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Models for Small Area Estimation for Census Tracts.

John R Logan1, Cici Bauer2, Jun Ke1

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This summary is machine-generated.

Small Area Estimation (SAE) using the American Community Survey (ACS) shows increased error with smaller samples. Bayesian models can correct point estimates but not measures of area variation.

Keywords:
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Area of Science:

  • Statistics
  • Demography
  • Spatial Analysis

Background:

  • The American Community Survey (ACS) replaced the decennial census long-form for tract-level data.
  • ACS relies on smaller samples compared to the previous decennial census long-form.
  • This shift raises concerns regarding the accuracy and reliability of Small Area Estimation (SAE).

Purpose of the Study:

  • To examine the issues of Small Area Estimation (SAE) arising from reliance on the American Community Survey (ACS).
  • To demonstrate the impact of smaller sample sizes on data accuracy and measurement error.
  • To evaluate potential solutions for improving SAE using ACS data.

Main Methods:

  • Utilized a 100% transcription of microdata from the 1940 census for demonstration.
  • Drew numerous samples from two major cities to analyze estimation patterns.
  • Evaluated three Bayesian models for correcting point estimates and an alternative method for variation estimates.

Main Results:

  • Smaller samples from the ACS lead to larger average measurement errors and increased risk of significant error in point estimates.
  • Sampling variability inflates estimates of measures of variation across areas, impacting spatial inequality analyses.
  • Bayesian models successfully reduce sampling variation for point estimates but artificially reduce variation measures.

Conclusions:

  • While Bayesian models can correct ACS small area point estimates, they are unsuitable for estimating variation across areas.
  • An alternative method using original sample data is demonstrated as an efficacious approach for estimating variation.
  • Further research into Bayesian approaches for estimating spatial variation is warranted.