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.
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.
(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.
**
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
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)
>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.
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.
**
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
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.
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
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!)
Hangovers are a myth
The way people downvote jokes on this forum lol. Really discourages it, I feel we need a decent chunk more humor here!
(this was a joke)
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.
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.