I suspect you are right to say that no one has carefully thought through the details of medical career choice in low and middle income countries—I regret I certainly haven’t. One challenge is that the particular details of medical careers will not only vary between higher and lower income countries but also within these groups: I would guess (e.g.) Kenya and the Phillipines differ more than US and UK. My excuse would be that I thought I’d write about what I knew, and that this would line up with the backgrounds of the expected audience. Maybe that was right in 2015, but much less so now, and—hopefully—clearly false in the near future.
Although I fear I’m little help in general, I can offer something more re. E2G vs. medical practice in Kenya.
First, some miscellaneous remarks/health warnings on the ‘life saved’ figure(s):
The effect size interval of ‘physician density’ crosses zero (P value ~ 0.4(!)). So with more sceptical priors/practices you might take this as a negative result. E.g. I imagine a typical Givewell analyst would interpret this work as an indication training more doctors is not a promising intervention.
Both wealth and education factors are much more predictive, which is at least indicative (if not decisive) of what stands better prospects of moving the population health needle. This fits with general doctrine in public health around the social determinants of health, and rhymes with the typically unimpressive impacts of generally greater medical care/expenditure in lottery studies, RCTs, etc.
Ecological methods may be the best we (/I) have, but are tricky, ditto the relatively small dataset and bunch on confounds. If I wanted to give my best guess central estimate of the impact of a doctor, I would probably adjust down further due to likely residual confounding, probably by a factor of ~~3. The most obvious example is physician density likely proxies healthcare workers generally, and doctors are unlikely to contribute the majority of the impact of a ‘marginal block of healthcare staff’.
I typically think the best use of this work is something like an approximate upper-bound: “When you control for the obvious, it is hard to see any impact of physicians in the aggregate—but it is unlikely to be much greater than X”.
The ‘scaling’ effect of how much returns of physicians diminish as their density increases is a function of how the variables are linearized. Although this is indirectly data-driven (i.e. because the relationship is very non-linear, you linearise using a function which drives diminishing returns), it is not a ‘discovery’ from the analysis itself.
Although available data (and maybe reality) is much too underpowered to show this, I would guess this scaling overrates the direct impact of medical personnel in lower-income settings: advanced medical training is likely overkill for primary prevention (or sometimes typical treatment) of the main contributors to lower-income countries burden of disease (e.g., for Kenya). If indeed the skill-mix should be tilted away from highly trained staff like physicians in low-income settings versus higher-income ones, then there is less of outsized effect of physician density.
Anyway, bracketing all the caveats and plugging in Kenya’s current physician per capita figure into the old regression model gives a marginal response of ~40 DALYs, so a 15x multiplier versus the same for the UK. If one (very roughly) takes ~20-40 DALYs = 1 ‘life saved’, each year of Kenyan medical practice roughly roughly nets out to 5-10k USD of Givewell donations.
As you note, this is >>10% (at the upper end, >100%) of the average income of someone in Kenya. However, I’d take the upshot as less “maybe medical careers is a good idea for folks in lower-income countries”, but more “maybe E2G in lower-income countries is usually a bad idea” as (almost by definition) the opportunities to generate high incomes to support large donaions to worthy causes will be scarcer.
Notably, the Kenyan diaspora in the US reports a median houshold income of ~$61 000, whilst the average income for a Kenyan physician is something like $35 000, so ‘E2G + emirgration’ likely ends up ahead. Of course ‘Just move to a high income country’ is not some trivial undertaking, and much easier said than done—but then again, the same applies to ‘Just become a doctor’.
Hi Gregory, thank you so much for this thoughtful reply!
This is exactly the kind of discussion and analysis I was hoping to encourage with this post.
Your upshot totally makes sense to me: E2G is probably a bad idea in lower-income countries.
I also buy that being a practicing doctor likely is not the most impactful thing most people in lower-income countries can do either
So what is? A lot of the career considerations probably are still directionally right: government policy, org building at effective nonprofits, research into global priorities, etc.
But getting better answers on these questions is work to be done by EA groups worldwide (much of it on a country-by-country basis as you say—thanks for pushing me on this)
And for what it’s worth, I think the advice you gave in 2015 totally makes sense given the likely audience then. It’s exciting that the audience has changed now and will continue changing.
Very interesting analysis here, but I would add one potential drawback of moving to a high-income country: brain drain. In some countries, there is a lot of lost economic value that happens when highly skilled workers leave developing countries, and it appears that it sometimes (though not always) can harm economic growth. Harming economic growth in a developing country is a bad thing, and can potentially outweigh the benefits of earning to give. The second study I have linked focuses specifically on the negative effects of healthcare worker emigration in Kenya, and though it is a bit old, I think it’s an interesting read.
I was also wondering if you can explain what you mean by “The effect size interval of ‘physician density’ crosses zero (P value ~ 0.4(!)). So with more sceptical priors/practices you might take this as a negative result.” I haven’t encountered some of these terms before so if you could explain them to me I would really appreciate it!
Just off hand-having spent a couple of months in a rural part of Kenya with a severe doctor shortage, the estimate of 1 ‘life saved’ per year of Kenyan medical practice seems off to me, especially in rural areas, but I’m basing this purely off of discussions with people and anecdotal stories so I could be completely wrong. I’ve just heard a lot of stories of patients dying bc there wasn’t enough available healthcare staff in the area. I do think you’re right that investing in less intensive medical training like nursing is probably more cost-effective than investing in doctors.
Brian drain is an interesting topic. The brief research and thinking I’ve done on brain drain leaves me without clear answers as to what an individual facing a decision to emigrate should actually do.
Even if it is in aggregate bad that so many people move from poorer to richer countries (which is not obvious to me), it could still be the rational thing to do on an individual basis.
I would love to see a sort of guide based on EA-principles written for people in low-middle income countries considering moving to higher-income countries.
what are the benefits you might provide to the world working in a (potentially higher-leverage) higher-income country?
what about if you stayed in your home country?
for which careers is emigrating likely to make more sense vs. staying, and vice versa?
If earning to give if a key motivator in emigrating, how much would you have to believe you can earn in order to offset any downsides
what about moving to a high-income country for a few years, gaining experience, then moving back and?
Side note: the methods used in the second paper you shared don’t make sense to me. They say that “for every doctor that emigrated, a country lost about: (i) US$ 517,931”, but they arrive at this 517k figure by saying that education costs ~$65k, and then applying compound interest over 32 years. Seems to me it would be more accurate to say that the country lost $65k, plus the medical services that person would have provided.
Hello Luke,
I suspect you are right to say that no one has carefully thought through the details of medical career choice in low and middle income countries—I regret I certainly haven’t. One challenge is that the particular details of medical careers will not only vary between higher and lower income countries but also within these groups: I would guess (e.g.) Kenya and the Phillipines differ more than US and UK. My excuse would be that I thought I’d write about what I knew, and that this would line up with the backgrounds of the expected audience. Maybe that was right in 2015, but much less so now, and—hopefully—clearly false in the near future.
Although I fear I’m little help in general, I can offer something more re. E2G vs. medical practice in Kenya.
First, some miscellaneous remarks/health warnings on the ‘life saved’ figure(s):
The effect size interval of ‘physician density’ crosses zero (P value ~ 0.4(!)). So with more sceptical priors/practices you might take this as a negative result. E.g. I imagine a typical Givewell analyst would interpret this work as an indication training more doctors is not a promising intervention.
Both wealth and education factors are much more predictive, which is at least indicative (if not decisive) of what stands better prospects of moving the population health needle. This fits with general doctrine in public health around the social determinants of health, and rhymes with the typically unimpressive impacts of generally greater medical care/expenditure in lottery studies, RCTs, etc.
Ecological methods may be the best we (/I) have, but are tricky, ditto the relatively small dataset and bunch on confounds. If I wanted to give my best guess central estimate of the impact of a doctor, I would probably adjust down further due to likely residual confounding, probably by a factor of ~~3. The most obvious example is physician density likely proxies healthcare workers generally, and doctors are unlikely to contribute the majority of the impact of a ‘marginal block of healthcare staff’.
I typically think the best use of this work is something like an approximate upper-bound: “When you control for the obvious, it is hard to see any impact of physicians in the aggregate—but it is unlikely to be much greater than X”.
The ‘scaling’ effect of how much returns of physicians diminish as their density increases is a function of how the variables are linearized. Although this is indirectly data-driven (i.e. because the relationship is very non-linear, you linearise using a function which drives diminishing returns), it is not a ‘discovery’ from the analysis itself.
Although available data (and maybe reality) is much too underpowered to show this, I would guess this scaling overrates the direct impact of medical personnel in lower-income settings: advanced medical training is likely overkill for primary prevention (or sometimes typical treatment) of the main contributors to lower-income countries burden of disease (e.g., for Kenya). If indeed the skill-mix should be tilted away from highly trained staff like physicians in low-income settings versus higher-income ones, then there is less of outsized effect of physician density.
Anyway, bracketing all the caveats and plugging in Kenya’s current physician per capita figure into the old regression model gives a marginal response of ~40 DALYs, so a 15x multiplier versus the same for the UK. If one (very roughly) takes ~20-40 DALYs = 1 ‘life saved’, each year of Kenyan medical practice roughly roughly nets out to 5-10k USD of Givewell donations.
As you note, this is >>10% (at the upper end, >100%) of the average income of someone in Kenya. However, I’d take the upshot as less “maybe medical careers is a good idea for folks in lower-income countries”, but more “maybe E2G in lower-income countries is usually a bad idea” as (almost by definition) the opportunities to generate high incomes to support large donaions to worthy causes will be scarcer.
Notably, the Kenyan diaspora in the US reports a median houshold income of ~$61 000, whilst the average income for a Kenyan physician is something like $35 000, so ‘E2G + emirgration’ likely ends up ahead. Of course ‘Just move to a high income country’ is not some trivial undertaking, and much easier said than done—but then again, the same applies to ‘Just become a doctor’.
Hi Gregory, thank you so much for this thoughtful reply!
This is exactly the kind of discussion and analysis I was hoping to encourage with this post.
Your upshot totally makes sense to me: E2G is probably a bad idea in lower-income countries.
I also buy that being a practicing doctor likely is not the most impactful thing most people in lower-income countries can do either
So what is? A lot of the career considerations probably are still directionally right: government policy, org building at effective nonprofits, research into global priorities, etc.
But getting better answers on these questions is work to be done by EA groups worldwide (much of it on a country-by-country basis as you say—thanks for pushing me on this)
And for what it’s worth, I think the advice you gave in 2015 totally makes sense given the likely audience then. It’s exciting that the audience has changed now and will continue changing.
Hi Gregory,
Very interesting analysis here, but I would add one potential drawback of moving to a high-income country: brain drain. In some countries, there is a lot of lost economic value that happens when highly skilled workers leave developing countries, and it appears that it sometimes (though not always) can harm economic growth. Harming economic growth in a developing country is a bad thing, and can potentially outweigh the benefits of earning to give. The second study I have linked focuses specifically on the negative effects of healthcare worker emigration in Kenya, and though it is a bit old, I think it’s an interesting read.
I was also wondering if you can explain what you mean by “The effect size interval of ‘physician density’ crosses zero (P value ~ 0.4(!)). So with more sceptical priors/practices you might take this as a negative result.” I haven’t encountered some of these terms before so if you could explain them to me I would really appreciate it!
Just off hand-having spent a couple of months in a rural part of Kenya with a severe doctor shortage, the estimate of 1 ‘life saved’ per year of Kenyan medical practice seems off to me, especially in rural areas, but I’m basing this purely off of discussions with people and anecdotal stories so I could be completely wrong. I’ve just heard a lot of stories of patients dying bc there wasn’t enough available healthcare staff in the area. I do think you’re right that investing in less intensive medical training like nursing is probably more cost-effective than investing in doctors.
Sources:
https://wol.iza.org/articles/brain-drain-from-developing-countries/long
https://bmchealthservres.biomedcentral.com/articles/10.1186/1472-6963-6-89
Brian drain is an interesting topic. The brief research and thinking I’ve done on brain drain leaves me without clear answers as to what an individual facing a decision to emigrate should actually do.
Even if it is in aggregate bad that so many people move from poorer to richer countries (which is not obvious to me), it could still be the rational thing to do on an individual basis.
I would love to see a sort of guide based on EA-principles written for people in low-middle income countries considering moving to higher-income countries.
what are the benefits you might provide to the world working in a (potentially higher-leverage) higher-income country?
what about if you stayed in your home country?
for which careers is emigrating likely to make more sense vs. staying, and vice versa?
If earning to give if a key motivator in emigrating, how much would you have to believe you can earn in order to offset any downsides
what about moving to a high-income country for a few years, gaining experience, then moving back and?
Side note: the methods used in the second paper you shared don’t make sense to me. They say that “for every doctor that emigrated, a country lost about: (i) US$ 517,931”, but they arrive at this 517k figure by saying that education costs ~$65k, and then applying compound interest over 32 years. Seems to me it would be more accurate to say that the country lost $65k, plus the medical services that person would have provided.