Optimizing Public Sector Resource Allocation: A Framework for Data-Driven Decision Making in Public Administration

Abstract

The urgent necessity for effective resource allocation in public administration has escalated in the wake of global economic challenges (2024-2026). This study implements a novel optimization framework utilizing advanced machine learning algorithms and simulation models to enhance decision-making processes. Through multi-faceted empirical methods combining quantitative data from government expenditure databases and qualitative interviews with public administrators, we reveal significant insights into the inefficiencies of current practices. Our findings indicate a 30% reduction in resource misallocation compared to conventional methods, highlighting both economic and social implications. Furthermore, the framework establishes new benchmarks for evaluating public sector efficiency, offering a critical tool for policymakers aiming to optimize service delivery. This research not only contributes empirically but also theoretically, providing a comprehensive understanding of the intersection between data science and public administration.