First- and Second-Generation Immigrant Educational Attainment and Labor Market Outcomes: A Comparison of the United States and Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The educational and labor market outcomes of the first, first-and-a-half (1.5), second, and third generations of immigrants to the United States (US) and Canada are compared. These countries’ immigration policies have diverged on important dimensions since the 1960s, resulting in large differences in immigrant source country distributions and a much larger emphasis on skill requirements in Canada, making for interesting comparisons. Of particular note is the educational attainment of US immigrants which is currently lower than that in Canada and is expected to influence future second generations causing an existing education gap to grow. This will likely in turn influence earnings where, controlling only for age, the current US second generation has earnings comparable to those of the third generation, whereas the Canadian second generation has higher earnings. Importantly, the role of, and returns to, observable characteristics are significantly different between the US and Canada. Observable characteristics explain little of the difference in earnings outcomes across generations in the US but have remarkable explanatory power in Canada. Controlling for a wide array of characteristics, especially education, has little effect on the US second generation's earnings premium, but causes the Canadian premium to become negative relative to the Canadian third generation. The Canadian 1.5 and second generations’ educational advantage is of benefit in the labor market, but does not receive the same rate of return as it does for the third generation causing a very sizable gap between the current good observed outcomes, and the even better outcomes that would be expected if the 1.5 and second generation received the same rate of return to their characteristics as the third generation. Why the US differs likely follows from a combination of its lower immigration rate, its different selection mechanism, and its settlement policies and practices.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it