Cost of living variation, non-metropolitan America, and implications for the Supplemental Poverty Measure
Bibliographic record
Abstract
Poverty scholarship in the United States is increasingly reliant upon the Supplemental Poverty Measure (SPM) as opposed to the Official Poverty Measure of the United States for research and policy analysis. However, the SPM still faces several critiques from scholars focused on poverty in nonmetropolitan areas. Key among these critiques is the geographic adjustment for cost of living employed in the SPM, which is based solely upon median rental costs and pools together all nonmetropolitan counties within each state. Here, we evaluate the current geographic adjustment of the SPM using both microdata and aggregate data from the American Community Survey for 2014-2018. By comparing housing costs, tenure, and commuting, we determine median rent is likely an appropriate basis for geographic adjustment. However, by demonstrating the wide variability between median rents of nonmetropolitan counties within the same state, we show that the current operationalization of this geographic adjustment could be improved through the use of more-specific categories such as metropolitan adjacency or Rural Urban Continuum Codes.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".