Determinants of Poverty: A Spatial Analysis. (Journal of Tropical Resources and Sustainable Science (JTRSS), 2019)
Eradicating poverty has become the main concern for Malaysian government since independence. Recognising the incidence of poverty through standard statistical data tables alone is no longer adequate. This study examines socio-demographic effects on poverty and measures spatial patterns in poverty risk looking for high risk of areas. The poverty data were counts of the numbers of poverty cases occurring in every ten districts of Kelantan. To model these data, a spatial autocorrelation was detected prior to a Poisson Log Linear Leroux Conditional Autoregressive was fitted to the data. The result shows the variables household members, number of non-education of household head and log number of female household head significantly associated with the number of poor households. Tumpat was found as the highest risk area of poverty.
Keywords: Poverty; Spatial Autocorrelation; Conditional Autoregressive Models