Promise Keeping, Relational Closeness, And Identifiability: An Experimental Investigation In China
Bibliographic record
Abstract
We experimentally investigate some key features of internet peer-to-peer (P2P) lending: the borrower specifies the amount of money required and makes a contingent promise about the value of the generally higher repayment prior to the investor's decision to lend the required sum or not. We examine the role played by two factors related to traditional Chinese culture and ethics: whether (i) guanxi, relational closeness between the actors and (ii) the ability of the actors to observe each other's identity after the repayment decision (identifiability or mianzi concerns) affect the borrowers' decisions to make the promised repayments and ultimately the consequent aggregate realized social benefits. Using a two-by-two factorial design, we conduct four experimental treatments in China and also perform the identifiability treatment in New Zealand as a cultural control. We find that in China both manipulations were positively and significantly related to the probability of a repayment promise being kept. Moreover, these two factors were substitutes for each other. In New Zealand, there was no significant identifiability effect on promise keeping. Over time, relational closeness and identifiability both led investors in China to accept more proposals, resulting in more investment and the creation of greater social surplus.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".