Bibliographic record
Abstract
As the first in a trio of pieces devoted to incorporating immigration into policy models, this review of research on immigrant earnings trajectories brings to light several findings. Controlling for demographic and human capital characteristics, immigrants often start their U.S. lives at substantially lower earnings, but experience faster earnings growth than natives with comparable years of education and experience. The extent to which the earnings trajectories of immigrants and natives differ varies by country of origin, with the source-country's level of economic development being a key determinant of the size of the U.S.-born/ foreign-born difference. The earnings profiles of immigrants from economically developed countries such as Japan, Canada, or Western Europe resemble those of U.S. natives who are of the same age and education level. In contrast, the earnings of immigrants from developing nations tend to start well below those of U.S. natives with comparable education levels and experience, but rise more rapidly than their U.S. counterparts. Comparing the earnings profiles of immigrants of similar age, sex, and years of schooling, over time and across groups, a strong inverse relationship emerges between their initial earnings and their subsequent U.S. earnings growth. In other words, the lower (higher) the initial earnings are, the higher (lower) the earnings growth. These and other research results have important implications for the projection of immigrant earnings and emigration in microsimulation models, as discussed in the two articles following this one: (1) "Adding Immigrants to Microsimulation Models" and (2) "Incorporating Immigrant Flows into Microsimulation Models".
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 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 | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".