Following Borrowers through Forbearance
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".