Racial Disparities in Pre-tax Wages and Salaries in Largest Metropolitan Areas in the United States
Bibliographic record
Abstract
The article deals with racial disparities in the distribution of pre-tax wages and salaries for employed individuals in the USA between the ages of 18-65. This study is done for the ten largest metropolitan areas of the USA using the 2019 American Community Survey data. The metropolitan areas included in the study are Atlanta-Sandy Springs-Roswell, Chicago-Naperville-Elgin, Dallas-Fort Worth-Arlington, Houston-The Woodlands-Sugar Land, Los Angeles-Long Beach-Anaheim, Miami-Fort Lauderdale-West Palm Beach, New York-Newark-Jersey City, Philadelphia-Camden-Wilmington, San Francisco-Oakland-Hayward, and Washington-Arlington-Alexandria. Employing well over a quarter of the total employed labour force in the USA, these ten metropolitan areas are also some of the largest industrial worlds. Average pre-tax wages and salaries, the standard deviation of the mean and Gini coefficient by major racial categories are presented for each of these ten metropolitan areas. For each metropolitan area, black employed individuals earned less in pre-tax wages and salaries than white employed individuals. The Gini coefficient of black pre-tax wages and wages is also found to be smaller for each of the metropolitan areas compared to the white counterparts. It suggests a much tighter distribution in pre-tax wages and salaries for blacks compared to whites. Furthermore, employed workers from other races earned less in pre-tax wages and salaries than their white counterparts for each major metro. Except for Los Angeles-Long Beach-Anaheim metropolitan area, black employed workers also earned less pre-tax wages and salaries than members of the other races. The Gini coefficients of pre-tax wages and salaries for various metropolitan areas for different races are found to be broadly comparable and often larger than that of the whites. Together, these results point to the fact that the pre-tax wages and salaries of black workers are lower compared to both whites and other races and more tightly distributed. Lastly, the relative inequality between whites and blacks and others and blacks often point to the relatively broader dispersion in the later group compared to the former.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".