Working Poverty across the Metro-Nonmetro Divide: A Quarter Century in Perspective, 1979-2003
Bibliographic record
Abstract
Researchers are increasingly recognizing space as a key axis of inequality. Scholars concerned with spatial inequality have called for special attention to issues of comparative advantage and disadvantage across space as well as the consideration of the subnational scale. This study draws on these ideas by examining the relationship between work and poverty in the United States with an explicit comparative focus on metropolitan (metro) and nonmetropolitan (nonmetro) areas. Moreover, this study joins space with its counterpart time by exploring how this relationship has changed over the last quarter century. Using data from the March Current Population Survey, the results show that working poverty persistently had a disproportionate impact on nonmetro families between 1979 and 2003. However, the results also show a trend of residential convergence, as working poverty in metro areas has climbed toward the levels experienced in nonmetro areas. Logistic-regression models exploring the effects of residence, family labor supply, and period confirm that labor supply has consistently provided nonmetro families with less protection from poverty than their metro counterparts, but also show that this disadvantage has waned in recent years. The findings underscore the need for policies that support those working on the economic margins and recognize the variable opportunity costs of employment across the rural-urban continuum.
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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 | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".