Project Abstract

Supplemental Security Income (SSI) is a federal social safety net program that provides cash payments to disabled persons and adults over age 65, who are very low income. Many states offer SSI payments, but the information about these supplements is contained in text-heavy historical reports. It is difficult to show how support varies from state to state and over time. The proposed project will involve text analysis of historical reports, construction of performance metrics, and designing a public facing visualization to convey clear and objective insights about the SSI program on a state level to inform policymakers and the public.

Project Description & Overview

Policymakers, researchers, and the public lack information about state support for the population that receives Supplemental Security Income (SII). This lack of information hampers policy making, research on program efficacy, and public understanding and awareness of the program. The information needed to address this gap is provided in text-heavy historical reports with too much programmatic jargon and little time-series or state-to-state comparisons. This proposed project aims to gather the hidden information with the goal to provide an overview for policymakers to better understand the impact of the SSI program. Information will be extracted using natural language processing algorithms. The final deliverable of the project is a public facing dashboard/visualization that will help guide policymakers in future program decision making. This project is a collaboration across multiple academic institutions, thus the project will be fully remotely.

Datasets

Publicly available administrative data.

Competencies

Students should have the following competencies:

• Python or R, some experiences with extracting text from documents
• XML/html
• Interest in public policy and social support systems, creating dashboards

Learning Outcomes & Deliverables

Students will learn how to perform text analysis on documents, extract content from document by using natural language processing algorithms. Students will also learn how to design intuitive visualizations. Student will learn how to present results for different audiences.

Students

Chih-Yun Lu, Jingxuan Xiao, Samuel Zierler