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Confronting Statistical Uncertainty in Rural America: Toward More Certain Data-Driven Policymaking Using American Community Survey (ACS) Data

  • Jason R. JurjevichEmail author
Chapter
Part of the New Frontiers in Regional Science: Asian Perspectives book series (NFRSASIPER, volume 40)

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.

Keywords

American Community Survey (ACS) Rural Demographic data Margin of error (MOE) Data-driven policy 

Notes

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Toulan School of Urban Studies and Planning, Population Research Center (PRC)Portland State UniversityPortlandUSA

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