Platform Power and Subsidy Wars: Structural Distortions in China’s Online Food Delivery Market-Taking Meituan as an Example
Bibliographic record
Abstract
This essay examines the structural distortions and power asymmetries in China’s online food delivery market, focusing on the rise of Meituan and the subsequent hypercompetitive environment intensified by subsidy wars. While platforms such as UberEats and DoorDash in Western markets fostered balanced competition between restaurants, platforms, and consumers, the Chinese market evolved differently. Meituan consolidated monopoly power during the COVID era through commission fees, delivery charges, and exposure-based advertising schemes that disproportionately burdened small and medium restaurants. The later entry of Taobao and JD, rather than alleviating this imbalance, introduced aggressive subsidy strategies funded partly by merchants, further eroding profitability and creating conditions of negative gross margins. Consumers benefited from low prices, and platforms leveraged data advantages, but restaurants were reduced to expendable actors in a distorted market equilibrium. By analyzing market concentration, cross-subsidy pricing, and coupon-funded competition, this study highlights how unchecked platform dominance undermines industry sustainability and social welfare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".