Multidimensional well-being of US households at a fine spatial scale using fused household surveys: fusionACS

TL;DR

This paper introduces fusionACS, a method to integrate multiple U.S. household surveys into a single microdataset, enabling richer analysis of household well-being at fine spatial scales.

Contribution

The paper presents a novel statistical fusion approach and an open-source R package to combine diverse household survey data for enhanced social science research.

Findings

Integrated datasets from multiple surveys covering energy, transportation, housing, and expenditure.

Enabled detailed analysis of household well-being across different dimensions.

Provided open-source tools for data fusion, analysis, and validation.

Abstract

Social science often relies on surveys of households and individuals. Dozens of such surveys are regularly administered by the U.S. government. However, they field independent, unconnected samples with specialized questions, limiting research questions to those that can be answered by a single survey. The fusionACS project seeks to integrate data from multiple U.S. household surveys by statistically "fusing" variables from "donor" surveys onto American Community Survey (ACS) microdata. This results in an integrated microdataset of household attributes and well-being dimensions that can be analyzed to address research questions in ways that are not currently possible. The presented data comprise the fusion onto the ACS of select donor variables from the Residential Energy Consumption Survey (RECS) of 2015, the National Household Transportation Survey (NHTS) of 2017, the American Housing Survey (AHS) of 2019, and the Consumer Expenditure Survey - Interview (CEI) for the years 2015-2019. The underlying statistical techniques are included in an open-source $R$ package, fusionModel, that provides generic tools for the creation, analysis, and validation of fused microdata.