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Small-Area Multidimensional Poverty Estimates for Tonga 2016: Drawn from a Hierarchical Bayesian Estimator
Applied Spatial Analysis and Policy ( IF 2.0 ) Pub Date : 2019-07-08 , DOI: 10.1007/s12061-019-09304-8
Héctor E. Nájera Catalán , Viliami Konifelenisi Fifita , Winston Faingaanuku

Tonga has recently adopted the consensual approach to produce its official multidimensional poverty measure. This index is computed using data from the Household’s Income and Expenditure Survey (HIES). The population of Tonga is scattered across 5 main groups of islands and high-quality spatial data is vital to inform policies. One limitation is that HIES data originate from a nationally representative survey that cannot produce reliable estimates for small areas such as constituencies, villages or blocks, and governments require highly disaggregated data to better inform policies, for example, with regard to natural disasters. This paper produces small-area estimates of poverty based on a hybrid hierarchical Bayesian (HHB) estimator, which draws on the standard hierarchical Bayes (HB) approach but uses a more efficient computation process – Hamiltonian (hybrid) Monte Carlo (HMC) – to produce the posterior distributions. The HHB estimator is then applied to Tonga’s National Population Census (2016) to present estimates down to island, constituency and block level. The results suggest that the extent of poverty is lower in Tongatapu than in Eua, Vava’u, Ha’apai and Niuas, while its prevalence is very similar (around 35%) in Eua, Vava’u and Ha’apai. Constituencies in Tongatapu show lower poverty rates than in the rest of the islands, and block-level data show a clear spatial pattern of poverty distribution in the capital Tongatapu. These are the first small-area indirect poverty estimates based on a hierarchical Bayesian model and drawn from the consensual approach (CA).

中文翻译:

汤加2016年的小面积多维贫困估计:来自分层贝叶斯估计量

汤加最近采用了协商一致的方法来制定其官方的多维贫困衡量标准。该指数是使用家庭收入和支出调查(HIES)中的数据计算得出的。汤加的人口分布在5个主要岛屿群中,高质量的空间数据对于制定政策至关重要。一个局限性是,HIES数据来源于全国代表性的调查,而该调查无法对选区,村庄或街区等小区域产生可靠的估计,并且政府需要高度分类的数据以更好地为政策提供信息,例如有关自然灾害的信息。本文根据混合层次贝叶斯(HHB)估算器得出小面积贫困估算,它采用标准的分级贝叶斯(HB)方法,但使用更有效的计算过程-哈密顿(混合)蒙特卡洛(HMC)-来产生后验分布。然后,将HHB估算器应用于汤加的国家人口普查(2016),以提供岛屿,选区和街区级别的估算值。结果表明,汤加塔普的贫困程度低于Eua,Vava'u,Ha'apai和Niuas,而Eua,Vava'u和Ha'apai的贫困率非常相似(约35%)。汤加塔普的选区显示出的贫困率低于其他岛屿,并且块级数据显示首都汤加塔普的贫困分布具有明显的空间格局。这些是基于分层贝叶斯模型并根据共识方法(CA)得出的第一个小区域间接贫困估计。
更新日期:2019-07-08
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