Financial Health

Using technology to improve the financial health of low-income individuals can have long-term benefits for families and society.

Domain Statement

The opportunity for financial technology to improve outcomes for low-income people has been widely documented. Technology can be used to deliver products and services more efficiently, as well as to help individuals make informed decisions and follow through on financial planning.

We are working on research projects and collaborations related to how technology can be used to reduce barriers to financial inclusion. One area of interest is helping students find avenues to finance education. Another area is testing behavioral interventions such as digital “nudges” to induce individuals to take beneficial actions such as savings or applying for aid.

Project Abstracts

Read about one of the financial health research projects the lab is working on.

 

Reduce Unanticipated Financial Burdens of Ticketing and Fines Among Car Owners

Utilizing an email campaign through New York City’s Department of Finance, lab researchers and ideas42 are targeting identified behavioral bottlenecks preventing at-risk ticket holders from paying their tickets in New York City. Machine learning and casual inference tools are used in randomized experiments to test whether personalized emails reduce the number of vehicles being booted, especially vehicles of those likely not able to pay increased fines.

Social Sciences & Behavioral Nudges, Adaptive & Iterative Experimentation

Texts Encouraging Low-Income Students to Apply for Federal Financial Aid

Lab researchers Susan Athey, Henrike Steimer, Matt Schaelling, and Niall Keleher are working with ideas42 to examine the effects of an email and SMS program that aims to encourage community college students to maintain financial aid packages. Using administrative records, machine learning algorithms test differential impacts of the reminders on educational outcomes among different types of students.

Social Sciences & Behavioral Nudges, AI & Machine Learning

Academic Publications

Publication Search
Working Paper

Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal

Susan Athey, Niall Keleher, Jann Spiess
October2023
Working Paper

Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces

Susan Athey, Dean Karlan, Emil Palikot, Yuan Yuan
November2022

Interviews

Learn firsthand from finance researchers and practitioners associated with the lab.

Thought Leadership

Computational Applications to Behavioral Science

ideas42

A report of the collaboration between ideas42 and Golub Capital Social Impact Lab to advance practical applications of machine learning to behavioral science policy and field experimentation. The contents of this report are designed for any audience looking to learn more about how machine learning can add new techniques to behavioral design, causal inference, and experimentation.