Charging into Adulthood: Credit Cards and Young Consumers
Bibliographic record
Abstract
The New York Fed’s Center for Microeconomic Data today released the Quarterly Report on Household Debt and Credit for the fourth quarter of 2019. Total household debt balances grew by $193 billion in the fourth quarter, marking a $601 billion increase in household debt balances in 2019, the largest annual gain since 2007. The main driver was a $433 billion annual upswing in mortgage balances, also the largest since 2007. Auto loan and credit card balances both increased by a brisk $57 billion last year, while student loan balances climbed by a more muted $51 billion, well below the $114 billion increase recorded in 2013—the fastest pace of growth for the series. The source for the Quarterly Report is the New York Fed’s Consumer Credit Panel—a panel data set that now spans twenty-one years, 1999-2019. The unique panel design allows us to identify new entrants to the credit market: as young people age into having credit reports and using credit products, they are “born” into the panel, enabling us to observe the credit behavior of young borrowers.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".