DECISION ANALYSIS IN PUBLIC POLICY: MULTI-OBJECTIVE OPTIMIZATION FOR ECONOMIC MOBILITY POLICY PORTFOLIO MIX IN LOCAL URBAN GOVERNMENTS

This dissertation seeks to improve the budget allocation process for economic mobility policy portfolios by leveraging multi-objective optimization as a decision support tool, accounting for population, political, social, and budgetary constraints. Economic mobility is measured as the difference in income between Black and White populations, known as the racial wealth gap. First, I run regression, mediation, and moderated mediation analyses to understand the impact of local authority, consolidation, local partisanship, unified government, and racial demographics on aid, budget expenditures and economic mobility. I then propose a novel application of multi-objective optimization1 to identify optimal mixes aimed at increasing economic mobility in urban cities. In doing so, I seek to improve decision support tools available to local urban governments. My work intends to enable local urban governments to leverage multi-objective optimization to guide their decisions regarding policy selection and budget allocation. Better informed policy processes lead to a better mix of policies, which allows for more holistic solutions with greater societal returns. This not only improves outcomes for residents; it also recovers waste in the governmental process, increasing effectiveness and efficiency.