Listed below are some of our recent projects. If you would like more detail about these and our other projects, please contact us.
In partnership with PBS and the Center for Advanced Hindsight, we are developing a free, online game for the popular NOVA Labs platform for teens informed by the latest behavioral insights in the domain of financial decision-making. This game at the heart of the NOVA Financial Literacy Lab (working title) will feature cutting-edge behavioral change interventions and enjoy wide public distribution, with outreach focused on high-need communities. It will also serve as a testbed for research, providing insights for the education, behavioral science, and finance communities into how to cultivate healthy financial behavior in the real world—valuable lessons that should reverberate well beyond this initiative.
Financial Well-being of State and Local Retirees in North Carolina: Importance of Managing Assets and Debts
The primary goal of the research is to enhance our understanding of how low-income retirees manage and utilize their retirement savings in the drawdown phase of life. We are particularly interested in the role of financial literacy on asset management and financial distress. If lower income households have lower levels of financial literacy, then disparities in financial wellness in retirement may stem both from inadequate savings while working and from poor asset management in retirement.
CFSI has proposed designing a research engagement that will allow us to answer the following
- With which aspects of their financial lives do pre-retirees struggle the most?
- How, and how well, are pre-retirees making decisions about their retirement? (e.g. when
to retire, when to draw Social Security, whether to purchase long-term care insurance,
- What can we learn about the way that pre-retirees’ day-to-day financial health challenges
impact their ability to make decisions and/or take action that results in future positive
financial health outcomes?
In a recent project with ICMM, the Center for Economic and Social Research developed and fielded a survey measuring the prevalence of five behavioral factors that are likely to influence DMP outcomes (present bias, limited attention, locus of control, feeling overwhelmed, and income/expense mismatch) among new DMP participants across three Credit Counseling Agencies. They found that behavioral characteristics are prevalent among DMP clients – 91% of respondents to the survey exhibited at least one of the elicited behavioral characteristics. Limited attention, a feeling of being overwhelmed, and difficulties in living paycheck-to-paycheck were particularly common among our survey respondents. Building on the survey results, and insights from the behavioral economics literature, they developed five behavioral intervention ideas designed to improve repayment behavior and increase DMP retention.
Machine Learning is a technique for analyzing large datasets and applying them to a variety of predictive problems. Rather than writing explicit models, machine learning systems will progressively “learn” to improve their performance. This project aims to use machine learning to analyze the challenges faced by clients in debt management programs and provide insight into how credit counseling agencies can improve their clients’ success rates. Our core model attempts to predict client outcomes in a debt management program based on their financial and demographic characteristics. Armed with that knowledge, credit counseling agencies can identify clients at risk of dropping out of the program and help them stay on track to achieve financial stability.