Migrants from a Different Shore: Earnings and Economic Assimilation of Immigrants from China in the United States
Bibliographic record
Abstract
Abstract Using data from 1980, 1990, and 2000 U.S. censuses, as well as the 2010 and 2019 American Community Surveys and the 1993–2019 National Survey of College Graduates, we investigate the performance of Chinese immigrants in the U.S. labor market over the past 40 years since China initiated its economic reforms and open-door policy in 1978. The results indicate that by 1990, Chinese immigrants’ earnings surpassed those of immigrants from other countries, and by 2010, they exceeded the earnings of U.S.-born workers. Our Oaxaca-Blinder and Quantile decomposition analyses suggest that a significant portion of the earnings advantage held by Chinese immigrants, compared to other immigrant groups and U.S.-born workers over time, can be attributed to differences in observable characteristics, with education being the most crucial factor, both at the mean and across the earnings distribution. By employing national surveys that provide data on college graduates, we demonstrate that attaining the highest degree earned in the U.S. is associated with higher earnings for Chinese immigrants compared to all other immigrants. Furthermore, the difference in returns to U.S.-earned highest degrees can account for this earnings advantage. (JEL J31, J61, J24)
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".