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Effectiveness of Targeting and Implementation of Education Assistance Using Binary Logistic Regression: Evidence from Indonesia
Abstract
The dropout rate is higher at advanced educational levels. The Indonesian government seeks to ensure inclusive and equitable education by providing educational support to children, particularly those from low-income families in households and schools. The study analyzes the Smart Indonesia Program's (PIP) effectiveness in providing educational assistance to poor or vulnerable households. Due to budget constraints, it is crucial to target aid accurately; however, government-defined poverty criteria can lead to errors in both inclusion and exclusion in the distribution of social assistance. The data for this study were derived from the household surveys conducted in the National Socio-Economic Survey (Susenas) in Indonesia in March 2021. This research focused on sample households in Bogor Regency, West Java Province, and Nunukan Regency, North Kalimantan Province. The data were analyzed using Binary Logistic Regression to identify the factors influencing the receipt of the PIP. The regression analysis was performed using STATA 17.0. The findings indicate that identity ownership, status as a Family Hope Program (PKH) beneficiary, Family Welfare Card program (KKS) participation, and geographical region significantly impact the likelihood of receiving assistance. Households receiving the PKH had a 5.32 times higher probability of receiving the PIP assistance than those that did not. These findings align with the primary eligibility criteria of the PIP, which provides supplementary educational assistance to families enrolled in the PKH. Therefore, the results suggest that the targeting and distribution of the PIP assistance were significantly influenced by identity ownership status rather than the household characteristics traditionally used to define poverty and distributing aid.
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DOI: https://doi.org/10.20961/ijpte.v8i1.89316
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