Results-Based Accountability
Also known as: RBA, Outcomes-Based Accountability, OBA, Friedman Results-Based Accountability
Results-Based Accountability (RBA), also known as Outcomes-Based Accountability, is a disciplined performance framework developed by Mark Friedman and set out in his 2005 book Trying Hard Is Not Good Enough. It provides a simple, common-sense method for moving from talk about results to measurable action, organised around a sharp distinction between population accountability — the wellbeing of whole populations in a place — and performance accountability — how well a specific program, agency or service is doing. For each, RBA asks the same disciplined set of questions and drives toward concrete actions that 'turn the curve' on key indicators.
Key highlights
- Cuts through jargon with plain language that diverse partners can readily share.
- Sharply separates population wellbeing from program performance, avoiding a common conflation.
- Drives from measurement to concrete 'turn the curve' action rather than stopping at reporting.
- Scales from a single program to a multi-agency, community-wide collaborative effort.
Intuition
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How it works
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When to use it
Use RBA when an agency, partnership or community needs a clear, shared, action-oriented way to define and improve results — for performance management of programs and for collaborative efforts to improve population-level wellbeing. It is well suited to government, nonprofit and collective-impact settings where many partners must align around common measures and plain language. It assumes usable indicator data are available or can be developed and that stakeholders will commit to acting on trends. It is less suited where the priority is rigorous causal attribution of a program's net impact, since RBA's performance measures show whether clients are better off but do not establish a counterfactual. It complements logic models and theory of change, which can sit beneath its performance-accountability layer.
Strengths & limitations
- Cuts through jargon with plain language that diverse partners can readily share.
- Sharply separates population wellbeing from program performance, avoiding a common conflation.
- Drives from measurement to concrete 'turn the curve' action rather than stopping at reporting.
- Scales from a single program to a multi-agency, community-wide collaborative effort.
- Performance measures show whether clients are better off but do not establish causal attribution.
- Depends on the availability and quality of indicator data, which are often weak in practice.
- Simplicity can mask the difficulty of choosing valid indicators and bending stubborn trends.
- Population-level results are shared by many actors, making individual accountability genuinely diffuse.
Common pitfalls
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Applications
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Frequently asked
What is the difference between population and performance accountability in RBA?
Population accountability concerns the wellbeing of a whole population in a place — for example, the proportion of children in a region who are healthy — and is the shared responsibility of many partners, none of whom controls it alone. Performance accountability concerns how well a specific program or service performs for its own clients. RBA insists on keeping these levels distinct because confusing them leads either to blaming a program for conditions it cannot control or to a program claiming credit for population outcomes it could never deliver by itself.
What are the three performance-accountability questions?
For any program, RBA asks: how much did we do, how well did we do it, and is anyone better off? The first two capture the quantity and quality of effort, while the third — 'is anyone better off?' — focuses on client outcomes such as changes in skills, behaviour or circumstances. Organising performance measures into this three-part matrix keeps attention on results for clients rather than on activity alone, while remaining simple enough for staff to use routinely.
Does RBA prove that a program caused its results?
No. RBA tracks whether clients are better off and whether indicator trends move in the desired direction, but it does not construct a counterfactual or estimate a net causal effect. It is a performance and accountability framework, not an impact-evaluation design. When a rigorous causal claim is required, RBA's 'is anyone better off?' measures need to be supplemented with an experimental, quasi-experimental or theory-based evaluation capable of attributing change to the program.
Sources
- 1.Friedman, M. (2005). Trying Hard Is Not Good Enough: How to Produce Measurable Improvements for Customers and Communities. Victoria, BC: Trafford Publishing.ISBN 9781439237861
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Cite this page
ScholarGate. (2026, June 22). Results-Based Accountability. ScholarGate. https://scholargate.app/public-policy/results-based-accountability