Thanks for the thoughtful post, I really appreciate it!
Open Phil has thought some about arguments for higher eta but as far as I can find never written them up, so I’ll go through some of the relevant arguments in my mind:
I think the #1 issue is that as eta gets large, the modeled utility at stake at high income levels approaches zero, which makes it fragile/vulnerable to errors, and those errors are easily decisive because our models do a bad job capturing empirically relevant spillovers that are close to linear rather than logarithmic or worse in $s.
For instance, take the UK, with GDP per capita of ~$40K. Until recently they gave 0.7% of GNI to foreign aid. Let’s assume their foreign aid is on average roughly as good as GiveDirectly, which is giving income to people living on ~$400/year. With eta=1.5, which implies a marginal $ at $400 is worth 1,000x a marginal $ at $40,000, if we reduced UK GDP by 1%, the loss of the 0.7% going to foreign aid is 7x more important than the loss of the 1% of GDP we assumed was just consumed by people with average incomes of $40,000. So if we had been willing to trade UK GDP for incomes of people at $400/year at the 1,000x rate implied by eta=1.5, we would have destroyed 7x the value for low income people before even getting to the costs for people in the UK by ignoring this practically relevant spillover.
You might be inclined to try to correct/control for this, but I think that’s rare in practice and difficult in principle: I don’t think foreign aid is the only place with this kind of international spillover (think R&D, trade, immigration). I think we live in an interconnected world and the assumption from high etas that abstract away from that seem dangerously wrong to me.
Depending on what you hold fixed, higher etas can also sharpen the challenge of how to weigh tradeoffs between lifesaving and income-increasing interventions, which we discuss here. Basically, if you hold a high-income VSLY fixed at something like 4x GDPpc and let the intercept move, higher etas imply that absolute welfare at lower income levels are much lower, which on a ~standard utilitarian framework would imply that social willingness to pay to save lower-income lives should be much lower than for higher-income lives. I think that’s a pretty unattractive implication.
FWIW it’s not as important but I looked into it once a while ago and I thought the equal sacrifice approach in Evans and Groom didn’t make sense, though I haven’t discussed this with others and may be wrong. (It assumes taxpayers are sacrificing an equal amount of utility everywhere on the income spectrum, and estimates eta from that, but it seems to me that that’s wrong—a marginal $ for a high income person in the US is taxed at ~35% federally, compared to ~10% for someone who might be making 10x less money—but on logarithmic utility the high-income person’s taxes should be vastly higher.) If instead you instead look at work like Hendren’s Efficient Welfare Weights, you get a ratio on welfare weights at the top of the income distribution relative to the bottom that is <2. (This makes sense as a description of the tradeoffs the tax code is making because, while our tax codes are progressive, a tax code that was actually efficiently codifying eta=1.4 would place ~0 weight on high incomes and would be at the ~peak of the Laffer curve, which AFAIK is not an accurate characterization of US or UK tax structures.)
Other lines of evidence in Groom make IMO better arguments for higher eta, though overall I’m not sure how much weight to put on revealed preference vs other factors here. One source I’ve seen cited elsewhere that seems maybe better to me is Dropp et al. 2017, which surveys a couple hundred economists about the right eta and gets a median of 1 and mean of 1.35. But per the argument #1 above, you’d get a very different answer if you aggregated over implied welfare levels (which I think would make you effectively want to end up with an eta <1), rather than taking the mean of eta and then extrapolating welfare levels. (I think this is related to this insight from Weitzman.)
In practice, we actually originally chose an eta=1 for simplicity (you can do math more easily and don’t need to know whole distributions as much) and because it roughly accords with the life satisfaction data (though that is contested). I personally think that the #1 point above dominates and if we were to revisit this, it would make more sense to revisit down than up, but I still see eta=1 as a reasonable compromise and don’t see more work on this as currently one of our top priorities.
On your 36% adjustment within the log framework: I don’t think our estimates for this are accurate to anything like 36%; I’d be happy if they turn out to be within a factor of 2-3x. So I find it easy to believe you could be right here. But I think your changes come from a period when inequality increased substantially, to a historically unusual level, and I would be surprised if it made sense to predict a continuation of that increasing trend indefinitely over the relevant horizon for Tom’s model (many decades to centuries).
More broadly, I agree that the gains from redistribution can be substantial and I think our work reflects that (e.g., our Global Aid Policy program).
Thanks for the points, I should have done more due diligence into the arguments for each framework. That said, I don’t see these as fatal flaws:
I don’t know if I see this as a problem. I think it’s good for considerations about policy with international spillovers to be dominated by their effect on low-income countries. For example, I think that the welfare effects of US tariffs should be primarily judged by their impact on exporters in low-income countries, and that economic growth in the US is valuable primarily because of spillovers to the rest of the world. Insofar as log utility brackets this effect away, it doesn’t seem like the right reasoning process.
* Even if you’re uncomfortable with that philosophical commitment, you can still use high etas to evaluate policies that focus on low-income countries, such as growth advocacy. That is considerably narrower than I would like, because I think we should make that philosophical commitment, but it’s still a useful set of scenarios.
Sharpening the tradeoff between life and income is a much bigger problem to me, as I agree that it would be unattractive to place a low value on life. But I don’t think that high etas intrinsically imply a low total welfare. Utility functions are not normalized to scale. We can introduce a large constant for the baseline welfare of being alive, as is done in this framework which has a subsistence welfare s. A high value of s would increase the value of life relative to income, while still maintaining the intuition that each doubling of income is worth less than the last. That s would also be irrelevant for monetary considerations since it would cancel out when looking at the change in utility. Moreover, I think it should be possible to estimate s from IDinsight’s work on beneficiary preferences which retains tractability.
I have to admit that I did not scrutinize the studies and I am very open to them being flawed. But I think almost everyone would agree that 10% income increase is worth much more to a poor person than a rich person. The median economist in the Dropp survey might disagree, but I don’t really place a high weight on a survey of economists, who are a) very attached to log utility as a tractable model and thus incentivized to post-hoc justify it by saying eta = 1, b) not the arbiters of people’s utility functions.
I don’t think that aggregating over implied welfare levels is necessarily the right approach either, since isoelastic utility functions with reasonable values of η are inherently smaller in magnitude than log utility functions. If we arbitrarily squared all welfare levels (utility is invariant to strictly increasing transformations) before averaging them, we would also place a lot more weight on low η, even though nothing has intrinsically changed. More generally, the fact that isoelastic utilities give small numbers is not morally meaningful, because it can be changed by having a normalizing constant.
The simplicity of η=1 may be useful if we don’t know the whole distribution of income, but this kind of exercise for when we do know the whole distribution can produce discounting factors that we can use even when we don’t know the whole distribution of income. So I don’t think that higher η sacrifices much tractability.
Tl, dr; I think most of the features of high η that you identify can be solved by having a high baseline welfare component of the utility function, and the others are not problems.
Thanks Karthik. I think we might be talking past each other a bit, but replying in order on your first four replies:
My key issue with higher etas isn’t philosophical disagreement, it’s as guidance for practical decision-making. If I had taken your post at face value and used eta=1.5 to value UK GDP relative to other ways we could spend money, I think I would have predictably destroyed a lot of value for the global poor by failing to account for the full set of spillovers (because I think doing so is somewhere between very difficult and impossible). Even within low-income countries there are still pervasive tax, pecuniary, other externalities from high-income spending/consumption on lower-income co-nationals, that are closer to linear than logarithmic in $s. None of this is to deny the possibility or likelihood that in a totally abstract pure notion of consumption where it didn’t have any externalities at all and it was truly final personal consumption, it would be appropriate to have a log or steeper eta, it’s to say that that is a predictably bad approximation of our world and accordingly a bad decision rule given the actual data that we have. I think the main reply here has to be a defense of the feasibility of explicitly accounting for all relevant spillovers, and having made multiple (admittedly weak!) stabs in that direction, I’m personally pessimistic, but I’d certainly love to see others’ attempts.
In the blog post I linked in my #2 above we explicitly consider the set point implied by the IDInsight survey data, and we think it’s consistent with what we’re doing. We’re open to the argument for using a higher fixed constant on being alive, but instead of making you focus more on redistribution of income, the first order consequence of that decision would be to focus more on saving poor people’s lives (which is in fact what we predominantly do). It’s also worth noting that as your weight there gets high, it gets increasingly out of line with people’s revealed preferences and the VSL literature (and it’s not obvious to me why you’d take those revealed preferences less seriously than the revealed preferences around eta).
“I think almost everyone would agree that 10% income increase is worth much more to a poor person than a rich person”—I don’t think that’s right as a descriptive claim but again even if it were the point I’m making in #1 above still holds—if your income measure is imperfect as a measure of purely private consumption without any externalities, and I think they all are, then any small positive externalities that are ~linear in $ will dominate the effective utility calculation as eta gets to or above 1. I think there are many such externalities—taxes, philanthropy, aid, R&D, trade… - such that very high etas will lead to predictably bad policy advice.
You can add a constant normalizing function and it doesn’t change my original point—maybe it’s worth checking the Weitzman paper I linked to get an intuition? There’s genuinely more “at stake” in higher incomes when you have a lower eta vs a higher eta, and so if you’re trying make the correct utilitarian decision under true uncertainty, you don’t want to take a unweighted mean of eta and then run with it, you want to run your scenarios over different etas and weight by the stakes to get the best aggregate outcome. (I think how you specify the units might matter for the conclusion here though, a la the two envelope problem; I’m not sure.)
Got it, I think I misunderstood that point the first time. Yes, I am convinced that this is an issue that is worth choosing log over isoelastic for.
Yes, I agree with the first order consequence of focusing more on saving lives. The purpose of this is just to compare different approaches that only increase income, and I was just suggesting that a high set point is a sufficient way to avoid having that spill over into unappealing implications for saving lives. It is true that a very high set point is inconsistent with revealed preference VSLs, though. I don’t have a good way to resolve that. I have an intuition that low VSLs are a problem and we shouldn’t respect them, but it’s not one I can defend, so I think you’re right on this.
Agreed
I’m on board with the idea of averaging over scenarios ala Weitzman, my original thinking was that a normalizing constant would shrink the scale of differences between the scenarios and thus reduce the effect of outlier etas. But I was confusing two different concepts—a high normalizing constant would reduce the % difference between them, but not the absolute difference between them which is the important quantity for expected value.
Thanks, appreciate it! I sympathize with this for some definition of low FWIW: “I have an intuition that low VSLs are a problem and we shouldn’t respect them” but I think it’s just a question of what the relevant “low” is.
Hey Karthik,
Thanks for the thoughtful post, I really appreciate it!
Open Phil has thought some about arguments for higher eta but as far as I can find never written them up, so I’ll go through some of the relevant arguments in my mind:
I think the #1 issue is that as eta gets large, the modeled utility at stake at high income levels approaches zero, which makes it fragile/vulnerable to errors, and those errors are easily decisive because our models do a bad job capturing empirically relevant spillovers that are close to linear rather than logarithmic or worse in $s.
For instance, take the UK, with GDP per capita of ~$40K. Until recently they gave 0.7% of GNI to foreign aid. Let’s assume their foreign aid is on average roughly as good as GiveDirectly, which is giving income to people living on ~$400/year. With eta=1.5, which implies a marginal $ at $400 is worth 1,000x a marginal $ at $40,000, if we reduced UK GDP by 1%, the loss of the 0.7% going to foreign aid is 7x more important than the loss of the 1% of GDP we assumed was just consumed by people with average incomes of $40,000. So if we had been willing to trade UK GDP for incomes of people at $400/year at the 1,000x rate implied by eta=1.5, we would have destroyed 7x the value for low income people before even getting to the costs for people in the UK by ignoring this practically relevant spillover.
You might be inclined to try to correct/control for this, but I think that’s rare in practice and difficult in principle: I don’t think foreign aid is the only place with this kind of international spillover (think R&D, trade, immigration). I think we live in an interconnected world and the assumption from high etas that abstract away from that seem dangerously wrong to me.
Depending on what you hold fixed, higher etas can also sharpen the challenge of how to weigh tradeoffs between lifesaving and income-increasing interventions, which we discuss here. Basically, if you hold a high-income VSLY fixed at something like 4x GDPpc and let the intercept move, higher etas imply that absolute welfare at lower income levels are much lower, which on a ~standard utilitarian framework would imply that social willingness to pay to save lower-income lives should be much lower than for higher-income lives. I think that’s a pretty unattractive implication.
FWIW it’s not as important but I looked into it once a while ago and I thought the equal sacrifice approach in Evans and Groom didn’t make sense, though I haven’t discussed this with others and may be wrong. (It assumes taxpayers are sacrificing an equal amount of utility everywhere on the income spectrum, and estimates eta from that, but it seems to me that that’s wrong—a marginal $ for a high income person in the US is taxed at ~35% federally, compared to ~10% for someone who might be making 10x less money—but on logarithmic utility the high-income person’s taxes should be vastly higher.) If instead you instead look at work like Hendren’s Efficient Welfare Weights, you get a ratio on welfare weights at the top of the income distribution relative to the bottom that is <2. (This makes sense as a description of the tradeoffs the tax code is making because, while our tax codes are progressive, a tax code that was actually efficiently codifying eta=1.4 would place ~0 weight on high incomes and would be at the ~peak of the Laffer curve, which AFAIK is not an accurate characterization of US or UK tax structures.)
Other lines of evidence in Groom make IMO better arguments for higher eta, though overall I’m not sure how much weight to put on revealed preference vs other factors here. One source I’ve seen cited elsewhere that seems maybe better to me is Dropp et al. 2017, which surveys a couple hundred economists about the right eta and gets a median of 1 and mean of 1.35. But per the argument #1 above, you’d get a very different answer if you aggregated over implied welfare levels (which I think would make you effectively want to end up with an eta <1), rather than taking the mean of eta and then extrapolating welfare levels. (I think this is related to this insight from Weitzman.)
In practice, we actually originally chose an eta=1 for simplicity (you can do math more easily and don’t need to know whole distributions as much) and because it roughly accords with the life satisfaction data (though that is contested). I personally think that the #1 point above dominates and if we were to revisit this, it would make more sense to revisit down than up, but I still see eta=1 as a reasonable compromise and don’t see more work on this as currently one of our top priorities.
On your 36% adjustment within the log framework: I don’t think our estimates for this are accurate to anything like 36%; I’d be happy if they turn out to be within a factor of 2-3x. So I find it easy to believe you could be right here. But I think your changes come from a period when inequality increased substantially, to a historically unusual level, and I would be surprised if it made sense to predict a continuation of that increasing trend indefinitely over the relevant horizon for Tom’s model (many decades to centuries).
More broadly, I agree that the gains from redistribution can be substantial and I think our work reflects that (e.g., our Global Aid Policy program).
Thanks for the points, I should have done more due diligence into the arguments for each framework. That said, I don’t see these as fatal flaws:
I don’t know if I see this as a problem. I think it’s good for considerations about policy with international spillovers to be dominated by their effect on low-income countries. For example, I think that the welfare effects of US tariffs should be primarily judged by their impact on exporters in low-income countries, and that economic growth in the US is valuable primarily because of spillovers to the rest of the world. Insofar as log utility brackets this effect away, it doesn’t seem like the right reasoning process. * Even if you’re uncomfortable with that philosophical commitment, you can still use high etas to evaluate policies that focus on low-income countries, such as growth advocacy. That is considerably narrower than I would like, because I think we should make that philosophical commitment, but it’s still a useful set of scenarios.
Sharpening the tradeoff between life and income is a much bigger problem to me, as I agree that it would be unattractive to place a low value on life. But I don’t think that high etas intrinsically imply a low total welfare. Utility functions are not normalized to scale. We can introduce a large constant for the baseline welfare of being alive, as is done in this framework which has a subsistence welfare s. A high value of s would increase the value of life relative to income, while still maintaining the intuition that each doubling of income is worth less than the last. That s would also be irrelevant for monetary considerations since it would cancel out when looking at the change in utility. Moreover, I think it should be possible to estimate s from IDinsight’s work on beneficiary preferences which retains tractability.
I have to admit that I did not scrutinize the studies and I am very open to them being flawed. But I think almost everyone would agree that 10% income increase is worth much more to a poor person than a rich person. The median economist in the Dropp survey might disagree, but I don’t really place a high weight on a survey of economists, who are a) very attached to log utility as a tractable model and thus incentivized to post-hoc justify it by saying eta = 1, b) not the arbiters of people’s utility functions.
I don’t think that aggregating over implied welfare levels is necessarily the right approach either, since isoelastic utility functions with reasonable values of η are inherently smaller in magnitude than log utility functions. If we arbitrarily squared all welfare levels (utility is invariant to strictly increasing transformations) before averaging them, we would also place a lot more weight on low η, even though nothing has intrinsically changed. More generally, the fact that isoelastic utilities give small numbers is not morally meaningful, because it can be changed by having a normalizing constant.
The simplicity of η=1 may be useful if we don’t know the whole distribution of income, but this kind of exercise for when we do know the whole distribution can produce discounting factors that we can use even when we don’t know the whole distribution of income. So I don’t think that higher η sacrifices much tractability.
Tl, dr; I think most of the features of high η that you identify can be solved by having a high baseline welfare component of the utility function, and the others are not problems.
Thanks Karthik. I think we might be talking past each other a bit, but replying in order on your first four replies:
My key issue with higher etas isn’t philosophical disagreement, it’s as guidance for practical decision-making. If I had taken your post at face value and used eta=1.5 to value UK GDP relative to other ways we could spend money, I think I would have predictably destroyed a lot of value for the global poor by failing to account for the full set of spillovers (because I think doing so is somewhere between very difficult and impossible). Even within low-income countries there are still pervasive tax, pecuniary, other externalities from high-income spending/consumption on lower-income co-nationals, that are closer to linear than logarithmic in $s. None of this is to deny the possibility or likelihood that in a totally abstract pure notion of consumption where it didn’t have any externalities at all and it was truly final personal consumption, it would be appropriate to have a log or steeper eta, it’s to say that that is a predictably bad approximation of our world and accordingly a bad decision rule given the actual data that we have. I think the main reply here has to be a defense of the feasibility of explicitly accounting for all relevant spillovers, and having made multiple (admittedly weak!) stabs in that direction, I’m personally pessimistic, but I’d certainly love to see others’ attempts.
In the blog post I linked in my #2 above we explicitly consider the set point implied by the IDInsight survey data, and we think it’s consistent with what we’re doing. We’re open to the argument for using a higher fixed constant on being alive, but instead of making you focus more on redistribution of income, the first order consequence of that decision would be to focus more on saving poor people’s lives (which is in fact what we predominantly do). It’s also worth noting that as your weight there gets high, it gets increasingly out of line with people’s revealed preferences and the VSL literature (and it’s not obvious to me why you’d take those revealed preferences less seriously than the revealed preferences around eta).
“I think almost everyone would agree that 10% income increase is worth much more to a poor person than a rich person”—I don’t think that’s right as a descriptive claim but again even if it were the point I’m making in #1 above still holds—if your income measure is imperfect as a measure of purely private consumption without any externalities, and I think they all are, then any small positive externalities that are ~linear in $ will dominate the effective utility calculation as eta gets to or above 1. I think there are many such externalities—taxes, philanthropy, aid, R&D, trade… - such that very high etas will lead to predictably bad policy advice.
You can add a constant normalizing function and it doesn’t change my original point—maybe it’s worth checking the Weitzman paper I linked to get an intuition? There’s genuinely more “at stake” in higher incomes when you have a lower eta vs a higher eta, and so if you’re trying make the correct utilitarian decision under true uncertainty, you don’t want to take a unweighted mean of eta and then run with it, you want to run your scenarios over different etas and weight by the stakes to get the best aggregate outcome. (I think how you specify the units might matter for the conclusion here though, a la the two envelope problem; I’m not sure.)
Got it, I think I misunderstood that point the first time. Yes, I am convinced that this is an issue that is worth choosing log over isoelastic for.
Yes, I agree with the first order consequence of focusing more on saving lives. The purpose of this is just to compare different approaches that only increase income, and I was just suggesting that a high set point is a sufficient way to avoid having that spill over into unappealing implications for saving lives. It is true that a very high set point is inconsistent with revealed preference VSLs, though. I don’t have a good way to resolve that. I have an intuition that low VSLs are a problem and we shouldn’t respect them, but it’s not one I can defend, so I think you’re right on this.
Agreed
I’m on board with the idea of averaging over scenarios ala Weitzman, my original thinking was that a normalizing constant would shrink the scale of differences between the scenarios and thus reduce the effect of outlier etas. But I was confusing two different concepts—a high normalizing constant would reduce the % difference between them, but not the absolute difference between them which is the important quantity for expected value.
Thanks, appreciate it! I sympathize with this for some definition of low FWIW: “I have an intuition that low VSLs are a problem and we shouldn’t respect them” but I think it’s just a question of what the relevant “low” is.