Mathematical Optimization of Fiscal Policy under Budget Constraints

Abstract

Fiscal policy formulation increasingly requires analytically rigorous tools capable of addressing complex trade-offs under binding budget constraints. Mathematical optimization offers a structured framework for designing fiscal policies that maximize economic and social objectives while respecting revenue limitations, borrowing rules, and institutional constraints. This study develops a formal optimization-based approach to fiscal policy analysis, integrating welfare maximization, expenditure allocation, and fiscal sustainability considerations within a unified modeling framework. The analysis demonstrates how linear, nonlinear, and robust optimization techniques can be applied to public budgeting problems to improve allocative efficiency, enhance policy coherence, and support evidence-based decision-making. By incorporating fiscal space limitations and institutional realities, the framework enables systematic evaluation of alternative expenditure and revenue configurations and highlights the trade-offs between growth, stability, and equity. The findings underscore the value of mathematical optimization as a practical decision-support instrument for governments operating in constrained fiscal environments and contribute to the advancement of quantitative methods in public finance policy design.