Improving Low Response Score Estimates by Accounting for ACS Sampling Error

Thomas M. Menino Convention & Exhibition Center 

The Census Bureau's Planning Database provides statistics from the Decennial Census and the American Community Survey (ACS) 5-year estimates at the tract and block group levels. These files include the Low Response Score (LRS), which is a predicted value of expected mail self-response used to identify areas that may be hard to survey. Because some of the predictors used to create these measures come from ACS estimates, there are concerns that high sampling variance at lower geographic levels may introduce bias and reduce reliability. To address this issue, we use the Public Use Microdata Sample (PUMS) to produce custom estimates and evaluate the impact of ACS sampling error on these predictors. This paper applies recent Census Bureau research to develop a framework that incorporates LRS while accounting for ACS uncertainty through small-area estimation methods such as the Fay-Herriot model. The goal is to improve accuracy in hard-to-survey areas and provide stakeholders with estimates whose sampling errors are closer to the true variability at small geographic levels.

Low Response Score (LRS)

American Community Survey (ACS)

Planning Database

Sampling error

Small area estimation 

Government Statistics Section