Economic Assimilation of Immigrants in Quebec
Bibliographic record
Abstract
In this paper, I examine the wages and work hours of immigrants in Quebec and the rest of Canada. By analyzing data from the Survey of Labour and Income Dynamics (SLID) for the years 1999 to 2011, I explore three key questions about the economic integration of immigrants relative to native-born individuals. The first question examines the initial wage gap for new immigrants, defined as those who have lived in Canada for less than ten years at the time of data collection. The second question assesses whether this wage gap changes significantly over the sample period. The third question examines the extent of economic assimilation in immigrant earnings as their residency in Canada lengthens. The findings reveal a significant initial wage gap, with new immigrants earning 28.8% less than their native counterparts. Over the sample period, this gap narrowed by 1.3% annually. Additionally, the analysis indicates that economic assimilation is substantial, though rates vary between Quebec and the rest \nof Canada. Initially, assimilation occurs more quickly in Quebec, whereas over time, immigrant earnings rise more rapidly outside Quebec. To further explore the economic assimilation of immigrants, I investigate their work hours and find that new immigrants work fewer hours than their native counterparts. As with the wage gap, the immigrant-native gap in work hours narrows as immigrants stay longer in Canada. To understand these assimilation effects on both wages and work hours, I analyze a simple model of learning by doing (LBD) and show that the model can account for the key observed patterns of wages and hours worked among immigrants in the SLID.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".