This dissertation uses original datasets from the U.S. and Japan to explore issues in spatial economics and public finance. In the first chapter, I study how the relocation of inventors affects local and aggregate growth through technological diffusion across U.S. cities. I propose a quantitative spatial theory of growth and knowledge diffusion through internal migration. My model highlights two mechanisms by which productivity growth can be higher in one city than in another: (1) agglomeration forces and (2) knowledge inflows through internal migration. Using data on US cities, I find that knowledge diffusion explains approximately 40 percent of the spatial variation in productivity changes, and agglomeration forces explain the rest. I quantify the dynamic effects of place-based policies and find that reducing the costs of migrating to a small number of cities can improve aggregate efficiency while reducing disparities in productivity across cities. Growing spatial inequality has led policymakers to enact tax breaks to attract corporate investment and jobs to economically peripheral regions. In the second chapter, co-authored with Cameron LaPoint, we demonstrate the importance of multi-plant firms’ physical capital structure for the take-up and efficacy of place-based policies by studying a national bonus depreciation scheme in Japan which altered the relative cost of capital across locations, offering high-tech manufacturers immediate cost deductions from their corporate income tax bill. Combining corporate balance sheets with a registry containing investment by plant location and asset type, we find the policy generated big gains in employment and investment in building construction and in machines at pre-existing production sites, with an implied partial equilibrium fiscal cost per job created of $16,000. The policy produced a welfare gain of $56.72 billion, or roughly 40% of one year’s worth of average annual corporate profits. For eligible firms, plant-level hiring in ineligible areas outstripped that in eligible areas, suggesting reallocation of resources within firms’ internal capital and labor markets mitigates the spatial misallocation inherent in subsidizing low-productivity areas. How governments should choose the frequency of payments has received little attention in the literature on the optimal design of benefits programs. In the third chapter, co-authored with Cameron LaPoint, we propose a simple model in which the government chooses the interval length between payments, subject to a tradeoff between the costs of providing more frequent benefits and welfare gains from mitigating consumption non-smoothing. Using a high-frequency retail dataset that links consumers to their purchase history, we apply the model to the Japanese National Pension System. Our evidence suggests suboptimal intra-cycle consumption patterns with negligible retailer price discrimination. Model calibrations support the worldwide prevalence of monthly payment systems.