Interpolating Distributions for Populations in Nested Geographies using Public-use Data with Application to the American Community Survey
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Statistical agencies often publish multiple data products from the same survey. First, they produce aggregate estimates of various features of the distributions of 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 for the same socio-demographic variables. However, the public-use micro areas usually constitute relatively large geographies in order to protect against the identification of households or individuals included in the sample. These two data products represent a trade-off 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 these two data products to produce estimates of detailed features of a given variable at a high degree of spatial resolution. Our motivating example uses the disseminated tabulations and PUMS from the American Community Survey to estimate U.S. Census tract-level income distributions and statistics associated with these distributions.
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