Bibliographic record
Abstract
A link between immigration, imports, and exports has been found by a number of papers that have used the gravity equation to analyze bilateral trade patterns. We discuss what this research implies about the mechanisms through which immigrants expand trade and identify strengths and weakness of the various approaches. This paper also contributes to this literature by estimating immigrant effects for Canada using cross-province variation in international trade and immigration patterns. We derive an alternative functional form capturing the relationship between immigration and trade based on the proposition that immigrants use their connections and superior ‘market intelligence’ to exploit trade opportunities that non-immigrants do not access. We find that the average new immigrant expands exports to his=her native country by $312 and expands imports by $944. The gravity model of international trade has consistently revealed a strong association between immigration and trade. Not only have different studies revealed a robust relationship for different samples and specifications, the strength of the immigration effect varies in sensible ways for different trading partners, products, and types of immigrants. The estimated magnitude of the immigration effect, however, differs greatly across studies. The analysis of Head and Ries (1998), Dunlevy and Hutchinson (1999, 2001), Rauch and Trindade (2002), Girma and Yu (2002) and Combes et al. (2002) based on cross-sectional information find large effects whereas Gould’s (1994) estimation based on times series variation indicates smaller effects. The discrepancy from an econometric standpoint is easy to explain—cross-sectional estimates may be upwardly biased due to unobserved characteristics of trading relationships whereas fixed effect estimates may have the opposite bias due to the magnification of measurement error caused by this technique. Alternative explanations for the discrepancy are differences in specifications and samples.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".