Going Separate Ways: Spatial Sorting by Education, Work, and Real Income in the USA since 1970
Bibliographic record
Abstract
A growing literature within economic geography has been documenting the increas- ing skill and wage polarization within and across metropolitan areas in the US. This work has provided evidence that skill-biased technological changes in the second half of the 20th century made way to a significant upwards shift in the demand coupled with productivity gains for high-skill, college-educated, workers. These changes have not, however, unfolded uniformly across space. High skill workers have been increas- ingly concentrating in large metropolitan areas where higher housing costs are more than compensated by premiums in their wage. Such areas seemingly do not offer the same benefits to low-skill workers working in more ubiquitous, non-tradeable in- dustries, and burdened by high rents. The bipartite division of skill groups in most research has, however, largely obscured the realities of ‘middle class’ America; where they live; who they live with; the evolution of their real incomes and well being com- pared to other groups. In this study, we look at long-term changes in the spatial distribution of three skill groups and observe an important shift in the geography of medium-skill workers, which have gradually separated away from high-skill workers and grown in relative importance in smaller non-coastal areas, where increases in general income and housing costs have been more limited. From a regional perspec- tive, we identify a large set places that are dominated by the middle-skilled and discuss their general characteristics.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".