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.

翻译：本项目首先通过综合多项社会经济指标构建了经济不稳定指数。尽管单一贫困指标比复合指数更能准确预测SNAP（补充营养援助计划）参与率，但其解释力弱于预期，这与既往研究中“参与率无法仅由收入解释”的结论相呼应。这促使研究重点转向：识别贫困率高但SNAP参与率异常偏低的邮政编码区，此类区域被定义为存在“SNAP差距”，即该邮编区位于家庭贫困率前30%且SNAP注册率后10%的区间。利用2014年至2023年全国可获取的邮政编码层级数据，我基于四个可解释的结构性指标训练了逻辑分类模型：无车辆家庭比例、无网络接入比例、无电脑接入比例以及仅持有高中文凭的成人比例。最优模型仅依赖车辆获取与教育程度两个预测变量，在精确度与校准度上均优于基于决策树的分类器。结果表明，经济不稳定现象持续集中于农村邮政编码区，其中交通可达性成为阻碍项目参与的最稳定因素。本研究提供了一个全国性诊断框架，可为开发可扩展的筛查工具提供依据，从而针对服务不足社区开展定向推广并改善福利获取渠道。