Exploratory Study of Comparison Income and Individual Debt Using British Household Panel Survey
Bibliographic record
Abstract
The famous saying “keeping up with the Joneses” is a generational behaviour that is still deeply interwoven in the behavioural fabric of our modern-day society. This paper aims to address and contribute to the existing literature by investigating the determinants of individual non-mortgage debt, focusing on the role of comparison income. It also seeks to overcome certain empirical shortcomings by applying Tobit, fixed effects, and Tobit fixed effects regression models to a UK dataset. The study is motivated by previous research, which suggested the aspiration of borrowers is influential in the debt-decision process. Previous studies did not use empirical methods or UK data, however. Comparison income (the measure of the borrowers’ aspiration) is derived from the Mincer earnings equation. Tobit regression is applied in the cross-section analysis and is pertinent considering the censored nature of the dependent variable. In the panel analysis, fixed effects and Tobit fixed effects are used to control for unobserved attributes of sampled individuals that may affect demand for debt. Comparison income and non-mortgage debt as well as other economic and demographic variables are positively and significantly associated. The relationship between comparison income and non-mortgage debt suggests the latter may be incurred for status-maintenance purposes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".