And we do have to worry about homopholy here,

that's going to be an issue behind the scenes,

because it could be that part of the reason that I participate compared

to my friends is that there's things that we have in common that

we're not seeing, and it's not that my friends influenced me, but it's

just that I am friends with people who are very similar to me.

Okay, so homopholy could be behind the scenes.

It's actually not going to be so much of an

issue for us because we are going to find it

eventually when we properly do a diffusion model, we are

not going to find these peer-effects, but it is something that

we have to keep in mind. So let's run that standard regression.

If you run that with a whole series of characteristics you

could find all the details of these regressions in the paper.

But effectively what you're going to end up with

is, a parameter here of 2.5, highly significant.

Which would seem to indicate that the more my friends par, the, the higher

the fraction my friends participate, the more likely I am to participate.

We can't say causal, but, but it seems to be there's a high correlation there.

And in particular, how do we make sense of

2.5 given that we're looking at log of odds ratios.

So what does that mean?

So if you do some calculations, if, if you took my fraction of my friends

from zero to one, holding all the other

characteristics at their average, you would increase the

odds ratio by a factor of 12.

To make it relative likelihood of me

participating compared to not, 12 times higher, okay?

So, so that, that's a huge impact, and if you

took it from just 0.1 to 0.3, which is closer in

within one standard deviation of, of what the actual numbers

are, you'd still go up by a factor of about 1.65.

So you get, you still get a, a substantial impact of just moving

one standard deviation in the fraction of friends participating comes to,

to a 50% increase in the relative likelihood of participation, okay?

So, so we see a very strong effect if we just do the regression, and now

the only network information we're using is just

in terms of who are my friends, right?

So.

So basically we, now we're going to try and use bring a diffusion model into this.

And get a little more understanding of what that 2.5

really represents or what's going on behind the scenes there.

So we're going to use network information, not just with my friends.

But we'll keep track of people who

hear about microfinance or repeatedly pass information to

friends and then once I hear, will make a decision of whether or not to participate.

Okay, so we're going to bring diffusion officially into the picture now.

So let's stick first of all with the participation decision.

So once I'm informed, I'm going to make a decision of whether or not to participate.

And, we'll allow the, the choice to basically vary the same way it did before.

Okay, so exactly the same, logistic kind of regression we, we had before.

The log odds of ratio that I'll participate

once I'm informed, will look like something which depends

on my characteristics, and depends on the fraction

of, of friends I have who are also informed,

who are participating.

Okay, so now we'll keep track of who's informed and keep track of whether or

not I participate as a function of

my characteristics and the fraction of friends participating.

But what we're doing differently is we're actually going to

map out the information flow, and so whether or

not I participate whether or not, I choose to

participate will be conditional on whether or not I'm informed.

Okay. So how are we going to do that?

We're, we're going to have just a very simple model of passing information.