Bibliographic record
Abstract
Abstract Across countries, average incomes are related to population density, but not population per se. At the same time, the number of firms in both manufacturing and services are essentially proportional to population but unrelated to density (controlling for population). Why are these distinctions important? Moving from autarky to free trade is much more like increasing population while keeping density fixed. In this paper, I extend a simple variety model by considering an economy made up of a large number of geographical markets of fixed area. Firms must incur a cost to access each market, and this cost is convex in the number of markets entered. Assuming no firm chooses to access all markets within an economy, increasing both population and the number of markets proportionately has no effect on firms’ market access decision and so the number of firms per capita and average incomes remain unchanged. The implications of the model for trade are stark. If two identical countries open up to trade, there are no gains from trade. If two different countries open up to trade but no comparative advantage exists, there are no gains from trade. Countries trade (and gains from trade exist) only if comparative advantage exists. If comparative advantage exists, the gains from trade are lower than in a standard variety model. But if comparative advantage is ignored, the true gains from trade can be much higher than the gains calculated using a standard sufficient‐statistic formula.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.005 |
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".