Poverty and Poverty Reduction Among Non-Elderly, Nondisabled, Childless Adults in Affluent Countries: The United States in Cross-National Perspective
Bibliographic record
Abstract
Income supports in the U.S. rely heavily on targeting based on means testing, categorical eligibility, or both. One result is that some groups are relatively underserved, often because they fall between the cracks of existing categories. One such group in the U.S. is non-elderly, nondisabled, childless adults. We assess poverty rates and poverty reduction—the extent to which taxes and transfers reduce market-generated poverty—in the U.S. compared to six other high-income countries: Canada, Czech Republic, Finland, Ireland, Netherlands, and the United Kingdom. Each of these countries reduces poverty more than does the U.S. and/or achieves lower post-tax-post-transfer poverty rates. Based on our cross-national comparative assessment—drawing on both microdata and country-level indicators—we offer some lessons for the U.S. First, the U.S. workforce is notable for its large share of low-wage workers. The U.S. could lower the incidence of low-paid work, and thus reduce poverty among the employed, by increasing the minimum wage at the federal and/or state and local levels, and by expanding the share of the workforce covered by collective agreements. Second, both income taxes and social contributions are pushing childless adults into poverty—more so in the U.S. than elsewhere. The U.S. could mitigate poverty among childless adults via any of a number of tax-related reforms. Third, our results indicate that U.S. income transfers, for this group, stand out in how meager they are. The U.S. could ameliorate poverty in this often-overlooked group by providing more-extensive income transfers, to those both in and out of work. (Stone Center on Socio-Economic Inequality Working Paper)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".