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Abstract
In this dissertation, I take an interdisciplinary approach to improve the budget allocation process for economic mobility policy by leveraging multi-objective optimization as a decision support tool. Economic mobility is measured as the difference in income between Black and White populations, known as the racial wealth gap. Lack of quantitative data linking budget policy selections to mobility metrics hinders government efficiency and the effectiveness of public investment. I propose a novel application of multi-objective optimization1 to identify optimal mixes aimed at increasing economic mobility in urban cities, restoring rational comprehensive decision making to the process, reducing government waste and improving resident outcomes.The optimization model is the main contribution of my research to both the systems engineering and public policy fields. The first of its kind, it is the basis for a practical decision support tool that can be utilized to help cities determine where to direct funding for greater gains in economic mobility. It also enables cities to select true peer cities from which to benchmark, assess return on investment at various levels of spending, and identify potential private partners based on additional investment needs to achieve community aims. The results indicate that each of the above factors plays a role in the level of aid that a city receives, and how that aid is spent. I found that while these factors do impact spending choices, budget policy alone does not drive economic mobility outcomes.