Age effects on consumer demand: an additive partially linear regression model
Bibliographic record
Abstract
An additive partially linear regression model is used to estimate non‐parametrically the effects of total expenditure and age in the context of Engel curves and to investigate the specification and welfare interpretation of the age effects in parametric models of consumer behaviour. Empirical analysis based on data drawn from the U.K. Family Expenditure Survey shows that modelling of the effects of age requires a more sophisticated approach than that generally adopted in parametric demand analysis. It also shows that failing to adequately capture these effects can have misleading welfare implications. JEL Classification: C14, D12 L’effet de l’âge sur la demande des consommateurs : un modèle de régression additif partiellement linéaire. On utilise un modèle de régression additif partiellement linéaire pour faire la calibration non paramétrique des effets de la dépense totale et de l’âge dans le contexte de courbes de Engel, et pour enquêter sur la spécification des effets d’âge et leur interprétation en termes de bien‐être dans des modèles paramétriques de comportements du consommateur. L’analyse empirique est construite sur des données de la U.K. Family Expenditures Survey et montre que la modélisation des effets de l’âge réclame une approche plus sophistiquée que celle qui est généralement utilisée dans l’analyse paramétrique de la demande. On montre aussi que le manque à bien saisir ces effets peut conduire à des conclusions fausses pour ce qui est du niveau de bien‐être.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".