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
This research uses listing prices on StubHub, a secondary market for sports tickets, to show that sellers have reference-dependent preferences, affected by two types of reference points: face values and previous lowest transaction prices within the same section of the stadium. First, I show evidence of the bunching of listing prices at the reference points, which is consistent with the prediction from the theoretical model. Then, I use a structural model to estimate the parameters of the gain-loss utility and simulate results for the case without reference-dependent preferences. Compared with the actual data, the counterfactual results indicate that the listing prices for game tickets will be lower by around 19.30% on the last day before the game if sellers have no reference-dependent preferences. Furthermore, the probability of a typical listing being sold increases from 0.43 to 0.48 during the last two weeks. In addition, I use the number of listings in a season to proxy for the size of sellers, and the results show that big sellers are less likely to be affected by the face value than small sellers, which is consistent with the previous literature’s notion that market experience can eliminate the effect of reference points. However, sellers of different sizes are affected by the previous lowest transaction prices in a similar way, which suggests that market experience might only eliminate the effects of reference points such as the status quo, and not the effects of reference points such as recent outcomes.
This research uses a difference-in-differences framework to investigate the effect of new risk information on housing prices in Taiwan. The results show that this information changed individuals’ subjective risk perceptions, so that housing prices in the highest-risk areas dropped, but only temporarily in the first three months after the disclosure. Also, this information effect happened for those apartments lacking certain earthquake-resistant characteristics. In addition, we investigate the dynamics of the effect around the boundary. We demonstrate that individuals were able to form continuous risk beliefs based on discrete information, and the housing prices dropped more sharply for those apartments located closer to the center of the risk area. Furthermore, individuals had updated their risk beliefs differently for apartments with different levels of earthquake-resistance. For those apartments with the least earthquake-resistance, the immediate price drops were relative larger, and the housing prices returned to normal more slowly, relative to the safer apartments. The effect did not disappear at all for those apartments with the least earthquake-resistance that were also located in the center of the highest-risk area.
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 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.