English as the Lingua franca and the Economic Value of Other Languages: The Case of the Language of Work in the Montreal Labor Market
Bibliographic record
Abstract
An important feature of Canada is that it has two official languages, English and French, and that one of them, English, is also the international lingua franca. This situation may have particular policy implications. Within Canada, the Montreal metropolitan area presents an interesting case in point: it has a majority of native French speakers, an important minority of native English speakers, and many immigrants from various linguistic backgrounds who try to make their way into the labor market. Using confidential micro-data from the 2006 Canadian Census, this chapter investigates the determinants and the economic values of the use of different languages at work in Montreal. Workers are divided into three groups: French, English and Other mother tongues, and indices are defined for the use of French, English, and Other languages at work. It is found that the use of English at work by non-English native speakers is positively related to the education level of the workers, while there is no such relationship for the use of French by native English speakers. The returns to using at work a language that is different from one’s mother tongue are analyzed with ordinary least squares and instrumental variables regressions. For the English mother tongue group, using French at work has little or no reward, while using English at work pays a lot for the French mother tongue group. For the Other mother tongues group, there is a high payoff to using an official language at work, especially English. This situation is not due to the inferior economic status of the native French speakers; it is due to the fact that English is the international lingua franca. The policy implications of the above results are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".