Just Released: More Credit Cards, Higher Limits, and . . . an Uptick in Delinquency
Bibliographic record
Abstract
Today the New York Fed’s Center for Microeconomic Data released its Quarterly Report on Household Debt and Credit for the second quarter of 2017. Overall debt balances increased in the period, continuing their moderate growth since 2013. Nearly all types of balances grew, with mortgages and auto loans rising by $64 billion and $23 billion, respectively. Credit card balances increased by $20 billion, recovering from the typical seasonal first-quarter decline. The overall balance surpassed its previous peak in the first quarter. We wrote here about how the new peak poses little concern in and of itself—after all, the debt’s composition and characteristics are now very different than in 2008. There are, however, aspects of the household balance sheet that warrant close monitoring. For example, last year, we pointed out that there had been a moderate rise in the number of credit cards issued to nonprime borrowers. Separately, last quarter we noted an uptick in delinquency transitions for credit card balances, and we observed another climb in this quarter. So here, we further investigate how credit card balances, accounts, and delinquencies have evolved over the past year.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.091 | 0.024 |
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".