Forthcoming at the Journal of International Economics
Bibliographic record
Abstract
In this paper, we provide causal evidence that firms serve new mar-kets which are geographically close to their prior export destinations with a higher probability than standard gravity models predict. We quantify the impact of this spatial pattern using a data set of Chi-nese firms which had never exported to the EU, the United States, and Canada before 2005. These countries imposed import quotas on textile and apparel products until 2005 and experienced a subsequent increase in imports of previously constrained Chinese firms. Controlling for firm-destination specific effects and accounting for potential true state de-pendence we show that the probability to export to a country increases by about two percentage points for each prior export destination which shares a common border with this country. We find little evidence for other forms of proximity to previous export destinations like common colonizer, language or income group. ∗A previous version of this paper has been circulated under the title “Spatial Exporter Dynamics ” (mimeo, January 2010). We thank two anonymous referees and the assigned co-editor for their comments which have considerably improved the paper. We also thank
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.232 | 0.071 |
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".