Open AccessA monthly spatio-temporal modelling of patterns for children on maintenance grant in Namibia
Author(s) -
Dismas Ntirampeba,
B. Nshimyimana,
C.R. Kikawa
Publication year - 2020
Publication title -
statistical journal of the iaos
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.286
H-Index - 16
eISSN - 1875-9254
pISSN - 1874-7655
DOI - 10.3233/sji-200729
Subject(s) - poverty , bayesian probability , rate ratio , incidence (geometry) , geography , unemployment , demography , econometrics , statistics , demographic economics , economics , confidence interval , economic growth , mathematics , sociology , geometry
BACKGROUND: Child support grant (CSG) is one of the social protection strategies which is today widely seen as an intervention contributing to poverty reduction. However, despite substantial expenses, it has been documented that Namibia’s social protection benefits do not reach intended beneficiaries in an efficient manner. AIMS: This study aims to determine factors associated with spatial and temporal variation in maintenance child grant and as well as identify regions with elevated incidence rate ratios of maintenance grant in Namibia. METHODS: We fitted a Bayesian spatio-temporal regression model on maintenance grant data available over 9 years in Namibia. RESULTS: The number of children on grant has almost tripled between March 2007 and October 2015 (it changed from 50596 in 2007 to 132840). Unemployment and orphanhood were significantly associated with the incidence rate ratio of maintenance grant (CI:(1.634, 2.627) and CI: (1.000, 1.004), respectively. The adjustment of measurement error in orphanhood through the Berkson error model has ensured the stability of its effect. CONCLUSIONS: This study has shown the strength of using measurement error models for analysing child grant data. Furthermore, the study has demonstrated that the northern regions of Namibia have the highest child incidence rate ratio of maintenance grant whereas the regions in central and south are at low incidence rate at present. The maps produced in this study can be particularly helpful in allocating efficiently limited resources in poor settings.