Ponder, Mark2022-09-132022-09-132022-06https://hdl.handle.net/11299/241609University of Minnesota Ph.D. dissertation. 2022. Major: Economics. Advisor: Amil Petrin. 1 computer file (PDF); 160 pages.This dissertation is comprised of three essays, each dealing with topics in empirical Industrial Organization and Applied Microeconomics. The second chapter was co-authored with Amil Petrin and Boyoung Seo and the third chapter was co-authored with Veronica Postal.\\ \noindent In the first chapter, I develop a dynamic model of the oil pipeline industry to estimate the impact of direct price regulation on investment. Since the shale boom began in 2010, crude oil production in the United States has surged over 100\% leading to a dramatic increase in demand for pipeline transportation. However, the profitability of investing in oil pipelines is constrained as transportation rates are set subject to a price cap. In this chapter, I examine the impact of direct price regulation on pipeline investment in response to the shale boom. I develop a theoretical model of the pipeline industry, where firms make production and investment decisions while being subject to a dynamically changing price ceiling. I estimate the model using detailed operational data derived from regulatory filings and compare welfare under three separate regulatory environments: price cap regulation, cost-of-service regulation, and price deregulation. I find that price cap regulation was superior to the alternative mechanisms considered, as it increased market entry by 15\% and incentivized firms to operate 17\% more efficiently. I find evidence suggesting that prices were allowed to increase too quickly. While this led to an increased rate of entry into new markets it came at the expense of higher prices in existing markets. This ultimately resulted in a transfer in consumer surplus from existing customers to new customers and a slight decrease in total relative to what could have been achieved under a fixed price ceiling. \\ \noindent In the second chapter, we propose a novel approach to estimating supply and demand in a discrete choice setting. The standard Berry, Levinsohn, and Pakes (1995) (BLP) approach to estimation of demand and supply parameters assumes that the product characteristic unobserved to the researcher but observed by consumers and producers is conditionally mean independent of all characteristics observed by the researcher. We extend this framework to allow all product characteristics to be endogenous, so the unobserved characteristic can be correlated with the other observed characteristics. We derive moment conditions based on the assumption that firms - when choosing product characteristics - are maximizing expected profits given their beliefs at that time about preferences, costs, and competitors' actions with respect to the product characteristics they choose. Following \cite{Hansen1982}, we assume that the ``mistake'' in the choice of the amount of the characteristic that is revealed once all products are on the market is conditionally mean independent of anything the firm knows when it chooses its product characteristics. We develop an approximation to the optimal instruments and we also show how to use the standard BLP instruments. Using the original BLP automobile data we find all parameters to be of the correct sign and to be much more precisely estimated. Our estimates imply observed and unobserved product characteristics are highly positively correlated, biasing demand elasticities upward significantly, as our average estimated price elasticities double in absolute value and average markups fall by 50\%. \\ \noindent In the third chapter, we estimate the benefit households derived from the introduction of light rail transit in Minneapolis. The primary goal of this chapter is to decompose this benefit into two components: the direct effect from improved access to public transportation and the indirect-effect from the endogenous change in local amenities. The literature has predominantly relied on two methods to estimate the impact of public transportation: difference-in-differences models and hedonic pricing models. Difference-in-difference models yield convincing treatment effect estimates but do not readily provide a decomposition of the direct and indirect effect. Hedonic pricing models can provide such a decomposition but have historically relied on parsimonious specifications that do not control for omitted variable bias. Recently, researchers have proposed refining the hedonic pricing approach by incorporating predictive modeling, where the researcher trains a predictive model on a control group using a high-dimensional dataset and then uses this model to predict what prices would have been in the ``but-for" world for the treatment group. The difference between actual and predicted prices provides a valid estimate of the average treatment effect. However, if important sources of heterogeneity are excluded from the model then this approach will still suffer from omitted variable bias. We propose augmenting the estimation of the predictive model with instrumental variables allowing us to control for the selection bias induced by unobserved heterogeneity. We find close agreement between our predictive model and the difference-in-differences approach, estimating an increase in house prices of 10.4-11.3\%. Using the predictive model, we estimate that prices increased by 5.5\% due to improved access to public transportation and 5.8\% due to improved access to amenities. \\enCausal effectsDiscrete choice estimationIndustrial organizationInvestmentMachine learningPrice regulationThesis or Dissertation