Interpersonal Networks in International Trade: Evidence on the Role of Immigrants in Promoting Exports from the American States *
Bibliographic record
Abstract
Economics Department provided funding to acquire the MISER data. I am grateful to Cletus Coughlin, Bill Even, and William Hutchinson for assistance and helpful comments. Earlier versions of this paper were presented at the Conference of the International Society for the New Institutional Economics (September 2003) and at the Southern Economic Association Conference (Nov. 2003); helpful comments were given by the participants, especially by Madeline Zavodny, at these conferences. The author, of course, is solely responsible for all remaining shortcoming. 2 The effect of immigrants on the export performance of the 50 American states and the District of Columbia to 87 foreign countries is studied. Mark Granovetter’s (1973) discussion of weak and strong ties is used to motivate the proposition that immigrants are well situtated with their knowledge of two societies and their strong ties to their countrymen to lower the transactions costs for prospective exporters, and, hence, that immigrants have a pro-trade effect on exports between their host and origin countries. This proposition (which has been confirmed in several studies at the national level and for the Canadian provinces) and its several corollaries are tested using state-level trade data averaged over the 1990 – 1992 period. The proposition and its corollaries, that the immigrants ’ ties are more important when the export destination economy
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".