边疆地区农村贫困程度的测度与模拟分析——以新疆维吾尔自治区为例

利用2000-2009年新疆农村收入分组数据,首先计算历年新疆农村FGT贫困指数,分析新疆农村贫困程度的变化情况;其次对新疆农村FGT贫困指数进行分解,探讨经济增长、收入分配和贫困线变动对新疆农村贫困程度的影响;最后模拟分析各个贫困指标对贫困线变动的敏感程度。研究结果表明：新千年开始后新疆农村贫困程度变化具有一定的阶段性特点,大体经历了大幅下降、平缓变动、小幅上扬三个阶段;FGT贫困指数分解后发现,各期影响因素作用不一,经济增长的减贫效应最为明显,收入分配状况的改善或者恶化产生了不一样的减贫作用,贫困线上调最明显的效应是新疆农村贫困面扩大;模拟贫困线上调后发现各贫困指标对贫困线较小幅度的变动具有较高的敏感性。新疆农村的贫困问题比较复杂,最近有进一步加重的趋势,尤其是最贫困人口的境况需要获得更多的关注,基于此提出了相关政策建议。     

Poverty as a System: Human Contestability Approach to Poverty Measurement

Since Sen's (1976) paper on poverty measurement, a substantial literature, both theoretical and empirical, has emerged. There have been several recent efforts to drive poverty measures based on different approaches and axioms. These poverty indices are based on head count ratio, poverty gaps and distribution of income. These are very narrow in approach and suffer from several drawbacks. However, the purpose of the present paper is to introduce a new poverty measure based on a holistic and system modelling approach. Based on Chopra's human contestability (Chopra, 2003, 2007) approach to poverty, this new approach to measuring poverty has been developed using a structure equation model based on Kanji's business excellence model (Kanji, 2002) to create the proposed poverty model. We construct a latent variable structural equation model to measure the contestability excellence within certain boundaries of the societal system. It will provide us with a measurement of poverty in a society or community in terms of human contestability. A higher human contestability index will indicate the lower poverty within the society. Strengths and weakness as of various components will also indicate that a characteristic of the individual requires extra society or government support to remove poverty. However, there remains considerable disagreement on the best way to achieve this.     

中国城乡多维贫困的测度及比较

传统的仅用收入或消费来衡量贫困的方法只能提供单一的信息,从多维角度则能够较全面地认识贫困。本文利用CHNS数据库中2000年和2009年数据,借鉴了多维贫困指数的构造及分解方法,对近十年来中国城乡多维贫困进行了测度、分解及分析,认为总体上城乡贫困程度均有所减轻,农村贫困严重于城市,应重视医疗健康保险、卫生设施、城市住房、做饭燃料等维度上的贫困,同时降低中西部省份的贫困。     

安徽省城镇贫困线的测度及贫困状况分析——基于ELES方法

贫困线度量是贫困研究的关键步骤和政府制定扶贫政策的重要依据。本文利用安徽省统计年鉴数据,采用"扩展线性支出法(ELES)",对安徽省1996-2005年的贫困线做了测度,并以此为标准与其他数据进行了比较分析。结果显示,贫困线整体上保持了一个上升的趋势,但是其中在1999、2001和2004年出现比往年低的情况;低收入群体贫困状况在恶化,现有贫困线距离扶贫的标准还很遥远。     

多维极化的测度:新的方法及其应用

鉴于现有多维极化测度方法未能体现维度之间的相关性,本文引入余弦相似度,并基于相对剥夺理论提出了新的多维极化测度方法。该方法还可以进行静态和动态分解,能够分析每个维度及其变动对多维极化的贡献率。运用中国家庭追踪调查(CFPS)数据,本文从收入、财产、交通通信支出和受教育水平四个维度实证分析了我国城乡居民家庭之间的多维极化。实证结果表明,城乡长期多维极化呈现明显下降趋势,其中教育、交通通信支出和收入三个维度的极化效应是主要原因,而财产极化效应不利于多维极化的下降。     

数字金融、数字鸿沟与多维贫困

以互联网为基础的第三次技术革命,推动了数字金融的发展,对居民经济行为的影响巨大,因而也可能具有减贫作用。本文首次利用中国数字普惠金融发展指数和中国家庭追踪调查(CFPS)的匹配数据,通过克服内生性的工具变量估计,探讨了数字经济发展对家庭多维贫困的影响。然而,与预期不同,本文的初步研究发现数字金融发展提升了贫困发生的概率,也加深了多维贫困的程度,且影响逐年增大。不过,进一步研究表明,数字金融发展可能存在结构性问题:数字金融发展给能够接触到互联网的居民带来便利以及机会,挤占了未能接触到互联网的居民原先所可能获得的资源,这种数字鸿沟使得位于贫困线附近和贫困线以下的居民由于数字金融的发展而愈加贫困。观察背后机制发现,数字金融发展主要导致了面临数字劣势的贫困居民失业概率的提升。因此,在推进数字金融发展的同时,应着力提高金融服务的覆盖率和可得性,尤其对于农村的贫困人口而言,积极应对数字经济对就业市场的挑战,引导其成为脱贫攻坚的重要支撑。     

我国农村贫困的动态转化、持续时间与状态依赖研究——基于收入贫困与多维贫困的双重视角

本文使用中国健康与营养调查2000—2015年跟踪数据,比较分析了农村家庭收入贫困与多维贫困的长期变动情况。在此基础上,使用Cox比例风险与动态Probit模型实证研究了跨期贫困的动态转化概率、状态依赖及其影响因素等问题。研究发现,农村多维贫困发生率下降幅度比收入贫困发生率下降幅度高出近20个百分点,但未脱贫的多维贫困家庭比收入贫困家庭具有更明显的贫困适应性。随着贫困持续时间的增加,无论是收入贫困还是多维贫困,中断当前贫困状态的可能性都下降了。进一步分析发现,子代职业地位、子代教育、城镇化水平和交通便捷度等因素显著降低了贫困家庭的贫困适应性与状态依赖,而贫困补贴对部分贫困家庭产生了补贴依赖效应,从而一定程度上促进了其贫困适应性。本研究在理论上丰富了有关贫困动态性的探讨,为有效破解低收入群体的贫困状态依赖提供了经验证据。     

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.     

A Dynamic Latent Model for Poverty Measurement

The main purpose of this paper is the longitudinal analysis of the poverty phenomenon. By interpreting poverty as a latent variable, we are able to resort to the statistical methodology developed for latent structure analysis. In particular, we propose to use the mixture latent Markov model which allows us to achieve two goals: (i) a time-invariant classification of households into homogenous groups, representing different levels of poverty; (ii) the dynamic analysis of the poverty phenomenon which highlights the distinction between transitory and permanent poverty situations. Furthermore, we exploit the flexibility provided by the model in order to achieve the measurement of poverty in a multidisciplinary framework, using several socio-economic indicators as covariates and identifying the main relevant factors which influence permanent and transitory poverty. The analysis of the longitudinal data of the Survey on Households Income and Wealth of the Bank of Italy provides the identification of two groups of households which are characterized by different dynamic features. Moreover, the inclusion of socio-economic covariates such as level of education, employment status, geographical area and residence size of the household head shows a direct association with permanent poverty.     

农村家庭多维贫困测度与分析

多维贫困理论与方法更有助于对贫困的精准识别和量化.本文构建了新型多维贫困指标体系,利用中国家庭追踪调查(CFPS)数据,引人人工神经网络方法,测度并分解了农村家庭多维贫困的广度、深度和强度水平.研究结论表明:随着贫困维度的增加,多维贫困的广度、深度和强度指数下降,表明农村家庭不易发生多维极端贫困;农村家庭多维贫困指数(MPI)呈西高东低态势,表明农村家庭多维贫困具有典型的区域分布特征.此外,多维贫困指数分解结果显示,收入、金融和教育等因素是我国农村家庭致贫的主因.其中,东部地区金融因素影响最大,而中西部地区则为收入因素.研究结论为贫困的识别和精准扶贫提供了政策依据.     

贫困测量中恩格尔系数的失效及分析

 恩格尔系数在我国的适用性问题是学术界重要议题之一。本文利用对哈尔滨市和沈阳市贫困人口的调查数据,分析了恩格尔系数在我国城市贫困测量中失效的问题及原因。研究者尝试对恩格尔系数的分子、分母进行了重新界定和适当的修正,研究检验表明,从宏观测量和微观测量两个方面,修正恩格尔系数都能更准确的反映我国城市贫困家庭的真实生活水平。     

Weights in Multidimensional Indices of Wellbeing: An Overview

Multidimensional indices are becoming increasingly important instruments to assess the wellbeing of societies. They move beyond the focus on a single indicator and yet they are easy to present and communicate. A crucial step in the construction of a multidimensional index of wellbeing is the selection of the relative weights for the different dimensions. The aim of this article is to study the role of these weights and to critically survey eight different approaches to set them. We categorize the approaches in three classes: data-driven, normative, and hybrid weighting, and compare their respective advantages and drawbacks.     

贫困测度指标及其评价

一、引言一般来说,研究贫困问题主要涉及三个方面:一是贫困的识别;二是贫困程度测定;三是反贫困效果判定以及相应的战略选择。贫困识别问题主要指贫困的基本范畴及贫困线的合理确定,关系到贫困集合的准确界定;贫困程度的测定主要指贫困指数的定义和计算,关系到贫困程度大小的判     

边境连片特困区多维贫困测算与空间分布——以新疆南疆三地州为例

利用2012年国家统计局新疆调查总队农村住户调查数据,采用AF多维贫困测量方法,测算了新疆南疆三地州多维贫困状况,研究了贫困空间分布与区域差异,得到以下结论:从多维贫困程度看,多维贫困发生率为24.5%,多维贫困指数为0.138;从多维贫困组成看,收入低、饮水困难、教育水平低、卫生条件差、少数民族汉语能力差等是核心问题,且收入贫困户几乎都是多维贫困户;从多维贫困空间分布看,克州地区最为严重,其次为和田地区和喀什地区,阿克陶县和疏勒县形成第一贫困级,阿图什市和和田县形成第二贫困级,疏附县和墨玉县形成第三贫困级,其他各县形成第四贫困级,且边境县多维贫困状况更加恶劣。     

Inference on Survival Data with Covariate Measurement Error – An Imputation-based Approach

Abstract.  We propose a new method for fitting proportional hazards models with error-prone covariates. Regression coefficients are estimated by solving an estimating equation that is the average of the partial likelihood scores based on imputed true covariates. For the purpose of imputation, a linear spline model is assumed on the baseline hazard. We discuss consistency and asymptotic normality of the resulting estimators, and propose a stochastic approximation scheme to obtain the estimates. The algorithm is easy to implement, and reduces to the ordinary Cox partial likelihood approach when the measurement error has a degenerate distribution. Simulations indicate high efficiency and robustness. We consider the special case where error-prone replicates are available on the unobserved true covariates. As expected, increasing the number of replicates for the unobserved covariates increases efficiency and reduces bias. We illustrate the practical utility of the proposed method with an Eastern Cooperative Oncology Group clinical trial where a genetic marker, c- myc expression level, is subject to measurement error.     

Estimation and Inference in Two-Step Econometric Models

A commonly used procedure in a wide class of empirical applications is to impute unobserved regressors, such as expectations, from an auxiliary econometric model. This two-step (T-S) procedure fails to account for the fact that imputed regressors are measured with sampling error, so hypothesis tests based on the estimated covariance matrix of the second-step estimator are biased, even in large samples. We present a simple yet general method of calculating asymptotically correct standard errors in T-S models. The procedure may be applied even when joint estimation methods, such as full information maximum likelihood, are inappropriate or computationally infeasible. We present two examples from recent empirical literature in which these corrections have a major impact on hypothesis testing.     

Estimation and Inference of Distributional Partial Effects: Theory and Application

This article considers nonparametric and semiparametric estimation and inference of the effects of a covariate, either discrete or continuous, on the conditional distribution of a response outcome. It also proposes various uniform tests following estimation. This type of analysis is useful in situations where the econometrician or policy-maker is interested in knowing the effect of a variable or policy on the whole distribution of the response outcome conditional on covariates and is not willing to make parametric functional form assumptions. Monte Carlo experiments show that the proposed estimators and tests are well-behaved in small samples. The empirical section studies the effect of minimum wage hikes on household labor earnings. It is found that the minimum wage has a heterogenous impact on household earnings in the U.S. and that small hikes in the minimum wage are more effective in improving the household earnings distribution.     

Inference of Seasonal Long‐memory Time Series with Measurement Error

We consider the Whittle likelihood estimation of seasonal autoregressive fractionally integrated moving‐average models in the presence of an additional measurement error and show that the spectral maximum Whittle likelihood estimator is asymptotically normal. We illustrate by simulation that ignoring measurement errors may result in incorrect inference. Hence, it is pertinent to test for the presence of measurement errors, which we do by developing a likelihood ratio (LR) test within the framework of Whittle likelihood. We derive the non‐standard asymptotic null distribution of this LR test and the limiting distribution of LR test under a sequence of local alternatives. Because in practice, we do not know the order of the seasonal autoregressive fractionally integrated moving‐average model, we consider three modifications of the LR test that takes model uncertainty into account. We study the finite sample properties of the size and the power of the LR test and its modifications. The efficacy of the proposed approach is illustrated by a real‐life example.     

Poverty and inequality in European regions

The European Union Statistics on Income and Living Conditions (EU-SILC) is the main source of information about poverty and economic inequality in the member states of the European Union. The sample sizes of its annual national surveys are sufficient for reliable estimation at the national level but not for inferences at the sub-national level, failing to respond to a rising demand from policy-makers and local authorities. We provide a comprehensive map of median income, inequality (Gini coefficient and Lorenz curve) and poverty (poverty rates) based on the equivalised household income in the countries in which the EU-SILC is conducted. We study the distribution of income of households (pro-rated to its members), not merely its median (or mean), because we regard its dispersion and frequency of lower extremes (relative poverty) as important characteristics. The estimation for the regions with small sample sizes is improved by the small-area methods. The uncertainty of complex nonlinear statistics is assessed by bootstrap. Household-level sampling weights are taken into account in both the estimates and the associated bootstrap standard errors.