In my last blog, I considered what good evidence looks like through the lens of process tracing and argued that assessing the quality of evidence is vital to causal explanations. In this blog, I want to talk about two methods with useful similarities: Outcome Mapping and the Actor-based Change (ABC) framework. Both these methods are concerned with issues of complexity, they are actor-based, and focus on behaviour change (who changes and how). They both acknowledge that our direct influence over other actors is limited and thus we’re talking about contribution rather than attribution. I believe both methods can teach us something together about processes of behaviour change.
Boundaries of influence
Outcome Mapping and the Actor-based Change (ABC) framework embrace a similar approach to behaviour change. Partly inspired by Montague’s circles of influence, Outcome Mapping’s spheres of change (see Deprez, 2008) have commonly been used to illustrate theories of change, as a recent diagram from the Institute for Development Studies (IDS) shows:
The actor-centred nature of “(boundary) partners” in Outcome Mapping acknowledges that you have to work through people you have a (good) relationship with in order to reach or influence other people. Both Outcome Mapping and ABC focus on specific individuals or small groups rather than aggregate impersonal organisations, such as the Ministry of Health. This actor focus may be a residue of baby ontology (seeing things as either “agents” or “stuff” rather than “institutional relationships”), but it does chime well with my own experience of errors people make with political economy analysis (e.g. treating Ministry of Health as if it were a single agent). While being problem-driven may be an important first step to making change happen, in my view, the second step is about being “actor-driven.”
Reflecting on learning from developing Planning and Navigating Social Change: Tools for Pacific Voyagers, Doug Orr mentioned that concepts of spheres of control, influence, and concern were useful because we need to be realistic about what we can actually change. Or as one outcome mapper put it,
“Stop trying to change the world; focus on your sphere of influence.”
This echoes what has been said recently within the World Bank’s Global Partnership for Social Accountability (GPSA). As Florencia Guerzovich points out, to understand the politics of service delivery “we need more meaningful monitoring, evaluation, research and learning tools (theories of change and action, indicators, more realistic benchmarks).” David Jacobstein of USAID’s Democracy Rights and Governance (DRG) centre also notes that “our ability to define and assess meaningful intermediate outcomes is essential.” While methods like Outcome Mapping have gained some traction over the last few decades, in general, we pay relatively little attention to intermediate behaviour changes at either individual or organisational level.
I believe we should take these behaviour changes more seriously. From a systems perspective, the ABC authors remind us that the behaviour changes of one actor will affect the practices and relationships of other actors in the system [and this can] stimulate a “more honest and realistic discussions of the types of impacts a program[me] can expect (and in what time horizon) (Koleros et al. 2018: 8).” To me, this suggests we need to focus on the journey as much as the destination.
Focus on the journey
As Michael Quinn Patton points out, “being attentive along the journey is as important as, and critical to, arriving at a destination.”
One way to focus on the journey and one of the most intuitively appealing things about Outcome Mapping is progress markers. Whether you call them progress markers or not doesn’t really matter. They are essentiallymilestones (or “mini indicators”) for key actors whose behaviour you intend to influence. These are articulated as “expect to see” (in the foreground), “like to see” (in the middle distance) and “love to see” outcomes (on the horizon). Behaviour changes may not be linear, but these three levels are intended to denote different depth of change, such as different levels of commitment in policymaker ratings or champion scorecards.
You can’t realistically pre-plan changes in all relationships of a system. Things often won’t turn out as expected. However, you will likely focus attention on building relationships and pushing for changes in specific actors because you believe their behaviour change matters to the functioning of that system. Progress markers are a relatively flexible and unpretentious way of illustrating actor-based contingencies. They are guideposts designed to help us keep our eyes open during the journey, whichever specific direction the journey takes us. As Hamish Nixon has put it, “marking progress is not really the science of indicators and means of verification, but the process of navigating together what seems to be permissible, possible, and probable in delivering desired outcomes (Nixon, 2018).”
To the point of permissibility, an interesting innovation of KPMG’s Accountability in Tanzania (AcT) Programme was to include changes partners would “not like to see,” reflecting potential backsliding on progress, as relationships may deteriorate as well as improve (Dyer, in ODI, 2018). This is something we almost never do, but it’s obviously crucial. Particularly in contexts where there is backsliding on civic space, such as Tanzania, documenting (possibly) unanticipated roll-backs and explaining these may be just as valuable for learning as documenting anticipated progress.
Building, nurturing, and sustaining relationships
Even though we know that behaviour change is contingent and relationships can go backwards as well as forwards, we rarely talk about these movements openly, as backwards movement isn’t permissible in many organisational contexts.
I recently co-facilitated a meeting of forty plus transparency, accountability and participation practitioners, evaluators, funders, and researchers from 19 countries in a side event at the GPSA Global Partners Forum. Few participants reported the ins and outs of relational shifts because they did not fit plans, log frames, or the situation changed.
Small demonstrations of trust and collaboration really matter, yet these remain mostly invisible. Modest and informal shifts in procedures and protocols for service providers or other state agencies enabled by those actors whose behaviour you’re trying to influence can be very significant in many contexts. So, we shouldn’t just huff and puff about “broken” or “weakening social contracts,” grumble about the collapse of political settlements or shriek at shrinking civic space. Instead, development organisations should be focusing at a lower level of change over which they can have some meaningful degree of influence. These are incremental and mid-range relational changes. Outcome Mapping, in particular, attempts to signpost these kinds of changes.
The Strengthening Advocacy and Civic Engagement (SACE) Programme in Nigeria, implemented by Root Change and Chemonics, has also adopted the Outcome Mapping’s represtation of spheres of control, influence, and interest to underpin their theory of change, blending this with Outcome Harvesting and an innovative array of other methods. They also adopt a systems approach to emergent change and look beyond individual actors to consider accountability ecosystems.
While I’m not entirely convinced by the programme’s claims about political economy analysis, where I believe SACE takes the conversation forward is by including network maps, which represent the 1,300 organisations and 2,800 relationships of the programme’s partners.
This may reflect an emerging trend which can strengthen approaches like Outcome Mapping. We also find the use network analysis at Pact World such as in the REACH programme in Malawi and the International Rescue Committee’s (IRC) has also used social network analysis in various projects. I still have questions about the depth of analysis this brings (see Davies, 2009 for some helpful background), but it looks to be a promising addition.
While our first step may be taking a problem-driven view and a second step is being actor-driven, this third step is about seeing relationships among actors in systems. What people get wrong by putting the Ministry of Health on a stakeholder map is not just that the Ministry is multiple actors, or that there are relationships between multiple actors with differential power within the Ministry, it is that these actors and relationships collectively cultivate authorising ideas, norms, and institutions, which go beyond these actors and relationships. It’s Ministry with a capital M. This is why when asked what he wanted to be reincarnated as, Bill Clinton advisor James Carville said he didn’t want to come back as the president or the pope or a .400 baseball hitter, but rather as the bond market. After all, the bond market can intimidate everybody. That’s institutional power.
Actor-based systems maps
An alternative approach from the ABC framework to get to the second and third steps is developing an actor-based systems map. This depicts the current practices and relationships among relevant actors in the system and macro-level dynamics that result from the interactions of these actors vis-à-vis the development problem to be addressed by the programme, as the diagram below shows in the case of Gender-based Violence survivors in Nepal:
The theory of action for each actor group in the framework then describes the expected pathway from programme intervention to changes in behaviour for each actor (behaviour change statements), as well as the causal link assumptions at each step of the pathway.
What drives behaviour change?
ABC is partly based on the Capability, Opportunity, and Motivation for Behaviour Change framework (or “COM-B”) which is used a great deal in the UK’s health sector and has recently gained some traction for theory of change methodologists (see Mayne, 2015, 2016; Rogers, 2016). The three key dimensions of COM-B are following:
- Capability — psychological or physical ability to enact a behaviour;
- Opportunity — physical and social environment that enables the behaviour, and;
- Motivation — reflective and automatic mechanisms that activate or inhibit the behaviour (Michie et al., 2011).
Where I believe COM-B is most helpful is in reminding us that we need a combination of all of these features in order for an actor to change their behaviour. We assume that when this actor (or actor group) is reached with a new resource and has a positive reaction to it, this will then lead to changes in their capacity, which will ultimately lead to changes in behaviour. This kind of model is a way to help us harness complexity in a more realistic way. In my view, COM-B is potentially reconcilable with Outcome Mapping’s strategy maps at both individual and environmental level and may help to define which causal, persuasive, or supportive strategies should be employed. Interrogating whether C, O, or M is missing (or weak) can help identify whether you need carrots or sticks, whether the blockage is individual or collective, and whether you need to anchor or frame ideas differently for different audiences. So, it can help tailor the nature of your strategy.
Perhaps more importantly still, COM-B also fits with an actor-based approach to political economy analysis (officially, there is no such thing, but I’m coining that here). In my own experience, I’ve found you can generally reduce political economy analysis to just a handful of questions about specific actors’ roles and responsibilities, their personal ideasand interests, the assumed incentives provided by actors, whether the actors you target actually have the capacity and resources to change something, who they’re connected toand who we believe they have influence over in relation to a particular problem. Christian Aid’s Strengthening Community-led Accountability to Improve Service Delivery (SABI) programme in Sierra Leone has also recently employed COM-B for political economy analysis. So, I look forward to hearing more about the potential overlaps.
In the next blog I will look at the value of outcome statements from Outcome Harvesting. Stay tuned.
Thanks to Kaia Ambrose, David Jacobstein, and Richard Smith for helpful comments and to Chris Roche for prompting me to think about baby ontology.