Some argue that we need more longitudinal studies. Others advocate the use of Randomised Control Trials (RCTs). And a recent study CARE conducted on the influence of Community Score Cards on reproductive health-related outcomes in Malawi shows that RCTs have a place, and do demonstrate that social accountability makes a difference.
But, once you consider that outcomes are behavioural changes of real (complicated) people, you quickly see, as Marina Apgar recently suggested, why we need to move “beyond measurement of linear pre-defined change and intervention-effect alone and [use] mixed-methods to help us understand emergent complex social change.” Social accountability outcomes (such as mayors changing budgets to benefit poorer areas, or even procuring a new ambulance) don’t fit neatly into boxes. They rely on our capacity to influence behaviour, and this is behaviour we can’t (fully) control. So, we need to better explain HOW change happened, not merely to assert that it did.
Recognising this has led CARE to explore various theory-based methods such as Most Significant Change and Outcome Mapping. With a particular emphasis on the change process, we are now piloting Contribution Tracing with Pamoja Evaluation Services in Ghana and Bangladesh to help us better understand CARE’s contribution to social accountability outcomes.
Contribution Tracing is all about increasing your confidence in making claims about impact. Essentially, you make a “claim” about your intervention’s role in achieving an outcome that really happened (your contribution), and then find evidence to defend your claim.
To do this, like other theory-based methods, you need a hypothesis (a proposed explanation) about how you think change happened. You then review the connection between different steps (or components) in that process:
You identify evidence that would help support (or undermine) your proposed explanation using the four tests of Process Tracing (Straws-in-the-wind, Hoops, Smoking Guns, Doubly Decisive).
What matters is not how much evidence you have, but how good that evidence is to help confirm that each part of your proposed explanation for your claim really exists (“probative value”).
In Contribution Tracing, you use Baysian (Confidence) Updating to assign a probability (how likely it is) that the various components of your contribution claim exist; and ultimately whether your claim holds true. You then update your confidence after gathering data precisely tailored to your claim (increasing or decreasing the probability using the four tests), compare this against rival explanations, and then put it up for “trial”, inviting others in to peer review your claim.
We’re right at the beginning of the journey, but to me, what our learning already suggests is that:
- You can show your contribution, even when change processes are complex;
- You can make credible impact claims, without a counterfactual;
- You can tighten up your loose theory of change as you go along, and;
- You may not need to gather as much data as you think you do to prove it.
But don’t take my word for it; listen to some reflections from staff on the experience so far. And watch this space for more to come.
