How efficiently do markets reallocate capital in booms and busts? Using a novel dataset of offshore drilling contracts I examine the role of matching in shaping industry reallocation. Oil companies search and match with capital (rigs) in a decentralized market. I find oil and gas booms increase the option value of searching which leads agents to avoid bad matches, reducing mismatch through a sorting effect. I provide an identification strategy to disentangle unobserved demand changes from the sorting effect. Estimating a model, I find substantial benefits to the sorting effect and an intermediary but that demand smoothing policies are ineffective.
How effective is local regulation when production occurs in a global market and capital is mobile? When environmental regulations differ between regions capital may relocate, resulting in spatial misallocation and ‘leakage’ of pollution and profits. In this paper I build an empirical framework to study incomplete regulation in a decentralized capital market, where capital is mobile and production occurs across multiple locations. The model extends the spatial location-choice and matching literature in industrial organization to accommodate two-sided vertical firm heterogeneity. Using novel contract and location data, I apply the framework to the global market for deepwater oil rigs. Offshore rigs are marine vessels that move around the ocean drilling wells, matching with oil companies like BP and Chevron to produce wells. Local changes in drilling standards and other regulations spur equilibrium rig relocation. The main policy finding is that incomplete regulations, such as unilateral increases in drilling standards, cause large shifts in profits and (expected) oil spilled to other markets through the capital relocation channel. In contrast, I find that more complete regulation - like a coordinated global agreement on drilling standards - would be significantly more effective than uncoordinated policies.
We evaluate the efficiency of dynamic linked environmental regulation. Linked regulation allows inspectors who uncover violations at one plant to increase future enforcement at other plants that share a common owner. When compliance costs are correlated, regulators can then target scarce enforcement resources towards bad actors without inspecting everyone. We develop an empirical framework of dynamic moral hazard under linked regulation. Plants choose pollution mitigation efforts, while regulators selectively target inspections. Our framework allows for large portfolios of plants and for choices to be interdependent within the portfolio of plants and across time. We apply the framework to the Texas Commission on Environmental Quality who uses a scoring-based system of linked regulation. We evaluate this program using a novel panel of plant inspections, violations, and scores. We find that linked regulation performs substantially better than both unlinked regulation and untargeted regulation.