariance matrices can be easily estimated. A class of weight functions are introduced to result in the estimators whose asymptotic covariance matrices do not involve the derivative of the unknown hazard function. Based on the estimators obtained from different weight functions, some goodness-of-fit tests are constructed to check the adequacy of the accelerated hazards regression model. Numerical simulations show that the estimators and tests perform well. The procedures are illustrated in the real world example of leukemia cancer. For the leukemia cancer data, the issue of interest is a comparison of two groups of patients that had two different kinds of bone marrow transplants. It is found that the difference of the two groups are well described by a time-scale change in hazard functions, i.e., the accelerated hazards model.     

Improved inference for a linear mixed-effects model when the subpopulation effects are clustered

We provide Bayesian methodology to relax the assumption that all subpopulation effects in a linear mixed-effects model have, after adjustment for covariates, a common mean. We expand the model specification by assuming that the m subpopulation effects are allowed to cluster into d groups where the value of d, 1?d?m, and the composition of the d groups are unknown, a priori. Specifically, for each partition of the m effects into d groups we only assume that the subpopulation effects in each group are exchangeable and are independent across the groups. We show that failure to take account of this clustering, as with the customary method, will lead to serious errors in inference about the variances and subpopulation effects, but the proposed, expanded, model leads to appropriate inferences. The efficacy of the proposed method is evaluated by contrasting it with both the customary method and use of a Dirichlet process prior. We use data from small area estimation to illustrate our method.     

A practical approach for assessing the effect of grouping in hierarchical spatio-temporal models

Hierarchical spatio-temporal models allow for the consideration and estimation of many sources of variability. A general spatio-temporal model can be written as the sum of a spatio-temporal trend and a spatio-temporal random effect. When spatial locations are considered to be homogeneous with respect to some exogenous features, the groups of locations may share a common spatial domain. Differences between groups can be highlighted both in the large-scale, spatio-temporal component and in the spatio-temporal dependence structure. When these differences are not included in the model specification, model performance and spatio-temporal predictions may be weak. This paper proposes a method for evaluating and comparing models that progressively include group differences. Hierarchical modeling under a Bayesian perspective is followed, allowing flexible models and the statistical assessment of results based on posterior predictive distributions. This procedure is applied to tropospheric ozone data in the Italian Emilia–Romagna region for 2001, where 30 monitoring sites are classified according to environmental laws into two groups by their relative position with respect to traffic emissions.