Small-Area Analyses Using Public American Community Survey Data: A Tree-Based Spatial Microsimulation Technique
56 Pages Posted: 5 May 2020
Date Written: April 13, 2020
The American Community Survey (ACS) is the largest household survey in the United States and indispensable for detailed analysis of specific places and populations. This paper introduces a technique to produce “small area” (e.g. census tract) estimates of any person- or household-level phenomenon that can be derived from ACS microdata variables. This is demonstrated by producing novel, tract-level estimates of 1) excess housing capacity, 2) prevalence of traditional living arrangements, and 3) household energy burden. We combine conventional spatial microsimulation techniques with binary-split decision trees to efficiently select local population margins from a large set of candidates. The result is place-specific microdata samples that are calibrated to match an information-rich set of known constraints (e.g. number of households by income group). A validation exercise indicates agreement between model output and known values (mean R2 = 0.78). We conclude by discussing potential extensions of the technique to derive small area estimates of variables observed in surveys other than the ACS.
Keywords: Spatial microsimulation, small area estimation, decision trees, American Community Survey
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