Modelling the composition of household portfolios: A latent class approach
Bibliographic record
Abstract
Abstract We explore portfolio allocation in Great Britain by introducing a latent class modelling approach using household panel data based on a nationally representative sample of the population, namely the Wealth and Assets Survey. The latent class aspect of the model splits households into four groups, from lowest‐wealth and least‐diversified through to highest‐wealth and most‐diversified, which serves to unveil a more detailed picture of the determinants of portfolio diversification than existing econometric approaches. A pattern of class heterogeneity is revealed that conventional econometric models are unable to identify because the statistical significance and the direction of the effect of some explanatory variables vary across the groups. For example, the effect of labour income on the number of financial assets held influences the level of diversification for the two middle classes, whereas no effect is found for households with the lowest or the highest levels of diversification. Noticeable differences in the magnitude of the effects of pension wealth and occupation are also revealed across the four classes. Such findings demonstrate the importance of accounting for latent heterogeneity when modelling financial behaviour. Ultimately, treating the population as a single homogeneous group may lead to biased parameter estimates, whereby policy based on such models could be inappropriate or erroneous.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".