Patterns and causes of uncertainty in the American Community Survey

By Seth E. Spielman, David C. Folch, N. Nagle. 2014

253 citations recorded by the source corpus.

In 2010 the American Community Survey (ACS) replaced the long form of the United States decennial census. The ACS is now the principal source of high-resolution geographic information about the U.S. population. The margins of error on ACS census tract-level data are on average 75 percent larger than those of the corresponding 2000 long-form estimate. The practical implications of this increase is that data are sometimes so imprecise that they are difficult to use. This paper explains why the ACS tract and block group estimates have large margins of error. Statistical concepts are explained in plain English. ACS margins of error are attributed to specific methodological decisions made by the Census Bureau. These decisions are best seen as compromises that attempt to balance financial constraints against concerns about data quality, timeliness, and geographic precision. In addition, demographic and geographic patterns in ACS data quality are identified. These patterns are associated with demographic composition of census tracts. Understanding the fundamental causes of uncertainty in the survey suggests a number of geographic strategies for improving the usability and quality ACS.

Subfields: Economics and Econometrics, Management Science and Operations Research, Sociology and Political Science

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Citations in the corpus

Fair decision making using privacy-protected data (2019, background): “We are not aware of systematic studies of potential bias in the statistics currently released by statistical agencies, however Spielman observed that margins-of-error can be correlated with income levels in some Census products, leading to greater inaccuracies for low-income persons [29, 30].”
Green gentrification or ‘just green enough’: Do park location, size and function affect whether a place gentrifies or not? (2020, methodology): “We use census tracts instead of smaller units of analysis such as census block groups because, for the latter, the American Community Survey provides estimates with very large margins of error (Spielman et al., 2014).”
Flood recovery and property acquisition in Cedar Rapids, Iowa (2016, background): “This results in a high margin of error that makes the ACS estimates unreliable for many variables (Spielman et al. 2014).”
Reducing Uncertainty in the American Community Survey through Data-Driven Regionalization (2015, background): “The particular reasons for this decline in data quality are complex and are discussed in detail elsewhere [3].”
New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings (2016, background): “However, it represents a very small sample of households and can produce inaccurate data (Macdonald 2006; Spielman, Folch, and Nagle 2014).”