[edited] Inequality is a (small) problem for EA and economic growth
This post was formerly titled “Inequality is a problem for EA and economic growth”. Alexander Berger pointed out a spreadsheet error, and after updated calculations the extent to which inequality is bad for growth is much, much smaller (25% vs 90%). I’ve thus changed the conceptual takeaways from this exercise. You can find the original version of the post here.
Recently, EAs have considered economic growth as a potential way to improve overall wellbeing and also help the worst-off people. The recently-influential Progress Studies movement focuses on economic growth as the most important way to improve people’s lives. In an essay that won the EA Forum Decade Review, Hauke Hillebrandt and John Halstead argued that we should focus on promoting economic growth in developing countries as a better alternative to targeting the extreme poor with health programs or cash transfers.
This essay quantifies a common objection to economic growth as the best way to improve wellbeing: the objection that growth is unequally distributed. Inequality has been shockingly neglected by EAs, to the point that I literally had to create the inequality tag for posting this essay. This is probably because EAs care about maximizing welfare, not about reducing inequality: but I demonstrate that inequality can reduce the welfare gains from economic growth, so we have to consider it as a factor that could reduce the social value of economic growth.
To quantify this argument, I build on Open Philanthropy’s framework for modelling the cost-effectiveness of growth. I extend this framework to account for inequality in two ways:
I use an empirically grounded (isoelastic) utility function, rather than the more commonly used (logarithmic) utility function that overvalues consumption growth for well-off people. This change reduces the social value of economic growth by 25%.
I use data on inequality of income growth, and show that adjusting for this inequality reduces the social value of economic growth by 36%, independently of the above change.
These magnitudes are not dramatic, but they are not trivial. They are worth considering for close comparisons between economic growth and other interventions, but they do not change the returns to growth by an order of magnitude.
Why inequality matters, even to utilitarians
Inequality is usually framed as a concern for egalitarian-minded people. But if you want to maximize utility, you also have to care about inequality, because of the simple fact of diminishing marginal utility. Inequality means that more income is accruing to people who don’t derive as much utility from that income. Consider a toy example:
There is an economy with two agents, Alice and Bob.
Alice and Bob each have the logarithmic utility function .
The social utility function is the sum of their utilities, .
Note that this social utility function is completely neutral to inequality: it does not place any inherent weight on Alice and Bob having similar incomes, or penalize deviations from that.
Initial GDP is $100, split unevenly: Alice has an income of $80 and Bob has an income of $20.
Now consider two scenarios of economic growth:
Scenario 1: GDP grows by 10% ($10). Alice and Bob split this surplus evenly, so Alice gets $5 and Bob gets $5. The change in social welfare is so utility increases by 0.28 log units.
Scenario 2: GDP grows by 10% ($10). Alice and Bob split this surplus unevenly, but proportional to their income, so that Alice gets $7 and Bob gets $3. The change in social welfare would be so utility increases by 0.19 log units, which is a smaller increase than in scenario 1.
What’s going on here? Even though aggregate income growth is the same in both scenarios, in the second, Alice gets a larger share of the surplus. Since she has more money than Bob, she has a lower marginal utility of consumption, so this uneven growth raises social welfare less than if both of them experienced the same $5 income growth. Inequality hurts social welfare even when you don’t care intrinsically about inequality at all.
Utility functions and inequality
The above example uses a logarithmic utility function to translate income to welfare (the quantity we care about). This is very common in cost-effectiveness analysis. Open Philanthropy uses a logarithmic utility framework for all of their cost-effectiveness analyses.
Logarithmic utility functions display diminishing marginal utility of consumption, as the above example shows. However, the rate at which utility diminishes is unrealistically slow. For individuals with log utility, percentage changes in income are enough to summarize changes in utility: where is the percentage growth of income. This means that a 10% increase in income is equally valued by millionaires and by people in poverty. This is an implausible description of how changes in income actually benefit people.
An alternative approach to modelling utility is the isoelastic utility function: is the elasticity of marginal utility of income: how much the marginal utility of income changes as income changes. This is actually a logarithmic utility function when . Researchers have tried to estimate empirically from people’s decisions and found that people’s behavior is better described by (Evans 2005, Maddison and Groom 2019).
The isoelastic utility function with places much higher weight on income growth for the poor, and much lower weight on income growth for the rich, compared to logarithmic utility. Acknowledging this, GiveWell uses an isoelastic utility function to calculate their discount rate with .
This difference in modelling is especially important for economic growth, which improves aggregate wellbeing and not just the wellbeing of the extreme poor. Logarithmic utility will substantially overstate the benefits of economic growth, by overvaluing increases in consumption for already well-off people, compared to how much they actually value it.
To concretely calculate how much this overstatement is, I used Open Philanthropy’s cost-effectiveness estimates for productivity growth and adjusted the calculations for them in this spreadsheet.
OP’s modelling framework is used for computing the cost-effectiveness of R&D specifically, so I removed discounting factors that were specific to R&D. These changes mean that OP’s framework is estimating the cost-effectiveness of globally increasing productivity growth, which has a new cost-effectiveness estimate of 169X (1.69 times as good as cash transfers to the poor).
Using an isoelastic utility function with reduces cost-effectiveness from 169X to 130X, a 25% reduction in the estimated social returns to growth.
Thus, even conservative groundings of the utility function reduce the returns to growth by a tremendous amount.
An important caveat is that this idea relies on Open Philanthropy’s framework for modelling growth, and their productivity growth parameters are calibrated with data from the US. This means that the discount factors could be different for other countries, especially for LMICs.
Inequality in income growth
Even if you believe that logarithmic utility is the best approach to modelling utility, you can’t ignore inequality in income growth. That is, the reality of growth is not that every person is seeing their income grow by [average growth rate]%: income growth is highly unequally distributed. If the vast majority of people are seeing very little growth while a few see large growth, then economic growth is not increasing utility very much.
To incorporate these concerns, we need data on how much each income group has actually seen its income grow. Blanchet, Saez and Zucman (2022) provide such estimates for the US from 1976 onwards. I calculate the change in log utility using their data, and I find that inequality in income growth reduces cost-effectiveness from 169X to 108X, a 36% reduction in the social value of growth, even when assuming utility is logarithmic in income.
It’s worth noting that unlike with the section above, this discount factor of 36% doesn’t rely on any modelling assumptions for how growth arises. It is directly calculated from the income growth data.
This 36% discount is robust to different specifications on what “income” is, but it is unfortunately limited to the US in the past 50 years. How unequal income growth is globally is an open question. On the one hand, the US over the past 50 years has seen exceptionally unequal growth, and the stylized fact that LMICs are growing much faster than rich countries suggests that growth is accruing more to the poor than to the rich. On the other hand, the US’s inequality over the past 50 years is not exceptional in a global context: most LMICs are comparably unequal to or more unequal than the US by Gini coefficient. So their aggregate growth does not necessarily translate into large income growth for their poor or middle class.
In the end, I would stand by this adjustment factor applying to global income growth, because the US is comparably unequal to the world’s largest countries (China, India, Indonesia are slightly more equal while Brazil is much more unequal), and the US is also in between more equal OECD countries and less equal LMICs. For people who want to emphasize growth in LMICs which are even more unequal than the US, inequality would discount the social value of economic growth even more than what I’ve calculated.
Conjecture—redistribution as a cause area?
A natural objection to these calculations is that inequality might be causing growth to be higher, so the right comparison is not as if everyone got the average growth rate, but if everyone got a lower growth rate of income. This would be the right comparison if I were making an argument about promoting growth vs promoting large-scale redistribution to reduce inequality as an EA cause area. That would require knowing how changing inequality would change growth. But the comparison scenario in all of these calculations is not equitable growth or large-scale redistribution—the comparison is always cash transfers to individuals in Open Philanthropy’s framework (recall that 1X is giving cash transfers to someone on $50,000 a year, and 100X is cash transfers to the extreme poor).
My goal with these calculations is just to show that our estimates of the social return to growth—that is, the actual utility that people derive from growth—is biased by ignoring inequality of income growth. Once we include inequality, economic growth looks less beneficial, and possibly less beneficial than interventions that target the extreme poor.
That said, a reasonable takeaway from this analysis is that reducing inequality itself would be a desirable goal for EAs to promote. Since unequal distributions of income lead to unequal marginal utility of consumption, they are inefficient: we could increase overall welfare by redistributing income. Of course, to quantify how desirable this would be as a policy agenda, we would need to know the relationship between inequality and growth. If an economic system that creates high levels of inequality also creates the most growth, then we need to quantitatively compare the benefits of reducing inequality with the costs of reducing growth.
Unfortunately, the academic literature on this question is not very confident. All studies I could find caution about error in measurement of inequality, omitted variable bias and reverse causality. Banerjee and Duflo (2003) showed an inverse U-shaped relationship: changes in inequality in either direction were associated with reduced growth. This nonlinear relationship between inequality and growth is generally plausible, and it makes it impossible to draw universal conclusions about the value of reducing inequality: its effect on growth will depend on the current level of growth and inequality.
So I am sympathetic to the extreme conclusion that large-scale redistribution is a desirable agenda based purely on the marginal utility of consumption. But that is an area where my intuition is not supported by any data.
Conclusion
When focusing narrowly on policies to target the extreme poor, inequality is small and thus may not change our conclusions very much: but as EA has expanded its focus to aggregate interventions like technological innovation and economic growth, inequality becomes more and more important to factor into our estimates. In the quantification exercise I have considered, it reduces the social value of economic growth by a modest amount, which could matter in close comparisons but would not change our estimates by an order of magnitude.
In general, considering inequality will reduce the value of aggregate policies like technological innovation and economic growth compared to analyses that assume they change everyone’s income by the same amount. Redistribution may even be an important end on its own because of how much welfare is lost through the rich consuming income that they don’t derive much utility from—but without knowing how redistribution affects growth, that’s just a conjecture.
Hey Karthik, starting separate thread for a different issue. I opened your main spreadsheet for the first time, and I’m not positive but I think the 90% reduction claim is due to a spreadsheet error? The utility gain in B5 that flows through to your bottom line takeaway is hardcoded as being in log terms, but if eta changes than the utility gain to $s at the global average should change (and by the way I think it would really matter if you were denominating in units of global average, global median, or global poverty level). In this copy I made a change to reimplement isoelastic utility in B7 and B8. In this version, when eta=1.00001, OP ROI is 169, and when eta=1.5, OP ROI is 130, for a difference of ~25% rather than 90%. I didn’t really follow what was happening in the rest of the sheet so it’s possible this is wrong or misguided or implemented incorrectly.
You… are absolutely right. That’s a very good catch. I think your calculation is correct, as the utility translation only happens twice—utility from productivity growth, which I adjusted, and utility from cash transfers, which I did not. Everything else is unchanged from the original framework.
You’re definitely right that it matters whether this is global average/median/poverty level. I think that the issue stems from using productivity A as the input to the utility function, rather than income. This is not an issue for log utility if income is directly proportional to A, since it cancels out, but it is probably better to redo this with income statistics/income growth and see how that changes things.
I’ll make a note about this at the top of the post and update it with a more substantive change to the conclusion when I’ve dug into it further.
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.
“ When focusing narrowly on policies to target the extreme poor, inequality is small and thus may not change our conclusions very much: but as EA has expanded its focus to aggregate interventions like technological innovation and economic growth, inequality becomes more and more important to factor into our estimates.”
I thought this was a great point!
The Open Philanthropy report is not narrowly about the cost-effectiveness of increasing economic growth, it is about the cost-effectiveness of spending on R&D, which is one way to increase economic growth but not the only way.
Indeed, that’s why I removed the discounting factors that were specific to R&D from their model (only crediting R&D with 40% of growth, and assuming only 70% diffusion of R&D to the rest of the world). Once you remove these factors, their model is a classic semi-endogenous growth model and is thus focused on the cost-effectiveness of TFP growth in the aggregate—no matter what the cause. Insofar as any path to sustained economic growth goes through productivity growth, I think their modelling framework is still very useful for evaluating the social returns to economic growth.
The only thing that is really specific to R&D is that in a semi-endogenous growth model, the number of researchers is an important input to the growth trajectory. But I am not focusing on the growth trajectory itself (which is affected by how you choose to model growth and what path is taken) - rather, I am focusing on the population welfare that arises from any given level of growth. Different paths to economic growth may yield different levels of economic growth, but they do not change the fundamental growth --> welfare mapping. So I don’t think that the focus on R&D takes away from this argument in any way.
You say things like “Once we include inequality, economic growth looks a lot less beneficial than interventions that target the extreme poor.” Insofar as I understand this sentence, you cannot conclude this unless you know about the cost of the intervention to increase economic growth. Your argument implies that the benefits of growth are lower than some models suggest, but since we don’t know anything about costs, you cannot conclude anything about cost-effectiveness. So, it’s not possible for you to conclude anything about the relative cost-effectiveness of different approaches
It’s fair to say that I’m not rigorously comparing these two approaches. What I am doing is showing that one has a 90% lower value than estimated, and the other is not affected. In general, this would lead you to update in favor of targeted interventions—hence saying that it looks better. The strength of that update may not be enough to overcome your prior. But I’m not litigating the entire growth vs RD debate here. The argument is just “inequality is a big problem for growth”.
Agreed. I would like to see this done for LMICs and not rich countries as it seems that could make a big difference.
For what it’s worth the Open Phil framework (with R&D discount factors removed) is looking at the effect of global growth, not growth in rich countries. That should attenuate the gap between their results and the results of modelling this just in LMICs. And I don’t know how big a big difference is, but to take it from my final estimate of 12X to 1000X would require growth promotion in LMICs to be over 80 times more cost effective than global growth promotion, which seems like a lot.
Yes and to be clear, I think the analysis is well done and think this adds to the debate, so I appreciate you doing this
Another point: it seems intuitively plausible that redistribution promotes growth because it would likely tend to increase human capital. If someone is consuming $2.30/day with subsistence farming, there is low human capital, but redistribution allows them to increase human capital, this will tend to promote economic growth.
Does anyone know where literature is on the growth effects of Global health and development charities vs traditional economic investment?
This is not usually the comparison that is made in the literature, so it’s not definitively answered. This comment thread on Hauke and John’s original post has some back and forth. Basically human capital interventions definitely have an aggregate effect, but the strength of that effect compared to growth-promoting policies is unclear.
It seems odd that this has not been explored more thoroughly by the EA community, especially given its proclivity for consideration of the long-term.
Great work – I’ve been waiting for someone to use the isoelastic utility model! Are you going to submit this to the Criticism and Red Teaming Contest?
Thanks! I hadn’t thought about it and frankly don’t know if this is substantive criticism/red teaming, but I’ll think about it.
I’m confused how this squares with Lant Pritchett’s observation that variation in headcount poverty rates across nations, regardless of where you set the poverty line, is completely accounted for by variation in the median of the distribution of consumption expenditures.
Pritchett’s argument is about the correlation between average income and poverty rates. My argument is about the welfare that people experience from any given level of growth. I’m claiming that conventional evaluations of growth overestimate the value of growth because they weight income growth of middle-income and rich people too heavily. Once you adjust for that, the population welfare from economic growth is now driven mostly by increase in incomes for poor people, and it is much lower than before (90% lower)
If you wanted to value growth solely based on its ability to reduce poverty, an isoelastic utility function does that as well. In the spreadsheet calculations I did, the isoelastic utility penalizes inequality less (24% vs 36%) because the bottom 50%’s income growth of 50% is almost as good on its own as the whole population income growing 90%.
Separately, I don’t interpret Pritchett’s observation as meaning “and therefore the best way to minimize poverty is to maximize median consumption”. That doesn’t follow at all from a cross-country correlation. For one thing, correlation is not causation and this correlation does not prove that increasing median consumption will decrease poverty. For another thing, we have to consider the costs as well: increasing median consumption through growth could be much more expensive than giving all that money to poor people directly.
While it is true that correlation is not causation, the fact that high median income is empirically necessary and sufficient for eliminating poverty on any poverty line is extremely strong evidence that increasing median income is a cause of eliminating poverty at any poverty line. There’s also a fairly obvious causal explanation i.e. growth increases everyone’s income. What else could be going on that explains the connection?
You say that “increasing median consumption through growth could be much more expensive than giving all that money to poor people directly.” This is very implausible. Globally, around $180bn is spent on aid per year—roughly $500 million per day. There are 500 million people who are extremely poor. Assuming that all the extreme poor have $1 per day already, we could double their income with the entire global aid budget. But, on any reasonable definition of poverty people with $2 per day are still extremely poor. The only thing that has ever pulled extremely poor people above more humane high bar poverty lines is economic growth.
I agree that your (excellent) analysis shows that the welfare increase is dominated by lifting the bottom half of the income distribution. I agree that this welfare effect is what we want. Pritchett’s argument is linked to yours because he claims the only (and therefore best) way to cause this effect is national development. He writes: “all plausible, general, measures of the basics of human material wellbeing [including headcount poverty] will have a strong, non-linear, empirically sufficient and empirically necessary relationship to GDPPC.” (Here non-linear refers to a stronger elasticity of these wellbeing metrics at lower than higher levels of GDPPC).
Of course as you point out national development can’t really be the only thing that decreases poverty—redistribution would too. But every single data point we have of countries shows that the rich got rich through development, not redistribution. And every single data point we have of rich countries shows that the bottom half of their income distributions is doing very well, relative to LMICs. So yes, redistribution would cause great welfare gains for a bit, but it’s not going to turn a $5000 GDPPC nation to a $50000 one. And the welfare gains from that nation’s decreased poverty headcount are going to dwarf the redistribution-caused welfare gains, even given your adjustments. (This isn’t an argument against redistribution as EA cause area, which could still be great; it’s an argument that redistribution’s efficacy isn’t really a point against the greater importance of the search for growth).
Regarding the correlation/causation, I’d be more sympathetic to your point if it was a nice and average correlation. Pritchett: “The simple correlation between the actual $3.20/day or $5.50/day headcount poverty rate and headcount poverty as predicted using only the median of the country distribution is .994 and for $1.90 it is .991. These are about as high a correlation as real world data can produce.” It’s very implausible that this incredibly strong relationship would break with some new intervention that increases median consumption. Not a single policy in the history of the world that changed a country’s median consumption has broken it.
To your final point that the cost of increasing median consumption might be way too high (relative to redistribution) - first of all, as Hillebrandt/Halstead pointed out, evaluating that claim should be a much larger priority in EA than it is right now. But development economics seems to have worked in the past, with just the expenses associated with a normal academic field! I’m sorry but I’m going to quote Pritchett again:
TL;DR: Increasing productivity still beats redistribution in the long-term given reasonable assumptions about costs.
I’m not really interested in dismissing growth as a cause area. (I am annoyed at how little EAs mechanize it beyond “advocate for policies --> ??? --> growth”, but I’m going to write that up soon!) I wrote this because I think people who advocate for growth largely ignore inequality and should discount growth heavily because of inequality. If growth still beats targeted interventions after that heavy discounting, then so be it.
That makes sense! I was interpreting your post and comment as a bit more categorical than was probably intended. Looking forward to your post.
FYI, I think one of your hyperlinks is linking to the wrong place.
I think that should instead link to: https://www.openphilanthropy.org/research/social-returns-to-productivity-growth/
Eek, good catch!