Policy Uncertainty and Bank Mortgage Credit
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 document that banks reduce supply of jumbo mortgage loans when policy uncertainty increases as measured by the timing of US gubernatorial elections in banks' headquarter states. The reduction is larger for more uncertain elections. We utilize high-frequency, geographically granular loan data to address an identification problem arising from changing demand for loans: (1) the microeconomic data allow for state/time (quarter) fixed effects; (2) we observe banks reduce lending not just in their home states but also outside their home states when their home states hold elections; (3) we observe important cross-sectional differences in the way banks with different characteristics respond to policy uncertainty. Overall, the findings suggest that policy uncertainty has a real effect on residential housing markets through banks' credit supply decisions and that it can spill over across states through lending by banks serving multiple states.
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 | 0.000 | 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 | 0.001 |
| 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