Essays in Estimation of Discrete Choice Demand Models
Open Access
- Author:
- Sagl, Stephan
- Graduate Program:
- Economics
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- April 19, 2024
- Committee Members:
- Marc Henry, Professor in Charge/Director of Graduate Studies
Edward Jaenicke, Outside Unit & Field Member
Joris Pinkse, Major Field Member
Mark Roberts, Major Field Member
Paul Grieco, Chair & Dissertation Advisor - Keywords:
- demand estimation
personalized pricing
price discrimination
automobile market
pickup trucks
demand estimation
personalized pricing
price discrimination
automobile market
price dispersion - Abstract:
- This dissertation consists of three chapters, each in the field of empirical Industrial Organization. In chapter 1, using a novel dataset on the Texas pickup truck market linking pickup trucks to their respective buyers, I document evidence for personalized pricing. In particular, using repeat purchase data on pickup trucks, I establish that the same consumers pay persistently high or persistently low prices across vehicle purchases. Less than 1% of this persistence can be explained by observed demographics. This result suggests that automobile dealers use consumer information beyond coarse demographics to personalize prices. Chapter 2 is motivated by the evidence for personalized pricing in chapter 1. Developing a novel discrete choice model with personalized pricing, I study the role of consumer information firms use for pricing in the welfare effects of price discrimination in the Texas market for pickup trucks. To do so, I overcome a common problem in settings with transaction data: personalized prices of non-chosen alternatives are unobservable. I solve this problem by recovering unobserved personalized prices and consumer-specific price sensitivity from the observed transaction price via firms’ first-order conditions. I simulate two counterfactuals: uniform pricing and price discrimination based on coarse demographic groups. Compared to uniform pricing, personalized pricing increases profits and total welfare but, on average, harms consumers. On the other hand, compared to uniform pricing, price discrimination based only on demographics is not profitable. This highlights the importance of the amount of consumer information firms can use for pricing in the welfare effects of price discrimination. Lastly, in chapter 3, which is joint work with Paul L. E. Grieco, Charles Murry, and Joris Pinkse and currently circulating as Grieco et al. (2023), we propose a conformant likelihood-based estimator with exogeneity restrictions (CLER) for random coefficients discrete choice demand models that is applicable in a broad range of data settings. It combines the likelihoods of two mixed logit estimators—one for consumer-level data, and one for product-level data—with product-level exogeneity restrictions. Our estimator is both efficient and conformant: its rates of convergence will be the fastest possible given the variation available in the data. The researcher does not need to pre-test or adjust the estimator and the inference procedure is valid across a wide variety of scenarios. Moreover, it can be tractably applied to large datasets. We illustrate the features of our estimator by comparing it to alternatives in the literature.