Small Area Poverty Estimation under Heteroskedasticity

IF 1.6 4区 数学 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS
Sumonkanti Das, Ray Chambers
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引用次数: 0

Abstract

Multilevel models with nested errors are widely used in poverty estimation. An important application in this context is estimating the distribution of poverty as defined by the distribution of income within a set of domains that cover the population of interest. Since unit-level values of income are usually heteroskedastic, the standard homoskedasticity assumptions implicit in popular multilevel models may not be appropriate and can lead to bias, particularly when used to estimate domain-specific income distributions. This article addresses this problem when the income values in the population of interest can be characterized by a two-level mixed linear model with independent and identically distributed domain effects and with independent but not identically distributed individual effects. Estimation of poverty indicators that are functionals of domain-level income distributions is also addressed, and a nonparametric bootstrap procedure is used to estimate mean squared errors and confidence intervals. The proposed methodology is compared with the well-known World Bank poverty mapping methodology for this situation, using model-based simulation experiments as well as an empirical study based on Bangladesh poverty data.
异方差下的小地区贫困估计
具有嵌套误差的多层次模型被广泛应用于贫困估算。在这种情况下,一个重要的应用就是估算贫困的分布情况,而贫困的分布情况是由覆盖相关人口的一系列领域内的收入分布情况来定义的。由于单位水平的收入值通常是异方差的,因此流行的多层次模型中隐含的标准同方差假设可能并不合适,并可能导致偏差,尤其是在用于估计特定领域的收入分布时。当相关人群的收入值可以用具有独立且同分布的领域效应和具有独立但非同分布的个体效应的两级混合线性模型来描述时,本文就可以解决这个问题。文章还讨论了作为领域级收入分布函数的贫困指标的估算问题,并使用了非参数自举程序来估算均方误差和置信区间。利用基于模型的模拟实验以及基于孟加拉国贫困数据的实证研究,将所提出的方法与著名的世界银行贫困绘图方法进行了比较。
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来源期刊
CiteScore
4.30
自引率
9.50%
发文量
40
期刊介绍: The Journal of Survey Statistics and Methodology, sponsored by AAPOR and the American Statistical Association, began publishing in 2013. Its objective is to publish cutting edge scholarly articles on statistical and methodological issues for sample surveys, censuses, administrative record systems, and other related data. It aims to be the flagship journal for research on survey statistics and methodology. Topics of interest include survey sample design, statistical inference, nonresponse, measurement error, the effects of modes of data collection, paradata and responsive survey design, combining data from multiple sources, record linkage, disclosure limitation, and other issues in survey statistics and methodology. The journal publishes both theoretical and applied papers, provided the theory is motivated by an important applied problem and the applied papers report on research that contributes generalizable knowledge to the field. Review papers are also welcomed. Papers on a broad range of surveys are encouraged, including (but not limited to) surveys concerning business, economics, marketing research, social science, environment, epidemiology, biostatistics and official statistics. The journal has three sections. The Survey Statistics section presents papers on innovative sampling procedures, imputation, weighting, measures of uncertainty, small area inference, new methods of analysis, and other statistical issues related to surveys. The Survey Methodology section presents papers that focus on methodological research, including methodological experiments, methods of data collection and use of paradata. The Applications section contains papers involving innovative applications of methods and providing practical contributions and guidance, and/or significant new findings.
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