Immigration Selection and the Educational Composition of the US Labor Force
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
Abstract Immigration policy is often viewed as an important regulator of the flow of labor and human capital into the labor market. In the US context, this perspective underlies efforts to raise the educational levels of newly admitted US immigrants, which has been proposed through a variety of mechanisms. Yet it remains unclear whether and under what circumstances such changes would significantly raise the educational level of the US labor force. We use a microsimulation model to evaluate the effects of various policy proposals that would seek to admit more highly educated immigrants. Results suggest that adopting a Canadian‐style admissions policy that explicitly selects immigrants based on educational attainment would lead to a better educated labor force, especially among immigrants and their descendants. Eliminating all unauthorized immigration or family reunification and diversity admission categories, however, would have minimal impact. Additionally, the effects of all policy scenarios on the educational composition of the entire labor force are likely to be modest and would be conditional on the continuation of intergenerational mobility and high levels of immigration.
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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 | 0.000 | 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 | 0.000 |
| 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