Impact of FTC's Allegation on Amazon's Marketplace: Evidence From Coffee Products
Bibliographic record
Abstract
ABSTRACT How do firms respond to regulatory allegations? The monopolist lawsuit by the Federal Trade Commission (FTC) against Amazon provides an avenue to answer this question. Applying difference‐in‐differences to panel data of ground coffee products from Amazon's marketplace in the United States and Canada, we find that Fulfillment by Amazon (FBA) fees were reduced by $0.27–$0.29 per product in the United States due to the FTC's monopolist allegation. We also find that the allegation led to price reductions of $0.79–$0.92 per product in the country. Since 42% of the 7.5 billion products sold on Amazon United States in 2023 were by FBA sellers, we estimate that the allegation led to an annual savings of $0.85 billion to $0.91 billion for the FBA sellers and $5.92 billion to $6.85 billion for Amazon's customers on average. These results suggest that the FTC's allegation had sizeable and positive welfare impacts on Amazon's third‐party sellers and customers, making it a significant win for the regulatory agency. Moreover, our study provides insights into the implications of the FTC allegation for agribusiness stakeholders and the future of grocery shopping.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".