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...

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Main Author:	Nuril Asyikin, Mohamad
Format:	Thesis
Language:	eng eng
Published:	2023
Subjects:	QA299.6-433 Analysis
Online Access:	https://etd.uum.edu.my/10914/1/Depositpermision_826270.pdf https://etd.uum.edu.my/10914/2/s826270_01.pdf
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id	my-uum-etd.10914
record_format	uketd_dc
spelling	my-uum-etd.109142024-01-23T07:19:44Z Development of poverty index with aggregated weights and beta regression analysis 2023 Nuril Asyikin, Mohamad Mat Desa, Nor Hasliza Mat Kasim, Maznah Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences QA299.6-433 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. 2023 Thesis https://etd.uum.edu.my/10914/ https://etd.uum.edu.my/10914/1/Depositpermision_826270.pdf text eng staffonly https://etd.uum.edu.my/10914/2/s826270_01.pdf text eng public other masters Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Mat Desa, Nor Hasliza Mat Kasim, Maznah
topic	QA299.6-433 Analysis
spellingShingle	QA299.6-433 Analysis Nuril Asyikin, Mohamad Development of poverty index with aggregated weights and beta regression analysis
description	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.
format	Thesis
qualification_name	other
qualification_level	Master's degree
author	Nuril Asyikin, Mohamad
author_facet	Nuril Asyikin, Mohamad
author_sort	Nuril Asyikin, Mohamad
title	Development of poverty index with aggregated weights and beta regression analysis
title_short	Development of poverty index with aggregated weights and beta regression analysis
title_full	Development of poverty index with aggregated weights and beta regression analysis
title_fullStr	Development of poverty index with aggregated weights and beta regression analysis
title_full_unstemmed	Development of poverty index with aggregated weights and beta regression analysis
title_sort	development of poverty index with aggregated weights and beta regression analysis
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2023
url	https://etd.uum.edu.my/10914/1/Depositpermision_826270.pdf https://etd.uum.edu.my/10914/2/s826270_01.pdf
_version_	1794023788038324224

Development of poverty index with aggregated weights and beta regression analysis

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