Worth Less? Exploring the Effects of Subminimum Wages on Poverty among U.S. Hourly Workers
Bibliographic record
Abstract
The Fair Labor Standards Act’s minimum wage laws provide important protections for workers. However, it still permits employers to pay subminimum wages to youth under age 20, student-vocational learners, full-time students, individuals with disabilities, and tipped workers. This has important economic consequences, especially for economically vulnerable workers in the low-wage sector. Using 2009–2019 Current Population Survey–Merged Outgoing Rotation Group (CPS-MORG) data ( n = 502,976), we find that 3.7 percent (about 1,565,805) of hourly workers were paid subminimum wages based on state minimum wage laws, and subminimum wages were associated with increases in family poverty by 1.4 percentage points. Importantly, the relationship between subminimum wages and poverty differed across workers with particularly telling results for disability. Unlike for youth and students for whom access to subminimum wage labor was associated with decreased family poverty, subminimum wage work compounded already high poverty rates for hourly workers with disabilities. Within a broader context of low-wage work, this research speaks to the impacts of subminimum pay on economic insecurity and poverty—an ongoing social problem disproportionately affecting people with disabilities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".