Weakly non‐separable preferences and the Harberger‐Laursen‐Metzler effect
Bibliographic record
Abstract
We examine the current account effect of a terms‐of‐trade deterioration for a small country model, incorporating weakly non‐separable preferences à la Shi (1994) under endogenous time preference. This enables us to emphasize a welfare change as an important determinant of the current account. Even with increasing marginal impatience, the Harberger‐Laursen‐Metzler effect occurs if consumers' preference toward imports is sufficiently wealth enhanced: in that case a terms‐of‐trade deterioration must reduce steady‐state welfare to shift preference away from imports to exports. Several empirical implications are also derived. JEL Classification: F41; F32 Préférences faiblement nonséparables et l'effet de Harberger‐Laursen‐Metzler. L'auteur examine l'effet sur le compte courant d'une détérioration des termes d'échange dans un modèle d'un petit pays, quand les préférences sont faiblement nonséparables à la Shi (1994) et que la préférence temporelle est endogène. Cela permet de souligner qu'un changement dans le niveau de bien‐être est un déterminant important du compte courant. Contrairement à ce qu'on trouve dans la littérature spécialisée, même quand on postule une impatience marginale croissante, l'effet Harberger‐Laursen‐Metzler se produit si la préférence des consommateurs pour les importations est suffisamment dépendante du niveau de richesse: dans ce cas‐là, une détérioration des termes d'échange doit réduire le niveau de bien‐être en régime permanent de manière à déplacer les préférences des importations vers les exportations. On déduit certaines implications empiriques de ces résultats.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".