The Center for Applied Behavioral Science (CABS) combines MDRC’s decades of experience tackling social policy issues with insights from behavioral science. This graphic explains the CABS’s approach to solving problems.
To improve outcomes among high-interest borrowers, policymakers need to understand what is driving usage. This second post in MDRC’s Reflections on Methodology series discusses how a data discovery process revealed clusters of borrowers who differed greatly in the kinds of loans and lenders they used and in their loan outcomes.
The SIMPLER framework was developed for the Behavioral Interventions to Advance Self-Sufficiency (BIAS) project ― the first major effort to apply behavioral insights to human services programs in the United States. SIMPLER summarizes several key behavioral concepts that can guide practitioners interested in using behavioral insights to enhance service delivery.
MDRC launches the first of a five-part web series from the Chicago Community Networks study — a mixed-methods initiative that combines formal social network analysis with in-depth field surveys of community practitioners. It measures how community organizations collaborate on local improvement projects and how they come together to shape public policy.
As the first major effort to use a behavioral economics lens to examine human services programs that serve poor and vulnerable families in the United States, the BIAS project demonstrated the value of applying behavioral insights to improve the efficacy of human services programs.
WorkAdvance connects low-income job seekers to high-demand sectors that offer quality jobs with strong career pathways. This infographic describes the program model and its implementation in four locations and presents encouraging evidence of WorkAdvance’s effects on boosting earnings.
Jobs-Plus – a “place-based,” workforce-development model proven to help public housing residents find employment – is about to be replicated across the country. This infographics depicts the program model, its effects on earnings, and the history of its development over the past 20 years.
Using an alternative to classical statistics, this paper reanalyzes results from three published studies of interventions to increase employment and reduce welfare dependency. The analysis formally incorporates prior beliefs about the interventions, characterizing the results in terms of the distribution of possible effects, and generally confirms the earlier published findings.