Information Asymmetry in Mauritius Slave Auctions
Bibliographic record
Abstract
Evidence on adverse selection in slave markets remains inconclusive. A necessary prerequisite is that buyers and sellers have different information. We study informational asymmetry on the slave markets through notarial acts on public slave auctions in Mauritius between 1825 and 1835, involving 4,286 slaves. In addition to slave characteristics, the acts document the identities of buyers and sellers. We use this information to determine whether the buyer of a slave was related (e.g. a relative or a spouse) to the original slave owner, and thus most likely better–informed than other bidders. Auction–theoretic models predict that bidding should be more aggressive when informed bidders are present in open-bid, ascending auctions, such as slave auctions. By proxying informed bidders by related bidders, our results consistently indicate that this is the case, pointing toward the presence of information asymmetry in the market for slaves in Mauritius. JEL Classification: D 82, N 37
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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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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".