Browsing by Author "Chen, Yibin"

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    Essays on Supply Chain Finance
    (2016-10) Chen, Yibin
    This thesis contains essays addressing three different problems in supply chain finance, studying two important source of financing – trade credit and crowdfunding. In the first essay, we model a web-based retailer offering direct financing to its capital-constrained supplier who sells its products on a consignment basis. The supplier has access to bank financing and the retailer can specify debt-seniority. We show that if the retailer requires debt-seniority, then the supplier will use either bank or direct financing (but not both), and it may produce more than the first-best quantity. In contrast, if the retailer does not request seniority, the supplier may take both bank and direct financing, but it always produces less than the first-best quantity. We find that the retailer does not always prefer debt-seniority. In the second essay, we study variants of net-terms trade credit contracts in a traditional market, with market frictions of bankruptcy costs, information asymmetry and transaction costs. We provide a possible explanation for the prevalence of debt forgiveness in practice. We show that the supplier, by contracting and preannouncing forgiveness, can use one type of friction to mitigate another, and benefit both supply chain partners. The third essay studies the social proof effect in the rapidly diffused crowdfunding market. We study how prior capital accumulation and the volume of owner-issued referrals impact subsequent fundraising. By analyzing a campaign-level web traffic data set, we show that higher capital accumulation increases the probability of contribution for the owner-referrals, the volume of organic visitors and the total volume of organic contributors, although it may decrease probability of contribution from the organic visitors. To study the optimal policy for entrepreneurs to use their referrals, we build a Markov-decision-process model and calibrate its parameters using our data. Our finding indicates that entrepreneurs in general should distribute their referrals over the course of the entire campaign.
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    Statistical Analysis of Fare Compliance
    (Center for Transportation Studies, University of Minnesota, 2014-08) Gupta, Diwakar; Chen, Yibin
    Metro Transit uses a Proof of Payment (PoP) system with barrier-free stations on its Hiawatha Light Rail Line. It measures fare compliance to calculate missed revenue and to determine the effectiveness of its efforts to improve compliance. In this project, researchers employ a suite of statistical methodologies for estimating compliance from ticket sales and tagged rides' data as well as data from the Mobile Phone Validator (MPV) device used by Metro Transit police officers and Patrol Activity Logs. Researchers calculate point and interval estimates of the noncompliance rate and the minimum sample sizes needed to realize a desired degree of precision. As a first step in their analysis, researchers confirm that both ridership and noncompliance rate vary by direction, station, stratum, and day-of-week. These factors also affect the precision of noncompliance rate estimates. Researchers estimated the overall noncompliance rate to be 0.55% for weekdays and 0.7% for weekends. They also found that the noncompliance rate was relatively much higher among cardholders. One of their recommendations was that in order to realize a reasonable precision (95% confidence interval to be no more than 10% of the mean rate), Metro Transit would need to reduce weekday inspection frequency by about 30% but increase weekends' inspection frequency by 50% relative to the current practice. Researchers also found that ticket sales and tagged rides' data (currently automated) could provide reliable estimates of ridership, reducing the need to perform manual counts, and could help improve the pricing of Metro Transit's fare products.