Making It in America: Social Mobility in the Immigrant Population
Why this work is in the frame
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Bibliographic record
Abstract
In his survey of research on social mobility and U.S. immigration, George Borjas underscores two insights. First, most immigrants are at a sizable earnings disadvantage, relative to native-born workers. Second, the earnings of different groups of immigrants vary widely. The children of immigrants "catch up" to native-born workers slowly. The jump in relative wages between the first and second generations is somewhere between 5 and 10 percentage points. Of particular concern is that the age-adjusted relative wage of both immigrants and second-generation workers has been falling--a trend with bleak implications for the children of immigrants. The wide ethnic variation in the earnings of immigrants has equally important implications. National origin groups from advanced economies, such as Canada, do much better in the U.S. labor market than those from poorer countries, such as Mexico. And the initial ethnic differences tend to persist. In rough terms, about half of the difference in relative economic status persists from one generation to the next. Thus a 20 percentage point wage gap among ethnic groups in the immigrant generation implies a 10 point gap among second-generation groups and a 5 point gap among third-generation groups. Again in rough terms, Borjas attributes about half of that persistence to the ethnic environment in which children are raised. Borjas cautions that the rate of social mobility that immigrants enjoyed over much of the twentieth century may not continue in the future. The employment sectors seeking immigrants today are unlikely to provide the same growth opportunities as did the rapidly expanding manufacturing sector a century ago. And in contrast to the many and diverse ethnic groups that made up early twentieth-century immigrants, the large ethnic groups of immigrants today may develop separate economies and social structures, in effect hindering their social mobility.
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.
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 | 0.000 | 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