On September 1st, 2014 I joined the Family Independence Initiative (FII) as the Director of Analytics. When I decided to shut down Idealistics in the summer of 2013 I figured the next step for me would be joining a team focusing on domestic and/or international poverty. In the time between shutting down Idealistics and joining FII I consulted with a range of nonprofits, foundations, and businesses, none of which felt quite like something I wanted to dedicate several years of my life to.
I decided to close Idealistics in part because I had lost faith that the company’s technologies were really helping the nonprofits I worked with to create social impact. Indeed, I had lost faith that anti-poverty interventions had much effect at all.
I had come to realize that so much of my work, like the social sector itself, had been based on a misguided paradigm of a nonprofit sector providing solutions to distressed “clients” in “need” of answers. This paradigm doesn’t only oversimplify the poor, it plainly gets them wrong.
As I was losing faith in the efficacy of the program driven nonprofit model, my interest in cash transfers was growing considerably. In my personal giving, I support GiveDirectly, a nonprofit that experiments with giving unconditional cash transfers to those living in extreme poverty in the developing world. While the evidence around conditional cash transfers is pretty compelling, and the evidence base for unconditional cash transfers is growing, what I find most compelling about the unconditional cash transfer model has less to do with the transfer of money and more to do with the underlying notion of trusting people living in poverty.
Positive deviance is a phenomenon whereby certain individuals given the same circumstance and access to raw materials are able to achieve better outcomes than their peers. The term was first coined by nutritionists and applied by those studying how certain families in rural Vietnam in the 1990s were able to provide better nutrition for their children than most families in the same areas.
I had not learned about the term positive deviance until joining FII, but it instantly resolved much of what I’ve been uncomfortable with about the social sector for so long and why I was attracted to models like GiveDirectly and FII.
I have never lived in any type of poverty, from extreme poverty in the developing world to domestic poverty as defined by the U.S. federal poverty line. It is asinine to believe I should see a pathway out of poverty, given my complete ignorance of any of its realities. Yet asinine I’ve been for the last decade of my career.
In FII I found an organization that is less interested in solving the problems of the poor, and instead more interested in learning about how the poor improve themselves, their families, and their own communities.
Data driven nonprofit
There’s a lot of talk in the social sector about data driven nonprofits. I spent eight years at Idealistics and an additional year as an independent consultant working with nonprofits to try to help them improve their data infrastructures to little success.
I’ve written before about the pitfalls of poor data literacy in the social sector, but data literacy is something that can be overcome and hired into an organization. What cannot be acquired through new hires is a data culture. A data culture requires the organization from top to bottom to truly adhere to not only investing in the ability to mine data for feedback, but then taking that feedback and turning it into organizational change.
Given the nonprofit model of developing a theory of change, then fundraising around that model, it’s not terribly surprising that nonprofits struggle to be data driven. A data driven nonprofit must be willing to accept not only that its theory of change might be wrong, but instead it must expect that it is most likely wrong.
Indeed, in graduate school my econometrics professor taught me that “all models are wrong, but some are useful”. An analyst’s role is to develop models that are knowingly wrong that hopefully get less wrong through time, as more data is acquired. This approach of iterative improvement and willingness to shift key assumptions is antithetical to how nonprofits are largely financed. Too often, a funder invests in a nonprofit on the assumption that the nonprofit’s theory of change is correct, leaving the nonprofit to use data to justify the funder’s investment rather than to use data to identify where the organization is wrong, and how to improve.
Investing in people, not nonprofits
I did not get into the social sector because I love nonprofits. I got into the social sector because I love people. Somewhere along the way, my career became about serving nonprofits, not serving people, not serving communities.
The word “service” is a popular way to describe our line of work in the nonprofit sector. Of course, when I receive “service” I expect to get what I want. I seek out services to help me achieve a goal, my goal, not a goal someone else has determined for me.
At FII, my job is to use data to learn from families how they improve themselves and their communities. I’m not tasked with proving a particular model, instead I’m learning about how families define success on their own terms, and how we (collectively) can invest in the incredible initiatives already underway by people that we (in the social sector) for too long have considered objects of change instead of agents of change.
I couldn’t be more thrilled.