The changing shape of spatial inequality in the United States
Bibliographic record
Abstract
Spatial income disparities have increased in the United States since 1980. Growth in this form of inequality is linked to major social, economic and political challenges. Yet, contemporary patterns, and how they relate to those of the past, remain insufficiently well understood. Building on population survey microdata spanning 1940-2019, this paper uses group-based trajectory modelling techniques to identify distinct sets of local labor markets based on the evolution of their income levels. We find that the increase in spatial inequality since 1980 is almost entirely driven by a small number of populous, economically-important, and resiliently high-performing `superstar' city-regions. Meanwhile, since 1940, much of the rest of the urban system has continued to converge toward the mean. We examine the demographic, economic and social characteristics of these different trajectories, identifying catch-up regions, declining regions, long-term winners, and possible future superstars. There is considerable turbulence within the convergence process, consisting of regions that are moving both upward and downward in the system. We conclude by exploring implications for the American urban-regional system in the mid-21st century, considering the challenges in overcoming the growing split between superstar locations and the rest of the country.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".