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Variation in Insurance Coverage Across Congressional Districts

New Estimates from 2008

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Document date: October 05, 2009
Released online: October 05, 2009

The text below is an excerpt from the complete document. Read the full analysis in PDF format.


New data on health insurance coverage from the American Community Survey show extensive variation in rates of private and public coverage and uninsurance across congressional districts in the United States. Rates of private coverage are lowest in districts that have higher poverty rates which tend to be concentrated in the South and West and uninsurance remains most serious in districts with low rates of private coverage. This analysis identifies the districts in which residents would have the most to gain from health reforms that are designed to increase health insurance coverage toward a higher and more uniform national standard.


Numerous studies have documented considerable state and regional variation in uninsured rates across the country (Holahan 2002; Cohen and Makuc 2008; Ahluwalia et al. 2009; Fronstin 2007; SHADAC 2009; Kaiser Family Foundation 2009). For example, residents in the South and West are about 1.5 times as likely to be uninsured compared to those in the Northeast and Midwest (DeNavas-Walt et al. 2009). Likewise, newly released American Community Survey (ACS) data for 2008 from the U.S. Census Bureau confirm dramatic variation in uninsured rates across states (Turner et al. 2009). These data indicate that children in Nevada are over nine times as likely to be uninsured as children in Massachusetts (20.2 percent compared to 2.1 percent).

Studies suggest that state variation in uninsured rates is driven by underlying economic and demographic factors, such as the employment mix in the state (e.g., firm size, industry and occupation, and the degree of unionization), eligibility requirements for public programs such as Medicaid, and the demographic/socioeconomic composition of state residents (Brown et al. 2000; Fronstin 2007; Holahan 2002). State variation in employersponsored coverage provided through employers appears to be driven in part by employee characteristics, such as industry and length of time spent with an employer, and local labor market characteristics, such as state-level unionization (Shen and Zuckerman 2003). There is also state variation in rates of public coverage due to varying income eligibility guidelines and the income distribution in the state (Holahan 2002). The fewer studies that have examined substate variation in coverage (Brown et al. 2000; Mendez et al. 2003) have found substantial variation in uninsured rates within particular states. For example, in California, areas with larger concentrations of young adults, Latinos, and low-income residents had uninsured rates up to four times higher than districts with the lowest uninsured rates (Mendez et al. 2003).

The potential for examining state and local variation in health insurance coverage and the composition of the uninsured has increased dramatically with the addition in 2008 of information on insurance coverage to the ACS, an annual survey of close to two million households conducted by the U.S. Census. Annual estimates are now possible for each congressional district and for 25 percent of counties nationwide. This brief examines variation across congressional districts in rates of private coverage, public coverage and uninsurance among the nonelderly. We find that there is substantial variation in these three rates across congressional districts and that districts with higher rates of private insurance coverage are likely to have lower rates of both public coverage and uninsurance. Coverage patterns vary systematically with the percent of residents living below the federal poverty level: districts with fewer people living in poverty have much higher rates of private coverage and lower rates of public coverage and uninsurance compared to the districts with more people living in poverty. In future work, we will examine how coverage rates and characteristics of the uninsured vary across other geographic areas, such as counties and Metropolitan Statistical Areas (MSAs).

(End of excerpt. The entire analysis is available in PDF format.)

Topics/Tags: | Health/Healthcare

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