Beyond Dispersion: Density, Industry, and Political Fragmentation in the Geography of Regional Job Centers

Zakhary Mallett & Nicholas Wilsey

Journal of Regional Science2026https://doi.org/10.1111/jors.70058article
AJG 3ABDC A
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0.50

Abstract

Most research on the spatial organization of employment focuses on individual metropolitan areas, limiting insight into cross‐regional and generalizable patterns. At the same time, recent studies often rely on broad indicators of employment dispersion—such as the share of urbanized land occupied by jobs—while methodological advances in identifying job centers have lagged. As a result, comparative analyses of regional employment geographies, particularly those focused on job center formation and dominance, remain scarce. Using block‐level employment data, we adapt a spatial clustering model to identify job centers across the 100 most populous regions of the United States at three relative density thresholds. We then document variation in employment centralization and concentration across regions and density strata, and test—via regression models—how these patterns relate to metropolitan age, industry composition, and political fragmentation, serving as proxies for car orientation, agglomeration dynamics, and inter‐municipal competition, respectively. Our findings show that professional services are strongly associated with dense employment clustering, health care tends to agglomerate at lower densities, and the regional prevalence of finance is linked to greater employment concentration and central business district (CBD) dominance. Additionally, denser regions tend to have fewer but more dominant job centers, and political fragmentation is associated with flatter job center hierarchies and weaker CBD primacy.

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https://doi.org/https://doi.org/10.1111/jors.70058

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@article{zakhary2026,
  title        = {{Beyond Dispersion: Density, Industry, and Political Fragmentation in the Geography of Regional Job Centers}},
  author       = {Zakhary Mallett & Nicholas Wilsey},
  journal      = {Journal of Regional Science},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/jors.70058},
}

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