中文摘要
基于16个国家142项家庭调查的家庭收入与消费回归模型，对地区层面和家庭层面的特异性误差分布进行估计，结果表明这些误差分布明显偏离正态分布；这类回归模型正是贫困地图的基础。Elbers等（2003）提出的贫困地图绘制方法展示了考虑这种非正态性的价值。遗憾的是，他们用于估计误差分布函数的非参数估计量并不适用于经验贝叶斯（EB）预测。本文提出了一种既适用于EB预测、又能处理非正态分布误差的方法。研究发现，在地方福利的经验最优估计中考虑非正态性，相较于基于正态分布假设的EB估计，能够提高精度。尽管精度提升的幅度从可观到微小不等，但始终为正。能够获得精确的地方福利估计，有助于政府和发展机构识别并监测落后地区，并更高效地配置资源。
Abstract
Estimates of the area- and household idiosyncratic error distributions from household income and consumption regression models across 142 household surveys from 16 different countries, the type of models that underpin poverty maps, points to significant deviations from normality. The poverty mapping approach put forward by Elbers et al. (2003) demonstrates the value of accommodating this non-normality. Unfortunately, their non-parametric estimator for the error distribution functions does not lend itself for Empirical Bayes (EB) prediction. This paper puts forward an approach that accommodates both EB prediction and non-normally distributed errors. Accounting for non-normality in Empirical Best estimation of local welfare is found to increase precision relative to normal-EB estimation. While the gains in precision range between substantial and marginal, it is always positive. Being able to obtain precise local estimates of welfare help governments and development agencies identify and monitor lagging areas and allocate their resources in a more efficient manner.