Heterogeneous firms, trade liberalization and agglomeration
Bibliographic record
Abstract
Abstract In this study, we develop an economic model to examine agglomeration of heterogeneous firms following trade liberalization. In a closed economy, we show that high-productivity firms are more likely to agglomerate because they benefit more from agglomeration than their low-productivity counterparts. However, trade liberalization, especially with a high-productivity partner, favours partial agglomeration; that is, low-productivity firms relocate away from the region where high-productivity firms agglomerate. Consequently, the welfare gap between the domestic regions of an economy narrows following trade liberalization. The latter result suggests that trade liberalization promotes regional economic development. On développe un modèle économique pour examiner l'agglomération de firmes hétérogènes à la suite de la libéralisation du commerce. Dans une économie fermée, on montre que les firmes à haute productivité sont davantage susceptibles de s'agglomérer parce qu'elles tirent plus d'avantages de l'agglomération que les firmes à basse productivité. Cependant, la libéralisation du commerce, en particulier avec un partenaire à haute productivité, favorise l'agglomération partielle, i.e., que les firmes à basse productivité se relocalise hors de la région où les firmes à haute productivité s'agglomèrent. En conséquence, l'écart de bien-être entre les régions domestiques d'une économie s'amenuise à la suite de la libéralisation du commerce. Ce dernier résultat suggère que la libéralisation du commerce favorise le développement économique régional.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".