In many human services programs, enrollees must make a series of decisions and take a number of active steps in order to access a benefit. From deciding which programs to apply for, to completing forms, attending meetings, showing proof of eligibility, and arranging travel and child care, program designers often assume that individuals make decisions about how to proceed based on careful consideration of their options and what is best for them. But over the past 30 years, innovative behavioral science research has demonstrated that human decision-making is often imperfect and imprecise. People — clients and program administrators alike — procrastinate, get overwhelmed by choices, miss details, lose their self-control, rely on mental shortcuts, and are influenced by small changes in the environment. As a result, both program operators and participants may not always achieve their intended goals, affecting the efficiency and effectiveness of government programs.
The Behavioral Interventions to Advance Self-Sufficiency (BIAS) project, sponsored by the Office of Planning, Research and Evaluation in the Administration for Children and Families (ACF), U.S. Department of Health and Human Services, and led by MDRC, was the first major effort to view programs for low-income U.S. families, including working poor families, through a behavioral science lens. Conducted from 2010 to 2016, the BIAS project used behavioral insights to design and test interventions that were intended to improve the operations and efficiency of human services programs. BIAS conducted 15 random assignment tests in seven states with nearly 100,000 sample members. The results of these tests demonstrated the promise of applying behavioral science principles to improve human services program outcomes. BIAS interventions increased child care subsidy renewal rates and the use of quality-rated child care by low-income working families; boosted requests for child support modifications and frequency of child support payments; and fostered engagement in welfare-to-work activities and in other social service appointments and activities.
Launched in 2015, the BIAS-Next Generation initiative is expanding the use of behavioral science to a wider range of ACF programs, going beyond testing simple “nudges” to include more implementation research, and developing tools to help program administrators and operators apply lessons from behavioral science to their work. Partners in BIAS-Next Generation include Lawrence Katz of Harvard University, who is serving as co-principal investigator; MEF Associates; Child Trends; and Public Strategies.