Estimating Distributions for Populations Within Nested Geographies with Public-Use Data

• Activity Number: 454 - Advances in Spatial and Spatio-Temporal Methodology with Applications to Official Statistics
• Type: Topic Contributed
• Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Government Statistics Section
• Author(s): Matthew Simpson* and Scott H. Holan and Christopher Wikle and Jonathan R Bradley
• Companies: University of Missouri - Columbia and University of Missouri and University of Missouri and Florida State University
• Keywords: American Community Survey ; Bayesian ; Multiscale model ; Poverty mapping ; Small area estimation ; Spatial statistics

Abstract

Statistical agencies often publish multiple data products from the same survey. First, they produce aggregate estimates of various features of the distributions of a several socio-demographic quantities of interest. Often these area-level estimates are tabulated at small geographies. Second, statistical agencies frequently produce weighted public-use microdata samples (PUMS) that provide detailed information of the entire distribution of the same socio-demographic variables. However, the public-use micro areas usually constitute larger geographies in order to protect against the identification of households or individuals included in the sample. These two data products represent a tradeoff in official statistics: publicly available data products can either provide detailed spatial information or detailed distributional information, but not both. We propose a model-based method to combine both two data products to produce estimates of detailed features of a given variable at a high degree of spatial resolution. Our working example uses estimates and PUMS from the American Community Survey to estimate U.S. Census tract-level income distributions and statistics of these distributions.