My dissertation consists of three chapters that evaluate the social welfare effect of either antitrust policy or industrial transition, all using discrete choice model estimation as the front end for counterfactual analysis. In the first chapter, I investigate the economic impact of the merger that created the world's largest hotel chain, Marriott's acquisition of Starwood, thereby shedding light on the antitrust authorities' performance in protecting competitive markets for the benefit of consumers.
Different from traditional merger analysis that focuses on the tradeoff between the upward pricing pressure and the cost synergy among the merging parties while fixing the market structure, I endogenize firms’ entry decisions into an oligopoly price competition model. To tackle the associated multiple equilibria issue, I use moment inequality estimation and propose a novel lower probability bound that reduces the computational burden from being exponential to being linear in the number of players. It also adds to the scant empirical evidence on post-merger cost synergy by showing that every one more affiliated hotel in the local market reduces a hotel's marginal cost by up to 2.3%. Then a comparison between the simulated with-merger and without-merger equilibria indicates that this merger enhances social welfare. In particular, for those markets that are previously not profitable for any firm to enter, because of the post-merger cost saving, Marriott or Starwood would enter 6% - 24% of them, which provides a new perspective for merger reviews.
The second chapter, joint with Mingli Chen, Marc Rysman and Krzysztof Wozniak, studies the determinants of the US payment system's shift from paper payment instruments, namely cash and check, to digital instruments, such as debit cards and credit cards. With a 5-year transaction-level panel data, for the first time in the literature, we can distinguish the short-term effects of transaction size from the long-term changes in households’ preferences. To do so, we incorporate a household-product-quarter fixed effect into a multinomial logit model. We develop a new method based on the Minorization-Maximization (MM) algorithm to address the prohibitive computational challenge of estimating over one million fixed effects in such a nonlinear model. Results show that over a short horizon (within a quarter), the probability of using card increases with transaction sizes in general but exhibits substantial household heterogeneity. While over long horizon (five-year period of the data), with the estimated household-product-quarter fixed effects, we decompose the increase in card usage into different channels and find that only a third of it is due to the changes in household preferences. Another significant driver is the households' entry and exit into the sample.
In the third chapter, my coauthors Jacob LaRiviere, Aadharsh Kannan, and I explore the "death of distance” hypothesis with a novel anonymized customer-level dataset on demand for cloud computing, accounting for both spatial and price competition among public cloud providers. We introduce a mixed logit demand model of spatial competition estimable with detailed data of a single firm but only aggregate sales data of a second. We leverage the Expectation-Maximization (EM) algorithm to tackle the customer-level missing data problem of the second firm. Estimation results and counterfactuals show that standard spatial competition economics hold even when distance for cloud latency is trivial.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/43306 |
| Date | 06 November 2021 |
| Creators | Wang, Shuang |
| Contributors | Rysman, Marc |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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