Government Intervention Strategy in Poverty Reduction

Abstract

Poverty remains a significant challenge in Indonesia, with a national rate of around 9.36% in 2023 and 16 provinces exceeding this poverty level. This study aims to analyze poverty patterns across districts and cities by using K-Means Clustering and examine the effectiveness of government interventions in poverty reduction through panel data regression with the Fixed Effect Model (FEM). Results indicate that higher-poverty areas benefit most from social assistance, village funds, credit schemes, and GRDP, while capital spending exacerbates poverty. On the other hand, lower-poverty areas achieve reductions through capital spending, village funds, and GRDP, with minimal impact from social assistance and credit schemes. Also, these results emphasize the need for targeted interventions, such as optimizing social assistance and credit access programs in higher-poverty areas and implementing growth-focused strategies, including physical infrastructure development, in lower-poverty regions.

Keywords

• Poverty Reduction
• K-Means Clustering
• Local Government Expenditure
• Government Credit Program