<i>Latent Cumulative Disadvantage:</i> US Immigrants’ Reversed Economic Assimilation in Later Life
Bibliographic record
Abstract
Abstract One of the most salient findings in research on immigration has been that immigrants experience substantial economic mobility as they accumulate more years in the host-society labor force and eventually approach earnings parity with their native-born counterparts. However, we do not know whether this progress is sustained in retirement. In this paper, I develop a framework of Latent Cumulative (Dis)advantage and hypothesize that even as immigrants are approaching parity with the native-born in terms of current earnings, they accumulate disadvantages in lifetime earnings, job benefits, and retirement planning that eventually lead them to have growing disadvantages in income in later life. Drawing on decades of longitudinal data from the Health and Retirement Study, I find that while foreign- and native-born men in the United States both experience a decline in income after age 50, the decline is much more substantial among foreign-born men. As a result, immigrant men’s economic assimilation is reversed in later life. I find evidence that this phenomenon is driven mainly by immigrants’ lower lifetime earnings and cumulative exposure to worse job benefits. Given that the foreign-born elderly population in the United States is projected to quadruple by 2050, findings from this paper have important implications for long-term policy planning.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".