Open Philanthropy’s newest focus area: Global Public Health Policy

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.

1. ^{^}

   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.

Having said that, this paper suggests that most the median age of death would be around 30-40, not among older people. 

**

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?

>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!)

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.

John G. Halstead @ 2023-11-26T15:17 (+4)

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. 

NickLaing @ 2023-11-26T16:18 (+4)

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.

John G. Halstead @ 2023-11-30T16:04 (+4)

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. 

NickLaing @ 2023-11-30T23:11 (+4)

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."

John G. Halstead @ 2023-12-04T16:14 (+2)

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. 

John G. Halstead @ 2023-12-01T12:31 (+2)

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.

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. 

The $5-10M figure is inclusive of $5M per year from that grant, which was recommended by GiveWell but funded by Open Philanthropy.

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.