The Changing Shape of Spatial Income Disparities in the United States
Bibliographic record
Abstract
Spatial income disparities have increased in the US since 1980, a pattern linked to major social, economic, and political challenges. Yet, today’s spatial inequality, and how it relates to the past, remains insufficiently well understood. The primary contribution of this article is to demonstrate a deep polarization in the American spatial system—yet one whose character differs from that commonly reported on in the literature. The increase in spatial inequality since 1980 is almost entirely driven by a small number of populous, economically important, and resiliently high-income superstar city-regions. But we also show that the rest of the system exhibits a long-run pattern of income convergence over the study period. A secondary contribution is historical: today’s superstars have sat durably atop the urban hierarchy since at least 1940. Third, we describe six distinctive pathways of development that regions follow between 1940 and 2019, with certain locations catching up, falling behind, and surging ahead. We explore the role played by initial endowments in driving locations down these pathways, finding population, education, industrial structure, and immigrant attraction to be key distinguishing features. These insights are enabled by a fourth contribution: methodologically, we use group-based trajectory modeling—an approach new to the field that integrates top-down and bottom-up views of the evolving national spatial system. We conclude by exploring implications for the mid-twenty-first century.
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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 | 0.001 | 0.002 |
| 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".