Screening incentives and privacy protection in financial markets: a theoretical and empirical analysis
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
We study a model in which firms offer financial products to individuals, post prices for their products, and screen consumers who apply to purchase them. Any information obtained in the screening process may be traded to another firm selling related products. We show that firms' ability to sell consumer information can lead to lower prices, higher screening intensities, and increased social welfare. By exploiting variations in the adoption of local financial‐privacy ordinances in five California Bay Area counties, we are able to provide simple estimates of the effects of stricter financial‐privacy laws on mortgage denial rates during 2001–2006. Consistent with the model's predictions, denial rates for home‐purchase loans and refinancing loans decreased in counties where opt‐in privacy ordinances were adopted. Moreover, estimated foreclosure start rates during the financial crisis of 2007–2008 were higher in counties where the privacy ordinance was adopted.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it