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This dissertation contains three chapters, each focusing on a different aspect of the micro structure of trade flows and shipments to and within the United States. All three chapters make use of rich micro level data on trade flows. In the first chapter, I study imports of heavy goods and how these products compete with local production. Although imports of heavy goods are often thought to stay near the coast, these goods frequently travel much farther. I develop a structural model of demand with large choice sets where all transaction prices are observed, but offered prices are unknown. Using unique micro data linking shipments to locations, I find that the rail network plays an important role in allowing shipments to reach distant locations. Once firms pay the fixed cost of accessing this network, shipments can cheaply go far. As a result, the ability of imports to easily reach all locations disciplines prices even in areas with low import shares. Because domestic producers have access to the same transportation infrastructure, they also discipline prices in distant locations. I also find that the ability to price discriminate by location enables this type of competition over long distances. The second chapter is coauthored with Thomas Holmes. This chapter studies the shipment of internationally-traded goods, focusing on the path the goods take to get to their final destination, and in particular taking into account the internal geography of the destination country. We use Wal-Mart's distribution network as our primary empirical example, modeling the flow of shipments from origination country to U.S. port to import distribution center and finally on to the final consumer. The paper estimates the costs incurred by Wal-Mart on account of transit time, by studying the choice behavior, as Wal-Mart trades off shorter transit times in exchange for higher freight rates. The paper assembles a variety of new data sources including bills of lading for ocean shipping transactions that have been processed to match to data on selected individual firms, and that have been merged with GPS data on ocean vessels to ascertain shipping times. As an application, the paper considers the recent labor market disturbance on the West Coast ports of the United States. The paper produces estimates of the cost of this disruption as well as the gains to Wal-Mart of having a ''four corners'' distribution network. The third chapter looks at internal shipments within the United States. Shipment distances in the Commodity Flow Survey (CFS) are disproportionately very short. This chapter looks at the distribution of shipment distances by industry using the 2012 public CFS. I use this data to support the explanation that wholesale networks result in low average distances. When measuring the total distance from manufacturer to consumer, if all layers of the wholesale network are considered then distances would be much larger.

While there has been a great deal of research into how to increase the number of responses to a mail survey, very little has been done on the question of how many surveys should be mailed out. The first part of this work deals with how to model the survey response pattern. This is then used as a foundation for a two stage optimization problem to determine the optimal number of questionnaires. Past experience and tuition are included by means of a prior distribution. A confidence constraint is imposed whereby there is a prespecified probability of meeting some desired level of returns by a specified deadline. The resulting problem can be solved using numerical integration and search routines. Simulations are performed to compare this procedure with the previous work of Parasuraman (1982). The last part of this work introduces a new regression model, the Odds Ratio Model, for problems with a binary response variable. This provides a conceptually attractive alternative to the logistic regression model.