Wage Gap between White non-Latinos and Latinos by Nativity and Gender in the Pacific Northwest, U.S.A.
Bibliographic record
Abstract
We estimate the effects of demographic and socioeconomic factors in the wage income gap between White non-Latinos and Latinos the Pacific Northwest (PNW) of the U.S. by nativity and gender for the period 2005-2007 using data from the American Community Survey (ACS). Linear decomposition of annual wage income showed that age and education are the two most relevant factors that contribute to explain differences in wage income among the U.S. born males and females. Age explains 43% of the differences in males and 55% in females; education explains about 20% for both. In contrast, occupation and education are the most relevant factors that explain wage gap among male and female immigrants; occupations explain about 30% and education about 27%; in addition, while age explains 9% of the wage gap among males, the number of years in the United States explains 11% of the wage gap among females. Among immigrants, the type of occupation explains slightly more of the wage difference than education. Our findings support general recommendations: to urge Latinos in the PNW to successfully complete high school, present to them the availability of apprenticeship programs, but also encourage them to attend college with the possibility of pursuing postgraduate or specialized studies. Lower returns to Latino education at all levels warrant attention to the quality of education in accordance to the demand for a qualified labor force. Further research on returns to education for White non-Latinos and Latinos (including apprenticeship programs) and occupations, together with evaluation instruments to measure the efficacy of educational investments, could benefit both the workforce and employers.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".