“• Determine whether to “Build First” or “Build Together.” When determining whether to develop and prove a model alone or to build together with government, enterprises considered their partnership goal and need for ownership, and typically ended up customizing a solution between the two extremes.
• Determine type and level of evidence needed. Enterprises went beyond impact evidence and recognized that evidence needs may become more complex in later stages or in donor-dependent countries.
• Find and cultivate the right champions. Interviewees leveraged organizations already working in-country, sought contacts interested in iteration, institutionalized relationships through MOUs and contracts, and found ways to decrease the physical distance between champions and solutions.
• Demonstrate true partnership with listening, humility, and respect. It may seem obvious, but according to interviewees, it is worth repeating: approach government with respect and humility, communicate regularly, and show how you are responding to feedback with change.
• Proactively manage—or avoid—politics. Enterprises spread out risk by engaging across political ideologies, working with technical experts, managing multiple projects simultaneously, and being wary of promises made around elections.
• Help maintain quality of impact over time. To ensure continued quality of programs (especially when government takes over implementation), enterprises recommended the following: breaking solutions into small steps; creating roadmaps while still empowering partners to adapt; using test sites to iterate; creating monitoring tools; and seeking sustainable funding sources.”
We think this is great advice for all teams looking to scale innovation with governments! Understanding the type and level of evidence needed (especially when it comes to costs) and helping maintain quality as the government takes over are crucial areas that we see overlooked all the time.
The article hits on a few our favorite, and often repeated, maxims. For example, how difficult it is to go from piloting with NGO staff to scaling with government staff:
“When a program scales, it has to hire and train many new people — or in some cases transition to using government resources and civil servants. And that can change a program’s effectiveness. “You turn over a program from a highly motivated NGO…. to people who know less about it and are less driven to see it succeed — or informed about what it will take. A lot can be lost in transmission”[Mushfiq Mobarak – Yale’s Research Initiative on Innovation and Scale].
Another great point for practitioners to think about at the design phase (that isn’t often cited) is the equilibrium effect – for example, if you take a great worker training program to scale, you might actually drive down wages if there are now too many skilled workers available. While CARE is careful to consider the impact of our programs on markets, it can be difficult to accurately predict unintended consequences at scale. Being mindful of the equilibrium effect while designing for scale can help us set appropriate targets.
It’s great to see UNDP’s Innovation Facility focus on scale! We were particularly excited to read UNDP’s Administrator Achim Steiner emphasize scale in his list of critical points to ensure that innovation goes beyond “gadgets” and gizmos”
Prove the comparative advantage of innovation: The hype cycle of innovation has peaked in most industries. Initiatives that are designed for outputs, rather than outcomes are still dominating innovation news updates from a number of organizations. But overall the sector is maturing and with it,the ambition and metrics to measure the impact of innovation. In the context of human development and social change, innovation must not happen for innovation’s sake but rather to find more effective ways of working. Innovation means foremost testing hypothesis with solid monitoring frameworks and a focus on inclusivity.”
Scaling checklists are a trendy tool…luckily, they’re also pretty useful! A number of organizations and programs have them, how do you find or develop right one for you? (Want to check out SxD’s? Click here)
In order scale, your solution must be simple. But the world we live in is not simple, it’s complex. Development sector practitioners strive to design holistic interventions and models that address the real-world needs of program participants. How do we address this tension?
” There is a tendency in the development industry to try and approach problems from a holistic perspective. But when you talk that way, it becomes very hard to find an entry point. Yes, everything is connected; yes, everything is complicated — but if you let that be the framework through which you start, you won’t get anywhere.
That’s why the world is scattered with pilot projects. Lovely pilot projects that are trying to deal with holistic issues, but are never going to get beyond 50 schools or 50 villages. If you look at the things that have achieved massive scale, they are well-defined interventions — or at least started that way.
I always tell people: don’t try and paint the masterpiece — do one layer, and do it well, then do another on top of that. Just creating or strengthening a platform to deliver something simply but well gives you the opportunity to build other stuff on top of it.”
We’re big fans of Apolitical’s blog series on scaling social impact! One of the hidden but crucial concepts the series surfaces is understanding what is core, or “fixed” and a non-negotiable element of your innovation:
“For one, it’s not always clear whether or how something is working. Impact evaluation is growing, but billions of dollars of policy expenditure remain inadequately unevaluated. And, even when impact is proven, it’s not always clear what exactly is responsible. Scaling up often requires paring down a program to its essentials — and for this you need to know what can and cannot be compromised.”
In Scale X Design, we emphasize learning and documenting “fixed” vs. “flexible” elements of your model. That’s the easy part. The tough part is testing, learning and iterating and managing this knowledge across loosely connected practitioners.