Using Multiple Discontinuities to Estimate Broad Effects of Public Need-Based Aid for College

This project estimates the effects of financial aid for college, among Wisconsin residents from low-income backgrounds. It links multiple data sources to track students from high school, to financial aid applications, through college enrollment and completion, and into the workforce. 

The core of the analysis is a regression discontinuity design, using eligibility rules for the Wisconsin Grant. The Wisconsin Grant program spends over $100 million each year, serving over 60,000 students pursuing two- and four-year undergraduate degrees, and covering one quarter of college tuition for the average recipient. The Wisconsin Grant is not fully funded, meaning not all eligible applicants can receive support. A key question is therefore whether students who receive grant offers achieve better educational or other outcomes, as compared to similar applicants who do not receive grant offers. This question can be answered by comparing students who receive different levels of grant support but have only slightly different eligibility criteria, in terms of family finances and/or the date they file a financial aid application. We apply this strategy among students in various income groups to measure how the effects of financial aid vary by financial need. This will inform efforts to efficiently target of state funding to low-income students.


This project is supported by a Spencer Foundation Small Research Grant. This project has also been selected to receive a grant for services as part of the Using Linked Data to Advance Evidence-Based Policymaking: Helping Projects Utilize the U.S. Census Bureau Linkage Infrastructure, a partnership of Chapin Hall and the U.S. Census Bureau, supported by the Laura and John Arnold Foundation.


Leadership

Drew Anderson

Status

Active through August 31, 2017

Contact Information

Drew Anderson