The Labor Force Trajectories of Immigrant Women in the United States: Intersecting Individual and Gendered Cohort Characteristics
Bibliographic record
Abstract
Research on immigrant women's labor market incorporation has increased in recent years, yet systematic comparisons of employment trajectories by national origin and over time remain rare. Likewise, the literature on immigrant assimilation remains dominated by attention to men, with little focus on larger gendered migration dynamics. Using US Census and ACS data from 1990 to 2016, we construct synthetic migration cohorts by national/regional origin, period, and age at arrival to track immigrant women's labor force participation (LFP) over time. We propose and model a typology of workforce incorporation, adjusting for individual characteristics and gendered migration-cohort characteristics (i.e., the gender ratio, share of women arriving single, and share of men arriving with a college education). Results indicate that immigrant women gradually join the workforce over time, though with significant variation in starting employment levels and growth rates. We classify the observed patterns into a five-group typology: Gradual incorporation (cohorts from Europe, Canada, Africa, China, and Vietnam), delayed incorporation with low entry LFP level (cohorts from Mexico), delayed incorporation with moderate entry LFP level (cohorts from Central America, South America, and Cuba), accelerated incorporation (cohorts from India, Korea, and other Asian countries), and continuous intensive employment (cohorts from the Philippines and the Caribbean). We show that gendered migration cohort characteristics explain a substantial share of national/regional origin variation in immigrant women's workforce participation, highlighting the importance of broader cultural and structural forces shaping gendered patterns of immigrant labor market incorporation.
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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 | 0.002 |
| 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) | 0.002 | 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".