Abstract
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).
Highlights
The Kingdom of Tonga has recently adopted the consensual approach (CA), which draws on Townsend’s theory of relative deprivation and has been applied in both high income and low-income countries, to produce its official poverty estimates (Guio et al 2012; Guio et al 2017; Lau et al 2015; Nandy and Pomati 2015; Pantazis et al 2006; Townsend 1979)
The aims of the study are to compute for the first time small-area multidimensional poverty estimates for Tonga, advance the production of these small-area estimates for poverty measures that rely on the consensual approach and contribute to the illustration of the advantages of using a Hierarchical Bayesian estimator that relies on novel computational tools
Since information is available at different levels, hierarchical or multi-level models are suited to the task, as they permit to incorporate into one model household-level data, area-level information and an estimate of uncertainty for the model, i.e. the unaccounted variation by the variables included in the model (Gelman et al 2014; Goldstein 1999)
Summary
The Kingdom of Tonga has recently adopted the consensual approach (CA), which draws on Townsend’s theory of relative deprivation and has been applied in both high income and low-income countries, to produce its official poverty estimates (Guio et al 2012; Guio et al 2017; Lau et al 2015; Nandy and Pomati 2015; Pantazis et al 2006; Townsend 1979). Tonga has been the first small island state in the South Pacific region to adopt an official multidimensional poverty measure to report the sustainable development goal (SDG) indicator 1.2 which aims to: “By 2030, reduce at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions” This is a major shift from the widely used calorie-based measure in that the CA is a multidimensional approach that focuses on people deprived of socially perceived necessities in the society to which they belong (see section 1) (Mack and Lansley 1985; Townsend 1979). This is a major disadvantage in that the location and concentration of poverty is vital in designing anti-poverty policies and responses to assist the most vulnerable and affected inhabitants when catastrophic events hit the islands, such as the recent Cyclone Gita in 2018
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
