Essays on Demand Estimation

This dissertation is a collection of two essays in the fields of empirical industrial organization. The overall theme of the dissertation is the estimation of the demand models. The estimated models can be used to study the implications of policy experiments in different industries. The first essay investigates competition in the taxi industry in New York City. With the advance of internet-based mobile technology, ride-hailing services, including Uber, have created new competition in the market that is traditionally dominated by the government-regulated Yellow Cab. Facing with new competition, the government, however, has not changed its pricing of Yellow Cab. What happens to the market if the government decides on different pricing policies is an empirical question. To study it, I adopt a discrete choice model where taxi consumers can choose among products offered by Yellow Cab and Uber, or an outside option. Using a comprehensive Yellow Cab data set, combined with unique Uber data, I estimate the parameters of consumers’ utility function. The estimated model is used to assess the changes in the market share of Yellow Cab and Uber in different counter-factual scenarios. I find that a small decrease in Yellow Cab’s fare increase its market share significantly. Simulating the effect of recent city regulations, I find that if Uber were banned, Yellow Cab’s market share would increase by 9%. I also find that consumers value brand characteristics. If Uber could replicate the characteristics of Yellow Cab, its market share would more than double.
In the second chapter, Andrea Szabo and I investigate recent government decisions related to net neutrality rules. Net neutrality rules limit internet providers’ ability to change the download speeds of competing online content providers. To understand the impact of such government regulations, we estimate consumer demand for download speed in the video-on-demand market using an original data set. We collect our data using a hypothetical choice experiment in which subjects choose between different platforms for viewing specific video content. Estimating the model using this data, we find that consumers are sensitive to both price and download times. In counterfactual experiments, we find that changes in download times for streaming have large impact on the market share of cable-on-demand. These findings show that a provider of both internet and cable has an incentive to differentiate download speeds in the absence of Net neutrality rules.