Repeated payment delinquency among young adults in the United States
Bibliographic record
Abstract
Abstract There is concern in the United States about young adults falling behind financially due to the increased use of student loans and low wages. This study investigates payment delinquency as a measure of financial distress to better understand how young adults might be struggling. Personality traits are incorporated into the model to determine the extent to which behavioural factors are correlated with financial behaviors and if they predict a habit trend of payment delinquency. The 1997 National Longitudinal Survey of Youth, a nationally representative longitudinal data set, is used in the study. A random effects probit model and a dynamic random effects probit model are used to examine late bill pay (harassed by bill collectors) and late rent or mortgage payments (more than 60 days late) over a period of 8 years (2007–2015). Results from the analysis indicate that payment delinquency in a previous period increases the likelihood of payment delinquency by 10 percentage points in a subsequent period. Conscientiousness decreases the likelihood by 2.1 percentage points, while neuroticism increases the likelihood by 1.6 percentage points.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".