Bibliographic record
Abstract
We present a general equilibrium model of monopolistic competition featuring pro-competitive effects and a competitive limit, and investigate the impact of trade on welfare and efficiency. Contrary to the constant elasticity case, in which all gains from trade are due to product diversity, our model allows for a welfare decomposition between gains from product diversity and gains from pro-competitive effects. We show that the market outcome is not efficient because too many firms operate at an inefficiently small scale by charging too high markups. We further illustrate that trade raises efficiency by narrowing the gap between the equilibrium utility and the optimal utility. As the population gets arbitrarily large in the integrated economy, the equilibrium utility converges to the optimal utility because of the competitive limit. We finally extend the variable elasticity model to a multi-sector setting, and show that intersectoral distortions are eliminated in the limit. The multi-sector model allows us to illustrate some new aspects arising from intersectoral and intrasectoral allocations, namely that trade leads to structural convergence, rather than sectoral specialization, and that trade induces domestic exit in the nontraded sector.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".