By Pranav Ganapathy
We propose and evaluate an auction mechanism for the priority review voucher program. The 2007 voucher program rewards drug developers for regulatory approval of novel treatments for neglected tropical diseases. Previous papers have proposed auctioning vouchers for the priority review voucher program but have offered neither a mathematical model nor a framework. We present a mechanism design problem with one pharmaceutical company producing one drug for a neglected tropical disease. The mechanism that maximizes the regulator’s expected surplus is a take-it-or-leave-it offer, with three different offers based on low, intermediate, and high neglected disease burdens. We demonstrate how mechanism design can be applied to settings in which the buyer pays for public access to a product with regulatory speed. Finally, this paper may be useful to policymakers seeking to improve access to voucher drugs through modifications of the program.
Advisors: Professor David Ridley, Professor Giuseppe Lopomo, Professor Michelle Connolly| JEL Codes: I1, D44, D82
By Lucas Do
The state of Michigan administers oil and gas lease auctions semiannually through the Department of Natural Resources. In June 2012, the international news outlet Reuters published allegations of bid-rigging following the Department’s May 2010 auction. This paper empirically investigates the validity of Reuters’ allegations by analyzing auction bid sheets from 2008 to 2018 as well as other data reflecting market conditions over time. To this end, I first formulate a benchmark structural model of bidders’ valuations and estimate it with auction data from a period during which I assume no collusion occurred. Then, I extend the benchmark model by endogenizing bidders’ decision to collude. Using the extended model and the estimated benchmark parameters, I apply the simulated method of moments to solve for the collusive probability that “best” explains the observed bids during the alleged period of collusion. After discovering strong evidence for bid-rigging, I run counterfactual simulations to estimate the revenue damage caused to the state of Michigan by this non-competitive bidding behavior. I find that the hypothetical revenue damage, summed over the entire alleged collusive period, totals over $450 million. However, although these findings lend support to Reuters’ allegations and are contrary to the Department of Justice’s conclusion in 2014 after they had probed the case, they should be approached only with caution, given the limitations of the available data on the potential bidders.
Advisors: Professor Jame Roberts, Professor Michelle Connolly | JEL Codes: L4, D44, L71
By Elizabeth Lim, Akshaya Trivedi and Frances Mitchell
On March 29, 2016, the FCC initiated its first ever two-sided spectrum auction. The auction closed approximately one year later, having repurposed a total of 84 megahertz (MHz) of spectrum. The “Incentive Auction” included three primary components: (1) a reverse auction where broadcasters bid on the price at which they would voluntarily relinquish their current spectrum usage rights, (2) a forward ascending clock auction for flexible use wireless licenses which determined the winning bids for licenses within a given geographic region, and (3) an assignment phase, where winning bidders from the forward auction participated in single-bid, second price sealed auctions to determine the exact frequencies individual licenses would be assigned within that geographic region. The reverse auction and the forward auction together constituted a “stage.” To guarantee that sufficient MHz were cleared, the auction included a “final stage rule” which, if not met, triggered a clearing of the previous stage and the start of a new stage. This rule led to a total of four stages taking place in the Incentive Auction before the final assignment phase took place. Even at first glance, the Incentive Auction is unique among FCC spectrum auctions. Here we consider the estimated true valuation for these licenses based on market conditions. We further compare these results to more recent outcomes in previous FCC spectrum auctions for wireless services to determine if this novel auction mechanism
impacted auction outcomes.
Advisor: Michelle Connolly | JEL Codes: L5, O3, K2, D44, L96
BIDDING FOR PARKING: The Impact of University Afﬁliation on Predicting Bid Values in Dutch Auctions of On-Campus Parking Permits
By Grant Kelly
Parking is often underpriced and expanding its capacity is expensive; universities need a better way of reducing congestion outside of building costly parking garages. Demand based pricing mechanisms, such as auctions, offer a possible solution to the problem by promising to reduce parking at peak times. However, faculty, students, and staff at universities have systematically different parking needs, leading to different parking valuations. In this study, I determine the impact university afﬁliation has on predicting bid values cast in three Dutch Auctions of on-campus parking permits sold at Chapman University in Fall 2010. Using clustering techniques crosschecked with university demographic information to detect afﬁliation groups, I ran a log-linear regression, ﬁnding that university afﬁliation had a larger effect on bid amount than on lot location and fraction of auction duration. Generally, faculty were predicted to have higher bids whereas students were predicted to have lower bids.
Advisor: Alison Hagy, Allan Collard-Wexler, Kent Kimbrough | JEL Codes: C38, C57, D44, R4, R49 | Tagged: Auctions, Parking, University Parking, Bidder Afﬁliation, Dutch Auction, Clustering
Optimal Ordering in Sequential English Auctions: A Revenue-Comparison Model for 18th Century Art Auctions in London and Paris
By Amaan Mitha
We develop a model based on several auction parameters to test the widely held notion that in a sequential English auction, it is optimal for the seller to arrange the lots in order of decreasing value. We test this model against two datasets of 18th century auctions, one of various auctions from Paris and the other from Christie’s sales in London. We find that the Paris data support the claim, while the Christie’s data seem to refute the optimal strategy. We also find a rationale for bidders in the Christie’s auctions to alter their strategies, accounting for the discrepancy.
Advisor: Neil De Marchi | JEL Codes: D4, Z11 | Tagged: