The Prevalence and Demography of Insufficient Earnings
Bibliographic record
Abstract
This research measures the prevalence and demography of full-year full-time workers whose earnings in 2018 or 2019 were insufficient to exceed the Supplemental Poverty Measure (SPM) poverty thresholds. Earnings sufficiency is then recalculated by subtracting the FICA taxes (Social Security and Medicare), adding the Earned Income Tax Credit, and subtracting work expenses to generate a measure of “expendable earnings.” This recalculation changes the prevalence of earnings insufficiency more for some demographic groups than others. The demographic breakdown examines racial/ethnic groups, separates immigrant workers from the native born, divides gender groups by marital status and the presence of children and examines three age groups, four educational attainment groups, and three groups divided by metropolitan status. The wage rates of workers with insufficient earnings are assigned to wage bins tailored to current debates about minimum wages, finding that almost 25% of full-year full-time workers with insufficient expendable earnings have wages that exceed $15 per hour, and that allocations into wage bins differ substantially among demographic groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".