Development of poverty index with aggregated weights and beta regression analysis

Poverty is a serious socio-economic issue faced by the world. Despite decades of efforts that have been made to reduce poverty, in reality, Malaysia has yet to break free from the issue of poverty. Kelantan and Kedah have been identified as having the highest poverty incidence in Peninsular Malaysia. Most of the past research has focused on developing Poverty Index (PI) at the federal and state-level which does not really reflect the actual situation in district-level. Therefore, this study aimed to develop a PI for the 12 districts in Kedah. Since the PI value is bounded to 0 and 1, hence the beta regression model was fitted to investigate the relationship between main indicators and sub-indicators of PI. Poverty indicators weight has been computed by aggregating subjective and objective weights using Rank Order Centroid (ROC) and CRiteria Importance Through Intercriteria Correlation (CRITIC) methods respectively. Hence, PI has been developed by using Simple Additive Weighting (SAW) method. From the results of PI, beta regression analysis was applied to investigate the relationship between main indicators and subindicators of PI. The result shows the most important indicator of poverty is occupation. It is also found that Kuala Muda and Sik have the highest PI value for poor and hardcore poor categories respectively. Finally, there is a positive relationship between the main indicators and sub-indicators of PI. The developed PI can be used by relevant authorities to provide related initiatives that could help poor people enhance their economic productivity for the betterment of their lives.