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Challenging Demographic Representativeness at State Borders: Implications for Policy Research

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Abstract

This study examines the demographic characteristics of U.S. state border counties, comparing them with those of nonborder counties. The demographic representativeness of border counties is essential for the interpretation of the results in state border-county difference-in-difference analyses, used in state policy evaluations. Our findings reveal that border counties generally have higher proportions of White, older, and disabled populations. We also see occasional instances of wide demographic differences across state boundaries. These differences potentially undermine the external validity and identification of policy evaluations. We illustrate the implications of these finding through a case study, highlighting the need for robustness checks and demographic considerations in border-county policy research.

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  • Benjamin S. Kay & Albina Khatiwoda, 2025. "Challenging Demographic Representativeness at State Borders: Implications for Policy Research," Finance and Economics Discussion Series 2025-018, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2025-18
    DOI: 10.17016/FEDS.2025.018
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    More about this item

    Keywords

    Demographics; Difference-in-Difference Estimates; Event Studies; Natural Experiments; Policy Experiments; US state border counties;
    All these keywords.

    JEL classification:

    • D78 - Microeconomics - - Analysis of Collective Decision-Making - - - Positive Analysis of Policy Formulation and Implementation
    • J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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