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

Hsieh, Yu-Wei and Shum, Matthew and Yang, Sha (2015) To Score or Not to Score? Estimates of a Sponsored Search Auction Model. Social Science Working Paper, 1402. California Institute of Technology , Pasadena, CA. (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20160329-095057403

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Abstract

We estimate a structural model of a sponsored search auction model. To accomodate the "position paradox", we relax the assumption of decreasing click volumes with position ranks, which is often assumed in the literature. Using data from "Website X", one of the largest online market places in China, we find that merchants of different qualities adopt different bidding strategies: high quality merchants bid more aggressively for informative keywords, while low quality merchants are more likely to be sorted to the top positions for value keywords. Counterfactual evaluations show that the price trend becomes steeper after moving to a score-weighted generalized second price auction, with much higher prices obtained for the top position but lower prices for the other positions. Overall, there is only a very modest change in total revenue from introducing popularity scoring, despite the intent in bid scoring to reward popular merchants with price discounts.


Item Type:Report or Paper (Working Paper)
ORCID:
AuthorORCID
Shum, Matthew0000-0002-6262-915X
Additional Information:February 2015. Acknowledgement: We thank Arie Beresteanu, Baiyu Dong, Hashem Pesaran, Joris Pinkse, Sergio Montero, Roger Moon, Geert Ridder and Guofu Tan for their helpful discussions. Thanks also go to the seminar participants at Colorado-Boulder, Penn State, Rice, Rochester, USC, UC-Riverside, UCLA, Yale-SOM, 2013 Annual Conference of Taiwan Econometric Society, 2014 California Econometrics Conference and 2014 North American Summer Meeting of the Econometric Society.
Group:Social Science Working Papers
Subject Keywords:Sponsored-search advertising; Auctions; Market design; Two-sided Matching; Bayesian estimation
Series Name:Social Science Working Paper
Issue or Number:1402
Classification Code:JEL: D44; D47; C11; C15
Record Number:CaltechAUTHORS:20160329-095057403
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160329-095057403
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65731
Collection:CaltechAUTHORS
Deposited By: Susan Vite
Deposited On:30 Mar 2016 23:16
Last Modified:03 Oct 2019 09:49

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