Abstract
Aging and lacking infrastructure are major impediments to economic development in rural America. To address these issues, civic leaders often look to state and federal infrastructure grant/loan funding, where eligibility is often based on income requirements established by the US Census Bureau’s American Community Survey (ACS). The problem, especially for rural communities, is that ACS data contain a high degree of statistical uncertainty (i.e., margin of error) that is often disregarded for determining program eligibility. For rural communities with unreliable income estimates, the most common work-around involves hiring a consultant to conduct an income census or survey to formally challenge the US Census Bureau’s ACS estimate. Many rural communities, however, elect not to formally challenge unreliable ACS estimates either because they are unaware that reimbursement for conducting an income survey is an allowable expense under some grant/loan programs or they are dissuaded by the necessary time and resources. First, I summarize whether federal infrastructure grant/loan programs incorporate MOE values when determining community eligibility. Second, I examine the degree to which ACS estimates are statistically reliable for communities across rural America. Finally, using an example from Oregon, I recommend guidelines for how states can assist rural communities with statistically unreliable ACS estimates. These findings can help rural communities secure infrastructure funding that advances economic development and quality of life, and potentially support reliable data-driven policy and decision-making more broadly.
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- 1.
Sampling odds are an effective way to compare the sampling frame of the ACS to the decennial long form. However, to be clear, the ACS sample is drawn from housing units and the group quarters population and is not based on a sampling rate. The 2017 ACS sample, for example, was drawn from roughly 2.1 million housing units and almost 300,000 individuals rising in group quarters, which together include more than 5,000,000 individuals.
- 2.
The standard error is calculated by dividing the MOE values (3 and 15%) by 1.645 (z-score at 90% statistical confidence, which is the statistical level of confidence at which the USCB reports the MOE value).
- 3.
The specific programs examined by Nesse and Rahe (2015) include the Housing Choice Voucher Program, Supplemental Nutrition Assistance Program (SNAP), and the Urbanized Area Formula Program.
- 4.
Although HUD considers MOE for developing annual income limits, the agency does not consistently report MOE across all data programs. For example, see the Comprehensive Housing Affordability Strategy (CHAS) dataset at: http://www.huduser.org/portal/datasets/cp.html
- 5.
As Usowski et al. (2008) point out, annual income limits determine eligibility for the Public Housing program, Section 8 Housing Assistance Payments program, Section 2020 Supportive Housing for the Elderly, and Section 811 Supportive Housing for Persons with Disabilities.
- 6.
In response to the directive, HUD now publishes the MOE data for all block groups and all places. See: https://www.hudexchange.info/programs/acs-low-mod-summary-data/
- 7.
Ratcliffe et al. (2016) note that a minimum of 1500 people must reside outside of group quarters in order for an area to be classified as urban.
- 8.
Scholars have developed innovative approaches, including the ERC Rural-Urban continuum codes, that contextualize the degree of rurality.
- 9.
According to Census 2010 data, the population of Lancaster, WI was 3868. Roughly 94% (3642 persons) of the population was classified as urban and the remaining 6% (226 persons) was classified as rural (US Census 2010a).
- 10.
The mean and median CV values calculated for places with fewer than 20,000 residents include both incorporated towns and cities (e.g., Drain, OR), as well as for census-designated places (CDPs) (e.g., Glide, OR). This analysis excludes instances where MHI estimates are unavailable for a place/CDP.
- 11.
The chief aim of the Oregon IFA is to help Oregon communities apply, receive, and manage federal and state loan/grant funds for water, sewer, roads, and other infrastructure development.
- 12.
In the recent past, Oregon IFA covered up to $7500 of costs for a census enumeration for cities with a population less than 500 and 50% of costs up to $5000 for a survey with cities with a population of 500 or more. Currently (through June 2019), Oregon IFA covers up to $1000 in costs for conducting a survey.
- 13.
This link also contains information for conducting an income census/survey (for communities in the Great Lakes RCAP region): http://greatlakesrcap.org/uploads/PDF/Winter2014RCAPConnectionFINAL.pdf
- 14.
This example uses 90% as the cutoff for statistical significance based on prevailing practice in social science research, but determining the level of statistical significance (i.e., how much uncertainty one is willing to tolerate) is somewhat arbitrary, and ultimately up to each state.
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Acknowledgments
This research was partially funded with support from Oregon State University (OSU) and Portland State University (PSU). Special thanks to the following individuals, who provided valuable insight and constructive feedback on this manuscript: Paul Lask, Beth Emshoff, Nick Chun, Lena Etuk, Mallory Rahe, Charles Rynerson, Jeff Sherman, David Tetrick, Kevin Tracy, and Bruce Weber. Thanks also to Business Oregon. All errors and oversights are solely the responsibility of the author.
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Appendix: Census Regions
Appendix: Census Regions
Source: US Census Bureau
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Jurjevich, J.R. (2019). Confronting Statistical Uncertainty in Rural America: Toward More Certain Data-Driven Policymaking Using American Community Survey (ACS) Data. In: Franklin, R. (eds) Population, Place, and Spatial Interaction. New Frontiers in Regional Science: Asian Perspectives, vol 40. Springer, Singapore. https://doi.org/10.1007/978-981-13-9231-3_7
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