When Platforms Go Public, Standards Drop
Bibliographic record
Abstract
Peer-to-peer (P2P) platforms facilitate the direct exchange of goods, services, or financial transactions between individuals without the involvement of intermediaries. To maintain trust among their user bases, these platforms must implement stringent access controls to determine which users are eligible to participate in their digital marketplace. We argue that when a platform transitions from private to public ownership, it may be incentivized to strategically lower its access standards and admit users who might otherwise have been deemed unqualified. Lowering standards before an initial public offering (IPO) can enable platforms to rapidly increase their user base—and, consequently, enhance their perceived valuation—which could appeal to stock investors and positively influence the IPO price. While this strategy may bolster short-term growth, it could be costly to the platform’s user base. We support this hypothesis using data from two major P2P lending platforms—one that went public and one that remained private. Using a difference-in-differences analysis, we find that the platform preparing for an IPO admitted borrowers who exhibited higher risk levels. Additionally, lenders, in some cases, did not effectively screen out these subpar borrowers and ended up issuing loans to them, leading to higher default rates and lower returns for the lenders. This effect was particularly evident for a specific segment of lenders—namely, those who brokered small-valued loans and loans for necessary purchases (e.g., health emergencies). By contrast, lenders who screened for large-valued loans or loans for discretionary expenditures (e.g., vacations or weddings) were more successful in screening out these subpar borrowers and not issuing loans to them. These results fill a gap in the operations management literature on platform governance by integrating capital-raising objectives into the discussion of access control and operational screening. Our study highlights the nuanced trade-off between quality control and user expansion during capital-raising events, emphasizing both the opportunities and potential inefficiencies that arise in this context.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".