Enhancing Poverty Alleviation Through Village-Level Multidimensional Poverty Measurement
Keywords:
multidimensional poverty, village-level poverty measurement, policy targeting, logistic regression, poverty alleviationAbstract
Poverty in Indonesia remains a persistent challenge, particularly in rural areas, where national strategies often fail to address local complexities. This study aims to enhance poverty alleviation efforts by constructing a village-level Multidimensional Poverty Index (MPI) tailored to the specific context of Candimas Village. Indicators were selected through literature review and consultation with village officials, followed by descriptive, contingency, and logistic regression analysis. The results reveal that 14.13% of households are multidimensionally poor, with inadequate housing, health vulnerability, and joblessness as key contributing factors. However, only 6.53% of these families receive government assistance, while 32.18% of non-poor families do—highlighting critical targeting gaps. Spatial disparities also suggest misallocation across neighborhood units. Regression analysis confirms the significant influence of BPJS PBI ownership, housing conditions, and unsafe drinking water on low-income levels. The study concludes that a locally-adapted MPI is more effective in identifying and prioritizing vulnerable households than conventional approaches. Its application can inform village-level resource allocation (e.g., BLT Desa) and support more equitable and data-driven poverty alleviation strategies.
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