Geographic Networks, Fiscal Incentives, and Coercive Federalism: Policy Diffusion of 287(g) Immigration Enforcement Agreements

When 287(g) immigration enforcement agreements surged from 135 to 1,035 across U.S. agencies in 9 months during 2025, adoption patterns revealed surprising dynamics of local policy diffusion under federal-state coercion. Using comprehensive county-level data and Cox proportional hazards models with time-varying covariates, I find that geographic proximity dominated adoption decisions despite unprecedented top-down pressure. Each neighboring adopter increased a focal county’s adoption likelihood by 25%, the single strongest predictor. State-level peer effects operated independently, creating compounding regional pressures. Fiscal findings challenge conventional wisdom: counties more dependent on intergovernmental transfers were 64% less likely to adopt, while debt burdens and deficits showed no effects, revealing that institutional capacity, not fiscal desperation, determines responsiveness to federal incentives. State mandates accelerated adoption but did not eliminate geographic variation in timing. These findings demonstrate that spatial networks structure local policy decisions even when higher governments actively direct outcomes, with important implications for understanding federalism, policy diffusion mechanisms, and the limits of coercive intergovernmental relations.

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Publication Details

Journal
Public Administration Quarterly
Published
2026-04-16
DOI
https://doi.org/10.1177/07349149261433590
Primary Topic
Policy Transfer and Learning
Type
article
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article

Geographic Networks, Fiscal Incentives, and Coercive Federalism: Policy Diffusion of 287(g) Immigration Enforcement Agreements

Dallin Overstreet
Public Administration Quarterly
Policy Transfer and Learning
article

Geographic Networks, Fiscal Incentives, and Coercive Federalism: Policy Diffusion of 287(g) Immigration Enforcement Agreements

Dallin Overstreet
article en

Abstract

When 287(g) immigration enforcement agreements surged from 135 to 1,035 across U.S. agencies in 9 months during 2025, adoption patterns revealed surprising dynamics of local policy diffusion under federal-state coercion. Using comprehensive county-level data and Cox proportional hazards models with time-varying covariates, I find that geographic proximity dominated adoption decisions despite unprecedented top-down pressure. Each neighboring adopter increased a focal county’s adoption likelihood by 25%, the single strongest predictor. State-level peer effects operated independently, creating compounding regional pressures. Fiscal findings challenge conventional wisdom: counties more dependent on intergovernmental transfers were 64% less likely to adopt, while debt burdens and deficits showed no effects, revealing that institutional capacity, not fiscal desperation, determines responsiveness to federal incentives. State mandates accelerated adoption but did not eliminate geographic variation in timing. These findings demonstrate that spatial networks structure local policy decisions even when higher governments actively direct outcomes, with important implications for understanding federalism, policy diffusion mechanisms, and the limits of coercive intergovernmental relations.

Public Administration Quarterly
Arizona State University (US)
Reduced inequalities
Openalex Percentile: Top 9%
Policy Transfer and Learning
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