To Score or Not to Score? Estimates of a Sponsored Search Auction Model

27 Pages Posted: 18 Feb 2015 Last revised: 25 Jul 2018

See all articles by Yu-Wei Hsieh

Yu-Wei Hsieh

Amazon

Matthew Shum

California Institute of Technology

Sha Yang

University of Southern California - Marshall School of Business

Date Written: February 1, 2015

Abstract

Using data from "WebsiteX", one of the largest online marketplaces in the world, we estimate a structural model of sponsored search auctions where bidders have heterogeneous click-through curves. Unlike earlier studies, our model accommodates two stylized empirical facts: the advertiser prominence eff ect and the position paradox. Using our estimates, we simulate the e ffects of introducing bid-scoring to the auctions. We fi nd that scoring reduces equilibrium per-click prices, but boosts the number of clicks by sorting prestigious merchants to the top positions. Overall there is only a very modest reduction in total revenues from introducing bid-scoring, despite the intent to reward high-quality merchants with price discounts. Methodologically, this paper also illustrates an application of a novel "approximate Bayesian" estimation method to a structural multi-item auction model.

Keywords: Sponsored-search advertising, Auctions, Market design, Two-sided Matching, Bayesian estimation

JEL Classification: D44, D47, C11, C15

Suggested Citation

Hsieh, Yu-Wei and Shum, Matthew and Yang, Sha, To Score or Not to Score? Estimates of a Sponsored Search Auction Model (February 1, 2015). USC-INET Research Paper No. 15-09, Marshall School of Business Working Paper No. FBE 04.16, Available at SSRN: https://ssrn.com/abstract=2564735 or http://dx.doi.org/10.2139/ssrn.2564735

Matthew Shum

California Institute of Technology ( email )

Pasadena, CA 91125
United States

Sha Yang

University of Southern California - Marshall School of Business ( email )

701 Exposition Blvd
Los Angeles, CA California 90089
United States

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