Bibliographic record
Abstract
Abstract Shapiro (2015) points out an inherent fragility of dynamic incentives in microfinance without collateral or long‐term loans. He shows that the dynamic incentive mechanism unravels for all except a single value of initial beliefs. That is, under any parameter setup, only a single efficient equilibrium exists, in which loan terms improve over time, while an infinite number of inefficient equilibria exist, in which loan terms eventually deteriorate and even the most patient borrowers would default. I show that his concern vanishes if one introduces to the environment any positive proportion of “commitment‐type” borrowers, who never default by nature. Adding an infinitesimal proportion of commitment‐type borrowers eliminates all inefficient equilibria, while preserving the unique efficient equilibrium. Moreover, given any setup of parameters, a unique efficient equilibrium exists, in which the loan terms become more favourable over time, and the proportion of non‐defaulters converges to one. Résumé Incitations dynamiques en micro‐crédit avec emprunteur engagé . En matière de micro‐crédit, Shapiro (2015) montre qu’en l’absence de prêts à long terme ou sur nantissement, une fragilité inhérente aux incitations dynamiques demeure. Shapiro montre également qu’en matière de convictions premières, le mécanisme d’incitation dynamique échoue pour toutes les valeurs sauf une. Autrement dit, pour tous les paramètres de base, il n’existe qu’un seul équilibre efficace au sein duquel les conditions de prêt s’améliorent dans la durée, alors que parallèlement, il existe un nombre infini d’équilibres inefficaces au sein desquels les conditions de prêt finissent par se détériorer rendant le crédit impossible à rembourser même pour l’emprunteur le plus patient. Je montre que le problème soulevé par Shapiro disparaît dès lors que l’on introduit à cet environnement une proportion positive d’emprunteurs engagés qui, par nature, remboursent toujours leur dette. En introduisant une proportion infime d’emprunteurs engagés, tous les équilibres inefficaces disparaissent tout en préservant le seul qui s’avère efficace. En outre, quels que soient les paramètres de base, il n’existe qu’un seul équilibre efficace au sein duquel les conditions de prêt s’amélioreront avec le temps et où le taux de remboursement approchera un (1).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".