Spatial disparity in household indebtedness across the UK
Bibliographic record
Abstract
Purpose This paper assesses the lending risks associated with the level of total household indebtedness at the local authority level across the UK. Design/methodology/approach Using GIS-based Exploratory Data Analysis and mapping, the paper identifies local concentrations of household borrowing, both secured and unsecured, which is referenced against regional Gross Added Value. Findings Significant local differences are revealed which are tracked over the period 2013–2019. Total debt relative to the size of economy is larger in London and local authorities around London. A positive correlation was revealed between areas of multiple deprivation in England and those local authorities with proportionally high unsecured lending, confirming that the less well-off require access to debt facilities and in the absence of availability of secured loans, resort to unsecured borrowing. Originality/value Understanding where the additional lending risks are located across the UK is relevant when evaluating the robustness of the economy to recession, with its uneven effects on different sectors and households and the impact of monetary policy changes, particularly sharp rises in interest rates. The mapping of these risks is illuminating and aids understanding.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".