Worth Less? Exploring the Effects of Subminimum Wages on Poverty among U.S. Hourly Workers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it