Updated: Jul 02 2015
- Under review at Management Science.
- Co-authored with Yitian(Sky) Liang and Xinlei(Jack) Chen.
The daily deals platform has become an important format in the online-to-offline (O2O) business model. However, questions have been raised over whether merchants really benefit from participating in daily deals, with some evidence suggesting that a significant number of merchants are losing money from them. In this paper, we address this question by quantifying the economic value of daily deals using a structural approach. Using data from the Chinese daily deals market, we find that merchants not only profit from daily deals, but also take the biggest share of the economic value created by them. However, the gain mainly comes from future revenue, with merchants typically incurring a loss during the promotion period. We also find that competition among platforms increases the merchant’s share of the economic value. Finally, we show that the competition in the current market is close to the optimal situation from a policy maker’s point of view.
Keywords: Daily Deals, Platform Competition, Economic Value
Updated: Jul 01 2015
- Co-authored with Yanwen Wang and Ting Zhu.
The taxi industry has undergone dramatic changes in recent years with the introduction of mobile application based cab hailing systems. New mobile applications, such as UBER and Lyft, have changed the way the taxi industry works. Meantime in many countries and regions including New York City, Las Vegas, Canada and Germany are hot debates as to imposing or lifting bans on UBER and similar mobile hailing apps. Supporters argue that mobile hailing apps are welfare-enhancing, as they lead to more efficient customer-and-driver allocations and help increase taxi drivers' productivity. The oppositions hold viewpoints that mobile hailing apps, as a new technology, may create digital divide as well as digital inequality against low-skilled taxi drivers thus increasing social disparities.
In this paper we provide evidence to confront two arguments about (1) whether mobile hailing apps increase an average driver's productivity and (2) whether the use of mobile hailing apps affects productivity disparities among taxi drivers. To investigate the two questions we take leverage of a natural experiment setting in a metro city where mobile hailing apps were regulated to be only adopted by the city's registered taxi drivers. We collected a 1TB data set on 6,000 taxi drivers' minute-by-minute trip geolocations before and after the introduction of two major cab hailing apps.
We build a productivity function and an unobserved mobile hailing app adoption decision separately for each taxi driver, and estimate with MCMC Bayesian method. We find that the adoption of mobile hailing apps has significantly increased drivers' productivity, although the advantage diminishes over time as more and more drivers adopt it. At the same time, the use of mobile hailing apps is found to have actually reduced the productivity gap between high and low-skilled taxi drivers. The pattern holds after controlling for observed and unobserved heterogeneity in the adoption decision of cab hailing apps. Our results are of great interest to public policy makers as to better evaluate the the impacts of mobile hailing services.
Keywords: Mobile Hailing Apps, Productivity, Productivity Disparity, Technology Adoption
Updated: Jul 01 2015
- Forthcoming at Marketing Science.
Advertising networks have recently played an increasingly important role in the online advertising market. Critical to the success of an advertising network are two mechanisms: an allocation mechanism that efficiently matches advertisers with publishers and a pricing scheme that maximally extracts surplus from the matches. In this paper, we quantify the value and investigate the determinants of a successful advertiser-publisher match, using data from Taobao’s advertising network. A counterfactual experiment reveals that the platform’s profit under a decentralized allocation mechanism is close to the profit level when the platform centrally assigns the matching under perfect knowledge. In another counterfactual experiment, we explore the effect of platform technology and revenue model on the strategic choice of the pricing schemes of list price vs. GSP auction pricing. We find that platforms that profit from the advertiser side may have less incentive to adopt GSP auction than platforms that profit from the publisher side.
Keywords: Advertising Network, Matching Game, Maximum Score Estimation, Generalized Second Price Auction, Platform Design