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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".