Closing the SNAP Gap: Identifying Under-Enrollment in High-Poverty ZIP Codes
Auyona Ray

TL;DR
This study develops a nationwide diagnostic framework to identify high-poverty ZIP codes with low SNAP participation, highlighting transportation and education as key barriers to improve outreach and benefit access.
Contribution
It introduces a novel method using interpretable structural indicators to detect under-enrollment in high-poverty areas, focusing on transportation and education barriers.
Findings
Transportation access is a key barrier in rural ZIP codes.
A simple model with vehicle access and education predicts SNAP gaps effectively.
Economic insecurity is concentrated in rural areas.
Abstract
This project began by constructing an index of economic insecurity using multiple socioeconomic indicators. Although poverty alone predicted SNAP participation more accurately than the composite index, its explanatory power was weaker than anticipated, echoing past findings that enrollment cannot be explained by income alone. This led to a shift in focus: identifying ZIP codes with high poverty but unexpectedly low SNAP participation, areas defined here as having a SNAP Gap, where ZIPs fall in the top 30 percent of family poverty and the bottom 10 percent of SNAP enrollment. Using nationally available ZIP level data from 2014 to 2023, I trained logistic classification models on four interpretable structural indicators: lack of vehicle, lack of internet access, lack of computer access, and percentage of adults with only a high school diploma. The most effective model relies on just two…
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Taxonomy
TopicsIncome, Poverty, and Inequality · Urban Transport and Accessibility · Human Mobility and Location-Based Analysis
