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

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.