Racial-Ethnic Poverty Gaps in Later Life: A Role for Late Career Employment Quality?
Bibliographic record
Abstract
Abstract Racial-ethnic disparities in poverty are an important form of inequality in older age. Recent scholarship on racial-ethnic poverty gaps demonstrates that, beyond individual characteristics and behaviors, racialized structural factors like employment contribute to such gaps. Yet surprisingly little is known about the role of employment quality, despite observed racial-ethnic disparities in employment quality and the role of employment history in shaping later life well-being. Using data from the 2002–2018 waves of the Health and Retirement Study (HRS) and three poverty measures, we decompose the proportions of the Black-white and Hispanic-white poverty gaps among households led by 65-year-olds that are attributable to disparities in late career employment quality. We find that racial-ethnic disparities in late career employment quality account for 17–28 percent of the observed Black-white and 18–32 percent of the observed Hispanic-white poverty gaps, thus explaining a greater proportion of such gaps than many common individual or behavioral explanations. Disaggregating employment quality into its component measures, we find racial-ethnic disparities in access to employer-provided health insurance and hourly wages account for the largest proportion of racial-ethnic poverty gaps. Our findings suggest that employment quality captures important racialized dimensions of labor market context that help account for racial-ethnic inequalities in later life poverty in the United States.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".