GiveWell, and the EA community at large, often emphasize the “cost of saving a life” as a key metric, $5,000 being the most commonly cited approximation. At first glance, GiveWell might seem to be in the business of finding the cheapest lives that can be saved, and then saving them. More precisely, GiveWell is in the business of finding the cheapest DALY it can buy. But implicit in that is the assumption that all DALYs are equal, or that disability or health effects are the only factors that we need to adjust for while assessing the value of a life year.. However, If DALYs vary significantly in quality (as I’ll argue and GiveWell acknowledges we have substantial evidence for), then simply minimizing the cost of buying a DALY risks adverse selection.
It’s indisputable that each dollar goes much further in the poorest parts of the world. But it goes further towards saving lives in one the poorest parts of the world, often countries with terrible political institutions, fewer individual freedoms and oppressive social norms. More importantly, these conditions are not exogenous to the cost of saving a life. They are precisely what drive that cost down.
Most EAs won’t need convincing of the fact that the average life in New Zealand is much, much better than the average life in the Democratic Republic of Congo. In fact, those of us who donate to GiveDirectly do so precisely because this is the case. Extreme poverty and the suffering it entails is worth alleviating, wherever it can be found. But acknowledging this contradicts the notion that while saving lives, philanthropists are suddenly in no position to make judgements on how anything but physical disability affects the value/quality of life.
To be clear, GiveWell won’t be shocked by anything I’ve said so far. They’ve commissioned work and published reports on this. But as you might expect, these quality of life adjustments wouldnt feature in GiveWell’s calculations anyway, since the pitch to donors is about the price paid for a life, or a DALY. But the idea that life is worse in poorer countries significantly understates the problem - that the project of minimizing the cost of lives saved while making no adjustments for the quality of lives said will systematically bias you towards saving the lives least worth living.
In advanced economies, prosperity is downstream of institutions that preserve the rule of law, guarantee basic individual freedoms, prevent the political class from raiding the country, etc. Except for the Gulf Monarchies, there are no countries that have delivered prosperity for their citizens who don’t at least do this. This doesn’t need to take the form of liberal democracy; countries like China and Singapore are more authoritarian but the political institutions are largely non-corrupt, preserve the will of the people, and enable the creation of wealth and development of human capital. One can’t say this about the countries in sub Saharan Africa.
High rates of preventable death and disease in these countries are symptoms of institutional dysfunction that touches every facet of life. The reason it’s so cheap to save a life in these countries is also because of low hanging fruit that political institutions in these countries somehow managed to stand in the way of. And one has to consider all the ways in which this bad equilibrium touches the ability to live a good life.
More controversially, these political institutions aren’t just levitating above local culture and customs. They interact and shape each other. The oppressive conditions that women (50% of the population) and other sexual minorities face in these countries isn’t a detail that we can gloss over. If you are both a liberal and a consequentialist, you should probably believe and act as if individual liberties and freedom from oppression actually cash out in a significantly better life.
You can get a better sense of this by looking at the list of countries AMF buys most of its DALYs in:
Democratic Republic of Congo is the country that tops the list, with over 100 million bednets. These excerpts from the World Bank country profile may not come as a surprise to most of you:
“DRC ranks 164 out of 174 countries on the 2020 Human Capital Index, reflecting decades of conflict and fragility, and constraining development..”
“Congolese women face significant barriers to economic opportunities and empowerment, including high rates of gender-based violence (GBV) and discrimination. Half of women report having experienced physical violence, and almost a third have experienced sexual violence, most commonly at the hands of an intimate partner...”
“DRC has one of the highest stunting rates in SSA (42% of children under age five), and malnutrition is the underlying cause of almost half of the deaths of children under the age of five. Unlike other African countries, the prevalence of stunting in the DRC has not decreased over the past 20 years. Due to the very high fertility rate, the number of stunted children has increased by 1.5 million.”
Quantifying quality of life
Valuing a life (or life year) has three components:
Hedonic value of the life itself
Psychological trauma/grief averted by family members (when you save a life)
Externalities (how the person’s life affects others)
Whether you save a life in Congo, Sri Lanka or Australia, I can’t think of strong reasons for why #2 would vary all that much.
We should expect #1 and #3 to be some function of per capita GDP, human capital development, individual freedoms etc. As Give Well reports “People in poor countries report that they are on average less satisfied with their lives than people in rich countries. The average resident of a low-income country rated their satisfaction as 4.3 using a subjective 1-10 scale, while the average was 6.7 among residents of G8 countries”. But this doesn’t help us quantify the differential value of lives.
You could ask reasonably well off people in the developed world at what level of fixed yearly income in their own country they’d be indifferent to moving to sub-Saharan Africa with all their money. But we’d need to deal with the challenge of disentangling how much of that effect is simply an attachment to one’s own relationships, sentimentality etc. ANother way into this would be to study demand for immigration from the poorest countries. For example, “In 1990, an estimated 300,000 Congolese migrants and refugees resided in one of the nine neighboring countries. By 2000, their number had more than doubled by 2000 (to approximately 700,000), and by mid-2015, had risen to more than 1 million in the neighboring countries.”. The vast majority of migration out of Congo took place after the official end of the war, which tells us something about the baseline conditions, not just threat of imminent violence. But we should note that economic migration, both legal and illegal, is not affordable and accessible to the people who are worst off within the poorest of countries. And trying to find the cheapest lives to save will systematically bias you towards lives which are worse than any estimate gathered from immigration data would suggest.
Present vs future quality of life
Notwithstanding the methodology used, the adjustments here need to incorporate two factors—the present quality of life and expected future quality of life, especially since most life saving interventions are targeted at children.
(1) Present quality of life is a function of per capita income, income inequality and measures of human development and freedoms. It’s absurd to end up with a framework that believes a life for a woman in Saudi Arabia is just as good as life for a woman in some other country with similarly high per capita income.
(2) The expected future quality of life is some function of growth prospects, institutional quality and trends in institutional quality.
What does this point to?
At first glance, this favors saving lives in countries that are still poor or have very poor parts but much better state capacity and institutional quality and thus better prospects.(eg. Bangaladesh, India vs DRC) In these instances, DALYs may still be available at a low price but those future DALYs are much higher quality DALYs than the ones you’d be buying in countries that seem to struggle with bad political equilibria.
More generally of course, based on the magnitude of adjustments, it could just move one away from the project of saving lives in the developing world altogether, perhaps towards more of alleviating acute suffering or interventions that would have an impact on human capital (like lead removal) and institutional quality in the long run.
Conclusion
Here’s how GiveWell concludes its analysis on standard of living in poor countries :
“On one hand, people in the developing world have a tangibly lower quality of life. On the other hand, a life saved probably means many more years of functional life. We feel strongly that it’s worth addressing a major problem (such as tuberculosis or immunizations) even if other problems remain unaddressed.”
While I agree with that general sentiment, we still have to contend with the fact that these other problems remained unaddressed are not independent of how valuable it is to solve specific problems within these countries. The conclusion may or may not look vastly different from the status quo but the prospect of adverse selection means that we shouldn’t be too surprised if the shift is significant.
I think you may be in danger of overloading “better” in statements like this, and more implicitly throughout your argument. (Similarly “good” in statements like “It’s absurd to… believe[] a life for a woman in Saudi Arabia is just as good as life for a woman in some other country with similarly high per capita income”.)
Consider if I said something like this: “We are constantly told that it would be better for us if we ate fewer calories. But this ignores the quality of those calories. Everybody who has tasted both knows that calorie for calorie, chocolate eclairs are better than lima beans. So it only makes sense that it would be better to reduce the better calories last.”
It might help if you explicitly stated better how (and maybe for whom) throughout, or use more precise adjectives than the generic good or better.
You also seem to me to be making an implicit “the goal of EA ought not to be to relieve existing suffering but to maximize future hedons” argument that maybe should be made explicit instead.
Thanks for the feedback. Thinking about it for a minute, it seems like your first point is more than just stylistic criticism. By “better” i meant we have strong intuitions about first person subjective experience, but i now realize the way I phrased it might be begging the question.
Why do you think I’m making that assumption? I assume EAs care about all of these things with some reasonable exchange rate between all the three. Assuming you only care about doesn’t this bias you towards enhaving subjective experience, pain relief etc (eg. Give Directly, Strong Minds etc) versus life saving interventions that might be barely net positive anyway, especially because things like malarial bed nets don’t have other positive externalities (unlike something like deworming) I agree it’s also an update towards any other things EAs could plausibly do, such as institutional imrprovements/human capital development etc.
Has anyone done a survey where they just ask what risk of dying people would accept to get a chance at a substantially higher standard of living, or how many years of life at their current standard of living they would trade off aganist a wester standard for living? I wouldn’t super trust people’s judgement to actually track what’s good here, but it seems like a very rough first pass (and is also how we are already constructing the “disability-adjustment” that’s happening in Disability-Adjusted-Life-Years, IIRC).
Curious if anyone has any references. It seems like the kind of thing someone working on DALYs might have already done.
Back in 2019 GiveWell surveyed 2,000 people in Kenya and Ghana.
Hmm, I can’t find the relevant question in the linked results PDF. I can only find comparisons within a country on what their tradeoff is between money for personal consumption and death (which is somewhat related but doesn’t really obviously interface with the point made in the OP)
Agree, this would be a more pertinent to answering this question than what GiveWell has commissioned thus far. I’m meeting someone this weekend who is working on DALYs at the Effective Institutions Project. Will update here if I hear something interesting.
I strongly downvoted this post.
1 . The optics of actually implementing this idea would be awful. It would permanently damage EA’s public image and be raised as a cudgel in every single expose written about the movement. To the average person, concluding that years in the life of the poorest are worth less than those of someone in a rich, first world country is an abhorrent statement, regardless of how well crafted your argument is.
2.1 It would be also be extremely difficult for rich foreigners to objectively assess the value of QALYs in the most globally impoverished nations, regardless of good intentions and attempts to overcome biases.
2.2 There is a fair amount of arbitrariness to metrics chosen to value someones life. You’ve mentioned womens rights, but we could look alternatively look at the suicide rate as a lower bound on the number of women in a society who believe more years of their life has negative value. By choosing this reasonable sounding metric, we can conclude that a year of a womans life in South Korea is much worse than a year of a womans life in Afghanistan. How confident are you that you’ll be able to find metrics which accurately reflect the value of a year of someones life?
The error in reasoning comes from making a utilitarian calculation without giving enough weight to the potential for flaws within the reasoning machine itself.
How much would we have to pay you to move to Congo ?
Assuming I blend in and speak the local language, within an order of magnitude of 5 million (edit: USD)
I don’t feel your response meaningfully engaged with either of my objections.
You could similarly argue a life for a woman in Saudi Arabia is worse than for a man, but it seems absurd to conclude from that that saving lives of SA men is better than saving lives of SA women.
It seems to me there are obvious differences: 1. family size (in the limit, the saved person may have no family at all); 2. how expected the person’s death is otherwise.
A thoughtful post! I think about this kind of stuff a lot, and wonder what the implications are. If we’re more pessimistic about saving lives in sub-saharan africa, should we:
promote things like lead removal (similar evidence-backed, scalable intervention as bednets, but aimed more directly at human capital)?
promote things like charter cities (untested crazy longshot megaproject, but aimed squarely at transformative political / societal improvements)?
switch to bednet-style lifesaving charities in South Asia, like you mention?
keep on trucking with our original Givewell-style africa-based lifesaving charities, because even after considering all the above, the original charities still look better than any of the three ideas above?
I would love it if you cross-posted this to the EA Forum (I’m sure you’d get some more criticism there vs Lesswrong, but I think it would nevertheless be a good conversation for them to have!) https://forum.effectivealtruism.org/
I posted this on the EA forum a couple of weeks ago—https://forum.effectivealtruism.org/posts/7WKiW4fTvJMzJwPsk/adverse-selection-in-minimizing-cost-per-life-saved
No surprise that people on the forum seem to think #4 is the right answer (although they did acknowledge this is a valid consideration). But a lot of it was “this is so cheap that this is probably still the right answer” and “we should be humble and not violate the intuition people have that all lives are equal”.
This is pretty classic Goodhart, right? The metric DALY is misaligned with what donors actually want (some other calculation about value).
Yes, unless what donors really want is to think no further than the cost of a DALY. Sure, GiveWell donors care about “actually having an impact” in that they’re doing more than most donors to understand who to best delegate resource allocation to, but how many would actually change their allocation based on this information? I don’t really know, but i’m not confident it’s a high proportion.