Essays in industrial organization

• Main Author: Liu, David (David Jing)
• Other Authors: Glenn Ellison and Nikhil Agarwal.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2018
• Subjects: Economics.
• Online Access: http://hdl.handle.net/1721.1/118045

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 2018. === Cataloged from PDF version of thesis. === Includes bibliographical references (pages 93-95). === This thesis consists of three empirical essays in industrial organization. The first chapter examines the welfare effects of intertemporal price discrimination in market for airfare using a novel structural model of dynamic consumer search. I model how consumers search for airline tickets over time using data gathered from an internet travel agent. My model features sophisticated consumers with rational beliefs about future price movements who make optimal decisions based on their beliefs. Using data on daily price and quantity in monopoly markets, I estimate the demand for airfare and calculate consumer welfare. I compare welfare to a counterfactual market in which airlines cannot price discriminate. The second chapter quantifies the impact of company-wide incentive plans on total company performance. I identify five airlines that have introduced employee incentive programs that offer monthly bonuses to all company employees as long as the company achieves a certain performance level in flight on-time performance. I present evidence that these programs are effective at increasing employee performance in spite of the temptation to be a free rider. My analysis uses Mahalanobis matching to compare each route's performance with the best matching control flight, taking advantage of the large volume of flight data available. In the third chapter I examine the role of reputation in an online marketplace that specializes in unreliable products. Using data gathered from video game resale platform G2A, I examine how buyers and sellers utilize the wide array of reputation information available. I find that buyers on this platform have an understanding of their probability of encountering a negative transaction and will utilize more reputation information in less reliable markets. === by David Liu. === Ph. D.