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An Empirical Examination of Representational Equity in Consolidated Governments, 1965-2002

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  • Acuff, Christopher

Abstract

Research on the impacts of city-county consolidation often focus on issues relating to efficiency, effectiveness, and economic development; yet, relatively few studies have addressed the issue of racial and ethnic minority representation. While existing research is limited, findings indicate that consolidating city and county governments dilutes minority voting strength and has a disparate impact on minority representation. However, it is not clear if this is a nationwide trend, particularly in preclearance states previously covered by the Voting Rights Act. Thus, the question becomes, does consolidation negatively affect minority representation, and to what extent? This study employs a quasi-experimental interrupted time-series analysis in order to ascertain the overall impact of consolidation on the descriptive representation of African Americans since 1965. Results indicate that while representation has increased in recent decades, there are discernible declines in following consolidation, and noticeable representational disparities in counties previously covered by the Voting Rights Act.

Suggested Citation

  • Acuff, Christopher, 2021. "An Empirical Examination of Representational Equity in Consolidated Governments, 1965-2002," SocArXiv bupfs, Center for Open Science.
    • Handle: RePEc:osf:socarx:bupfs
    DOI: 10.31219/osf.io/bupfs
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    References listed on IDEAS

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    1. Francisco Jose Veiga & Linda Goncalves Veiga & Atsuyoshi Morozumi, 2015. "Political budget cycles and media freedom," Discussion Papers 2015/14, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Morgan, David R. & Pelissero, John P., 1980. "Urban Policy: Does Political Structure Matter?," American Political Science Review, Cambridge University Press, vol. 74(4), pages 999-1006, December.
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