Comments
This is interesting thanks for sharing. With respect to alcohol, will you also be considering the benefits of alcohol, i.e. that people enjoy drinking? Or will you just focus on the costs?
Yes, we do consider the benefits of alcohol, including that many people enjoy it.
James Snowden put together a short document discussing this when he made the largest current Open Philanthropy alcohol grant in 2021 (the grant was recommended by GiveWell but funded by Open Philanthropy; any extension / renewal will sit within Open Philanthropy). At the time GiveWell / James applied a 10% reduction to the (implicitly net) burden of alcohol harm on this basis.
I'm reviewing this issue in greater detail now.
The dismissal of consumer surplus-based ways to value alcohol consumption is puzzling. The main justification is that "it seems likely to us that consumers are behaving irrationally", but this is an overly broad statement. What fraction of alcohol consumption is irrational? If you believe that 30% of consumers are consuming irrationally high amounts, you could easily exclude the 30% heaviest drinkers and estimate consumer surplus for the remainder. In general, you can choose a population where you believe people are consuming more out of enjoyment than addiction.
This would require a bit of primary investigation, but you could use Nielsen scanner data with alcohol prices and consumption to a) drop the people who drink the most, b) estimate consumer surplus on the remainder. I'm pretty sure Nielsen has similar data in some LMICs if you want a more representative population. My prior is that you would arrive at substantially more than a 10% downward adjustment.
Thanks for the thoughts Kartik!
(Speaking for myself; the 10% estimate comes from work I did at GiveWell but others at Open Phil and GiveWell may disagree with me)
I agree we shouldn’t dismiss consumer surplus entirely, and in retrospect would soften some of the wording in that doc – I think the irrationality point is important but not totalizing. The Nielsen idea is interesting and I’d like to think about it more. I think internalities are less bimodally distributed between people than your model, which muddies the waters, but I wonder if an analysis like that could still be informative.
Fwiw the program we funded is primarily focused on taxation, which is a nice mechanism to balance a recognition of externalities / internalities with a general prior towards personal choice. I'd estimate higher than 10% if that wasn’t the case. A focus on tax means the reduced consumption will be from the drinks for which people had the lowest willingness to pay, limiting lost consumer surplus.[1] It also results in increased tax revenue, so could be considered as trading off against alternative ways of raising tax revenue with their own deadweight loss in consumer surplus.
TBC, I recognize the inherent fragility / subjectivity of the 10% estimate and I suspect different people would come to quite different conclusions about what input to use, so I’d be excited to see more efforts to estimate this considering the broad sweep of evidence.
- ^
Of the two studies I could find on consumer surplus, the one which attempted to estimate consumer surplus from a marginal increase in price (rather than for typical consumption) estimates a loss of €58 million in consumer surplus, compared to a €700m improvement in “health, productivity, and non-financial welfare losses” (Anderson and Baumberg 2010, pg 34), implying an offsetting impact of ~8%. (Though I think there are a bunch of ways in which that study isn’t analogous to the models we use, including a higher estimate of non-health impacts, so difficult to know what to make of it).
This also raises a separate worry about the extent to which taxation affects heavy drinkers, where the marginal harm is likely highest, which we tried to account for separately in the effect size estimate.
Fair points, I agree that taxation has a lower bar. The bimodal point was illustrative, you could take some other individual characteristics as proxies for the extent of internalities (e.g. education) and weight people by that when estimating.
Thanks for this. I'm not sure I follow the claim that if you assume that alcohol taxation merely shifts the tax burden, there aren't strong reasons to think the deadweight loss will be greater from alcohol taxation vs other forms of taxation. The subjective wellbeing study found that drinking increases people's wellbeing by almost as much as spending time with friends. It seems unlikely to me that if the tax were instead eg on income that the benefits of the income would be as large as this. Intuitively, this seems off.
On your botec on the benefits of alcohol, a lot rides on you assuming that a death from alcohol accounts for 40 units of value (I'm assuming this means life years lost, but not sure). But in the sheet you suggest that most deaths would be among older people. If you revise this figure to 10 years of life lost (which seems much more plausible to me), on the median case, the value wiped out by reduced booze enjoyment is 67% on the median case and 134% on the pessimistic (or optimistic, depending on your view) case. If you reduce the years of life lost to 5 years, then on the median case, the 134% of the value is wiped out. i.e. it seems like on some more plausible assumptions, the policy is net negative.
On the other hand, none of this considers hangovers.
Having said that, this paper suggests that most the median age of death would be around 30-40, not among older people.
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Ah, I was looking at the spreadsheet and I think a unit of value is different to a year of life lost.
Another thought - you measure the effects of alcohol on subjective wellbeing as a fraction of someone's waking hours. This seems right from a subjective wellbeing perspective. But is that also the way you think about the value lost by a death? By consistency, you would also need to implicitly downweight the disvalue a death by a third for the time people spend asleep. Or do you already do that in your moral weights?
>Another thought - you measure the effects of alcohol on subjective wellbeing as a fraction of someone's waking hours. This seems right from a subjective wellbeing perspective. But is that also the way you think about the value lost by a death? By consistency, you would also need to implicitly downweight the disvalue a death by a third for the time people spend asleep. Or do you already do that in your moral weights?
Oh that's interesting. It's been a while now since I did this, but I think I was implicitly doing that with this calc
Yeah wrt to your botec, I wasn't sure whether you were implicitly writing off time spent asleep.
I suppose you would also have to do the same for measuring the effects of money on wellbeing. Do you do that?
Implicitly, yes. Though don’t use that exact formulation. money vs daly comparison is based on reported preference not swb. Daly vs swb comparison implicitly writes off time spent asleep where I assumed 1 daly = difference between 40->100 on swb scale.
If didn’t exclude sleep in botec, would make alcohol look worse as happiness bump from alcohol would be for lower % of time. (Set row 21 to 24)
Yeah I'm not making the 'you've underestimated net benefits of alcohol' point, just trying to think through your assumptions
>I'm not sure I follow the claim that if you assume that alcohol taxation merely shifts the tax burden, there aren't strong reasons to think the deadweight loss will be greater from alcohol taxation vs other forms of taxation. The subjective wellbeing study found that drinking increases people's wellbeing by almost as much as spending time with friends. It seems unlikely to me that if the tax were instead eg on income that the benefits of the income would be as large as this. Intuitively, this seems off.
Interesting. That doesn't seem off to me. If I'm understanding correctly, the implication of your view is that people would generally be better off if they consumed more alcohol and less of other goods on the margin. Is that right?
To put it another way: increasing taxes on alcohol has two effects on consumer surplus: (i) deadweight loss (ii) a transfer from consumers to the government. I think (ii) is probably positive. Almost all taxes involve some amount of deadweight loss, but we do them anyway because we think public goods and redistribution are worth it.
TBC, I'm not claiming that higher excise taxes on alcohol relative to other goods merely shifts the tax burden. If we assume perfect rationality (which I believe would be mistaken), having unequal marginal taxes between goods does result in some additional deadweight loss. But it is a counterveiling factor.
>On your botec on the benefits of alcohol, a lot rides on you assuming that a death from alcohol accounts for 40 units of value
A unit of value (in GiveWell's terms) is equivalent to doubling consumption for a person for a year. A DALY is 2.3 units of value. So you want to be dividing your estimates by 2.3.
The Global Burden of Disease estimates ~30 YLLs and ~10 YLDs per death (I didn't include YLDs in the BOTEC and I underestimated YLLs which makes it conservative, though I also didn't discount the GBD estimates for imperfect evidence quality and black market consumption not addressable through policy which makes it optimistic. I'd guess these ~cancel).
Edit: didn't see your second comment when writing this where you saw this
>On the other hand, none of this considers hangovers.
Yeah, although interestingly (IIRC) the Baumberg study didn't find any effect on SWB the day after drinking (though I'm skeptical -- maybe people didn't feel like inputting how sad they were on their phone when they were hungover!)
Hey John, just to throw in a couple of subjective narratives here. Basically I'm very skeptical that there would be any net happiness benefit to alcohol.
The negative effects of drinking are immense here in LMICs like Uganda - acknowleged almost everyone in society, even sadly by most drinkers. Ask Ugandans "Do you think alcohol makes people more happy or more sad?" and I can guess the answer. Alcohol is a multifaceted life sucker, it eats too many people's (especially men's) productive hours, reduces household income, fuels gender based violence and precipitates and potentiates depression.
Just drive through any village center here at 3:00pm at the afternoon - the side of the road ain't a pretty site. Not only the plastic alcohol bottles littering the street, but also the hundreds drunk men in bar shacks who could have "counterfactually" been digging in the garden, or mentoring their sons, or doing basically anything else mildly productive. The negative happiness "spillovers" to their families might beat even Strongmind's positive ones....
And even leaving that serious stuff aside and looking at "happiness" isolated (if that's possible). If there was no alcohol available in the whole world tomorrow, if we just looked at point-in-time-whole-world happiness, say the day before and the day after alcohol was vanished away, I would bet (with low certainty of course) that happiness might even go up. For every 5 people that might get a slight happiness benefit from drinking, I reckon there's one person ii whom alcohol illicits at least moderate depression and negativity that wipes out the benefit of those 5 people who drink casually and (perhaps) healthily and it makes their life perhaps a tiny fraction better.
I read the little alcohol enjoyment paper James and co wrote, which seem to have one study showing no change in happiness, three longitudinal (better) studies showing negative changes for heavy drinkers and no changes for moderate drinkers (expected), one large cross-sectional survey showing an incredible third of drinkers want to drink less next year which is telling/
And just one "I phone based" cross-sectional subjective study showing a increase in moment-to-moment happiness while drinking alcohol (I mean of course), while not looking at future negative effects. Above anecdotes like mine, basically the lowest level of evidence. Typed in by an unrepresentative sample on Iphones.... I mean...
If it was me I would possibly be discounting for happiness by -10% :D, or at least by 0%.
Hi Nick, yeah I get that there are costs to alcohol, I just think it is important to consider the benefits when deciding what to do. A lot of public health policies are defended by only focusing on the costs of doing something, not the benefits. So, I think it is important to consider the benefits.
My own intuition is that if there were no alcohol, happiness would go down for the vast majority of people and would go up for a small minority. From my own experience, people often seem close to their happiest when drinking, and it is a very important social lubricant.
I think the best study in James' review was the subjective wellbeing one because it measures what, in my view, actually matters, which is people's moment to moment wellbeing. It's also a very large sample. I wouldn't class the benefits found in the study as 'tiny'. The increase in wellbeing while drinking is nearly as large as that produced during spending time with friends. And this is a benefit which would be spread across billions of people.
Thanks John those are reasonable points. Completely agree we should consider the benefits, just disagree that alcohol is probably net positive for happiness in LMICs, no comment really on Staten countries.
You are right the difference isn't "tiny" in the study have deleted.
From a study design standpoint, a cross sectional phone study where people opt in, would fall close to the bottom of the heirachy of evidence, traditionally at least. Personally I think it's not useless, but at best very weak. I can see your argument that what they measure is what "actually matters", but the bias and confounding of this kind of the study makes it so so hard to put much weight on. Given that, a huge sample size doesnt really help at all.
If your rate moment by moment wellbeing as the most important thing to measure, there would have to be some kind of random sampling and ways to avoid the kind of "I'm drinking and happy now" bias, while not attributing the hangover or bad mood the next day to alcohol.
I'm not sure the phone study has the traditional weaknesses as cross-sectional studies. It's a bit more like a panel study where you can track in very fine-grained detail what events are happening and what happens to subjective wellbeing at the time. Because the event data is so fine-grained and there are so many contiguous datapoints, it provides very good evidence of causation. These sorts of studies also give intuitively plausible results for all other events. People don't like work, getting divorced, being unemployed, being widowed; people like sex, seeing their friends etc.
It's true that people opt in, but I don't see any particular reasons to think that this would have a bias towards 'happy drinkers'. The same is true for other life events. Like maybe there is some bias such that people who enjoy sex are more likely to opt in to phone-based subjective wellbeing studies, but I don't think that is what is driving the results.
I'm surprised you think it provides "good evidence of causation". Having fine grained data and many datapoints doesn't as far as I can tell do anything to counter selection bias and confounding. Usually these kind of studies would not even claim that themselves. I'm going to have to read the study properly now rather than just skimming lol.
How do you get away from the confounding? People drink at social events with friends, people drink in the evening when they are relaxing anyway. Are people drinking because they are happy it happy because they are drinking?
And self selection seems like a pretty massive deal why do you think it isn't? Seems likely to heavily select for "happy" drinkers f again I could be missing something.
Re confounding, the headline estimate that James uses is adjusted for various potential confounders.
"Aside from controlling for all time-invariant factors using FE models, we control for a variety of moment-specific factors, including: what people were doing (40 activities), who they were doing it with (7 types), time of day (three-hour blocks split by weekday vs. weekend/bank holiday), location Page 16 (inside/outside/in vehicle and home/work/other), and how many responses the participant has previously given. OLS estimates also include time-invariant controls for gender, employment status, marital and relationship status, household income, general health, children, single parent status, region, age and age squared at baseline. Derivations/descriptive statistics are given in Web Appendix S5.
I don't have a strong view on what effect the potential selection effect would point.
Thanks John. It's great that hey adjusted for confounders (and any similar study would). That such a lot of controlling for confounders needed to be done at all shows the major weakness in this kind of study design.
I'm not saying it's a bad study, just that it's not fit for analysis of causation.
I think you would struggle to find many (if any) researchers who would say this study provided any more than a decent correlation between drinking and increased happiness, rather than evidence of causation. Happy to be proved wrong here and others can feel free to weigh in!
Along those lines it was mainly this comment I disagreed with.
"Because the event data is so fine-grained and there are so many contiguous datapoints, it provides very good evidence of causation."
Another thought - I think there is a risk of overcontrolling with some of the controls used in that paper. The controls in effect assume that if people were not drinking, they would do the same thing they were in fact doing while drinking, except without drinking. But drinking might lead people to be more likely to spend time with friends, go dancing etc. If you control for what people were doing and who they were doing it with, you assume that if they weren't drinking, people would see their friends and go dancing sober. I think this is unlikely. I don't like dancing, no matter what I have imbibed, but I think most people would basically never go dancing with friends if they didn't drink. The uncontrolled effect on SWB is 10 points, though some of the controls seems sensible, so that probably overstates it.
Yeah that's fair enough re that part of the comment.
Yeah I suppose I would disagree with how a lot of researchers view the strength of evidence provided by cross-sectional studies. I think a lot of researchers seem to endorse the proposition 'if this could be confounded, it provides no evidence of causation', which I don't think is right. It depends on one's prior on how plausible the confounder is. I think this is why a lot of economics has stopped trying to focus on some of the more important macro questions, and I think this is a mistake.
eg consider the potential effects of climate change on economic performance. I do think cross-sectional evidence is highly relevant and should update one's view. If economic performance were very strongly climatically determined, I would expect this to show up strongly in the cross-section. I wouldn't expect to see California being way richer than Baja California. I wouldn't expect gross state product for US states to look like this as a function of state average temperature:
I would expect growth rates to be uniformly low in climatically exposed places like Vietnam, Bangladesh, Indonesia, India etc, which is not what we see. So, I do think this sort of evidence should update one's view, even though there are obviously loads of potential confounders.
In climate economics, people don't like this, so they have started using panel data approaches which aim to test the exogenous effects of weather changes on economic performance in particular periods of time. This supposedly provides better evidence of causation, but I think should be completely ignored because of huge researcher degrees of freedom, reporting bias and political bias. I think they leave the door open for econometric skullduggery to provide inflated estimates. In part because the cross-sectional evidence is more transparent, I think it is more reliable.
I assume many potential alcohol-reduction efforts would reduce an individual's alcohol consumption at the margin, not lead to a world with no alcohol. I don't drink myself, but it would seem challenging to measure the lost happiness from -- e.g., going down from four beers at a social occasion to three due to higher taxation. I don't think extrapolating from the status quo to an alcohol-free world would work here.
The latest reports of CEARCH might be of interest to the new team:
Hypertension reduction through salt taxation:
https://drive.google.com/file/d/1R2ul47NtD-dJ7D7rcHFZ0z7h0JqcFxK_/view
Diabetes through sugar-soda tax:
https://drive.google.com/file/d/1UrYZUGbLn5LeTRVRZYdiY2EorsmXxQwR/view
Hi James,
Have you considered interventions pushing for healthier diets? Based on an analysis from the EAT-Lancet Commision, it looks like the global adoption of a predominantly plant-based healthy diet (described in Table 1) would decrease premature deaths of adults by 21.7 % (= (0.19 + 0.224 + 0.236)/3). A pretty large benefit!
It would be very hard for it to be adopted globally, given a healthy diet is much more expensive than a calorie or macro sufficient diet, but maybe there are still effective interventions. I have the impression decreasing the consumption of animals is not super tractable, but, from the above, the major drivers for decreasing mortality are eating more fruits, vegetables, whole grains, and nuts, and less sodium.
Thanks James I think this is a great initiative. instinctively 3 of those policy areas seem tractable and at least somewhat neglected. I've seen first hand through my wife's work in Northern Uganda (albeit on a small scale) the power of alcohol legislation .
Air pollution on the other hand has large amounts of academic research and advocacy attached to it, and is also getting increasing funding through climate change mitigation money bags. Clean air fund estimates around 4 billion (ish) spent annually in aid towards clean air, which may still be not enough but is similar to malaria funding, with cleaning the air arguably far less tractable than malaria.
One of your numbers here does look like it could be a bit of misleading strawman, the "10-20 million" spent in aid on clean are in India. Its true that India isn't getting much philanthropic aid here, possibly because their government is already spending quite a lot on cleaning the air. There may also be political reasons why they aren't getting more funding. India has pledged 1.7 billion over the next 5 years to clean up their air, which may not be nearly enough but dwarfs aid funding. The World Bank are also supporting this program logistically, which at a wild guess might be 5-30 million dollars of support??? Also the World Bank has dedicated 1.5 billion dollars towards greening India's energy supply, which is a relatively small contributor to the clean air problem, but still significant - OpenPhil estimated around 15% of the problem in their report. Looking at aid money alone can be misleading and make causes seem more neglected than they are.
https://www.worldbank.org/en/country/india/publication/catalyzing-clean-air-in-india
If you had picked "China" rather than "India" to display, then clean air aid funding would have been around 1.5 billion dollars a year which paints a different picture. Picking India as your example seems potentially like misleading cherry picking, and I'm interested to hear why you chose India as your example here, rather than another country, or the world combined like for other examples?
As a final very uncertain side note I'm a little dubious about the death estimates from things like clean air and lead. There is far more uncertainty around them compared with deaths from the big 3 infectious diseases (where uncertainty is still surprisingly high), and given the motivations of the people researching them they are at risk of being inflated.
At least I think it's good where possible to display uncertainty around these estimates, although I'm guilty of not doing this myself at times as I know it makes graphics look messy.
Also I think DALYs lost is a better metric than "deaths", as it captures more meaningful information, but I get using the easier to understand metric. Using DALYs lost, malaria and lead poisoning would likely look like bigger problems comparatively than they do using only the death metric.
Some excellent points.
In addition, I'm confused about the figure of $5-10m for spending on alcohol. This is roughly how much is spent by just two alcohol charities in the UK (Drinkaware and Alcohol Research UK). So global philanthropic spending on alcohol is presumably much higher - and then there's also any government spending.
Perhaps the $5-10m figure is supposed to only apply to low and middle income countries, or money moved as part of development assistance for health?
The $5-10M for alcohol work is indeed LMIC only - GiveWell document from 2021 here. I think the main funder missed from that is the DG Murray Trust in South Africa, whose alcohol harms reduction work is exclusively South Africa oriented.
There isn't a development assistance for health estimate from the IHME for alcohol policy work, lead exposure, or suicide prevention through means restriction in the way that there is for tobacco. One reason for displaying these funding estimates as a range is that they are very uncertain and vulnerable to questions of what gets included or not.
There is some HIC alcohol policy funding. I'd personally be leery of including Drinkaware, since it is funded by alcoholic beverage manufacturers (and some other broader industry participants) and so I think sits in quite a different category.
Thanks for clarifying!
Interesting point about Drinkaware - I didn't know it was partly industry-funded. Given this, even though I'd hope the information they provide is broadly accurate, I'm assuming it is more likely to be framed through the lens of personal choice rather than advocating for government action (e.g. higher taxes on alcohol).
I presume the $5-10M also only refers to alcohol-specific philanthropy? I would expect there to be some funding for it via adjacent topics, such as organisations that work on drugs/addiction more broadly, or ones that focus on promoting nutrition and healthy lifestyles.
That's very true, and after a 30 second google search here's a 15 million GiveWell grant recommended in December 2021 given to a bunch of Orgs in the space - Actually I think this may have been funded by OpenPhil directly but then does it count or not? Unsure.
https://www.givewell.org/research/grants/RESET-alcohol-December-2021
The $5-10M figure is inclusive of $5M per year from that grant, which was recommended by GiveWell but funded by Open Philanthropy.
(I don't lead on the air quality work, so be more careful with this comment that the others that I've left here).
India wasn't picked as an example to illustrate the importance and neglectedness of air quality work. Rather, India has been the dominant setting for Open Philanthropy's air quality work to date - it even has its own updated web page. You can read more about why Open Philanthropy launched the work on South Asian air quality here and here. Santosh Harish, the Program Officer who leads that work, recently gave an excellent interview to the 80,000 hours podcast - transcript and recording here.
I agree domestic financing complicates relative neglectedness - the effort here was to be as consistent as reasonably possible between the risk factors. Neglectedness comparisons are very tricky to nail down in general (e.g. how to attribute non-specific health systems spending across both causes and risks, whether to include treatment for linked health conditions like lung cancer or cirrhosis, how to think about relatively ineffective uses of money like e.g. biofuel subsidies for climate change, or a more relevant example here would be smog towers for air quality). One nod to the uncertainty of both measurement and scope here is the use of ranges; but yeah, we're trading off a bunch of different considerations here.
There's a lot of internal research stress testing the IHME burden estimates for lead and air quality, and some on alcohol - I'm doing more on alcohol specifically at the moment. Here we're pointing to the IHME GBD study for several reasons: it's widely recognized, easy to interact with, has a largely consistent / common methodology between different causes of death and disease, and importantly doesn't allow for deaths to be attributed to more than one cause. This works well for problems where the Open Philanthropy way of conceptualizing the problem (e.g. malaria, lead exposure) matches a GBD cause (e.g. malaria) or risk (e.g. lead exposure). This doesn't mean we uncritically use the GBD in all of our own decision making - but this set of reasons make it very helpful to refer to when communicating externally. It might be that we publish more of our internal research on this in the future, but honestly it's a serious time investment and I don't want to over promise.
DALYs sit behind the framework but can be understandably offputting for many audiences. The BOTECs / grant decisions are in line with our usual GHW cause prioritization framework of valuing increases in healthy life and log-income.
Thanks heaps for the reply Chris! Yes I have read the OpenPhil work on air quality. That explains to some extent the India choice, although I still feel like its a misleading graphic.
I would be interested to hear what you found interesting and compelling about the 80,000 hours interview? Maybe though you can't say much (or anything) given your position at GiveWell. I was impressed by his knowledge and passion about the issue, but found his arguments about potentially tractable interventions somewhat unconvincing. I struggled to see a clear theory of change in his proposed interventions. I can definitely be convinced, but just aren't right now. think its likely India will greatly reduce air pollution over the next 20 years, but its hard for me right now to envisage how marginal funding will make a difference. Obviously I'm not an expert at all though so could easily just be dead wrong.
If anything the interview made me a bit less convinced than I was before that air quality in India was likely to be a cost-effective cause area. I was also surprised that he didn't talk about the significant domestic funding going towards cleaning the air as well.
Hi Nick, thanks for your thoughts.
I agree air quality is meaningfully different from the other areas we highlight in terms of domestic salience (at least in India). But it’s not clear to me whether the existence of nascent government funding (and the consequent opportunity to improve the allocation of that funding) make philanthropic opportunities better or worse.
Efforts like the NCAP framework and 15th Finance Commission budget allocations in India are fairly new, and there aren’t well-developed playbooks for prioritizing and addressing sources of air pollution in this context. So we think there are large potential benefits to helping improve the effectiveness of those efforts. Among other things, we’re doing that by supporting organizations to help governments develop and implement specific action plans (e.g.), developing better models to enable cost-effectiveness analysis (e.g.), and providing independent assessment of progress against policy goals (e.g.). We think these are areas where philanthropic funding may be able to have an outsized impact. Santosh gives some more examples of grants he’s made in this part of his podcast. We’re also exploring working in other countries in South Asia with fewer government resources allocated to air quality than India. The program hasn’t been running long enough to make confident claims of impact yet.
Thanks so much for your great explanation James and the other ones on the post. Great to see direct engagement from the people running the program.
the theory of change of trying to change the government's current allocation of makes some sense (I didn't pick it up in the podcast, but reading back it is there to some degree), but its very difficult. Its always a difficult task influence governments to spend their money in more impactful directions - especially just with science and logic. Advocacy skills might be at least as important to your cause as the data the projects you fund produce.
I would be interested to see if you have examples of philanthropies with small amounts of money and no real carrot or stick, influencing governments with larger amounts of money on the issue. Here in Uganda philanthropies can definitely influence the direction of healthcare for example (HIV, Malaria national programs. etc.), but its largely through pouring significant resources into that area, often more than the government can even which gives them hard power and carrots and sticks to wield.
I'm also interested if Indian governments have shown in any practical way that they might be genuinely interested in cost-effectiveness analysis driving fund allocation? As far as I know in East Africa here cost-effectiveness analysis is almost completely unutilised by governments, I've certainly never heard it referred to from any level of government in any intervention here, health or otherwise. I hope India is more switched on than that and might pay more attention. I know nothing about the region really so maybe they do already use it.
Anyway we will see what happens! Obvious clean air is wildly important and causes ludicrous amnounts of suffering and death. It may well be a hard thing to judge the impact on - a rough one for the grantmaker and those doing the interventions. If the quality doesn't improve obviously that will be a fail, but if it does (as is likely) its going to be hard to pin down the counterfactual impact of the grants. If they do allocate more efforts to rural areas that's a win you could tick up I'd imagine, but if air quality gets better in general it may well be hard to figure out what portion of it was due to your guys work - even if most of it was.
Anyway thanks again for the reply and all the best with the project!
Interesting! This is a very similar reasoning to what CE suggested from the start, nice to see this going forward and more supported financially.
Thanks for sharing, James!
Will the grants made in this new area mostly save people in low income countries? If not, I would be specially worried about the meat-eater problem. I estimated this reduces the cost-effectiveness of GiveWell's top charities by 8.72 % for the current consumption per capita[1]. However, for the mean global diet, I estimated the badness of the effects on animals to be 4.64 times the goodness of the effects on humans. This suggests saving lives leads to net suffering in the nearterm (relatedly), although I am kind of agnostic with respect to the overall value of saving lives due to other considerations.
Does Open Phil consider effects on animals when launching new areas in the GHW portfolio? Holding constant the effects on humans, I would say interventions mostly improving quality of life (as opposed to extending life), like ones aiming to improve mental health, should be preferred due smaller negative effects on animals.
If Open Phil thinks effects on animals should not be considered when making choices about interventions which aim to help humans, reasoning transparency about the topic would still be appreciated.
- ^
The consumption per capita of farmed animals will tend to increase as the target countries get richer.
I'm very pleased to see public health become a distinct part of OP's portfolio. In particular, the continued support for suicide prevention is very welcome.
I'm curious about how you model the future wellbeing of the people whose suicides are prevented. Given that your focus is on LMICs, it's very unlikely they'll receive mental health treatment, so what happens to them? Do they make a full recovery or do they (and their families) continue to suffer?
I don't think this was your intention, but this post implies that OP only cares about reducing the deaths and you're not planning to do anything to alleviate the suffering.
Within the field of global mental health, there is growing interest in how to influence social determinants of mental health. Lund (2023) makes some tentative suggestions for public health interventions which may be of interest to you.
Thanks Barry,
At GiveWell (where I was working when we started the suicide prevention work), we discounted the impact to account for people who would otherwise die by suicide potentially living somewhat worse lives than a typical person in their context. Given the empirical and moral uncertainty, that estimate was based on a deliberative process and preference aggregation of different staff views rather than a single bottom-up model. Open Phil hasn't yet decided whether to incorporate a similar discount.
An overview of how GiveWell thought about it is available on this page and a selection of the evidence considered is in this Google Doc.
Speaking for myself, the evidence in that doc did update me towards valuing suicide prevention through means restriction highly. Interviews with survivors and psychological autopsies suggests that suicide (particularly pesticide suicide) is often impulsive and in response to short term life events. Under 5% of suicide attempt survivors go on to die by suicide in the next 5 years, which suggests that most survivors regret their first attempts and prefer to be alive.
I agree that, all else equal, addressing the social determinants of mental health would be preferable to preventing suicide by means restriction. But means restriction has empirically been very successful at reducing suicide rates.
Thanks for sharing the Lund paper!
Thanks for providing such a thoughtful response. These value judgments are extremely difficult and it looks like you did the best you could with the evidence available. I haven't looked into the subjective wellbeing of suicide survivors but, if there's enough data, this could provide a helpful sense-check to your original discount rate.
Although means restriction is very successful at reducing suicide rates, I'm curious how it compares to social determinants (or psychotherapy) if the goal is DALYs/QALYs/WELLBYs. It seems plausible that public health interventions that focus on improving quality of life could lead to a larger overall benefit (for a larger population) than ones that focus solely on reducing suicides (depending on philosophical views of course!)