Nonparametric Estimation of Sponsored Search Auctions and Impact of Ad Quality on Search Revenue
Bibliographic record
Abstract
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 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".