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How Can States Regulate De-Spatialized Phenomena? The Case of Occupational Licensing

Adrienne Edisis

American Review of Public Administration2025https://doi.org/10.1177/02750740241305416article
AJG 3ABDC B
Weight
0.37

Abstract

The Covid-19 pandemic compelled U.S. states to forge their own paths to guide their citizens through new dangers and challenges, yet made clear that state borders cannot hold against pathogens, people, or technology. How can states regulate increasingly de-spatialized phenomena? State legislators and regulators face this dilemma in the case of occupational licensing. This paper identifies a spatial mismatch between the individual state jurisdiction of occupational licensing regulations and the interstate reach of services provision by practitioners of licensed occupations. It investigates the origins of this mismatch, as well as a functional mismatch between state occupational licensing regimes established before 1900 and the current context. By building on insights from historical analysis of legacy state occupational licensing regimes; learning from examples of regulation of de-spatialized and digitalized economic activity; enlisting historical institutionalist-defined modes of gradual institutional change; and updating a classic typology of policy tools for regulation, a risk-based classification template for state regulation of services provided by professionals and skilled workers in currently licensed occupations is proposed. More broadly, analysis of the case of occupational licensing illustrates the potential of diversifying regulation of de-spatialized phenomena from authoritative mandates to regulatory programs comprised of a policy mix of mandate, information, incentive, and multi-mechanism tools better suited to porous political borders.

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https://doi.org/https://doi.org/10.1177/02750740241305416

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@article{adrienne2025,
  title        = {{How Can States Regulate De-Spatialized Phenomena? The Case of Occupational Licensing}},
  author       = {Adrienne Edisis},
  journal      = {American Review of Public Administration},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/02750740241305416},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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