Abstract
The Multidimensional Poverty Index (MPI) is an income-based poverty index which measures multiple deprivations alongside other relevant factors to determine and classify poverty. The implementation of a reliable MPI is one of the significant efforts by the Malaysian government to improve measures in alleviating poverty, in line with the recent policy for Bottom 40 Percent (B40) group. However, using this measurement, only 0.86% of Malaysians are regarded as multidimensionally poor, and this measurement was claimed to be irrelevant for Malaysia as a country that has rapid economic development. Therefore, this study proposes a B40 clustering-based K-Means with cosine similarity architecture to identify the right indicators and dimensions that will provide data driven MPI measurement. In order to evaluate the approach, this study conducted extensive experiments on the Malaysian Census dataset. A series of data preprocessing steps were implemented, including data integration, attribute generation, data filtering, data cleaning, data transformation and attribute selection. The clustering model produced eight clusters of B40 group. The study included a comprehensive clustering analysis to meaningfully understand each of the clusters. The analysis discovered seven indicators of multidimensional poverty from three dimensions encompassing education, living standard and employment. Out of the seven indicators, this study proposed six indicators to be added to the current MPI to establish a more meaningful scenario of the current poverty trend in Malaysia. The outcomes from this study may help the government in properly identifying the B40 group who suffers from financial burden, which could have been currently misclassified.
Highlights
Malaysia has experienced significant progress in poverty reduction over half a century ago with tremendous initiatives made by the government since the introduction of the New Economic Policy (NEP) in 1971 [1]
Malaysia has taken steps to develop its custom Multidimensional Poverty Index (MPI) model at the national level as outlined in the Eleventh Malaysia Plan (11MP), following the footsteps of 100 countries worldwide that have already adopted the methods launched by Oxford Poverty and Human Development Initiative (OPHI) in 2010 [7]
This study looks at the relationship between deprivation and demographic characteristics based on the clusters produced
Summary
Malaysia has experienced significant progress in poverty reduction over half a century ago with tremendous initiatives made by the government since the introduction of the New Economic Policy (NEP) in 1971 [1]. On July 2010, the Oxford Poverty and Human Development Initiative (OPHI) and the United Nations Development Programme (UNDP) proposed a new poverty measure They introduced the Multidimensional Poverty Index (MPI), which complements traditional income-based poverty indices by measuring multiple dimensions and different factors to determine and classify poverty. Malaysia has taken steps to develop its custom Multidimensional Poverty Index (MPI) model at the national level as outlined in the Eleventh Malaysia Plan (11MP), following the footsteps of 100 countries worldwide that have already adopted the methods launched by OPHI in 2010 [7] It complements the PLI by considering other aspects apart from income. Outcomes from this study help government to efficiently identify B40 group, which otherwise could be misclassified
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