Regression trees for poverty mapping

Bilton Penelope; Jones Geoff; Ganesh Siva; Haslett Stephen

摘要

Poverty mapping is used to facilitate efficient allocation of aid resources, with the objective of ending poverty, the first of the United Nations Sustainable Development Goals. Levels of poverty across small geographic domains within a country are estimated using a statistical model, and the resulting estimates displayed on a poverty map. Current methodology for small area estimation of poverty utilises various forms of regression modelling of household income or expenditure. Fitting sound models requires skill and time, especially where there are many candidate regressors and even more possible interactions. Tree-based methods have the potential to screen more quickly for interactions and also to provide reliable small area estimates in their own right. A classification tree technique has been presented by Bilton et al. (Comput Stat Data Anal115: 53-66, 2017) for estimating poverty incidence, but although adjustments were made to incorporate complex survey designs and estimate mean square error, classification trees are unable to estimate the associated non-categorical deprivation measures of poverty gap and poverty severity. The focus of this paper is regression trees, because they enable all three core poverty measures of incidence, gap and severity to be estimated. Using regression trees, two alternative methodologies, parametric and non-parametric, are explored for producing household level predictions that are then amalgamated up to small-area level. New methods are developed for mean square error estimation. The properties of the small area estimates based on these regression tree techniques are then evaluated and compared with linear mixed models both by simulation and by using real poverty data from Nepal.

机译：贫困映射用于促进援助资源的高效配置，凭借结束贫困的目标，是联合国可持续发展目标的第一个。使用统计模型估计一个国家/地区的小地理域中的贫困水平，并在贫困地图上显示的估算结果。目前对贫困的小区估计的目前的方法利用家庭收入或支出的各种形式的回归建模。拟合声音模型需要技能和时间，尤其是存在许多候选人回归器和更可能的交互。基于树的方法有可能更快地筛选交互，并在自己的权利中提供可靠的小面积估计。 Bilton等人介绍了分类树技术。（计算统计数据ANAN115：53-66,2017）用于估算贫困发病率，但虽然调整融合了复杂的调查设计并估计均方误差，但分类树无法估计贫困间隙的相关非分类剥夺措施贫困严重程度。本文的重点是回归树，因为它们可以估计所有三个核心贫困措施，差距和严重程度。使用回归树，两种替代方法，参数和非参数，用于产生家庭水平预测，然后达到小面积水平。开发了新方法，用于均方误差估计。然后评估基于这些回归树技术的小面积估计的性质，并通过模拟和使用来自尼泊尔的真实贫困数据的线性混合模型进行比较。

Regression trees for poverty mapping

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