The Equilibrium Effects of Information Deletion: Evidence from Consumer Credit Markets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper studies the equilibrium effects of information restrictions in credit markets using a large-scale natural experiment. In 2012, Chilean credit bureaus were forced to stop reporting defaults for 2.8 million individuals (21% of the adult population). Using panel data on the universe of bank borrowers in Chile combined with the deleted registry information, we implement machine learning techniques to measure changes in the predictions lenders can make about default rates following deletion. Deletion lowers (raises) predicted default the most for poorer defaulters (non-defaulters) with limited borrowing histories. Using a difference-in-differences design, we show that individuals exposed to increases in predicted default reduce borrowing by 6.4% following deletion, while those exposed to decreases raise borrowing by 11.8%. In aggregate, deletion reduces borrowing by 3.5%. Taking the difference-in-difference estimates as inputs into a model of borrowing under adverse selection, we find that deletion reduces surplus under a variety of assumptions about lenders' pricing strategies.
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.
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 | 0.008 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it