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This research aims to explore the effectiveness of non-income poverty indicators in targeting household income through machine learning techniques.

June 3, 2026Open Access

Applying an Unsupervised Machine Learning Approach to Analyze the Non-Income Poverty Indicators Used in the Listahanan 2

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This research aims to explore the effectiveness of non-income poverty indicators in targeting household income through machine learning techniques.
Applied an unsupervised machine learning approach using the UMAP algorithm for dimension reduction.
Analyzed household-specific non-income poverty indicators from the Listahanan 2 database.
Investigated clustering of households based on these indicators to assess income differentiation.
Households naturally clustered into three to four groups using UMAP.
Clusters were not correlated with income and expenditure levels.
Rich and poor households exhibited similar characteristics, indicating limitations in using non-income indicators for accurate poverty targeting.

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Randomized trial applies machine learning to assess non-income poverty indicators in households, suggesting limitations for targeting accuracy.

Dominique Ysobel Ting (2022) studied this question.

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Randomized trial applies machine learning to assess non-income poverty indicators in households, suggesting limitations for targeting accuracy.