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

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 predictors, vehicle access and education, and outperforms tree based classifiers in both precision and calibration. Results show that economic insecurity is consistently concentrated in rural ZIP codes, with transportation access emerging as the most stable barrier to program take up. This study provides a nationwide diagnostic framework that can inform the development of scalable screening tools for targeting outreach and improving benefit access in underserved communities.