Mo Xiao

Do Entry Conditions Vary over Time? Entry and Competition in the Broadband Market: 1999-2003

We extend Bresnahan and Reiss’s (1991) model of local oligopoly to allow firm entry and exit over time. In our framework, entrants have to incur sunk costs in order to enter a market. After becoming incumbents, they disregard these entry costs in deciding whether to continue operating or to exit. We apply this framework to study market structure and competitive conduct in local markets for high-speed Internet service from 1999 to 2003. Replication of Bresnahan and Reiss’s framework generates unreasonable variation in firms’ competitive conduct over time. This variation disappears when entry costs are allowed. We find that once the market has one to three firms, the next entrant has little effect on competitive conduct. We also find that entry costs vary with the order of entry, especially for early entrants. Our findings highlight the importance of sunk costs in determining entry conditions and inferences about firm conduct.

Entry Threat and Entry Deterrence: The Timing of Broadband Rollout

Past empirical literature provides strong evidence that competition increases when new firms enter a market. However, rarely have economists been able to examine how competition changes with the threat of entry. This paper uses the evolution of the zip code level market structure of facilities-based broadband providers from 1999 to 2004 to investigate how a firm adjusts its entry strategy when facing the threat of additional entrants. We identify the potential entrant into a local market as threatened when a neighboring market houses more than firms providing broadband services. We first document that such a market is more likely to accommodate more than firms in the long run. Taking account of endogeneity of entry into neighboring markets, we find that the first 1 to 3 entrants significantly delay their entrance into an open local market facing entry threat. We do not find evidence of delayed entry for firms following the 3rd entrant. The evidence suggests that the mere threat of entry may curb market power associated with oligopolistic market structure.

For Whom to Tweet? Evidence from a Large-Scale Social Media Platform

We study the effects of peer-group sizes on content generating and sharing in a large-scale and influential social media platform. User-generated content, particularly tweets in social media, disseminates information and exerts social influence. However, 50% of the users in this platform post less than 6 tweets per month and contribute to less than 15% of the total tweets in stock, while the top 10% post on average 40 tweets a month and contribute to more than half of the tweets in stock. We attribute the highly unbalanced contribution to a users conflicting incentives of free-riding and maximizing social influence. We exploit the asymmetry of a users peer groups (followers and followees, groups of people following and being followed by the user respectively) to disentangle these incentives, and devise empirical strategies to deal with the endogenous formation of ones networks. We find asymmetric effects, both in signs and sizes, of followers and followees on content generating and sharing. A larger group of followers leads a user to tweet more, while a larger group of followees leads a user to tweet less. As the follower effects dominate the followee effects in size, our simulations indicate that the platform could increase the number of total tweets by 25% if it randomly adds 1% new links to existing links. Targeting occasional tweeters is even more effective in promoting the activeness of this social media platform.We study the e↵ects of peer-group sizes on tweeting in a large-scale and influential social media platform. Tweets in social media disseminate information and exert social influence. However, 50% of the users post less than 6 tweets per month and contribute to less than 15% of the tweets in stock, while the top 10% post over 40 tweets a month and contribute to more than half of the tweets in stock. We attribute the highly unbalanced contribution to a user’s conflicting incentives of free-riding and maximizing social influence. We exploit the asymmetry of a user’s peer groups (followers and followees, groups of people following and being followed by the user) to disentangle these incentives, and devise empirical strategies to deal with the endogenous network formation. We find asymmetric e↵ects, in both signs and sizes, of followers and followees. A larger group of followers leads a user to tweet more, while a larger group of followees leads a user to tweet less. As the follower e↵ects are dominant, our simulations indicate that by randomly adding 1% new connections the platform could increase the total tweets by 25%. Targeting occasional tweeters is even more e↵ective in promoting the activeness of this platform.