Small-Area Analyses Using Public American Community Survey Data: A Tree-Based Spatial Microsimulation Technique
36 Pages Posted: 5 May 2020 Last revised: 2 Mar 2021
Date Written: January 28, 2021
Quantitative sociologists and social policymakers are increasingly interested in local context. While some city-specific studies have developed new primary data collection efforts to analyze inequality at the neighborhood level, methods from spatial microsimulation have yet to be broadly utilized in sociology to take better advantage of existing public datasets. The American Community Survey is the largest household survey in the United States and indispensable for detailed analysis of specific places and populations. We propose a technique – tree-based spatial microsimulation (TBSM) – to produce “small area” (Census tract) estimates of any person- or household-level phenomenon that can be derived from ACS microdata variables. Our approach is straightforward, computationally efficient, based only on publicly available data, and it provides more reliable estimates than prevailing methods of microsimulation. We demonstrate our technique’s capabilities by producing tract-level estimates, stratified by race-ethnicity, of 1) the proportion of people in the Census tract population who have children and work in an essential occupation and 2) the proportion of people in the Census tract population living below the federal poverty threshold and in a household that spends greater than 50% of monthly income on rent or owner costs; these examples are relevant to understanding the socio-spatial inequalities dramatized by the COVID-19 pandemic. We discuss 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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