In today’s issue, I’m going to share 3 beliefs about scaling change that I think are lies.

When I say “lies”, I don’t mean that proponents are intentionally misleading people, but rather that they’ve been led astray themselves.

And in a world of philanthropists and government bureaucrats encouraged to get as much impact as possible, the same bad recommendations are shouted from the rooftops again and again.

So, people are compelled to “get to scale”, only to find that it doesn’t work.

Let’s end that naive mindset.

Solutions are not universal at all scales

So much of social impact activism can be characterized as finding what works and then trying to scale it. It’s the dream of finding a successful intervention for one person and expanding it to an entire community. Or taking an impactful program and spreading it across the nation.

It’s assumed that a big impact requires this type of growth engineering. Like the world is a big factory and we can just manufacture more impact with new markets and economies of scale.

But unlike widgets that can be mass produced with enough inputs, interventions in complex environments have different effects at different scales.

A hypothetical walk home from school illustrates the point:

Your kid trips on the sidewalk and scrapes her knee. After applying some band-aids, you give her an ice cream cone. The treat cheers her right up.

At the next PTA meeting, you suggest that ice cream cones are a fun and easy way to quickly comfort injured kids. Wouldn’t the school benefit from a policy of giving out ice cream cones in the nurse’s office? You volunteer for the job.

The injured students love ice cream. And you love giving out the treats.

But soon teachers complain. Students are intentionally tripping each other at recess to instigate nurse visits. And parents aren’t too happy either. There is a marked increase in skinned knees and uneaten bag lunches coming home.

You get the point. 

What worked in a simple interaction with one kid won’t have the same effect when it’s repeated in a network of student interactions.

The scale transformation – a dramatic increase in actors, interconnected variables and feedback loops – makes the aggregate effect of the ice cream cone policy difficult to predict. (I admit that veteran teachers were probably not surprised in the least.)

Nonetheless, this inclination for scale happens in real life all the time. 

Reducing gang violence in Panama

In a recent Stanford Social Innovation Review article, KC Hardin writes about the challenges of trying to scale a gang violence reduction initiative in Panama City.

His nonprofit had previously succeeded in demobilizing four gangs in the San Felipe neighborhood. Based on their successful model, his nonprofit thought it could scale their work to similar gang problems just 15 blocks away.

However, different gang dynamics in the Santa Ana neighborhood meant that what had worked in San Felipe didn’t translate to the new environment. His nonprofit’s costs and complexity increased while effectiveness dropped dramatically.

As he recounts,

“In retrospect, the trouble started when it stopped feeling like enough to be a hypothesis-driven community organization learning how to solve one big problem in one small neighborhood. I began believing that maybe Esperanza did have an answer to the wider gang problem. That hubris grew every time I was asked to present Esperanza at a forum or to meet with an NGO or multilateral; concepts like “leverage,” “scaling,” and “knowledge transfer” became part of my everyday vocabulary.”

The effect here is a little different than the unintended network effects of the ice cream cone policy. In this example, the intervention isn’t scalable because gang violence in San Felipe and Santa Ana are materially different phenomena. They may choose to use the same intervention, but it is not treating the same problem. 

With these warning stories in mind, here are 3 beliefs about scaling that I think are incorrect, and some updates from me, to make them right.

#1. What works for individuals will work for communities → What works is scale-dependent

Life isn’t a changemaker’s dream, where some magic intervention is discovered and philanthropic investment scales it to the whole world.

Has it happened? In rare cases. (Gavi’s vaccine campaign saving millions of lives comes to mind.)

But can every intervention be scaled like that? No.

A pilot program evaluation showing that an intervention had a positive impact for 12 individuals does not mean that the intervention will have the same positive effects at a community or societal level. 

Evidence is scale-dependent, meaning it is only applicable to the scale of the investigation. In other words, claims at an individual level cannot be extrapolated to the community or societal level.

So, the next time someone claims an intervention is “evidence-based”, the best question to ask is: “at what level?”

#2. There are universal solutions → There are no non-contextual solutions

Complex problems like gang violence are a mix of interrelated factors. 

One systems thinking insight is that a complex problem’s unique structure (i.e. how its parts are connected and interact) determines the problem’s behavior.

In a practical sense, this means that each complex problem will respond to intervention in its own way. It’s entirely dependent on the problem’s structure and environment.

So, from the outside it may seem like San Felipe and Santa Ana have the same gang violence problem and could use the same solution.

But local context is everything.

#3. Large-scale impact requires large-scale changes → Small changes can have big effects

One of the most common narratives in social impact is that large-scale impact requires large-scale changes.

People will point to efforts to totally transform the criminal justice or education systems, or movements to completely reimagine policing. But so far these have been more successful in gaining public attention than creating change.

Instead of thinking about what systems you want to change, ask yourself this:

“What small changes could I make to become just 10% more effective?”

Imagine if everyone did that.

Seems like a big change to me.

Action Steps:

• Ask “at what scale?” for evidence-based claims
• Consider the problem’s unique context before applying “best practices”
• Look for ways to be just a little bit better
  

That’s it for today.

See you again next week.

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