It is not enough for us to just count the number of jobs that we have created. We really want to
figure out are these jobs improving lives because at the end of the day that's why we
exist. So to answer that question we turn to a logic model to help us very systematically
break down our intervention and think through step by step, what is the cause and effect of
what we do. With any logic model you should start with the inputs. What do you have or
how do you think about your mission? What is your reason for existence? With those inputs in hand,
what will you actually do to carry out your mission? Now this is where you really list
out the activities. Now outputs. What happens as a direct result of those activities? This
can also be thought of as outreach, those numbers we were just talking about. How many
lives has your program touched? Right and this is what you have direct control over.
With those outputs, we then think about outcomes and this is where it starts to get interesting.
This is the, so what? What happens as a result of those outputs? You touched a thousand lives,
great. But did those lives improve? These are really the meaty questions and these can also
happen perhaps a little bit outside your direct sphere of influence. This is what happens over
a longer period of time. And then lastly as part of the logic model we have impact. In an academic
sense, to prove impact technically means you need to be able to show statistical evidence
to link your action to the observed outcomes. This is where you prove that causal connection
and most of the time to do this requires undertaking a randomized control trial or
some other rigorous research method. But here I want to drive home a point and this is my opinion
of course. We mission-driven organizations should not be scared off by the intensity of an RCT or
rigorous research that prevents us from measuring anything. This is not an all-or-nothing game so we
should not say that just because we cannot afford rigorous research that we shouldn't measure social
outcomes. I feel like too many people stop at just the outputs or outreach and what I really want to
be the key takeaway of this presentation is that we can and should move to this middle ground of
outcomes and even if we don't do anything more rigorous than that to really prove impact,
measuring outcomes and being very systematic about it can yield just a wealth of information
and improve your models and I will talk about how that has helped us. So here I show a pious version
of the logic model. As I said before we start with our mission to create dignified jobs for
the forest of the port by investing and scalable businesses. The primary activities we undertake
is a very rigorous screening and selection process and then we put the businesses we select through a
four-month accelerator program and provide them deeper support. After the accelerator program,
we will choose a handful of them that actually receive patient equity investment and then more
hands-on consulting support over the next two or three years and by doing all these activities what
we are really hoping is the immediate output are profitable, self-sustaining and scalable
enterprises. And if these businesses are all those things they should create new jobs for the poor
and these are the things that we have some control over as Upaya because this is how we work hands-on
with our entrepreneurs and we really advise them on how to make their systems more robust
and their businesses more profitable and so we hope that yields more jobs but then after that
what happens is a little bit out of our control, but we certainly hope that happens because that's
what our mission is and that is these new jobs should provide stable and increased income for
job holders and that income should move those job holders out of poverty and we will look at
how it moves job holders out of poverty, along a number of dimensions. Once we have defined all the
steps of our logic model, we can then define the metrics that correspond with each of these steps
and these are the quantitative measures that give us an idea of whether we are on track or
not with our mission statement. For example, if we want to track whether our portfolio companies
are scaling or not, we would measure quarterly revenue growth and see does this revenue growth
track to the entrepreneurs financial projections. We look at you know what percentage of companies
in our portfolio have hit profitability. How much follow-on capital have they attracted? Are they
able to meet their growth goals and keep scaling? To track progress out of poverty, we look at a
number of different things. So first and foremost ,we would measure the increase in income and I'm
going to talk about that a little later and then observe what this increased income helps that
person in their household do? Are they putting this new income towards improving the condition
of their house? Are they able to purchase and eat more food? Are they purchasing more assets for
the household or productive assets? Are they sending their children to school? We collect
this information directly from the job holders and let them tell us what they are doing and
how they are being helped. The more a person's income allows them to make decisions about their
well-being and their aspirations the greater progress they will make out of poverty, so we
try and capture like I said directly from them what they claim these jobs are helping them to.