Pre-Tax Wage and Salary Income Inequalities in Largest Metropolitan Areas in the United States
Bibliographic record
Abstract
The distribution of pre-tax wages and salaries for employed individuals between the ages of 18-65 in the ten largest metropolitan areas of the USA are studied in this paper using the American Community Survey data from 2019. The included metropolitan areas 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. These ten metropolitan areas employed over 39 million individuals representing well over a quarter of the total employed labour force in the USA. Mean, median, standard error of the mean, 25th percentile, 50th percentile, and the Gini coefficient of pre-tax wages and salaries are presented for each metropolitan area. The metros differ significantly in terms of average pre-tax wages and salaries. They differ significantly in terms of the spread in the distribution of pre-tax wages and salaries measured both in terms of the inter-quartile range (the difference between 75th and 25th percentiles) and the Gini coefficient. San Francisco-Oakland-Hayward is found to have both the highest average pre-tax wages and salaries and widest inequality as measured by the Gini coefficient. The Smallest Gini coefficient is observed in Washington-Arlington-Alexandria metropolitan area. Inequality measured in terms of the Gini coefficient is nearly 15% higher in San Francisco-Oakland-Hayward as compared to Washington-Arlington-Alexandria. The average pre-tax wages and salaries are about 83% higher in San Francisco-Oakland-Hayward than Miami-Fort Lauderdale-West Palm Beach, the lowest in the nation. While aggregate nationwide inequalities attract intense attention, these regional variations point to significant and wide-ranging variations between different regions (metropolitan cities). By focusing on the pre-tax wages and salaries, this study allows us to tie inequalities that are most closely related to the labour market conditions, unlike other sources of income like capital gains, inheritance, government transfers, etc.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".