A nonprofit, nonpartisan organization, the Coalition seeks to increase government effectiveness through the use of rigorous evidence about what works. In the field of medicine, public policies based on scientifically-rigorous evidence have produced extraordinary advances in health over the past 50 years. By contrast, in most areas of social policy – such as education, poverty reduction, and crime prevention – government programs often are implemented with little regard to evidence, costing billions of dollars yet failing to address critical social problems. However, rigorous studies have identified a few highly-effective program models and strategies (“interventions”), suggesting that a concerted government effort to build the number of these proven interventions, and spur their widespread use, could bring rapid progress to social policy similar to that which transformed medicine.
The Coalition advocates many types of research to identify the most promising social interventions. However, a central theme of our advocacy, consistent with the recommendation of a recent National Academy of Sciences report, is that evidence of effectiveness generally cannot be considered definitive without ultimate confirmation in well-conducted randomized controlled trials.
The Coalition’s work with key Executive Branch and Congressional officials has helped inform and/or shape major new policy initiatives enacted into law, such as:
- Evidence-Based Home Visitation Program for at-risk families with young children (Department of Health and Human Services – HHS, $1.5 billion over 2010-2014)
- Evidence-Based Teen Pregnancy Prevention Program (HHS, $109 million in FY14)
- Investing in Innovation Fund, to fund development and scale-up of evidence-based K-12 educational interventions (Department of Education, $142 million in FY14)
- First in the World Initiative, to fund development and scale-up of evidence-based interventions in postsecondary education (Department of Education, $75 million in FY14)
- Social Innovation Fund, to support public/private investment in evidence-based programs in low-income communities (Corporation for National and Community Service, $70 million in FY14)
- Trade Adjustment Assistance Community College and Career Training Grants Program, to fund development and scale-up of evidence-based education and career training programs for dislocated workers (Department of Labor – DOL, $2 billion over 2011-2014)
- Workforce Innovation Fund, to fund development and scale-up of evidence-based strategies to improve education/employment outcomes for U.S. workers (DOL, $47 million in FY14).
Some of the key initiatives are summarized here on the Office of Management and Budget (OMB) website by then-Director Peter Orszag (his second paragraph links to our website).
ASSESSMENTS OF OUR WORK
An independent assessment of the Coalition over 2004-2009, conducted under our grant agreement with the William T. Grant Foundation, found that:
The Coalition has successfully influenced legislative language, increased funding for evidence-based evaluations and programs, … and raised the level of debate in the policy process regarding standards of evidence. The Coalition has established a generally positive reputation as a rigorous, responsive, honest, and impartial advocate for evidence-based approaches, primarily at the federal level.
The full report is available (pdf).
In a March 2011 external review based on not-for-attribution interviews with federal policy officials:
The Coalition … was given credit by multiple interviewees for OMB’s establishment of a requirement that many discretionary domestic programs be subject to rigorous evaluation … [and] for certain pieces of legislation carrying similar requirements. As one interviewee stated, ‘The Coalition played a central role in securing this Administration’s commitment to high standards of evidence.’ And another interviewee stated, ‘The push for strong evidence would not have happened as quickly and widely and with so relatively little controversy without the Coalition.’
The report is linked here (pdf).