Jian-Da Zhu 朱建達
Department of Economics, National Taiwan University
Contact InformationE-mail: email@example.com
Address: No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan
Research InterestsIndustrial Organization, Applied Microeconomics, Econometrics
I use Major League Baseball ticket data, both in the primary market and in StubHub, for one anonymous franchise in the 2011 season to study how the franchise can price dynamically to increase its revenue. Compared using a uniform price schedule over time, the revenue for the franchise can be increased by decreasing prices as the game date approaches in a manner estimated by my model. In the counterfactual experiment, the revenue for the franchise can be increased by around 6.93% if consumers are assumed not strategic in both markets. However, if consumers are strategic in purchasing tickets, the revenue for the franchise can only be increased by around 3.67%.
This paper uses both listing and transaction data on StubHub to study how different types of sellers price their tickets. Two types of sellers, single sellers and brokers, are identified from the data. The single sellers only post a few listings in the whole season, while the brokers sell lots of tickets in many listings. The results show that the listing prices set by the brokers are higher than those set by the single sellers in the early days before an event, but this reverses in the last few days before an event. This study also proposes a dynamic pricing model based on the reference-dependent preferences of sellers to support this finding. The estimation result further leads to the conclusion that single sellers tend to use the face values as reference points to determine the listing prices every day before an event.
In this paper, we investigate how disclosure of liquefaction risks affects prices in the housing market in Taiwan. We apply difference-in-differences method for the sample along the borders crossing different levels of liquefaction risk to identify its effect on housing prices. Using the sample in no-risk regions in Taipei City as the benchmark, we find that prices increase by around 3.58% for those moderate-risk real property along the high-risk border after the dissemination of information. In addition, starting from the border, housing prices are 1.91% lower per 100 meters closer to the center of high-risk regions only after the disclosure. Finally, this observed effect is mainly driven by real property such as apartments with elevators or recently built ones, specifically after 2011.
This paper investigates how a gasoline station chooses the location for entry, especially for spatial differentiation. Two measures of spatial differentiation are directly calculated for each gasoline station: (1) the distance from the nearest incumbent, and (2) the number of incumbents inside 2-kilometer radius. The result shows that Formosa dealer-owned gasoline stations has 332.1 meters more close to the nearest station, compared with the distance choice of CPC dealer- owned stations. In addition, Formosa company-operated stations tend to locate at the point with more competitors inside 2-kilometer radius. Compared with CPC dealer-owned stations, Formosa company-operated stations on average has 1.9 more competitors within 2 kilometers.