<![CDATA[Socio-economic inequality across regions remains a structural challenge in Indonesia’s national development, particularly in the distribution of social assistance programs, which often generates perceptions of fiscal unfairness among provinces. Differences in the number of beneficiaries are frequently interpreted as unequal budget allocation, although such variation does not necessarily reflect bias in policy design based on fixed formulas. This study aims to analyze spatial inequality in the distribution of the 2024 Food Social Assistance Program and to examine fiscal neutrality in budget allocation across provinces in Indonesia. The research employs a quantitative explanatory approach with a cross-sectional design using provincial-level secondary data derived from official government reports. Data analysis is conducted through simple linear regression to examine the relationship between the number of Beneficiary Families and allocated budgets, inequality measurement using the Coefficient of Variation and the Gini Index, and spatial autocorrelation analysis using Moran’s I and the Local Indicator of Spatial Association to identify geographic clustering patterns. The results reveal a deterministic linear relationship between the number of beneficiaries and budget allocation, indicating that the distribution folLows a fixed formula-based mechanism and is fiscally neutral without discretionary intervention across regions. The distribution of beneficiaries exhibits a high level of inequality and forms significant spatial clusters, particularly in regions characterized by higher socio-economic vulnerability and geographic proximity. The novelty of this study lies in integrating fiscal neutrality testing with inequality measurement and spatial statistical analysis within a single comprehensive analytical framework that remains rarely applied in social assistance studies in Indonesia. These findings provide empirical contributions to data-driven public administration research and offer policy implications for designing more spatially responsive social interventions to improve targeting accuracy, distribution effectiveness, and the overall quality of redistributive policies at the national level.]]>
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