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A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group

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  • Mariah Abdul Rahman
  • Nor Samsiah Sani
  • Rusnita Hamdan
  • Zulaiha Ali Othman
  • Azuraliza Abu Bakar

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.

Suggested Citation

  • Mariah Abdul Rahman & Nor Samsiah Sani & Rusnita Hamdan & Zulaiha Ali Othman & Azuraliza Abu Bakar, 2021. "A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-25, August.
    • Handle: RePEc:plo:pone00:0255312
    DOI: 10.1371/journal.pone.0255312
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    References listed on IDEAS

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    1. Alkire, Sabina & Santos, Maria Emma, 2014. "Measuring Acute Poverty in the Developing World: Robustness and Scope of the Multidimensional Poverty Index," World Development, Elsevier, vol. 59(C), pages 251-274.
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    1. Prakash C Bhattarai & Milan Shrestha & Prakash Kumar Paudel, 2023. "Measuring multi-dimensional disparity index: A case of Nepal," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-14, October.

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