Nonparametric Estimation of Sponsored Search Auctions and Impact of Ad Quality on Search Revenue
建立了一个赞助搜索拍卖的实证模型，利用雅虎搜索拍卖数据估计广告商估值分布，并通过反事实分析发现产品特定质量评分规则可使拍卖商收入最多提高24.3%，但会损害广告商利润和消费者福利。
This paper presents an empirical model of sponsored search auctions where advertisers are ranked by bid and ad quality. Our model is developed under the “incomplete information” setting with a general quality scoring rule. We establish nonparametric identification of the advertiser’s valuation and its distribution given observed bids and introduce novel nonparametric estimators. Using Yahoo! search auction data, we estimate value distributions and study the bidding behavior across product categories. We also conduct a counterfactual analysis to evaluate the impact of different quality scoring rules on the auctioneer’s revenue. Product-specific scoring rules can enhance auctioneer revenue by at most 24.3% at the expense of advertiser profit (−28.3%) and consumer welfare (−30.2%). The revenue-maximizing scoring rule depends on market competitiveness. This paper was accepted by Jean-Pierre Dube, marketing. Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada under the Insight Development Grant [Grant 430-2022-00841] to D. Kim. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02052 .