1) … I think the chance of failure could be significantly higher.
Possibly, but they are already starting to operate in the country in question, and my understanding is that’s been going pretty well. My impression is that they’re much more competent than Safaricom. My inside view is much higher than 50%, and getting down to 50% was a discount from there.
Sounds like you definitely have inside info that I don’t have, so for now I’d have to rely on my outside view, but I can work to acquire that inside info if I look into this more.
-
2) … I expect future roll-outs will take place in countries with higher base consumption
I’m confused. I was trying to talk about the counterfactual for a specific very poor country if Wave were not working there. So if future mobile money rollouts by other organizations happen first in countries with higher base consumption then that increases the counterfactual impact of Wave choosing to come into a country with very low consumption.
I don’t know what country Wave is looking at or how they are doing what they do because you have inside info that I don’t have. If it has consumption comparable to Kenya than my point is invalid. I just was concerned that it wouldn’t.
-
3) … I expect them to continue at AMF levels (or greater) for at least a few more years
Cool. Sounds like this isn’t a disagreement between us then.
-
4) … I really don’t know how many staff years it would take
That, combined with estimating marginal impact, makes this pretty awkward. I figure something like 40 person years?
Agreed that it is pretty awkward to estimate. I modified my model to use some of your inputs—such as a 40% chance of 1-10M subscribers and a 10% chance of >10M subscribers and 40 person years—and it comes out to $383/hr (95%: $145/hr to $834/hr). The new mean is still in my old 95% interval which is about the best I can hope for with this level of uncertainty.
-
5) … I’m confused about why GiveDirectly is stated to be 5x more cost-effective than AMF
This comes from cell F31 of the “Results” tab. I haven’t put time into understanding how that’s calculated, but it looked like the relevant bottom line number.
Oh, I see that now. I suppose this is a question for GiveWell and not you. I’ll ask them.
Sounds like you definitely have inside info that I don’t have, so for now I’d have to rely on my outside view, but I can work to acquire that inside info if I look into this more.
If you’re interested in working for Wave, or are advising other people on whether it’s a good idea for them, I could imagine they’d be quite interested in talking to you!
if it has consumption comparable to Kenya than my point is invalid. I just was concerned that it wouldn’t.
Thanks Jeff!
-
Sounds like you definitely have inside info that I don’t have, so for now I’d have to rely on my outside view, but I can work to acquire that inside info if I look into this more.
-
I don’t know what country Wave is looking at or how they are doing what they do because you have inside info that I don’t have. If it has consumption comparable to Kenya than my point is invalid. I just was concerned that it wouldn’t.
-
Cool. Sounds like this isn’t a disagreement between us then.
-
Agreed that it is pretty awkward to estimate. I modified my model to use some of your inputs—such as a 40% chance of 1-10M subscribers and a 10% chance of >10M subscribers and 40 person years—and it comes out to $383/hr (95%: $145/hr to $834/hr). The new mean is still in my old 95% interval which is about the best I can hope for with this level of uncertainty.
-
Oh, I see that now. I suppose this is a question for GiveWell and not you. I’ll ask them.
If you’re interested in working for Wave, or are advising other people on whether it’s a good idea for them, I could imagine they’d be quite interested in talking to you!
It’s poorer than Kenya.
That sounds pretty awesome, who do you think would be a good person to reach out to when I’m ready?
Ben Kuhn maybe?