Thanks Vasco, good q. I’m not too worried about temperature, since some of their specifications control for it (though we haven’t gone back and checked the 13 pp precisely). I think the stronger critique here is prolly on external validity grounds. On what basis do we go from one extreme year in Tanzania to a sustained 10-30% drop over many years in other regions? There are arguments that cut both ways on whether a persistent shift like this is worse or better than a one-off shock. We say worse in part because we see less potential for adaptation to a long-term weather-induced shock than to a price shock. But who knows, and I think that our note calling our estimate “likely conservative” probably overstates our stance. I’d say it's maybe more of a best guess than a conservative one. 

On the practical end of things, as we both have flagged, this damage stream is responsible for most of the harm, so small changes here will tend to generate moderate/large changes in the overall picture. (Not fatally direction-wise though.) I think that looking back, we should have flagged this as a particularly sensitive parameter. But in our defense, we do say we have only medium confidence in this BOTEC because of the long causal chain to mortality, which this parameter belongs to.

Thanks @Vasco Grilo🔸 , this is a reasonable flag. This particular assumption is a judgment call that seemed reasonable to us and in line with what we thought a 30% loss in European yields might deliver (this would be quite a large food shock). But happy to update if there were stronger priors. Most importantly, however, most of the harm from AMOC comes from the damage streams in lower-income areas, so even if we were to significantly reduce our parameter here, our topline estimate wouldn’t move much (to the point of it being almost trivial). 

Helpful!

On the impact side, some would suggest smaller numbers than the ones you point to here after discounting, and on the cost side some programs are fairly expensive. This all works to make the cost-effectiveness case less attractive, which explains limited attention in EA, I think.

We recently wrote a paper with colleagues from Gates trying to incorporate health and social benefits alongside economic ones to some set of schooling (and schooling related interventions)*, and find that they more than double the benefits, which makes them more competitive under some bar.

(*Though in our paper we just model additional years of schooling, rather than learning. One would imagine the benefits of more learning would be larger, but I haven't followed that literature as closely.)

Good q. We did not directly adjust the endogenous growth term through any discount, it enters the model only as a multiplier. That said, the multipliers we use have underlying discount rates that they use (generally larger than 0). We probably could’ve harmonized in a smarter way to be able to answer your question, but the general point you are trying to make, I think, is that higher discount rates here might be appropriate because of long stream of future harms/benefits (or perhaps just uncertainty). This would mean that our SCC is too large. Fair; and maybe!

Recall that our main SCC estimates are log-linear in income. In a linear model with 0% time preference, yes, the estimate would balloon because of the long tail. But in a log-linear model where people are getting richer, a 0% time preference essentially sets the effective discount to the growth rate (because future people are wealthier, and so additional dollar damages mean less to them). So this would mean that our initial SCC is probably smaller than you’d think *and* it is less sensitive than linear models to discount rates (by a nontrivial but not super large amount I would say). 

Now the multipliers themselves, as I said above, use some discount rate and are not log-linear in income the way our base estimate is. So the actual discount rates are important here and might mean our multipliers modify our baseline SCC too muuch (our log-framework already downweighs far-off damages, but our current multiplier does not, making it large) or not enough (if the pure time preference is really closer to 0% than the rates they were estimated at, given how back-loaded growth damages are, making it too small). 

All to say, the multiplier bit was a significant source of uncertainty to us when we wrote it (we labeled the endogenous growth multiplier as an adjustment with high uncertainty in our report) and happy to have had a bit more time to rethink some of these things and share our views on it now.

My understanding is that GWs pure time preference is similar to ours and that any differnece in effective discounts is related to how they treat uncertainty over the future, but I have't looked into this. 

Fair point Alex. We mostly interrogated the climate side of things, and in that sense the work itself is asymmetric. GW does incorporate second order effects into some of its CEAs, but I guess the larger question is whether or not there is any such large multiplier to apply to some GHD interventions for some amount of time. My quick reading of the literature there is simply that there isn't strong evidence on the direction of growth effects for GHD, whereas the estimates we found on the climate side were at least consistent in direction (though I may be wrong here).

I think being skeptical here is probably smart. But even if Nan is off by some order of magnitude, and we believe there is some low probability the money comes through, the amounts of new philanthropic funding are very plausibly still very large, especially considering what goes into certain cause areas. 

I find your advice to organizations compelling as well. Though on balance I'm likely to let their leadership figure what they are likely to win/lose from making such bets. Feels perfectly reasonable to get a whole lot of orgs that make heavy bets (alongside some, even perhaps very few, who don't).

Thanks for jumping in Nick. I appreciate the distinction. 

To be clear, what I meant by "in practice" is the actual amount of effort, time, and resources RP dedicates to GHD internally, which is distinct from its public footprint and its ultimate impact. My point is simply that characterizing RP as having "shifted" to animal welfare doesn't capture my sense of internal resource allocation and the external impact of our GHD work (some of which may be not in the public domain), even if that's how it appears externally.

A late comment to say that I don't think RP takes the view that any given cause area is more important than another, either philosophically or in practice. Our GHD team produces a steady stream of-I think-interesting and helpful reports. Perhaps this perception stems from the fact that a lot of our GHD work is not public (for various reasons), or simply that people don't engage with it as much as they might have in the past.

This was a fun read, thanks for sharing! 

I agree with you that cash benchmarking is a helpful, relatively intuitive metric. But I also think that all benchmarks in our space sometimes provide a veneer of precision, when ultimately there are a bunch of non-trivial subjective beliefs that help build out even a quantitative-looking cash benchmark. Concretely, how much do we weigh a life relative to hard cash? This is not an easy question, and I worry sometimes that cash benchmarking makes people believe it is some kind of purely objective metric.

Thanks Nick, helpful!