Working Paper: NBER ID: w23569
Authors: Joachim Freyberger; Bradley J. Larsen
Abstract: This study provides new identification and estimation results for ascending (traditional English or online) auctions with unobserved auction-level heterogeneity and an unknown number of bidders. When the seller's reserve price and two order statistics of bids are observed, we derive conditions under which the distributions of buyer valuations, unobserved heterogeneity, and number of participants are point identified. We also derive conditions for point identification in cases where reserve prices are binding (in which case bids may be unobserved in some auctions) and present general conditions for partial identification. We propose a nonparametric maximum likelihood approach for estimation and inference. We apply our approach to the online market for used iPhones and analyze the effects of recent regulatory changes banning consumers from circumventing digital rights management technologies used to lock phones to service providers. We find that buyer valuations for unlocked phones dropped after the unlocking ban took effect.
Keywords: ascending auctions; digital rights management; buyer valuations; unobserved heterogeneity; nonparametric maximum likelihood
JEL Codes: C14; C57; D44; L96; O3
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| regulatory changes (G18) | buyer valuations for unlocked smartphones (L96) |
| unlocking ban (Y60) | buyer valuations for unlocked smartphones (L96) |
| regulatory changes (G18) | consumer perceptions and behaviors regarding illegally tampered products (D18) |