Hidden Disadvantage Asian American Unemployment and the Great Recession
Bibliographic record
Abstract
Nationally, Asian Americans have the lowest unemployment rate of the major racial groups. But a closer look at unemployment by educational attainment shows a more complicated picture. Asian Americans with bachelor’s degrees have a higher unemployment rate than whites with comparable education, but Asian American high school dropouts are more successful than comparable whites at finding jobs. As a result, the economic hardships and disadvantages for Asian Americans are sometimes overlooked. This Issue Brief shows that Asian American workers experience a complex mix of advantages and disadvantages in finding jobs when education level is considered. It concludes that if Asian Americans had the same unemployment rates by education level as whites, the Asian American unemployment rate would have been almost a percentage point lower in the fourth quarter of 2009. Thus, Asian American workers are disadvantaged relative to white workers in finding jobs. This paper examines the Asian American unemployment rate nationally and in five states: California, Hawaii, New Jersey, New York, and Texas. Only these five states had Current Population Survey sample sizes large enough for reliable statistics on Asian American unemployment. These analyses of Asian Americans exclude Pacific Islander, multi-racial, and Hispanic workers. 1 The data for white workers also exclude multi-racials and Hispanics. This Issue Brief shows that Asian American workers experience hidden disadvantages in the labor market: Asian American workers are more concentrated at both the high and low end of the education spectrum. A larger
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".