Case Study
Policy Analysis

TANF Data Collaborative Pilot: Analyzing Application Denial Rates in Michigan

TANF Data Collaborative Pilot: Analyzing Application Denial Rates in Michigan
TANF Data Collaborative Pilot: Analyzing Application Denial Rates in Michigan
Project Partners
MDRC, Chapin Hall at the University of Chicago, Actionable Intelligence for Social Policy, Coleridge Initiative
Sector of partners
Benefits Program
TANF: Temporary Assistance for Needy Families
Level of government

Problem Statement

The project aimed to analyze reasons behind Michigan's higher TANF application denial rate compared to other states, seeking to refine the application process, enhance program accessibility for eligible individuals, and alleviate staff workload by minimizing redundant applications. Research focused on identifying factors relating to eligibility rules and application processing steps. 

Project Description

Michigan transferred de-identified MDHHS data on TANF applications from a statewide data warehouse to the ADRF. MDHHS’s pilot team worked with graduate student researchers from the University of Chicago to define a series of coding and analysis tasks to better understand the characteristics of TANF applicants and applications and the outcomes of those applications. The pilot team analyzed data on TANF applications and denials and coded approximately 70 denial reasons into a set of eight major categories such as applicants not meeting eligibility criteria, failing to verify their application information or withdrawing from the program. The trends in the frequency of these denial groupings over time were analyzed, and the team also examined duplicate applications, county-by-county denial patterns, and the income levels and demographic characteristics of approved and denied applicants. Using multivariate regression analyses, the team generated these and other analyses to interpret TANF application and denial patterns. 

Project Outcomes and Impact

The pilot project created a secure cloud environment for MDHHS TANF data, identified top denial reasons, emphasized documentation for knowledge transfer, and improved communication. Future plans include regular data uploads, exploring research questions, and analyzing COVID-related policy changes and income thresholds. 

Replicable Takeaways

This project serves as a replicable model for staff from other agencies looking for examples of efforts that support the use of administrative data for learning and improvement. Policymakers, researchers, and organizations seeking to expand the use of data in state TANF agencies may find interest in this project.  

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