Economic returns of participation in the enclave and mainstream economy for Chinese and South Asian immigrants in Canada
Bibliographic record
Abstract
Economic integration of immigrants has been studied from three theoretical perspectives: assimilation theory, social capital theory and immigrant enclave economy thesis. These theoretical perspectives differ on whether immigrants’ ethnic attachments are seen as advancing or limiting their economic interests. The enclave economy thesis suggests that immigrants benefit from enclave participation by making use of common ethnic language and cultural ties to advance their economic interests. Using individual data from the 2006 Census of Canada, this thesis investigates whether Chinese and South Asian immigrants who participate in the enclave economy have better or worse returns compared to their counterparts in the mainstream economy. There are several major general findings. First, Chinese and South Asian immigrants who immigrated to Canada at an older age, those with less human capital, and those who lived in large metropolitan centres are more likely to participate in the enclave economy. Second, the returns for Chinese and South Asian immigrants in the enclave are lower than the returns of their counterparts in the mainstream economy, but the relative enclave earnings disadvantage is smaller for self-employed than for wage workers. Third, the returns to human capital for Chinese and South Asian in the enclave tend to be lower. Fourth, when the interaction terms measuring unequal human capital returns are further controlled, there is a positive effect associated with enclave participation. Such an effect indicates unmeasured positive influences associated with enclave participation after variations in other factors and unequal returns to human capital have been controlled. The positive effect may be understood as results of ethnic solidarity and cultural attachment. At the same time, the study suggests that the enclave economy provides an alternative opportunity to some immigrants, but such an opportunity is not as good as the opportunity in the mainstream 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".