Following Borrowers through Forbearance
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
Today, the New York Fed’s Center for Microeconomic Data reported that total household debt balances increased slightly in the third quarter of 2020, according to the latest Quarterly Report on Household Debt and Credit. This increase marked a reversal from the modest decline in the second quarter of 2020, a downturn driven by a sharp contraction in credit card balances. In the third quarter, credit card balances declined again, even as consumer spending recovered somewhat; meanwhile, mortgage originations came in at a robust $1.049 trillion, the highest level since 2003. Many of the efforts to stabilize the economy in response to the COVID-19 crisis have focused on consumer balance sheets, both through direct cash transfers and through forbearances on federally backed debts. Here, we examine the uptake of forbearances on mortgage and auto loans and its impact on their delinquency status and the borrower’s credit score. This analysis, as well as the Quarterly Report on Household Debt and Credit, is based on anonymized Equifax credit report data.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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