Maximum impact

I had a fascinating conversation with the Constellation Fund's Cooper Hanning a few weeks back, where I was introduced to the framework for their funding. I've been hoping to connect with someone from Constellation for some time, intrigued by their board and the stated mission around data-driven impact.

Like most funders, Constellation is looking for the maximum return on their investment. Where most large funders judge prospective projects based on their qualitative merits, Constellation looks to judge each proposal on its quantitative impact, measured, it appears, by quality-adjusted life years (QALY) or income increases. There are likely other measures or outputs, but those were the focus of our brief conversation.

Constellation's website mentions that their approach is based on Andrew Dayton's experiences working with the Tipping Point Community in San Fransisco, where he got a front row seat to see the effects of this data-driven approach to philanthropy. At first glance, this approach takes a lot of the sloppiness and favoritism out of the world of foundation funding. Or, it at least moves the game toward something approaching a set of rules. I still cringe at the thought of actuarial tables and managed-care-generated measurements of quality driving the work of nonprofits, but with the organization aligned with consultancies like McKinsey and individuals like former UnitedHealth Group CEO Stephen Hemsley, it makes sense that they would pursue this approach. And also, what the hell else would they use? Imperfect estimates and data derived from organizations whose agendas may not align with the stated mission aside, there aren’t any other options for securing this information. Are there?

Cooper shared that projects selected for estimates as part of the selection process are converted into measured impact via a proprietary algorithm, which I believe is developed through exploration of the existing body of research on the intervention in addition to looking at historical impact to estimate reach. Constellation is focused on alleviating poverty in the Twin Cities metro and all projects must tie into this focus.

The impact would likely be simplified into two measures in our case: estimated increase in income and estimated health cost savings. For people working in the world of substance use disorder (SUD), these measures would provide a compelling argument for scaling effective services. For people to reduce harms related to substance use or achieve some measure of recovery is likely correlated to both income realization and health cost savings. For people with complex health issues related to IV drug use, such as repeated bouts of endocarditis, heart valve replacements, and Hepatitis C, the savings generated have the potential to be significant.

All this to say that this approach to funding is going to be fascinating to watch as our country continues to move toward universal healthcare while at the same confronting the widespread mistrust of large institutions and the principles that guide them. As the thinking of skeptics of this approach (like Anand Giridharadas) continues to become more common, I wonder if it will drive further distance between the “story” and “data” camps, or if these designations become more nuanced and lead toward collaboration and reform.

I am cautiously optimistic that this approach — if tempered by the understanding that many communities might need assistance translating testimony and anecdote into data — will be a very positive move for philanthropy in the future.